def resize_numpy_image()

in src/image_gen_aux/image_processor.py [0:0]

• 17 lines of code
• 7 McCabe index (conditional complexity)

    def resize_numpy_image(image: np.ndarray, scale: float, multiple_factor: int = 8) -> np.ndarray:
        """
        Resizes a NumPy image while maintaining aspect ratio and ensuring dimensions are multiples of `multiple_factor`.

        Args:
            image (`np.ndarray`): The input image array of shape (height, width, channels) or (height, width) for grayscale.
            scale (`float`): The scaling factor applied to the image dimensions.
            multiple_factor (`int`, *optional*, defaults to 8): The factor by which the new dimensions should be divisible.

        Returns:
            `np.ndarray`: The resized image array.
        """

        if scale == 1.0:
            return image, scale

        if len(image.shape) == 3:  # Single image without batch dimension
            image = np.expand_dims(image, axis=0)

        batch_size, height, width, channels = image.shape

        # Calculate new dimensions while maintaining aspect ratio
        new_height = int(height * scale)
        new_width = int(width * scale)

        # Ensure new dimensions are multiples of multiple_factor
        new_height = (new_height // multiple_factor) * multiple_factor
        new_width = (new_width // multiple_factor) * multiple_factor

        # if the final height and widht changed because of the multiple_factor, we need to set the scale too
        scale = new_height / height

        # Resize each image in the batch
        resized_images = []
        for i in range(batch_size):
            resized_image = cv2.resize(image[i], (new_width, new_height), interpolation=cv2.INTER_LINEAR)
            resized_images.append(resized_image)

        # Stack resized images back into a single array
        resized_images = np.stack(resized_images, axis=0)

        return resized_images, scale