def pre_process()

in models/01_YoloV5/01_Pytorch/processing.py [0:0]

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

    def pre_process(self, image):
        pad_y, pad_x = 0, 0
        if self.keep_ratio:
            # Make the image square if needed by adding extra pads to
            # preserve the original height to width ratio after resize
            h, w = image.shape[:2]  
            if h != w:
                max_size = max(h, w)
                pad_y, pad_x = max_size - h, max_size - w
                img = np.zeros((max_size, max_size, 3), dtype="float32")
                img[0:max_size-pad_y, 0:max_size-pad_x] = image[:]
                image = img
        
        img = cv2.resize(image, (self.input_shape[0], self.input_shape[1]))
        img = img.astype(np.float32) / 255.
        
        # Convert to an expected shape
        img = img.transpose(2, 0, 1)  # to CHW
        img = np.expand_dims(img, axis=0).copy()  # Must use a copy here, otherwise it may not work as expected on Panorama
 
        return img