def fetch(subjects, action_filter=None, subset=1, parse_3d_poses=True):
out_poses_3d = []
out_poses_2d = []
out_camera_params = []
subject = 'S1'
action = 'Directions'
poses_2d = keypoints[subject][action]
for i in range(len(poses_2d)): # Iterate across cameras
out_poses_2d.append(poses_2d[i])
if len(out_camera_params) == 0:
out_camera_params = None
if len(out_poses_3d) == 0:
out_poses_3d = None
stride = args.downsample
if subset < 1:
for i in range(len(out_poses_2d)):
n_frames = int(round(len(out_poses_2d[i])//stride * subset)*stride)
start = deterministic_random(0, len(out_poses_2d[i]) - n_frames + 1, str(len(out_poses_2d[i])))
out_poses_2d[i] = out_poses_2d[i][start:start+n_frames:stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][start:start+n_frames:stride]
elif stride > 1:
# Downsample as requested
for i in range(len(out_poses_2d)):
out_poses_2d[i] = out_poses_2d[i][::stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][::stride]
return out_camera_params, out_poses_3d, out_poses_2d
def fetch(subjects, action_filter=None, subset=1, parse_3d_poses=True):
out_poses_3d = []
out_poses_2d = []
out_camera_params = []
subject = 'S1'
action = 'Directions 1'
poses_2d = keypoints[subject][action]
for i in range(len(poses_2d)): # Iterate across cameras
out_poses_2d.append(poses_2d[i])
if len(out_camera_params) == 0:
out_camera_params = None
if len(out_poses_3d) == 0:
out_poses_3d = None
stride = args.downsample
if subset < 1:
for i in range(len(out_poses_2d)):
n_frames = int(
round(len(out_poses_2d[i]) // stride * subset) * stride)
start = deterministic_random(0,
len(out_poses_2d[i]) - n_frames + 1,
str(len(out_poses_2d[i])))
out_poses_2d[i] = out_poses_2d[i][start:start + n_frames:stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][start:start +
n_frames:stride]
elif stride > 1:
# Downsample as requested
for i in range(len(out_poses_2d)):
out_poses_2d[i] = out_poses_2d[i][::stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][::stride]
return out_camera_params, out_poses_3d, out_poses_2d
def fetch(subjects,
action_filter=None,
subset=1,
parse_3d_poses=True,
validation=False):
out_poses_3d = []
out_poses_2d = []
out_camera_params = []
for subject in subjects:
for action in keypoints[subject].keys():
if action_filter is not None:
found = False
for a in action_filter:
if action.startswith(a):
found = True
break
if validation and not found:
continue
if not validation and found:
continue
poses_2d = keypoints[subject][action]
for i in range(len(poses_2d)): # Iterate across cameras
out_poses_2d.append(poses_2d[i])
if subject in dataset.cameras():
cams = dataset.cameras()[subject]
assert len(cams) == len(poses_2d), 'Camera count mismatch'
for cam in cams:
if 'intrinsic' in cam:
out_camera_params.append(cam['intrinsic'])
if parse_3d_poses and 'positions_3d' in dataset[subject][action]:
poses_3d = dataset[subject][action]['positions_3d']
assert len(poses_3d) == len(poses_2d), 'Camera count mismatch'
for i in range(len(poses_3d)): # Iterate across cameras
out_poses_3d.append(poses_3d[i])
if len(out_camera_params) == 0:
out_camera_params = None
if len(out_poses_3d) == 0:
out_poses_3d = None
stride = args.downsample
if subset < 1:
for i in range(len(out_poses_2d)):
n_frames = int(
round(len(out_poses_2d[i]) // stride * subset) * stride)
start = deterministic_random(0,
len(out_poses_2d[i]) - n_frames + 1,
str(len(out_poses_2d[i])))
out_poses_2d[i] = out_poses_2d[i][start:start + n_frames:stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][start:start +
n_frames:stride]
elif stride > 1:
# Downsample as requested
for i in range(len(out_poses_2d)):
out_poses_2d[i] = out_poses_2d[i][::stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][::stride]
return out_camera_params, out_poses_3d, out_poses_2d
def fetch(subjects, action_filter=None, subset=1, parse_3d_poses=True):
out_poses_3d = []
out_poses_2d = []
out_camera_params = []
for subject in subjects:
for action in keypoints[subject].keys():
if action_filter is not None:
found = False
for a in action_filter:
if action.startswith(a):
found = True
break
if not found:
continue
poses_2d = keypoints[subject][action]
for i in range(len(poses_2d)): # Iterate across cameras
out_poses_2d.append(poses_2d[i])
if subject in dataset.cameras():
cams = dataset.cameras()[subject]
assert len(cams) == len(poses_2d), 'Camera count mismatch'
for cam in cams:
if 'intrinsic' in cam:
out_camera_params.append(cam['intrinsic'])
if parse_3d_poses and 'positions_3d' in dataset[subject][action]:
poses_3d = dataset[subject][action]['positions_3d']
assert len(poses_3d) == len(poses_2d), 'Camera count mismatch'
for i in range(len(poses_3d)): # Iterate across cameras
out_poses_3d.append(poses_3d[i])
if len(out_camera_params) == 0:
out_camera_params = None
if len(out_poses_3d) == 0:
out_poses_3d = None
stride = args.downsample
if subset < 1:
for i in range(len(out_poses_2d)):
n_frames = int(
round(len(out_poses_2d[i]) // stride * subset) * stride)
start = deterministic_random(0,
len(out_poses_2d[i]) - n_frames + 1,
str(len(out_poses_2d[i])))
out_poses_2d[i] = out_poses_2d[i][start:start + n_frames:stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][start:start +
n_frames:stride]
elif stride > 1:
# Downsample as requested
for i in range(len(out_poses_2d)):
out_poses_2d[i] = out_poses_2d[i][::stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][::stride]
# ------------------------------Add 3D data into input(2d)---------------------------
# addJoints = [9, 10, 15] # 9:Head, 10:RWrist, 15:LWrist
#
# out_poses_2d_added = []
# for out_pose_2d, out_pose_3d in zip(out_poses_2d, out_poses_3d):
# out_pose_2d_added = np.zeros((out_pose_3d.shape[0],out_pose_3d.shape[1],out_pose_2d.shape[2] + out_pose_3d.shape[2]))
# out_pose_2d_added[:,:,0:2] = out_pose_2d
#
# added3D = np.zeros((out_pose_2d_added.shape[0], out_pose_2d_added.shape[1], 3))
# added3D[:,addJoints,:] = out_pose_3d[:,addJoints,:]
# out_pose_2d_added[:, :, 2:5] = added3D
# out_poses_2d_added.append(out_pose_2d_added)
#
# out_poses_2d = out_poses_2d_added
# -----------------------------------------------------------------------------------
return out_camera_params, out_poses_3d, out_poses_2d