def augmentation()

in common/utils/preprocessing.py [0:0]

• 23 lines of code
• 4 McCabe index (conditional complexity)

def augmentation(img, bbox, joint_coord, joint_valid, hand_type, mode, joint_type):
    img = img.copy(); 
    joint_coord = joint_coord.copy(); 
    hand_type = hand_type.copy();

    original_img_shape = img.shape
    joint_num = len(joint_coord)
    
    if mode == 'train':
        trans, scale, rot, do_flip, color_scale = get_aug_config()
    else:
        trans, scale, rot, do_flip, color_scale = [0,0], 1.0, 0.0, False, np.array([1,1,1])
    
    bbox[0] = bbox[0] + bbox[2] * trans[0]
    bbox[1] = bbox[1] + bbox[3] * trans[1]
    img, trans, inv_trans = generate_patch_image(img, bbox, do_flip, scale, rot, cfg.input_img_shape)
    img = np.clip(img * color_scale[None,None,:], 0, 255)
    
    if do_flip:
        joint_coord[:,0] = original_img_shape[1] - joint_coord[:,0] - 1
        joint_coord[joint_type['right']], joint_coord[joint_type['left']] = joint_coord[joint_type['left']].copy(), joint_coord[joint_type['right']].copy()
        joint_valid[joint_type['right']], joint_valid[joint_type['left']] = joint_valid[joint_type['left']].copy(), joint_valid[joint_type['right']].copy()
        hand_type[0], hand_type[1] = hand_type[1].copy(), hand_type[0].copy()
    for i in range(joint_num):
        joint_coord[i,:2] = trans_point2d(joint_coord[i,:2], trans)
        joint_valid[i] = joint_valid[i] * (joint_coord[i,0] >= 0) * (joint_coord[i,0] < cfg.input_img_shape[1]) * (joint_coord[i,1] >= 0) * (joint_coord[i,1] < cfg.input_img_shape[0])

    return img, joint_coord, joint_valid, hand_type, inv_trans