replace maskrcnn-benchmark nms with torchvision nms
This commit is contained in:
Zhongdao
parent
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commit
be116014d6
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@ -1,112 +1,112 @@
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results/
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weights/
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data/mot17
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data/mot19
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tmp/
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external/
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=======
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
|
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*.so
|
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!utils/*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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||||
dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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|
||||
# PyInstaller
|
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# Usually these files are written by a python script from a template
|
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
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*.manifest
|
||||
*.spec
|
||||
|
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# Installer logs
|
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pip-log.txt
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pip-delete-this-directory.txt
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||||
|
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# Unit test / coverage reports
|
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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.pytest_cache/
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||||
|
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# Translations
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*.mo
|
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*.pot
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|
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# Django stuff:
|
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*.log
|
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local_settings.py
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||||
db.sqlite3
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||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
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target/
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|
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# Jupyter Notebook
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.ipynb_checkpoints
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|
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# pyenv
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.python-version
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|
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# celery beat schedule file
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celerybeat-schedule
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|
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# SageMath parsed files
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*.sage.py
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|
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# Environments
|
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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||||
|
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# Spyder project settings
|
||||
.spyderproject
|
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.spyproject
|
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|
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# Rope project settings
|
||||
.ropeproject
|
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|
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# mkdocs documentation
|
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/site
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|
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# mypy
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.mypy_cache/
|
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results/
|
||||
weights/
|
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data/mot17
|
||||
data/mot19
|
||||
tmp/
|
||||
external/
|
||||
=======
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
!utils/*.so
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
MANIFEST
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
|
||||
.tox/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
.hypothesis/
|
||||
.pytest_cache/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
*.log
|
||||
local_settings.py
|
||||
db.sqlite3
|
||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
|
||||
|
||||
# pyenv
|
||||
.python-version
|
||||
|
||||
# celery beat schedule file
|
||||
celerybeat-schedule
|
||||
|
||||
# SageMath parsed files
|
||||
*.sage.py
|
||||
|
||||
# Environments
|
||||
.env
|
||||
.venv
|
||||
env/
|
||||
venv/
|
||||
ENV/
|
||||
env.bak/
|
||||
venv.bak/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
.spyproject
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
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||||
|
|
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352
DATASET_ZOO.md
352
DATASET_ZOO.md
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@ -1,176 +1,176 @@
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# Dataset Zoo
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We provide several relevant datasets for training and evaluating the Joint Detection and Embedding (JDE) model.
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Annotations are provided in a unified format. If you want to use these datasets, please **follow their licenses**,
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and if you use these datasets in your research, please cite the original work (you can find the bibtex in the bottom).
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## Data Format
|
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All the dataset has the following structrue:
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```
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Caltech
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|——————images
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| └——————00001.jpg
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| |—————— ...
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| └——————0000N.jpg
|
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└——————labels_with_ids
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└——————00001.txt
|
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|—————— ...
|
||||
└——————0000N.txt
|
||||
```
|
||||
Every image corresponds to an annation text. Given an image path,
|
||||
the annotation text path can be easily generated by replacing the string `images` with `labels_with_ids` and replacing `.jpg` with `.txt`.
|
||||
|
||||
In the annotation text, each line is a bounding box and has the following format,
|
||||
```
|
||||
[class] [identity] [x_center] [y_center] [width] [height]
|
||||
```
|
||||
The field `[class]` is not used in this project since we only care about a single class, i.e., pedestrian here.
|
||||
|
||||
The field `[identity]` is an integer from `0` to `num_identities - 1`, or `-1` if this box has no identity annotation.
|
||||
|
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***Note** that the values of `[x_center] [y_center] [width] [height]` are normalized by the width/height of the image, so they are float numbers ranging from 0 to 1.
|
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## Download
|
||||
|
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### Caltech Pedestrian
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Baidu NetDisk:
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[[0]](https://pan.baidu.com/s/1sYBXXvQaXZ8TuNwQxMcAgg)
|
||||
[[1]](https://pan.baidu.com/s/1lVO7YBzagex1xlzqPksaPw)
|
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[[2]](https://pan.baidu.com/s/1PZXxxy_lrswaqTVg0GuHWg)
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[[3]](https://pan.baidu.com/s/1M93NCo_E6naeYPpykmaNgA)
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[[4]](https://pan.baidu.com/s/1ZXCdPNXfwbxQ4xCbVu5Dtw)
|
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[[5]](https://pan.baidu.com/s/1kcZkh1tcEiBEJqnDtYuejg)
|
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[[6]](https://pan.baidu.com/s/1sDjhtgdFrzR60KKxSjNb2A)
|
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[[7]](https://pan.baidu.com/s/18Zvp_d33qj1pmutFDUbJyw)
|
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|
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Google Drive: [[annotation]](https://drive.google.com/file/d/1h8vxl_6tgi9QVYoer9XcY9YwNB32TE5k/view?usp=sharing) ,
|
||||
please download all the `.tar` file from [this page](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/datasets/USA/) and unzip the images under `Caltech/images`
|
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|
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Original dataset webpage: [CaltechPedestrians](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/)
|
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### CityPersons
|
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Baidu NetDisk:
|
||||
[[0]](https://pan.baidu.com/s/1g24doGOdkKqmbgbJf03vsw)
|
||||
[[1]](https://pan.baidu.com/s/1mqDF9M5MdD3MGxSfe0ENsA)
|
||||
[[2]](https://pan.baidu.com/s/1Qrbh9lQUaEORCIlfI25wdA)
|
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[[3]](https://pan.baidu.com/s/1lw7shaffBgARDuk8mkkHhw)
|
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Google Drive:
|
||||
[[0]](https://drive.google.com/file/d/1DgLHqEkQUOj63mCrS_0UGFEM9BG8sIZs/view?usp=sharing)
|
||||
[[1]](https://drive.google.com/file/d/1BH9Xz59UImIGUdYwUR-cnP1g7Ton_LcZ/view?usp=sharing)
|
||||
[[2]](https://drive.google.com/file/d/1q_OltirP68YFvRWgYkBHLEFSUayjkKYE/view?usp=sharing)
|
||||
[[3]](https://drive.google.com/file/d/1VSL0SFoQxPXnIdBamOZJzHrHJ1N2gsTW/view?usp=sharing)
|
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|
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Original dataset webpage: [Citypersons pedestrian detection dataset](https://bitbucket.org/shanshanzhang/citypersons)
|
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|
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### CUHK-SYSU
|
||||
Baidu NetDisk:
|
||||
[[0]](https://pan.baidu.com/s/1YFrlyB1WjcQmFW3Vt_sEaQ)
|
||||
|
||||
Google Drive:
|
||||
[[0]](https://drive.google.com/file/d/1D7VL43kIV9uJrdSCYl53j89RE2K-IoQA/view?usp=sharing)
|
||||
|
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Original dataset webpage: [CUHK-SYSU Person Search Dataset](http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html)
|
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|
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### PRW
|
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Baidu NetDisk:
|
||||
[[0]](https://pan.baidu.com/s/1iqOVKO57dL53OI1KOmWeGQ)
|
||||
|
||||
Google Drive:
|
||||
[[0]](https://drive.google.com/file/d/116_mIdjgB-WJXGe8RYJDWxlFnc_4sqS8/view?usp=sharing)
|
||||
|
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Original dataset webpage: [Person Search in the Wild datset](http://www.liangzheng.com.cn/Project/project_prw.html)
|
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|
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### ETHZ (overlapping videos with MOT-16 removed):
|
||||
Baidu NetDisk:
|
||||
[[0]](https://pan.baidu.com/s/14EauGb2nLrcB3GRSlQ4K9Q)
|
||||
|
||||
Google Drive:
|
||||
[[0]](https://drive.google.com/file/d/19QyGOCqn8K_rc9TXJ8UwLSxCx17e0GoY/view?usp=sharing)
|
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Original dataset webpage: [ETHZ pedestrian datset](https://data.vision.ee.ethz.ch/cvl/aess/dataset/)
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### MOT-17
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Baidu NetDisk:
|
||||
[[0]](https://pan.baidu.com/s/1lHa6UagcosRBz-_Y308GvQ)
|
||||
|
||||
Google Drive:
|
||||
[[0]](https://drive.google.com/file/d/1ET-6w12yHNo8DKevOVgK1dBlYs739e_3/view?usp=sharing)
|
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Original dataset webpage: [MOT-17](https://motchallenge.net/data/MOT17/)
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### MOT-16 (for evaluation )
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Baidu NetDisk:
|
||||
[[0]](https://pan.baidu.com/s/10pUuB32Hro-h-KUZv8duiw)
|
||||
|
||||
Google Drive:
|
||||
[[0]](https://drive.google.com/file/d/1254q3ruzBzgn4LUejDVsCtT05SIEieQg/view?usp=sharing)
|
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|
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Original dataset webpage: [MOT-16](https://motchallenge.net/data/MOT16/)
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# Citation
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Caltech:
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```
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@inproceedings{ dollarCVPR09peds,
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author = "P. Doll\'ar and C. Wojek and B. Schiele and P. Perona",
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title = "Pedestrian Detection: A Benchmark",
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booktitle = "CVPR",
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month = "June",
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year = "2009",
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city = "Miami",
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}
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```
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Citypersons:
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```
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@INPROCEEDINGS{Shanshan2017CVPR,
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Author = {Shanshan Zhang and Rodrigo Benenson and Bernt Schiele},
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Title = {CityPersons: A Diverse Dataset for Pedestrian Detection},
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Booktitle = {CVPR},
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Year = {2017}
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}
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@INPROCEEDINGS{Cordts2016Cityscapes,
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title={The Cityscapes Dataset for Semantic Urban Scene Understanding},
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author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
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booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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year={2016}
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}
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```
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CUHK-SYSU:
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```
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@inproceedings{xiaoli2017joint,
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title={Joint Detection and Identification Feature Learning for Person Search},
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author={Xiao, Tong and Li, Shuang and Wang, Bochao and Lin, Liang and Wang, Xiaogang},
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booktitle={CVPR},
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year={2017}
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}
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```
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PRW:
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```
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@inproceedings{zheng2017person,
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title={Person re-identification in the wild},
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author={Zheng, Liang and Zhang, Hengheng and Sun, Shaoyan and Chandraker, Manmohan and Yang, Yi and Tian, Qi},
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booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
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pages={1367--1376},
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year={2017}
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}
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```
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ETHZ:
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```
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@InProceedings{eth_biwi_00534,
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author = {A. Ess and B. Leibe and K. Schindler and and L. van Gool},
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title = {A Mobile Vision System for Robust Multi-Person Tracking},
|
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booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08)},
|
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year = {2008},
|
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month = {June},
|
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publisher = {IEEE Press},
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keywords = {}
|
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}
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```
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MOT-16&17:
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```
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@article{milan2016mot16,
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title={MOT16: A benchmark for multi-object tracking},
|
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author={Milan, Anton and Leal-Taix{\'e}, Laura and Reid, Ian and Roth, Stefan and Schindler, Konrad},
|
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journal={arXiv preprint arXiv:1603.00831},
|
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year={2016}
|
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}
|
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```
|
||||
# Dataset Zoo
|
||||
We provide several relevant datasets for training and evaluating the Joint Detection and Embedding (JDE) model.
|
||||
Annotations are provided in a unified format. If you want to use these datasets, please **follow their licenses**,
|
||||
and if you use these datasets in your research, please cite the original work (you can find the bibtex in the bottom).
|
||||
## Data Format
|
||||
All the dataset has the following structrue:
|
||||
```
|
||||
Caltech
|
||||
|——————images
|
||||
| └——————00001.jpg
|
||||
| |—————— ...
|
||||
| └——————0000N.jpg
|
||||
└——————labels_with_ids
|
||||
└——————00001.txt
|
||||
|—————— ...
|
||||
└——————0000N.txt
|
||||
```
|
||||
Every image corresponds to an annation text. Given an image path,
|
||||
the annotation text path can be easily generated by replacing the string `images` with `labels_with_ids` and replacing `.jpg` with `.txt`.
|
||||
|
||||
In the annotation text, each line is a bounding box and has the following format,
|
||||
```
|
||||
[class] [identity] [x_center] [y_center] [width] [height]
|
||||
```
|
||||
The field `[class]` is not used in this project since we only care about a single class, i.e., pedestrian here.
|
||||
|
||||
The field `[identity]` is an integer from `0` to `num_identities - 1`, or `-1` if this box has no identity annotation.
|
||||
|
||||
***Note** that the values of `[x_center] [y_center] [width] [height]` are normalized by the width/height of the image, so they are float numbers ranging from 0 to 1.
|
||||
|
||||
## Download
|
||||
|
||||
### Caltech Pedestrian
|
||||
Baidu NetDisk:
|
||||
[[0]](https://pan.baidu.com/s/1sYBXXvQaXZ8TuNwQxMcAgg)
|
||||
[[1]](https://pan.baidu.com/s/1lVO7YBzagex1xlzqPksaPw)
|
||||
[[2]](https://pan.baidu.com/s/1PZXxxy_lrswaqTVg0GuHWg)
|
||||
[[3]](https://pan.baidu.com/s/1M93NCo_E6naeYPpykmaNgA)
|
||||
[[4]](https://pan.baidu.com/s/1ZXCdPNXfwbxQ4xCbVu5Dtw)
|
||||
[[5]](https://pan.baidu.com/s/1kcZkh1tcEiBEJqnDtYuejg)
|
||||
[[6]](https://pan.baidu.com/s/1sDjhtgdFrzR60KKxSjNb2A)
|
||||
[[7]](https://pan.baidu.com/s/18Zvp_d33qj1pmutFDUbJyw)
|
||||
|
||||
Google Drive: [[annotation]](https://drive.google.com/file/d/1h8vxl_6tgi9QVYoer9XcY9YwNB32TE5k/view?usp=sharing) ,
|
||||
please download all the `.tar` file from [this page](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/datasets/USA/) and unzip the images under `Caltech/images`
|
||||
|
||||
Original dataset webpage: [CaltechPedestrians](http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/)
|
||||
### CityPersons
|
||||
Baidu NetDisk:
|
||||
[[0]](https://pan.baidu.com/s/1g24doGOdkKqmbgbJf03vsw)
|
||||
[[1]](https://pan.baidu.com/s/1mqDF9M5MdD3MGxSfe0ENsA)
|
||||
[[2]](https://pan.baidu.com/s/1Qrbh9lQUaEORCIlfI25wdA)
|
||||
[[3]](https://pan.baidu.com/s/1lw7shaffBgARDuk8mkkHhw)
|
||||
|
||||
Google Drive:
|
||||
[[0]](https://drive.google.com/file/d/1DgLHqEkQUOj63mCrS_0UGFEM9BG8sIZs/view?usp=sharing)
|
||||
[[1]](https://drive.google.com/file/d/1BH9Xz59UImIGUdYwUR-cnP1g7Ton_LcZ/view?usp=sharing)
|
||||
[[2]](https://drive.google.com/file/d/1q_OltirP68YFvRWgYkBHLEFSUayjkKYE/view?usp=sharing)
|
||||
[[3]](https://drive.google.com/file/d/1VSL0SFoQxPXnIdBamOZJzHrHJ1N2gsTW/view?usp=sharing)
|
||||
|
||||
Original dataset webpage: [Citypersons pedestrian detection dataset](https://bitbucket.org/shanshanzhang/citypersons)
|
||||
|
||||
### CUHK-SYSU
|
||||
Baidu NetDisk:
|
||||
[[0]](https://pan.baidu.com/s/1YFrlyB1WjcQmFW3Vt_sEaQ)
|
||||
|
||||
Google Drive:
|
||||
[[0]](https://drive.google.com/file/d/1D7VL43kIV9uJrdSCYl53j89RE2K-IoQA/view?usp=sharing)
|
||||
|
||||
Original dataset webpage: [CUHK-SYSU Person Search Dataset](http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html)
|
||||
|
||||
### PRW
|
||||
Baidu NetDisk:
|
||||
[[0]](https://pan.baidu.com/s/1iqOVKO57dL53OI1KOmWeGQ)
|
||||
|
||||
Google Drive:
|
||||
[[0]](https://drive.google.com/file/d/116_mIdjgB-WJXGe8RYJDWxlFnc_4sqS8/view?usp=sharing)
|
||||
|
||||
Original dataset webpage: [Person Search in the Wild datset](http://www.liangzheng.com.cn/Project/project_prw.html)
|
||||
|
||||
### ETHZ (overlapping videos with MOT-16 removed):
|
||||
Baidu NetDisk:
|
||||
[[0]](https://pan.baidu.com/s/14EauGb2nLrcB3GRSlQ4K9Q)
|
||||
|
||||
Google Drive:
|
||||
[[0]](https://drive.google.com/file/d/19QyGOCqn8K_rc9TXJ8UwLSxCx17e0GoY/view?usp=sharing)
|
||||
|
||||
Original dataset webpage: [ETHZ pedestrian datset](https://data.vision.ee.ethz.ch/cvl/aess/dataset/)
|
||||
|
||||
### MOT-17
|
||||
Baidu NetDisk:
|
||||
[[0]](https://pan.baidu.com/s/1lHa6UagcosRBz-_Y308GvQ)
|
||||
|
||||
Google Drive:
|
||||
[[0]](https://drive.google.com/file/d/1ET-6w12yHNo8DKevOVgK1dBlYs739e_3/view?usp=sharing)
|
||||
|
||||
Original dataset webpage: [MOT-17](https://motchallenge.net/data/MOT17/)
|
||||
|
||||
### MOT-16 (for evaluation )
|
||||
Baidu NetDisk:
|
||||
[[0]](https://pan.baidu.com/s/10pUuB32Hro-h-KUZv8duiw)
|
||||
|
||||
Google Drive:
|
||||
[[0]](https://drive.google.com/file/d/1254q3ruzBzgn4LUejDVsCtT05SIEieQg/view?usp=sharing)
|
||||
|
||||
Original dataset webpage: [MOT-16](https://motchallenge.net/data/MOT16/)
|
||||
|
||||
|
||||
# Citation
|
||||
Caltech:
|
||||
```
|
||||
@inproceedings{ dollarCVPR09peds,
|
||||
author = "P. Doll\'ar and C. Wojek and B. Schiele and P. Perona",
|
||||
title = "Pedestrian Detection: A Benchmark",
|
||||
booktitle = "CVPR",
|
||||
month = "June",
|
||||
year = "2009",
|
||||
city = "Miami",
|
||||
}
|
||||
```
|
||||
Citypersons:
|
||||
```
|
||||
@INPROCEEDINGS{Shanshan2017CVPR,
|
||||
Author = {Shanshan Zhang and Rodrigo Benenson and Bernt Schiele},
|
||||
Title = {CityPersons: A Diverse Dataset for Pedestrian Detection},
|
||||
Booktitle = {CVPR},
|
||||
Year = {2017}
|
||||
}
|
||||
|
||||
@INPROCEEDINGS{Cordts2016Cityscapes,
|
||||
title={The Cityscapes Dataset for Semantic Urban Scene Understanding},
|
||||
author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
|
||||
booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
|
||||
year={2016}
|
||||
}
|
||||
```
|
||||
CUHK-SYSU:
|
||||
```
|
||||
@inproceedings{xiaoli2017joint,
|
||||
title={Joint Detection and Identification Feature Learning for Person Search},
|
||||
author={Xiao, Tong and Li, Shuang and Wang, Bochao and Lin, Liang and Wang, Xiaogang},
|
||||
booktitle={CVPR},
|
||||
year={2017}
|
||||
}
|
||||
```
|
||||
PRW:
|
||||
```
|
||||
@inproceedings{zheng2017person,
|
||||
title={Person re-identification in the wild},
|
||||
author={Zheng, Liang and Zhang, Hengheng and Sun, Shaoyan and Chandraker, Manmohan and Yang, Yi and Tian, Qi},
|
||||
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
|
||||
pages={1367--1376},
|
||||
year={2017}
|
||||
}
|
||||
```
|
||||
ETHZ:
|
||||
```
|
||||
@InProceedings{eth_biwi_00534,
|
||||
author = {A. Ess and B. Leibe and K. Schindler and and L. van Gool},
|
||||
title = {A Mobile Vision System for Robust Multi-Person Tracking},
|
||||
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08)},
|
||||
year = {2008},
|
||||
month = {June},
|
||||
publisher = {IEEE Press},
|
||||
keywords = {}
|
||||
}
|
||||
```
|
||||
MOT-16&17:
|
||||
```
|
||||
@article{milan2016mot16,
|
||||
title={MOT16: A benchmark for multi-object tracking},
|
||||
author={Milan, Anton and Leal-Taix{\'e}, Laura and Reid, Ian and Roth, Stefan and Schindler, Konrad},
|
||||
journal={arXiv preprint arXiv:1603.00831},
|
||||
year={2016}
|
||||
}
|
||||
```
|
||||
|
|
|
|||
42
LICENSE
42
LICENSE
|
|
@ -1,21 +1,21 @@
|
|||
MIT License
|
||||
|
||||
Copyright (c) 2019 ZhongdaoWang
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2019 ZhongdaoWang
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
|
|
|
|||
104
README.md
104
README.md
|
|
@ -1,52 +1,52 @@
|
|||
# Towards-Realtime-MOT
|
||||
**NEWS:**
|
||||
- **[2019.10.11]** Training and evaluation data uploaded! Please see [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for details.
|
||||
- **[2019.10.01]** Demo code and pre-trained model released!
|
||||
|
||||
## Introduction
|
||||
This repo is the a codebase of the Joint Detection and Embedding (JDE) model. JDE is a fast and high-performance multiple-object tracker that learns the object detection task and appearance embedding task simutaneously in a shared neural network. Techical details are described in our [arXiv preprint paper](https://arxiv.org/pdf/1909.12605v1.pdf). By using this repo, you can simply achieve **MOTA 64%+** on the "private" protocol of [MOT-16 challenge](https://motchallenge.net/tracker/JDE), and with a near real-time speed at **18~24 FPS** (Note this speed is for the entire system, including the detection step! ) .
|
||||
|
||||
We hope this repo will help researches/engineers to develop more practical MOT systems. For algorithm development, we provide training data, baseline models and evaluation methods to make a level playground. For application usage, we also provide a small video demo that takes raw videos as input without any bells and whistles.
|
||||
|
||||
## Requirements
|
||||
* Python 3.6
|
||||
* [Pytorch](https://pytorch.org) >= 1.0.1
|
||||
* [syncbn](https://github.com/ytoon/Synchronized-BatchNorm-PyTorch) (Optional, compile and place it under utils/syncbn, or simply replace with nn.BatchNorm [here](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/models.py#L12))
|
||||
* [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) (Their GPU NMS is used in this project)
|
||||
* python-opencv
|
||||
* ffmpeg (Optional, used in the video demo)
|
||||
* [py-motmetrics](https://github.com/cheind/py-motmetrics) (Simply `pip install motmetrics`)
|
||||
|
||||
## Video Demo
|
||||
<img src="assets/MOT16-03.gif" width="400"/> <img src="assets/MOT16-14.gif" width="400"/>
|
||||
<img src="assets/IMG_0055.gif" width="400"/> <img src="assets/000011-00001.gif" width="400"/>
|
||||
|
||||
Usage:
|
||||
```
|
||||
python demo.py --input-video path/to/your/input/video --weights path/to/model/weights
|
||||
--output-format video --output-root path/to/output/root
|
||||
```
|
||||
## Dataset zoo
|
||||
Please see [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for detailed description of the training/evaluation datasets.
|
||||
## Pretrained model and baseline models
|
||||
Darknet-53 ImageNet pretrained: [[DarkNet Official]](https://pjreddie.com/media/files/darknet53.conv.74)
|
||||
|
||||
JDE-1088x608-uncertainty: [[Google Drive]](https://drive.google.com/open?id=1nlnuYfGNuHWZztQHXwVZSL_FvfE551pA) [[Baidu NetDisk]](https://pan.baidu.com/s/1Ifgn0Y_JZE65_qSrQM2l-Q)
|
||||
## Test on MOT-16 Challenge
|
||||
|
||||
## Training instruction
|
||||
- Download the training datasets.
|
||||
- Edit `cfg/ccmcpe.json`, config the training/validation combinations. A dataset is represented by an image list, please see `data/*.train` for example.
|
||||
- Run the training script:
|
||||
```
|
||||
CUDA_VISIBLE_DEIVCES=0,1,2,3,4,5,6,7 python train.py
|
||||
```
|
||||
|
||||
We use 8x Nvidia Titan Xp to train the model, with a batch size of 32. You can adjust the batch size (and the learning rate together) according to how many GPUs your have. You can also train with smaller image size, which will bring faster inference time. But note the image size had better to be multiples of 32 (the down-sampling rate).
|
||||
|
||||
### Train with custom datasets
|
||||
Adding custom datsets is quite simple, all you need to do is to organize your annotation files in the same format as in our training sets. Please refer to [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for the dataset format.
|
||||
|
||||
|
||||
## Acknowledgement
|
||||
A large portion of code is borrowed from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) and [longcw/MOTDT](https://github.com/longcw/MOTDT), many thanks to their wonderful work!
|
||||
# Towards-Realtime-MOT
|
||||
**NEWS:**
|
||||
- **[2019.10.11]** Training and evaluation data uploaded! Please see [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for details.
|
||||
- **[2019.10.01]** Demo code and pre-trained model released!
|
||||
|
||||
## Introduction
|
||||
This repo is the a codebase of the Joint Detection and Embedding (JDE) model. JDE is a fast and high-performance multiple-object tracker that learns the object detection task and appearance embedding task simutaneously in a shared neural network. Techical details are described in our [arXiv preprint paper](https://arxiv.org/pdf/1909.12605v1.pdf). By using this repo, you can simply achieve **MOTA 64%+** on the "private" protocol of [MOT-16 challenge](https://motchallenge.net/tracker/JDE), and with a near real-time speed at **18~24 FPS** (Note this speed is for the entire system, including the detection step! ) .
|
||||
|
||||
We hope this repo will help researches/engineers to develop more practical MOT systems. For algorithm development, we provide training data, baseline models and evaluation methods to make a level playground. For application usage, we also provide a small video demo that takes raw videos as input without any bells and whistles.
|
||||
|
||||
## Requirements
|
||||
* Python 3.6
|
||||
* [Pytorch](https://pytorch.org) >= 1.0.1
|
||||
* [syncbn](https://github.com/ytoon/Synchronized-BatchNorm-PyTorch) (Optional, compile and place it under utils/syncbn, or simply replace with nn.BatchNorm [here](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/models.py#L12))
|
||||
* [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) (Their GPU NMS is used in this project)
|
||||
* python-opencv
|
||||
* ffmpeg (Optional, used in the video demo)
|
||||
* [py-motmetrics](https://github.com/cheind/py-motmetrics) (Simply `pip install motmetrics`)
|
||||
|
||||
## Video Demo
|
||||
<img src="assets/MOT16-03.gif" width="400"/> <img src="assets/MOT16-14.gif" width="400"/>
|
||||
<img src="assets/IMG_0055.gif" width="400"/> <img src="assets/000011-00001.gif" width="400"/>
|
||||
|
||||
Usage:
|
||||
```
|
||||
python demo.py --input-video path/to/your/input/video --weights path/to/model/weights
|
||||
--output-format video --output-root path/to/output/root
|
||||
```
|
||||
## Dataset zoo
|
||||
Please see [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for detailed description of the training/evaluation datasets.
|
||||
## Pretrained model and baseline models
|
||||
Darknet-53 ImageNet pretrained: [[DarkNet Official]](https://pjreddie.com/media/files/darknet53.conv.74)
|
||||
|
||||
JDE-1088x608-uncertainty: [[Google Drive]](https://drive.google.com/open?id=1nlnuYfGNuHWZztQHXwVZSL_FvfE551pA) [[Baidu NetDisk]](https://pan.baidu.com/s/1Ifgn0Y_JZE65_qSrQM2l-Q)
|
||||
## Test on MOT-16 Challenge
|
||||
|
||||
## Training instruction
|
||||
- Download the training datasets.
|
||||
- Edit `cfg/ccmcpe.json`, config the training/validation combinations. A dataset is represented by an image list, please see `data/*.train` for example.
|
||||
- Run the training script:
|
||||
```
|
||||
CUDA_VISIBLE_DEIVCES=0,1,2,3,4,5,6,7 python train.py
|
||||
```
|
||||
|
||||
We use 8x Nvidia Titan Xp to train the model, with a batch size of 32. You can adjust the batch size (and the learning rate together) according to how many GPUs your have. You can also train with smaller image size, which will bring faster inference time. But note the image size had better to be multiples of 32 (the down-sampling rate).
|
||||
|
||||
### Train with custom datasets
|
||||
Adding custom datsets is quite simple, all you need to do is to organize your annotation files in the same format as in our training sets. Please refer to [DATASET_ZOO.md](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/DATASET_ZOO.md) for the dataset format.
|
||||
|
||||
|
||||
## Acknowledgement
|
||||
A large portion of code is borrowed from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) and [longcw/MOTDT](https://github.com/longcw/MOTDT), many thanks to their wonderful work!
|
||||
|
|
|
|||
|
|
@ -1,24 +1,26 @@
|
|||
{
|
||||
"root":"/home/wangzd/datasets/MOT",
|
||||
"train":
|
||||
{
|
||||
"mot17":"./data/mot17.train",
|
||||
"caltech":"./data/caltech.train",
|
||||
"citypersons":"./data/citypersons.train",
|
||||
"cuhksysu":"./data/cuhksysu.train",
|
||||
"prw":"./data/prw.train",
|
||||
"eth":"./data/eth.train"
|
||||
},
|
||||
"test_emb":
|
||||
{
|
||||
"caltech":"./data/caltech.10k.val",
|
||||
"cuhksysu":"./data/cuhksysu.val",
|
||||
"prw":"./data/prw.val"
|
||||
},
|
||||
"test":
|
||||
{
|
||||
"mot19":"./data/mot19.train",
|
||||
"caltech":"./data/caltech.val",
|
||||
"citypersons":"./data/citypersons.val"
|
||||
}
|
||||
}
|
||||
{
|
||||
"root":"/home/wangzd/datasets/MOT",
|
||||
"train":
|
||||
{
|
||||
"mot17":"./data/mot17.train",
|
||||
"caltech":"./data/caltech.train",
|
||||
"citypersons":"./data/citypersons.train",
|
||||
"cuhksysu":"./data/cuhksysu.train",
|
||||
"prw":"./data/prw.train",
|
||||
"eth":"./data/eth.train",
|
||||
"03":"./data/mot16-03.test",
|
||||
"01":"./data/mot16-01.test",
|
||||
"14":"./data/mot16-14.test"
|
||||
},
|
||||
"test_emb":
|
||||
{
|
||||
"caltech":"./data/caltech.10k.val",
|
||||
"cuhksysu":"./data/cuhksysu.val",
|
||||
"prw":"./data/prw.val"
|
||||
},
|
||||
"test":
|
||||
{
|
||||
"caltech":"./data/caltech.val",
|
||||
"citypersons":"./data/citypersons.val"
|
||||
}
|
||||
}
|
||||
|
|
|
|||
1666
cfg/yolov3.cfg
1666
cfg/yolov3.cfg
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,833 @@
|
|||
[net]
|
||||
# Testing
|
||||
#batch=1
|
||||
#subdivisions=1
|
||||
# Training
|
||||
batch=16
|
||||
subdivisions=1
|
||||
width=480
|
||||
height=864
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 500200
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=24
|
||||
activation=linear
|
||||
|
||||
######### embedding ###########
|
||||
[route]
|
||||
layers = -3
|
||||
|
||||
[convolutional]
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=linear
|
||||
|
||||
[route]
|
||||
layers = -3, -1
|
||||
###############################
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 8,9,10,11
|
||||
anchors = 6,19, 9,27, 13,38, 18,54, 25,76, 36,107, 51,152, 71,215, 102,305, 143, 429, 203,508, 407,508
|
||||
classes=1
|
||||
num=12
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
[route]
|
||||
layers = -7
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=24
|
||||
activation=linear
|
||||
|
||||
######### embedding ###########
|
||||
[route]
|
||||
layers = -3
|
||||
|
||||
[convolutional]
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=linear
|
||||
|
||||
[route]
|
||||
layers = -3, -1
|
||||
###############################
|
||||
|
||||
[yolo]
|
||||
mask = 4,5,6,7
|
||||
anchors = 6,19, 9,27, 13,38, 18,54, 25,76, 36,107, 51,152, 71,215, 102,305, 143, 429, 203,508, 407,508
|
||||
classes=1
|
||||
num=12
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -7
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=24
|
||||
activation=linear
|
||||
|
||||
|
||||
|
||||
######### embedding ###########
|
||||
[route]
|
||||
layers = -3
|
||||
|
||||
[convolutional]
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=linear
|
||||
|
||||
[route]
|
||||
layers = -3, -1
|
||||
###############################
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2,3
|
||||
anchors = 6,19, 9,27, 13,38, 18,54, 25,76, 36,107, 51,152, 71,215, 102,305, 143, 429, 203,508, 407,508
|
||||
classes=1
|
||||
num=12
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
20000
data/caltech.10k.val
20000
data/caltech.10k.val
File diff suppressed because it is too large
Load Diff
53476
data/caltech.train
53476
data/caltech.train
File diff suppressed because it is too large
Load Diff
59824
data/caltech.val
59824
data/caltech.val
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
|
|
@ -1,441 +1,441 @@
|
|||
Cityscapes/images/val/frankfurt/frankfurt_000001_066574_leftImg8bit.png
|
||||
Cityscapes/images/val/frankfurt/frankfurt_000001_067474_leftImg8bit.png
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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Cityscapes/images/val/lindau/lindau_000024_000019_leftImg8bit.png
|
||||
Cityscapes/images/val/lindau/lindau_000035_000019_leftImg8bit.png
|
||||
Cityscapes/images/val/lindau/lindau_000010_000019_leftImg8bit.png
|
||||
Cityscapes/images/val/lindau/lindau_000046_000019_leftImg8bit.png
|
||||
Cityscapes/images/val/lindau/lindau_000022_000019_leftImg8bit.png
|
||||
Cityscapes/images/val/lindau/lindau_000053_000019_leftImg8bit.png
|
||||
Cityscapes/images/val/lindau/lindau_000009_000019_leftImg8bit.png
|
||||
Cityscapes/images/val/lindau/lindau_000013_000019_leftImg8bit.png
|
||||
Cityscapes/images/val/lindau/lindau_000007_000019_leftImg8bit.png
|
||||
Cityscapes/images/val/lindau/lindau_000038_000019_leftImg8bit.png
|
||||
Cityscapes/images/val/lindau/lindau_000054_000019_leftImg8bit.png
|
||||
Cityscapes/images/val/lindau/lindau_000005_000019_leftImg8bit.png
|
||||
Cityscapes/images/val/lindau/lindau_000023_000019_leftImg8bit.png
|
||||
|
|
|
|||
22412
data/cuhksysu.train
22412
data/cuhksysu.train
File diff suppressed because it is too large
Load Diff
4000
data/cuhksysu.val
4000
data/cuhksysu.val
File diff suppressed because it is too large
Load Diff
4112
data/eth.train
4112
data/eth.train
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,23 @@
|
|||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000041.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000201.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000221.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000061.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000021.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000261.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000241.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000001.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000421.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000401.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000381.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000181.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000441.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000161.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000321.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000301.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000141.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000101.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000341.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000361.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000121.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000281.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-01/img1/000081.jpg
|
||||
|
|
@ -0,0 +1,52 @@
|
|||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000391.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000811.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001471.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001021.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000061.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001261.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000021.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000871.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000631.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000241.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001411.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000781.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001201.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000001.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000451.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000661.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000421.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001441.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000211.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000991.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001051.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000181.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000601.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001381.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000011.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000841.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001231.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000031.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000271.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000931.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000301.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000751.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000481.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000511.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001291.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001141.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000091.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000361.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000121.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000571.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001321.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001111.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000151.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001081.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001351.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000901.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000331.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000721.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/001171.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000961.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000541.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-03/img1/000691.jpg
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000201.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000041.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000581.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000221.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000061.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000021.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000241.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000261.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000001.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000661.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000421.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000401.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000381.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000641.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000601.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000181.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000441.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000461.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000621.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000161.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000321.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000301.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000481.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000141.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000681.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000101.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000341.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000361.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000121.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000741.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000501.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000281.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000521.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000721.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000561.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000541.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000081.jpg
|
||||
/home/wangzd/datasets/MOT/MOT16/images/test/MOT16-14/img1/000701.jpg
|
||||
10632
data/mot17.train
10632
data/mot17.train
File diff suppressed because it is too large
Load Diff
17862
data/mot19.train
17862
data/mot19.train
File diff suppressed because it is too large
Load Diff
11402
data/prw.train
11402
data/prw.train
File diff suppressed because it is too large
Load Diff
4000
data/prw.val
4000
data/prw.val
File diff suppressed because it is too large
Load Diff
128
demo.py
128
demo.py
|
|
@ -1,64 +1,64 @@
|
|||
import os
|
||||
import os.path as osp
|
||||
import cv2
|
||||
import logging
|
||||
import argparse
|
||||
import motmetrics as mm
|
||||
|
||||
from tracker.multitracker import JDETracker
|
||||
from utils import visualization as vis
|
||||
from utils.utils import *
|
||||
from utils.io import read_results
|
||||
from utils.log import logger
|
||||
from utils.timer import Timer
|
||||
from utils.evaluation import Evaluator
|
||||
import utils.datasets as datasets
|
||||
import torch
|
||||
from track import eval_seq
|
||||
|
||||
|
||||
def track(opt):
|
||||
logger.setLevel(logging.INFO)
|
||||
result_root = opt.output_root if opt.output_root!='' else '.'
|
||||
mkdir_if_missing(result_root)
|
||||
|
||||
# run tracking
|
||||
timer = Timer()
|
||||
accs = []
|
||||
n_frame = 0
|
||||
|
||||
logger.info('start tracking...')
|
||||
dataloader = datasets.LoadVideo(opt.input_video, opt.img_size)
|
||||
result_filename = os.path.join(result_root, 'results.txt')
|
||||
frame_rate = dataloader.frame_rate
|
||||
|
||||
frame_dir = None if opt.output_format=='text' else osp.join(result_root, 'frame')
|
||||
try:
|
||||
eval_seq(opt, dataloader, 'mot', result_filename,
|
||||
save_dir=frame_dir, show_image=False, frame_rate=frame_rate)
|
||||
except Exception as e:
|
||||
logger.info(e)
|
||||
|
||||
if opt.output_format == 'video':
|
||||
output_video_path = osp.join(result_root, 'result.mp4')
|
||||
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(osp.join(result_root, 'frame'), output_video_path)
|
||||
os.system(cmd_str)
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(prog='demo.py')
|
||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
|
||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
|
||||
parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression')
|
||||
parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes')
|
||||
parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer')
|
||||
parser.add_argument('--input-video', type=str, help='path to the input video')
|
||||
parser.add_argument('--output-format', type=str, default='video', help='expected output format, can be video, or text')
|
||||
parser.add_argument('--output-root', type=str, default='results', help='expected output root path')
|
||||
opt = parser.parse_args()
|
||||
print(opt, end='\n\n')
|
||||
|
||||
track(opt)
|
||||
|
||||
import os
|
||||
import os.path as osp
|
||||
import cv2
|
||||
import logging
|
||||
import argparse
|
||||
import motmetrics as mm
|
||||
|
||||
from tracker.multitracker import JDETracker
|
||||
from utils import visualization as vis
|
||||
from utils.utils import *
|
||||
from utils.io import read_results
|
||||
from utils.log import logger
|
||||
from utils.timer import Timer
|
||||
from utils.evaluation import Evaluator
|
||||
import utils.datasets as datasets
|
||||
import torch
|
||||
from track import eval_seq
|
||||
|
||||
|
||||
def track(opt):
|
||||
logger.setLevel(logging.INFO)
|
||||
result_root = opt.output_root if opt.output_root!='' else '.'
|
||||
mkdir_if_missing(result_root)
|
||||
|
||||
# run tracking
|
||||
timer = Timer()
|
||||
accs = []
|
||||
n_frame = 0
|
||||
|
||||
logger.info('start tracking...')
|
||||
dataloader = datasets.LoadVideo(opt.input_video, opt.img_size)
|
||||
result_filename = os.path.join(result_root, 'results.txt')
|
||||
frame_rate = dataloader.frame_rate
|
||||
|
||||
frame_dir = None if opt.output_format=='text' else osp.join(result_root, 'frame')
|
||||
try:
|
||||
eval_seq(opt, dataloader, 'mot', result_filename,
|
||||
save_dir=frame_dir, show_image=False, frame_rate=frame_rate)
|
||||
except Exception as e:
|
||||
logger.info(e)
|
||||
|
||||
if opt.output_format == 'video':
|
||||
output_video_path = osp.join(result_root, 'result.mp4')
|
||||
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(osp.join(result_root, 'frame'), output_video_path)
|
||||
os.system(cmd_str)
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(prog='demo.py')
|
||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
|
||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
|
||||
parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression')
|
||||
parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes')
|
||||
parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer')
|
||||
parser.add_argument('--input-video', type=str, help='path to the input video')
|
||||
parser.add_argument('--output-format', type=str, default='video', help='expected output format, can be video, or text')
|
||||
parser.add_argument('--output-root', type=str, default='results', help='expected output root path')
|
||||
opt = parser.parse_args()
|
||||
print(opt, end='\n\n')
|
||||
|
||||
track(opt)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,98 +1,98 @@
|
|||
import argparse
|
||||
import json
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
from sklearn import metrics
|
||||
from scipy import interpolate
|
||||
import torch.nn.functional as F
|
||||
from models import *
|
||||
from utils.utils import *
|
||||
from torchvision.transforms import transforms as T
|
||||
from utils.datasets import LoadImages, JointDataset, collate_fn
|
||||
|
||||
def extract_ped_per_frame(
|
||||
cfg,
|
||||
input_root,
|
||||
output_root,
|
||||
weights,
|
||||
batch_size=16,
|
||||
img_size=416,
|
||||
iou_thres=0.5,
|
||||
conf_thres=0.3,
|
||||
nms_thres=0.45,
|
||||
print_interval=40,
|
||||
nID=14455,
|
||||
):
|
||||
mkdir_if_missing(output_root)
|
||||
|
||||
# Initialize model
|
||||
model = Darknet(cfg, img_size, nID)
|
||||
|
||||
# Load weights
|
||||
if weights.endswith('.pt'): # pytorch format
|
||||
model.load_state_dict(torch.load(weights, map_location='cpu')['model'], strict=False)
|
||||
else: # darknet format
|
||||
load_darknet_weights(model, weights)
|
||||
|
||||
model = torch.nn.DataParallel(model)
|
||||
model.cuda().eval()
|
||||
|
||||
vlist = os.listdir(input_root)
|
||||
vlist = [osp.join(input_root, v, 'img1') for v in vlist]
|
||||
|
||||
for vpath in vlist:
|
||||
vroot = osp.join('/',*vpath.split('/')[:-1])
|
||||
out_vroot = vroot.replace(input_root, output_root)
|
||||
mkdir_if_missing(out_vroot)
|
||||
dataloader = LoadImages(vpath, img_size)
|
||||
for frame_id, (frame_path, frame, frame_ori) in enumerate(dataloader):
|
||||
frame_ground_id = frame_path.split('/')[-1].split('.')[0]
|
||||
if frame_id % 20 == 0:
|
||||
print('Processing frame {} of video {}'.format(frame_id, frame_path))
|
||||
blob = torch.from_numpy(frame).cuda().unsqueeze(0)
|
||||
pred = model(blob)
|
||||
pred = pred[pred[:,:,4] > conf_thres]
|
||||
if len(pred) > 0:
|
||||
dets = non_max_suppression(pred.unsqueeze(0), conf_thres, nms_thres)[0].cpu()
|
||||
scale_coords(img_size, dets[:, :4], frame_ori.shape).round()
|
||||
frame_dir = osp.join(out_vroot, frame_ground_id)
|
||||
mkdir_if_missing(frame_dir)
|
||||
dets = dets[:, :5]
|
||||
|
||||
for ped_id, det in enumerate(dets):
|
||||
box = det[:4].int()
|
||||
conf = det[4]
|
||||
ped = frame_ori[box[1]:box[3], box[0]:box[2]]
|
||||
ped_path = osp.join(frame_dir, ('{:04d}_'+ '{:d}_'*4 + '{:.2f}.jpg').format(ped_id, *box, conf))
|
||||
cv2.imwrite(ped_path, ped)
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(prog='test.py')
|
||||
parser.add_argument('--batch-size', type=int, default=40, help='size of each image batch')
|
||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||
parser.add_argument('--weights', type=str, default='weights/mot_64/latest.pt', help='path to weights file')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.3, help='iou threshold required to qualify as detected')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
|
||||
parser.add_argument('--nms-thres', type=float, default=0.3, help='iou threshold for non-maximum suppression')
|
||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
||||
parser.add_argument('--print-interval', type=int, default=10, help='size of each image dimension')
|
||||
parser.add_argument('--input-root', type=str, default='/home/wangzd/datasets/youtube/data/0004/frame', help='path to input frames')
|
||||
parser.add_argument('--output-root', type=str, default='/home/wangzd/datasets/youtube/data/0004/ped_per_frame', help='path to output frames')
|
||||
opt = parser.parse_args()
|
||||
print(opt, end='\n\n')
|
||||
|
||||
with torch.no_grad():
|
||||
extract_ped_per_frame(
|
||||
opt.cfg,
|
||||
opt.input_root,
|
||||
opt.output_root,
|
||||
opt.weights,
|
||||
opt.batch_size,
|
||||
opt.img_size,
|
||||
opt.iou_thres,
|
||||
opt.conf_thres,
|
||||
opt.nms_thres,
|
||||
opt.print_interval,
|
||||
)
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
from sklearn import metrics
|
||||
from scipy import interpolate
|
||||
import torch.nn.functional as F
|
||||
from models import *
|
||||
from utils.utils import *
|
||||
from torchvision.transforms import transforms as T
|
||||
from utils.datasets import LoadImages, JointDataset, collate_fn
|
||||
|
||||
def extract_ped_per_frame(
|
||||
cfg,
|
||||
input_root,
|
||||
output_root,
|
||||
weights,
|
||||
batch_size=16,
|
||||
img_size=416,
|
||||
iou_thres=0.5,
|
||||
conf_thres=0.3,
|
||||
nms_thres=0.45,
|
||||
print_interval=40,
|
||||
nID=14455,
|
||||
):
|
||||
mkdir_if_missing(output_root)
|
||||
|
||||
# Initialize model
|
||||
model = Darknet(cfg, img_size, nID)
|
||||
|
||||
# Load weights
|
||||
if weights.endswith('.pt'): # pytorch format
|
||||
model.load_state_dict(torch.load(weights, map_location='cpu')['model'], strict=False)
|
||||
else: # darknet format
|
||||
load_darknet_weights(model, weights)
|
||||
|
||||
model = torch.nn.DataParallel(model)
|
||||
model.cuda().eval()
|
||||
|
||||
vlist = os.listdir(input_root)
|
||||
vlist = [osp.join(input_root, v, 'img1') for v in vlist]
|
||||
|
||||
for vpath in vlist:
|
||||
vroot = osp.join('/',*vpath.split('/')[:-1])
|
||||
out_vroot = vroot.replace(input_root, output_root)
|
||||
mkdir_if_missing(out_vroot)
|
||||
dataloader = LoadImages(vpath, img_size)
|
||||
for frame_id, (frame_path, frame, frame_ori) in enumerate(dataloader):
|
||||
frame_ground_id = frame_path.split('/')[-1].split('.')[0]
|
||||
if frame_id % 20 == 0:
|
||||
print('Processing frame {} of video {}'.format(frame_id, frame_path))
|
||||
blob = torch.from_numpy(frame).cuda().unsqueeze(0)
|
||||
pred = model(blob)
|
||||
pred = pred[pred[:,:,4] > conf_thres]
|
||||
if len(pred) > 0:
|
||||
dets = non_max_suppression(pred.unsqueeze(0), conf_thres, nms_thres)[0].cpu()
|
||||
scale_coords(img_size, dets[:, :4], frame_ori.shape).round()
|
||||
frame_dir = osp.join(out_vroot, frame_ground_id)
|
||||
mkdir_if_missing(frame_dir)
|
||||
dets = dets[:, :5]
|
||||
|
||||
for ped_id, det in enumerate(dets):
|
||||
box = det[:4].int()
|
||||
conf = det[4]
|
||||
ped = frame_ori[box[1]:box[3], box[0]:box[2]]
|
||||
ped_path = osp.join(frame_dir, ('{:04d}_'+ '{:d}_'*4 + '{:.2f}.jpg').format(ped_id, *box, conf))
|
||||
cv2.imwrite(ped_path, ped)
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(prog='test.py')
|
||||
parser.add_argument('--batch-size', type=int, default=40, help='size of each image batch')
|
||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||
parser.add_argument('--weights', type=str, default='weights/mot_64/latest.pt', help='path to weights file')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.3, help='iou threshold required to qualify as detected')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
|
||||
parser.add_argument('--nms-thres', type=float, default=0.3, help='iou threshold for non-maximum suppression')
|
||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
||||
parser.add_argument('--print-interval', type=int, default=10, help='size of each image dimension')
|
||||
parser.add_argument('--input-root', type=str, default='/home/wangzd/datasets/youtube/data/0004/frame', help='path to input frames')
|
||||
parser.add_argument('--output-root', type=str, default='/home/wangzd/datasets/youtube/data/0004/ped_per_frame', help='path to output frames')
|
||||
opt = parser.parse_args()
|
||||
print(opt, end='\n\n')
|
||||
|
||||
with torch.no_grad():
|
||||
extract_ped_per_frame(
|
||||
opt.cfg,
|
||||
opt.input_root,
|
||||
opt.output_root,
|
||||
opt.weights,
|
||||
opt.batch_size,
|
||||
opt.img_size,
|
||||
opt.iou_thres,
|
||||
opt.conf_thres,
|
||||
opt.nms_thres,
|
||||
opt.print_interval,
|
||||
)
|
||||
|
||||
|
|
|
|||
754
models.py
754
models.py
|
|
@ -1,374 +1,380 @@
|
|||
import os
|
||||
from collections import defaultdict,OrderedDict
|
||||
|
||||
import torch.nn as nn
|
||||
|
||||
from utils.parse_config import *
|
||||
from utils.utils import *
|
||||
from utils.syncbn import SyncBN
|
||||
import time
|
||||
import math
|
||||
|
||||
batch_norm=SyncBN #nn.BatchNorm2d
|
||||
|
||||
def create_modules(module_defs):
|
||||
"""
|
||||
Constructs module list of layer blocks from module configuration in module_defs
|
||||
"""
|
||||
hyperparams = module_defs.pop(0)
|
||||
output_filters = [int(hyperparams['channels'])]
|
||||
module_list = nn.ModuleList()
|
||||
yolo_layer_count = 0
|
||||
for i, module_def in enumerate(module_defs):
|
||||
modules = nn.Sequential()
|
||||
|
||||
if module_def['type'] == 'convolutional':
|
||||
bn = int(module_def['batch_normalize'])
|
||||
filters = int(module_def['filters'])
|
||||
kernel_size = int(module_def['size'])
|
||||
pad = (kernel_size - 1) // 2 if int(module_def['pad']) else 0
|
||||
modules.add_module('conv_%d' % i, nn.Conv2d(in_channels=output_filters[-1],
|
||||
out_channels=filters,
|
||||
kernel_size=kernel_size,
|
||||
stride=int(module_def['stride']),
|
||||
padding=pad,
|
||||
bias=not bn))
|
||||
if bn:
|
||||
modules.add_module('batch_norm_%d' % i, batch_norm(filters))
|
||||
if module_def['activation'] == 'leaky':
|
||||
modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1))
|
||||
|
||||
elif module_def['type'] == 'maxpool':
|
||||
kernel_size = int(module_def['size'])
|
||||
stride = int(module_def['stride'])
|
||||
if kernel_size == 2 and stride == 1:
|
||||
modules.add_module('_debug_padding_%d' % i, nn.ZeroPad2d((0, 1, 0, 1)))
|
||||
maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2))
|
||||
modules.add_module('maxpool_%d' % i, maxpool)
|
||||
|
||||
elif module_def['type'] == 'upsample':
|
||||
upsample = Upsample(scale_factor=int(module_def['stride']))
|
||||
modules.add_module('upsample_%d' % i, upsample)
|
||||
|
||||
elif module_def['type'] == 'route':
|
||||
layers = [int(x) for x in module_def['layers'].split(',')]
|
||||
filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers])
|
||||
modules.add_module('route_%d' % i, EmptyLayer())
|
||||
|
||||
elif module_def['type'] == 'shortcut':
|
||||
filters = output_filters[int(module_def['from'])]
|
||||
modules.add_module('shortcut_%d' % i, EmptyLayer())
|
||||
|
||||
elif module_def['type'] == 'yolo':
|
||||
anchor_idxs = [int(x) for x in module_def['mask'].split(',')]
|
||||
# Extract anchors
|
||||
anchors = [float(x) for x in module_def['anchors'].split(',')]
|
||||
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
|
||||
anchors = [anchors[i] for i in anchor_idxs]
|
||||
nC = int(module_def['classes']) # number of classes
|
||||
img_size = (int(hyperparams['width']),int(hyperparams['height']))
|
||||
# Define detection layer
|
||||
yolo_layer = YOLOLayer(anchors, nC, hyperparams['nID'], img_size, yolo_layer_count, cfg=hyperparams['cfg'])
|
||||
modules.add_module('yolo_%d' % i, yolo_layer)
|
||||
yolo_layer_count += 1
|
||||
|
||||
# Register module list and number of output filters
|
||||
module_list.append(modules)
|
||||
output_filters.append(filters)
|
||||
|
||||
return hyperparams, module_list
|
||||
|
||||
|
||||
class EmptyLayer(nn.Module):
|
||||
"""Placeholder for 'route' and 'shortcut' layers"""
|
||||
|
||||
def __init__(self):
|
||||
super(EmptyLayer, self).__init__()
|
||||
|
||||
def forward(self, x):
|
||||
return x
|
||||
|
||||
|
||||
class Upsample(nn.Module):
|
||||
# Custom Upsample layer (nn.Upsample gives deprecated warning message)
|
||||
|
||||
def __init__(self, scale_factor=1, mode='nearest'):
|
||||
super(Upsample, self).__init__()
|
||||
self.scale_factor = scale_factor
|
||||
self.mode = mode
|
||||
|
||||
def forward(self, x):
|
||||
return F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
|
||||
|
||||
|
||||
class YOLOLayer(nn.Module):
|
||||
def __init__(self, anchors, nC, nID, img_size, yolo_layer, cfg):
|
||||
super(YOLOLayer, self).__init__()
|
||||
self.layer = yolo_layer
|
||||
nA = len(anchors)
|
||||
self.anchors = torch.FloatTensor(anchors)
|
||||
self.nA = nA # number of anchors (3)
|
||||
self.nC = nC # number of classes (80)
|
||||
self.nID = nID # number of identities
|
||||
self.img_size = 0
|
||||
self.emb_dim = 512
|
||||
|
||||
self.SmoothL1Loss = nn.SmoothL1Loss()
|
||||
self.SoftmaxLoss = nn.CrossEntropyLoss(ignore_index=-1)
|
||||
self.CrossEntropyLoss = nn.CrossEntropyLoss()
|
||||
self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1)
|
||||
self.s_c = nn.Parameter(-4.15*torch.ones(1)) # -4.15
|
||||
self.s_r = nn.Parameter(-4.85*torch.ones(1)) # -4.85
|
||||
self.s_id = nn.Parameter(-2.3*torch.ones(1)) # -2.3
|
||||
self.emb_scale = math.sqrt(2) * math.log(self.nID-1)
|
||||
|
||||
|
||||
def forward(self, p_cat, img_size, targets=None, classifier=None, test_emb=False):
|
||||
p, p_emb = p_cat[:, :24, ...], p_cat[:, 24:, ...]
|
||||
nB, nGh, nGw = p.shape[0], p.shape[-2], p.shape[-1]
|
||||
|
||||
if self.img_size != img_size:
|
||||
create_grids(self, img_size, nGh, nGw)
|
||||
|
||||
if p.is_cuda:
|
||||
self.grid_xy = self.grid_xy.cuda()
|
||||
self.anchor_wh = self.anchor_wh.cuda()
|
||||
|
||||
p = p.view(nB, self.nA, self.nC + 5, nGh, nGw).permute(0, 1, 3, 4, 2).contiguous() # prediction
|
||||
|
||||
p_emb = p_emb.permute(0,2,3,1).contiguous()
|
||||
p_box = p[..., :4]
|
||||
p_conf = p[..., 4:6].permute(0, 4, 1, 2, 3) # Conf
|
||||
|
||||
# Training
|
||||
if targets is not None:
|
||||
if test_emb:
|
||||
tconf, tbox, tids = build_targets_max(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw)
|
||||
else:
|
||||
tconf, tbox, tids = build_targets_thres(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw)
|
||||
tconf, tbox, tids = tconf.cuda(), tbox.cuda(), tids.cuda()
|
||||
mask = tconf > 0
|
||||
|
||||
# Compute losses
|
||||
nT = sum([len(x) for x in targets]) # number of targets
|
||||
nM = mask.sum().float() # number of anchors (assigned to targets)
|
||||
nP = torch.ones_like(mask).sum().float()
|
||||
if nM > 0:
|
||||
lbox = self.SmoothL1Loss(p_box[mask], tbox[mask])
|
||||
else:
|
||||
FT = torch.cuda.FloatTensor if p_conf.is_cuda else torch.FloatTensor
|
||||
lbox, lconf = FT([0]), FT([0])
|
||||
lconf = self.SoftmaxLoss(p_conf, tconf)
|
||||
lid = torch.Tensor(1).fill_(0).squeeze().cuda()
|
||||
emb_mask,_ = mask.max(1)
|
||||
|
||||
# For convenience we use max(1) to decide the id, TODO: more reseanable strategy
|
||||
tids,_ = tids.max(1)
|
||||
tids = tids[emb_mask]
|
||||
embedding = p_emb[emb_mask].contiguous()
|
||||
embedding = self.emb_scale * F.normalize(embedding)
|
||||
nI = emb_mask.sum().float()
|
||||
|
||||
if test_emb:
|
||||
if np.prod(embedding.shape)==0 or np.prod(tids.shape) == 0:
|
||||
return torch.zeros(0, self. emb_dim+1).cuda()
|
||||
emb_and_gt = torch.cat([embedding, tids.float()], dim=1)
|
||||
return emb_and_gt
|
||||
|
||||
if len(embedding) > 1:
|
||||
logits = classifier(embedding).contiguous()
|
||||
lid = self.IDLoss(logits, tids.squeeze())
|
||||
|
||||
# Sum loss components
|
||||
loss = torch.exp(-self.s_r)*lbox + torch.exp(-self.s_c)*lconf + torch.exp(-self.s_id)*lid + \
|
||||
(self.s_r + self.s_c + self.s_id)
|
||||
loss *= 0.5
|
||||
|
||||
return loss, loss.item(), lbox.item(), lconf.item(), lid.item(), nT
|
||||
|
||||
else:
|
||||
p_conf = torch.softmax(p_conf, dim=1)[:,1,...].unsqueeze(-1)
|
||||
p_emb = p_emb.unsqueeze(1).repeat(1,self.nA,1,1,1).contiguous()
|
||||
p_cls = torch.zeros(nB,self.nA,nGh,nGw,1).cuda() # Temp
|
||||
p = torch.cat([p_box, p_conf, p_cls, p_emb], dim=-1)
|
||||
p[..., :4] = decode_delta_map(p[..., :4], self.anchor_vec.to(p))
|
||||
p[..., :4] *= self.stride
|
||||
|
||||
return p.view(nB, -1, p.shape[-1])
|
||||
|
||||
|
||||
class Darknet(nn.Module):
|
||||
"""YOLOv3 object detection model"""
|
||||
|
||||
def __init__(self, cfg_path, img_size=(1088, 608), nID=1591, test_emb=False):
|
||||
super(Darknet, self).__init__()
|
||||
|
||||
self.module_defs = parse_model_cfg(cfg_path)
|
||||
self.module_defs[0]['cfg'] = cfg_path
|
||||
self.module_defs[0]['nID'] = nID
|
||||
self.hyperparams, self.module_list = create_modules(self.module_defs)
|
||||
self.img_size = img_size
|
||||
self.loss_names = ['loss', 'box', 'conf', 'id', 'nT']
|
||||
self.losses = OrderedDict()
|
||||
for ln in self.loss_names:
|
||||
self.losses[ln] = 0
|
||||
self.emb_dim = 512
|
||||
self.classifier = nn.Linear(self.emb_dim, nID)
|
||||
self.test_emb=test_emb
|
||||
|
||||
|
||||
def forward(self, x, targets=None, targets_len=None):
|
||||
self.losses = OrderedDict()
|
||||
for ln in self.loss_names:
|
||||
self.losses[ln] = 0
|
||||
is_training = (targets is not None) and (not self.test_emb)
|
||||
#img_size = x.shape[-1]
|
||||
layer_outputs = []
|
||||
output = []
|
||||
|
||||
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
|
||||
mtype = module_def['type']
|
||||
if mtype in ['convolutional', 'upsample', 'maxpool']:
|
||||
x = module(x)
|
||||
elif mtype == 'route':
|
||||
layer_i = [int(x) for x in module_def['layers'].split(',')]
|
||||
if len(layer_i) == 1:
|
||||
x = layer_outputs[layer_i[0]]
|
||||
else:
|
||||
x = torch.cat([layer_outputs[i] for i in layer_i], 1)
|
||||
elif mtype == 'shortcut':
|
||||
layer_i = int(module_def['from'])
|
||||
x = layer_outputs[-1] + layer_outputs[layer_i]
|
||||
elif mtype == 'yolo':
|
||||
if is_training: # get loss
|
||||
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
|
||||
x, *losses = module[0](x, self.img_size, targets, self.classifier)
|
||||
for name, loss in zip(self.loss_names, losses):
|
||||
self.losses[name] += loss
|
||||
elif self.test_emb:
|
||||
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
|
||||
x = module[0](x, self.img_size, targets, self.classifier, self.test_emb)
|
||||
else: # get detections
|
||||
x = module[0](x, self.img_size)
|
||||
output.append(x)
|
||||
layer_outputs.append(x)
|
||||
|
||||
if is_training:
|
||||
self.losses['nT'] /= 3
|
||||
output = [o.squeeze() for o in output]
|
||||
return sum(output), torch.Tensor(list(self.losses.values())).cuda()
|
||||
elif self.test_emb:
|
||||
return torch.cat(output, 0)
|
||||
return torch.cat(output, 1)
|
||||
|
||||
|
||||
def create_grids(self, img_size, nGh, nGw):
|
||||
self.stride = img_size[0]/nGw
|
||||
assert self.stride == img_size[1] / nGh
|
||||
|
||||
# build xy offsets
|
||||
grid_x = torch.arange(nGw).repeat((nGh, 1)).view((1, 1, nGh, nGw)).float()
|
||||
grid_y = torch.arange(nGh).repeat((nGw, 1)).transpose(0,1).view((1, 1, nGh, nGw)).float()
|
||||
#grid_y = grid_x.permute(0, 1, 3, 2)
|
||||
self.grid_xy = torch.stack((grid_x, grid_y), 4)
|
||||
|
||||
# build wh gains
|
||||
self.anchor_vec = self.anchors / self.stride
|
||||
self.anchor_wh = self.anchor_vec.view(1, self.nA, 1, 1, 2)
|
||||
|
||||
|
||||
def load_darknet_weights(self, weights, cutoff=-1):
|
||||
# Parses and loads the weights stored in 'weights'
|
||||
# cutoff: save layers between 0 and cutoff (if cutoff = -1 all are saved)
|
||||
weights_file = weights.split(os.sep)[-1]
|
||||
|
||||
# Try to download weights if not available locally
|
||||
if not os.path.isfile(weights):
|
||||
try:
|
||||
os.system('wget https://pjreddie.com/media/files/' + weights_file + ' -O ' + weights)
|
||||
except IOError:
|
||||
print(weights + ' not found')
|
||||
|
||||
# Establish cutoffs
|
||||
if weights_file == 'darknet53.conv.74':
|
||||
cutoff = 75
|
||||
elif weights_file == 'yolov3-tiny.conv.15':
|
||||
cutoff = 15
|
||||
|
||||
# Open the weights file
|
||||
fp = open(weights, 'rb')
|
||||
header = np.fromfile(fp, dtype=np.int32, count=5) # First five are header values
|
||||
|
||||
# Needed to write header when saving weights
|
||||
self.header_info = header
|
||||
|
||||
self.seen = header[3] # number of images seen during training
|
||||
weights = np.fromfile(fp, dtype=np.float32) # The rest are weights
|
||||
fp.close()
|
||||
|
||||
ptr = 0
|
||||
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
|
||||
if module_def['type'] == 'convolutional':
|
||||
conv_layer = module[0]
|
||||
if module_def['batch_normalize']:
|
||||
# Load BN bias, weights, running mean and running variance
|
||||
bn_layer = module[1]
|
||||
num_b = bn_layer.bias.numel() # Number of biases
|
||||
# Bias
|
||||
bn_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.bias)
|
||||
bn_layer.bias.data.copy_(bn_b)
|
||||
ptr += num_b
|
||||
# Weight
|
||||
bn_w = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.weight)
|
||||
bn_layer.weight.data.copy_(bn_w)
|
||||
ptr += num_b
|
||||
# Running Mean
|
||||
bn_rm = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_mean)
|
||||
bn_layer.running_mean.data.copy_(bn_rm)
|
||||
ptr += num_b
|
||||
# Running Var
|
||||
bn_rv = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_var)
|
||||
bn_layer.running_var.data.copy_(bn_rv)
|
||||
ptr += num_b
|
||||
else:
|
||||
# Load conv. bias
|
||||
num_b = conv_layer.bias.numel()
|
||||
conv_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(conv_layer.bias)
|
||||
conv_layer.bias.data.copy_(conv_b)
|
||||
ptr += num_b
|
||||
# Load conv. weights
|
||||
num_w = conv_layer.weight.numel()
|
||||
conv_w = torch.from_numpy(weights[ptr:ptr + num_w]).view_as(conv_layer.weight)
|
||||
conv_layer.weight.data.copy_(conv_w)
|
||||
ptr += num_w
|
||||
|
||||
|
||||
"""
|
||||
@:param path - path of the new weights file
|
||||
@:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
|
||||
"""
|
||||
|
||||
|
||||
def save_weights(self, path, cutoff=-1):
|
||||
fp = open(path, 'wb')
|
||||
self.header_info[3] = self.seen # number of images seen during training
|
||||
self.header_info.tofile(fp)
|
||||
|
||||
# Iterate through layers
|
||||
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
|
||||
if module_def['type'] == 'convolutional':
|
||||
conv_layer = module[0]
|
||||
# If batch norm, load bn first
|
||||
if module_def['batch_normalize']:
|
||||
bn_layer = module[1]
|
||||
bn_layer.bias.data.cpu().numpy().tofile(fp)
|
||||
bn_layer.weight.data.cpu().numpy().tofile(fp)
|
||||
bn_layer.running_mean.data.cpu().numpy().tofile(fp)
|
||||
bn_layer.running_var.data.cpu().numpy().tofile(fp)
|
||||
# Load conv bias
|
||||
else:
|
||||
conv_layer.bias.data.cpu().numpy().tofile(fp)
|
||||
# Load conv weights
|
||||
conv_layer.weight.data.cpu().numpy().tofile(fp)
|
||||
|
||||
fp.close()
|
||||
import os
|
||||
from collections import defaultdict,OrderedDict
|
||||
|
||||
import torch.nn as nn
|
||||
|
||||
from utils.parse_config import *
|
||||
from utils.utils import *
|
||||
from utils.syncbn import SyncBN
|
||||
import time
|
||||
import math
|
||||
|
||||
batch_norm=SyncBN #nn.BatchNorm2d
|
||||
#batch_norm=nn.BatchNorm2d
|
||||
def create_modules(module_defs):
|
||||
"""
|
||||
Constructs module list of layer blocks from module configuration in module_defs
|
||||
"""
|
||||
hyperparams = module_defs.pop(0)
|
||||
output_filters = [int(hyperparams['channels'])]
|
||||
module_list = nn.ModuleList()
|
||||
yolo_layer_count = 0
|
||||
for i, module_def in enumerate(module_defs):
|
||||
modules = nn.Sequential()
|
||||
|
||||
if module_def['type'] == 'convolutional':
|
||||
bn = int(module_def['batch_normalize'])
|
||||
filters = int(module_def['filters'])
|
||||
kernel_size = int(module_def['size'])
|
||||
pad = (kernel_size - 1) // 2 if int(module_def['pad']) else 0
|
||||
modules.add_module('conv_%d' % i, nn.Conv2d(in_channels=output_filters[-1],
|
||||
out_channels=filters,
|
||||
kernel_size=kernel_size,
|
||||
stride=int(module_def['stride']),
|
||||
padding=pad,
|
||||
bias=not bn))
|
||||
if bn:
|
||||
after_bn = batch_norm(filters)
|
||||
modules.add_module('batch_norm_%d' % i, after_bn)
|
||||
# BN is uniformly initialized by default in pytorch 1.0.1.
|
||||
# In pytorch>1.2.0, BN weights are initialized with constant 1,
|
||||
# but we find with the uniform initialization the model converges faster.
|
||||
nn.init.uniform_(after_bn.weight)
|
||||
nn.init.zeros_(after_bn.bias)
|
||||
if module_def['activation'] == 'leaky':
|
||||
modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1))
|
||||
|
||||
elif module_def['type'] == 'maxpool':
|
||||
kernel_size = int(module_def['size'])
|
||||
stride = int(module_def['stride'])
|
||||
if kernel_size == 2 and stride == 1:
|
||||
modules.add_module('_debug_padding_%d' % i, nn.ZeroPad2d((0, 1, 0, 1)))
|
||||
maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2))
|
||||
modules.add_module('maxpool_%d' % i, maxpool)
|
||||
|
||||
elif module_def['type'] == 'upsample':
|
||||
upsample = Upsample(scale_factor=int(module_def['stride']))
|
||||
modules.add_module('upsample_%d' % i, upsample)
|
||||
|
||||
elif module_def['type'] == 'route':
|
||||
layers = [int(x) for x in module_def['layers'].split(',')]
|
||||
filters = sum([output_filters[i + 1 if i > 0 else i] for i in layers])
|
||||
modules.add_module('route_%d' % i, EmptyLayer())
|
||||
|
||||
elif module_def['type'] == 'shortcut':
|
||||
filters = output_filters[int(module_def['from'])]
|
||||
modules.add_module('shortcut_%d' % i, EmptyLayer())
|
||||
|
||||
elif module_def['type'] == 'yolo':
|
||||
anchor_idxs = [int(x) for x in module_def['mask'].split(',')]
|
||||
# Extract anchors
|
||||
anchors = [float(x) for x in module_def['anchors'].split(',')]
|
||||
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
|
||||
anchors = [anchors[i] for i in anchor_idxs]
|
||||
nC = int(module_def['classes']) # number of classes
|
||||
img_size = (int(hyperparams['width']),int(hyperparams['height']))
|
||||
# Define detection layer
|
||||
yolo_layer = YOLOLayer(anchors, nC, hyperparams['nID'], img_size, yolo_layer_count, cfg=hyperparams['cfg'])
|
||||
modules.add_module('yolo_%d' % i, yolo_layer)
|
||||
yolo_layer_count += 1
|
||||
|
||||
# Register module list and number of output filters
|
||||
module_list.append(modules)
|
||||
output_filters.append(filters)
|
||||
|
||||
return hyperparams, module_list
|
||||
|
||||
|
||||
class EmptyLayer(nn.Module):
|
||||
"""Placeholder for 'route' and 'shortcut' layers"""
|
||||
|
||||
def __init__(self):
|
||||
super(EmptyLayer, self).__init__()
|
||||
|
||||
def forward(self, x):
|
||||
return x
|
||||
|
||||
|
||||
class Upsample(nn.Module):
|
||||
# Custom Upsample layer (nn.Upsample gives deprecated warning message)
|
||||
|
||||
def __init__(self, scale_factor=1, mode='nearest'):
|
||||
super(Upsample, self).__init__()
|
||||
self.scale_factor = scale_factor
|
||||
self.mode = mode
|
||||
|
||||
def forward(self, x):
|
||||
return F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
|
||||
|
||||
|
||||
class YOLOLayer(nn.Module):
|
||||
def __init__(self, anchors, nC, nID, img_size, yolo_layer, cfg):
|
||||
super(YOLOLayer, self).__init__()
|
||||
self.layer = yolo_layer
|
||||
nA = len(anchors)
|
||||
self.anchors = torch.FloatTensor(anchors)
|
||||
self.nA = nA # number of anchors (3)
|
||||
self.nC = nC # number of classes (80)
|
||||
self.nID = nID # number of identities
|
||||
self.img_size = 0
|
||||
self.emb_dim = 512
|
||||
|
||||
self.SmoothL1Loss = nn.SmoothL1Loss()
|
||||
self.SoftmaxLoss = nn.CrossEntropyLoss(ignore_index=-1)
|
||||
self.CrossEntropyLoss = nn.CrossEntropyLoss()
|
||||
self.IDLoss = nn.CrossEntropyLoss(ignore_index=-1)
|
||||
self.s_c = nn.Parameter(-4.15*torch.ones(1)) # -4.15
|
||||
self.s_r = nn.Parameter(-4.85*torch.ones(1)) # -4.85
|
||||
self.s_id = nn.Parameter(-2.3*torch.ones(1)) # -2.3
|
||||
self.emb_scale = math.sqrt(2) * math.log(self.nID-1)
|
||||
|
||||
|
||||
def forward(self, p_cat, img_size, targets=None, classifier=None, test_emb=False):
|
||||
p, p_emb = p_cat[:, :24, ...], p_cat[:, 24:, ...]
|
||||
nB, nGh, nGw = p.shape[0], p.shape[-2], p.shape[-1]
|
||||
|
||||
if self.img_size != img_size:
|
||||
create_grids(self, img_size, nGh, nGw)
|
||||
|
||||
if p.is_cuda:
|
||||
self.grid_xy = self.grid_xy.cuda()
|
||||
self.anchor_wh = self.anchor_wh.cuda()
|
||||
|
||||
p = p.view(nB, self.nA, self.nC + 5, nGh, nGw).permute(0, 1, 3, 4, 2).contiguous() # prediction
|
||||
|
||||
p_emb = p_emb.permute(0,2,3,1).contiguous()
|
||||
p_box = p[..., :4]
|
||||
p_conf = p[..., 4:6].permute(0, 4, 1, 2, 3) # Conf
|
||||
|
||||
# Training
|
||||
if targets is not None:
|
||||
if test_emb:
|
||||
tconf, tbox, tids = build_targets_max(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw)
|
||||
else:
|
||||
tconf, tbox, tids = build_targets_thres(targets, self.anchor_vec.cuda(), self.nA, self.nC, nGh, nGw)
|
||||
tconf, tbox, tids = tconf.cuda(), tbox.cuda(), tids.cuda()
|
||||
mask = tconf > 0
|
||||
|
||||
# Compute losses
|
||||
nT = sum([len(x) for x in targets]) # number of targets
|
||||
nM = mask.sum().float() # number of anchors (assigned to targets)
|
||||
nP = torch.ones_like(mask).sum().float()
|
||||
if nM > 0:
|
||||
lbox = self.SmoothL1Loss(p_box[mask], tbox[mask])
|
||||
else:
|
||||
FT = torch.cuda.FloatTensor if p_conf.is_cuda else torch.FloatTensor
|
||||
lbox, lconf = FT([0]), FT([0])
|
||||
lconf = self.SoftmaxLoss(p_conf, tconf)
|
||||
lid = torch.Tensor(1).fill_(0).squeeze().cuda()
|
||||
emb_mask,_ = mask.max(1)
|
||||
|
||||
# For convenience we use max(1) to decide the id, TODO: more reseanable strategy
|
||||
tids,_ = tids.max(1)
|
||||
tids = tids[emb_mask]
|
||||
embedding = p_emb[emb_mask].contiguous()
|
||||
embedding = self.emb_scale * F.normalize(embedding)
|
||||
nI = emb_mask.sum().float()
|
||||
|
||||
if test_emb:
|
||||
if np.prod(embedding.shape)==0 or np.prod(tids.shape) == 0:
|
||||
return torch.zeros(0, self. emb_dim+1).cuda()
|
||||
emb_and_gt = torch.cat([embedding, tids.float()], dim=1)
|
||||
return emb_and_gt
|
||||
|
||||
if len(embedding) > 1:
|
||||
logits = classifier(embedding).contiguous()
|
||||
lid = self.IDLoss(logits, tids.squeeze())
|
||||
|
||||
# Sum loss components
|
||||
loss = torch.exp(-self.s_r)*lbox + torch.exp(-self.s_c)*lconf + torch.exp(-self.s_id)*lid + \
|
||||
(self.s_r + self.s_c + self.s_id)
|
||||
loss *= 0.5
|
||||
|
||||
return loss, loss.item(), lbox.item(), lconf.item(), lid.item(), nT
|
||||
|
||||
else:
|
||||
p_conf = torch.softmax(p_conf, dim=1)[:,1,...].unsqueeze(-1)
|
||||
p_emb = p_emb.unsqueeze(1).repeat(1,self.nA,1,1,1).contiguous()
|
||||
p_cls = torch.zeros(nB,self.nA,nGh,nGw,1).cuda() # Temp
|
||||
p = torch.cat([p_box, p_conf, p_cls, p_emb], dim=-1)
|
||||
p[..., :4] = decode_delta_map(p[..., :4], self.anchor_vec.to(p))
|
||||
p[..., :4] *= self.stride
|
||||
|
||||
return p.view(nB, -1, p.shape[-1])
|
||||
|
||||
|
||||
class Darknet(nn.Module):
|
||||
"""YOLOv3 object detection model"""
|
||||
|
||||
def __init__(self, cfg_path, img_size=(1088, 608), nID=1591, test_emb=False):
|
||||
super(Darknet, self).__init__()
|
||||
|
||||
self.module_defs = parse_model_cfg(cfg_path)
|
||||
self.module_defs[0]['cfg'] = cfg_path
|
||||
self.module_defs[0]['nID'] = nID
|
||||
self.hyperparams, self.module_list = create_modules(self.module_defs)
|
||||
self.img_size = img_size
|
||||
self.loss_names = ['loss', 'box', 'conf', 'id', 'nT']
|
||||
self.losses = OrderedDict()
|
||||
for ln in self.loss_names:
|
||||
self.losses[ln] = 0
|
||||
self.emb_dim = 512
|
||||
self.classifier = nn.Linear(self.emb_dim, nID)
|
||||
self.test_emb=test_emb
|
||||
|
||||
|
||||
def forward(self, x, targets=None, targets_len=None):
|
||||
self.losses = OrderedDict()
|
||||
for ln in self.loss_names:
|
||||
self.losses[ln] = 0
|
||||
is_training = (targets is not None) and (not self.test_emb)
|
||||
#img_size = x.shape[-1]
|
||||
layer_outputs = []
|
||||
output = []
|
||||
|
||||
for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
|
||||
mtype = module_def['type']
|
||||
if mtype in ['convolutional', 'upsample', 'maxpool']:
|
||||
x = module(x)
|
||||
elif mtype == 'route':
|
||||
layer_i = [int(x) for x in module_def['layers'].split(',')]
|
||||
if len(layer_i) == 1:
|
||||
x = layer_outputs[layer_i[0]]
|
||||
else:
|
||||
x = torch.cat([layer_outputs[i] for i in layer_i], 1)
|
||||
elif mtype == 'shortcut':
|
||||
layer_i = int(module_def['from'])
|
||||
x = layer_outputs[-1] + layer_outputs[layer_i]
|
||||
elif mtype == 'yolo':
|
||||
if is_training: # get loss
|
||||
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
|
||||
x, *losses = module[0](x, self.img_size, targets, self.classifier)
|
||||
for name, loss in zip(self.loss_names, losses):
|
||||
self.losses[name] += loss
|
||||
elif self.test_emb:
|
||||
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
|
||||
x = module[0](x, self.img_size, targets, self.classifier, self.test_emb)
|
||||
else: # get detections
|
||||
x = module[0](x, self.img_size)
|
||||
output.append(x)
|
||||
layer_outputs.append(x)
|
||||
|
||||
if is_training:
|
||||
self.losses['nT'] /= 3
|
||||
output = [o.squeeze() for o in output]
|
||||
return sum(output), torch.Tensor(list(self.losses.values())).cuda()
|
||||
elif self.test_emb:
|
||||
return torch.cat(output, 0)
|
||||
return torch.cat(output, 1)
|
||||
|
||||
|
||||
def create_grids(self, img_size, nGh, nGw):
|
||||
self.stride = img_size[0]/nGw
|
||||
assert self.stride == img_size[1] / nGh
|
||||
|
||||
# build xy offsets
|
||||
grid_x = torch.arange(nGw).repeat((nGh, 1)).view((1, 1, nGh, nGw)).float()
|
||||
grid_y = torch.arange(nGh).repeat((nGw, 1)).transpose(0,1).view((1, 1, nGh, nGw)).float()
|
||||
#grid_y = grid_x.permute(0, 1, 3, 2)
|
||||
self.grid_xy = torch.stack((grid_x, grid_y), 4)
|
||||
|
||||
# build wh gains
|
||||
self.anchor_vec = self.anchors / self.stride
|
||||
self.anchor_wh = self.anchor_vec.view(1, self.nA, 1, 1, 2)
|
||||
|
||||
|
||||
def load_darknet_weights(self, weights, cutoff=-1):
|
||||
# Parses and loads the weights stored in 'weights'
|
||||
# cutoff: save layers between 0 and cutoff (if cutoff = -1 all are saved)
|
||||
weights_file = weights.split(os.sep)[-1]
|
||||
|
||||
# Try to download weights if not available locally
|
||||
if not os.path.isfile(weights):
|
||||
try:
|
||||
os.system('wget https://pjreddie.com/media/files/' + weights_file + ' -O ' + weights)
|
||||
except IOError:
|
||||
print(weights + ' not found')
|
||||
|
||||
# Establish cutoffs
|
||||
if weights_file == 'darknet53.conv.74':
|
||||
cutoff = 75
|
||||
elif weights_file == 'yolov3-tiny.conv.15':
|
||||
cutoff = 15
|
||||
|
||||
# Open the weights file
|
||||
fp = open(weights, 'rb')
|
||||
header = np.fromfile(fp, dtype=np.int32, count=5) # First five are header values
|
||||
|
||||
# Needed to write header when saving weights
|
||||
self.header_info = header
|
||||
|
||||
self.seen = header[3] # number of images seen during training
|
||||
weights = np.fromfile(fp, dtype=np.float32) # The rest are weights
|
||||
fp.close()
|
||||
|
||||
ptr = 0
|
||||
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
|
||||
if module_def['type'] == 'convolutional':
|
||||
conv_layer = module[0]
|
||||
if module_def['batch_normalize']:
|
||||
# Load BN bias, weights, running mean and running variance
|
||||
bn_layer = module[1]
|
||||
num_b = bn_layer.bias.numel() # Number of biases
|
||||
# Bias
|
||||
bn_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.bias)
|
||||
bn_layer.bias.data.copy_(bn_b)
|
||||
ptr += num_b
|
||||
# Weight
|
||||
bn_w = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.weight)
|
||||
bn_layer.weight.data.copy_(bn_w)
|
||||
ptr += num_b
|
||||
# Running Mean
|
||||
bn_rm = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_mean)
|
||||
bn_layer.running_mean.data.copy_(bn_rm)
|
||||
ptr += num_b
|
||||
# Running Var
|
||||
bn_rv = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_var)
|
||||
bn_layer.running_var.data.copy_(bn_rv)
|
||||
ptr += num_b
|
||||
else:
|
||||
# Load conv. bias
|
||||
num_b = conv_layer.bias.numel()
|
||||
conv_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(conv_layer.bias)
|
||||
conv_layer.bias.data.copy_(conv_b)
|
||||
ptr += num_b
|
||||
# Load conv. weights
|
||||
num_w = conv_layer.weight.numel()
|
||||
conv_w = torch.from_numpy(weights[ptr:ptr + num_w]).view_as(conv_layer.weight)
|
||||
conv_layer.weight.data.copy_(conv_w)
|
||||
ptr += num_w
|
||||
|
||||
|
||||
"""
|
||||
@:param path - path of the new weights file
|
||||
@:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
|
||||
"""
|
||||
|
||||
|
||||
def save_weights(self, path, cutoff=-1):
|
||||
fp = open(path, 'wb')
|
||||
self.header_info[3] = self.seen # number of images seen during training
|
||||
self.header_info.tofile(fp)
|
||||
|
||||
# Iterate through layers
|
||||
for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
|
||||
if module_def['type'] == 'convolutional':
|
||||
conv_layer = module[0]
|
||||
# If batch norm, load bn first
|
||||
if module_def['batch_normalize']:
|
||||
bn_layer = module[1]
|
||||
bn_layer.bias.data.cpu().numpy().tofile(fp)
|
||||
bn_layer.weight.data.cpu().numpy().tofile(fp)
|
||||
bn_layer.running_mean.data.cpu().numpy().tofile(fp)
|
||||
bn_layer.running_var.data.cpu().numpy().tofile(fp)
|
||||
# Load conv bias
|
||||
else:
|
||||
conv_layer.bias.data.cpu().numpy().tofile(fp)
|
||||
# Load conv weights
|
||||
conv_layer.weight.data.cpu().numpy().tofile(fp)
|
||||
|
||||
fp.close()
|
||||
|
|
|
|||
|
|
@ -0,0 +1,75 @@
|
|||
###################################################################
|
||||
# File Name: setup.py
|
||||
# Author: Zhongdao Wang
|
||||
# mail: wcd17@mails.tsinghua.edu.cn
|
||||
# Created Time: Thu 19 Dec 2019 07:29:02 PM CST
|
||||
###################################################################
|
||||
|
||||
from __future__ import print_function
|
||||
from __future__ import division
|
||||
from __future__ import absolute_import
|
||||
|
||||
import os
|
||||
import glob
|
||||
|
||||
import torch
|
||||
from setuptools import find_packages
|
||||
from setuptools import setup
|
||||
from torch.utils.cpp_extension import CUDA_HOME
|
||||
from torch.utils.cpp_extension import CppExtension
|
||||
from torch.utils.cpp_extension import CUDAExtension
|
||||
|
||||
|
||||
|
||||
def get_extensions():
|
||||
this_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
extensions_dir = os.path.join(this_dir, "utils", "nms")
|
||||
|
||||
main_file = glob.glob(os.path.join(extensions_dir, "*.cpp"))
|
||||
source_cpu = glob.glob(os.path.join(extensions_dir, "*.cpp"))
|
||||
source_cuda = glob.glob(os.path.join(extensions_dir, "*.cu"))
|
||||
|
||||
sources = main_file
|
||||
extension = CppExtension
|
||||
|
||||
extra_compile_args = {"cxx": []}
|
||||
define_macros = []
|
||||
|
||||
#if (torch.cuda.is_available() and CUDA_HOME is not None) or os.getenv("FORCE_CUDA", "0") == "1":
|
||||
if False:
|
||||
extension = CUDAExtension
|
||||
sources += source_cuda
|
||||
define_macros += [("WITH_CUDA", None)]
|
||||
extra_compile_args["nvcc"] = [
|
||||
"-DCUDA_HAS_FP16=1",
|
||||
"-D__CUDA_NO_HALF_OPERATORS__",
|
||||
"-D__CUDA_NO_HALF_CONVERSIONS__",
|
||||
"-D__CUDA_NO_HALF2_OPERATORS__",
|
||||
]
|
||||
|
||||
sources = [os.path.join(extensions_dir, s) for s in sources]
|
||||
|
||||
include_dirs = [extensions_dir]
|
||||
|
||||
ext_modules = [
|
||||
extension(
|
||||
"nms",
|
||||
sources,
|
||||
include_dirs=include_dirs,
|
||||
define_macros=define_macros,
|
||||
extra_compile_args=extra_compile_args,
|
||||
)
|
||||
]
|
||||
|
||||
return ext_modules
|
||||
|
||||
print(get_extensions())
|
||||
setup(
|
||||
name="nms",
|
||||
version="0.1",
|
||||
author="fmassa",
|
||||
url="https://github.com/facebookresearch/maskrcnn-benchmark",
|
||||
description="GPU supported NMS",
|
||||
ext_modules=get_extensions(),
|
||||
cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
|
||||
)
|
||||
530
test.py
530
test.py
|
|
@ -1,265 +1,265 @@
|
|||
import argparse
|
||||
import json
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
from sklearn import metrics
|
||||
from scipy import interpolate
|
||||
import torch.nn.functional as F
|
||||
from models import *
|
||||
from utils.utils import *
|
||||
from torchvision.transforms import transforms as T
|
||||
from utils.datasets import LoadImagesAndLabels, JointDataset, collate_fn
|
||||
|
||||
def test(
|
||||
cfg,
|
||||
data_cfg,
|
||||
weights,
|
||||
batch_size=16,
|
||||
img_size=416,
|
||||
iou_thres=0.5,
|
||||
conf_thres=0.3,
|
||||
nms_thres=0.45,
|
||||
print_interval=40,
|
||||
nID=14455,
|
||||
):
|
||||
|
||||
# Configure run
|
||||
f = open(data_cfg)
|
||||
data_cfg_dict = json.load(f)
|
||||
f.close()
|
||||
#nC = int(data_cfg_dict['classes']) # number of classes (80 for COCO)
|
||||
nC = 1
|
||||
test_path = data_cfg_dict['test']
|
||||
dataset_root = data_cfg_dict['root']
|
||||
|
||||
# Initialize model
|
||||
model = Darknet(cfg, img_size, nID)
|
||||
|
||||
# Load weights
|
||||
if weights.endswith('.pt'): # pytorch format
|
||||
model.load_state_dict(torch.load(weights, map_location='cpu')['model'], strict=False)
|
||||
else: # darknet format
|
||||
load_darknet_weights(model, weights)
|
||||
|
||||
model = torch.nn.DataParallel(model)
|
||||
model.cuda().eval()
|
||||
|
||||
# Get dataloader
|
||||
transforms = T.Compose([T.ToTensor()])
|
||||
dataset = JointDataset(dataset_root, test_path, img_size, augment=False, transforms=transforms)
|
||||
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False,
|
||||
num_workers=8, drop_last=False, collate_fn=collate_fn)
|
||||
|
||||
mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0
|
||||
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
|
||||
outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class, jdict = \
|
||||
[], [], [], [], [], [], [], [], []
|
||||
AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
|
||||
for batch_i, (imgs, targets, paths, shapes, targets_len) in enumerate(dataloader):
|
||||
t = time.time()
|
||||
output = model(imgs.cuda())
|
||||
output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres)
|
||||
for i, o in enumerate(output):
|
||||
if o is not None:
|
||||
output[i] = o[:, :6]
|
||||
|
||||
# Compute average precision for each sample
|
||||
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
|
||||
for si, (labels, detections) in enumerate(zip(targets, output)):
|
||||
seen += 1
|
||||
|
||||
if detections is None:
|
||||
# If there are labels but no detections mark as zero AP
|
||||
if labels.size(0) != 0:
|
||||
mAPs.append(0), mR.append(0), mP.append(0)
|
||||
continue
|
||||
|
||||
# Get detections sorted by decreasing confidence scores
|
||||
detections = detections.cpu().numpy()
|
||||
detections = detections[np.argsort(-detections[:, 4])]
|
||||
|
||||
|
||||
# If no labels add number of detections as incorrect
|
||||
correct = []
|
||||
if labels.size(0) == 0:
|
||||
# correct.extend([0 for _ in range(len(detections))])
|
||||
mAPs.append(0), mR.append(0), mP.append(0)
|
||||
continue
|
||||
else:
|
||||
target_cls = labels[:, 0]
|
||||
|
||||
# Extract target boxes as (x1, y1, x2, y2)
|
||||
target_boxes = xywh2xyxy(labels[:, 2:6])
|
||||
target_boxes[:, 0] *= img_size[0]
|
||||
target_boxes[:, 2] *= img_size[0]
|
||||
target_boxes[:, 1] *= img_size[1]
|
||||
target_boxes[:, 3] *= img_size[1]
|
||||
|
||||
detected = []
|
||||
for *pred_bbox, conf, obj_conf in detections:
|
||||
obj_pred = 0
|
||||
pred_bbox = torch.FloatTensor(pred_bbox).view(1, -1)
|
||||
# Compute iou with target boxes
|
||||
iou = bbox_iou(pred_bbox, target_boxes, x1y1x2y2=True)[0]
|
||||
# Extract index of largest overlap
|
||||
best_i = np.argmax(iou)
|
||||
# If overlap exceeds threshold and classification is correct mark as correct
|
||||
if iou[best_i] > iou_thres and obj_pred == labels[best_i, 0] and best_i not in detected:
|
||||
correct.append(1)
|
||||
detected.append(best_i)
|
||||
else:
|
||||
correct.append(0)
|
||||
|
||||
# Compute Average Precision (AP) per class
|
||||
AP, AP_class, R, P = ap_per_class(tp=correct,
|
||||
conf=detections[:, 4],
|
||||
pred_cls=np.zeros_like(detections[:, 5]), # detections[:, 6]
|
||||
target_cls=target_cls)
|
||||
|
||||
# Accumulate AP per class
|
||||
AP_accum_count += np.bincount(AP_class, minlength=nC)
|
||||
AP_accum += np.bincount(AP_class, minlength=nC, weights=AP)
|
||||
|
||||
# Compute mean AP across all classes in this image, and append to image list
|
||||
mAPs.append(AP.mean())
|
||||
mR.append(R.mean())
|
||||
mP.append(P.mean())
|
||||
|
||||
# Means of all images
|
||||
mean_mAP = np.sum(mAPs) / ( AP_accum_count + 1E-16)
|
||||
mean_R = np.sum(mR) / ( AP_accum_count + 1E-16)
|
||||
mean_P = np.sum(mP) / (AP_accum_count + 1E-16)
|
||||
|
||||
if batch_i % print_interval==0:
|
||||
# Print image mAP and running mean mAP
|
||||
print(('%11s%11s' + '%11.3g' * 4 + 's') %
|
||||
(seen, dataloader.dataset.nF, mean_P, mean_R, mean_mAP, time.time() - t))
|
||||
# Print mAP per class
|
||||
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
|
||||
|
||||
print('AP: %-.4f\n\n' % (AP_accum[0] / (AP_accum_count[0] + 1E-16)))
|
||||
|
||||
# Return mAP
|
||||
return mean_mAP, mean_R, mean_P
|
||||
|
||||
|
||||
def test_emb(
|
||||
cfg,
|
||||
data_cfg,
|
||||
weights,
|
||||
batch_size=16,
|
||||
img_size=416,
|
||||
iou_thres=0.5,
|
||||
conf_thres=0.3,
|
||||
nms_thres=0.45,
|
||||
print_interval=40,
|
||||
nID=14455,
|
||||
):
|
||||
|
||||
# Configure run
|
||||
f = open(data_cfg)
|
||||
data_cfg_dict = json.load(f)
|
||||
f.close()
|
||||
test_paths = data_cfg_dict['test_emb']
|
||||
dataset_root = data_cfg_dict['root']
|
||||
|
||||
# Initialize model
|
||||
model = Darknet(cfg, img_size, nID, test_emb=True)
|
||||
|
||||
# Load weights
|
||||
if weights.endswith('.pt'): # pytorch format
|
||||
model.load_state_dict(torch.load(weights, map_location='cpu')['model'], strict=False)
|
||||
else: # darknet format
|
||||
load_darknet_weights(model, weights)
|
||||
|
||||
model = torch.nn.DataParallel(model)
|
||||
model.cuda().eval()
|
||||
|
||||
# Get dataloader
|
||||
transforms = T.Compose([T.ToTensor()])
|
||||
dataset = JointDataset(dataset_root, test_paths, img_size, augment=False, transforms=transforms)
|
||||
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False,
|
||||
num_workers=8, drop_last=False, collate_fn=collate_fn)
|
||||
embedding, id_labels = [], []
|
||||
print('Extracting pedestrain features...')
|
||||
for batch_i, (imgs, targets, paths, shapes, targets_len) in enumerate(dataloader):
|
||||
t = time.time()
|
||||
output = model(imgs.cuda(), targets.cuda(), targets_len.cuda()).squeeze()
|
||||
|
||||
for out in output:
|
||||
feat, label = out[:-1], out[-1].long()
|
||||
if label != -1:
|
||||
embedding.append(feat)
|
||||
id_labels.append(label)
|
||||
|
||||
if batch_i % print_interval==0:
|
||||
print('Extracting {}/{}, # of instances {}, time {:.2f} sec.'.format(batch_i, len(dataloader), len(id_labels), time.time() - t))
|
||||
|
||||
print('Computing pairwise similairity...')
|
||||
if len(embedding) <1 :
|
||||
return None
|
||||
embedding = torch.stack(embedding, dim=0).cuda()
|
||||
id_labels = torch.LongTensor(id_labels)
|
||||
n = len(id_labels)
|
||||
print(n, len(embedding))
|
||||
assert len(embedding) == n
|
||||
|
||||
embedding = F.normalize(embedding, dim=1)
|
||||
pdist = torch.mm(embedding, embedding.t()).cpu().numpy()
|
||||
gt = id_labels.expand(n,n).eq(id_labels.expand(n,n).t()).numpy()
|
||||
|
||||
up_triangle = np.where(np.triu(pdist)- np.eye(n)*pdist !=0)
|
||||
pdist = pdist[up_triangle]
|
||||
gt = gt[up_triangle]
|
||||
|
||||
far_levels = [ 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1]
|
||||
far,tar,threshold = metrics.roc_curve(gt, pdist)
|
||||
interp = interpolate.interp1d(far, tar)
|
||||
tar_at_far = [interp(x) for x in far_levels]
|
||||
for f,fa in enumerate(far_levels):
|
||||
print('TPR@FAR={:.7f}: {:.4f}'.format(fa, tar_at_far[f]))
|
||||
return tar_at_far
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(prog='test.py')
|
||||
parser.add_argument('--batch-size', type=int, default=40, help='size of each image batch')
|
||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||
parser.add_argument('--data-cfg', type=str, default='cfg/ccmcpe.json', help='data config')
|
||||
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
|
||||
parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
|
||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
||||
parser.add_argument('--print-interval', type=int, default=10, help='size of each image dimension')
|
||||
parser.add_argument('--test-emb', action='store_true', help='test embedding')
|
||||
opt = parser.parse_args()
|
||||
print(opt, end='\n\n')
|
||||
|
||||
with torch.no_grad():
|
||||
if opt.test_emb:
|
||||
res = test_emb(
|
||||
opt.cfg,
|
||||
opt.data_cfg,
|
||||
opt.weights,
|
||||
opt.batch_size,
|
||||
opt.img_size,
|
||||
opt.iou_thres,
|
||||
opt.conf_thres,
|
||||
opt.nms_thres,
|
||||
opt.print_interval,
|
||||
)
|
||||
else:
|
||||
mAP = test(
|
||||
opt.cfg,
|
||||
opt.data_cfg,
|
||||
opt.weights,
|
||||
opt.batch_size,
|
||||
opt.img_size,
|
||||
opt.iou_thres,
|
||||
opt.conf_thres,
|
||||
opt.nms_thres,
|
||||
opt.print_interval,
|
||||
)
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
from sklearn import metrics
|
||||
from scipy import interpolate
|
||||
import torch.nn.functional as F
|
||||
from models import *
|
||||
from utils.utils import *
|
||||
from torchvision.transforms import transforms as T
|
||||
from utils.datasets import LoadImagesAndLabels, JointDataset, collate_fn
|
||||
|
||||
def test(
|
||||
cfg,
|
||||
data_cfg,
|
||||
weights,
|
||||
batch_size=16,
|
||||
img_size=416,
|
||||
iou_thres=0.5,
|
||||
conf_thres=0.3,
|
||||
nms_thres=0.45,
|
||||
print_interval=40,
|
||||
nID=14455,
|
||||
):
|
||||
|
||||
# Configure run
|
||||
f = open(data_cfg)
|
||||
data_cfg_dict = json.load(f)
|
||||
f.close()
|
||||
#nC = int(data_cfg_dict['classes']) # number of classes (80 for COCO)
|
||||
nC = 1
|
||||
test_path = data_cfg_dict['test']
|
||||
dataset_root = data_cfg_dict['root']
|
||||
|
||||
# Initialize model
|
||||
model = Darknet(cfg, img_size, nID)
|
||||
|
||||
# Load weights
|
||||
if weights.endswith('.pt'): # pytorch format
|
||||
model.load_state_dict(torch.load(weights, map_location='cpu')['model'], strict=False)
|
||||
else: # darknet format
|
||||
load_darknet_weights(model, weights)
|
||||
|
||||
model = torch.nn.DataParallel(model)
|
||||
model.cuda().eval()
|
||||
|
||||
# Get dataloader
|
||||
transforms = T.Compose([T.ToTensor()])
|
||||
dataset = JointDataset(dataset_root, test_path, img_size, augment=False, transforms=transforms)
|
||||
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False,
|
||||
num_workers=8, drop_last=False, collate_fn=collate_fn)
|
||||
|
||||
mean_mAP, mean_R, mean_P, seen = 0.0, 0.0, 0.0, 0
|
||||
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
|
||||
outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class, jdict = \
|
||||
[], [], [], [], [], [], [], [], []
|
||||
AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
|
||||
for batch_i, (imgs, targets, paths, shapes, targets_len) in enumerate(dataloader):
|
||||
t = time.time()
|
||||
output = model(imgs.cuda())
|
||||
output = non_max_suppression(output, conf_thres=conf_thres, nms_thres=nms_thres)
|
||||
for i, o in enumerate(output):
|
||||
if o is not None:
|
||||
output[i] = o[:, :6]
|
||||
|
||||
# Compute average precision for each sample
|
||||
targets = [targets[i][:int(l)] for i,l in enumerate(targets_len)]
|
||||
for si, (labels, detections) in enumerate(zip(targets, output)):
|
||||
seen += 1
|
||||
|
||||
if detections is None:
|
||||
# If there are labels but no detections mark as zero AP
|
||||
if labels.size(0) != 0:
|
||||
mAPs.append(0), mR.append(0), mP.append(0)
|
||||
continue
|
||||
|
||||
# Get detections sorted by decreasing confidence scores
|
||||
detections = detections.cpu().numpy()
|
||||
detections = detections[np.argsort(-detections[:, 4])]
|
||||
|
||||
|
||||
# If no labels add number of detections as incorrect
|
||||
correct = []
|
||||
if labels.size(0) == 0:
|
||||
# correct.extend([0 for _ in range(len(detections))])
|
||||
mAPs.append(0), mR.append(0), mP.append(0)
|
||||
continue
|
||||
else:
|
||||
target_cls = labels[:, 0]
|
||||
|
||||
# Extract target boxes as (x1, y1, x2, y2)
|
||||
target_boxes = xywh2xyxy(labels[:, 2:6])
|
||||
target_boxes[:, 0] *= img_size[0]
|
||||
target_boxes[:, 2] *= img_size[0]
|
||||
target_boxes[:, 1] *= img_size[1]
|
||||
target_boxes[:, 3] *= img_size[1]
|
||||
|
||||
detected = []
|
||||
for *pred_bbox, conf, obj_conf in detections:
|
||||
obj_pred = 0
|
||||
pred_bbox = torch.FloatTensor(pred_bbox).view(1, -1)
|
||||
# Compute iou with target boxes
|
||||
iou = bbox_iou(pred_bbox, target_boxes, x1y1x2y2=True)[0]
|
||||
# Extract index of largest overlap
|
||||
best_i = np.argmax(iou)
|
||||
# If overlap exceeds threshold and classification is correct mark as correct
|
||||
if iou[best_i] > iou_thres and obj_pred == labels[best_i, 0] and best_i not in detected:
|
||||
correct.append(1)
|
||||
detected.append(best_i)
|
||||
else:
|
||||
correct.append(0)
|
||||
|
||||
# Compute Average Precision (AP) per class
|
||||
AP, AP_class, R, P = ap_per_class(tp=correct,
|
||||
conf=detections[:, 4],
|
||||
pred_cls=np.zeros_like(detections[:, 5]), # detections[:, 6]
|
||||
target_cls=target_cls)
|
||||
|
||||
# Accumulate AP per class
|
||||
AP_accum_count += np.bincount(AP_class, minlength=nC)
|
||||
AP_accum += np.bincount(AP_class, minlength=nC, weights=AP)
|
||||
|
||||
# Compute mean AP across all classes in this image, and append to image list
|
||||
mAPs.append(AP.mean())
|
||||
mR.append(R.mean())
|
||||
mP.append(P.mean())
|
||||
|
||||
# Means of all images
|
||||
mean_mAP = np.sum(mAPs) / ( AP_accum_count + 1E-16)
|
||||
mean_R = np.sum(mR) / ( AP_accum_count + 1E-16)
|
||||
mean_P = np.sum(mP) / (AP_accum_count + 1E-16)
|
||||
|
||||
if batch_i % print_interval==0:
|
||||
# Print image mAP and running mean mAP
|
||||
print(('%11s%11s' + '%11.3g' * 4 + 's') %
|
||||
(seen, dataloader.dataset.nF, mean_P, mean_R, mean_mAP, time.time() - t))
|
||||
# Print mAP per class
|
||||
print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
|
||||
|
||||
print('AP: %-.4f\n\n' % (AP_accum[0] / (AP_accum_count[0] + 1E-16)))
|
||||
|
||||
# Return mAP
|
||||
return mean_mAP, mean_R, mean_P
|
||||
|
||||
|
||||
def test_emb(
|
||||
cfg,
|
||||
data_cfg,
|
||||
weights,
|
||||
batch_size=16,
|
||||
img_size=416,
|
||||
iou_thres=0.5,
|
||||
conf_thres=0.3,
|
||||
nms_thres=0.45,
|
||||
print_interval=40,
|
||||
nID=14455,
|
||||
):
|
||||
|
||||
# Configure run
|
||||
f = open(data_cfg)
|
||||
data_cfg_dict = json.load(f)
|
||||
f.close()
|
||||
test_paths = data_cfg_dict['test_emb']
|
||||
dataset_root = data_cfg_dict['root']
|
||||
|
||||
# Initialize model
|
||||
model = Darknet(cfg, img_size, nID, test_emb=True)
|
||||
|
||||
# Load weights
|
||||
if weights.endswith('.pt'): # pytorch format
|
||||
model.load_state_dict(torch.load(weights, map_location='cpu')['model'], strict=False)
|
||||
else: # darknet format
|
||||
load_darknet_weights(model, weights)
|
||||
|
||||
model = torch.nn.DataParallel(model)
|
||||
model.cuda().eval()
|
||||
|
||||
# Get dataloader
|
||||
transforms = T.Compose([T.ToTensor()])
|
||||
dataset = JointDataset(dataset_root, test_paths, img_size, augment=False, transforms=transforms)
|
||||
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False,
|
||||
num_workers=8, drop_last=False, collate_fn=collate_fn)
|
||||
embedding, id_labels = [], []
|
||||
print('Extracting pedestrain features...')
|
||||
for batch_i, (imgs, targets, paths, shapes, targets_len) in enumerate(dataloader):
|
||||
t = time.time()
|
||||
output = model(imgs.cuda(), targets.cuda(), targets_len.cuda()).squeeze()
|
||||
|
||||
for out in output:
|
||||
feat, label = out[:-1], out[-1].long()
|
||||
if label != -1:
|
||||
embedding.append(feat)
|
||||
id_labels.append(label)
|
||||
|
||||
if batch_i % print_interval==0:
|
||||
print('Extracting {}/{}, # of instances {}, time {:.2f} sec.'.format(batch_i, len(dataloader), len(id_labels), time.time() - t))
|
||||
|
||||
print('Computing pairwise similairity...')
|
||||
if len(embedding) <1 :
|
||||
return None
|
||||
embedding = torch.stack(embedding, dim=0).cuda()
|
||||
id_labels = torch.LongTensor(id_labels)
|
||||
n = len(id_labels)
|
||||
print(n, len(embedding))
|
||||
assert len(embedding) == n
|
||||
|
||||
embedding = F.normalize(embedding, dim=1)
|
||||
pdist = torch.mm(embedding, embedding.t()).cpu().numpy()
|
||||
gt = id_labels.expand(n,n).eq(id_labels.expand(n,n).t()).numpy()
|
||||
|
||||
up_triangle = np.where(np.triu(pdist)- np.eye(n)*pdist !=0)
|
||||
pdist = pdist[up_triangle]
|
||||
gt = gt[up_triangle]
|
||||
|
||||
far_levels = [ 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1]
|
||||
far,tar,threshold = metrics.roc_curve(gt, pdist)
|
||||
interp = interpolate.interp1d(far, tar)
|
||||
tar_at_far = [interp(x) for x in far_levels]
|
||||
for f,fa in enumerate(far_levels):
|
||||
print('TPR@FAR={:.7f}: {:.4f}'.format(fa, tar_at_far[f]))
|
||||
return tar_at_far
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(prog='test.py')
|
||||
parser.add_argument('--batch-size', type=int, default=40, help='size of each image batch')
|
||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||
parser.add_argument('--data-cfg', type=str, default='cfg/ccmcpe.json', help='data config')
|
||||
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
|
||||
parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
|
||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
||||
parser.add_argument('--print-interval', type=int, default=10, help='size of each image dimension')
|
||||
parser.add_argument('--test-emb', action='store_true', help='test embedding')
|
||||
opt = parser.parse_args()
|
||||
print(opt, end='\n\n')
|
||||
|
||||
with torch.no_grad():
|
||||
if opt.test_emb:
|
||||
res = test_emb(
|
||||
opt.cfg,
|
||||
opt.data_cfg,
|
||||
opt.weights,
|
||||
opt.batch_size,
|
||||
opt.img_size,
|
||||
opt.iou_thres,
|
||||
opt.conf_thres,
|
||||
opt.nms_thres,
|
||||
opt.print_interval,
|
||||
)
|
||||
else:
|
||||
mAP = test(
|
||||
opt.cfg,
|
||||
opt.data_cfg,
|
||||
opt.weights,
|
||||
opt.batch_size,
|
||||
opt.img_size,
|
||||
opt.iou_thres,
|
||||
opt.conf_thres,
|
||||
opt.nms_thres,
|
||||
opt.print_interval,
|
||||
)
|
||||
|
||||
|
|
|
|||
359
track.py
359
track.py
|
|
@ -1,175 +1,184 @@
|
|||
import os
|
||||
import os.path as osp
|
||||
import cv2
|
||||
import logging
|
||||
import argparse
|
||||
import motmetrics as mm
|
||||
|
||||
from tracker.multitracker import JDETracker
|
||||
from utils import visualization as vis
|
||||
from utils.log import logger
|
||||
from utils.timer import Timer
|
||||
from utils.evaluation import Evaluator
|
||||
import utils.datasets as datasets
|
||||
import torch
|
||||
from utils.utils import *
|
||||
|
||||
|
||||
def write_results(filename, results, data_type):
|
||||
if data_type == 'mot':
|
||||
save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
|
||||
elif data_type == 'kitti':
|
||||
save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n'
|
||||
else:
|
||||
raise ValueError(data_type)
|
||||
|
||||
with open(filename, 'w') as f:
|
||||
for frame_id, tlwhs, track_ids in results:
|
||||
if data_type == 'kitti':
|
||||
frame_id -= 1
|
||||
for tlwh, track_id in zip(tlwhs, track_ids):
|
||||
if track_id < 0:
|
||||
continue
|
||||
x1, y1, w, h = tlwh
|
||||
x2, y2 = x1 + w, y1 + h
|
||||
line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h)
|
||||
f.write(line)
|
||||
logger.info('save results to {}'.format(filename))
|
||||
|
||||
|
||||
def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_image=True, frame_rate=30):
|
||||
if save_dir:
|
||||
mkdir_if_missing(save_dir)
|
||||
tracker = JDETracker(opt, frame_rate=frame_rate)
|
||||
timer = Timer()
|
||||
results = []
|
||||
frame_id = 0
|
||||
for path, img, img0 in dataloader:
|
||||
if frame_id % 20 == 0:
|
||||
logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1./max(1e-5, timer.average_time)))
|
||||
|
||||
# run tracking
|
||||
timer.tic()
|
||||
blob = torch.from_numpy(img).cuda().unsqueeze(0)
|
||||
online_targets = tracker.update(blob, img0)
|
||||
online_tlwhs = []
|
||||
online_ids = []
|
||||
for t in online_targets:
|
||||
tlwh = t.tlwh
|
||||
tid = t.track_id
|
||||
vertical = tlwh[2] / tlwh[3] > 1.6
|
||||
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
|
||||
online_tlwhs.append(tlwh)
|
||||
online_ids.append(tid)
|
||||
timer.toc()
|
||||
# save results
|
||||
results.append((frame_id + 1, online_tlwhs, online_ids))
|
||||
if show_image or save_dir is not None:
|
||||
online_im = vis.plot_tracking(img0, online_tlwhs, online_ids, frame_id=frame_id,
|
||||
fps=1. / timer.average_time)
|
||||
if show_image:
|
||||
cv2.imshow('online_im', online_im)
|
||||
if save_dir is not None:
|
||||
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), online_im)
|
||||
frame_id += 1
|
||||
# save results
|
||||
write_results(result_filename, results, data_type)
|
||||
return frame_id, timer.average_time, timer.calls
|
||||
|
||||
|
||||
def main(opt, data_root='/data/MOT16/train', det_root=None, seqs=('MOT16-05',), exp_name='demo',
|
||||
save_images=False, save_videos=False, show_image=True):
|
||||
logger.setLevel(logging.INFO)
|
||||
result_root = os.path.join(data_root, '..', 'results', exp_name)
|
||||
mkdir_if_missing(result_root)
|
||||
data_type = 'mot'
|
||||
|
||||
# run tracking
|
||||
accs = []
|
||||
n_frame = 0
|
||||
timer_avgs, timer_calls = [], []
|
||||
for seq in seqs:
|
||||
output_dir = os.path.join(data_root, '..','outputs', exp_name, seq) if save_images or save_videos else None
|
||||
|
||||
logger.info('start seq: {}'.format(seq))
|
||||
dataloader = datasets.LoadImages(osp.join(data_root, seq, 'img1'), opt.img_size)
|
||||
result_filename = os.path.join(result_root, '{}.txt'.format(seq))
|
||||
meta_info = open(os.path.join(data_root, seq, 'seqinfo.ini')).read()
|
||||
frame_rate = int(meta_info[meta_info.find('frameRate')+10:meta_info.find('\nseqLength')])
|
||||
nf, ta, tc = eval_seq(opt, dataloader, data_type, result_filename,
|
||||
save_dir=output_dir, show_image=show_image, frame_rate=frame_rate)
|
||||
n_frame += nf
|
||||
timer_avgs.append(ta)
|
||||
timer_calls.append(tc)
|
||||
|
||||
# eval
|
||||
logger.info('Evaluate seq: {}'.format(seq))
|
||||
evaluator = Evaluator(data_root, seq, data_type)
|
||||
accs.append(evaluator.eval_file(result_filename))
|
||||
if save_videos:
|
||||
output_video_path = osp.join(output_dir, '{}.mp4'.format(seq))
|
||||
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(output_dir, output_video_path)
|
||||
os.system(cmd_str)
|
||||
timer_avgs = np.asarray(timer_avgs)
|
||||
timer_calls = np.asarray(timer_calls)
|
||||
all_time = np.dot(timer_avgs, timer_calls)
|
||||
avg_time = all_time / np.sum(timer_calls)
|
||||
logger.info('Time elapsed: {:.2f} seconds, FPS: {:.2f}'.format(all_time, 1.0 / avg_time))
|
||||
|
||||
# get summary
|
||||
metrics = mm.metrics.motchallenge_metrics
|
||||
mh = mm.metrics.create()
|
||||
summary = Evaluator.get_summary(accs, seqs, metrics)
|
||||
strsummary = mm.io.render_summary(
|
||||
summary,
|
||||
formatters=mh.formatters,
|
||||
namemap=mm.io.motchallenge_metric_names
|
||||
)
|
||||
print(strsummary)
|
||||
Evaluator.save_summary(summary, os.path.join(result_root, 'summary_{}.xlsx'.format(exp_name)))
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(prog='track.py')
|
||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
|
||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
|
||||
parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression')
|
||||
parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes')
|
||||
parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer')
|
||||
parser.add_argument('--test-mot16', action='store_true', help='tracking buffer')
|
||||
parser.add_argument('--save-images', action='store_true', help='save tracking results (image)')
|
||||
parser.add_argument('--save-videos', action='store_true', help='save tracking results (video)')
|
||||
opt = parser.parse_args()
|
||||
print(opt, end='\n\n')
|
||||
|
||||
if not opt.test_mot16:
|
||||
seqs_str = '''KITTI-13
|
||||
KITTI-17
|
||||
ADL-Rundle-6
|
||||
PETS09-S2L1
|
||||
TUD-Campus
|
||||
TUD-Stadtmitte'''
|
||||
data_root = '/home/wangzd/datasets/MOT/MOT15/train'
|
||||
else:
|
||||
seqs_str = '''MOT16-01
|
||||
MOT16-03
|
||||
MOT16-06
|
||||
MOT16-07
|
||||
MOT16-08
|
||||
MOT16-12
|
||||
MOT16-14'''
|
||||
data_root = '/home/wangzd/datasets/MOT/MOT16/images/test'
|
||||
seqs = [seq.strip() for seq in seqs_str.split()]
|
||||
|
||||
main(opt,
|
||||
data_root=data_root,
|
||||
seqs=seqs,
|
||||
exp_name=opt.weights.split('/')[-2],
|
||||
show_image=False,
|
||||
save_images=opt.save_images,
|
||||
save_videos=opt.save_videos)
|
||||
|
||||
import os
|
||||
import os.path as osp
|
||||
import cv2
|
||||
import logging
|
||||
import argparse
|
||||
import motmetrics as mm
|
||||
|
||||
from tracker.multitracker import JDETracker
|
||||
from utils import visualization as vis
|
||||
from utils.log import logger
|
||||
from utils.timer import Timer
|
||||
from utils.evaluation import Evaluator
|
||||
import utils.datasets as datasets
|
||||
import torch
|
||||
from utils.utils import *
|
||||
|
||||
|
||||
def write_results(filename, results, data_type):
|
||||
if data_type == 'mot':
|
||||
save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
|
||||
elif data_type == 'kitti':
|
||||
save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n'
|
||||
else:
|
||||
raise ValueError(data_type)
|
||||
|
||||
with open(filename, 'w') as f:
|
||||
for frame_id, tlwhs, track_ids in results:
|
||||
if data_type == 'kitti':
|
||||
frame_id -= 1
|
||||
for tlwh, track_id in zip(tlwhs, track_ids):
|
||||
if track_id < 0:
|
||||
continue
|
||||
x1, y1, w, h = tlwh
|
||||
x2, y2 = x1 + w, y1 + h
|
||||
line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h)
|
||||
f.write(line)
|
||||
logger.info('save results to {}'.format(filename))
|
||||
|
||||
|
||||
def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_image=True, frame_rate=30):
|
||||
if save_dir:
|
||||
mkdir_if_missing(save_dir)
|
||||
tracker = JDETracker(opt, frame_rate=frame_rate)
|
||||
timer = Timer()
|
||||
results = []
|
||||
frame_id = 0
|
||||
for path, img, img0 in dataloader:
|
||||
if frame_id % 20 == 0:
|
||||
logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1./max(1e-5, timer.average_time)))
|
||||
|
||||
# run tracking
|
||||
timer.tic()
|
||||
blob = torch.from_numpy(img).cuda().unsqueeze(0)
|
||||
online_targets = tracker.update(blob, img0)
|
||||
online_tlwhs = []
|
||||
online_ids = []
|
||||
for t in online_targets:
|
||||
tlwh = t.tlwh
|
||||
tid = t.track_id
|
||||
vertical = tlwh[2] / tlwh[3] > 1.6
|
||||
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
|
||||
online_tlwhs.append(tlwh)
|
||||
online_ids.append(tid)
|
||||
timer.toc()
|
||||
# save results
|
||||
results.append((frame_id + 1, online_tlwhs, online_ids))
|
||||
if show_image or save_dir is not None:
|
||||
online_im = vis.plot_tracking(img0, online_tlwhs, online_ids, frame_id=frame_id,
|
||||
fps=1. / timer.average_time)
|
||||
if show_image:
|
||||
cv2.imshow('online_im', online_im)
|
||||
if save_dir is not None:
|
||||
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), online_im)
|
||||
frame_id += 1
|
||||
# save results
|
||||
write_results(result_filename, results, data_type)
|
||||
return frame_id, timer.average_time, timer.calls
|
||||
|
||||
|
||||
def main(opt, data_root='/data/MOT16/train', det_root=None, seqs=('MOT16-05',), exp_name='demo',
|
||||
save_images=False, save_videos=False, show_image=True):
|
||||
logger.setLevel(logging.INFO)
|
||||
result_root = os.path.join(data_root, '..', 'results', exp_name)
|
||||
mkdir_if_missing(result_root)
|
||||
data_type = 'mot'
|
||||
|
||||
# run tracking
|
||||
accs = []
|
||||
n_frame = 0
|
||||
timer_avgs, timer_calls = [], []
|
||||
for seq in seqs:
|
||||
output_dir = os.path.join(data_root, '..','outputs', exp_name, seq) if save_images or save_videos else None
|
||||
|
||||
logger.info('start seq: {}'.format(seq))
|
||||
dataloader = datasets.LoadImages(osp.join(data_root, seq, 'img1'), opt.img_size)
|
||||
result_filename = os.path.join(result_root, '{}.txt'.format(seq))
|
||||
meta_info = open(os.path.join(data_root, seq, 'seqinfo.ini')).read()
|
||||
frame_rate = int(meta_info[meta_info.find('frameRate')+10:meta_info.find('\nseqLength')])
|
||||
nf, ta, tc = eval_seq(opt, dataloader, data_type, result_filename,
|
||||
save_dir=output_dir, show_image=show_image, frame_rate=frame_rate)
|
||||
n_frame += nf
|
||||
timer_avgs.append(ta)
|
||||
timer_calls.append(tc)
|
||||
|
||||
# eval
|
||||
logger.info('Evaluate seq: {}'.format(seq))
|
||||
evaluator = Evaluator(data_root, seq, data_type)
|
||||
accs.append(evaluator.eval_file(result_filename))
|
||||
if save_videos:
|
||||
output_video_path = osp.join(output_dir, '{}.mp4'.format(seq))
|
||||
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(output_dir, output_video_path)
|
||||
os.system(cmd_str)
|
||||
timer_avgs = np.asarray(timer_avgs)
|
||||
timer_calls = np.asarray(timer_calls)
|
||||
all_time = np.dot(timer_avgs, timer_calls)
|
||||
avg_time = all_time / np.sum(timer_calls)
|
||||
logger.info('Time elapsed: {:.2f} seconds, FPS: {:.2f}'.format(all_time, 1.0 / avg_time))
|
||||
|
||||
# get summary
|
||||
metrics = mm.metrics.motchallenge_metrics
|
||||
mh = mm.metrics.create()
|
||||
summary = Evaluator.get_summary(accs, seqs, metrics)
|
||||
strsummary = mm.io.render_summary(
|
||||
summary,
|
||||
formatters=mh.formatters,
|
||||
namemap=mm.io.motchallenge_metric_names
|
||||
)
|
||||
print(strsummary)
|
||||
Evaluator.save_summary(summary, os.path.join(result_root, 'summary_{}.xlsx'.format(exp_name)))
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(prog='track.py')
|
||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
|
||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='size of each image dimension')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
|
||||
parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression')
|
||||
parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes')
|
||||
parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer')
|
||||
parser.add_argument('--test-mot16', action='store_true', help='tracking buffer')
|
||||
parser.add_argument('--save-images', action='store_true', help='save tracking results (image)')
|
||||
parser.add_argument('--save-videos', action='store_true', help='save tracking results (video)')
|
||||
opt = parser.parse_args()
|
||||
print(opt, end='\n\n')
|
||||
|
||||
if not opt.test_mot16:
|
||||
#seqs_str = '''KITTI-13
|
||||
# KITTI-17
|
||||
# ADL-Rundle-6
|
||||
# PETS09-S2L1
|
||||
# TUD-Campus
|
||||
# TUD-Stadtmitte'''
|
||||
#data_root = '/home/wangzd/datasets/MOT/MOT15/train'
|
||||
seqs_str = '''MOT17-02-SDP
|
||||
MOT17-04-SDP
|
||||
MOT17-05-SDP
|
||||
MOT17-09-SDP
|
||||
MOT17-10-SDP
|
||||
MOT17-11-SDP
|
||||
MOT17-13-SDP
|
||||
'''
|
||||
data_root = '/home/wangzd/datasets/MOT/MOT17/images/train'
|
||||
else:
|
||||
seqs_str = '''MOT16-01
|
||||
MOT16-03
|
||||
MOT16-06
|
||||
MOT16-07
|
||||
MOT16-08
|
||||
MOT16-12
|
||||
MOT16-14'''
|
||||
data_root = '/home/wangzd/datasets/MOT/MOT16/images/test'
|
||||
seqs = [seq.strip() for seq in seqs_str.split()]
|
||||
|
||||
main(opt,
|
||||
data_root=data_root,
|
||||
seqs=seqs,
|
||||
exp_name=opt.weights.split('/')[-2],
|
||||
show_image=False,
|
||||
save_images=opt.save_images,
|
||||
save_videos=opt.save_videos)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,53 +1,53 @@
|
|||
import numpy as np
|
||||
from collections import OrderedDict
|
||||
|
||||
|
||||
class TrackState(object):
|
||||
New = 0
|
||||
Tracked = 1
|
||||
Lost = 2
|
||||
Removed = 3
|
||||
|
||||
|
||||
class BaseTrack(object):
|
||||
_count = 0
|
||||
|
||||
track_id = 0
|
||||
is_activated = False
|
||||
state = TrackState.New
|
||||
|
||||
history = OrderedDict()
|
||||
features = []
|
||||
curr_feature = None
|
||||
score = 0
|
||||
start_frame = 0
|
||||
frame_id = 0
|
||||
time_since_update = 0
|
||||
|
||||
# multi-camera
|
||||
location = (np.inf, np.inf)
|
||||
|
||||
@property
|
||||
def end_frame(self):
|
||||
return self.frame_id
|
||||
|
||||
@staticmethod
|
||||
def next_id():
|
||||
BaseTrack._count += 1
|
||||
return BaseTrack._count
|
||||
|
||||
def activate(self, *args):
|
||||
raise NotImplementedError
|
||||
|
||||
def predict(self):
|
||||
raise NotImplementedError
|
||||
|
||||
def update(self, *args, **kwargs):
|
||||
raise NotImplementedError
|
||||
|
||||
def mark_lost(self):
|
||||
self.state = TrackState.Lost
|
||||
|
||||
def mark_removed(self):
|
||||
self.state = TrackState.Removed
|
||||
|
||||
import numpy as np
|
||||
from collections import OrderedDict
|
||||
|
||||
|
||||
class TrackState(object):
|
||||
New = 0
|
||||
Tracked = 1
|
||||
Lost = 2
|
||||
Removed = 3
|
||||
|
||||
|
||||
class BaseTrack(object):
|
||||
_count = 0
|
||||
|
||||
track_id = 0
|
||||
is_activated = False
|
||||
state = TrackState.New
|
||||
|
||||
history = OrderedDict()
|
||||
features = []
|
||||
curr_feature = None
|
||||
score = 0
|
||||
start_frame = 0
|
||||
frame_id = 0
|
||||
time_since_update = 0
|
||||
|
||||
# multi-camera
|
||||
location = (np.inf, np.inf)
|
||||
|
||||
@property
|
||||
def end_frame(self):
|
||||
return self.frame_id
|
||||
|
||||
@staticmethod
|
||||
def next_id():
|
||||
BaseTrack._count += 1
|
||||
return BaseTrack._count
|
||||
|
||||
def activate(self, *args):
|
||||
raise NotImplementedError
|
||||
|
||||
def predict(self):
|
||||
raise NotImplementedError
|
||||
|
||||
def update(self, *args, **kwargs):
|
||||
raise NotImplementedError
|
||||
|
||||
def mark_lost(self):
|
||||
self.state = TrackState.Lost
|
||||
|
||||
def mark_removed(self):
|
||||
self.state = TrackState.Removed
|
||||
|
||||
|
|
|
|||
|
|
@ -1,122 +1,122 @@
|
|||
import cv2
|
||||
import numpy as np
|
||||
import scipy
|
||||
from scipy.spatial.distance import cdist
|
||||
from sklearn.utils import linear_assignment_
|
||||
|
||||
from utils.cython_bbox import bbox_ious
|
||||
from utils import kalman_filter
|
||||
import time
|
||||
|
||||
def merge_matches(m1, m2, shape):
|
||||
O,P,Q = shape
|
||||
m1 = np.asarray(m1)
|
||||
m2 = np.asarray(m2)
|
||||
|
||||
M1 = scipy.sparse.coo_matrix((np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P))
|
||||
M2 = scipy.sparse.coo_matrix((np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q))
|
||||
|
||||
mask = M1*M2
|
||||
match = mask.nonzero()
|
||||
match = list(zip(match[0], match[1]))
|
||||
unmatched_O = tuple(set(range(O)) - set([i for i, j in match]))
|
||||
unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match]))
|
||||
|
||||
return match, unmatched_O, unmatched_Q
|
||||
|
||||
|
||||
def _indices_to_matches(cost_matrix, indices, thresh):
|
||||
matched_cost = cost_matrix[tuple(zip(*indices))]
|
||||
matched_mask = (matched_cost <= thresh)
|
||||
|
||||
matches = indices[matched_mask]
|
||||
unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0]))
|
||||
unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1]))
|
||||
|
||||
return matches, unmatched_a, unmatched_b
|
||||
|
||||
|
||||
def linear_assignment(cost_matrix, thresh):
|
||||
"""
|
||||
Simple linear assignment
|
||||
:type cost_matrix: np.ndarray
|
||||
:type thresh: float
|
||||
:return: matches, unmatched_a, unmatched_b
|
||||
"""
|
||||
if cost_matrix.size == 0:
|
||||
return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
|
||||
|
||||
cost_matrix[cost_matrix > thresh] = thresh + 1e-4
|
||||
indices = linear_assignment_.linear_assignment(cost_matrix)
|
||||
|
||||
return _indices_to_matches(cost_matrix, indices, thresh)
|
||||
|
||||
|
||||
def ious(atlbrs, btlbrs):
|
||||
"""
|
||||
Compute cost based on IoU
|
||||
:type atlbrs: list[tlbr] | np.ndarray
|
||||
:type atlbrs: list[tlbr] | np.ndarray
|
||||
|
||||
:rtype ious np.ndarray
|
||||
"""
|
||||
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
|
||||
if ious.size == 0:
|
||||
return ious
|
||||
|
||||
ious = bbox_ious(
|
||||
np.ascontiguousarray(atlbrs, dtype=np.float),
|
||||
np.ascontiguousarray(btlbrs, dtype=np.float)
|
||||
)
|
||||
|
||||
return ious
|
||||
|
||||
|
||||
def iou_distance(atracks, btracks):
|
||||
"""
|
||||
Compute cost based on IoU
|
||||
:type atracks: list[STrack]
|
||||
:type btracks: list[STrack]
|
||||
|
||||
:rtype cost_matrix np.ndarray
|
||||
"""
|
||||
|
||||
if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):
|
||||
atlbrs = atracks
|
||||
btlbrs = btracks
|
||||
else:
|
||||
atlbrs = [track.tlbr for track in atracks]
|
||||
btlbrs = [track.tlbr for track in btracks]
|
||||
_ious = ious(atlbrs, btlbrs)
|
||||
cost_matrix = 1 - _ious
|
||||
|
||||
return cost_matrix
|
||||
|
||||
def embedding_distance(tracks, detections, metric='cosine'):
|
||||
"""
|
||||
:param tracks: list[STrack]
|
||||
:param detections: list[BaseTrack]
|
||||
:param metric:
|
||||
:return: cost_matrix np.ndarray
|
||||
"""
|
||||
|
||||
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
|
||||
if cost_matrix.size == 0:
|
||||
return cost_matrix
|
||||
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
|
||||
for i, track in enumerate(tracks):
|
||||
cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
|
||||
return cost_matrix
|
||||
|
||||
|
||||
def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):
|
||||
if cost_matrix.size == 0:
|
||||
return cost_matrix
|
||||
gating_dim = 2 if only_position else 4
|
||||
gating_threshold = kalman_filter.chi2inv95[gating_dim]
|
||||
measurements = np.asarray([det.to_xyah() for det in detections])
|
||||
for row, track in enumerate(tracks):
|
||||
gating_distance = kf.gating_distance(
|
||||
track.mean, track.covariance, measurements, only_position)
|
||||
cost_matrix[row, gating_distance > gating_threshold] = np.inf
|
||||
return cost_matrix
|
||||
import cv2
|
||||
import numpy as np
|
||||
import scipy
|
||||
from scipy.spatial.distance import cdist
|
||||
from sklearn.utils import linear_assignment_
|
||||
|
||||
from cython_bbox import bbox_overlaps as bbox_ious
|
||||
from utils import kalman_filter
|
||||
import time
|
||||
|
||||
def merge_matches(m1, m2, shape):
|
||||
O,P,Q = shape
|
||||
m1 = np.asarray(m1)
|
||||
m2 = np.asarray(m2)
|
||||
|
||||
M1 = scipy.sparse.coo_matrix((np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P))
|
||||
M2 = scipy.sparse.coo_matrix((np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q))
|
||||
|
||||
mask = M1*M2
|
||||
match = mask.nonzero()
|
||||
match = list(zip(match[0], match[1]))
|
||||
unmatched_O = tuple(set(range(O)) - set([i for i, j in match]))
|
||||
unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match]))
|
||||
|
||||
return match, unmatched_O, unmatched_Q
|
||||
|
||||
|
||||
def _indices_to_matches(cost_matrix, indices, thresh):
|
||||
matched_cost = cost_matrix[tuple(zip(*indices))]
|
||||
matched_mask = (matched_cost <= thresh)
|
||||
|
||||
matches = indices[matched_mask]
|
||||
unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0]))
|
||||
unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1]))
|
||||
|
||||
return matches, unmatched_a, unmatched_b
|
||||
|
||||
|
||||
def linear_assignment(cost_matrix, thresh):
|
||||
"""
|
||||
Simple linear assignment
|
||||
:type cost_matrix: np.ndarray
|
||||
:type thresh: float
|
||||
:return: matches, unmatched_a, unmatched_b
|
||||
"""
|
||||
if cost_matrix.size == 0:
|
||||
return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
|
||||
|
||||
cost_matrix[cost_matrix > thresh] = thresh + 1e-4
|
||||
indices = linear_assignment_.linear_assignment(cost_matrix)
|
||||
|
||||
return _indices_to_matches(cost_matrix, indices, thresh)
|
||||
|
||||
|
||||
def ious(atlbrs, btlbrs):
|
||||
"""
|
||||
Compute cost based on IoU
|
||||
:type atlbrs: list[tlbr] | np.ndarray
|
||||
:type atlbrs: list[tlbr] | np.ndarray
|
||||
|
||||
:rtype ious np.ndarray
|
||||
"""
|
||||
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
|
||||
if ious.size == 0:
|
||||
return ious
|
||||
|
||||
ious = bbox_ious(
|
||||
np.ascontiguousarray(atlbrs, dtype=np.float),
|
||||
np.ascontiguousarray(btlbrs, dtype=np.float)
|
||||
)
|
||||
|
||||
return ious
|
||||
|
||||
|
||||
def iou_distance(atracks, btracks):
|
||||
"""
|
||||
Compute cost based on IoU
|
||||
:type atracks: list[STrack]
|
||||
:type btracks: list[STrack]
|
||||
|
||||
:rtype cost_matrix np.ndarray
|
||||
"""
|
||||
|
||||
if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):
|
||||
atlbrs = atracks
|
||||
btlbrs = btracks
|
||||
else:
|
||||
atlbrs = [track.tlbr for track in atracks]
|
||||
btlbrs = [track.tlbr for track in btracks]
|
||||
_ious = ious(atlbrs, btlbrs)
|
||||
cost_matrix = 1 - _ious
|
||||
|
||||
return cost_matrix
|
||||
|
||||
def embedding_distance(tracks, detections, metric='cosine'):
|
||||
"""
|
||||
:param tracks: list[STrack]
|
||||
:param detections: list[BaseTrack]
|
||||
:param metric:
|
||||
:return: cost_matrix np.ndarray
|
||||
"""
|
||||
|
||||
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
|
||||
if cost_matrix.size == 0:
|
||||
return cost_matrix
|
||||
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
|
||||
for i, track in enumerate(tracks):
|
||||
cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
|
||||
return cost_matrix
|
||||
|
||||
|
||||
def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):
|
||||
if cost_matrix.size == 0:
|
||||
return cost_matrix
|
||||
gating_dim = 2 if only_position else 4
|
||||
gating_threshold = kalman_filter.chi2inv95[gating_dim]
|
||||
measurements = np.asarray([det.to_xyah() for det in detections])
|
||||
for row, track in enumerate(tracks):
|
||||
gating_distance = kf.gating_distance(
|
||||
track.mean, track.covariance, measurements, only_position)
|
||||
cost_matrix[row, gating_distance > gating_threshold] = np.inf
|
||||
return cost_matrix
|
||||
|
|
|
|||
|
|
@ -1,335 +1,335 @@
|
|||
import numpy as np
|
||||
from numba import jit
|
||||
from collections import deque
|
||||
import itertools
|
||||
import os
|
||||
import os.path as osp
|
||||
import time
|
||||
import torch
|
||||
|
||||
from utils.utils import *
|
||||
from utils.log import logger
|
||||
from utils.kalman_filter import KalmanFilter
|
||||
from models import *
|
||||
from tracker import matching
|
||||
from .basetrack import BaseTrack, TrackState
|
||||
|
||||
|
||||
class STrack(BaseTrack):
|
||||
|
||||
def __init__(self, tlwh, score, temp_feat, buffer_size=30):
|
||||
|
||||
# wait activate
|
||||
self._tlwh = np.asarray(tlwh, dtype=np.float)
|
||||
self.kalman_filter = None
|
||||
self.mean, self.covariance = None, None
|
||||
self.is_activated = False
|
||||
|
||||
self.score = score
|
||||
self.tracklet_len = 0
|
||||
|
||||
self.smooth_feat = None
|
||||
self.update_features(temp_feat)
|
||||
self.features = deque([], maxlen=buffer_size)
|
||||
self.alpha = 0.9
|
||||
|
||||
def update_features(self, feat):
|
||||
self.curr_feat = feat
|
||||
if self.smooth_feat is None:
|
||||
self.smooth_feat = feat
|
||||
else:
|
||||
self.smooth_feat = self.alpha *self.smooth_feat + (1-self.alpha) * feat
|
||||
self.features.append(feat)
|
||||
self.smooth_feat /= np.linalg.norm(self.smooth_feat)
|
||||
|
||||
def predict(self):
|
||||
mean_state = self.mean.copy()
|
||||
if self.state != TrackState.Tracked:
|
||||
mean_state[7] = 0
|
||||
self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
|
||||
|
||||
|
||||
def activate(self, kalman_filter, frame_id):
|
||||
"""Start a new tracklet"""
|
||||
self.kalman_filter = kalman_filter
|
||||
self.track_id = self.next_id()
|
||||
self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
|
||||
|
||||
self.tracklet_len = 0
|
||||
self.state = TrackState.Tracked
|
||||
#self.is_activated = True
|
||||
self.frame_id = frame_id
|
||||
self.start_frame = frame_id
|
||||
|
||||
def re_activate(self, new_track, frame_id, new_id=False):
|
||||
self.mean, self.covariance = self.kalman_filter.update(
|
||||
self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
|
||||
)
|
||||
|
||||
self.update_features(new_track.curr_feat)
|
||||
self.tracklet_len = 0
|
||||
self.state = TrackState.Tracked
|
||||
self.is_activated = True
|
||||
self.frame_id = frame_id
|
||||
if new_id:
|
||||
self.track_id = self.next_id()
|
||||
|
||||
def update(self, new_track, frame_id, update_feature=True):
|
||||
"""
|
||||
Update a matched track
|
||||
:type new_track: STrack
|
||||
:type frame_id: int
|
||||
:type update_feature: bool
|
||||
:return:
|
||||
"""
|
||||
self.frame_id = frame_id
|
||||
self.tracklet_len += 1
|
||||
|
||||
new_tlwh = new_track.tlwh
|
||||
self.mean, self.covariance = self.kalman_filter.update(
|
||||
self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
|
||||
self.state = TrackState.Tracked
|
||||
self.is_activated = True
|
||||
|
||||
self.score = new_track.score
|
||||
if update_feature:
|
||||
self.update_features(new_track.curr_feat)
|
||||
|
||||
@property
|
||||
@jit
|
||||
def tlwh(self):
|
||||
"""Get current position in bounding box format `(top left x, top left y,
|
||||
width, height)`.
|
||||
"""
|
||||
if self.mean is None:
|
||||
return self._tlwh.copy()
|
||||
ret = self.mean[:4].copy()
|
||||
ret[2] *= ret[3]
|
||||
ret[:2] -= ret[2:] / 2
|
||||
return ret
|
||||
|
||||
@property
|
||||
@jit
|
||||
def tlbr(self):
|
||||
"""Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
|
||||
`(top left, bottom right)`.
|
||||
"""
|
||||
ret = self.tlwh.copy()
|
||||
ret[2:] += ret[:2]
|
||||
return ret
|
||||
|
||||
@staticmethod
|
||||
@jit
|
||||
def tlwh_to_xyah(tlwh):
|
||||
"""Convert bounding box to format `(center x, center y, aspect ratio,
|
||||
height)`, where the aspect ratio is `width / height`.
|
||||
"""
|
||||
ret = np.asarray(tlwh).copy()
|
||||
ret[:2] += ret[2:] / 2
|
||||
ret[2] /= ret[3]
|
||||
return ret
|
||||
|
||||
def to_xyah(self):
|
||||
return self.tlwh_to_xyah(self.tlwh)
|
||||
|
||||
@staticmethod
|
||||
@jit
|
||||
def tlbr_to_tlwh(tlbr):
|
||||
ret = np.asarray(tlbr).copy()
|
||||
ret[2:] -= ret[:2]
|
||||
return ret
|
||||
|
||||
@staticmethod
|
||||
@jit
|
||||
def tlwh_to_tlbr(tlwh):
|
||||
ret = np.asarray(tlwh).copy()
|
||||
ret[2:] += ret[:2]
|
||||
return ret
|
||||
|
||||
def __repr__(self):
|
||||
return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)
|
||||
|
||||
|
||||
class JDETracker(object):
|
||||
def __init__(self, opt, frame_rate=30):
|
||||
self.opt = opt
|
||||
self.model = Darknet(opt.cfg, opt.img_size, nID=14455)
|
||||
# load_darknet_weights(self.model, opt.weights)
|
||||
self.model.load_state_dict(torch.load(opt.weights, map_location='cpu')['model'], strict=False)
|
||||
self.model.cuda().eval()
|
||||
|
||||
self.tracked_stracks = [] # type: list[STrack]
|
||||
self.lost_stracks = [] # type: list[STrack]
|
||||
self.removed_stracks = [] # type: list[STrack]
|
||||
|
||||
self.frame_id = 0
|
||||
self.det_thresh = opt.conf_thres
|
||||
self.buffer_size = int(frame_rate / 30.0 * opt.track_buffer)
|
||||
self.max_time_lost = self.buffer_size
|
||||
|
||||
self.kalman_filter = KalmanFilter()
|
||||
|
||||
def update(self, im_blob, img0):
|
||||
self.frame_id += 1
|
||||
activated_starcks = []
|
||||
refind_stracks = []
|
||||
lost_stracks = []
|
||||
removed_stracks = []
|
||||
|
||||
t1 = time.time()
|
||||
''' Step 1: Network forward, get detections & embeddings'''
|
||||
with torch.no_grad():
|
||||
pred = self.model(im_blob)
|
||||
pred = pred[pred[:, :, 4] > self.opt.conf_thres]
|
||||
if len(pred) > 0:
|
||||
dets = non_max_suppression(pred.unsqueeze(0), self.opt.conf_thres, self.opt.nms_thres)[0].cpu()
|
||||
scale_coords(self.opt.img_size, dets[:, :4], img0.shape).round()
|
||||
'''Detections'''
|
||||
detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f.numpy(), 30) for
|
||||
(tlbrs, f) in zip(dets[:, :5], dets[:, -self.model.emb_dim:])]
|
||||
else:
|
||||
detections = []
|
||||
|
||||
t2 = time.time()
|
||||
# print('Forward: {} s'.format(t2-t1))
|
||||
|
||||
''' Add newly detected tracklets to tracked_stracks'''
|
||||
unconfirmed = []
|
||||
tracked_stracks = [] # type: list[STrack]
|
||||
for track in self.tracked_stracks:
|
||||
if not track.is_activated:
|
||||
unconfirmed.append(track)
|
||||
else:
|
||||
tracked_stracks.append(track)
|
||||
|
||||
''' Step 2: First association, with embedding'''
|
||||
strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
|
||||
# Predict the current location with KF
|
||||
for strack in strack_pool:
|
||||
strack.predict()
|
||||
|
||||
dists = matching.embedding_distance(strack_pool, detections)
|
||||
dists = matching.gate_cost_matrix(self.kalman_filter, dists, strack_pool, detections)
|
||||
matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7)
|
||||
|
||||
for itracked, idet in matches:
|
||||
track = strack_pool[itracked]
|
||||
det = detections[idet]
|
||||
if track.state == TrackState.Tracked:
|
||||
track.update(detections[idet], self.frame_id)
|
||||
activated_starcks.append(track)
|
||||
else:
|
||||
track.re_activate(det, self.frame_id, new_id=False)
|
||||
refind_stracks.append(track)
|
||||
|
||||
''' Step 3: Second association, with IOU'''
|
||||
detections = [detections[i] for i in u_detection]
|
||||
r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state==TrackState.Tracked ]
|
||||
dists = matching.iou_distance(r_tracked_stracks, detections)
|
||||
matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5)
|
||||
|
||||
for itracked, idet in matches:
|
||||
track = r_tracked_stracks[itracked]
|
||||
det = detections[idet]
|
||||
if track.state == TrackState.Tracked:
|
||||
track.update(det, self.frame_id)
|
||||
activated_starcks.append(track)
|
||||
else:
|
||||
track.re_activate(det, self.frame_id, new_id=False)
|
||||
refind_stracks.append(track)
|
||||
|
||||
for it in u_track:
|
||||
track = r_tracked_stracks[it]
|
||||
if not track.state == TrackState.Lost:
|
||||
track.mark_lost()
|
||||
lost_stracks.append(track)
|
||||
|
||||
'''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
|
||||
detections = [detections[i] for i in u_detection]
|
||||
dists = matching.iou_distance(unconfirmed, detections)
|
||||
matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
|
||||
for itracked, idet in matches:
|
||||
unconfirmed[itracked].update(detections[idet], self.frame_id)
|
||||
activated_starcks.append(unconfirmed[itracked])
|
||||
for it in u_unconfirmed:
|
||||
track = unconfirmed[it]
|
||||
track.mark_removed()
|
||||
removed_stracks.append(track)
|
||||
|
||||
""" Step 4: Init new stracks"""
|
||||
for inew in u_detection:
|
||||
track = detections[inew]
|
||||
if track.score < self.det_thresh:
|
||||
continue
|
||||
track.activate(self.kalman_filter, self.frame_id)
|
||||
activated_starcks.append(track)
|
||||
|
||||
""" Step 5: Update state"""
|
||||
for track in self.lost_stracks:
|
||||
if self.frame_id - track.end_frame > self.max_time_lost:
|
||||
track.mark_removed()
|
||||
removed_stracks.append(track)
|
||||
t4 = time.time()
|
||||
# print('Ramained match {} s'.format(t4-t3))
|
||||
|
||||
self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
|
||||
self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
|
||||
self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
|
||||
# self.lost_stracks = [t for t in self.lost_stracks if t.state == TrackState.Lost] # type: list[STrack]
|
||||
self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
|
||||
self.lost_stracks.extend(lost_stracks)
|
||||
self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
|
||||
self.removed_stracks.extend(removed_stracks)
|
||||
self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
|
||||
|
||||
# get scores of lost tracks
|
||||
output_stracks = [track for track in self.tracked_stracks if track.is_activated]
|
||||
|
||||
logger.debug('===========Frame {}=========='.format(self.frame_id))
|
||||
logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks]))
|
||||
logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks]))
|
||||
logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks]))
|
||||
logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks]))
|
||||
t5 = time.time()
|
||||
# print('Final {} s'.format(t5-t4))
|
||||
return output_stracks
|
||||
|
||||
def joint_stracks(tlista, tlistb):
|
||||
exists = {}
|
||||
res = []
|
||||
for t in tlista:
|
||||
exists[t.track_id] = 1
|
||||
res.append(t)
|
||||
for t in tlistb:
|
||||
tid = t.track_id
|
||||
if not exists.get(tid, 0):
|
||||
exists[tid] = 1
|
||||
res.append(t)
|
||||
return res
|
||||
|
||||
def sub_stracks(tlista, tlistb):
|
||||
stracks = {}
|
||||
for t in tlista:
|
||||
stracks[t.track_id] = t
|
||||
for t in tlistb:
|
||||
tid = t.track_id
|
||||
if stracks.get(tid, 0):
|
||||
del stracks[tid]
|
||||
return list(stracks.values())
|
||||
|
||||
def remove_duplicate_stracks(stracksa, stracksb):
|
||||
pdist = matching.iou_distance(stracksa, stracksb)
|
||||
pairs = np.where(pdist<0.15)
|
||||
dupa, dupb = list(), list()
|
||||
for p,q in zip(*pairs):
|
||||
timep = stracksa[p].frame_id - stracksa[p].start_frame
|
||||
timeq = stracksb[q].frame_id - stracksb[q].start_frame
|
||||
if timep > timeq:
|
||||
dupb.append(q)
|
||||
else:
|
||||
dupa.append(p)
|
||||
resa = [t for i,t in enumerate(stracksa) if not i in dupa]
|
||||
resb = [t for i,t in enumerate(stracksb) if not i in dupb]
|
||||
return resa, resb
|
||||
|
||||
|
||||
import numpy as np
|
||||
from numba import jit
|
||||
from collections import deque
|
||||
import itertools
|
||||
import os
|
||||
import os.path as osp
|
||||
import time
|
||||
import torch
|
||||
|
||||
from utils.utils import *
|
||||
from utils.log import logger
|
||||
from utils.kalman_filter import KalmanFilter
|
||||
from models import *
|
||||
from tracker import matching
|
||||
from .basetrack import BaseTrack, TrackState
|
||||
|
||||
|
||||
class STrack(BaseTrack):
|
||||
|
||||
def __init__(self, tlwh, score, temp_feat, buffer_size=30):
|
||||
|
||||
# wait activate
|
||||
self._tlwh = np.asarray(tlwh, dtype=np.float)
|
||||
self.kalman_filter = None
|
||||
self.mean, self.covariance = None, None
|
||||
self.is_activated = False
|
||||
|
||||
self.score = score
|
||||
self.tracklet_len = 0
|
||||
|
||||
self.smooth_feat = None
|
||||
self.update_features(temp_feat)
|
||||
self.features = deque([], maxlen=buffer_size)
|
||||
self.alpha = 0.9
|
||||
|
||||
def update_features(self, feat):
|
||||
self.curr_feat = feat
|
||||
if self.smooth_feat is None:
|
||||
self.smooth_feat = feat
|
||||
else:
|
||||
self.smooth_feat = self.alpha *self.smooth_feat + (1-self.alpha) * feat
|
||||
self.features.append(feat)
|
||||
self.smooth_feat /= np.linalg.norm(self.smooth_feat)
|
||||
|
||||
def predict(self):
|
||||
mean_state = self.mean.copy()
|
||||
if self.state != TrackState.Tracked:
|
||||
mean_state[7] = 0
|
||||
self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
|
||||
|
||||
|
||||
def activate(self, kalman_filter, frame_id):
|
||||
"""Start a new tracklet"""
|
||||
self.kalman_filter = kalman_filter
|
||||
self.track_id = self.next_id()
|
||||
self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
|
||||
|
||||
self.tracklet_len = 0
|
||||
self.state = TrackState.Tracked
|
||||
#self.is_activated = True
|
||||
self.frame_id = frame_id
|
||||
self.start_frame = frame_id
|
||||
|
||||
def re_activate(self, new_track, frame_id, new_id=False):
|
||||
self.mean, self.covariance = self.kalman_filter.update(
|
||||
self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
|
||||
)
|
||||
|
||||
self.update_features(new_track.curr_feat)
|
||||
self.tracklet_len = 0
|
||||
self.state = TrackState.Tracked
|
||||
self.is_activated = True
|
||||
self.frame_id = frame_id
|
||||
if new_id:
|
||||
self.track_id = self.next_id()
|
||||
|
||||
def update(self, new_track, frame_id, update_feature=True):
|
||||
"""
|
||||
Update a matched track
|
||||
:type new_track: STrack
|
||||
:type frame_id: int
|
||||
:type update_feature: bool
|
||||
:return:
|
||||
"""
|
||||
self.frame_id = frame_id
|
||||
self.tracklet_len += 1
|
||||
|
||||
new_tlwh = new_track.tlwh
|
||||
self.mean, self.covariance = self.kalman_filter.update(
|
||||
self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
|
||||
self.state = TrackState.Tracked
|
||||
self.is_activated = True
|
||||
|
||||
self.score = new_track.score
|
||||
if update_feature:
|
||||
self.update_features(new_track.curr_feat)
|
||||
|
||||
@property
|
||||
@jit
|
||||
def tlwh(self):
|
||||
"""Get current position in bounding box format `(top left x, top left y,
|
||||
width, height)`.
|
||||
"""
|
||||
if self.mean is None:
|
||||
return self._tlwh.copy()
|
||||
ret = self.mean[:4].copy()
|
||||
ret[2] *= ret[3]
|
||||
ret[:2] -= ret[2:] / 2
|
||||
return ret
|
||||
|
||||
@property
|
||||
@jit
|
||||
def tlbr(self):
|
||||
"""Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
|
||||
`(top left, bottom right)`.
|
||||
"""
|
||||
ret = self.tlwh.copy()
|
||||
ret[2:] += ret[:2]
|
||||
return ret
|
||||
|
||||
@staticmethod
|
||||
@jit
|
||||
def tlwh_to_xyah(tlwh):
|
||||
"""Convert bounding box to format `(center x, center y, aspect ratio,
|
||||
height)`, where the aspect ratio is `width / height`.
|
||||
"""
|
||||
ret = np.asarray(tlwh).copy()
|
||||
ret[:2] += ret[2:] / 2
|
||||
ret[2] /= ret[3]
|
||||
return ret
|
||||
|
||||
def to_xyah(self):
|
||||
return self.tlwh_to_xyah(self.tlwh)
|
||||
|
||||
@staticmethod
|
||||
@jit
|
||||
def tlbr_to_tlwh(tlbr):
|
||||
ret = np.asarray(tlbr).copy()
|
||||
ret[2:] -= ret[:2]
|
||||
return ret
|
||||
|
||||
@staticmethod
|
||||
@jit
|
||||
def tlwh_to_tlbr(tlwh):
|
||||
ret = np.asarray(tlwh).copy()
|
||||
ret[2:] += ret[:2]
|
||||
return ret
|
||||
|
||||
def __repr__(self):
|
||||
return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)
|
||||
|
||||
|
||||
class JDETracker(object):
|
||||
def __init__(self, opt, frame_rate=30):
|
||||
self.opt = opt
|
||||
self.model = Darknet(opt.cfg, opt.img_size, nID=14455)
|
||||
# load_darknet_weights(self.model, opt.weights)
|
||||
self.model.load_state_dict(torch.load(opt.weights, map_location='cpu')['model'], strict=False)
|
||||
self.model.cuda().eval()
|
||||
|
||||
self.tracked_stracks = [] # type: list[STrack]
|
||||
self.lost_stracks = [] # type: list[STrack]
|
||||
self.removed_stracks = [] # type: list[STrack]
|
||||
|
||||
self.frame_id = 0
|
||||
self.det_thresh = opt.conf_thres
|
||||
self.buffer_size = int(frame_rate / 30.0 * opt.track_buffer)
|
||||
self.max_time_lost = self.buffer_size
|
||||
|
||||
self.kalman_filter = KalmanFilter()
|
||||
|
||||
def update(self, im_blob, img0):
|
||||
self.frame_id += 1
|
||||
activated_starcks = []
|
||||
refind_stracks = []
|
||||
lost_stracks = []
|
||||
removed_stracks = []
|
||||
|
||||
t1 = time.time()
|
||||
''' Step 1: Network forward, get detections & embeddings'''
|
||||
with torch.no_grad():
|
||||
pred = self.model(im_blob)
|
||||
pred = pred[pred[:, :, 4] > self.opt.conf_thres]
|
||||
if len(pred) > 0:
|
||||
dets = non_max_suppression(pred.unsqueeze(0), self.opt.conf_thres, self.opt.nms_thres)[0].cpu()
|
||||
scale_coords(self.opt.img_size, dets[:, :4], img0.shape).round()
|
||||
'''Detections'''
|
||||
detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f.numpy(), 30) for
|
||||
(tlbrs, f) in zip(dets[:, :5], dets[:, -self.model.emb_dim:])]
|
||||
else:
|
||||
detections = []
|
||||
|
||||
t2 = time.time()
|
||||
# print('Forward: {} s'.format(t2-t1))
|
||||
|
||||
''' Add newly detected tracklets to tracked_stracks'''
|
||||
unconfirmed = []
|
||||
tracked_stracks = [] # type: list[STrack]
|
||||
for track in self.tracked_stracks:
|
||||
if not track.is_activated:
|
||||
unconfirmed.append(track)
|
||||
else:
|
||||
tracked_stracks.append(track)
|
||||
|
||||
''' Step 2: First association, with embedding'''
|
||||
strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
|
||||
# Predict the current location with KF
|
||||
for strack in strack_pool:
|
||||
strack.predict()
|
||||
|
||||
dists = matching.embedding_distance(strack_pool, detections)
|
||||
dists = matching.gate_cost_matrix(self.kalman_filter, dists, strack_pool, detections)
|
||||
matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7)
|
||||
|
||||
for itracked, idet in matches:
|
||||
track = strack_pool[itracked]
|
||||
det = detections[idet]
|
||||
if track.state == TrackState.Tracked:
|
||||
track.update(detections[idet], self.frame_id)
|
||||
activated_starcks.append(track)
|
||||
else:
|
||||
track.re_activate(det, self.frame_id, new_id=False)
|
||||
refind_stracks.append(track)
|
||||
|
||||
''' Step 3: Second association, with IOU'''
|
||||
detections = [detections[i] for i in u_detection]
|
||||
r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state==TrackState.Tracked ]
|
||||
dists = matching.iou_distance(r_tracked_stracks, detections)
|
||||
matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5)
|
||||
|
||||
for itracked, idet in matches:
|
||||
track = r_tracked_stracks[itracked]
|
||||
det = detections[idet]
|
||||
if track.state == TrackState.Tracked:
|
||||
track.update(det, self.frame_id)
|
||||
activated_starcks.append(track)
|
||||
else:
|
||||
track.re_activate(det, self.frame_id, new_id=False)
|
||||
refind_stracks.append(track)
|
||||
|
||||
for it in u_track:
|
||||
track = r_tracked_stracks[it]
|
||||
if not track.state == TrackState.Lost:
|
||||
track.mark_lost()
|
||||
lost_stracks.append(track)
|
||||
|
||||
'''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
|
||||
detections = [detections[i] for i in u_detection]
|
||||
dists = matching.iou_distance(unconfirmed, detections)
|
||||
matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
|
||||
for itracked, idet in matches:
|
||||
unconfirmed[itracked].update(detections[idet], self.frame_id)
|
||||
activated_starcks.append(unconfirmed[itracked])
|
||||
for it in u_unconfirmed:
|
||||
track = unconfirmed[it]
|
||||
track.mark_removed()
|
||||
removed_stracks.append(track)
|
||||
|
||||
""" Step 4: Init new stracks"""
|
||||
for inew in u_detection:
|
||||
track = detections[inew]
|
||||
if track.score < self.det_thresh:
|
||||
continue
|
||||
track.activate(self.kalman_filter, self.frame_id)
|
||||
activated_starcks.append(track)
|
||||
|
||||
""" Step 5: Update state"""
|
||||
for track in self.lost_stracks:
|
||||
if self.frame_id - track.end_frame > self.max_time_lost:
|
||||
track.mark_removed()
|
||||
removed_stracks.append(track)
|
||||
t4 = time.time()
|
||||
# print('Ramained match {} s'.format(t4-t3))
|
||||
|
||||
self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
|
||||
self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
|
||||
self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
|
||||
# self.lost_stracks = [t for t in self.lost_stracks if t.state == TrackState.Lost] # type: list[STrack]
|
||||
self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
|
||||
self.lost_stracks.extend(lost_stracks)
|
||||
self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
|
||||
self.removed_stracks.extend(removed_stracks)
|
||||
self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
|
||||
|
||||
# get scores of lost tracks
|
||||
output_stracks = [track for track in self.tracked_stracks if track.is_activated]
|
||||
|
||||
logger.debug('===========Frame {}=========='.format(self.frame_id))
|
||||
logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks]))
|
||||
logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks]))
|
||||
logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks]))
|
||||
logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks]))
|
||||
t5 = time.time()
|
||||
# print('Final {} s'.format(t5-t4))
|
||||
return output_stracks
|
||||
|
||||
def joint_stracks(tlista, tlistb):
|
||||
exists = {}
|
||||
res = []
|
||||
for t in tlista:
|
||||
exists[t.track_id] = 1
|
||||
res.append(t)
|
||||
for t in tlistb:
|
||||
tid = t.track_id
|
||||
if not exists.get(tid, 0):
|
||||
exists[tid] = 1
|
||||
res.append(t)
|
||||
return res
|
||||
|
||||
def sub_stracks(tlista, tlistb):
|
||||
stracks = {}
|
||||
for t in tlista:
|
||||
stracks[t.track_id] = t
|
||||
for t in tlistb:
|
||||
tid = t.track_id
|
||||
if stracks.get(tid, 0):
|
||||
del stracks[tid]
|
||||
return list(stracks.values())
|
||||
|
||||
def remove_duplicate_stracks(stracksa, stracksb):
|
||||
pdist = matching.iou_distance(stracksa, stracksb)
|
||||
pairs = np.where(pdist<0.15)
|
||||
dupa, dupb = list(), list()
|
||||
for p,q in zip(*pairs):
|
||||
timep = stracksa[p].frame_id - stracksa[p].start_frame
|
||||
timeq = stracksb[q].frame_id - stracksb[q].start_frame
|
||||
if timep > timeq:
|
||||
dupb.append(q)
|
||||
else:
|
||||
dupa.append(p)
|
||||
resa = [t for i,t in enumerate(stracksa) if not i in dupa]
|
||||
resb = [t for i,t in enumerate(stracksb) if not i in dupb]
|
||||
return resa, resb
|
||||
|
||||
|
||||
|
|
|
|||
381
train.py
381
train.py
|
|
@ -1,191 +1,190 @@
|
|||
import argparse
|
||||
import json
|
||||
import time
|
||||
|
||||
import test
|
||||
from models import *
|
||||
from utils.datasets import JointDataset, collate_fn
|
||||
from utils.utils import *
|
||||
from utils.log import logger
|
||||
from torchvision.transforms import transforms as T
|
||||
|
||||
|
||||
def train(
|
||||
cfg,
|
||||
data_cfg,
|
||||
img_size=(1088,608),
|
||||
resume=False,
|
||||
epochs=100,
|
||||
batch_size=16,
|
||||
accumulated_batches=1,
|
||||
freeze_backbone=False,
|
||||
opt=None,
|
||||
):
|
||||
weights = 'weights'
|
||||
mkdir_if_missing(weights)
|
||||
latest = osp.join(weights, 'latest.pt')
|
||||
|
||||
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
|
||||
|
||||
# Configure run
|
||||
f = open(data_cfg)
|
||||
data_config = json.load(f)
|
||||
trainset_paths = data_config['train']
|
||||
dataset_root = data_config['root']
|
||||
f.close()
|
||||
|
||||
|
||||
transforms = T.Compose([T.ToTensor()])
|
||||
# Get dataloader
|
||||
dataset = JointDataset(dataset_root, trainset_paths, img_size, augment=True, transforms=transforms)
|
||||
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True,
|
||||
num_workers=8, pin_memory=True, drop_last=True, collate_fn=collate_fn)
|
||||
|
||||
# Initialize model
|
||||
model = Darknet(cfg, img_size, dataset.nID)
|
||||
|
||||
cutoff = -1 # backbone reaches to cutoff layer
|
||||
start_epoch = 0
|
||||
if resume:
|
||||
checkpoint = torch.load(latest, map_location='cpu')
|
||||
|
||||
# Load weights to resume from
|
||||
model.load_state_dict(checkpoint['model'])
|
||||
model.cuda().train()
|
||||
|
||||
# Set optimizer
|
||||
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr, momentum=.9)
|
||||
|
||||
start_epoch = checkpoint['epoch'] + 1
|
||||
if checkpoint['optimizer'] is not None:
|
||||
optimizer.load_state_dict(checkpoint['optimizer'])
|
||||
|
||||
del checkpoint # current, saved
|
||||
|
||||
else:
|
||||
# Initialize model with backbone (optional)
|
||||
if cfg.endswith('yolov3.cfg'):
|
||||
load_darknet_weights(model, osp.join(weights ,'darknet53.conv.74'))
|
||||
cutoff = 75
|
||||
elif cfg.endswith('yolov3-tiny.cfg'):
|
||||
load_darknet_weights(model, osp.join(weights , 'yolov3-tiny.conv.15'))
|
||||
cutoff = 15
|
||||
|
||||
model.cuda().train()
|
||||
|
||||
# Set optimizer
|
||||
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr, momentum=.9, weight_decay=1e-4)
|
||||
|
||||
model = torch.nn.DataParallel(model)
|
||||
# Set scheduler
|
||||
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
|
||||
milestones=[int(0.5*opt.epochs), int(0.75*opt.epochs)], gamma=0.1)
|
||||
|
||||
# An important trick for detection: freeze bn during fine-tuning
|
||||
if not opt.unfreeze_bn:
|
||||
for i, (name, p) in enumerate(model.named_parameters()):
|
||||
p.requires_grad = False if 'batch_norm' in name else True
|
||||
|
||||
model_info(model)
|
||||
t0 = time.time()
|
||||
for epoch in range(epochs):
|
||||
epoch += start_epoch
|
||||
|
||||
logger.info(('%8s%12s' + '%10s' * 6) % (
|
||||
'Epoch', 'Batch', 'box', 'conf', 'id', 'total', 'nTargets', 'time'))
|
||||
|
||||
# Update scheduler (automatic)
|
||||
scheduler.step()
|
||||
|
||||
|
||||
# Freeze darknet53.conv.74 for first epoch
|
||||
if freeze_backbone and (epoch < 2):
|
||||
for i, (name, p) in enumerate(model.named_parameters()):
|
||||
if int(name.split('.')[2]) < cutoff: # if layer < 75
|
||||
p.requires_grad = False if (epoch == 0) else True
|
||||
|
||||
ui = -1
|
||||
rloss = defaultdict(float) # running loss
|
||||
optimizer.zero_grad()
|
||||
for i, (imgs, targets, _, _, targets_len) in enumerate(dataloader):
|
||||
if sum([len(x) for x in targets]) < 1: # if no targets continue
|
||||
continue
|
||||
|
||||
# SGD burn-in
|
||||
burnin = min(1000, len(dataloader))
|
||||
if (epoch == 0) & (i <= burnin):
|
||||
lr = opt.lr * (i / burnin) **4
|
||||
for g in optimizer.param_groups:
|
||||
g['lr'] = lr
|
||||
|
||||
# Compute loss, compute gradient, update parameters
|
||||
loss, components = model(imgs.cuda(), targets.cuda(), targets_len.cuda())
|
||||
components = torch.mean(components.view(-1, 5),dim=0)
|
||||
|
||||
loss = torch.mean(loss)
|
||||
loss.backward()
|
||||
|
||||
# accumulate gradient for x batches before optimizing
|
||||
if ((i + 1) % accumulated_batches == 0) or (i == len(dataloader) - 1):
|
||||
optimizer.step()
|
||||
optimizer.zero_grad()
|
||||
|
||||
# Running epoch-means of tracked metrics
|
||||
ui += 1
|
||||
|
||||
for ii, key in enumerate(model.module.loss_names):
|
||||
rloss[key] = (rloss[key] * ui + components[ii]) / (ui + 1)
|
||||
|
||||
s = ('%8s%12s' + '%10.3g' * 6) % (
|
||||
'%g/%g' % (epoch, epochs - 1),
|
||||
'%g/%g' % (i, len(dataloader) - 1),
|
||||
rloss['box'], rloss['conf'],
|
||||
rloss['id'],rloss['loss'],
|
||||
rloss['nT'], time.time() - t0)
|
||||
t0 = time.time()
|
||||
if i % opt.print_interval == 0:
|
||||
logger.info(s)
|
||||
|
||||
|
||||
# Save latest checkpoint
|
||||
checkpoint = {'epoch': epoch,
|
||||
'model': model.module.state_dict(),
|
||||
'optimizer': optimizer.state_dict()}
|
||||
torch.save(checkpoint, latest)
|
||||
|
||||
|
||||
# Calculate mAP
|
||||
if epoch % opt.test_interval ==0:
|
||||
with torch.no_grad():
|
||||
mAP, R, P = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, print_interval=40, nID=dataset.nID)
|
||||
test.test_emb(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, print_interval=40, nID=dataset.nID)
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--epochs', type=int, default=30, help='number of epochs')
|
||||
parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
|
||||
parser.add_argument('--accumulated-batches', type=int, default=1, help='number of batches before optimizer step')
|
||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||
parser.add_argument('--data-cfg', type=str, default='cfg/ccmcpe.json', help='coco.data file path')
|
||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='pixels')
|
||||
parser.add_argument('--resume', action='store_true', help='resume training flag')
|
||||
parser.add_argument('--print-interval', type=int, default=40, help='print interval')
|
||||
parser.add_argument('--test-interval', type=int, default=9, help='test interval')
|
||||
parser.add_argument('--lr', type=float, default=1e-2, help='init lr')
|
||||
parser.add_argument('--unfreeze-bn', action='store_true', help='unfreeze bn')
|
||||
opt = parser.parse_args()
|
||||
|
||||
init_seeds()
|
||||
|
||||
train(
|
||||
opt.cfg,
|
||||
opt.data_cfg,
|
||||
img_size=opt.img_size,
|
||||
resume=opt.resume,
|
||||
epochs=opt.epochs,
|
||||
batch_size=opt.batch_size,
|
||||
accumulated_batches=opt.accumulated_batches,
|
||||
opt=opt,
|
||||
)
|
||||
import argparse
|
||||
import json
|
||||
import time
|
||||
|
||||
import test
|
||||
from models import *
|
||||
from utils.datasets import JointDataset, collate_fn
|
||||
from utils.utils import *
|
||||
from utils.log import logger
|
||||
from torchvision.transforms import transforms as T
|
||||
|
||||
|
||||
def train(
|
||||
cfg,
|
||||
data_cfg,
|
||||
img_size=(1088,608),
|
||||
resume=False,
|
||||
epochs=100,
|
||||
batch_size=16,
|
||||
accumulated_batches=1,
|
||||
freeze_backbone=False,
|
||||
opt=None,
|
||||
):
|
||||
weights = 'weights'
|
||||
mkdir_if_missing(weights)
|
||||
latest = osp.join(weights, 'latest.pt')
|
||||
|
||||
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
|
||||
|
||||
# Configure run
|
||||
f = open(data_cfg)
|
||||
data_config = json.load(f)
|
||||
trainset_paths = data_config['train']
|
||||
dataset_root = data_config['root']
|
||||
f.close()
|
||||
|
||||
|
||||
transforms = T.Compose([T.ToTensor()])
|
||||
# Get dataloader
|
||||
dataset = JointDataset(dataset_root, trainset_paths, img_size, augment=True, transforms=transforms)
|
||||
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True,
|
||||
num_workers=8, pin_memory=True, drop_last=True, collate_fn=collate_fn)
|
||||
|
||||
# Initialize model
|
||||
model = Darknet(cfg, img_size, dataset.nID)
|
||||
|
||||
cutoff = -1 # backbone reaches to cutoff layer
|
||||
start_epoch = 0
|
||||
if resume:
|
||||
checkpoint = torch.load(latest, map_location='cpu')
|
||||
|
||||
# Load weights to resume from
|
||||
model.load_state_dict(checkpoint['model'])
|
||||
model.cuda().train()
|
||||
|
||||
# Set optimizer
|
||||
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr, momentum=.9)
|
||||
|
||||
start_epoch = checkpoint['epoch'] + 1
|
||||
if checkpoint['optimizer'] is not None:
|
||||
optimizer.load_state_dict(checkpoint['optimizer'])
|
||||
|
||||
del checkpoint # current, saved
|
||||
|
||||
else:
|
||||
# Initialize model with backbone (optional)
|
||||
if cfg.endswith('yolov3.cfg'):
|
||||
load_darknet_weights(model, osp.join(weights ,'darknet53.conv.74'))
|
||||
cutoff = 75
|
||||
elif cfg.endswith('yolov3-tiny.cfg'):
|
||||
load_darknet_weights(model, osp.join(weights , 'yolov3-tiny.conv.15'))
|
||||
cutoff = 15
|
||||
|
||||
model.cuda().train()
|
||||
|
||||
# Set optimizer
|
||||
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr, momentum=.9, weight_decay=1e-4)
|
||||
|
||||
model = torch.nn.DataParallel(model)
|
||||
# Set scheduler
|
||||
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
|
||||
milestones=[int(0.5*opt.epochs), int(0.75*opt.epochs)], gamma=0.1)
|
||||
|
||||
# An important trick for detection: freeze bn during fine-tuning
|
||||
if not opt.unfreeze_bn:
|
||||
for i, (name, p) in enumerate(model.named_parameters()):
|
||||
p.requires_grad = False if 'batch_norm' in name else True
|
||||
|
||||
model_info(model)
|
||||
t0 = time.time()
|
||||
for epoch in range(epochs):
|
||||
epoch += start_epoch
|
||||
|
||||
logger.info(('%8s%12s' + '%10s' * 6) % (
|
||||
'Epoch', 'Batch', 'box', 'conf', 'id', 'total', 'nTargets', 'time'))
|
||||
|
||||
|
||||
|
||||
# Freeze darknet53.conv.74 for first epoch
|
||||
if freeze_backbone and (epoch < 2):
|
||||
for i, (name, p) in enumerate(model.named_parameters()):
|
||||
if int(name.split('.')[2]) < cutoff: # if layer < 75
|
||||
p.requires_grad = False if (epoch == 0) else True
|
||||
|
||||
ui = -1
|
||||
rloss = defaultdict(float) # running loss
|
||||
optimizer.zero_grad()
|
||||
for i, (imgs, targets, _, _, targets_len) in enumerate(dataloader):
|
||||
if sum([len(x) for x in targets]) < 1: # if no targets continue
|
||||
continue
|
||||
|
||||
# SGD burn-in
|
||||
burnin = min(1000, len(dataloader))
|
||||
if (epoch == 0) & (i <= burnin):
|
||||
lr = opt.lr * (i / burnin) **4
|
||||
for g in optimizer.param_groups:
|
||||
g['lr'] = lr
|
||||
|
||||
# Compute loss, compute gradient, update parameters
|
||||
loss, components = model(imgs.cuda(), targets.cuda(), targets_len.cuda())
|
||||
components = torch.mean(components.view(-1, 5),dim=0)
|
||||
|
||||
loss = torch.mean(loss)
|
||||
loss.backward()
|
||||
|
||||
# accumulate gradient for x batches before optimizing
|
||||
if ((i + 1) % accumulated_batches == 0) or (i == len(dataloader) - 1):
|
||||
optimizer.step()
|
||||
optimizer.zero_grad()
|
||||
|
||||
# Running epoch-means of tracked metrics
|
||||
ui += 1
|
||||
|
||||
for ii, key in enumerate(model.module.loss_names):
|
||||
rloss[key] = (rloss[key] * ui + components[ii]) / (ui + 1)
|
||||
|
||||
s = ('%8s%12s' + '%10.3g' * 6) % (
|
||||
'%g/%g' % (epoch, epochs - 1),
|
||||
'%g/%g' % (i, len(dataloader) - 1),
|
||||
rloss['box'], rloss['conf'],
|
||||
rloss['id'],rloss['loss'],
|
||||
rloss['nT'], time.time() - t0)
|
||||
t0 = time.time()
|
||||
if i % opt.print_interval == 0:
|
||||
logger.info(s)
|
||||
|
||||
# Save latest checkpoint
|
||||
checkpoint = {'epoch': epoch,
|
||||
'model': model.module.state_dict(),
|
||||
'optimizer': optimizer.state_dict()}
|
||||
torch.save(checkpoint, latest)
|
||||
|
||||
|
||||
# Calculate mAP
|
||||
if epoch % opt.test_interval ==0:
|
||||
with torch.no_grad():
|
||||
mAP, R, P = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, print_interval=40, nID=dataset.nID)
|
||||
test.test_emb(cfg, data_cfg, weights=latest, batch_size=batch_size, img_size=img_size, print_interval=40, nID=dataset.nID)
|
||||
|
||||
|
||||
# Call scheduler.step() after opimizer.step() with pytorch > 1.1.0
|
||||
scheduler.step()
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--epochs', type=int, default=30, help='number of epochs')
|
||||
parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
|
||||
parser.add_argument('--accumulated-batches', type=int, default=1, help='number of batches before optimizer step')
|
||||
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
||||
parser.add_argument('--data-cfg', type=str, default='cfg/ccmcpe.json', help='coco.data file path')
|
||||
parser.add_argument('--img-size', type=int, default=(1088, 608), help='pixels')
|
||||
parser.add_argument('--resume', action='store_true', help='resume training flag')
|
||||
parser.add_argument('--print-interval', type=int, default=40, help='print interval')
|
||||
parser.add_argument('--test-interval', type=int, default=9, help='test interval')
|
||||
parser.add_argument('--lr', type=float, default=1e-2, help='init lr')
|
||||
parser.add_argument('--unfreeze-bn', action='store_true', help='unfreeze bn')
|
||||
opt = parser.parse_args()
|
||||
|
||||
init_seeds()
|
||||
|
||||
train(
|
||||
opt.cfg,
|
||||
opt.data_cfg,
|
||||
img_size=opt.img_size,
|
||||
resume=opt.resume,
|
||||
epochs=opt.epochs,
|
||||
batch_size=opt.batch_size,
|
||||
accumulated_batches=opt.accumulated_batches,
|
||||
opt=opt,
|
||||
)
|
||||
|
|
|
|||
Binary file not shown.
Binary file not shown.
|
|
@ -1,411 +1,410 @@
|
|||
import glob
|
||||
import math
|
||||
import os
|
||||
import os.path as osp
|
||||
import random
|
||||
import time
|
||||
from collections import OrderedDict
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from torch.utils.data import Dataset
|
||||
from utils.utils import xyxy2xywh
|
||||
|
||||
class LoadImages: # for inference
|
||||
def __init__(self, path, img_size=(1088, 608)):
|
||||
if os.path.isdir(path):
|
||||
image_format = ['.jpg', '.jpeg', '.png', '.tif']
|
||||
self.files = sorted(glob.glob('%s/*.*' % path))
|
||||
self.files = list(filter(lambda x: os.path.splitext(x)[1].lower() in image_format, self.files))
|
||||
elif os.path.isfile(path):
|
||||
self.files = [path]
|
||||
|
||||
self.nF = len(self.files) # number of image files
|
||||
self.width = img_size[0]
|
||||
self.height = img_size[1]
|
||||
self.count = 0
|
||||
|
||||
assert self.nF > 0, 'No images found in ' + path
|
||||
|
||||
def __iter__(self):
|
||||
self.count = -1
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
self.count += 1
|
||||
if self.count == self.nF:
|
||||
raise StopIteration
|
||||
img_path = self.files[self.count]
|
||||
|
||||
# Read image
|
||||
img0 = cv2.imread(img_path) # BGR
|
||||
assert img0 is not None, 'Failed to load ' + img_path
|
||||
|
||||
# Padded resize
|
||||
img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
|
||||
|
||||
# Normalize RGB
|
||||
img = img[:, :, ::-1].transpose(2, 0, 1)
|
||||
img = np.ascontiguousarray(img, dtype=np.float32)
|
||||
img /= 255.0
|
||||
|
||||
# cv2.imwrite(img_path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
|
||||
return img_path, img, img0
|
||||
|
||||
def __getitem__(self, idx):
|
||||
idx = idx % self.nF
|
||||
img_path = self.files[idx]
|
||||
|
||||
# Read image
|
||||
img0 = cv2.imread(img_path) # BGR
|
||||
assert img0 is not None, 'Failed to load ' + img_path
|
||||
|
||||
# Padded resize
|
||||
img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
|
||||
|
||||
# Normalize RGB
|
||||
img = img[:, :, ::-1].transpose(2, 0, 1)
|
||||
img = np.ascontiguousarray(img, dtype=np.float32)
|
||||
img /= 255.0
|
||||
|
||||
return img_path, img, img0
|
||||
|
||||
def __len__(self):
|
||||
return self.nF # number of files
|
||||
|
||||
|
||||
class LoadVideo: # for inference
|
||||
def __init__(self, path, img_size=(1088, 608)):
|
||||
self.cap = cv2.VideoCapture(path)
|
||||
self.frame_rate = int(round(self.cap.get(cv2.CAP_PROP_FPS)))
|
||||
self.vw = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
self.vh = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
self.vn = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
|
||||
self.width = img_size[0]
|
||||
self.height = img_size[1]
|
||||
self.count = 0
|
||||
|
||||
self.w, self.h = self.get_size(self.vw, self.vh, self.width, self.height)
|
||||
print('Lenth of the video: {:d} frames'.format(self.vn))
|
||||
|
||||
def get_size(self, vw, vh, dw, dh):
|
||||
wa, ha = float(dw) / vw, float(dh) / vh
|
||||
a = min(wa, ha)
|
||||
return int(vw *a), int(vh*a)
|
||||
|
||||
def __iter__(self):
|
||||
self.count = -1
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
self.count += 1
|
||||
if self.count == len(self):
|
||||
raise StopIteration
|
||||
# Read image
|
||||
res, img0 = self.cap.read() # BGR
|
||||
assert img0 is not None, 'Failed to load frame {:d}'.format(self.count)
|
||||
img0 = cv2.resize(img0, (self.w, self.h))
|
||||
|
||||
# Padded resize
|
||||
img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
|
||||
|
||||
# Normalize RGB
|
||||
img = img[:, :, ::-1].transpose(2, 0, 1)
|
||||
img = np.ascontiguousarray(img, dtype=np.float32)
|
||||
img /= 255.0
|
||||
|
||||
# cv2.imwrite(img_path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
|
||||
return self.count, img, img0
|
||||
|
||||
def __len__(self):
|
||||
return self.vn # number of files
|
||||
|
||||
|
||||
class LoadImagesAndLabels: # for training
|
||||
def __init__(self, path, img_size=(1088,608), augment=False, transforms=None):
|
||||
with open(path, 'r') as file:
|
||||
self.img_files = file.readlines()
|
||||
self.img_files = [x.replace('\n', '') for x in self.img_files]
|
||||
self.img_files = list(filter(lambda x: len(x) > 0, self.img_files))
|
||||
|
||||
self.label_files = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')
|
||||
for x in self.img_files]
|
||||
|
||||
self.nF = len(self.img_files) # number of image files
|
||||
self.width = img_size[0]
|
||||
self.height = img_size[1]
|
||||
self.augment = augment
|
||||
self.transforms = transforms
|
||||
|
||||
|
||||
def __getitem__(self, files_index):
|
||||
img_path = self.img_files[files_index]
|
||||
label_path = self.label_files[files_index]
|
||||
return self.get_data(img_path, label_path)
|
||||
|
||||
def get_data(self, img_path, label_path):
|
||||
height = self.height
|
||||
width = self.width
|
||||
img = cv2.imread(img_path) # BGR
|
||||
if img is None:
|
||||
raise ValueError('File corrupt {}'.format(img_path))
|
||||
augment_hsv = True
|
||||
if self.augment and augment_hsv:
|
||||
# SV augmentation by 50%
|
||||
fraction = 0.50
|
||||
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
|
||||
S = img_hsv[:, :, 1].astype(np.float32)
|
||||
V = img_hsv[:, :, 2].astype(np.float32)
|
||||
|
||||
a = (random.random() * 2 - 1) * fraction + 1
|
||||
S *= a
|
||||
if a > 1:
|
||||
np.clip(S, a_min=0, a_max=255, out=S)
|
||||
|
||||
a = (random.random() * 2 - 1) * fraction + 1
|
||||
V *= a
|
||||
if a > 1:
|
||||
np.clip(V, a_min=0, a_max=255, out=V)
|
||||
|
||||
img_hsv[:, :, 1] = S.astype(np.uint8)
|
||||
img_hsv[:, :, 2] = V.astype(np.uint8)
|
||||
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
|
||||
|
||||
h, w, _ = img.shape
|
||||
img, ratio, padw, padh = letterbox(img, height=height, width=width)
|
||||
|
||||
# Load labels
|
||||
if os.path.isfile(label_path):
|
||||
labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)
|
||||
|
||||
# Normalized xywh to pixel xyxy format
|
||||
labels = labels0.copy()
|
||||
labels[:, 2] = ratio * w * (labels0[:, 2] - labels0[:, 4] / 2) + padw
|
||||
labels[:, 3] = ratio * h * (labels0[:, 3] - labels0[:, 5] / 2) + padh
|
||||
labels[:, 4] = ratio * w * (labels0[:, 2] + labels0[:, 4] / 2) + padw
|
||||
labels[:, 5] = ratio * h * (labels0[:, 3] + labels0[:, 5] / 2) + padh
|
||||
else:
|
||||
labels = np.array([])
|
||||
|
||||
# Augment image and labels
|
||||
if self.augment:
|
||||
img, labels, M = random_affine(img, labels, degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.50, 1.20))
|
||||
|
||||
plotFlag = False
|
||||
if plotFlag:
|
||||
import matplotlib
|
||||
matplotlib.use('Agg')
|
||||
import matplotlib.pyplot as plt
|
||||
plt.figure(figsize=(50, 50))
|
||||
plt.imshow(img[:, :, ::-1])
|
||||
plt.plot(labels[:, [1, 3, 3, 1, 1]].T, labels[:, [2, 2, 4, 4, 2]].T, '.-')
|
||||
plt.axis('off')
|
||||
plt.savefig('test.jpg')
|
||||
time.sleep(10)
|
||||
|
||||
nL = len(labels)
|
||||
if nL > 0:
|
||||
# convert xyxy to xywh
|
||||
labels[:, 2:6] = xyxy2xywh(labels[:, 2:6].copy()) #/ height
|
||||
labels[:, 2] /= width
|
||||
labels[:, 3] /= height
|
||||
labels[:, 4] /= width
|
||||
labels[:, 5] /= height
|
||||
if self.augment:
|
||||
# random left-right flip
|
||||
lr_flip = True
|
||||
if lr_flip & (random.random() > 0.5):
|
||||
img = np.fliplr(img)
|
||||
if nL > 0:
|
||||
labels[:, 2] = 1 - labels[:, 2]
|
||||
|
||||
img = np.ascontiguousarray(img[ :, :, ::-1]) # BGR to RGB
|
||||
if self.transforms is not None:
|
||||
img = self.transforms(img)
|
||||
|
||||
return img, labels, img_path, (h, w)
|
||||
|
||||
def __len__(self):
|
||||
return self.nF # number of batches
|
||||
|
||||
|
||||
def letterbox(img, height=608, width=1088, color=(127.5, 127.5, 127.5)): # resize a rectangular image to a padded rectangular
|
||||
shape = img.shape[:2] # shape = [height, width]
|
||||
ratio = min(float(height)/shape[0], float(width)/shape[1])
|
||||
new_shape = (round(shape[1] * ratio), round(shape[0] * ratio)) # new_shape = [width, height]
|
||||
dw = (width - new_shape[0]) / 2 # width padding
|
||||
dh = (height - new_shape[1]) / 2 # height padding
|
||||
top, bottom = round(dh - 0.1), round(dh + 0.1)
|
||||
left, right = round(dw - 0.1), round(dw + 0.1)
|
||||
img = cv2.resize(img, new_shape, interpolation=cv2.INTER_AREA) # resized, no border
|
||||
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # padded rectangular
|
||||
return img, ratio, dw, dh
|
||||
|
||||
|
||||
def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
|
||||
borderValue=(127.5, 127.5, 127.5)):
|
||||
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
|
||||
# https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
|
||||
|
||||
border = 0 # width of added border (optional)
|
||||
height = img.shape[0]
|
||||
width = img.shape[1]
|
||||
|
||||
# Rotation and Scale
|
||||
R = np.eye(3)
|
||||
a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
|
||||
# a += random.choice([-180, -90, 0, 90]) # 90deg rotations added to small rotations
|
||||
s = random.random() * (scale[1] - scale[0]) + scale[0]
|
||||
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
|
||||
|
||||
# Translation
|
||||
T = np.eye(3)
|
||||
T[0, 2] = (random.random() * 2 - 1) * translate[0] * img.shape[0] + border # x translation (pixels)
|
||||
T[1, 2] = (random.random() * 2 - 1) * translate[1] * img.shape[1] + border # y translation (pixels)
|
||||
|
||||
# Shear
|
||||
S = np.eye(3)
|
||||
S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # x shear (deg)
|
||||
S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # y shear (deg)
|
||||
|
||||
M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
|
||||
imw = cv2.warpPerspective(img, M, dsize=(width, height), flags=cv2.INTER_LINEAR,
|
||||
borderValue=borderValue) # BGR order borderValue
|
||||
|
||||
# Return warped points also
|
||||
if targets is not None:
|
||||
if len(targets) > 0:
|
||||
n = targets.shape[0]
|
||||
points = targets[:, 2:6].copy()
|
||||
area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1])
|
||||
|
||||
# warp points
|
||||
xy = np.ones((n * 4, 3))
|
||||
xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
|
||||
xy = (xy @ M.T)[:, :2].reshape(n, 8)
|
||||
|
||||
# create new boxes
|
||||
x = xy[:, [0, 2, 4, 6]]
|
||||
y = xy[:, [1, 3, 5, 7]]
|
||||
xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
|
||||
|
||||
# apply angle-based reduction
|
||||
radians = a * math.pi / 180
|
||||
reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
|
||||
x = (xy[:, 2] + xy[:, 0]) / 2
|
||||
y = (xy[:, 3] + xy[:, 1]) / 2
|
||||
w = (xy[:, 2] - xy[:, 0]) * reduction
|
||||
h = (xy[:, 3] - xy[:, 1]) * reduction
|
||||
xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
|
||||
|
||||
# reject warped points outside of image
|
||||
np.clip(xy[:, 0], 0, width, out=xy[:, 0])
|
||||
np.clip(xy[:, 2], 0, width, out=xy[:, 2])
|
||||
np.clip(xy[:, 1], 0, height, out=xy[:, 1])
|
||||
np.clip(xy[:, 3], 0, height, out=xy[:, 3])
|
||||
w = xy[:, 2] - xy[:, 0]
|
||||
h = xy[:, 3] - xy[:, 1]
|
||||
area = w * h
|
||||
ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
|
||||
i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10)
|
||||
|
||||
targets = targets[i]
|
||||
targets[:, 2:6] = xy[i]
|
||||
|
||||
return imw, targets, M
|
||||
else:
|
||||
return imw
|
||||
|
||||
def collate_fn(batch):
|
||||
imgs, labels, paths, sizes = zip(*batch)
|
||||
batch_size = len(labels)
|
||||
imgs = torch.stack(imgs, 0)
|
||||
max_box_len = max([l.shape[0] for l in labels])
|
||||
labels = [torch.from_numpy(l) for l in labels]
|
||||
filled_labels = torch.zeros(batch_size, max_box_len, 6)
|
||||
labels_len = torch.zeros(batch_size)
|
||||
|
||||
for i in range(batch_size):
|
||||
isize = labels[i].shape[0]
|
||||
if len(labels[i])>0:
|
||||
filled_labels[i, :isize, :] = labels[i]
|
||||
labels_len[i] = isize
|
||||
|
||||
return imgs, filled_labels, paths, sizes, labels_len.unsqueeze(1)
|
||||
|
||||
|
||||
class JointDataset(LoadImagesAndLabels): # for training
|
||||
def __init__(self, root, paths, img_size=(1088,608), augment=False, transforms=None):
|
||||
|
||||
dataset_names = paths.keys()
|
||||
self.img_files = OrderedDict()
|
||||
self.label_files = OrderedDict()
|
||||
self.tid_num = OrderedDict()
|
||||
self.tid_start_index = OrderedDict()
|
||||
for ds, path in paths.items():
|
||||
with open(path, 'r') as file:
|
||||
self.img_files[ds] = file.readlines()
|
||||
self.img_files[ds] = [osp.join(root, x.strip()) for x in self.img_files[ds]]
|
||||
self.img_files[ds] = list(filter(lambda x: len(x) > 0, self.img_files[ds]))
|
||||
|
||||
self.label_files[ds] = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')
|
||||
for x in self.img_files[ds]]
|
||||
|
||||
for ds, label_paths in self.label_files.items():
|
||||
max_index = -1
|
||||
for lp in label_paths:
|
||||
lb = np.loadtxt(lp)
|
||||
if len(lb) < 1:
|
||||
continue
|
||||
if len(lb.shape) < 2:
|
||||
img_max = lb[1]
|
||||
else:
|
||||
img_max = np.max(lb[:,1])
|
||||
if img_max >max_index:
|
||||
max_index = img_max
|
||||
self.tid_num[ds] = max_index + 1
|
||||
|
||||
last_index = 0
|
||||
for i, (k, v) in enumerate(self.tid_num.items()):
|
||||
self.tid_start_index[k] = last_index
|
||||
last_index += v
|
||||
|
||||
self.nID = int(last_index+1)
|
||||
self.nds = [len(x) for x in self.img_files.values()]
|
||||
self.cds = [sum(self.nds[:i]) for i in range(len(self.nds))]
|
||||
self.nF = sum(self.nds)
|
||||
self.width = img_size[0]
|
||||
self.height = img_size[1]
|
||||
self.augment = augment
|
||||
self.transforms = transforms
|
||||
|
||||
print('='*80)
|
||||
print('dataset summary')
|
||||
print(self.tid_num)
|
||||
print('total # identities:', self.nID)
|
||||
print('start index')
|
||||
print(self.tid_start_index)
|
||||
print('='*80)
|
||||
|
||||
|
||||
def __getitem__(self, files_index):
|
||||
|
||||
for i, c in enumerate(self.cds):
|
||||
if files_index >= c:
|
||||
ds = list(self.label_files.keys())[i]
|
||||
start_index = c
|
||||
|
||||
img_path = self.img_files[ds][files_index - start_index]
|
||||
label_path = self.label_files[ds][files_index - start_index]
|
||||
|
||||
imgs, labels, img_path, (h, w) = self.get_data(img_path, label_path)
|
||||
for i, _ in enumerate(labels):
|
||||
if labels[i,1] > -1:
|
||||
labels[i,1] += self.tid_start_index[ds]
|
||||
|
||||
return imgs, labels, img_path, (h, w)
|
||||
|
||||
|
||||
import glob
|
||||
import math
|
||||
import os
|
||||
import os.path as osp
|
||||
import random
|
||||
import time
|
||||
from collections import OrderedDict
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from torch.utils.data import Dataset
|
||||
from utils.utils import xyxy2xywh
|
||||
|
||||
class LoadImages: # for inference
|
||||
def __init__(self, path, img_size=(1088, 608)):
|
||||
if os.path.isdir(path):
|
||||
image_format = ['.jpg', '.jpeg', '.png', '.tif']
|
||||
self.files = sorted(glob.glob('%s/*.*' % path))
|
||||
self.files = list(filter(lambda x: os.path.splitext(x)[1].lower() in image_format, self.files))
|
||||
elif os.path.isfile(path):
|
||||
self.files = [path]
|
||||
|
||||
self.nF = len(self.files) # number of image files
|
||||
self.width = img_size[0]
|
||||
self.height = img_size[1]
|
||||
self.count = 0
|
||||
|
||||
assert self.nF > 0, 'No images found in ' + path
|
||||
|
||||
def __iter__(self):
|
||||
self.count = -1
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
self.count += 1
|
||||
if self.count == self.nF:
|
||||
raise StopIteration
|
||||
img_path = self.files[self.count]
|
||||
|
||||
# Read image
|
||||
img0 = cv2.imread(img_path) # BGR
|
||||
assert img0 is not None, 'Failed to load ' + img_path
|
||||
|
||||
# Padded resize
|
||||
img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
|
||||
|
||||
# Normalize RGB
|
||||
img = img[:, :, ::-1].transpose(2, 0, 1)
|
||||
img = np.ascontiguousarray(img, dtype=np.float32)
|
||||
img /= 255.0
|
||||
|
||||
# cv2.imwrite(img_path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
|
||||
return img_path, img, img0
|
||||
|
||||
def __getitem__(self, idx):
|
||||
idx = idx % self.nF
|
||||
img_path = self.files[idx]
|
||||
|
||||
# Read image
|
||||
img0 = cv2.imread(img_path) # BGR
|
||||
assert img0 is not None, 'Failed to load ' + img_path
|
||||
|
||||
# Padded resize
|
||||
img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
|
||||
|
||||
# Normalize RGB
|
||||
img = img[:, :, ::-1].transpose(2, 0, 1)
|
||||
img = np.ascontiguousarray(img, dtype=np.float32)
|
||||
img /= 255.0
|
||||
|
||||
return img_path, img, img0
|
||||
|
||||
def __len__(self):
|
||||
return self.nF # number of files
|
||||
|
||||
|
||||
class LoadVideo: # for inference
|
||||
def __init__(self, path, img_size=(1088, 608)):
|
||||
self.cap = cv2.VideoCapture(path)
|
||||
self.frame_rate = int(round(self.cap.get(cv2.CAP_PROP_FPS)))
|
||||
self.vw = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
self.vh = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
self.vn = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
|
||||
self.width = img_size[0]
|
||||
self.height = img_size[1]
|
||||
self.count = 0
|
||||
|
||||
self.w, self.h = self.get_size(self.vw, self.vh, self.width, self.height)
|
||||
print('Lenth of the video: {:d} frames'.format(self.vn))
|
||||
|
||||
def get_size(self, vw, vh, dw, dh):
|
||||
wa, ha = float(dw) / vw, float(dh) / vh
|
||||
a = min(wa, ha)
|
||||
return int(vw *a), int(vh*a)
|
||||
|
||||
def __iter__(self):
|
||||
self.count = -1
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
self.count += 1
|
||||
if self.count == len(self):
|
||||
raise StopIteration
|
||||
# Read image
|
||||
res, img0 = self.cap.read() # BGR
|
||||
assert img0 is not None, 'Failed to load frame {:d}'.format(self.count)
|
||||
img0 = cv2.resize(img0, (self.w, self.h))
|
||||
|
||||
# Padded resize
|
||||
img, _, _, _ = letterbox(img0, height=self.height, width=self.width)
|
||||
|
||||
# Normalize RGB
|
||||
img = img[:, :, ::-1].transpose(2, 0, 1)
|
||||
img = np.ascontiguousarray(img, dtype=np.float32)
|
||||
img /= 255.0
|
||||
|
||||
# cv2.imwrite(img_path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # save letterbox image
|
||||
return self.count, img, img0
|
||||
|
||||
def __len__(self):
|
||||
return self.vn # number of files
|
||||
|
||||
|
||||
class LoadImagesAndLabels: # for training
|
||||
def __init__(self, path, img_size=(1088,608), augment=False, transforms=None):
|
||||
with open(path, 'r') as file:
|
||||
self.img_files = file.readlines()
|
||||
self.img_files = [x.replace('\n', '') for x in self.img_files]
|
||||
self.img_files = list(filter(lambda x: len(x) > 0, self.img_files))
|
||||
|
||||
self.label_files = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')
|
||||
for x in self.img_files]
|
||||
|
||||
self.nF = len(self.img_files) # number of image files
|
||||
self.width = img_size[0]
|
||||
self.height = img_size[1]
|
||||
self.augment = augment
|
||||
self.transforms = transforms
|
||||
|
||||
|
||||
def __getitem__(self, files_index):
|
||||
img_path = self.img_files[files_index]
|
||||
label_path = self.label_files[files_index]
|
||||
return self.get_data(img_path, label_path)
|
||||
|
||||
def get_data(self, img_path, label_path):
|
||||
height = self.height
|
||||
width = self.width
|
||||
img = cv2.imread(img_path) # BGR
|
||||
if img is None:
|
||||
raise ValueError('File corrupt {}'.format(img_path))
|
||||
augment_hsv = True
|
||||
if self.augment and augment_hsv:
|
||||
# SV augmentation by 50%
|
||||
fraction = 0.50
|
||||
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
|
||||
S = img_hsv[:, :, 1].astype(np.float32)
|
||||
V = img_hsv[:, :, 2].astype(np.float32)
|
||||
|
||||
a = (random.random() * 2 - 1) * fraction + 1
|
||||
S *= a
|
||||
if a > 1:
|
||||
np.clip(S, a_min=0, a_max=255, out=S)
|
||||
|
||||
a = (random.random() * 2 - 1) * fraction + 1
|
||||
V *= a
|
||||
if a > 1:
|
||||
np.clip(V, a_min=0, a_max=255, out=V)
|
||||
|
||||
img_hsv[:, :, 1] = S.astype(np.uint8)
|
||||
img_hsv[:, :, 2] = V.astype(np.uint8)
|
||||
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
|
||||
|
||||
h, w, _ = img.shape
|
||||
img, ratio, padw, padh = letterbox(img, height=height, width=width)
|
||||
|
||||
# Load labels
|
||||
if os.path.isfile(label_path):
|
||||
labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 6)
|
||||
|
||||
# Normalized xywh to pixel xyxy format
|
||||
labels = labels0.copy()
|
||||
labels[:, 2] = ratio * w * (labels0[:, 2] - labels0[:, 4] / 2) + padw
|
||||
labels[:, 3] = ratio * h * (labels0[:, 3] - labels0[:, 5] / 2) + padh
|
||||
labels[:, 4] = ratio * w * (labels0[:, 2] + labels0[:, 4] / 2) + padw
|
||||
labels[:, 5] = ratio * h * (labels0[:, 3] + labels0[:, 5] / 2) + padh
|
||||
else:
|
||||
labels = np.array([])
|
||||
|
||||
# Augment image and labels
|
||||
if self.augment:
|
||||
img, labels, M = random_affine(img, labels, degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.50, 1.20))
|
||||
|
||||
|
||||
plotFlag = False
|
||||
if plotFlag:
|
||||
import matplotlib
|
||||
matplotlib.use('Agg')
|
||||
import matplotlib.pyplot as plt
|
||||
plt.figure(figsize=(50, 50))
|
||||
plt.imshow(img[:, :, ::-1])
|
||||
plt.plot(labels[:, [1, 3, 3, 1, 1]].T, labels[:, [2, 2, 4, 4, 2]].T, '.-')
|
||||
plt.axis('off')
|
||||
plt.savefig('test.jpg')
|
||||
time.sleep(10)
|
||||
|
||||
nL = len(labels)
|
||||
if nL > 0:
|
||||
# convert xyxy to xywh
|
||||
labels[:, 2:6] = xyxy2xywh(labels[:, 2:6].copy()) #/ height
|
||||
labels[:, 2] /= width
|
||||
labels[:, 3] /= height
|
||||
labels[:, 4] /= width
|
||||
labels[:, 5] /= height
|
||||
if self.augment:
|
||||
# random left-right flip
|
||||
lr_flip = True
|
||||
if lr_flip & (random.random() > 0.5):
|
||||
img = np.fliplr(img)
|
||||
if nL > 0:
|
||||
labels[:, 2] = 1 - labels[:, 2]
|
||||
|
||||
img = np.ascontiguousarray(img[ :, :, ::-1]) # BGR to RGB
|
||||
if self.transforms is not None:
|
||||
img = self.transforms(img)
|
||||
|
||||
return img, labels, img_path, (h, w)
|
||||
|
||||
def __len__(self):
|
||||
return self.nF # number of batches
|
||||
|
||||
|
||||
def letterbox(img, height=608, width=1088, color=(127.5, 127.5, 127.5)): # resize a rectangular image to a padded rectangular
|
||||
shape = img.shape[:2] # shape = [height, width]
|
||||
ratio = min(float(height)/shape[0], float(width)/shape[1])
|
||||
new_shape = (round(shape[1] * ratio), round(shape[0] * ratio)) # new_shape = [width, height]
|
||||
dw = (width - new_shape[0]) / 2 # width padding
|
||||
dh = (height - new_shape[1]) / 2 # height padding
|
||||
top, bottom = round(dh - 0.1), round(dh + 0.1)
|
||||
left, right = round(dw - 0.1), round(dw + 0.1)
|
||||
img = cv2.resize(img, new_shape, interpolation=cv2.INTER_AREA) # resized, no border
|
||||
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # padded rectangular
|
||||
return img, ratio, dw, dh
|
||||
|
||||
|
||||
def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
|
||||
borderValue=(127.5, 127.5, 127.5)):
|
||||
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
|
||||
# https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
|
||||
|
||||
border = 0 # width of added border (optional)
|
||||
height = img.shape[0]
|
||||
width = img.shape[1]
|
||||
|
||||
# Rotation and Scale
|
||||
R = np.eye(3)
|
||||
a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
|
||||
# a += random.choice([-180, -90, 0, 90]) # 90deg rotations added to small rotations
|
||||
s = random.random() * (scale[1] - scale[0]) + scale[0]
|
||||
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
|
||||
|
||||
# Translation
|
||||
T = np.eye(3)
|
||||
T[0, 2] = (random.random() * 2 - 1) * translate[0] * img.shape[0] + border # x translation (pixels)
|
||||
T[1, 2] = (random.random() * 2 - 1) * translate[1] * img.shape[1] + border # y translation (pixels)
|
||||
|
||||
# Shear
|
||||
S = np.eye(3)
|
||||
S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # x shear (deg)
|
||||
S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # y shear (deg)
|
||||
|
||||
M = S @ T @ R # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
|
||||
imw = cv2.warpPerspective(img, M, dsize=(width, height), flags=cv2.INTER_LINEAR,
|
||||
borderValue=borderValue) # BGR order borderValue
|
||||
|
||||
# Return warped points also
|
||||
if targets is not None:
|
||||
if len(targets) > 0:
|
||||
n = targets.shape[0]
|
||||
points = targets[:, 2:6].copy()
|
||||
area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1])
|
||||
|
||||
# warp points
|
||||
xy = np.ones((n * 4, 3))
|
||||
xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
|
||||
xy = (xy @ M.T)[:, :2].reshape(n, 8)
|
||||
|
||||
# create new boxes
|
||||
x = xy[:, [0, 2, 4, 6]]
|
||||
y = xy[:, [1, 3, 5, 7]]
|
||||
xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
|
||||
|
||||
# apply angle-based reduction
|
||||
radians = a * math.pi / 180
|
||||
reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
|
||||
x = (xy[:, 2] + xy[:, 0]) / 2
|
||||
y = (xy[:, 3] + xy[:, 1]) / 2
|
||||
w = (xy[:, 2] - xy[:, 0]) * reduction
|
||||
h = (xy[:, 3] - xy[:, 1]) * reduction
|
||||
xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
|
||||
|
||||
# reject warped points outside of image
|
||||
np.clip(xy[:, 0], 0, width, out=xy[:, 0])
|
||||
np.clip(xy[:, 2], 0, width, out=xy[:, 2])
|
||||
np.clip(xy[:, 1], 0, height, out=xy[:, 1])
|
||||
np.clip(xy[:, 3], 0, height, out=xy[:, 3])
|
||||
w = xy[:, 2] - xy[:, 0]
|
||||
h = xy[:, 3] - xy[:, 1]
|
||||
area = w * h
|
||||
ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
|
||||
i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10)
|
||||
|
||||
targets = targets[i]
|
||||
targets[:, 2:6] = xy[i]
|
||||
|
||||
return imw, targets, M
|
||||
else:
|
||||
return imw
|
||||
|
||||
def collate_fn(batch):
|
||||
imgs, labels, paths, sizes = zip(*batch)
|
||||
batch_size = len(labels)
|
||||
imgs = torch.stack(imgs, 0)
|
||||
max_box_len = max([l.shape[0] for l in labels])
|
||||
labels = [torch.from_numpy(l) for l in labels]
|
||||
filled_labels = torch.zeros(batch_size, max_box_len, 6)
|
||||
labels_len = torch.zeros(batch_size)
|
||||
|
||||
for i in range(batch_size):
|
||||
isize = labels[i].shape[0]
|
||||
if len(labels[i])>0:
|
||||
filled_labels[i, :isize, :] = labels[i]
|
||||
labels_len[i] = isize
|
||||
|
||||
return imgs, filled_labels, paths, sizes, labels_len.unsqueeze(1)
|
||||
|
||||
|
||||
class JointDataset(LoadImagesAndLabels): # for training
|
||||
def __init__(self, root, paths, img_size=(1088,608), augment=False, transforms=None):
|
||||
|
||||
dataset_names = paths.keys()
|
||||
self.img_files = OrderedDict()
|
||||
self.label_files = OrderedDict()
|
||||
self.tid_num = OrderedDict()
|
||||
self.tid_start_index = OrderedDict()
|
||||
for ds, path in paths.items():
|
||||
with open(path, 'r') as file:
|
||||
self.img_files[ds] = file.readlines()
|
||||
self.img_files[ds] = [osp.join(root, x.strip()) for x in self.img_files[ds]]
|
||||
self.img_files[ds] = list(filter(lambda x: len(x) > 0, self.img_files[ds]))
|
||||
|
||||
self.label_files[ds] = [x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')
|
||||
for x in self.img_files[ds]]
|
||||
|
||||
for ds, label_paths in self.label_files.items():
|
||||
max_index = -1
|
||||
for lp in label_paths:
|
||||
lb = np.loadtxt(lp)
|
||||
if len(lb) < 1:
|
||||
continue
|
||||
if len(lb.shape) < 2:
|
||||
img_max = lb[1]
|
||||
else:
|
||||
img_max = np.max(lb[:,1])
|
||||
if img_max >max_index:
|
||||
max_index = img_max
|
||||
self.tid_num[ds] = max_index + 1
|
||||
|
||||
last_index = 0
|
||||
for i, (k, v) in enumerate(self.tid_num.items()):
|
||||
self.tid_start_index[k] = last_index
|
||||
last_index += v
|
||||
|
||||
self.nID = int(last_index+1)
|
||||
self.nds = [len(x) for x in self.img_files.values()]
|
||||
self.cds = [sum(self.nds[:i]) for i in range(len(self.nds))]
|
||||
self.nF = sum(self.nds)
|
||||
self.width = img_size[0]
|
||||
self.height = img_size[1]
|
||||
self.augment = augment
|
||||
self.transforms = transforms
|
||||
|
||||
print('='*80)
|
||||
print('dataset summary')
|
||||
print(self.tid_num)
|
||||
print('total # identities:', self.nID)
|
||||
print('start index')
|
||||
print(self.tid_start_index)
|
||||
print('='*80)
|
||||
|
||||
|
||||
def __getitem__(self, files_index):
|
||||
for i, c in enumerate(self.cds):
|
||||
if files_index >= c:
|
||||
ds = list(self.label_files.keys())[i]
|
||||
start_index = c
|
||||
img_path = self.img_files[ds][files_index - start_index]
|
||||
label_path = self.label_files[ds][files_index - start_index]
|
||||
|
||||
imgs, labels, img_path, (h, w) = self.get_data(img_path, label_path)
|
||||
for i, _ in enumerate(labels):
|
||||
if labels[i,1] > -1:
|
||||
labels[i,1] += self.tid_start_index[ds]
|
||||
|
||||
return imgs, labels, img_path, (h, w)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,101 +1,101 @@
|
|||
import os
|
||||
import numpy as np
|
||||
import copy
|
||||
import motmetrics as mm
|
||||
|
||||
from utils.io import read_results, unzip_objs
|
||||
|
||||
|
||||
class Evaluator(object):
|
||||
|
||||
def __init__(self, data_root, seq_name, data_type):
|
||||
self.data_root = data_root
|
||||
self.seq_name = seq_name
|
||||
self.data_type = data_type
|
||||
|
||||
self.load_annotations()
|
||||
self.reset_accumulator()
|
||||
|
||||
def load_annotations(self):
|
||||
assert self.data_type == 'mot'
|
||||
|
||||
gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt')
|
||||
self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True)
|
||||
self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True)
|
||||
|
||||
def reset_accumulator(self):
|
||||
self.acc = mm.MOTAccumulator(auto_id=True)
|
||||
|
||||
def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
|
||||
# results
|
||||
trk_tlwhs = np.copy(trk_tlwhs)
|
||||
trk_ids = np.copy(trk_ids)
|
||||
|
||||
# gts
|
||||
gt_objs = self.gt_frame_dict.get(frame_id, [])
|
||||
gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
|
||||
|
||||
# ignore boxes
|
||||
ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
|
||||
ignore_tlwhs = unzip_objs(ignore_objs)[0]
|
||||
|
||||
# remove ignored results
|
||||
keep = np.ones(len(trk_tlwhs), dtype=bool)
|
||||
iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5)
|
||||
match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
|
||||
match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])
|
||||
match_ious = iou_distance[match_is, match_js]
|
||||
|
||||
match_js = np.asarray(match_js, dtype=int)
|
||||
match_js = match_js[np.logical_not(np.isnan(match_ious))]
|
||||
keep[match_js] = False
|
||||
trk_tlwhs = trk_tlwhs[keep]
|
||||
trk_ids = trk_ids[keep]
|
||||
|
||||
# get distance matrix
|
||||
iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)
|
||||
|
||||
# acc
|
||||
self.acc.update(gt_ids, trk_ids, iou_distance)
|
||||
|
||||
if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'):
|
||||
events = self.acc.last_mot_events # only supported by https://github.com/longcw/py-motmetrics
|
||||
else:
|
||||
events = None
|
||||
return events
|
||||
|
||||
def eval_file(self, filename):
|
||||
self.reset_accumulator()
|
||||
|
||||
result_frame_dict = read_results(filename, self.data_type, is_gt=False)
|
||||
frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys())))
|
||||
for frame_id in frames:
|
||||
trk_objs = result_frame_dict.get(frame_id, [])
|
||||
trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
|
||||
self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)
|
||||
|
||||
return self.acc
|
||||
|
||||
@staticmethod
|
||||
def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')):
|
||||
names = copy.deepcopy(names)
|
||||
if metrics is None:
|
||||
metrics = mm.metrics.motchallenge_metrics
|
||||
metrics = copy.deepcopy(metrics)
|
||||
|
||||
mh = mm.metrics.create()
|
||||
summary = mh.compute_many(
|
||||
accs,
|
||||
metrics=metrics,
|
||||
names=names,
|
||||
generate_overall=True
|
||||
)
|
||||
|
||||
return summary
|
||||
|
||||
@staticmethod
|
||||
def save_summary(summary, filename):
|
||||
import pandas as pd
|
||||
writer = pd.ExcelWriter(filename)
|
||||
summary.to_excel(writer)
|
||||
writer.save()
|
||||
import os
|
||||
import numpy as np
|
||||
import copy
|
||||
import motmetrics as mm
|
||||
|
||||
from utils.io import read_results, unzip_objs
|
||||
|
||||
|
||||
class Evaluator(object):
|
||||
|
||||
def __init__(self, data_root, seq_name, data_type):
|
||||
self.data_root = data_root
|
||||
self.seq_name = seq_name
|
||||
self.data_type = data_type
|
||||
|
||||
self.load_annotations()
|
||||
self.reset_accumulator()
|
||||
|
||||
def load_annotations(self):
|
||||
assert self.data_type == 'mot'
|
||||
|
||||
gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt')
|
||||
self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True)
|
||||
self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True)
|
||||
|
||||
def reset_accumulator(self):
|
||||
self.acc = mm.MOTAccumulator(auto_id=True)
|
||||
|
||||
def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
|
||||
# results
|
||||
trk_tlwhs = np.copy(trk_tlwhs)
|
||||
trk_ids = np.copy(trk_ids)
|
||||
|
||||
# gts
|
||||
gt_objs = self.gt_frame_dict.get(frame_id, [])
|
||||
gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
|
||||
|
||||
# ignore boxes
|
||||
ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
|
||||
ignore_tlwhs = unzip_objs(ignore_objs)[0]
|
||||
|
||||
# remove ignored results
|
||||
keep = np.ones(len(trk_tlwhs), dtype=bool)
|
||||
iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5)
|
||||
match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
|
||||
match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])
|
||||
match_ious = iou_distance[match_is, match_js]
|
||||
|
||||
match_js = np.asarray(match_js, dtype=int)
|
||||
match_js = match_js[np.logical_not(np.isnan(match_ious))]
|
||||
keep[match_js] = False
|
||||
trk_tlwhs = trk_tlwhs[keep]
|
||||
trk_ids = trk_ids[keep]
|
||||
|
||||
# get distance matrix
|
||||
iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)
|
||||
|
||||
# acc
|
||||
self.acc.update(gt_ids, trk_ids, iou_distance)
|
||||
|
||||
if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'):
|
||||
events = self.acc.last_mot_events # only supported by https://github.com/longcw/py-motmetrics
|
||||
else:
|
||||
events = None
|
||||
return events
|
||||
|
||||
def eval_file(self, filename):
|
||||
self.reset_accumulator()
|
||||
|
||||
result_frame_dict = read_results(filename, self.data_type, is_gt=False)
|
||||
frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys())))
|
||||
for frame_id in frames:
|
||||
trk_objs = result_frame_dict.get(frame_id, [])
|
||||
trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
|
||||
self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)
|
||||
|
||||
return self.acc
|
||||
|
||||
@staticmethod
|
||||
def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')):
|
||||
names = copy.deepcopy(names)
|
||||
if metrics is None:
|
||||
metrics = mm.metrics.motchallenge_metrics
|
||||
metrics = copy.deepcopy(metrics)
|
||||
|
||||
mh = mm.metrics.create()
|
||||
summary = mh.compute_many(
|
||||
accs,
|
||||
metrics=metrics,
|
||||
names=names,
|
||||
generate_overall=True
|
||||
)
|
||||
|
||||
return summary
|
||||
|
||||
@staticmethod
|
||||
def save_summary(summary, filename):
|
||||
import pandas as pd
|
||||
writer = pd.ExcelWriter(filename)
|
||||
summary.to_excel(writer)
|
||||
writer.save()
|
||||
|
|
|
|||
222
utils/io.py
222
utils/io.py
|
|
@ -1,112 +1,112 @@
|
|||
import os
|
||||
from typing import Dict
|
||||
import numpy as np
|
||||
|
||||
from utils.log import logger
|
||||
|
||||
|
||||
def write_results(filename, results_dict: Dict, data_type: str):
|
||||
if not filename:
|
||||
return
|
||||
path = os.path.dirname(filename)
|
||||
if not os.path.exists(path):
|
||||
os.makedirs(path)
|
||||
|
||||
if data_type in ('mot', 'mcmot', 'lab'):
|
||||
save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
|
||||
elif data_type == 'kitti':
|
||||
save_format = '{frame} {id} pedestrian -1 -1 -10 {x1} {y1} {x2} {y2} -1 -1 -1 -1000 -1000 -1000 -10 {score}\n'
|
||||
else:
|
||||
raise ValueError(data_type)
|
||||
|
||||
with open(filename, 'w') as f:
|
||||
for frame_id, frame_data in results_dict.items():
|
||||
if data_type == 'kitti':
|
||||
frame_id -= 1
|
||||
for tlwh, track_id in frame_data:
|
||||
if track_id < 0:
|
||||
continue
|
||||
x1, y1, w, h = tlwh
|
||||
x2, y2 = x1 + w, y1 + h
|
||||
line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, score=1.0)
|
||||
f.write(line)
|
||||
logger.info('Save results to {}'.format(filename))
|
||||
|
||||
|
||||
def read_results(filename, data_type: str, is_gt=False, is_ignore=False):
|
||||
if data_type in ('mot', 'lab'):
|
||||
read_fun = read_mot_results
|
||||
else:
|
||||
raise ValueError('Unknown data type: {}'.format(data_type))
|
||||
|
||||
return read_fun(filename, is_gt, is_ignore)
|
||||
|
||||
|
||||
"""
|
||||
labels={'ped', ... % 1
|
||||
'person_on_vhcl', ... % 2
|
||||
'car', ... % 3
|
||||
'bicycle', ... % 4
|
||||
'mbike', ... % 5
|
||||
'non_mot_vhcl', ... % 6
|
||||
'static_person', ... % 7
|
||||
'distractor', ... % 8
|
||||
'occluder', ... % 9
|
||||
'occluder_on_grnd', ... %10
|
||||
'occluder_full', ... % 11
|
||||
'reflection', ... % 12
|
||||
'crowd' ... % 13
|
||||
};
|
||||
"""
|
||||
|
||||
|
||||
def read_mot_results(filename, is_gt, is_ignore):
|
||||
valid_labels = {1}
|
||||
ignore_labels = {2, 7, 8, 12}
|
||||
results_dict = dict()
|
||||
if os.path.isfile(filename):
|
||||
with open(filename, 'r') as f:
|
||||
for line in f.readlines():
|
||||
linelist = line.split(',')
|
||||
if len(linelist) < 7:
|
||||
continue
|
||||
fid = int(linelist[0])
|
||||
if fid < 1:
|
||||
continue
|
||||
results_dict.setdefault(fid, list())
|
||||
|
||||
if is_gt:
|
||||
if 'MOT16-' in filename or 'MOT17-' in filename:
|
||||
label = int(float(linelist[7]))
|
||||
mark = int(float(linelist[6]))
|
||||
if mark == 0 or label not in valid_labels:
|
||||
continue
|
||||
score = 1
|
||||
elif is_ignore:
|
||||
if 'MOT16-' in filename or 'MOT17-' in filename:
|
||||
label = int(float(linelist[7]))
|
||||
vis_ratio = float(linelist[8])
|
||||
if label not in ignore_labels and vis_ratio >= 0:
|
||||
continue
|
||||
else:
|
||||
continue
|
||||
score = 1
|
||||
else:
|
||||
score = float(linelist[6])
|
||||
|
||||
tlwh = tuple(map(float, linelist[2:6]))
|
||||
target_id = int(linelist[1])
|
||||
|
||||
results_dict[fid].append((tlwh, target_id, score))
|
||||
|
||||
return results_dict
|
||||
|
||||
|
||||
def unzip_objs(objs):
|
||||
if len(objs) > 0:
|
||||
tlwhs, ids, scores = zip(*objs)
|
||||
else:
|
||||
tlwhs, ids, scores = [], [], []
|
||||
tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)
|
||||
|
||||
import os
|
||||
from typing import Dict
|
||||
import numpy as np
|
||||
|
||||
from utils.log import logger
|
||||
|
||||
|
||||
def write_results(filename, results_dict: Dict, data_type: str):
|
||||
if not filename:
|
||||
return
|
||||
path = os.path.dirname(filename)
|
||||
if not os.path.exists(path):
|
||||
os.makedirs(path)
|
||||
|
||||
if data_type in ('mot', 'mcmot', 'lab'):
|
||||
save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
|
||||
elif data_type == 'kitti':
|
||||
save_format = '{frame} {id} pedestrian -1 -1 -10 {x1} {y1} {x2} {y2} -1 -1 -1 -1000 -1000 -1000 -10 {score}\n'
|
||||
else:
|
||||
raise ValueError(data_type)
|
||||
|
||||
with open(filename, 'w') as f:
|
||||
for frame_id, frame_data in results_dict.items():
|
||||
if data_type == 'kitti':
|
||||
frame_id -= 1
|
||||
for tlwh, track_id in frame_data:
|
||||
if track_id < 0:
|
||||
continue
|
||||
x1, y1, w, h = tlwh
|
||||
x2, y2 = x1 + w, y1 + h
|
||||
line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, score=1.0)
|
||||
f.write(line)
|
||||
logger.info('Save results to {}'.format(filename))
|
||||
|
||||
|
||||
def read_results(filename, data_type: str, is_gt=False, is_ignore=False):
|
||||
if data_type in ('mot', 'lab'):
|
||||
read_fun = read_mot_results
|
||||
else:
|
||||
raise ValueError('Unknown data type: {}'.format(data_type))
|
||||
|
||||
return read_fun(filename, is_gt, is_ignore)
|
||||
|
||||
|
||||
"""
|
||||
labels={'ped', ... % 1
|
||||
'person_on_vhcl', ... % 2
|
||||
'car', ... % 3
|
||||
'bicycle', ... % 4
|
||||
'mbike', ... % 5
|
||||
'non_mot_vhcl', ... % 6
|
||||
'static_person', ... % 7
|
||||
'distractor', ... % 8
|
||||
'occluder', ... % 9
|
||||
'occluder_on_grnd', ... %10
|
||||
'occluder_full', ... % 11
|
||||
'reflection', ... % 12
|
||||
'crowd' ... % 13
|
||||
};
|
||||
"""
|
||||
|
||||
|
||||
def read_mot_results(filename, is_gt, is_ignore):
|
||||
valid_labels = {1}
|
||||
ignore_labels = {2, 7, 8, 12}
|
||||
results_dict = dict()
|
||||
if os.path.isfile(filename):
|
||||
with open(filename, 'r') as f:
|
||||
for line in f.readlines():
|
||||
linelist = line.split(',')
|
||||
if len(linelist) < 7:
|
||||
continue
|
||||
fid = int(linelist[0])
|
||||
if fid < 1:
|
||||
continue
|
||||
results_dict.setdefault(fid, list())
|
||||
|
||||
if is_gt:
|
||||
if 'MOT16-' in filename or 'MOT17-' in filename:
|
||||
label = int(float(linelist[7]))
|
||||
mark = int(float(linelist[6]))
|
||||
if mark == 0 or label not in valid_labels:
|
||||
continue
|
||||
score = 1
|
||||
elif is_ignore:
|
||||
if 'MOT16-' in filename or 'MOT17-' in filename:
|
||||
label = int(float(linelist[7]))
|
||||
vis_ratio = float(linelist[8])
|
||||
if label not in ignore_labels and vis_ratio >= 0:
|
||||
continue
|
||||
else:
|
||||
continue
|
||||
score = 1
|
||||
else:
|
||||
score = float(linelist[6])
|
||||
|
||||
tlwh = tuple(map(float, linelist[2:6]))
|
||||
target_id = int(linelist[1])
|
||||
|
||||
results_dict[fid].append((tlwh, target_id, score))
|
||||
|
||||
return results_dict
|
||||
|
||||
|
||||
def unzip_objs(objs):
|
||||
if len(objs) > 0:
|
||||
tlwhs, ids, scores = zip(*objs)
|
||||
else:
|
||||
tlwhs, ids, scores = [], [], []
|
||||
tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)
|
||||
|
||||
return tlwhs, ids, scores
|
||||
36
utils/log.py
36
utils/log.py
|
|
@ -1,18 +1,18 @@
|
|||
import logging
|
||||
|
||||
|
||||
def get_logger(name='root'):
|
||||
formatter = logging.Formatter(
|
||||
# fmt='%(asctime)s [%(levelname)s]: %(filename)s(%(funcName)s:%(lineno)s) >> %(message)s')
|
||||
fmt='%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
|
||||
|
||||
handler = logging.StreamHandler()
|
||||
handler.setFormatter(formatter)
|
||||
|
||||
logger = logging.getLogger(name)
|
||||
logger.setLevel(logging.DEBUG)
|
||||
logger.addHandler(handler)
|
||||
return logger
|
||||
|
||||
|
||||
logger = get_logger('root')
|
||||
import logging
|
||||
|
||||
|
||||
def get_logger(name='root'):
|
||||
formatter = logging.Formatter(
|
||||
# fmt='%(asctime)s [%(levelname)s]: %(filename)s(%(funcName)s:%(lineno)s) >> %(message)s')
|
||||
fmt='%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
|
||||
|
||||
handler = logging.StreamHandler()
|
||||
handler.setFormatter(formatter)
|
||||
|
||||
logger = logging.getLogger(name)
|
||||
logger.setLevel(logging.DEBUG)
|
||||
logger.addHandler(handler)
|
||||
return logger
|
||||
|
||||
|
||||
logger = get_logger('root')
|
||||
|
|
|
|||
|
|
@ -1,7 +0,0 @@
|
|||
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
# from ._utils import _C
|
||||
from utils import _C
|
||||
|
||||
nms = _C.nms
|
||||
# nms.__doc__ = """
|
||||
# This function performs Non-maximum suppresion"""
|
||||
|
|
@ -0,0 +1,32 @@
|
|||
// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
#pragma once
|
||||
#include <torch/extension.h>
|
||||
at::Tensor nms_cpu(const at::Tensor& dets, const at::Tensor& scores, const float threshold);
|
||||
#ifdef WITH_CUDA
|
||||
at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh);
|
||||
#endif
|
||||
|
||||
|
||||
at::Tensor nms(const at::Tensor& dets,
|
||||
const at::Tensor& scores,
|
||||
const float threshold) {
|
||||
|
||||
if (dets.type().is_cuda()) {
|
||||
#ifdef WITH_CUDA
|
||||
// TODO raise error if not compiled with CUDA
|
||||
if (dets.numel() == 0)
|
||||
return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU));
|
||||
auto b = at::cat({dets, scores.unsqueeze(1)}, 1);
|
||||
return nms_cuda(b, threshold);
|
||||
#else
|
||||
AT_ERROR("Not compiled with GPU support");
|
||||
#endif
|
||||
}
|
||||
|
||||
at::Tensor result = nms_cpu(dets, scores, threshold);
|
||||
return result;
|
||||
}
|
||||
|
||||
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m){
|
||||
m.def("nms", &nms, "non-maximum suppression");
|
||||
}
|
||||
|
|
@ -0,0 +1,74 @@
|
|||
// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
|
||||
#include "nms.h"
|
||||
template <typename scalar_t>
|
||||
at::Tensor nms_cpu_kernel(const at::Tensor& dets,
|
||||
const at::Tensor& scores,
|
||||
const float threshold) {
|
||||
AT_ASSERTM(!dets.type().is_cuda(), "dets must be a CPU tensor");
|
||||
AT_ASSERTM(!scores.type().is_cuda(), "scores must be a CPU tensor");
|
||||
AT_ASSERTM(dets.type() == scores.type(), "dets should have the same type as scores");
|
||||
|
||||
if (dets.numel() == 0) {
|
||||
return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU));
|
||||
}
|
||||
|
||||
auto x1_t = dets.select(1, 0).contiguous();
|
||||
auto y1_t = dets.select(1, 1).contiguous();
|
||||
auto x2_t = dets.select(1, 2).contiguous();
|
||||
auto y2_t = dets.select(1, 3).contiguous();
|
||||
|
||||
at::Tensor areas_t = (x2_t - x1_t + 1) * (y2_t - y1_t + 1);
|
||||
|
||||
auto order_t = std::get<1>(scores.sort(0, /* descending=*/true));
|
||||
|
||||
auto ndets = dets.size(0);
|
||||
at::Tensor suppressed_t = at::zeros({ndets}, dets.options().dtype(at::kByte).device(at::kCPU));
|
||||
|
||||
auto suppressed = suppressed_t.data<uint8_t>();
|
||||
auto order = order_t.data<int64_t>();
|
||||
auto x1 = x1_t.data<scalar_t>();
|
||||
auto y1 = y1_t.data<scalar_t>();
|
||||
auto x2 = x2_t.data<scalar_t>();
|
||||
auto y2 = y2_t.data<scalar_t>();
|
||||
auto areas = areas_t.data<scalar_t>();
|
||||
|
||||
for (int64_t _i = 0; _i < ndets; _i++) {
|
||||
auto i = order[_i];
|
||||
if (suppressed[i] == 1)
|
||||
continue;
|
||||
auto ix1 = x1[i];
|
||||
auto iy1 = y1[i];
|
||||
auto ix2 = x2[i];
|
||||
auto iy2 = y2[i];
|
||||
auto iarea = areas[i];
|
||||
|
||||
for (int64_t _j = _i + 1; _j < ndets; _j++) {
|
||||
auto j = order[_j];
|
||||
if (suppressed[j] == 1)
|
||||
continue;
|
||||
auto xx1 = std::max(ix1, x1[j]);
|
||||
auto yy1 = std::max(iy1, y1[j]);
|
||||
auto xx2 = std::min(ix2, x2[j]);
|
||||
auto yy2 = std::min(iy2, y2[j]);
|
||||
|
||||
auto w = std::max(static_cast<scalar_t>(0), xx2 - xx1 + 1);
|
||||
auto h = std::max(static_cast<scalar_t>(0), yy2 - yy1 + 1);
|
||||
auto inter = w * h;
|
||||
auto ovr = inter / (iarea + areas[j] - inter);
|
||||
if (ovr >= threshold)
|
||||
suppressed[j] = 1;
|
||||
}
|
||||
}
|
||||
return at::nonzero(suppressed_t == 0).squeeze(1);
|
||||
}
|
||||
|
||||
at::Tensor nms_cpu(const at::Tensor& dets,
|
||||
const at::Tensor& scores,
|
||||
const float threshold) {
|
||||
at::Tensor result;
|
||||
AT_DISPATCH_FLOATING_TYPES(dets.type(), "nms", [&] {
|
||||
result = nms_cpu_kernel<scalar_t>(dets, scores, threshold);
|
||||
});
|
||||
return result;
|
||||
}
|
||||
|
||||
|
|
@ -0,0 +1,131 @@
|
|||
#include <ATen/ATen.h>
|
||||
#include <ATen/cuda/CUDAContext.h>
|
||||
|
||||
#include <THC/THC.h>
|
||||
#include <THC/THCDeviceUtils.cuh>
|
||||
|
||||
#include <vector>
|
||||
#include <iostream>
|
||||
|
||||
|
||||
int const threadsPerBlock = sizeof(unsigned long long) * 8;
|
||||
|
||||
__device__ inline float devIoU(float const * const a, float const * const b) {
|
||||
float left = max(a[0], b[0]), right = min(a[2], b[2]);
|
||||
float top = max(a[1], b[1]), bottom = min(a[3], b[3]);
|
||||
float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f);
|
||||
float interS = width * height;
|
||||
float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1);
|
||||
float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1);
|
||||
return interS / (Sa + Sb - interS);
|
||||
}
|
||||
|
||||
__global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh,
|
||||
const float *dev_boxes, unsigned long long *dev_mask) {
|
||||
const int row_start = blockIdx.y;
|
||||
const int col_start = blockIdx.x;
|
||||
|
||||
// if (row_start > col_start) return;
|
||||
|
||||
const int row_size =
|
||||
min(n_boxes - row_start * threadsPerBlock, threadsPerBlock);
|
||||
const int col_size =
|
||||
min(n_boxes - col_start * threadsPerBlock, threadsPerBlock);
|
||||
|
||||
__shared__ float block_boxes[threadsPerBlock * 5];
|
||||
if (threadIdx.x < col_size) {
|
||||
block_boxes[threadIdx.x * 5 + 0] =
|
||||
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0];
|
||||
block_boxes[threadIdx.x * 5 + 1] =
|
||||
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1];
|
||||
block_boxes[threadIdx.x * 5 + 2] =
|
||||
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2];
|
||||
block_boxes[threadIdx.x * 5 + 3] =
|
||||
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3];
|
||||
block_boxes[threadIdx.x * 5 + 4] =
|
||||
dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4];
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
if (threadIdx.x < row_size) {
|
||||
const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x;
|
||||
const float *cur_box = dev_boxes + cur_box_idx * 5;
|
||||
int i = 0;
|
||||
unsigned long long t = 0;
|
||||
int start = 0;
|
||||
if (row_start == col_start) {
|
||||
start = threadIdx.x + 1;
|
||||
}
|
||||
for (i = start; i < col_size; i++) {
|
||||
if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) {
|
||||
t |= 1ULL << i;
|
||||
}
|
||||
}
|
||||
const int col_blocks = THCCeilDiv(n_boxes, threadsPerBlock);
|
||||
dev_mask[cur_box_idx * col_blocks + col_start] = t;
|
||||
}
|
||||
}
|
||||
|
||||
// boxes is a N x 5 tensor
|
||||
at::Tensor nms_cuda(const at::Tensor boxes, float nms_overlap_thresh) {
|
||||
using scalar_t = float;
|
||||
AT_ASSERTM(boxes.type().is_cuda(), "boxes must be a CUDA tensor");
|
||||
auto scores = boxes.select(1, 4);
|
||||
auto order_t = std::get<1>(scores.sort(0, /* descending=*/true));
|
||||
auto boxes_sorted = boxes.index_select(0, order_t);
|
||||
|
||||
int boxes_num = boxes.size(0);
|
||||
|
||||
const int col_blocks = THCCeilDiv(boxes_num, threadsPerBlock);
|
||||
|
||||
scalar_t* boxes_dev = boxes_sorted.data<scalar_t>();
|
||||
|
||||
THCState *state = at::globalContext().lazyInitCUDA(); // TODO replace with getTHCState
|
||||
|
||||
unsigned long long* mask_dev = NULL;
|
||||
//THCudaCheck(THCudaMalloc(state, (void**) &mask_dev,
|
||||
// boxes_num * col_blocks * sizeof(unsigned long long)));
|
||||
|
||||
mask_dev = (unsigned long long*) THCudaMalloc(state, boxes_num * col_blocks * sizeof(unsigned long long));
|
||||
|
||||
dim3 blocks(THCCeilDiv(boxes_num, threadsPerBlock),
|
||||
THCCeilDiv(boxes_num, threadsPerBlock));
|
||||
dim3 threads(threadsPerBlock);
|
||||
nms_kernel<<<blocks, threads>>>(boxes_num,
|
||||
nms_overlap_thresh,
|
||||
boxes_dev,
|
||||
mask_dev);
|
||||
|
||||
std::vector<unsigned long long> mask_host(boxes_num * col_blocks);
|
||||
THCudaCheck(cudaMemcpy(&mask_host[0],
|
||||
mask_dev,
|
||||
sizeof(unsigned long long) * boxes_num * col_blocks,
|
||||
cudaMemcpyDeviceToHost));
|
||||
|
||||
std::vector<unsigned long long> remv(col_blocks);
|
||||
memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks);
|
||||
|
||||
at::Tensor keep = at::empty({boxes_num}, boxes.options().dtype(at::kLong).device(at::kCPU));
|
||||
int64_t* keep_out = keep.data<int64_t>();
|
||||
|
||||
int num_to_keep = 0;
|
||||
for (int i = 0; i < boxes_num; i++) {
|
||||
int nblock = i / threadsPerBlock;
|
||||
int inblock = i % threadsPerBlock;
|
||||
|
||||
if (!(remv[nblock] & (1ULL << inblock))) {
|
||||
keep_out[num_to_keep++] = i;
|
||||
unsigned long long *p = &mask_host[0] + i * col_blocks;
|
||||
for (int j = nblock; j < col_blocks; j++) {
|
||||
remv[j] |= p[j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
THCudaFree(state, mask_dev);
|
||||
// TODO improve this part
|
||||
return std::get<0>(order_t.index({
|
||||
keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep).to(
|
||||
order_t.device(), keep.scalar_type())
|
||||
}).sort(0, false));
|
||||
}
|
||||
|
|
@ -1,35 +1,35 @@
|
|||
def parse_model_cfg(path):
|
||||
"""Parses the yolo-v3 layer configuration file and returns module definitions"""
|
||||
file = open(path, 'r')
|
||||
lines = file.read().split('\n')
|
||||
lines = [x for x in lines if x and not x.startswith('#')]
|
||||
lines = [x.rstrip().lstrip() for x in lines] # get rid of fringe whitespaces
|
||||
module_defs = []
|
||||
for line in lines:
|
||||
if line.startswith('['): # This marks the start of a new block
|
||||
module_defs.append({})
|
||||
module_defs[-1]['type'] = line[1:-1].rstrip()
|
||||
if module_defs[-1]['type'] == 'convolutional':
|
||||
module_defs[-1]['batch_normalize'] = 0
|
||||
else:
|
||||
key, value = line.split("=")
|
||||
value = value.strip()
|
||||
module_defs[-1][key.rstrip()] = value.strip()
|
||||
|
||||
return module_defs
|
||||
|
||||
|
||||
def parse_data_cfg(path):
|
||||
"""Parses the data configuration file"""
|
||||
options = dict()
|
||||
options['gpus'] = '0'
|
||||
options['num_workers'] = '10'
|
||||
with open(path, 'r') as fp:
|
||||
lines = fp.readlines()
|
||||
for line in lines:
|
||||
line = line.strip()
|
||||
if line == '' or line.startswith('#'):
|
||||
continue
|
||||
key, value = line.split('=')
|
||||
options[key.strip()] = value.strip()
|
||||
return options
|
||||
def parse_model_cfg(path):
|
||||
"""Parses the yolo-v3 layer configuration file and returns module definitions"""
|
||||
file = open(path, 'r')
|
||||
lines = file.read().split('\n')
|
||||
lines = [x for x in lines if x and not x.startswith('#')]
|
||||
lines = [x.rstrip().lstrip() for x in lines] # get rid of fringe whitespaces
|
||||
module_defs = []
|
||||
for line in lines:
|
||||
if line.startswith('['): # This marks the start of a new block
|
||||
module_defs.append({})
|
||||
module_defs[-1]['type'] = line[1:-1].rstrip()
|
||||
if module_defs[-1]['type'] == 'convolutional':
|
||||
module_defs[-1]['batch_normalize'] = 0
|
||||
else:
|
||||
key, value = line.split("=")
|
||||
value = value.strip()
|
||||
module_defs[-1][key.rstrip()] = value.strip()
|
||||
|
||||
return module_defs
|
||||
|
||||
|
||||
def parse_data_cfg(path):
|
||||
"""Parses the data configuration file"""
|
||||
options = dict()
|
||||
options['gpus'] = '0'
|
||||
options['num_workers'] = '10'
|
||||
with open(path, 'r') as fp:
|
||||
lines = fp.readlines()
|
||||
for line in lines:
|
||||
line = line.strip()
|
||||
if line == '' or line.startswith('#'):
|
||||
continue
|
||||
key, value = line.split('=')
|
||||
options[key.strip()] = value.strip()
|
||||
return options
|
||||
|
|
|
|||
|
|
@ -1,45 +1,45 @@
|
|||
# --------------------------------------------------------
|
||||
# Fast R-CNN
|
||||
# Copyright (c) 2015 Microsoft
|
||||
# Licensed under The MIT License [see LICENSE for details]
|
||||
# Written by Ross Girshick
|
||||
# --------------------------------------------------------
|
||||
|
||||
import time
|
||||
|
||||
|
||||
class Timer(object):
|
||||
"""A simple timer."""
|
||||
def __init__(self):
|
||||
self.total_time = 0.
|
||||
self.calls = 0
|
||||
self.start_time = 0.
|
||||
self.diff = 0.
|
||||
self.average_time = 0.
|
||||
|
||||
self.duration = 0.
|
||||
|
||||
def tic(self):
|
||||
# using time.time instead of time.clock because time time.clock
|
||||
# does not normalize for multithreading
|
||||
self.start_time = time.time()
|
||||
|
||||
def toc(self, average=True):
|
||||
self.diff = time.time() - self.start_time
|
||||
self.total_time += self.diff
|
||||
self.calls += 1
|
||||
self.average_time = self.total_time / self.calls
|
||||
if average:
|
||||
self.duration = self.average_time
|
||||
else:
|
||||
self.duration = self.diff
|
||||
return self.duration
|
||||
|
||||
def clear(self):
|
||||
self.total_time = 0.
|
||||
self.calls = 0
|
||||
self.start_time = 0.
|
||||
self.diff = 0.
|
||||
self.average_time = 0.
|
||||
self.duration = 0.
|
||||
|
||||
# --------------------------------------------------------
|
||||
# Fast R-CNN
|
||||
# Copyright (c) 2015 Microsoft
|
||||
# Licensed under The MIT License [see LICENSE for details]
|
||||
# Written by Ross Girshick
|
||||
# --------------------------------------------------------
|
||||
|
||||
import time
|
||||
|
||||
|
||||
class Timer(object):
|
||||
"""A simple timer."""
|
||||
def __init__(self):
|
||||
self.total_time = 0.
|
||||
self.calls = 0
|
||||
self.start_time = 0.
|
||||
self.diff = 0.
|
||||
self.average_time = 0.
|
||||
|
||||
self.duration = 0.
|
||||
|
||||
def tic(self):
|
||||
# using time.time instead of time.clock because time time.clock
|
||||
# does not normalize for multithreading
|
||||
self.start_time = time.time()
|
||||
|
||||
def toc(self, average=True):
|
||||
self.diff = time.time() - self.start_time
|
||||
self.total_time += self.diff
|
||||
self.calls += 1
|
||||
self.average_time = self.total_time / self.calls
|
||||
if average:
|
||||
self.duration = self.average_time
|
||||
else:
|
||||
self.duration = self.diff
|
||||
return self.duration
|
||||
|
||||
def clear(self):
|
||||
self.total_time = 0.
|
||||
self.calls = 0
|
||||
self.start_time = 0.
|
||||
self.diff = 0.
|
||||
self.average_time = 0.
|
||||
self.duration = 0.
|
||||
|
||||
|
|
|
|||
1094
utils/utils.py
1094
utils/utils.py
File diff suppressed because it is too large
Load Diff
|
|
@ -1,90 +1,90 @@
|
|||
import numpy as np
|
||||
import cv2
|
||||
|
||||
|
||||
def tlwhs_to_tlbrs(tlwhs):
|
||||
tlbrs = np.copy(tlwhs)
|
||||
if len(tlbrs) == 0:
|
||||
return tlbrs
|
||||
tlbrs[:, 2] += tlwhs[:, 0]
|
||||
tlbrs[:, 3] += tlwhs[:, 1]
|
||||
return tlbrs
|
||||
|
||||
|
||||
def get_color(idx):
|
||||
idx = idx * 3
|
||||
color = ((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255)
|
||||
|
||||
return color
|
||||
|
||||
|
||||
def resize_image(image, max_size=800):
|
||||
if max(image.shape[:2]) > max_size:
|
||||
scale = float(max_size) / max(image.shape[:2])
|
||||
image = cv2.resize(image, None, fx=scale, fy=scale)
|
||||
return image
|
||||
|
||||
|
||||
def plot_tracking(image, tlwhs, obj_ids, scores=None, frame_id=0, fps=0., ids2=None):
|
||||
im = np.ascontiguousarray(np.copy(image))
|
||||
im_h, im_w = im.shape[:2]
|
||||
|
||||
top_view = np.zeros([im_w, im_w, 3], dtype=np.uint8) + 255
|
||||
|
||||
text_scale = max(1, image.shape[1] / 1600.)
|
||||
text_thickness = 1 if text_scale > 1.1 else 1
|
||||
line_thickness = max(1, int(image.shape[1] / 500.))
|
||||
|
||||
radius = max(5, int(im_w/140.))
|
||||
cv2.putText(im, 'frame: %d fps: %.2f num: %d' % (frame_id, fps, len(tlwhs)),
|
||||
(0, int(15 * text_scale)), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 0, 255), thickness=2)
|
||||
|
||||
for i, tlwh in enumerate(tlwhs):
|
||||
x1, y1, w, h = tlwh
|
||||
intbox = tuple(map(int, (x1, y1, x1 + w, y1 + h)))
|
||||
obj_id = int(obj_ids[i])
|
||||
id_text = '{}'.format(int(obj_id))
|
||||
if ids2 is not None:
|
||||
id_text = id_text + ', {}'.format(int(ids2[i]))
|
||||
_line_thickness = 1 if obj_id <= 0 else line_thickness
|
||||
color = get_color(abs(obj_id))
|
||||
cv2.rectangle(im, intbox[0:2], intbox[2:4], color=color, thickness=line_thickness)
|
||||
cv2.putText(im, id_text, (intbox[0], intbox[1] + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 0, 255),
|
||||
thickness=text_thickness)
|
||||
return im
|
||||
|
||||
|
||||
def plot_trajectory(image, tlwhs, track_ids):
|
||||
image = image.copy()
|
||||
for one_tlwhs, track_id in zip(tlwhs, track_ids):
|
||||
color = get_color(int(track_id))
|
||||
for tlwh in one_tlwhs:
|
||||
x1, y1, w, h = tuple(map(int, tlwh))
|
||||
cv2.circle(image, (int(x1 + 0.5 * w), int(y1 + h)), 2, color, thickness=2)
|
||||
|
||||
return image
|
||||
|
||||
|
||||
def plot_detections(image, tlbrs, scores=None, color=(255, 0, 0), ids=None):
|
||||
im = np.copy(image)
|
||||
text_scale = max(1, image.shape[1] / 800.)
|
||||
thickness = 2 if text_scale > 1.3 else 1
|
||||
for i, det in enumerate(tlbrs):
|
||||
x1, y1, x2, y2 = np.asarray(det[:4], dtype=np.int)
|
||||
if len(det) >= 7:
|
||||
label = 'det' if det[5] > 0 else 'trk'
|
||||
if ids is not None:
|
||||
text = '{}# {:.2f}: {:d}'.format(label, det[6], ids[i])
|
||||
cv2.putText(im, text, (x1, y1 + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 255, 255),
|
||||
thickness=thickness)
|
||||
else:
|
||||
text = '{}# {:.2f}'.format(label, det[6])
|
||||
|
||||
if scores is not None:
|
||||
text = '{:.2f}'.format(scores[i])
|
||||
cv2.putText(im, text, (x1, y1 + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 255, 255),
|
||||
thickness=thickness)
|
||||
|
||||
cv2.rectangle(im, (x1, y1), (x2, y2), color, 2)
|
||||
|
||||
return im
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
|
||||
def tlwhs_to_tlbrs(tlwhs):
|
||||
tlbrs = np.copy(tlwhs)
|
||||
if len(tlbrs) == 0:
|
||||
return tlbrs
|
||||
tlbrs[:, 2] += tlwhs[:, 0]
|
||||
tlbrs[:, 3] += tlwhs[:, 1]
|
||||
return tlbrs
|
||||
|
||||
|
||||
def get_color(idx):
|
||||
idx = idx * 3
|
||||
color = ((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255)
|
||||
|
||||
return color
|
||||
|
||||
|
||||
def resize_image(image, max_size=800):
|
||||
if max(image.shape[:2]) > max_size:
|
||||
scale = float(max_size) / max(image.shape[:2])
|
||||
image = cv2.resize(image, None, fx=scale, fy=scale)
|
||||
return image
|
||||
|
||||
|
||||
def plot_tracking(image, tlwhs, obj_ids, scores=None, frame_id=0, fps=0., ids2=None):
|
||||
im = np.ascontiguousarray(np.copy(image))
|
||||
im_h, im_w = im.shape[:2]
|
||||
|
||||
top_view = np.zeros([im_w, im_w, 3], dtype=np.uint8) + 255
|
||||
|
||||
text_scale = max(1, image.shape[1] / 1600.)
|
||||
text_thickness = 1 if text_scale > 1.1 else 1
|
||||
line_thickness = max(1, int(image.shape[1] / 500.))
|
||||
|
||||
radius = max(5, int(im_w/140.))
|
||||
cv2.putText(im, 'frame: %d fps: %.2f num: %d' % (frame_id, fps, len(tlwhs)),
|
||||
(0, int(15 * text_scale)), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 0, 255), thickness=2)
|
||||
|
||||
for i, tlwh in enumerate(tlwhs):
|
||||
x1, y1, w, h = tlwh
|
||||
intbox = tuple(map(int, (x1, y1, x1 + w, y1 + h)))
|
||||
obj_id = int(obj_ids[i])
|
||||
id_text = '{}'.format(int(obj_id))
|
||||
if ids2 is not None:
|
||||
id_text = id_text + ', {}'.format(int(ids2[i]))
|
||||
_line_thickness = 1 if obj_id <= 0 else line_thickness
|
||||
color = get_color(abs(obj_id))
|
||||
cv2.rectangle(im, intbox[0:2], intbox[2:4], color=color, thickness=line_thickness)
|
||||
cv2.putText(im, id_text, (intbox[0], intbox[1] + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 0, 255),
|
||||
thickness=text_thickness)
|
||||
return im
|
||||
|
||||
|
||||
def plot_trajectory(image, tlwhs, track_ids):
|
||||
image = image.copy()
|
||||
for one_tlwhs, track_id in zip(tlwhs, track_ids):
|
||||
color = get_color(int(track_id))
|
||||
for tlwh in one_tlwhs:
|
||||
x1, y1, w, h = tuple(map(int, tlwh))
|
||||
cv2.circle(image, (int(x1 + 0.5 * w), int(y1 + h)), 2, color, thickness=2)
|
||||
|
||||
return image
|
||||
|
||||
|
||||
def plot_detections(image, tlbrs, scores=None, color=(255, 0, 0), ids=None):
|
||||
im = np.copy(image)
|
||||
text_scale = max(1, image.shape[1] / 800.)
|
||||
thickness = 2 if text_scale > 1.3 else 1
|
||||
for i, det in enumerate(tlbrs):
|
||||
x1, y1, x2, y2 = np.asarray(det[:4], dtype=np.int)
|
||||
if len(det) >= 7:
|
||||
label = 'det' if det[5] > 0 else 'trk'
|
||||
if ids is not None:
|
||||
text = '{}# {:.2f}: {:d}'.format(label, det[6], ids[i])
|
||||
cv2.putText(im, text, (x1, y1 + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 255, 255),
|
||||
thickness=thickness)
|
||||
else:
|
||||
text = '{}# {:.2f}'.format(label, det[6])
|
||||
|
||||
if scores is not None:
|
||||
text = '{:.2f}'.format(scores[i])
|
||||
cv2.putText(im, text, (x1, y1 + 30), cv2.FONT_HERSHEY_PLAIN, text_scale, (0, 255, 255),
|
||||
thickness=thickness)
|
||||
|
||||
cv2.rectangle(im, (x1, y1), (x2, y2), color, 2)
|
||||
|
||||
return im
|
||||
|
|
|
|||
Loading…
Reference in New Issue