def __call__(self, results):
image_size = results['ann_info']['image_size']
img = results['img']
joints_3d = results['joints_3d']
joints_3d_visible = results['joints_3d_visible']
c = results['center']
s = results['scale']
r = results['rotation']
if self.use_udp:
trans = get_warp_matrix(r, c * 2.0, image_size - 1.0, s * 200.0)
img = cv2.warpAffine(
img,
trans, (int(image_size[0]), int(image_size[1])),
flags=cv2.INTER_LINEAR)
joints_3d[:, 0:2] = \
warp_affine_joints(joints_3d[:, 0:2].copy(), trans)
else:
trans = get_affine_transform(c, s, r, image_size)
img = cv2.warpAffine(
img,
trans, (int(image_size[0]), int(image_size[1])),
flags=cv2.INTER_LINEAR)
for i in range(results['ann_info']['num_joints']):
if joints_3d_visible[i, 0] > 0.0:
joints_3d[i,
0:2] = affine_transform(joints_3d[i, 0:2], trans)
results['img'] = img
results['joints_3d'] = joints_3d
results['joints_3d_visible'] = joints_3d_visible
return results
def __call__(self, results):
image_size = results['ann_info']['image_size']
img = results['img']
joints_3d = results['joints_3d']
joints_3d_visible = results['joints_3d_visible']
c = results['center']
s = results['scale']
r = results['rotation']
trans = get_affine_transform(c, s, r, image_size)
img = cv2.warpAffine(
img,
trans, (int(image_size[0]), int(image_size[1])),
flags=cv2.INTER_LINEAR)
for i in range(results['ann_info']['num_joints']):
if joints_3d_visible[i, 0] > 0.0:
joints_3d[i, 0:2] = affine_transform(joints_3d[i, 0:2], trans)
results['img'] = img
results['joints_3d'] = joints_3d
results['joints_3d_visible'] = joints_3d_visible
return results
def __call__(self, results):
"""Perform random affine transformation to joints coordinates."""
c = results['center']
s = results['scale'] / 200.0
r = results['rotation']
image_size = results['ann_info']['image_size']
assert self.item in results
joints = results[self.item]
if self.visible_item is not None:
assert self.visible_item in results
joints_vis = results[self.visible_item]
else:
joints_vis = [np.ones_like(joints[0]) for _ in range(len(joints))]
trans = get_affine_transform(c, s, r, image_size)
nposes = len(joints)
for n in range(nposes):
for i in range(len(joints[0])):
if joints_vis[n][i, 0] > 0.0:
joints[n][i,
0:2] = affine_transform(joints[n][i, 0:2], trans)
if (np.min(joints[n][i, :2]) < 0
or joints[n][i, 0] >= image_size[0]
or joints[n][i, 1] >= image_size[1]):
joints_vis[n][i, :] = 0
results[self.item] = joints
if self.visible_item is not None:
results[self.visible_item] = joints_vis
return results
def __call__(self, results):
image_size = results['ann_info']['image_size']
img = results['img']
joints_2d = results['joints_2d']
joints_2d_visible = results['joints_2d_visible']
joints_3d = results['joints_3d']
pose = results['pose']
c = results['center']
s = results['scale']
r = results['rotation']
trans = get_affine_transform(c, s, r, image_size)
img = cv2.warpAffine(img,
trans, (int(image_size[0]), int(image_size[1])),
flags=cv2.INTER_LINEAR)
for i in range(results['ann_info']['num_joints']):
if joints_2d_visible[i, 0] > 0.0:
joints_2d[i] = affine_transform(joints_2d[i], trans)
joints_3d = _rotate_joints_3d(joints_3d, r)
pose = _rotate_smpl_pose(pose, r)
results['img'] = img
results['joints_2d'] = joints_2d
results['joints_2d_visible'] = joints_2d_visible
results['joints_3d'] = joints_3d
results['pose'] = pose
if 'iuv' in results.keys():
iuv = results['iuv']
if iuv is not None:
iuv_size = results['ann_info']['iuv_size']
iuv = cv2.warpAffine(iuv,
trans,
(int(iuv_size[0]), int(iuv_size[1])),
flags=cv2.INTER_NEAREST)
results['iuv'] = iuv
return results
def __call__(self, results):
image_size = results['ann_info']['image_size']
img = results['img']
joints_3d = results['joints_3d']
joints_3d_visible = results['joints_3d_visible']
c = results['center']
s = results['scale']
r = results['rotation']
if self.use_udp:
trans = get_warp_matrix(r, c * 2.0, image_size - 1.0, s * 200.0)
img = cv2.warpAffine(
img,
trans, (int(image_size[0]), int(image_size[1])),
flags=cv2.INTER_LINEAR)
joints_3d[:, 0:2] = \
warp_affine_joints(joints_3d[:, 0:2].copy(), trans)
else:
trans = get_affine_transform(c, s, r, image_size)
img = cv2.warpAffine(
img,
trans, (int(image_size[0]), int(image_size[1])),
flags=cv2.INTER_LINEAR)
for i in range(results['ann_info']['num_joints']):
if joints_3d_visible[i, 0] > 0.0:
joints_3d[i,
0:2] = affine_transform(joints_3d[i, 0:2], trans)
# print('save image')
# im_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# cv2.imwrite('ori_images/cropped_image'+str(c[0])+'.jpg', im_rgb)
#time.sleep(3)
results['img'] = img
results['joints_3d'] = joints_3d
results['joints_3d_visible'] = joints_3d_visible
return results
