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Visualizing intermediate steps.

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Daniel
2024-11-15 15:05:25 +01:00
parent caa4a0cb51
commit 1c0eeb9147
10 changed files with 473 additions and 1 deletions
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Image source:
```bash
cp '/datasets/chi3d/chi3d_train/train/s02/images/50591643/Grab-1/000000.jpg' ./50591643-img_000000.jpg
cp '/datasets/chi3d/chi3d_train/train/s02/images/58860488/Grab-1/000000.jpg' ./58860488-img_000000.jpg
cp '/datasets/chi3d/chi3d_train/train/s02/images/60457274/Grab-1/000000.jpg' ./60457274-img_000000.jpg
cp '/datasets/chi3d/chi3d_train/train/s02/images/65906101/Grab-1/000000.jpg' ./65906101-img_000000.jpg
```

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data/q1/sample.json Normal file
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{
"index": 0,
"cameras": [
{
"name": "50591643",
"width": 900,
"height": 900,
"R": [
[
-0.860039,
0.507047,
0.05688
],
[
0.092576,
0.264702,
-0.959876
],
[
-0.501759,
-0.820266,
-0.274595
]
],
"T": [
[
1.857531
],
[
3.150466
],
[
1.475396
]
],
"K": [
[
1133.847176,
0.0,
479.499984
],
[
0.0,
1129.822225,
461.364033
],
[
0.0,
0.0,
1.0
]
],
"DC": [
-0.205751,
0.242908,
-0.001062,
0.001299,
0.013546
]
},
{
"name": "65906101",
"width": 900,
"height": 900,
"R": [
[
0.88179,
0.471104,
-0.022521
],
[
0.060883,
-0.161048,
-0.985067
],
[
-0.467696,
0.867251,
-0.170693
]
],
"T": [
[
1.954908
],
[
-3.565101
],
[
1.531835
]
],
"K": [
[
1024.220433,
0.0,
454.055744
],
[
0.0,
1021.947708,
461.90052
],
[
0.0,
0.0,
1.0
]
],
"DC": [
-0.136866,
0.061446,
-0.011862,
-0.001411,
-4e-05
]
}
],
"imgpaths": [
"/datasets/chi3d/50591643-img_000000.jpg",
"/datasets/chi3d/65906101-img_000000.jpg"
],
"dataset": "chi3d",
"action": "Grab",
"setup": "s02",
"num_persons": 2,
"room_size": [
4.0,
5.8,
2.0
],
"room_center": [
0,
0,
1.0
],
"id": "s02_Grab-1_000000"
}

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data/q1/vis_steps.py Normal file
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import json
import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
from skelda import utils_pose, utils_view
# ==================================================================================================
filepath = os.path.dirname(os.path.realpath(__file__)) + "/"
core_triangs = [
[
[0.287, -0.282, 1.264, 1.000],
[0.504, -0.052, 1.272, 1.000],
[0.276, -0.160, 0.764, 1.000],
[0.443, -0.099, 0.768, 1.000],
[0.258, -0.313, 0.999, 1.000],
[0.513, -0.009, 1.008, 1.000],
[0.204, -0.126, 0.439, 1.000],
[0.422, -0.132, 0.436, 1.000],
[0.195, -0.265, 0.807, 1.000],
[0.415, 0.039, 0.823, 1.000],
[0.113, -0.103, 0.096, 1.000],
[0.389, -0.175, 0.097, 1.000],
],
[
[0.322, -0.192, 1.349, 1.000],
[0.268, -0.594, 1.336, 1.000],
[0.272, -0.100, 0.882, 1.000],
[0.281, -0.379, 0.870, 1.000],
[0.336, -0.104, 1.124, 1.000],
[0.249, -0.578, 1.089, 1.000],
[0.229, 0.009, 0.571, 1.000],
[0.269, -0.345, 0.553, 1.000],
[0.289, -0.016, 0.951, 1.000],
[0.216, -0.327, 0.908, 1.000],
[0.188, 0.128, 0.268, 1.000],
[0.267, -0.273, 0.243, 1.000],
],
[
[0.865, 1.058, 1.613, 1.000],
[0.862, 0.870, 1.604, 1.000],
[0.927, 1.562, 1.491, 1.000],
[0.954, 1.505, 1.486, 1.000],
[0.908, 1.309, 1.542, 1.000],
[0.905, 1.170, 1.525, 1.000],
[0.968, 1.911, 1.454, 1.000],
[1.019, 1.919, 1.457, 1.000],
[0.921, 1.542, 1.514, 1.000],
[0.931, 1.539, 1.506, 1.000],
[1.008, 2.230, 1.455, 1.000],
[1.071, 2.271, 1.460, 1.000],
],
[
[-0.260, 0.789, 1.316, 1.000],
[0.039, 1.073, 1.322, 1.000],
[-0.236, 0.798, 0.741, 1.000],
[-0.048, 0.952, 0.759, 1.000],
[-0.315, 0.734, 0.995, 1.000],
[0.080, 1.026, 1.046, 1.000],
[-0.291, 0.721, 0.339, 1.000],
[-0.101, 0.887, 0.366, 1.000],
[-0.300, 0.600, 0.742, 1.000],
[0.066, 0.768, 0.897, 1.000],
[-0.381, 0.685, -0.113, 1.000],
[-0.169, 0.775, -0.040, 1.000],
],
[
[-0.199, 0.854, 1.414, 1.000],
[-0.401, 0.566, 1.409, 1.000],
[-0.242, 0.818, 0.870, 1.000],
[-0.343, 0.654, 0.856, 1.000],
[-0.176, 0.903, 1.140, 1.000],
[-0.398, 0.480, 1.132, 1.000],
[-0.245, 0.812, 0.492, 1.000],
[-0.380, 0.642, 0.471, 1.000],
[-0.145, 0.817, 0.912, 1.000],
[-0.251, 0.396, 0.973, 1.000],
[-0.255, 0.879, 0.107, 1.000],
[-0.383, 0.633, 0.116, 1.000],
],
[
[0.641, 1.796, 1.681, 1.000],
[0.603, 1.719, 1.680, 1.000],
[0.711, 2.000, 1.518, 1.000],
[0.706, 1.970, 1.515, 1.000],
[0.689, 1.920, 1.588, 1.000],
[0.651, 1.784, 1.585, 1.000],
[0.786, 2.190, 1.448, 1.000],
[0.780, 2.167, 1.444, 1.000],
[0.747, 1.994, 1.531, 1.000],
[0.720, 1.783, 1.546, 1.000],
[0.868, 2.432, 1.427, 1.000],
[0.849, 2.341, 1.410, 1.000],
],
]
core_joints = [
"shoulder_left",
"shoulder_right",
"hip_left",
"hip_right",
"elbow_left",
"elbow_right",
"knee_left",
"knee_right",
"wrist_left",
"wrist_right",
"ankle_left",
"ankle_right",
]
poses_2d = [
[
[
[383.443, 144.912, 0.923],
[382.629, 135.143, 0.83],
[374.488, 134.329, 1.0],
[349.251, 136.771, 0.478],
[343.552, 139.213, 1.0],
[356.578, 201.899, 0.73],
[323.2, 201.899, 0.825],
[357.392, 282.494, 0.663],
[324.014, 289.821, 0.854],
[378.558, 339.481, 0.621],
[355.764, 356.577, 0.821],
[370.417, 357.391, 0.714],
[332.155, 359.834, 0.71],
[391.584, 452.641, 0.768],
[331.341, 458.34, 0.789],
[414.379, 547.076, 0.864],
[332.969, 550.333, 0.9],
[351.286, 358.613, 0.71],
[339.889, 201.899, 0.73],
[346.402, 137.992, 0.478],
],
[
[640.948, 116.443, 0.908],
[650.057, 100.249, 0.788],
[642.972, 100.249, 0.681],
[682.445, 103.285, 0.862],
[684.469, 100.249, 0.518],
[707.748, 181.219, 0.836],
[693.578, 180.207, 0.705],
[702.688, 290.528, 0.867],
[664.227, 276.358, 0.786],
[662.202, 375.547, 0.847],
[605.523, 319.88, 0.881],
[692.566, 373.522, 0.758],
[679.409, 372.51, 0.739],
[679.409, 500.038, 0.844],
[672.324, 494.978, 0.837],
[679.409, 635.663, 0.913],
[659.166, 599.226, 0.894],
[685.987, 373.016, 0.739],
[700.663, 180.713, 0.705],
[683.457, 101.767, 0.518],
],
],
[
[
[495.125, 304.671, 0.581],
[492.338, 301.885, 0.462],
[502.091, 301.885, 0.295],
[495.125, 308.851, 0.92],
[528.562, 306.064, 0.754],
[477.013, 359.703, 0.822],
[557.819, 355.523, 0.855],
[466.564, 431.452, 0.855],
[565.481, 425.879, 0.836],
[458.902, 480.911, 0.85],
[544.583, 464.889, 0.596],
[491.642, 490.663, 0.741],
[539.707, 492.056, 0.746],
[480.496, 569.379, 0.779],
[531.348, 577.041, 0.784],
[464.475, 646.005, 0.872],
[518.809, 661.33, 0.913],
[515.675, 491.36, 0.741],
[517.416, 357.613, 0.822],
[511.843, 307.458, 0.754],
],
[
[472.982, 273.983, 0.911],
[477.875, 266.645, 0.848],
[464.421, 266.645, 0.896],
[483.378, 268.48, 0.599],
[448.521, 268.48, 0.88],
[493.163, 308.841, 0.753],
[425.894, 311.899, 0.837],
[502.336, 363.268, 0.625],
[417.944, 368.16, 0.847],
[499.278, 407.91, 0.495],
[438.736, 410.357, 0.844],
[484.602, 426.868, 0.682],
[448.521, 427.48, 0.681],
[485.825, 504.534, 0.745],
[441.794, 505.757, 0.796],
[489.494, 571.803, 0.688],
[442.405, 577.918, 0.867],
[466.561, 427.174, 0.681],
[459.528, 310.37, 0.753],
[465.95, 268.48, 0.599],
],
[
[702.349, 208.747, 0.215],
[705.862, 207.944, 0.212],
[700.341, 207.944, 0.209],
[708.472, 196.399, 0.182],
[699.538, 196.299, 0.193],
[708.071, 196.7, 0.194],
[696.927, 196.098, 0.22],
[709.175, 206.137, 0.191],
[696.526, 206.94, 0.188],
[707.368, 210.052, 0.128],
[699.738, 209.751, 0.145],
[704.658, 215.172, 0.172],
[701.445, 215.273, 0.168],
[705.159, 224.007, 0.179],
[703.654, 225.211, 0.185],
[705.059, 225.914, 0.23],
[704.457, 230.532, 0.241],
[703.051, 215.223, 0.168],
[702.499, 196.399, 0.194],
[704.005, 196.349, 0.182],
],
],
]
joints_2d = [
"nose",
"eye_left",
"eye_right",
"ear_left",
"ear_right",
"shoulder_left",
"shoulder_right",
"elbow_left",
"elbow_right",
"wrist_left",
"wrist_right",
"hip_left",
"hip_right",
"knee_left",
"knee_right",
"ankle_left",
"ankle_right",
"hip_middle",
"shoulder_middle",
"head",
]
# ==================================================================================================
def main():
with open(os.path.join(filepath, "sample.json"), "r", encoding="utf-8") as file:
sample = json.load(file)
camparams = sample["cameras"]
roomparams = {
"room_size": sample["room_size"],
"room_center": sample["room_center"],
}
fig2 = utils_view.draw_poses3d(core_triangs, core_joints, roomparams, camparams)
fig2.axes[0].view_init(elev=30, azim=0)
fig2.savefig(os.path.join(filepath, "core-triangs.png"), dpi=fig2.dpi)
core_projections = []
for i in range(len(camparams)):
b2d, _ = utils_pose.project_poses(np.array(core_triangs), camparams[i])
core_projections.append(b2d)
img_size = [900, 900]
scale = 0.66
fig_size = 2
plotsize = (35, 30)
fig, axs = plt.subplots(fig_size, fig_size, figsize=plotsize)
fig.suptitle("core reprojections", fontsize=20)
fig.tight_layout(rect=[0, 0, 1, 0.97])
num_persons = max((len(b2d) for b2d in core_projections))
colors = plt.cm.hsv(np.linspace(0, 1, num_persons, endpoint=False)).tolist()
colors = [[int(c[0] * 255), int(c[1] * 255), int(c[2] * 255)] for c in colors]
for i in range(len(camparams)):
img = np.ones((img_size[0], img_size[1], 3), dtype=np.uint8) * 255
for j in range(len(core_projections[i])):
color = colors[j]
body = np.array(core_projections[i][j])
img = utils_view.draw_body_in_image(img, body, core_joints, color)
for k in range(len(poses_2d[i])):
body = np.array(poses_2d[i][k])
cjids = [joints_2d.index(j) for j in core_joints]
body = body[cjids]
img = utils_view.draw_body_in_image(
img, body, core_joints, [0, 0, 0], thickness=2
)
img = cv2.resize(img, (int(img.shape[1] * scale), int(img.shape[0] * scale)))
title = str(i)
x, y = divmod(i, fig_size)
axs[x][y].imshow(img)
axs[x][y].set_title(title)
# Delete empty plots
for i in range(2, fig_size**2):
x, y = divmod(i, fig_size)
fig.delaxes(axs[x][y])
fig.savefig(os.path.join(filepath, "core-reprojections.png"), dpi=fig.dpi)
# ==================================================================================================
if __name__ == "__main__":
main()

2
scripts/test_triangulate.py
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@ -294,7 +294,7 @@ def main():
if not os.path.isdir(dirpath): if not os.path.isdir(dirpath):
continue continue
if (dirname[0] not in ["p", "h", "e"]) or len(dirname) != 2: if (dirname[0] not in ["p", "h", "e", "q"]) or len(dirname) != 2:
continue continue
# Load sample infos # Load sample infos