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...
cbab35e6d3
| Author | SHA1 | Date |
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Ruben van de Ven
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cbab35e6d3 | |
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Ruben van de Ven
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7c3433e456 |
41
demo.py
41
demo.py
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@ -31,6 +31,8 @@ from utils.log import logger
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from utils.timer import Timer
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from utils.parse_config import parse_model_cfg
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import utils.datasets as datasets
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from pathlib import Path
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import tempfile
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from track import eval_seq
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@ -49,26 +51,29 @@ def track(opt):
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n_frame = 0
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logger.info('Starting tracking...')
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if os.path.isdir(opt.input_video):
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print('Use image sequence')
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dataloader = datasets.LoadImages(opt.input_video, opt.img_size)
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frame_rate = 30 # hack for now; see https://motchallenge.net/data/MOT16/
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else:
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dataloader = datasets.LoadVideo(opt.input_video, opt.img_size)
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frame_rate = dataloader.frame_rate
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result_filename = os.path.join(result_root, 'results.txt')
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with tempfile.TemporaryDirectory() as tmpdirname:
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if os.path.isdir(opt.input_video):
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print('Use image sequence')
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dataloader = datasets.LoadImages(opt.input_video, opt.img_size)
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frame_rate = 30 # hack for now; see https://motchallenge.net/data/MOT16/
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else:
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dataloader = datasets.LoadVideo(opt.input_video, opt.img_size)
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frame_rate = dataloader.frame_rate
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result_filename = os.path.join(result_root, 'results.txt')
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frame_dir = None if opt.output_format=='text' else osp.join(result_root, 'frame')
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try:
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eval_seq(opt, dataloader, 'mot', result_filename,
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save_dir=frame_dir, show_image=False, frame_rate=frame_rate)
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except Exception as e:
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logger.info(e)
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frame_dir = None if opt.output_format=='text' else tmpdirname
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try:
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eval_seq(opt, dataloader, 'mot', result_filename,
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save_dir=frame_dir, show_image=False, save_img=True, frame_rate=frame_rate)
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except Exception as e:
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logger.info(e)
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if opt.output_format == 'video':
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output_video_path = osp.join(result_root, 'result.mp4')
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cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(osp.join(result_root, 'frame'), output_video_path)
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os.system(cmd_str)
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if opt.output_format == 'video':
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name = 'result-' + Path(opt.input_video).stem + '.mp4'
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output_video_path = osp.join(result_root, name)
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cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v mjpeg -q:v 1 {}'.format(frame_dir, output_video_path)
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os.system(cmd_str)
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if __name__ == '__main__':
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101
live_track.ipynb
101
live_track.ipynb
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 47,
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"execution_count": 64,
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"id": "d1489f9f-328c-4812-9cdb-0a2dee44ae88",
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"metadata": {},
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"outputs": [],
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@ -28,7 +28,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"execution_count": 65,
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"id": "edec1b34-64ad-4610-856a-68d886a45142",
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"metadata": {},
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"outputs": [],
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@ -38,7 +38,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 49,
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"execution_count": 66,
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"id": "010bf567-8845-46d4-8500-883efce2d010",
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"metadata": {},
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"outputs": [],
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@ -51,7 +51,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"execution_count": 67,
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"id": "8a413424-13c4-4bdc-825a-0aa6164e89e2",
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"metadata": {},
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"outputs": [],
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@ -67,7 +67,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"execution_count": 68,
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"id": "7b291b67-93ad-4b51-934a-dbaf095f7704",
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"metadata": {},
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"outputs": [],
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@ -103,7 +103,28 @@
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"execution_count": 77,
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"id": "c87f2b73-6109-4509-a0ce-62a690c44030",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'./OUT/embedding_test/track-test'"
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]
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},
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"execution_count": 77,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"result_frame_path"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"id": "648faf4b-d692-473a-a99d-06b50a2e2261",
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"metadata": {},
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"outputs": [],
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@ -140,7 +161,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"execution_count": 70,
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"id": "a28ef404-2031-43cf-aeb1-357aa1be0934",
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"metadata": {},
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"outputs": [],
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@ -158,6 +179,28 @@
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"Load video file and get it's properties. Use that to calculate the dimension to fit the loaded model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 74,
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"id": "eb63b1c4-fdde-48e9-977e-21ceb10c5316",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(2, 512)"
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]
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},
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"execution_count": 74,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# array with shape n-components, n-features\n",
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"reducer.components_.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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@ -202,7 +245,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"execution_count": 75,
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"id": "c623aa17-5ce2-4948-9adf-d4c9a6d1ccd2",
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"metadata": {},
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"outputs": [
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@ -212,7 +255,7 @@
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"(-1.0, 1.0)"
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]
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},
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"execution_count": 61,
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"execution_count": 75,
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"metadata": {},
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"output_type": "execute_result"
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},
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@ -242,7 +285,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 62,
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"execution_count": 76,
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"id": "e8952235-7e56-4606-858a-a9165b967726",
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"metadata": {},
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"outputs": [],
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@ -256,7 +299,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 63,
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"id": "f9cc6fd1-b9c2-4303-a21c-a193c6045526",
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"metadata": {},
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"outputs": [
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@ -273,6 +316,17 @@
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"ename": "error",
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"evalue": "OpenCV(4.7.0) /io/opencv/modules/imgproc/src/resize.cpp:4062: error: (-215:Assertion failed) !ssize.empty() in function 'resize'\n",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31merror\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[63], line 10\u001b[0m\n\u001b[1;32m 8\u001b[0m ret, frame \u001b[38;5;241m=\u001b[39m stream\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# scale down/up frame to fit tracker\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m frame \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# letterbox as to have a constant size for the model\u001b[39;00m\n\u001b[1;32m 14\u001b[0m img, _, _, _ \u001b[38;5;241m=\u001b[39m datasets\u001b[38;5;241m.\u001b[39mletterbox(frame, height\u001b[38;5;241m=\u001b[39mopt\u001b[38;5;241m.\u001b[39mimg_size[\u001b[38;5;241m1\u001b[39m], width\u001b[38;5;241m=\u001b[39mopt\u001b[38;5;241m.\u001b[39mimg_size[\u001b[38;5;241m0\u001b[39m])\n",
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"\u001b[0;31merror\u001b[0m: OpenCV(4.7.0) /io/opencv/modules/imgproc/src/resize.cpp:4062: error: (-215:Assertion failed) !ssize.empty() in function 'resize'\n"
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]
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}
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],
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"source": [
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@ -366,10 +420,31 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 78,
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"id": "8f21e642-2320-4a7e-a0ea-af32e8d1a182",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ffmpeg version 4.3 Copyright (c) 2000-2020 the FFmpeg developers\n",
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" built with gcc 7.3.0 (crosstool-NG 1.23.0.449-a04d0)\n",
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" configuration: --prefix=/opt/conda/conda-bld/ffmpeg_1597178665428/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placeh --cc=/opt/conda/conda-bld/ffmpeg_1597178665428/_build_env/bin/x86_64-conda_cos6-linux-gnu-cc --disable-doc --disable-openssl --enable-avresample --enable-gnutls --enable-hardcoded-tables --enable-libfreetype --enable-libopenh264 --enable-pic --enable-pthreads --enable-shared --disable-static --enable-version3 --enable-zlib --enable-libmp3lame\n",
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" libavutil 56. 51.100 / 56. 51.100\n",
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" libavcodec 58. 91.100 / 58. 91.100\n",
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" libavformat 58. 45.100 / 58. 45.100\n",
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" libavdevice 58. 10.100 / 58. 10.100\n",
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" libavfilter 7. 85.100 / 7. 85.100\n",
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" libavresample 4. 0. 0 / 4. 0. 0\n",
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" libswscale 5. 7.100 / 5. 7.100\n",
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" libswresample 3. 7.100 / 3. 7.100\n",
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"\u001b[1;31mUnrecognized option 'crf'.\n",
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"\u001b[0m\u001b[4;31mError splitting the argument list: \u001b[0m\u001b[4;31mOption not found\n",
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"\u001b[0m"
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]
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}
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],
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"source": [
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"! ffmpeg -i OUT/embedding_test/track-test/%04d.png -c:v libx264 -crf 10 OUT/embedding_test/track-test.mp4"
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]
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5
track.py
5
track.py
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@ -100,7 +100,8 @@ def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, save_im
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online_targets, frame_embeddings = tracker.update(blob, img0)
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online_tlwhs = []
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online_ids = []
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for t in online_targets:
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# for t in online_targets:
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for t in tracker.tracked_stracks:
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tlwh = t.tlwh
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tid = t.track_id
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vertical = tlwh[2] / tlwh[3] > 1.6
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@ -118,7 +119,7 @@ def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, save_im
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if save_dir is not None:
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base_fn = os.path.join(save_dir, '{:05d}'.format(frame_id))
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if save_img:
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cv2.imwrite(base_fn+'.jpg', online_im)
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cv2.imwrite(base_fn+'.jpg', online_im, [cv2.IMWRITE_JPEG_QUALITY, 100])
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if save_figures:
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for i, fe in enumerate(frame_embeddings):
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tlwh, curr_feat = fe
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