crosstyan/RapidPoseTriangulation
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Small speed improvements.

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Daniel
2024-08-05 16:28:25 +02:00
parent d935e1cda5
commit 6002aeddde
2 changed files with 33 additions and 23 deletions
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34
media/RESULTS.md
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@ -7,9 +7,9 @@ Results of the model in various experiments on different datasets.
(duration 00:01:20) (duration 00:01:20)
```json ```json
{ {
"avg_time_2d": 0.07095112719778288, "avg_time_2d": 0.0706314975932493,
"avg_time_3d": 0.0030404001979504602, "avg_time_3d": 0.002891659332534014,
"avg_fps": 13.515060915713226 "avg_fps": 13.6011569934277
} }
{ {
"person_nums": { "person_nums": {
@ -278,9 +278,9 @@ Results of the model in various experiments on different datasets.
(duration 00:00:56) (duration 00:00:56)
```json ```json
{ {
"avg_time_2d": 0.10204474868643325, "avg_time_2d": 0.10373067446181045,
"avg_time_3d": 0.017180594382007506, "avg_time_3d": 0.01558385436067876,
"avg_fps": 8.38747848623052 "avg_fps": 8.381208976551004
} }
{ {
"person_nums": { "person_nums": {
@ -549,9 +549,9 @@ Results of the model in various experiments on different datasets.
(duration 00:00:29) (duration 00:00:29)
```json ```json
{ {
"avg_time_2d": 0.05710376546068011, "avg_time_2d": 0.059855266562047996,
"avg_time_3d": 0.005011487681910677, "avg_time_3d": 0.004884123802185059,
"avg_fps": 16.09910528263349 "avg_fps": 15.446546443731663
} }
{ {
"person_nums": { "person_nums": {
@ -820,9 +820,9 @@ Results of the model in various experiments on different datasets.
(duration 00:02:31) (duration 00:02:31)
```json ```json
{ {
"avg_time_2d": 0.05584677240189905, "avg_time_2d": 0.05529122129203044,
"avg_time_3d": 0.0030521013817609993, "avg_time_3d": 0.0028780115247735685,
"avg_fps": 16.978253330837436 "avg_fps": 17.191218649029846
} }
{ {
"person_nums": { "person_nums": {
@ -858,8 +858,8 @@ Results of the model in various experiments on different datasets.
"ap-0.05": 9.5e-05, "ap-0.05": 9.5e-05,
"ap-0.1": 0.257803, "ap-0.1": 0.257803,
"ap-0.15": 0.500043, "ap-0.15": 0.500043,
"ap-0.25": 0.610092, "ap-0.25": 0.610091,
"ap-0.5": 0.638299 "ap-0.5": 0.638298
}, },
"head": { "head": {
"count": 753, "count": 753,
@ -1031,9 +1031,9 @@ Results of the model in various experiments on different datasets.
(duration 00:02:28) (duration 00:02:28)
```json ```json
{ {
"avg_time_2d": 0.10889388700810874, "avg_time_2d": 0.10983607013051103,
"avg_time_3d": 0.02615876488569306, "avg_time_3d": 0.023344272520483995,
"avg_fps": 7.404519540914655 "avg_fps": 7.508615611693869
} }
{ {
"person_nums": { "person_nums": {

22
scripts/triangulate_poses.py
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@ -46,7 +46,7 @@ def undistort_points(points: np.ndarray, caminfo: dict):
points = cv2.undistortPoints(points, K, DC, P=newK) points = cv2.undistortPoints(points, K, DC, P=newK)
points = points.reshape(pshape) points = points.reshape(pshape)
return points return points, caminfo
# ================================================================================================== # ==================================================================================================
@ -104,9 +104,7 @@ def calc_pose_scored(pose1, pose2, cam1, cam2, roomparams):
# Triangulate points # Triangulate points
points1 = pose1[mask, 0:2].T points1 = pose1[mask, 0:2].T
points2 = pose2[mask, 0:2].T points2 = pose2[mask, 0:2].T
P1 = get_camera_P(cam1) points3d = cv2.triangulatePoints(cam1["P"], cam2["P"], points1, points2)
P2 = get_camera_P(cam2)
points3d = cv2.triangulatePoints(P1, P2, points1, points2)
points3d = (points3d / points3d[3, :])[0:3, :].T points3d = (points3d / points3d[3, :])[0:3, :].T
pose3d = np.zeros([len(pose1), 4]) pose3d = np.zeros([len(pose1), 4])
pose3d[mask] = np.concatenate([points3d, np.ones([points3d.shape[0], 1])], axis=-1) pose3d[mask] = np.concatenate([points3d, np.ones([points3d.shape[0], 1])], axis=-1)
@ -295,14 +293,26 @@ def get_3d_pose(poses_2d, camparams, roomparams, joint_names_2d, min_score=0.95)
camparams[i]["K"] = np.array(camparams[i]["K"]) camparams[i]["K"] = np.array(camparams[i]["K"])
camparams[i]["R"] = np.array(camparams[i]["R"]) camparams[i]["R"] = np.array(camparams[i]["R"])
camparams[i]["T"] = np.array(camparams[i]["T"]) camparams[i]["T"] = np.array(camparams[i]["T"])
camparams[i]["DC"] = np.array(camparams[i]["DC"][0:5]) camparams[i]["DC"] = np.array(camparams[i]["DC"])
# Undistort 2D points # Undistort 2D points
for i in range(len(camparams)): for i in range(len(camparams)):
poses = poses_2d[i] poses = poses_2d[i]
cam = camparams[i] cam = camparams[i]
poses[:, :, 0:2] = undistort_points(poses[:, :, 0:2], cam) poses[:, :, 0:2], cam = undistort_points(poses[:, :, 0:2], cam)
# Mask out points that are far outside the image (points slightly outside are still valid)
offset = (cam["width"] + cam["height"]) / 40
mask = (
(poses[:, :, 0] >= 0 - offset)
& (poses[:, :, 0] < cam["width"] + offset)
& (poses[:, :, 1] >= 0 - offset)
& (poses[:, :, 1] < cam["height"] + offset)
)
poses = poses * np.expand_dims(mask, axis=-1)
poses_2d[i] = poses poses_2d[i] = poses
# Calc projection matrix with updated camera parameters
cam["P"] = get_camera_P(cam)
camparams[i] = cam
# Create pairs of persons # Create pairs of persons
num_persons = [len(p) for p in poses_2d] num_persons = [len(p) for p in poses_2d]