def replace_joint_img(self, joint_img, bbox, trans):
if self.input_joint_name == 'coco':
joint_img_coco = joint_img
if self.data_split == 'train':
xmin, ymin, xmax, ymax = bbox[0], bbox[
1], bbox[0] + bbox[2], bbox[1] + bbox[3]
pt1 = affine_transform(np.array([xmin, ymin]), trans)
pt2 = affine_transform(np.array([xmax, ymin]), trans)
pt3 = affine_transform(np.array([xmax, ymax]), trans)
area = math.sqrt(
pow(pt2[0] - pt1[0], 2) +
pow(pt2[1] - pt1[1], 2)) * math.sqrt(
pow(pt3[0] - pt2[0], 2) + pow(pt3[1] - pt2[1], 2))
joint_img_coco[:17, :] = synthesize_pose(
joint_img_coco[:17, :], area, num_overlap=0)
return joint_img_coco
elif self.input_joint_name == 'human36':
joint_img_h36m = joint_img
if self.data_split == 'train':
joint_syn_error = (self.generate_syn_error() / 256) * np.array(
[cfg.MODEL.input_shape[1], cfg.MODEL.input_shape[0]],
dtype=np.float32)
joint_img_h36m = joint_img_h36m[:, :2] + joint_syn_error
return joint_img_h36m
def replace_joint_img(self, idx, img_id, joint_img, bbox, trans):
if self.input_joint_name == 'coco':
joint_img_coco = joint_img
if self.data_split == 'train':
xmin, ymin, xmax, ymax = bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]
pt1 = affine_transform(np.array([xmin, ymin]), trans)
pt2 = affine_transform(np.array([xmax, ymin]), trans)
pt3 = affine_transform(np.array([xmax, ymax]), trans)
area = math.sqrt(pow(pt2[0] - pt1[0], 2) + pow(pt2[1] - pt1[1], 2)) * math.sqrt(
pow(pt3[0] - pt2[0], 2) + pow(pt3[1] - pt2[1], 2))
joint_img_coco[:17, :] = synthesize_pose(joint_img_coco[:17, :], area, num_overlap=0)
return joint_img_coco
else:
joint_img_coco = self.datalist_pose2d_det[img_id]
joint_img_coco = self.add_pelvis_and_neck(joint_img_coco)
for i in range(self.coco_joint_num):
joint_img_coco[i, :2] = affine_transform(joint_img_coco[i, :2].copy(), trans)
return joint_img_coco
elif self.input_joint_name == 'human36':
joint_img_h36m = joint_img
if self.data_split == 'train':
joint_syn_error = (self.generate_syn_error() / 256) * np.array(
[cfg.MODEL.input_shape[1], cfg.MODEL.input_shape[0]], dtype=np.float32)
joint_img_h36m = joint_img_h36m[:, :2] + joint_syn_error
return joint_img_h36m
else:
det_data = self.datalist_pose2d_det[idx]
# assert img_name == det_data['img_name'], f"check: {img_name} / {det_data['img_name']}"
joint_img_h36m = det_data['pose2d'][:, :2]
for i in range(self.human36_joint_num):
joint_img_h36m[i, :2] = affine_transform(joint_img_h36m[i, :2].copy(), trans)
return joint_img_h36m