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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
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"source": [
"from pathlib import Path\n",
"from typing import List\n",
"from trap.frame_emitter import Track\n",
"from trap.frame_emitter import Camera\n",
"from trap.tracker import TrackReader\n",
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"import numpy as np\n",
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"\n",
"\n",
"path = Path(\"EXPERIMENTS/raw/hof3/\")\n",
"calibration_path = Path(\"../DATASETS/hof3/calibration.json\")\n",
"homography_path = Path(\"../DATASETS/hof3/homography.json\")\n",
"camera = Camera.from_paths(calibration_path, homography_path, 12)\n"
]
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
"outputs": [],
"source": [
"reader = TrackReader(path, camera.fps, exclude_whitelisted = False, include_blacklisted=False)"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [],
"source": [
"from trap.tracker import FinalDisplacementFilter\n",
"\n",
"\n",
"tracks: List[Track] = [t for t in reader]\n",
"filter = FinalDisplacementFilter(2)\n",
"tracks = filter.apply(tracks, camera)"
]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [
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{
"name": "stdout",
"output_type": "stream",
"text": [
"2379\n"
]
},
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{
"data": {
"text/plain": [
"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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"execution_count": 4,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"print(len(tracks))\n",
"track =tracks[0]\n",
"track"
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]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [],
"source": [
"\n",
"from trap.tracker import Smoother\n",
"\n",
"\n",
"smoother = Smoother(12, convolution=True)"
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib.axes import Axes\n",
"import tqdm\n"
]
},
{
"cell_type": "code",
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"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'x': 12.0, 'y': 7.0},\n",
" {'x': 12.5, 'y': 6.5},\n",
" {'x': 13.0, 'y': 6.0},\n",
" {'x': 13.0, 'y': 5.5},\n",
" {'x': 13.0, 'y': 5.0},\n",
" {'x': 13.0, 'y': 4.5},\n",
" {'x': 13.0, 'y': 4.0},\n",
" {'x': 13.5, 'y': 3.5},\n",
" {'x': 13.5, 'y': 3.0}]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"track.get_binned(.5, camera, True)"
]
},
{
"cell_type": "code",
"execution_count": null,
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"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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"100%|██████████| 2379/2379 [00:20<00:00, 113.44it/s]\n"
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]
},
{
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"data": {
"image/png": "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
"text/plain": [
"<Figure size 2000x1600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(20,16))\n",
"ax1, ax2 = fig.subplots(2)\n",
"\n",
"im = cv2.imread(\"../DATASETS/hof3/output.png\")\n",
"ax2.imshow(im)\n",
"ax1.set_aspect(1)\n",
"ax2.set_aspect(1)\n",
"\n",
"for track in (bar:= tqdm.tqdm(tracks)):\n",
" t = track.get_with_interpolated_history()\n",
" t = smoother.smooth_track(t)\n",
" binned_points = t.get_binned(.5, camera, bin_start=True)\n",
" points = np.array([[p['x'],p['y']] for p in binned_points])\n",
" x, y = points[:,0], points[:,1]\n",
" ax1.plot(x, y, alpha=.2)\n",
" ax1.scatter(x, y, marker='x', alpha=.2)\n",
"\n",
" \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",
"\n",
" # print (binned_points, points)\n",
"\n",
"\n",
"\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",
"\n",
" # if bar.n > 25:\n",
" # break\n",
" # if bar.n > 100:\n",
" # break"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'tracks' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 11\u001b[0m\n\u001b[1;32m 6\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset_aspect(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 7\u001b[0m ax2\u001b[38;5;241m.\u001b[39mset_aspect(\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m track \u001b[38;5;129;01min\u001b[39;00m (bar\u001b[38;5;241m:=\u001b[39m tqdm\u001b[38;5;241m.\u001b[39mtqdm(\u001b[43mtracks\u001b[49m)):\n\u001b[1;32m 12\u001b[0m t \u001b[38;5;241m=\u001b[39m track\u001b[38;5;241m.\u001b[39mget_with_interpolated_history()\n\u001b[1;32m 13\u001b[0m t \u001b[38;5;241m=\u001b[39m smoother\u001b[38;5;241m.\u001b[39msmooth_track(t)\n",
"\u001b[0;31mNameError\u001b[0m: name 'tracks' is not defined"
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]
},
{
"data": {
2024-12-27 16:12:50 +01:00
"image/png": "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
2024-12-06 08:28:48 +01:00
"text/plain": [
"<Figure size 2000x1600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"fig = plt.figure(figsize=(20,16))\n",
"ax1, ax2 = fig.subplots(2)\n",
"\n",
"im = cv2.imread(\"../DATASETS/hof3/output.png\")\n",
"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": {},
2024-12-27 16:12:50 +01:00
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 1%| | 20/2379 [00:00<00:11, 196.76it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'x': 12.419917, 'y': 6.629327}, {'x': 11.919917106628418, 'y': 6.129326820373535}, {'x': 11.419917106628418, 'y': 5.629326820373535}, {'x': 11.419917106628418, 'y': 5.129326820373535}, {'x': 11.919917106628418, 'y': 4.629326820373535}, {'x': 12.419917106628418, 'y': 4.129326820373535}, {'x': 12.419917106628418, 'y': 3.629326820373535}, {'x': 12.919917106628418, 'y': 3.129326820373535}] [[ 12.42 6.6293]\n",
" [ 12.445 6.6081]\n",
" [ 12.49 6.5677]\n",
" [ 12.549 6.5107]\n",
" [ 12.612 6.4391]\n",
" [ 12.673 6.354]\n",
" [ 12.729 6.2547]\n",
" [ 12.782 6.1409]\n",
" [ 12.834 6.0165]\n",
" [ 12.882 5.888]\n",
" [ 12.927 5.7595]\n",
" [ 12.967 5.6316]\n",
" [ 13.004 5.5034]\n",
" [ 13.039 5.3739]\n",
" [ 13.071 5.2438]\n",
" [ 13.101 5.1151]\n",
" [ 13.128 4.9896]\n",
" [ 13.151 4.8678]\n",
" [ 13.171 4.7487]\n",
" [ 13.187 4.63]\n",
" [ 13.201 4.5105]\n",
" [ 13.214 4.3919]\n",
" [ 13.225 4.2758]\n",
" [ 13.236 4.1613]\n",
" [ 13.247 4.0459]\n",
" [ 13.26 3.927]\n",
" [ 13.276 3.803]\n",
" [ 13.292 3.6741]\n",
" [ 13.308 3.5422]\n",
" [ 13.321 3.4103]\n",
" [ 13.33 3.2815]\n",
" [ 13.335 3.1578]\n",
" [ 13.337 3.0399]\n",
" [ 13.338 2.929]\n",
" [ 13.339 2.8303]\n",
" [ 13.34 2.754]\n",
" [ 13.341 2.7115]]\n",
"[{'x': 13.354624, 'y': 3.0794778}] [[ 13.355 3.0795]\n",
" [ 13.351 3.144]\n",
" [ 13.345 3.2546]\n",
" [ 13.341 3.3842]\n",
" [ 13.339 3.5117]\n",
" [ 13.339 3.6272]\n",
" [ 13.339 3.7305]\n",
" [ 13.338 3.8291]\n",
" [ 13.336 3.931]\n",
" [ 13.334 4.0411]\n",
" [ 13.335 4.159]\n",
" [ 13.341 4.2809]\n",
" [ 13.351 4.4053]\n",
" [ 13.364 4.5349]\n",
" [ 13.376 4.6715]\n",
" [ 13.385 4.8133]\n",
" [ 13.389 4.9542]\n",
" [ 13.391 5.0857]\n",
" [ 13.391 5.2017]\n",
" [ 13.393 5.3019]\n",
" [ 13.396 5.3919]\n",
" [ 13.401 5.4812]\n",
" [ 13.407 5.5779]\n",
" [ 13.414 5.6827]\n",
" [ 13.422 5.7918]\n",
" [ 13.432 5.9022]\n",
" [ 13.442 6.017]\n",
" [ 13.448 6.1443]\n",
" [ 13.448 6.2903]\n",
" [ 13.441 6.454]\n",
" [ 13.43 6.6263]\n",
" [ 13.419 6.7923]\n",
" [ 13.413 6.9398]\n",
" [ 13.415 7.0623]\n",
" [ 13.424 7.1607]\n",
" [ 13.439 7.2461]\n",
" [ 13.458 7.3318]\n",
" [ 13.479 7.4261]\n",
" [ 13.5 7.5321]\n",
" [ 13.523 7.6508]\n",
" [ 13.544 7.7827]\n",
" [ 13.563 7.9268]\n",
" [ 13.578 8.0792]\n",
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"[{'x': 13.3166485, 'y': 4.2331495}] [[ 13.317 4.2331]\n",
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" ...\n",
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" [ 13.321 2.0274]\n",
" [ 13.311 1.9402]\n",
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" [ 13.366 1.2885]]\n",
"[{'x': 14.167434, 'y': 9.248635}, {'x': 14.667433738708496, 'y': 8.748635292053223}, {'x': 15.167433738708496, 'y': 8.248635292053223}, {'x': 15.167433738708496, 'y': 7.748635292053223}, {'x': 14.667433738708496, 'y': 7.248635292053223}, {'x': 14.167433738708496, 'y': 6.748635292053223}, {'x': 13.667433738708496, 'y': 6.248635292053223}] [[ 14.167 9.2486]\n",
" [ 14.138 9.2194]\n",
" [ 14.087 9.1601]\n",
" [ 14.023 9.0734]\n",
" [ 13.956 8.9657]\n",
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" [ 13.619 8.267]\n",
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" [ 13.479 7.8266]\n",
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" [ 13.42 7.5961]\n",
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" [ 13.367 7.3647]\n",
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" [ 13.291 6.935]\n",
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" [ 13.306 6.259]\n",
" [ 13.318 6.1269]\n",
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" [ 13.34 5.8948]\n",
" [ 13.35 5.7885]\n",
" [ 13.359 5.684]\n",
" [ 13.368 5.5767]\n",
" [ 13.376 5.4607]\n",
" [ 13.385 5.3324]\n",
" [ 13.394 5.1923]\n",
" [ 13.403 5.0456]\n",
" [ 13.413 4.8995]\n",
" [ 13.425 4.7587]\n",
" [ 13.44 4.6243]\n",
" [ 13.458 4.4947]\n",
" [ 13.478 4.3673]\n",
" [ 13.498 4.2413]\n",
" [ 13.514 4.1172]\n",
" [ 13.523 3.9953]\n",
" [ 13.523 3.8762]\n",
" [ 13.517 3.7592]\n",
" [ 13.506 3.6425]\n",
" [ 13.493 3.524]\n",
" [ 13.483 3.4036]\n",
" [ 13.476 3.2835]\n",
" [ 13.475 3.1641]\n",
" [ 13.479 3.045]\n",
" [ 13.486 2.9252]\n",
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" [ 13.507 2.6825]\n",
" [ 13.514 2.5628]\n",
" [ 13.517 2.4479]\n",
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" [ 13.502 2.157]]\n",
"[{'x': 13.3994665, 'y': 1.8823287}] [[ 13.399 1.8823]\n",
" [ 13.393 1.9072]\n",
" [ 13.383 1.9579]\n",
" ...\n",
" [ 13.441 13.093]\n",
" [ 13.441 13.093]\n",
" [ 13.441 13.093]]\n",
"[{'x': 13.854554, 'y': 11.539255}, {'x': 13.854554176330566, 'y': 11.039255142211914}, {'x': 13.854554176330566, 'y': 10.539255142211914}, {'x': 13.854554176330566, 'y': 10.039255142211914}, {'x': 13.854554176330566, 'y': 9.539255142211914}, {'x': 13.854554176330566, 'y': 9.039255142211914}, {'x': 13.854554176330566, 'y': 8.539255142211914}, {'x': 13.354554176330566, 'y': 8.039255142211914}] [[ 13.855 11.539]\n",
" [ 13.864 11.481]\n",
" [ 13.88 11.374]\n",
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" [ 13.31 1.1557]]\n",
"[{'x': 13.40759, 'y': 11.859976}, {'x': 12.90758991241455, 'y': 11.359975814819336}] [[ 13.408 11.86]\n",
" [ 13.402 11.858]\n",
" [ 13.393 11.854]\n",
" ...\n",
" [ 12.502 4.2348]\n",
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" [ 12.5 4.244]]\n",
"[{'x': 13.653291, 'y': 3.7475975}, {'x': 14.153290748596191, 'y': 4.2475974559783936}, {'x': 14.653290748596191, 'y': 4.7475974559783936}, {'x': 15.153290748596191, 'y': 5.2475974559783936}] [[ 13.653 3.7476]\n",
" [ 13.659 3.7767]\n",
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" [ 15.55 9.9001]]\n",
"[{'x': 13.207353, 'y': 3.9187326}, {'x': 12.707352638244629, 'y': 3.4187326431274414}, {'x': 12.207352638244629, 'y': 2.9187326431274414}, {'x': 12.707352638244629, 'y': 2.4187326431274414}] [[ 13.207 3.9187]\n",
" [ 13.215 3.9274]\n",
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" [ 13.155 1.2851]]\n",
"[{'x': 17.723541, 'y': 10.326171}, {'x': 17.223541259765625, 'y': 10.826170921325684}, {'x': 16.723541259765625, 'y': 11.326170921325684}] [[ 17.724 10.326]\n",
" [ 17.701 10.332]\n",
" [ 17.662 10.341]\n",
" ...\n",
" [ 15.764 11.039]\n",
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" [ 15.73 10.948]]\n",
"[{'x': 14.579937, 'y': 9.73415}, {'x': 15.079936981201172, 'y': 9.234149932861328}, {'x': 15.579936981201172, 'y': 8.734149932861328}, {'x': 15.079936981201172, 'y': 8.234149932861328}, {'x': 14.579936981201172, 'y': 7.734149932861328}, {'x': 14.579936981201172, 'y': 7.234149932861328}, {'x': 14.079936981201172, 'y': 6.734149932861328}, {'x': 13.579936981201172, 'y': 6.234149932861328}] [[ 14.58 9.7341]\n",
" [ 14.563 9.7031]\n",
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]
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"text": [
"[{'x': 13.174498, 'y': 2.6993177}, {'x': 13.674497604370117, 'y': 3.199317693710327}] [[ 13.174 2.6993]\n",
" [ 13.181 2.7241]\n",
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{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 2000x1600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
2024-12-06 08:28:48 +01:00
"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
}
