2024-12-06 08:28:48 +01:00
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{
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"cells": [
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{
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"cell_type": "code",
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2024-12-10 15:43:30 +01:00
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"execution_count": 2,
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2024-12-06 08:28:48 +01:00
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"metadata": {},
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2024-12-10 15:43:30 +01:00
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"outputs": [],
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2024-12-06 08:28:48 +01:00
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"source": [
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"from pathlib import Path\n",
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"from typing import List\n",
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"from trap.frame_emitter import Track\n",
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"from trap.frame_emitter import Camera\n",
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"from trap.tracker import TrackReader\n",
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"\n",
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"\n",
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"path = Path(\"EXPERIMENTS/raw/hof3/\")\n",
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"calibration_path = Path(\"../DATASETS/hof3/calibration.json\")\n",
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"homography_path = Path(\"../DATASETS/hof3/homography.json\")\n",
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"camera = Camera.from_paths(calibration_path, homography_path, 12)\n"
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]
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},
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{
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"cell_type": "code",
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2024-12-10 15:43:30 +01:00
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"execution_count": 3,
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2024-12-06 08:28:48 +01:00
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"metadata": {},
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"outputs": [],
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"source": [
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"reader = TrackReader(path, camera.fps, exclude_whitelisted = False, include_blacklisted=False)"
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]
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},
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{
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"cell_type": "code",
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2024-12-10 15:43:30 +01:00
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"execution_count": 4,
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2024-12-06 08:28:48 +01:00
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"metadata": {},
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"outputs": [],
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"source": [
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"from trap.tracker import FinalDisplacementFilter\n",
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"\n",
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"\n",
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"tracks: List[Track] = [t for t in reader]\n",
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"filter = FinalDisplacementFilter(2)\n",
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"tracks = filter.apply(tracks, camera)"
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]
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},
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{
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"cell_type": "code",
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2024-12-10 15:43:30 +01:00
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"execution_count": 5,
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2024-12-06 08:28:48 +01:00
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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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"Track(track_id='7', history=[Detection(track_id='7', l=1320.8199462890625, t=251.9683380126953, w=111.053955078125, h=234.67745971679688, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3264, det_class=1), Detection(track_id='7', l=1339.9742431640625, t=238.920166015625, w=105.037109375, h=222.37574768066406, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3265, det_class=1), Detection(track_id='7', l=1352.3323974609375, t=229.05804443359375, w=104.625732421875, h=221.97633361816406, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3266, det_class=1), Detection(track_id='7', l=1359.267822265625, t=220.7544708251953, w=106.5965576171875, h=226.98056030273438, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3267, det_class=1), Detection(track_id='7', l=1362.710693359375, t=213.61810302734375, w=109.2371826171875, h=233.524169921875, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3268, det_class=1), Detection(track_id='7', l=1364.126708984375, t=203.14120483398438, w=111.1407470703125, h=238.55650329589844, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3269, det_class=1), Detection(track_id='7', l=1367.715576171875, t=190.77456665039062, w=112.6883544921875, h=245.13031005859375, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3270, det_class=1), Detection(track_id='7', l=1375.32275390625, t=181.7355194091797, w=108.6031494140625, h=238.25833129882812, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3271, det_class=1), Detection(track_id='7', l=1380.20654296875, t=170.72500610351562, w=103.9525146484375, h=230.0364227294922, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3272, det_class=1), Detection(track_id='7', l=1385.44189453125, t=161.61102294921875, w=97.076416015625, h=216.43988037109375, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3273, det_class=1), Detection(track_id='7', l=1387.068603515625, t=149.4908447265625, w=97.640869140625, h=220.21246337890625, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3274, det_class=1), Detection(track_id='7', l=1389.26220703125, t=143.24551391601562, w=97.9940185546875, h=224.08978271484375, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3275, det_class=1), Detection(track_id='7', l=1388.7193603515625, t=135.30145263671875, w=98.697021484375, h=228.9639129638672, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3276, det_class=1), Detection(track_id='7', l=1391.044921875, t=126.18832397460938, w=95.349853515625, h=223.96878051757812, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3277, det_class=1), Detection(track_id='7', l=1392.5245361328125, t=115.1940689086914, w=91.294921875, h=215.87339782714844, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3278, det_class=1), Detection(track_id='7', l=1394.88720703125, t=104.04446411132812, w=87.3721923828125, h=207.13995361328125, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3279, det_class=1), Detection(track_id='7', l=1395.7288818359375, t=94.92156982421875, w=87.2978515625, h=207.88204956054688, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3280, det_class=1), Detection(track_id='7', l=1395.14404296875, t=89.08332061767578, w=87.2071533203125, h=209.1320037841797, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3281, det_class=1), Detection(track_id='7', l=1393.6158447265625, t=83.175048828125, w=88.0908203125, h=213.34136962890625, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3282, det_class=1), Detection(track_id='7', l=1393.5189208984375, t=79.1900634765625, w=87.788330078125, h=212.45541381835938, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3283, det_class=1), Detection(track_id='7', l=1392.7437744140625, t=71.73565673828125, w=84.64892578125, h=204.18603515625, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3284, det_class=1), Detection(track_id='7', l=1391.8515625, t=61.526771545410156, w=79.6556396484375, h=191.9879913330078, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3285, det_class=1), Detection(track_id='7', l=1390.78076171875, t=58.899993896484375, w=77.8787
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]
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},
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2024-12-10 15:43:30 +01:00
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"execution_count": 5,
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2024-12-06 08:28:48 +01:00
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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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"tracks[0]"
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]
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},
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{
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"cell_type": "code",
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2024-12-10 15:43:30 +01:00
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"execution_count": 6,
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2024-12-06 08:28:48 +01:00
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"from trap.tracker import Smoother\n",
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"\n",
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"\n",
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"smoother = Smoother(12, convolution=True)"
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]
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},
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{
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"cell_type": "code",
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2024-12-10 15:43:30 +01:00
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"execution_count": 7,
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2024-12-06 08:28:48 +01:00
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"from matplotlib import pyplot as plt\n",
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"from matplotlib.axes import Axes\n",
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"import tqdm\n"
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]
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},
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{
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"cell_type": "code",
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2024-12-10 15:43:30 +01:00
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"name": "stderr",
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"output_type": "stream",
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"text": [
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAABBsAAAT7CAYAAADfBrhGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9yY+s25nf+33XWm8ffWSfuXPvffY+PA3JIqtIlbqSr0rNvboFWRMDhuFBQRBwB5pIAwka6C8Q5FHJgO7AhmHBFx4YFiwB17At68qSqm5JZBXJYnf63Wef0TdvvxoP9uEpHpKiqsRTbNcHCOwd8b75xkJmZCLWL571LOGcc3ie53me53me53me531C5E96AJ7neZ7neZ7neZ7n/XzxYYPneZ7neZ7neZ7neZ8oHzZ4nud5nud5nud5nveJ8mGD53me53me53me53mfKB82eJ7neZ7neZ7neZ73ifJhg+d5nud5nud5nud5nygfNnie53me53me53me94kKftID+F7WWi4vL+n1egghftLD8TzP8zzP8zzP8zwPcM6x2Ww4Pj5Gyh9eu/BTFzZcXl5yenr6kx6G53me53me53me53k/wNnZGXfu3Pmh5/zUhQ29Xg94Ofh+v/8THo3neZ7neZ7neZ7neQDr9ZrT09OP5u0/zE9d2PCdpRP9ft+HDZ7neZ7neZ7neZ73U+aP0vLAN4j0PM/zPM/zPM/zPO8T5cMGz/M8z/M8z/M8z/M+UT5s8DzP8zzP8zzP8zzvE+XDBs/zPM/zPM/zPM/zPlE+bPA8z/M8z/M8z/M87xPlwwbP8zzP8zzP8zzP8z5RPmzwPM/zPM/zPM/zPO8T5cMGz/M8z/M8z/M8z/M+UT5s8DzP8zzP8zzP8zzvE+XDBs/zPM/zPM/zPM/zPlE+bPA8z/M8z/M8z/M87xPlwwbP8zzP8zzP8zzP8z5RPmzwPM/zPM/zPM/zPO8T5cMGz/M8z/M8z/M8z/M+UT5s8DzP8zzP8zzP8zzvE+XDBs/zPM/zPM/zPM/zPlF/7LDht3/7t/kbf+NvcHx8jBCCf/kv/+V/8ty//bf/NkIIfuu3futHGKLneZ7neZ7neZ7neT9L/thhQ57nfP7zn+ef/tN/+kPP+xf/4l/wpS99iePj4//iwXme53me53me53me97Mn+ON+wW/8xm/wG7/xGz/0nIuLC/7O3/k7/Kt/9a/463/9r//Qc+u6pq7rj+6v1+s/7pA8z/M8z/N+4qx1GG2RUqCCj3+eY7TFWocKJFKKn9AIPc/zPO/H5xPv2WCt5Td/8zf5B//gH/CZz3zmP3v+P/pH/4jBYPDR7fT09JMekud5nud53p8oax2L65zzd+bML7cYbT86ZrRldrnl/J05i+sca91PcKSe53me9+PxiYcN//gf/2OCIODv/t2/+0c6/x/+w3/IarX66HZ2dvZJD8nzPM/zPO8Hcs7Qtivqeoq1zceOWdtQ1zPado1z5odex2hLW2t0a5le5Zw/XWG0xWjL5GzN8qZEt5Zt3lI3P/xanud5nvfz4I+9jOKH+epXv8o/+Sf/hK997WsI8UcrEYzjmDiOP8lheJ7neZ7n/Wc5Z9hs3uH88l9R65a7R3+R0eiLSBlhbcNy+XXOrv4dgYQ7J/8t/d5nEEJ933WsdWznFUiB6CjeencJl2vKTUO3G3L7bINzju5pl2/nJemzlr/w6i5R4DcF8zzP835+faJhw+/8zu9we3vL3bt3P3rMGMPf//t/n9/6rd/i2bNnn+TTeZ7neZ7n/RdzzlBUW778QpAXG9rmn/PQaAbjX2G9fptnF/9fvvzUEIWKv5p+QDd9gAq733edpjGsNw2Xq5KLRcnEtqTblq9+rWQ3jsh6EUvR8M13FwyHGfd2Yuq2JQpefthSbteoKCSK0h/3t8DzPM/z/sR8omHDb/7mb/JX/+pf/dhjf+2v/TV+8zd/k7/1t/7WJ/lUnud5nud5PxIpI+Lk0xT1BTebii8/3uLq/xOH2z/PdHvN7z1zXK9iRtkuqhCI+XPYewPkH1Y35JXm629PmMxzKt0iECR2zXKzISwi5jamjeHb+halDK8/HPDGqwZhGpx7SJXnfPvf/k/IIOAzf+kvkyTfH2Z4nud53s+iP3bYsN1uefTo0Uf3nz59yte//nXG4zF3795lZ2fnY+eHYcjh4SGvv/76jz5az/M8z/O8T4o1JMLxhcNj/udiwbKY8KUXIceLf8tNfcL1epdePOaX+ksyNUI4C9ZgkDTaUjWG//mday4uV6SiwJg5tDHtRMDKcbYuWYgaEwJ2RSQnnJ4tkW98FmtPKDZL3v73/57F1QXWGG6fPuTO628g5fcv1fA8z/O8nzV/7LDhK1/5Cn/pL/2lj+7/vb/39wD4m3/zb/LP/tk/+8QG5nme53neLyatG5qqQkpJGMWoIPzoWFMVtHWFEpKo2/u+iblrGlAKof4zE3ZrMJNHXMxLOjt3+FPNFV892+N8E3BT9Mj0FvQeJ/2cTx/tE8YJbvwKVoa8f73h0c2KYpozWS1Ih5b1vGZY3LK+ipgVI5YFzKVlFTSMg4pX7Zzj8hksHI9+5zm9v/arPPv6y6BhO59x8OBThFGIs/ZjlROe53me97Pqjx02/Pqv/zrO/dG3bPJ9GjzP8zzP+08pm5LGNAzSAfAyaHj6ta9y9eg9iEPuvPEZ7jx8jSjJaKqCR7/3Ja6/8XWyToe7v/YX2H/4KYIgAl4GDfUHH0AUET948IMDB2te3oCmbanqGltf4qKaXnTL42qHMZekasuB+hYHvc+gZQnDu4gwZV3UvHU24Xx6C3lLL5IUS0vSsUzfk6xWEVtTsQ4NJqo5Hl3zmeyCaN2lEq9giyVcrvny/+OfE0QxxWpJNtohzjJGR0cfC1a8ny7OGDAGEUXff+yPGnJ5nuf9AvlEezZ4nud5nuf9IO+//T+wWf0Bv/Ir/ztk8vLtx6pY8T8++x85e/4v+G92/hq/8oX/Dqs1uqlZrmc8vf2At59/jT/zy/81Dz73BS7ffZvzt9+iuLzAdbtcf+2rVI3l7puvoyxU77xDc3aOzFKavSOSbvbxHR+sgdljsBozesiFOsGKR2yL97hZfI3zm4h+syQKAioRMxV9HqivYpP/FissRbXh3337MednOf1Iscocm8KSyYbFo/eYtwKQiHRLL90w7sO9ZMqd5JqcHufiNWy8g1t+G8wc4yyDvUMGe3scfup1wij5yfxwvP8sZwz1kyfQNMSf+tTHAoc/UsjleZ73C8iHDZ7neZ7n/Yl6/+3/gfk3/wXJ8iG/9/7f4k//b/7P6MDyu9e/y3947/9Cb93yr8/+JxbvfZO//L/935O9do8/eO//jnQ5vWnDk69+mbNvfAMRhLSrJYNuj17W5eZswfPJ72JbzdgY2m98g0Ap+Nwv85WnC8K0+PgWk9aA1WBqzPQRrdllrUqul28zeVFRbvsIYrK0YSpP2DrL16/3SIJ/g5ARIm64XNZoDevW0E0li7Dm5uyGuNjQiQzLYIO1jgfxJcLGnARTwiDApjFPNhsoW7auy2mxIBKaYjXn8MGrhFGMkH4rzJ9Wtqponj7FaQ3wUeDgmuZjIVd05xSR+rDB8zwPfNjgeZ7ned5/AdMYTNHQtoY6bwgCSZJFqCTEtQZrHHX58vjZu/89ne2bRKsTovVdfu//9rf47P/qn/Li8f+V47Xlqs6Im5ivuBue/b9+nefjv8j6WDIyXU7Wh2xvJtR1zTocMN6/y+FBl6jWlFcTktsJb/3za8JswKDT4e7dfb5hulQIetrSaEsk3cugIYhg51WYPSIyNfe4ZpZvmV8alkWMcCkijRjvh4ypuCi6TPOGr17GJME3yJK73I9j3skMm0nFcl3Tck2z2RA1IXG2IlCOunXUteC15ClKVDRmj/c3r7DcaMpW0DcVoiNoCoPKc55942vEnQ7jkxNk5CeqP22cMbTn5x8to6ifP8c2DdHpXdqLc/KvfAUhFdmf+iJC+cDI8zzvO3zY4Hme53neD2W1xZQttjU4YzGtoXprTv1iRT3Z0q4cgYJoT8Feh8CC2WjqeYW
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"text/plain": [
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"<Figure size 2000x1600 with 2 Axes>"
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]
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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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"source": [
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"\n",
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"fig = plt.figure(figsize=(20,16))\n",
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"ax1, ax2 = fig.subplots(2)\n",
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"\n",
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"im = cv2.imread(\"../DATASETS/hof3/output.png\")\n",
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"ax2.imshow(im)\n",
|
|
|
|
|
"ax1.set_aspect(1)\n",
|
|
|
|
|
"ax2.set_aspect(1)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"for track in (bar:= tqdm.tqdm(tracks)):\n",
|
|
|
|
|
" t = track.get_with_interpolated_history()\n",
|
|
|
|
|
" t = smoother.smooth_track(t)\n",
|
|
|
|
|
" points = t.get_projected_history(None, camera)\n",
|
|
|
|
|
" x, y = points[:,0], points[:,1]\n",
|
|
|
|
|
" ax1.plot(x, y, alpha=.2)\n",
|
|
|
|
|
" ax1.scatter(x, y, marker='x', alpha=.2)\n",
|
|
|
|
|
" l = [d.get_foot_coords()[0] for d in track.history]\n",
|
|
|
|
|
" t = [d.get_foot_coords()[1] for d in track.history]\n",
|
|
|
|
|
" ax2.plot(l, t, alpha=.2)\n",
|
|
|
|
|
" ax2.scatter(l, t, marker='x', alpha=.2)\n",
|
|
|
|
|
" # if bar.n > 100:\n",
|
|
|
|
|
" # break"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 10,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 2000x1600 with 6 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"import numpy as np\n",
|
|
|
|
|
"from trajectron.environment import Environment\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"from trap.tracker import Smoother\n",
|
|
|
|
|
"env = Environment(node_type_list=['PEDESTRIAN'], standardization=None)\n",
|
|
|
|
|
"n = 9\n",
|
|
|
|
|
"# n = 20\n",
|
|
|
|
|
"# n = 21\n",
|
|
|
|
|
"# print([d.frame_nr for d in tracks[n].history])\n",
|
|
|
|
|
"track = tracks[n].get_with_interpolated_history()\n",
|
|
|
|
|
"node_s = smoother.smooth_track(track).to_trajectron_node(camera, env)\n",
|
|
|
|
|
"node_o = track.to_trajectron_node(camera, env)\n",
|
|
|
|
|
"# print(node.data[:, {'velocity': ['x', 'y']}] * 12)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"fig = plt.figure(figsize=(20,16))\n",
|
|
|
|
|
"(ax1, ax2), (ax3, ax4), (ax5, ax6) = fig.subplots(3,2)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"for i in range(len(track.history)):\n",
|
|
|
|
|
" ax1.plot(node_o.data[:, {'position': ['x']}])\n",
|
|
|
|
|
" ax1.plot(node_s.data[:, {'position': ['x']}])\n",
|
|
|
|
|
" ax2.plot(node_o.data[:, {'position': ['y']}])\n",
|
|
|
|
|
" ax2.plot(node_s.data[:, {'position': ['y']}])\n",
|
|
|
|
|
" ax3.plot(node_o.data[:, {'velocity': ['x']}])\n",
|
|
|
|
|
" ax3.plot(node_s.data[:, {'velocity': ['x']}])\n",
|
|
|
|
|
" ax4.plot(node_o.data[:, {'velocity': ['y']}])\n",
|
|
|
|
|
" ax4.plot(node_s.data[:, {'velocity': ['y']}])\n",
|
|
|
|
|
" ax5.plot(np.linalg.norm(node_o.data[:, {'velocity': ['x', 'y']}], axis=1) * 12)\n",
|
|
|
|
|
" ax5.plot(np.linalg.norm(node_s.data[:, {'velocity': ['x', 'y']}], axis=1) * 12)\n",
|
|
|
|
|
" \n",
|
|
|
|
|
" ax6.plot(np.linalg.norm(node_o.data[:, {'acceleration': ['x', 'y']}], axis=1) * 12)\n",
|
|
|
|
|
" ax6.plot(np.linalg.norm(node_s.data[:, {'acceleration': ['x', 'y']}], axis=1) * 12)\n",
|
|
|
|
|
" # np.linalg.norm(node.data[:, {'acceleration': ['x', 'y']}], axis=1) * 12\n",
|
|
|
|
|
"# node.data[:, {'acceleration': ['x', 'y']}]"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
|
|
|
|
"display_name": ".venv",
|
|
|
|
|
"language": "python",
|
|
|
|
|
"name": "python3"
|
|
|
|
|
},
|
|
|
|
|
"language_info": {
|
|
|
|
|
"codemirror_mode": {
|
|
|
|
|
"name": "ipython",
|
|
|
|
|
"version": 3
|
|
|
|
|
},
|
|
|
|
|
"file_extension": ".py",
|
|
|
|
|
"mimetype": "text/x-python",
|
|
|
|
|
"name": "python",
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
"pygments_lexer": "ipython3",
|
|
|
|
|
"version": "3.10.4"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 2
|
|
|
|
|
}
|
