def _create_submask_annotation()

in gt_converter/convert_coco.py [0:0]

• 29 lines of code
• 6 McCabe index (conditional complexity)

    def _create_submask_annotation(sub_mask, image_id, category_id, annotation_id):
        """
        Find contours (boundary lines) around each sub-mask
        Note: there could be multiple contours if the object is partially occluded. (E.g. an elephant behind a tree)
        :param sub_mask: Numpy array of single labels masks
        :param image_id: Integer ID specifying which image this is
        :param category_id: Label ID being processed
        :param annotation_id: Integer ID specifying which annotation this is
        :return: Dictionary of COCO annotation
        """
        contours = measure.find_contours(sub_mask, 0.5, positive_orientation="low")

        segmentations = []
        polygons = []
        for contour in contours:
            # Flip from (row, col) representation to (x, y) and subtract the padding pixel
            for i in range(len(contour)):
                row, col = contour[i]
                contour[i] = (col - 1, row - 1)

            # Make a polygon and simplify it
            poly = Polygon(contour)
            poly = poly.simplify(1.0, preserve_topology=False)
            polygons.append(poly)
            segmentation = np.array(poly.exterior.coords).ravel().tolist()
            segmentations.append(segmentation)

        # Combine the polygons to calculate the bounding box and area
        multi_poly = MultiPolygon(polygons)
        x, y, max_x, max_y = multi_poly.bounds
        width = max_x - x
        height = max_y - y
        bbox = (x, y, width, height)
        area = multi_poly.area

        annotation = {
            "segmentation": segmentations,
            "iscrowd": 0,
            "image_id": image_id,
            "category_id": category_id,
            "id": annotation_id,
            "bbox": bbox,
            "area": area,
        }

        return annotation