Python rect.Rect类代码示例

    def get_suggested_focal_point(self):
        with self.get_willow_image() as willow:
            faces = willow.detect_faces()

            if faces:
                # Create a bounding box around all faces
                left = min(face[0] for face in faces)
                top = min(face[1] for face in faces)
                right = max(face[2] for face in faces)
                bottom = max(face[3] for face in faces)
                focal_point = Rect(left, top, right, bottom)
            else:
                features = willow.detect_features()
                if features:
                    # Create a bounding box around all features
                    left = min(feature[0] for feature in features)
                    top = min(feature[1] for feature in features)
                    right = max(feature[0] for feature in features)
                    bottom = max(feature[1] for feature in features)
                    focal_point = Rect(left, top, right, bottom)
                else:
                    return None

        # Add 20% to width and height and give it a minimum size
        x, y = focal_point.centroid
        width, height = focal_point.size

        width *= 1.20
        height *= 1.20

        width = max(width, 100)
        height = max(height, 100)

        return Rect.from_point(x, y, width, height)

 def get_focal_point(self):
     if (
         self.focal_point_x is not None
         and self.focal_point_y is not None
         and self.focal_point_width is not None
         and self.focal_point_height is not None
     ):
         return Rect.from_point(
             self.focal_point_x, self.focal_point_y, self.focal_point_width, self.focal_point_height
         )

    def get_suggested_focal_point(self, backend_name='default'):
        backend = get_image_backend(backend_name)
        image_file = self.file.file

        # Make sure image is open and seeked to the beginning
        image_file.open('rb')
        image_file.seek(0)

        # Load the image
        image = backend.open_image(self.file.file)
        image_data = backend.image_data_as_rgb(image)

        # Make sure we have image data
        # If the image is animated, image_data_as_rgb will return None
        if image_data is None:
            return

        # Use feature detection to find a focal point
        feature_detector = FeatureDetector(image.size, image_data[0], image_data[1])

        faces = feature_detector.detect_faces()
        if faces:
            # Create a bounding box around all faces
            left = min(face.left for face in faces)
            top = min(face.top for face in faces)
            right = max(face.right for face in faces)
            bottom = max(face.bottom for face in faces)
            focal_point = Rect(left, top, right, bottom)
        else:
            features = feature_detector.detect_features()
            if features:
                # Create a bounding box around all features
                left = min(feature[0] for feature in features)
                top = min(feature[1] for feature in features)
                right = max(feature[0] for feature in features)
                bottom = max(feature[1] for feature in features)
                focal_point = Rect(left, top, right, bottom)
            else:
                return None

        # Add 20% to width and height and give it a minimum size
        x, y = focal_point.centroid
        width, height = focal_point.size

        width *= 1.20
        height *= 1.20

        width = max(width, 100)
        height = max(height, 100)

        return Rect.from_point(x, y, width, height)

    def run(self, willow, image):
        image_width, image_height = willow.get_size()
        focal_point = image.get_focal_point()

        # Get crop aspect ratio
        crop_aspect_ratio = self.width / self.height

        # Get crop max
        crop_max_scale = min(image_width, image_height * crop_aspect_ratio)
        crop_max_width = crop_max_scale
        crop_max_height = crop_max_scale / crop_aspect_ratio

        # Initialise crop width and height to max
        crop_width = crop_max_width
        crop_height = crop_max_height

        # Use crop closeness to zoom in
        if focal_point is not None:
            # Get crop min
            crop_min_scale = max(focal_point.width, focal_point.height * crop_aspect_ratio)
            crop_min_width = crop_min_scale
            crop_min_height = crop_min_scale / crop_aspect_ratio

            # Sometimes, the focal point may be bigger than the image...
            if not crop_min_scale >= crop_max_scale:
                # Calculate max crop closeness to prevent upscaling
                max_crop_closeness = max(
                    1 - (self.width - crop_min_width) / (crop_max_width - crop_min_width),
                    1 - (self.height - crop_min_height) / (crop_max_height - crop_min_height)
                )

                # Apply max crop closeness
                crop_closeness = min(self.crop_closeness, max_crop_closeness)

                if 1 >= crop_closeness >= 0:
                    # Get crop width and height
                    crop_width = crop_max_width + (crop_min_width - crop_max_width) * crop_closeness
                    crop_height = crop_max_height + (crop_min_height - crop_max_height) * crop_closeness

        # Find focal point UV
        if focal_point is not None:
            fp_x, fp_y = focal_point.centroid
        else:
            # Fall back to positioning in the centre
            fp_x = image_width / 2
            fp_y = image_height / 2

        fp_u = fp_x / image_width
        fp_v = fp_y / image_height

        # Position crop box based on focal point UV
        crop_x = fp_x - (fp_u - 0.5) * crop_width
        crop_y = fp_y - (fp_v - 0.5) * crop_height

        # Convert crop box into rect
        rect = Rect.from_point(crop_x, crop_y, crop_width, crop_height)

        # Make sure the entire focal point is in the crop box
        if focal_point is not None:
            rect = rect.move_to_cover(focal_point)

        # Don't allow the crop box to go over the image boundary
        rect = rect.move_to_clamp(Rect(0, 0, image_width, image_height))

        # Crop!
        willow = willow.crop(rect.round())

        # Get scale for resizing
        # The scale should be the same for both the horizontal and
        # vertical axes
        aftercrop_width, aftercrop_height = willow.get_size()
        scale = self.width / aftercrop_width

        # Only resize if the image is too big
        if scale < 1.0:
            # Resize!
            willow = willow.resize((self.width, self.height))

        return willow

 def test_from_point(self):
     rect = Rect.from_point(100, 200, 50, 20)
     self.assertEqual(rect, Rect(75, 190, 125, 210))

 def test_set_centroid_with_vector(self):
     rect = Rect(100, 150, 200, 350)
     rect.centroid = Vector(500, 500)
     self.assertEqual(rect, (450, 400, 550, 600))

 def test_set_size_with_vector(self):
     rect = Rect(100, 150, 200, 350)
     rect.size = Vector(200, 400)
     self.assertEqual(rect, (50, 50, 250, 450))

 def test_as_tuple(self):
     rect = Rect(100, 150, 200, 250)
     self.assertEqual(rect.as_tuple(), (100, 150, 200, 250))

 def test_get_key(self):
     rect = Rect(100, 150, 200, 250)
     self.assertEqual(rect.get_key(), "150-200-100x100")