def __call__()

in src/controlnet_aux/leres/__init__.py [0:0]

• 38 lines of code
• 8 McCabe index (conditional complexity)

    def __call__(self, input_image, thr_a=0, thr_b=0, boost=False, detect_resolution=512, image_resolution=512, output_type="pil"):
        device = next(iter(self.model.parameters())).device
        if not isinstance(input_image, np.ndarray):
            input_image = np.array(input_image, dtype=np.uint8)
        
        input_image = HWC3(input_image)
        input_image = resize_image(input_image, detect_resolution)

        assert input_image.ndim == 3
        height, width, dim = input_image.shape

        with torch.no_grad():

            if boost:
                depth = estimateboost(input_image, self.model, 0, self.pix2pixmodel, max(width, height))
            else:
                depth = estimateleres(input_image, self.model, width, height)

            numbytes=2
            depth_min = depth.min()
            depth_max = depth.max()
            max_val = (2**(8*numbytes))-1

            # check output before normalizing and mapping to 16 bit
            if depth_max - depth_min > np.finfo("float").eps:
                out = max_val * (depth - depth_min) / (depth_max - depth_min)
            else:
                out = np.zeros(depth.shape)
            
            # single channel, 16 bit image
            depth_image = out.astype("uint16")

            # convert to uint8
            depth_image = cv2.convertScaleAbs(depth_image, alpha=(255.0/65535.0))

            # remove near
            if thr_a != 0:
                thr_a = ((thr_a/100)*255) 
                depth_image = cv2.threshold(depth_image, thr_a, 255, cv2.THRESH_TOZERO)[1]

            # invert image
            depth_image = cv2.bitwise_not(depth_image)

            # remove bg
            if thr_b != 0:
                thr_b = ((thr_b/100)*255)
                depth_image = cv2.threshold(depth_image, thr_b, 255, cv2.THRESH_TOZERO)[1]

        detected_map = depth_image
        detected_map = HWC3(detected_map)      

        img = resize_image(input_image, image_resolution)
        H, W, C = img.shape

        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
        
        if output_type == "pil":
            detected_map = Image.fromarray(detected_map)
            
        return detected_map