Option to save tracker output as training data

switch to not predict video, but training data
Auto-select latest iteration of model
2023-10-20 13:27:51 +02:00 · 2023-10-20 13:27:04 +02:00 · 2023-10-20 13:25:59 +02:00 · 2023-10-20 13:25:10 +02:00 · 2023-10-20 13:24:43 +02:00 · 2023-10-20 13:23:38 +02:00
8 changed files with 597 additions and 336 deletions
--- a/poetry.lock
+++ b/poetry.lock
@ -1,4 +1,4 @@
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    {file = "psutil-5.9.6-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:daecbcbd29b289aac14ece28eca6a3e60aa361754cf6da3dfb20d4d32b6c7f57"},
    {file = "psutil-5.9.6.tar.gz", hash = "sha256:e4b92ddcd7dd4cdd3f900180ea1e104932c7bce234fb88976e2a3b296441225a"},
 ]
 [package.extras]
@ -2100,6 +2165,31 @@ files = [
 [package.extras]
 tests = ["pytest"]
 [[package]]
 name = "pyasn1"
 version = "0.5.0"
 description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
    {file = "pyasn1-0.5.0-py2.py3-none-any.whl", hash = "sha256:87a2121042a1ac9358cabcaf1d07680ff97ee6404333bacca15f76aa8ad01a57"},
    {file = "pyasn1-0.5.0.tar.gz", hash = "sha256:97b7290ca68e62a832558ec3976f15cbf911bf5d7c7039d8b861c2a0ece69fde"},
 ]
 [[package]]
 name = "pyasn1-modules"
 version = "0.3.0"
 description = "A collection of ASN.1-based protocols modules"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
    {file = "pyasn1_modules-0.3.0-py2.py3-none-any.whl", hash = "sha256:d3ccd6ed470d9ffbc716be08bd90efbd44d0734bc9303818f7336070984a162d"},
    {file = "pyasn1_modules-0.3.0.tar.gz", hash = "sha256:5bd01446b736eb9d31512a30d46c1ac3395d676c6f3cafa4c03eb54b9925631c"},
 ]
 [package.dependencies]
 pyasn1 = ">=0.4.6,<0.6.0"
 [[package]]
 name = "pycocotools"
 version = "2.0.7"
@ -2455,6 +2545,24 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 [[package]]
 name = "requests-oauthlib"
 version = "1.3.1"
 description = "OAuthlib authentication support for Requests."
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
    {file = "requests-oauthlib-1.3.1.tar.gz", hash = "sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a"},
    {file = "requests_oauthlib-1.3.1-py2.py3-none-any.whl", hash = "sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5"},
 ]
 [package.dependencies]
 oauthlib = ">=3.0.0"
 requests = ">=2.0.0"
 [package.extras]
 rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
 [[package]]
 name = "rfc3339-validator"
 version = "0.1.4"
@ -2588,6 +2696,20 @@ files = [
    {file = "rpds_py-0.10.6.tar.gz", hash = "sha256:4ce5a708d65a8dbf3748d2474b580d606b1b9f91b5c6ab2a316e0b0cf7a4ba50"},
 ]
 [[package]]
 name = "rsa"
 version = "4.9"
 description = "Pure-Python RSA implementation"
 optional = false
 python-versions = ">=3.6,<4"
 files = [
    {file = "rsa-4.9-py3-none-any.whl", hash = "sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7"},
    {file = "rsa-4.9.tar.gz", hash = "sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21"},
 ]
 [package.dependencies]
 pyasn1 = ">=0.1.3"
 [[package]]
 name = "scikit-learn"
 version = "1.3.1"
@ -2840,25 +2962,50 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 [[package]]
 name = "tensorboard"
-version = "1.14.0"
+version = "2.11.0"
 description = "TensorBoard lets you watch Tensors Flow"
 optional = false
-python-versions = ">= 2.7, != 3.0.*, != 3.1.*"
+python-versions = ">=3.7"
 files = [
-    {file = "tensorboard-1.14.0-py2-none-any.whl", hash = "sha256:c35ba681a52d4922be6b225623cf77285033e7e4e68bac5c1d5490e47d129bb2"},
+    {file = "tensorboard-2.11.0-py3-none-any.whl", hash = "sha256:a0e592ee87962e17af3f0dce7faae3fbbd239030159e9e625cce810b7e35c53d"},
    {file = "tensorboard-1.14.0-py3-none-any.whl", hash = "sha256:50e0b1bdcd488dbe39fd9416976e089b2ff4df18d9f1fab47abf4c498209c3fc"},
 ]
 [package.dependencies]
 absl-py = ">=0.4"
-grpcio = ">=1.6.3"
+google-auth = ">=1.6.3,<3"
 google-auth-oauthlib = ">=0.4.1,<0.5"
 grpcio = ">=1.24.3"
 markdown = ">=2.6.8"
 numpy = ">=1.12.0"
-protobuf = ">=3.6.0"
+protobuf = ">=3.9.2,<4"
 requests = ">=2.21.0,<3"
 setuptools = ">=41.0.0"
-six = ">=1.10.0"
+tensorboard-data-server = ">=0.6.0,<0.7.0"
-werkzeug = ">=0.11.15"
+tensorboard-plugin-wit = ">=1.6.0"
-wheel = {version = ">=0.26", markers = "python_version >= \"3\""}
+werkzeug = ">=1.0.1"
 wheel = ">=0.26"
 [[package]]
 name = "tensorboard-data-server"
 version = "0.6.1"
 description = "Fast data loading for TensorBoard"
 optional = false
 python-versions = ">=3.6"
 files = [
    {file = "tensorboard_data_server-0.6.1-py3-none-any.whl", hash = "sha256:809fe9887682d35c1f7d1f54f0f40f98bb1f771b14265b453ca051e2ce58fca7"},
    {file = "tensorboard_data_server-0.6.1-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:fa8cef9be4fcae2f2363c88176638baf2da19c5ec90addb49b1cde05c95c88ee"},
    {file = "tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl", hash = "sha256:d8237580755e58eff68d1f3abefb5b1e39ae5c8b127cc40920f9c4fb33f4b98a"},
 ]
 [[package]]
 name = "tensorboard-plugin-wit"
 version = "1.8.1"
 description = "What-If Tool TensorBoard plugin."
 optional = false
 python-versions = "*"
 files = [
    {file = "tensorboard_plugin_wit-1.8.1-py3-none-any.whl", hash = "sha256:ff26bdd583d155aa951ee3b152b3d0cffae8005dc697f72b44a8e8c2a77a8cbe"},
 ]
 [[package]]
 name = "tensorboardx"
@ -2967,44 +3114,6 @@ typing-extensions = "*"
 type = "url"
 url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl"
 [[package]]
 name = "torchvision"
 version = "0.13.1"
 description = "image and video datasets and models for torch deep learning"
 optional = false
 python-versions = ">=3.7"
 files = [
    {file = "torchvision-0.13.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:19286a733c69dcbd417b86793df807bd227db5786ed787c17297741a9b0d0fc7"},
    {file = "torchvision-0.13.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:08f592ea61836ebeceb5c97f4d7a813b9d7dc651bbf7ce4401563ccfae6a21fc"},
    {file = "torchvision-0.13.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:ef5fe3ec1848123cd0ec74c07658192b3147dcd38e507308c790d5943e87b88c"},
    {file = "torchvision-0.13.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:099874088df104d54d8008f2a28539ca0117b512daed8bf3c2bbfa2b7ccb187a"},
    {file = "torchvision-0.13.1-cp310-cp310-win_amd64.whl", hash = "sha256:8e4d02e4d8a203e0c09c10dfb478214c224d080d31efc0dbf36d9c4051f7f3c6"},
    {file = "torchvision-0.13.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:5e631241bee3661de64f83616656224af2e3512eb2580da7c08e08b8c965a8ac"},
    {file = "torchvision-0.13.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:899eec0b9f3b99b96d6f85b9aa58c002db41c672437677b553015b9135b3be7e"},
    {file = "torchvision-0.13.1-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:83e9e2457f23110fd53b0177e1bc621518d6ea2108f570e853b768ce36b7c679"},
    {file = "torchvision-0.13.1-cp37-cp37m-win_amd64.whl", hash = "sha256:7552e80fa222252b8b217a951c85e172a710ea4cad0ae0c06fbb67addece7871"},
    {file = "torchvision-0.13.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:f230a1a40ed70d51e463ce43df243ec520902f8725de2502e485efc5eea9d864"},
    {file = "torchvision-0.13.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:e9a563894f9fa40692e24d1aa58c3ef040450017cfed3598ff9637f404f3fe3b"},
    {file = "torchvision-0.13.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:7cb789ceefe6dcd0dc8eeda37bfc45efb7cf34770eac9533861d51ca508eb5b3"},
    {file = "torchvision-0.13.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:87c137f343197769a51333076e66bfcd576301d2cd8614b06657187c71b06c4f"},
    {file = "torchvision-0.13.1-cp38-cp38-win_amd64.whl", hash = "sha256:4d8bf321c4380854ef04613935fdd415dce29d1088a7ff99e06e113f0efe9203"},
    {file = "torchvision-0.13.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:0298bae3b09ac361866088434008d82b99d6458fe8888c8df90720ef4b347d44"},
    {file = "torchvision-0.13.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c5ed609c8bc88c575226400b2232e0309094477c82af38952e0373edef0003fd"},
    {file = "torchvision-0.13.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:3567fb3def829229ec217c1e38f08c5128ff7fb65854cac17ebac358ff7aa309"},
    {file = "torchvision-0.13.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:b167934a5943242da7b1e59318f911d2d253feeca0d13ad5d832b58eed943401"},
    {file = "torchvision-0.13.1-cp39-cp39-win_amd64.whl", hash = "sha256:0e77706cc90462653620e336bb90daf03d7bf1b88c3a9a3037df8d111823a56e"},
 ]
 [package.dependencies]
 numpy = "*"
 pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
 requests = "*"
 torch = "1.12.1"
 typing-extensions = "*"
 [package.extras]
 scipy = ["scipy"]
 [[package]]
 name = "torchvision"
 version = "0.13.1+cu113"
@ -3086,7 +3195,7 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.5.1)", "pre-commit", "pytest (>=7.0,
 [[package]]
 name = "trajectron-plus-plus"
-version = "0.1.0"
+version = "0.1.1"
 description = "Predict trajectories for anomaly detection"
 optional = false
 python-versions = "^3.9,<3.12"
@ -3109,11 +3218,11 @@ scikit-learn = "^1.3.1"
 scipy = "^1.11.3"
 seaborn = "^0.13.0"
 setuptools = "^68.2.2"
-tensorboard = "1.14"
+tensorboard = "2.11"
 tensorboardx = "^2.6.2.2"
 torch = [
    {version = "1.12.1", markers = "python_version < \"3.10\" or python_version >= \"4.0\" or sys_platform != \"linux\""},
    {url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl", markers = "python_version ~= \"3.10\" and sys_platform == \"linux\""},
    {version = "1.12.1", markers = "python_version < \"3.10\" or python_version >= \"4.0\" or sys_platform != \"linux\""},
 ]
 tqdm = "^4.65.0"
@ -3170,13 +3279,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 [[package]]
 name = "urllib3"
-version = "2.0.6"
+version = "2.0.7"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "urllib3-2.0.6-py3-none-any.whl", hash = "sha256:7a7c7003b000adf9e7ca2a377c9688bbc54ed41b985789ed576570342a375cd2"},
+    {file = "urllib3-2.0.7-py3-none-any.whl", hash = "sha256:fdb6d215c776278489906c2f8916e6e7d4f5a9b602ccbcfdf7f016fc8da0596e"},
-    {file = "urllib3-2.0.6.tar.gz", hash = "sha256:b19e1a85d206b56d7df1d5e683df4a7725252a964e3993648dd0fb5a1c157564"},
+    {file = "urllib3-2.0.7.tar.gz", hash = "sha256:c97dfde1f7bd43a71c8d2a58e369e9b2bf692d1334ea9f9cae55add7d0dd0f84"},
 ]
 [package.extras]
@ -3272,4 +3381,4 @@ test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10,<3.12,"
-content-hash = "06852d16ba90b0438b84e1c51c084c6aa6a5c7a78a6af1e17b7d59e5304ed5a7"
+content-hash = "54eabff3ca832efc7790a1044ace59b882e134b536d45c42df91701a7001a6c0"
--- a/pyproject.toml
+++ b/pyproject.toml
@ -5,10 +5,20 @@ description = "Art installation with trajectory prediction"
 authors = ["Ruben van de Ven <git@rubenvandeven.com>"]
 readme = "README.md"
 [tool.poetry.scripts]
 trapserv = "trap.plumber:start"
 [tool.poetry.dependencies]
 python = "^3.10,<3.12,"
 trajectron-plus-plus = { path = "../Trajectron-plus-plus/", develop = true }
 torch = [
        { version="1.12.1" },
 #       { url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp38-cp38-linux_x86_64.whl", markers = "python_version ~= '3.8' and sys_platform == 'linux'" },
        { url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl", markers = "python_version ~= '3.10' and sys_platform == 'linux'" },
 ]
 torchvision = [
       { version="0.13.1" },
 #       { url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp38-cp38-linux_x86_64.whl", markers = "python_version ~= '3.8' and sys_platform == 'linux'" },
--- a/trap/config.py
+++ b/trap/config.py
@ -1,9 +1,25 @@
 import argparse
 from pathlib import Path
 import types
 from pyparsing import Optional
-parser = argparse.ArgumentParser()
+class LambdaParser(argparse.ArgumentParser):
    """Execute lambda functions
    """
    def parse_args(self, args=None, namespace=None):
        args = super().parse_args(args, namespace)
        for key in vars(args):
            f = args.__dict__[key]
            if type(f) == types.LambdaType:
                print(f'Getting default value for {key}')
                args.__dict__[key] = f()
        return args
 parser = LambdaParser()
 # parser.parse_args()
 parser.add_argument(
@ -34,7 +50,8 @@ render_parser = parser.add_argument_group('Renderer')
 inference_parser.add_argument("--model_dir",
                    help="directory with the model to use for inference",
                    type=str, # TODO: make into Path
-                    default='../Trajectron-plus-plus/experiments/pedestrians/models/models_04_Oct_2023_21_04_48_eth_vel_ar3')
+                    default='../Trajectron-plus-plus/experiments/trap/models/models_18_Oct_2023_19_56_22_virat_vel_ar3/')
                    # default='../Trajectron-plus-plus/experiments/pedestrians/models/models_04_Oct_2023_21_04_48_eth_vel_ar3')
 inference_parser.add_argument("--conf",
                    help="path to json config file for hyperparameters, relative to model_dir",
@ -129,6 +146,10 @@ inference_parser.add_argument('--seed',
                    type=int,
                    default=123)
 inference_parser.add_argument('--predict_training_data',
                    help='Ignore tracker and predict data from the training dataset',
                    action='store_true')
 # Internal connections.
@ -167,7 +188,13 @@ connection_parser.add_argument('--bypass-prediction',
 frame_emitter_parser.add_argument("--video-src",
                    help="source video to track from",
                    type=Path,
-                    default='../DATASETS/VIRAT_subset_0102x/VIRAT_S_010200_00_000060_000218.mp4')
+                    nargs='+',
                    default=lambda: list(Path('../DATASETS/VIRAT_subset_0102x/').glob('*.mp4')))
 #TODO: camera as source
 frame_emitter_parser.add_argument("--video-no-loop",
                    help="By default it emitter will run indefiniately. This prevents that and plays every video only once.",
                    action='store_true')
 #TODO: camera as source
@ -177,11 +204,14 @@ tracker_parser.add_argument("--homography",
                    help="File with homography params",
                    type=Path,
                    default='../DATASETS/VIRAT_subset_0102x/VIRAT_0102_homography_img2world.txt')
 tracker_parser.add_argument("--save-for-training",
                    help="Specify the path in which to save",
                    type=Path,
                    default=None)
 # Renderer
-# render_parser.add_argument("--output-dir",
+render_parser.add_argument("--render-preview",
-#                     help="Target image dir",
+                    help="Render a video file previewing the prediction, and its delay compared to the current frame",
-#                     type=Optional[Path],
+                    action='store_true')
 #                     default=None)
--- a/trap/frame_emitter.py
+++ b/trap/frame_emitter.py
@ -1,5 +1,6 @@
 from argparse import Namespace
 from dataclasses import dataclass, field
 from itertools import cycle
 import logging
 from multiprocessing import Event
 import pickle
@ -28,39 +29,57 @@ class FrameEmitter:
        self.is_running = is_running
        context = zmq.Context()
        # TODO: to make things faster, a multiprocessing.Array might be a tad faster: https://stackoverflow.com/a/65201859
        self.frame_sock = context.socket(zmq.PUB)
        self.frame_sock.setsockopt(zmq.CONFLATE, 1) # only keep latest frame. make sure to set BEFORE connect/bind
        self.frame_sock.bind(config.zmq_frame_addr)
        logger.info(f"Connection socket {config.zmq_frame_addr}")
        if self.config.video_no_loop:
            self.video_srcs = self.config.video_src
        else:
            self.video_srcs = cycle(self.config.video_src)
    def emit_video(self):
-        video = cv2.VideoCapture(str(self.config.video_src))
+        for video_path in self.video_srcs:
-        fps = video.get(cv2.CAP_PROP_FPS)
+            logger.info(f"Play from '{str(video_path)}'")
-        frame_duration = 1./fps
+            video = cv2.VideoCapture(str(video_path))
-        logger.info(f"Emit frames at {fps} fps")
+            fps = video.get(cv2.CAP_PROP_FPS)
            frame_duration = 1./fps
            logger.info(f"Emit frames at {fps} fps")
-        prev_time = time.time()
+            prev_time = time.time()
-        while self.is_running.is_set():
+            while self.is_running.is_set():
            ret, img = video.read()
            # seek to 0 if video has finished. Infinite loop
            if not ret:
                video.set(cv2.CAP_PROP_POS_FRAMES, 0)
                ret, img = video.read()
                assert ret is not False # not really error proof...
            frame = Frame(img=img)
            # TODO: this is very dirty, need to find another way.
            # perhaps multiprocessing queue?
            self.frame_sock.send(pickle.dumps(frame))
-            # defer next loop
+                # seek to 0 if video has finished. Infinite loop
-            new_frame_time = time.time()
+                if not ret:
-            time_diff = (new_frame_time - prev_time)
+                    # now loading multiple files        
-            if time_diff < frame_duration:
+                    break
-                time.sleep(frame_duration - time_diff)
+                    # video.set(cv2.CAP_PROP_POS_FRAMES, 0)
-                new_frame_time += frame_duration - time_diff
+                    # ret, img = video.read()
-            else:
+                    # assert ret is not False # not really error proof...
-                prev_time = new_frame_time
+                frame = Frame(img=img)
                # TODO: this is very dirty, need to find another way.
                # perhaps multiprocessing Array?
                self.frame_sock.send(pickle.dumps(frame))
                # defer next loop
                new_frame_time = time.time()
                time_diff = (new_frame_time - prev_time)
                if time_diff < frame_duration:
                    time.sleep(frame_duration - time_diff)
                    new_frame_time += frame_duration - time_diff
                else:
                    prev_time = new_frame_time
            if not self.is_running.is_set():
                # if not running, also break out of infinite generator loop
                break
        logger.info("Stopping")
--- a/trap/plumber.py
+++ b/trap/plumber.py
@ -35,6 +35,7 @@ class ExceptionHandlingProcess(Process):
        try:
            super(Process, self).run()
        except Exception as e:
            logger.critical(f"Exception in {self.name}")
            logger.exception(e)
            self._kwargs['is_running'].clear()
@ -70,8 +71,13 @@ def start():
        ExceptionHandlingProcess(target=run_ws_forwarder, kwargs={'config': args, 'is_running': isRunning}, name='forwarder'),
        ExceptionHandlingProcess(target=run_frame_emitter, kwargs={'config': args, 'is_running': isRunning}, name='frame_emitter'),
        ExceptionHandlingProcess(target=run_tracker, kwargs={'config': args, 'is_running': isRunning}, name='tracker'),
        ExceptionHandlingProcess(target=run_renderer, kwargs={'config': args, 'is_running': isRunning}, name='renderer'),
    ]
    if args.render_preview:
        procs.append(
            ExceptionHandlingProcess(target=run_renderer, kwargs={'config': args, 'is_running': isRunning}, name='renderer')
        )
    if not args.bypass_prediction:
        procs.append(
            ExceptionHandlingProcess(target=run_prediction_server, kwargs={'config': args, 'is_running':isRunning}, name='inference'),
--- a/trap/prediction_server.py
+++ b/trap/prediction_server.py
@ -192,8 +192,12 @@ class PredictionServer:
        eval_scene = eval_env.scenes[scene_idx]
        online_env = create_online_env(eval_env, hyperparams, scene_idx, init_timestep)
        # auto-find highest iteration
        model_registrar = ModelRegistrar(self.config.model_dir, self.config.eval_device)
-        model_registrar.load_models(iter_num=100)
+        model_iterations = pathlib.Path(self.config.model_dir).glob('model_registrar-*.pt')
        highest_iter = max([int(p.stem.split('-')[-1]) for p in model_iterations])
        model_registrar.load_models(iter_num=highest_iter)
        trajectron = OnlineTrajectron(model_registrar,
                                    hyperparams,
@ -210,13 +214,13 @@ class PredictionServer:
        prev_run_time = 0
        while self.is_running.is_set():
            timestep += 1
-            this_run_time = time.time()
+            
-            logger.debug(f'test {prev_run_time - this_run_time}')
+            # this_run_time = time.time()
-            time.sleep(max(0, prev_run_time - this_run_time + .5))
+            # logger.debug(f'test {prev_run_time - this_run_time}')
-            prev_run_time = time.time()
+            # time.sleep(max(0, prev_run_time - this_run_time + .5))
-            # for timestep in range(init_timestep + 1, eval_scene.timesteps):
+            # prev_run_time = time.time()
            # input_dict = eval_scene.get_clipped_input_dict(timestep, hyperparams['state'])
            # TODO: see process_data.py on how to create a node, the provide nodes + incoming data columns
            # data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
            # x = node_values[:, 0]
@ -235,62 +239,66 @@ class PredictionServer:
            # node_data = pd.DataFrame(data_dict, columns=data_columns)
            # node = Node(node_type=env.NodeType.PEDESTRIAN, node_id=node_id, data=node_data)
-            
+            if self.config.predict_training_data:
-            data = self.trajectory_socket.recv()
+                input_dict = eval_scene.get_clipped_input_dict(timestep, hyperparams['state'])
-            frame: Frame = pickle.loads(data)
+            else:
-            trajectory_data = frame.trajectories # TODO: properly refractor
+                data = self.trajectory_socket.recv()
-            # trajectory_data = json.loads(data)
+                frame: Frame = pickle.loads(data)
-            logger.debug(f"Receive {trajectory_data}")
+                trajectory_data = frame.trajectories # TODO: properly refractor
                # trajectory_data = json.loads(data)
                logger.debug(f"Receive {trajectory_data}")
-            # class FakeNode:
+                # class FakeNode:
-            #     def __init__(self, node_type: NodeType):
+                #     def __init__(self, node_type: NodeType):
-            #         self.type = node_type
+                #         self.type = node_type
-            input_dict = {}
+                input_dict = {}
-            for identifier, trajectory in trajectory_data.items():
+                for identifier, trajectory in trajectory_data.items():
-                # if len(trajectory['history']) < 7:
+                    # if len(trajectory['history']) < 7:
-                #     # TODO: these trajectories should still be in the output, but without predictions
+                    #     # TODO: these trajectories should still be in the output, but without predictions
-                #     continue
+                    #     continue
-                # TODO: modify this into a mapping function between JS data an the expected Node format
+                    # TODO: modify this into a mapping function between JS data an the expected Node format
-                # node = FakeNode(online_env.NodeType.PEDESTRIAN)
+                    # node = FakeNode(online_env.NodeType.PEDESTRIAN)
-                history = [[h['x'], h['y']] for h in trajectory['history']]
+                    history = [[h['x'], h['y']] for h in trajectory['history']]
-                history = np.array(history)
+                    history = np.array(history)
-                x = history[:, 0]
+                    x = history[:, 0]
-                y = history[:, 1]
+                    y = history[:, 1]
-                # TODO: calculate dt based on input
+                    # TODO: calculate dt based on input
-                vx = derivative_of(x, 0.2) #eval_scene.dt
+                    vx = derivative_of(x, 0.2) #eval_scene.dt
-                vy = derivative_of(y, 0.2)
+                    vy = derivative_of(y, 0.2)
-                ax = derivative_of(vx, 0.2)
+                    ax = derivative_of(vx, 0.2)
-                ay = derivative_of(vy, 0.2)
+                    ay = derivative_of(vy, 0.2)
-                data_dict = {('position', 'x'): x[:],
+                    data_dict = {('position', 'x'): x[:],
-                            ('position', 'y'): y[:],
+                                ('position', 'y'): y[:],
-                            ('velocity', 'x'): vx[:],
+                                ('velocity', 'x'): vx[:],
-                            ('velocity', 'y'): vy[:],
+                                ('velocity', 'y'): vy[:],
-                            ('acceleration', 'x'): ax[:],
+                                ('acceleration', 'x'): ax[:],
-                            ('acceleration', 'y'): ay[:]}
+                                ('acceleration', 'y'): ay[:]}
-                data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
+                    data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
-                node_data = pd.DataFrame(data_dict, columns=data_columns)
+                    node_data = pd.DataFrame(data_dict, columns=data_columns)
-                node = Node(
+                    node = Node(
-                    node_type=online_env.NodeType.PEDESTRIAN,
+                        node_type=online_env.NodeType.PEDESTRIAN,
-                    node_id=identifier,
+                        node_id=identifier,
-                    data=node_data,
+                        data=node_data,
-                    first_timestep=timestep
+                        first_timestep=timestep
-                    )
+                        )
-                input_dict[node] = np.array([x[-1],y[-1],vx[-1],vy[-1],ax[-1],ay[-1]])
+                    input_dict[node] = np.array([x[-1],y[-1],vx[-1],vy[-1],ax[-1],ay[-1]])
-            # print(input_dict)
+                # print(input_dict)
-            if not len(input_dict):
+                if not len(input_dict):
-                # skip if our input is empty
+                    # skip if our input is empty
-                # TODO: we want to send out empty result...
+                    # TODO: we want to send out empty result...
                    # And want to update the network
-                data = json.dumps({})
+                    data = json.dumps({})
-                self.prediction_socket.send_string(data)
+                    self.prediction_socket.send_string(data)
-                continue
+                    
                    continue
            maps = None
            if hyperparams['use_map_encoding']:
@ -307,12 +315,14 @@ class PredictionServer:
                # robot_present_and_future += adjustment
            start = time.time()
-            dists, preds = trajectron.incremental_forward(input_dict,
+            with warnings.catch_warnings():
-                                                        maps,
+                warnings.simplefilter('ignore') # prevent deluge of UserWarning from torch's rrn.py
-                                                        prediction_horizon=20, # TODO: make variable
+                dists, preds = trajectron.incremental_forward(input_dict,
-                                                        num_samples=2, # TODO: make variable
+                                                            maps,
-                                                        robot_present_and_future=robot_present_and_future,
+                                                            prediction_horizon=25, # TODO: make variable
-                                                        full_dist=True)
+                                                            num_samples=20, # TODO: make variable
                                                            robot_present_and_future=robot_present_and_future,
                                                            full_dist=True)
            end = time.time()
            logger.debug("took %.2f s (= %.2f Hz) w/ %d nodes and %d edges" % (end - start,
                                                                            1. / (end - start), len(trajectron.nodes),
@ -361,7 +371,10 @@ class PredictionServer:
                }
            data = json.dumps(response)
-            logger.info(f"Total frame delay = {time.time()-frame.time}s ({len(trajectron.nodes)} nodes & {trajectron.scene_graph.get_num_edges()} edges. Trajectron: {end - start}s)")
+            if self.config.predict_training_data:
                logger.info(f"Frame prediction: {len(trajectron.nodes)} nodes & {trajectron.scene_graph.get_num_edges()} edges. Trajectron: {end - start}s")
            else:
                logger.info(f"Total frame delay = {time.time()-frame.time}s ({len(trajectron.nodes)} nodes & {trajectron.scene_graph.get_num_edges()} edges. Trajectron: {end - start}s)")
            self.prediction_socket.send_string(data)
        logger.info('Stopping')
--- a/trap/renderer.py
+++ b/trap/renderer.py
@ -30,9 +30,9 @@ class Renderer:
        self.frame_sock.connect(config.zmq_frame_addr)
-        H = np.loadtxt(self.config.homography, delimiter=',')
+        self.H = np.loadtxt(self.config.homography, delimiter=',')
-        self.inv_H = np.linalg.pinv(H)
+        self.inv_H = np.linalg.pinv(self.H)
        if not self.config.output_dir.exists():
            raise FileNotFoundError("Path does not exist")
@ -57,8 +57,19 @@ class Renderer:
                predictions = self.prediction_sock.recv_json(zmq.NOBLOCK)
            except zmq.ZMQError as e:
                logger.debug(f'reuse prediction')
-            
+
            img = frame.img
            # all not working:
            # if i == 1:
            #     # thanks to GpG for fixing scaling issue: https://stackoverflow.com/a/39668864
            #     scale_factor = 1./20 # from 10m to 1000px
            #     S = np.array([[scale_factor, 0,0],[0,scale_factor,0 ],[ 0,0,1 ]])
            #     new_H = S * self.H * np.linalg.inv(S)
            #     warpedFrame = cv2.warpPerspective(img, new_H, (1000,1000))
            #     cv2.imwrite(str(self.config.output_dir / "orig.png"), warpedFrame)
            for track_id, prediction in predictions.items():
                if not 'history' in prediction or not len(prediction['history']):
                    continue
@ -94,7 +105,9 @@ class Renderer:
            # cv2.imwrite(str(img_path), img)
            self.out.write(img)
        logger.info('Stopping')
-        self.out.release() 
+
        if i>2:
            self.out.release() 
--- a/trap/tracker.py
+++ b/trap/tracker.py
@ -1,7 +1,9 @@
 from argparse import Namespace
 import csv
 import json
 import logging
 from multiprocessing import Event
 from pathlib import Path
 import pickle
 import time
 import numpy as np
@ -18,6 +20,12 @@ from trap.frame_emitter import Frame
 Detection = [int, int, int, int, float, int]
 Detections = [Detection]
 # This is the dt that is also used by the scene. 
 # as this needs to be rather stable, try to adhere
 # to it by waiting when we are faster. Value chosen based
 # on a rough estimate of tracker duration
 TARGET_DT = .1
 logger = logging.getLogger("trap.tracker")
 class Tracker:
@ -56,11 +64,36 @@ class Tracker:
    def track(self):
        prev_run_time = 0
        training_fp = None
        training_csv = None
        training_frames = 0
        if self.config.save_for_training is not None:
            if not isinstance(self.config.save_for_training, Path):
                raise ValueError("save-for-training should be a path")
            if not self.config.save_for_training.exists():
                logger.info(f"Making path for training data: {self.config.save_for_training}")
                self.config.save_for_training.mkdir(parents=True, exist_ok=False)
            else:
                logger.warning(f"Path for training-data exists: {self.config.save_for_training}. Continuing assuming that's ok.")
            training_fp = open(self.config.save_for_training / 'all.txt', 'w')
            # following https://github.com/StanfordASL/Trajectron-plus-plus/blob/master/experiments/pedestrians/process_data.py
            training_csv = csv.DictWriter(training_fp, fieldnames=['frame_id', 'track_id', 'x', 'y'], delimiter='\t', quoting=csv.QUOTE_NONE)
        frame_i = 0
        while self.is_running.is_set():
            this_run_time = time.time()
            # logger.debug(f'test {prev_run_time - this_run_time}')
            time.sleep(max(0, prev_run_time - this_run_time + TARGET_DT))
            prev_run_time = time.time()
            msg = self.frame_sock.recv()
            frame: Frame = pickle.loads(msg) # frame delivery in current setup: 0.012-0.03s
            # logger.info(f"Frame delivery delay = {time.time()-frame.time}s")
            start_time = time.time()
            detections = self.detect_persons(frame.img)
            tracks: [Track] = self.mot_tracker.update_tracks(detections, frame=frame.img)
@ -91,6 +124,34 @@ class Tracker:
            #TODO calculate fps (also for other loops to see asynchonity)
            # fpsfilter=fpsfilter*.9+(1/dt)*.1    #trust value in order to stabilize fps display
            if training_csv:
                training_csv.writerows([{
                    'frame_id': round(frame_i * 10., 1), # not really time
                    'track_id': t['id'],
                    'x': t['history'][-1]['x'],
                    'y': t['history'][-1]['y'],
                } for t in trajectories.values()])
                training_frames += len(trajectories)
            frame_i += 1
        if training_fp:
            training_fp.close()
            lines = {
                'train': int(training_frames * .8),
                'val': int(training_frames * .12),
                'test': int(training_frames * .08),
            }
            logger.info(f"Splitting gathered data from {training_fp.name}")
            with open(training_fp.name, 'r') as source_fp:
                for name, line_nrs in lines.items():
                    dir_path = self.config.save_for_training / name
                    dir_path.mkdir(exist_ok=True)
                    file = dir_path / 'tracked.txt'
                    logger.debug(f"- Write {line_nrs} lines to {file}")
                    with file.open('w') as target_fp:
                        for i in range(line_nrs):
                            target_fp.write(source_fp.readline())
        logger.info('Stopping')
Author	SHA1	Message	Date
Ruben van de Ven	06c5bde997	Option to save tracker output as training data	2023-10-20 13:27:51 +02:00
Ruben van de Ven	2171dd459a	switch to not predict video, but training data	2023-10-20 13:27:04 +02:00
Ruben van de Ven	f3ac903555	Auto-select latest iteration of model	2023-10-20 13:25:59 +02:00
Ruben van de Ven	0a02422733	allow disabling render preview	2023-10-20 13:25:10 +02:00
Ruben van de Ven	c903d07b49	Frame emitter loops over files in folder	2023-10-20 13:24:43 +02:00
Ruben van de Ven	3a64c438eb	update deps, now having scripts in pyproject	2023-10-20 13:23:38 +02:00
Ruben van de Ven	5423b84788	keep experiment as commented code	2023-10-18 12:52:40 +02:00