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 get_group_preds(grouped_joints,
center,
scale,
heatmap_size,
use_udp=False):
"""Transform the grouped joints back to the image.
Args:
grouped_joints (list): Grouped person joints.
center (np.ndarray[2, ]): Center of the bounding box (x, y).
scale (np.ndarray[2, ]): Scale of the bounding box
wrt [width, height].
heatmap_size (np.ndarray[2, ]): Size of the destination heatmaps.
use_udp (bool): Unbiased data processing.
Paper ref: Huang et al. The Devil is in the Details: Delving into
Unbiased Data Processing for Human Pose Estimation (CVPR 2020).
Returns:
list: List of the pose result for each person.
"""
if use_udp:
if grouped_joints[0].shape[0] > 0:
heatmap_size_t = np.array(heatmap_size, dtype=np.float32) - 1.0
trans = get_warp_matrix(theta=0,
size_input=heatmap_size_t,
size_dst=scale,
size_target=heatmap_size_t)
grouped_joints[0][..., :2] = \
warp_affine_joints(grouped_joints[0][..., :2], trans)
results = [person for person in grouped_joints[0]]
else:
results = []
for person in grouped_joints[0]:
joints = transform_preds(person, center, scale, heatmap_size)
results.append(joints)
return results