crosstyan/RapidPoseTriangulation
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Delete old py code.

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Daniel
2024-09-26 12:10:05 +02:00
parent d2efb66767
commit 3318afc3f1
3 changed files with 0 additions and 679 deletions
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118
scripts/test_triangulate.py
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@ -228,124 +228,6 @@ def load_image(path: str):
# ==================================================================================================
def filter_poses(poses3D, poses2D, roomparams, joint_names, drop_few_limbs=True):
drop = []
for i, pose in enumerate(poses3D):
pose = np.array(pose)
valid_joints = [j for j in pose if j[-1] > 0.1]
# Drop persons with too few joints
if np.sum(pose[..., -1] > 0.1) < 5:
drop.append(i)
continue
# Drop too large or too small persons
mins = np.min(valid_joints, axis=0)
maxs = np.max(valid_joints, axis=0)
diff = maxs - mins
if any(((d > 2.3) for d in diff)):
drop.append(i)
continue
if all(((d < 0.3) for d in diff)):
drop.append(i)
continue
if (
(diff[0] < 0.2 and diff[1] < 0.2)
or (diff[1] < 0.2 and diff[2] < 0.2)
or (diff[2] < 0.2 and diff[0] < 0.2)
):
drop.append(i)
continue
# Drop persons outside room
mean = np.mean(valid_joints, axis=0)
mins = np.min(valid_joints, axis=0)
maxs = np.max(valid_joints, axis=0)
rsize = [r / 2 for r in roomparams["room_size"]]
rcent = roomparams["room_center"]
if any(
(
# Center of mass outside room
mean[j] > rsize[j] + rcent[j] or mean[j] < -rsize[j] + rcent[j]
for j in range(3)
)
) or any(
(
# One limb more than 10cm outside room
maxs[j] > rsize[j] + rcent[j] + 0.1
or mins[j] < -rsize[j] + rcent[j] - 0.1
for j in range(3)
)
):
drop.append(i)
continue
if drop_few_limbs:
# Drop persons with less than 3 limbs
found_limbs = 0
for limb in main_limbs:
start_idx = joint_names.index(limb[0])
end_idx = joint_names.index(limb[1])
if pose[start_idx, -1] > 0.1 and pose[end_idx, -1] > 0.1:
found_limbs += 1
if found_limbs < 3:
drop.append(i)
continue
# Drop persons with too small or high average limb length
total_length = 0
total_limbs = 0
for limb in main_limbs:
start_idx = joint_names.index(limb[0])
end_idx = joint_names.index(limb[1])
if pose[start_idx, -1] < 0.1 or pose[end_idx, -1] < 0.1:
continue
limb_length = np.linalg.norm(pose[end_idx, :3] - pose[start_idx, :3])
total_length += limb_length
total_limbs += 1
if total_limbs == 0:
drop.append(i)
continue
average_length = total_length / total_limbs
if average_length < 0.1:
drop.append(i)
continue
if total_limbs > 4 and average_length > 0.5:
drop.append(i)
continue
new_poses3D = []
new_poses2D = [[] for _ in range(len(poses2D))]
for i in range(len(poses3D)):
if len(poses3D[i]) != len(joint_names):
# Sometimes some joints of a poor detection are missing
continue
if i not in drop:
new_poses3D.append(poses3D[i])
for j in range(len(poses2D)):
new_poses2D[j].append(poses2D[j][i])
else:
new_pose = np.array(poses3D[i])
new_pose[..., -1] = 0.001
new_poses3D.append(new_pose)
for j in range(len(poses2D)):
new_pose = np.array(poses2D[j][i])
new_pose[..., -1] = 0.001
new_poses2D[j].append(new_pose)
new_poses3D = np.array(new_poses3D)
new_poses2D = np.array(new_poses2D)
if new_poses3D.size == 0:
new_poses3D = np.zeros([1, len(joint_names), 4])
new_poses2D = np.zeros([len(poses2D), 1, len(joint_names), 3])
return new_poses3D, new_poses2D
# ==================================================================================================
def update_keypoints(poses_2d: list, joint_names: List[str]) -> list:
new_views = []
for view in poses_2d: