smartcropper.py

class SmartCropper:
    """Smart cropper that uses a saliency map to determine the most interesting point in an image."""

    def __init__(self):
        # Initialize the saliency detector (using the spectral residual method)
        try:
            self.saliency = cv2.saliency.StaticSaliencySpectralResidual_create()
        except AttributeError:
            print("run `pip install opencv_contrib_python`")
            raise

    def find_interesting_centroid(self, image_path):
        """
        Analyze an image using a saliency map and return the centroid of the most salient region.

        Args:
            image_path: Path to the image file.

        Returns:
            tuple: (x_percent, y_percent) coordinates of the most interesting point as percentages.
        """
        img = cv2.imread(str(image_path))
        if img is None:
            print(f"Error: Cannot read image at {image_path}")
            return None

        # Compute the saliency map
        success, saliencyMap = self.saliency.computeSaliency(img)
        if not success:
            print("Error: Saliency computation failed")
            return None

        saliencyMap = (saliencyMap * 255).astype("uint8")

        # Calculate image moments of the saliency map to determine the centroid
        moments = cv2.moments(saliencyMap)
        if moments["m00"] != 0:
            cX = int(moments["m10"] / moments["m00"])
            cY = int(moments["m01"] / moments["m00"])
        else:
            # Fall back to the image center if moments are zero
            cX, cY = img.shape[1] // 2, img.shape[0] // 2

        centroid_x_percent = (cX / img.shape[1]) * 100.0
        centroid_y_percent = (cY / img.shape[0]) * 100.0

        return centroid_x_percent, centroid_y_percent