<jupyter_start><jupyter_text>MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking Original paper: https://arxiv.org/pdf/1504.01942.pdfThe authors gathered 22 sequences which are devided into training and test set. The data contains over 10 minutes of footage and 61440 annotated bounding boxs.<jupyter_code>%matplotlib inline

import sys
sys.path.insert(0,'../')
from pak.datasets.MOT import MOT152D
from pak import utils
import matplotlib.pyplot as plt

root = '/home/julian/Data/pak_test1'

mot15 = MOT152D(root)
X, Y_det, Y_gt = mot15.get_train("TUD-Stadtmitte", memmapped=True)<jupyter_output>/home/julian/Data/pak_test1/2DMOT2015 found :)
2DMOT2015: load memmapped file /home/julian/Data/pak_test1/2DMOT2015/train/TUD-Stadtmitte/data.memmap
2DMOT2015 X loaded
2DMOT2015 Y_det loaded
2DMOT2015 Y_gt loaded<jupyter_text>sub-datasets Training:<jupyter_code>for f in mot15.get_train_folders():
    print(f)<jupyter_output>ADL-Rundle-6
ETH-Bahnhof
ETH-Sunnyday
KITTI-17
TUD-Campus
Venice-2
ADL-Rundle-8
ETH-Pedcross2
KITTI-13
PETS09-S2L1
TUD-Stadtmitte<jupyter_text>Test:<jupyter_code>for f in mot15.get_test_folders():
    print(f)<jupyter_output>ADL-Rundle-1
ADL-Rundle-3
AVG-TownCentre
ETH-Jelmoli
KITTI-16
PETS09-S2L2
Venice-1
ETH-Crossing
ETH-Linthescher
KITTI-19
TUD-Crossing<jupyter_text>Y-detection | Position  | Name | Description || ------------- | ------------- | ---------- || 0  | Frame number  | Indicates at which frame the object is present  || 2  | Bounding box left  | Coors of top-left corner of pedestrian bb || 3  | Bounding box top  | Coors of top-left corner of pedestrian bb || 4  | Bounding box width  | Coors of top-left corner of pedestrian bb || 5  | Bounding box height  | Coors of top-left corner of pedestrian bb|| 6  | Confidence score | Indicates how confident the detector is that this instance is a pedestrian |Positions that are not described yield no function and can be ignored Y-ground-truth| Position  | Name | Description || ------------- | ------------- | ---------- || 0  | Frame number  | Indicates at which frame the object is present  || 1  | Identity number | Each pedestrian trjaectory is identified by a unique ID || 2  | Bounding box left  | Coors of top-left corner of pedestrian bb || 3  | Bounding box top  | Coors of top-left corner of pedestrian bb || 4  | Bounding box width  | Coors of top-left corner of pedestrian bb || 5  | Bounding box height  | Coors of top-left corner of pedestrian bb|| 6  | Confidence score | Flag wheather the entry is to be considered |<jupyter_code>print("Y_det:\t",Y_det.shape)
print("Y_gt:\t", Y_gt.shape)
frame = 1

Y_gt_frame1 = utils.extract_eq(Y_gt, col=0, value=frame)
left = Y_gt_frame1[:,2]
top = Y_gt_frame1[:,3]
width = Y_gt_frame1[:,4]
height = Y_gt_frame1[:,5]

img = X[frame]
fig = plt.figure(figsize=(16,8))

ax = fig.add_subplot(111)
ax.imshow(img)

for x, y, w, h in zip(*[left, top, width, height]):
    bbX, bbY = utils.bb_to_plt_plot(x, y, w, h)
    ax.plot(bbX, bbY, linewidth=3)

plt.show()<jupyter_output><empty_output>
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{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '59425', 'license': 'MIT', 'n_tokens_mistral': 1202, 'n_tokens_neox': 1099, 'n_words': 316, 'path': 'samples/MOT15_2D.ipynb', 'repo_name': 'jutanke/pak', 'size': '3039'}