Function image_editing

harsh_lighting_utils.py:75–135

(data_root: str,
                  marker_path: str, 
                  scene_path:str, 
                  src_path:str,                  
                  niid_net,
                  estimator,
                  poster_brightness=1/2.5,
                  save_decomposed=False
)

edit single image
'''
def image_editing(data_root: str,
                  marker_path: str, 
                  scene_path:str, 
                  src_path:str,                  
                  niid_net,
                  estimator,
                  poster_brightness=1/2.5,
                  save_decomposed=False
):
    # =========== Decompose Scene Image ===========
    print('===> Decompose Scene Image')
    resized_scene_name = os.path.split(scene_path)[-1]
    if save_decomposed:
        decompose_dir = os.path.join(data_root, 'decompose')
        os.mkdir(decompose_dir, exist_ok=True)
        print(f"save decompose scene images to {decompose_dir}")
    else:        
        decompose_dir = None 

    decompose_result = decompose_image( data_root = data_root, 
                                img_name  = resized_scene_name, 
                                model = niid_net,
                                save = save_decomposed, 
                                decompose_dir = decompose_dir,    
                                **{ 'pretrained_file': './third_party/NIID/pretrained_model/final.pth.tar',
                                    'offline': True,
                                    'gpu_devices': [0],
                                }
                               )
    
    # =========== Load Image ===========
    print('===> Load Image')    
    starget = cv2.imread(marker_path, cv2.IMREAD_UNCHANGED) #bgr        
    src = cv2.imread(src_path, cv2.IMREAD_UNCHANGED) #bgr
    if not src.shape[2] == 3:        
        raise ValueError("replace image should have 3 channels")    

    dst = np.float64(decompose_result['rgb'])    
    dst = dst[:,:,::-1] #rgb to bgr  
    sdst = (dst*255.0).astype(np.uint8)        
  
    # =========== Estimate & Warp =============
    print('===> Estimate Flow & Warp Image')   
    ori_H, ori_W = dst.shape[:2] 
    flow = estimator.estimate(sdst, starget) 
    src = cv2.GaussianBlur(src,(3,3),1,borderType=cv2.BORDER_CONSTANT)
    out = image_flow_warp(src, flow[0].permute([1,2,0]), padding_mode='border')              
    mask_origin = (np.ones(shape=(src.shape[0], src.shape[1], 1)) * 255).astype(np.uint8)
    mask_origin = image_flow_warp(mask_origin, flow[0].permute([1,2,0]),padding_mode='zeros')    
    mask = (255 - mask_origin).astype(np.float64) / 255.0
    mask = cv2.GaussianBlur(mask,(3,3),1, borderType=cv2.BORDER_REPLICATE)
    mask = mask[:,:,np.newaxis]
    
    result = (out*(1-mask) + sdst*mask).astype(np.uint8)
    replace_result = cv2.resize(result, (ori_W, ori_H)) 
    mask = cv2.resize(mask, (ori_W, ori_H))        

    # =========== Render Light ============