6.1 MiB
6.1 MiB
In [1]:
from pathlib import Path
from typing import List
from trap.frame_emitter import Track
from trap.frame_emitter import Camera
from trap.tracker import TrackReader
import numpy as np
path = Path("EXPERIMENTS/raw/hof3/")
calibration_path = Path("../DATASETS/hof3/calibration.json")
homography_path = Path("../DATASETS/hof3/homography.json")
camera = Camera.from_paths(calibration_path, homography_path, 12)
/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 from .autonotebook import tqdm as notebook_tqdm
In [2]:
reader = TrackReader(path, camera.fps, exclude_whitelisted = False, include_blacklisted=False)
In [3]:
from trap.tracker import FinalDisplacementFilter
tracks: List[Track] = [t for t in reader]
filter = FinalDisplacementFilter(2)
tracks = filter.apply(tracks, camera)
In [4]:
print(len(tracks))
track =tracks[0]
track
2379
Out[4]:
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=77.8787841796875, h=187.45034790039062, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3286, det_class=1), Detection(track_id='7', l=1389.577392578125, t=54.75666809082031, w=77.93115234375, h=188.03045654296875, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3287, det_class=1), Detection(track_id='7', l=1387.60986328125, t=49.06880187988281, w=79.3365478515625, h=193.26239013671875, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3288, det_class=1), Detection(track_id='7', l=1385.806396484375, t=39.1656494140625, w=80.3294677734375, h=197.7169189453125, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3289, det_class=1), Detection(track_id='7', l=1385.39453125, t=38.04023742675781, w=78.2938232421875, h=193.96432495117188, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3290, det_class=1), Detection(track_id='7', l=1387.2510986328125, t=26.112152099609375, w=71.546875, h=176.45062255859375, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3291, det_class=1), Detection(track_id='7', l=1384.037353515625, t=11.176986694335938, w=72.8916015625, h=180.7523193359375, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3292, det_class=1), Detection(track_id='7', l=1383.413818359375, t=2.1067962646484375, w=74.3514404296875, h=185.46435546875, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3293, det_class=1), Detection(track_id='7', l=1383.9927978515625, t=0.0, w=74.515625, h=185.3157501220703, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3294, det_class=1), Detection(track_id='7', l=1381.23828125, t=0.0, w=74.3914794921875, h=185.56240844726562, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3295, det_class=1), Detection(track_id='7', l=1375.34130859375, t=0.0, w=68.0294189453125, h=170.82057189941406, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3296, det_class=1), Detection(track_id='7', l=1377.630126953125, t=0.0, w=62.1622314453125, h=153.26541137695312, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3297, det_class=1), Detection(track_id='7', l=1377.28564453125, t=0.0, w=60.074951171875, h=145.32818603515625, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3298, det_class=1), Detection(track_id='7', l=1374.962890625, t=0.0, w=60.5577392578125, h=145.00042724609375, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3299, det_class=1), Detection(track_id='7', l=1371.738525390625, t=0.0, w=60.5992431640625, h=142.3328094482422, conf=1, state=<DetectionState.Confirmed: 2>, frame_nr=3300, det_class=1)], predictor_history=None, predictions=None, fps=12, source=1)
In [5]:
from trap.tracker import Smoother
smoother = Smoother(12, convolution=True)
In [6]:
import cv2
from matplotlib import pyplot as plt
from matplotlib.axes import Axes
import tqdm
In [7]:
track.get_binned(.5, camera, True)
Out[7]:
[{'x': 12.0, 'y': 7.0},
{'x': 12.5, 'y': 6.5},
{'x': 13.0, 'y': 6.0},
{'x': 13.0, 'y': 5.5},
{'x': 13.0, 'y': 5.0},
{'x': 13.0, 'y': 4.5},
{'x': 13.0, 'y': 4.0},
{'x': 13.5, 'y': 3.5},
{'x': 13.5, 'y': 3.0}]
In [ ]:
fig = plt.figure(figsize=(20,16))
ax1, ax2 = fig.subplots(2)
im = cv2.imread("../DATASETS/hof3/output.png")
ax2.imshow(im)
ax1.set_aspect(1)
ax2.set_aspect(1)
for track in (bar:= tqdm.tqdm(tracks)):
t = track.get_with_interpolated_history()
t = smoother.smooth_track(t)
binned_points = t.get_binned(.5, camera, bin_start=True)
points = np.array([[p['x'],p['y']] for p in binned_points])
x, y = points[:,0], points[:,1]
ax1.plot(x, y, alpha=.2)
ax1.scatter(x, y, marker='x', alpha=.2)
# points = t.get_projected_history(None, camera)
# x, y = points[:,0], points[:,1]
# ax1.plot(x, y, alpha=.2)
# ax1.scatter(x, y, marker='x', alpha=.2)
# print (binned_points, points)
l = [d.get_foot_coords()[0] for d in track.history]
t = [d.get_foot_coords()[1] for d in track.history]
ax2.plot(l, t, alpha=.2)
ax2.scatter(l, t, marker='x', alpha=.2)
# if bar.n > 25:
# break
# if bar.n > 100:
# break
100%|██████████| 2379/2379 [00:20<00:00, 113.44it/s]
In [2]:
fig = plt.figure(figsize=(20,16))
ax1, ax2 = fig.subplots(2)
im = cv2.imread("../DATASETS/hof3/output.png")
ax2.imshow(im)
ax1.set_aspect(1)
ax2.set_aspect(1)
for track in (bar:= tqdm.tqdm(tracks)):
t = track.get_with_interpolated_history()
t = smoother.smooth_track(t)
points = t.get_projected_history(None, camera)
x, y = points[:,0], points[:,1]
ax1.plot(x, y, alpha=.2)
ax1.scatter(x, y, marker='x', alpha=.2)
l = [d.get_foot_coords()[0] for d in track.history]
t = [d.get_foot_coords()[1] for d in track.history]
ax2.plot(l, t, alpha=.2)
ax2.scatter(l, t, marker='x', alpha=.2)
# if bar.n > 100:
# break
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[2], line 11 6 ax1.set_aspect(1) 7 ax2.set_aspect(1) ---> 11 for track in (bar:= tqdm.tqdm(tracks)): 12 t = track.get_with_interpolated_history() 13 t = smoother.smooth_track(t) NameError: name 'tracks' is not defined
In [10]:
import numpy as np
from trajectron.environment import Environment
from trap.tracker import Smoother
env = Environment(node_type_list=['PEDESTRIAN'], standardization=None)
n = 9
# n = 20
# n = 21
# print([d.frame_nr for d in tracks[n].history])
track = tracks[n].get_with_interpolated_history()
node_s = smoother.smooth_track(track).to_trajectron_node(camera, env)
node_o = track.to_trajectron_node(camera, env)
# print(node.data[:, {'velocity': ['x', 'y']}] * 12)
fig = plt.figure(figsize=(20,16))
(ax1, ax2), (ax3, ax4), (ax5, ax6) = fig.subplots(3,2)
for i in range(len(track.history)):
ax1.plot(node_o.data[:, {'position': ['x']}])
ax1.plot(node_s.data[:, {'position': ['x']}])
ax2.plot(node_o.data[:, {'position': ['y']}])
ax2.plot(node_s.data[:, {'position': ['y']}])
ax3.plot(node_o.data[:, {'velocity': ['x']}])
ax3.plot(node_s.data[:, {'velocity': ['x']}])
ax4.plot(node_o.data[:, {'velocity': ['y']}])
ax4.plot(node_s.data[:, {'velocity': ['y']}])
ax5.plot(np.linalg.norm(node_o.data[:, {'velocity': ['x', 'y']}], axis=1) * 12)
ax5.plot(np.linalg.norm(node_s.data[:, {'velocity': ['x', 'y']}], axis=1) * 12)
ax6.plot(np.linalg.norm(node_o.data[:, {'acceleration': ['x', 'y']}], axis=1) * 12)
ax6.plot(np.linalg.norm(node_s.data[:, {'acceleration': ['x', 'y']}], axis=1) * 12)
# np.linalg.norm(node.data[:, {'acceleration': ['x', 'y']}], axis=1) * 12
# node.data[:, {'acceleration': ['x', 'y']}]
In [ ]:
1%| | 20/2379 [00:00<00:11, 196.76it/s]
[{'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]
[ 12.445 6.6081]
[ 12.49 6.5677]
[ 12.549 6.5107]
[ 12.612 6.4391]
[ 12.673 6.354]
[ 12.729 6.2547]
[ 12.782 6.1409]
[ 12.834 6.0165]
[ 12.882 5.888]
[ 12.927 5.7595]
[ 12.967 5.6316]
[ 13.004 5.5034]
[ 13.039 5.3739]
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[ 13.339 2.8303]
[ 13.34 2.754]
[ 13.341 2.7115]]
[{'x': 13.354624, 'y': 3.0794778}] [[ 13.355 3.0795]
[ 13.351 3.144]
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[ 13.339 3.5117]
[ 13.339 3.6272]
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[ 13.389 4.9542]
[ 13.391 5.0857]
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[ 13.393 5.3019]
[ 13.396 5.3919]
[ 13.401 5.4812]
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[ 13.414 5.6827]
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[ 13.442 6.017]
[ 13.448 6.1443]
[ 13.448 6.2903]
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[ 13.43 6.6263]
[ 13.419 6.7923]
[ 13.413 6.9398]
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[ 13.424 7.1607]
[ 13.439 7.2461]
[ 13.458 7.3318]
[ 13.479 7.4261]
[ 13.5 7.5321]
[ 13.523 7.6508]
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[ 13.563 7.9268]
[ 13.578 8.0792]
[ 13.587 8.2333]
[ 13.591 8.3792]
[ 13.592 8.5071]
[ 13.59 8.6094]
[ 13.587 8.6799]
[ 13.585 8.7155]]
[{'x': 14.312386, 'y': 9.749995}, {'x': 13.812385559082031, 'y': 9.249995231628418}, {'x': 13.812385559082031, 'y': 8.749995231628418}, {'x': 13.812385559082031, 'y': 8.249995231628418}, {'x': 13.812385559082031, 'y': 7.749995231628418}, {'x': 13.812385559082031, 'y': 7.249995231628418}, {'x': 13.812385559082031, 'y': 6.749995231628418}, {'x': 13.312385559082031, 'y': 6.249995231628418}] [[ 14.312 9.75]
[ 14.294 9.7358]
[ 14.256 9.7105]
[ 14.199 9.682]
[ 14.124 9.6613]
[ 14.034 9.6556]
[ 13.935 9.6644]
[ 13.834 9.6742]
[ 13.74 9.6632]
[ 13.662 9.6113]
[ 13.607 9.505]
[ 13.576 9.3412]
[ 13.568 9.1293]
[ 13.576 8.8903]
[ 13.595 8.6534]
[ 13.616 8.4464]
[ 13.634 8.2843]
[ 13.648 8.165]
[ 13.66 8.0696]
[ 13.668 7.9774]
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[ 13.546 7.4144]
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[ 13.342 6.0255]
[ 13.34 5.8859]
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[ 13.324 5.4764]
[ 13.317 5.344]
[ 13.31 5.2101]
[ 13.304 5.0756]
[ 13.298 4.9429]
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[ 13.285 4.5623]
[ 13.284 4.4372]
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[ 13.287 4.2052]
[ 13.288 4.108]
[ 13.287 4.0202]
[ 13.285 3.9351]
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[ 13.279 3.7422]
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[ 13.28 3.4881]
[ 13.286 3.3436]
[ 13.295 3.1928]
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[ 13.344 2.7103]
[ 13.362 2.5403]
[ 13.377 2.3887]
[ 13.388 2.2752]
[ 13.393 2.2141]]
[{'x': 13.545586, 'y': 8.109212}, {'x': 14.045585632324219, 'y': 7.6092119216918945}, {'x': 14.045585632324219, 'y': 7.1092119216918945}, {'x': 13.545585632324219, 'y': 6.6092119216918945}, {'x': 13.045585632324219, 'y': 6.1092119216918945}] [[ 13.546 8.1092]
[ 13.528 8.0543]
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[ 13.046 5.9857]
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[ 13.001 5.4055]
[ 13.007 5.2536]
[ 13.019 5.1069]
[ 13.038 4.969]
[ 13.059 4.8414]
[ 13.081 4.7214]
[ 13.1 4.604]
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[ 13.137 3.9431]
[ 13.137 3.8]
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[ 13.164 3.0457]
[ 13.166 2.9365]
[ 13.167 2.8433]
[ 13.167 2.7749]
[ 13.166 2.7387]]
[{'x': 12.752447, 'y': 6.9348097}, {'x': 12.252447128295898, 'y': 6.434809684753418}, {'x': 11.752447128295898, 'y': 5.934809684753418}, {'x': 12.252447128295898, 'y': 5.434809684753418}, {'x': 12.252447128295898, 'y': 4.934809684753418}] [[ 12.752 6.9348]
[ 12.759 6.9068]
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[ 12.832 6.619]
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[ 13.028 6.015]
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[ 13.2 5.4677]
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[ 13.283 5.0991]
[ 13.299 4.9941]
[ 13.309 4.9]
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[{'x': 13.3994665, 'y': 1.8823287}] [[ 13.399 1.8823]
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...
[ 13.441 13.093]
[ 13.441 13.093]
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[{'x': 13.40759, 'y': 11.859976}, {'x': 12.90758991241455, 'y': 11.359975814819336}] [[ 13.408 11.86]
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[ 13.393 11.854]
...
[ 12.502 4.2348]
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[{'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]
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...
[ 15.764 11.039]
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[{'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]
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2%|▏ | 40/2379 [00:00<00:13, 175.85it/s]
[{'x': 13.174498, 'y': 2.6993177}, {'x': 13.674497604370117, 'y': 3.199317693710327}] [[ 13.174 2.6993]
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