def __call__(self, results):
"""Perform data augmentation with random image flip."""
img = results['img']
joints_3d = results['joints_3d']
joints_3d_visible = results['joints_3d_visible']
center = results['center']
# A flag indicating whether the image is flipped,
# which can be used by child class.
flipped = False
if np.random.rand() <= self.flip_prob:
flipped = True
if not isinstance(img, list):
img = img[:, ::-1, :]
else:
img = [i[:, ::-1, :] for i in img]
if not isinstance(img, list):
joints_3d, joints_3d_visible = fliplr_joints(
joints_3d, joints_3d_visible, img.shape[1],
results['ann_info']['flip_pairs'])
center[0] = img.shape[1] - center[0] - 1
else:
joints_3d, joints_3d_visible = fliplr_joints(
joints_3d, joints_3d_visible, img[0].shape[1],
results['ann_info']['flip_pairs'])
center[0] = img[0].shape[1] - center[0] - 1
results['img'] = img
results['joints_3d'] = joints_3d
results['joints_3d_visible'] = joints_3d_visible
results['center'] = center
results['flipped'] = flipped
return results
def __call__(self, results):
"""Perform data augmentation with random image flip."""
img = results['img']
joints_3d = results['joints_3d']
joints_3d_visible = results['joints_3d_visible']
center = results['center']
# A flag indicating whether the image is flipped,
# which can be used by child class.
flipped = False
if np.random.rand() <= self.flip_prob:
flipped = True
img = img[:, ::-1, :]
joints_3d, joints_3d_visible = fliplr_joints(
joints_3d, joints_3d_visible, img.shape[1],
results['ann_info']['flip_pairs'])
center[0] = img.shape[1] - center[0] - 1
results['img'] = img
results['joints_3d'] = joints_3d
results['joints_3d_visible'] = joints_3d_visible
results['center'] = center
results['flipped'] = flipped
# import cv2
# print("R", results.keys())
# # print("center", results["center"])
# # print("bbox", results["bbox"])
# # print("scale", results["scale"])
# # print("rotation", results["rotation"])
# # print("j3d", results["joints_3d"])
# import time
# cv2.imwrite("xtest/" + str(time.time()) + ".jpg", results['img'][:,:,::-1])
return results
def __call__(self, results):
img = results['img']
joints_3d = results['joints_3d']
joints_3d_visible = results['joints_3d_visible']
center = results['center']
if np.random.rand() <= self.flip_prob:
img = img[:, ::-1, :]
joints_3d, joints_3d_visible = fliplr_joints(
joints_3d, joints_3d_visible, img.shape[1],
results['ann_info']['flip_pairs'])
center[0] = img.shape[1] - center[0] - 1
results['img'] = img
results['joints_3d'] = joints_3d
results['joints_3d_visible'] = joints_3d_visible
results['center'] = center
return results
def __call__(self, results):
"""Perform data augmentation with random image flip."""
if np.random.rand() > self.flip_prob:
return results
img = results['img']
joints_2d = results['joints_2d']
joints_2d_visible = results['joints_2d_visible']
joints_3d = results['joints_3d']
joints_3d_visible = results['joints_3d_visible']
pose = results['pose']
center = results['center']
img = img[:, ::-1, :]
pose = _flip_smpl_pose(pose)
joints_2d, joints_2d_visible = fliplr_joints(
joints_2d, joints_2d_visible, img.shape[1],
results['ann_info']['flip_pairs'])
joints_3d, joints_3d_visible = _flip_joints_3d(
joints_3d, joints_3d_visible, results['ann_info']['flip_pairs'])
center[0] = img.shape[1] - center[0] - 1
if 'iuv' in results.keys():
iuv = results['iuv']
if iuv is not None:
iuv = _flip_iuv(iuv, results['ann_info']['uv_type'])
results['iuv'] = iuv
results['img'] = img
results['joints_2d'] = joints_2d
results['joints_2d_visible'] = joints_2d_visible
results['joints_3d'] = joints_3d
results['joints_3d_visible'] = joints_3d_visible
results['pose'] = pose
results['center'] = center
return results
