def compute_cord_warp(source_pose_loc,
target_pose_loc,
_warp_skip='mask',
_image_size=(256, 192),
pose_dim=16):
_image_size = (256, 192)
if _warp_skip == 'full':
# warp = [np.empty([1, pose_dim-10]), 1]
warp = [np.empty([1, 8]), 1]
else:
# warp = [np.empty([10, pose_dim-10]),
# np.empty([10] + list(_image_size))]
warp = [np.empty([10, 8]), np.empty([10] + list(_image_size))]
kp_array1 = source_pose_loc
kp_array2 = target_pose_loc
if _warp_skip == 'mask':
warp[0] = pose_transform.affine_transforms(kp_array1, kp_array2,
pose_dim)
warp[1] = pose_transform.pose_masks(kp_array2, _image_size, pose_dim)
else:
warp[0] = pose_transform.estimate_uniform_transform(
kp_array1, kp_array2, pose_dim)
return warp
def compute_interpol_cord_warp(inp_map, interpol_pose):
interpol_pose = [inp_map] + interpol_pose
num_interpol = len(interpol_pose)
interpol_warps, interpol_masks = [], []
_warp_skip = 'mask'
pose_dim = 18
_image_size = (256, 192)
# possibly causing nan error
kp_array1 = map_to_cord(inp_map, pose_dim)
for pose in interpol_pose:
if _warp_skip == 'full':
warp = [np.empty([1, 8]), 1]
else:
warp = [np.empty([10, 8]), np.empty([10] + list(_image_size))]
kp_array2 = map_to_cord(pose, pose_dim)
if _warp_skip == 'mask':
warp[0] = pose_transform.affine_transforms(kp_array1, kp_array2,
pose_dim)
warp[1] = pose_transform.pose_masks(kp_array2, _image_size,
pose_dim)
else:
warp[0] = pose_transform.estimate_uniform_transform(
kp_array1, kp_array2, pose_dim)
interpol_warps.append(warp[0])
interpol_masks.append(warp[1])
kp_array1 = kp_array2
return np.array(interpol_warps), np.array(interpol_masks)
def compute_interpol_cord_warp(self, inp_map, interpol_pose):
interpol_pose = [inp_map] + interpol_pose
num_interpol = len(interpol_pose)
interpol_warps, interpol_masks = [], []
# possibly causing nan error
kp_array1 = pose_utils.map_to_cord(np.transpose(inp_map, [1, 2, 0]),
self.pose_dim)
for pose in interpol_pose:
if self._warp_skip == 'full':
warp = [np.empty([1, 8]), 1]
else:
warp = [
np.empty([10, 8]),
np.empty([10] + list(self._image_size))
]
kp_array2 = pose_utils.map_to_cord(np.transpose(pose, [1, 2, 0]),
self.pose_dim)
if self._warp_skip == 'mask':
warp[0] = pose_transform.affine_transforms(
kp_array1, kp_array2, self.pose_dim)
warp[1] = pose_transform.pose_masks(kp_array2,
self._image_size,
self.pose_dim)
else:
warp[0] = pose_transform.estimate_uniform_transform(
kp_array1, kp_array2, self.pose_dim)
interpol_warps.append(warp[0])
interpol_masks.append(warp[1])
kp_array1 = kp_array2
return np.array(interpol_warps), np.array(interpol_masks)
def compute_cord_warp(self, pair):
if self._warp_skip == 'full':
warp = [np.empty([1, 8])]
else:
warp = [np.empty([10, 8]), np.empty([10] + list(self._image_size))]
fr = self._annotations_file.loc[p['from']]
to = self._annotations_file.loc[p['to']]
kp_array1 = pose_utils.load_pose_cords_from_strings(
fr['keypoints_y'], fr['keypoints_x'])
kp_array2 = pose_utils.load_pose_cords_from_strings(
to['keypoints_y'], to['keypoints_x'])
if self._warp_skip == 'mask':
warp[0] = pose_transform.affine_transforms(kp_array1, kp_array2)
warp[1] = pose_transform.pose_masks(kp_array2, self._image_size)
else:
warp[0] = pose_transform.estimate_uniform_transform(
kp_array1, kp_array2)
return warp
def compute_cord_warp_batch(self, pair_data):
if self._use_warp == 'full':
warp = [np.empty([self._batch_size] + [1, 8])]
elif self._use_warp == 'mask':
warp = [
np.empty([self._batch_size] + [10, 8]),
np.empty([self._batch_size, 10] + list(self._im_size))
]
elif self._use_warp == 'stn':
warp = [np.empty([self._batch_size] + [72])]
else:
return []
for i, p in enumerate(pair_data):
pairs = p.split(',')
from_d = '_'.join(pairs[0].split('/'))
to_d = '_'.join(pairs[1].split('/'))
from_row = self._annotations_file.loc[from_d]
to_row = self._annotations_file.loc[to_d]
from_pose_corordinates = pose_utils.load_pose_cords_from_strings(
from_row['keypoints_y'], from_row['keypoints_x'])
to_pose_corordinates = pose_utils.load_pose_cords_from_strings(
to_row['keypoints_y'], to_row['keypoints_x'])
if self._use_warp == 'mask':
warp[0][i] = pose_transform.affine_transforms(
from_pose_corordinates, to_pose_corordinates)
warp[1][i] = pose_transform.pose_masks(to_pose_corordinates,
self._im_size)
elif self._use_warp == 'full':
warp[0][i] = pose_transform.estimate_uniform_transform(
from_pose_corordinates, to_pose_corordinates)
else: # sel._use_warp == 'stn'
warp[0][i][:36] = from_pose_corordinates.reshape((-1, ))
warp[0][i][36:] = to_pose_corordinates.reshape((-1, ))
return warp
