Python measure.approximate_polygon方法代碼示例

# 需要導入模塊: from skimage import measure [as 別名]
# 或者: from skimage.measure import approximate_polygon [as 別名]
def vectorize_regions(im: np.ndarray, threshold: float = 0.5):
    """
    Vectorizes lines from a binarized array.

    Args:
        im (np.ndarray): Array of shape (H, W) with the first dimension
                         being a probability distribution over the region.
        threshold (float): Threshold for binarization

    Returns:
        [[x0, y0, ... xn, yn], [xm, ym, ..., xk, yk], ... ]
        A list of lists containing the region polygons.
    """
    bin = im > threshold
    contours = find_contours(bin, 0.5, fully_connected='high', positive_orientation='high')
    if len(contours) == 0:
        return contours
    approx_contours = []
    for contour in contours:
        approx_contours.append(approximate_polygon(contour[:,[1,0]], 1).astype('uint').tolist())
    return approx_contours 

# 需要導入模塊: from skimage import measure [as 別名]
# 或者: from skimage.measure import approximate_polygon [as 別名]
def binary_mask_to_polygon(binary_mask, tolerance=0):
    """Converts a binary mask to COCO polygon representation
    Args:
        binary_mask: a 2D binary numpy array where '1's represent the object
        tolerance: Maximum distance from original points of polygon to approximated
            polygonal chain. If tolerance is 0, the original coordinate array is returned.
    """
    polygons = []
    # pad mask to close contours of shapes which start and end at an edge
    padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)
    contours = measure.find_contours(padded_binary_mask, 0.5)
    contours = np.subtract(contours, 1)
    for contour in contours:
        contour = close_contour(contour)
        contour = measure.approximate_polygon(contour, tolerance)
        if len(contour) < 3:
            continue
        contour = np.flip(contour, axis=1)
        segmentation = contour.ravel().tolist()
        # after padding and subtracting 1 we may get -0.5 points in our segmentation
        segmentation = [0 if i < 0 else i for i in segmentation]
        polygons.append(segmentation)

    return polygons 

# 需要導入模塊: from skimage import measure [as 別名]
# 或者: from skimage.measure import approximate_polygon [as 別名]
def binary_mask_to_polygon(binary_mask, tolerance=0):
    """Converts a binary mask to COCO polygon representation
    Args:
        binary_mask: a 2D binary numpy array where '1's represent the object
        tolerance: Maximum distance from original points of polygon to approximated
            polygonal chain. If tolerance is 0, the original coordinate array is returned.
    """
    polygons = []
    # pad mask to close contours of shapes which start and end at an edge
    padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)
    contours = measure.find_contours(padded_binary_mask, 0.5)
    contours = np.subtract(contours, 1)
    for contour in contours:
        contour = close_contour(contour)
        contour = measure.approximate_polygon(contour, tolerance)
        if len(contour) < 3:
            continue
        contour = np.flip(contour, axis=1)
        segmentation = contour
        # after padding and subtracting 1 we may get -0.5 points in our segmentation
        segmentation = [np.clip(i,0.0,i).tolist() for i in segmentation]
        polygons.append(segmentation)

    return polygons 

# 需要導入模塊: from skimage import measure [as 別名]
# 或者: from skimage.measure import approximate_polygon [as 別名]
def __call__(self, roi):
        smoothed_polygons = []
        coords = roi.coords
        for polygon in coords:
            if polygon.shape[0] > self.min_verts:
                plane = polygon[0, -1]
                smoothed_coords = approximate_polygon(polygon[:, :2],
                                                      self.tolerance)
                smoothed_coords = np.hstack(
                    (smoothed_coords, plane * np.ones(
                        (smoothed_coords.shape[0], 1))))

                if smoothed_coords.shape[0] < self.min_verts:
                    smoothed_coords = polygon

            else:
                smoothed_coords = polygon

            smoothed_polygons += [smoothed_coords]

        return ROI(polygons=smoothed_polygons, im_shape=roi.im_shape) 

# 需要導入模塊: from skimage import measure [as 別名]
# 或者: from skimage.measure import approximate_polygon [as 別名]
def binary_mask_to_polygon(binary_mask, tolerance=0):
    """Converts a binary mask to COCO polygon representation

    Args:
        binary_mask: a 2D binary numpy array where '1's represent the object
        tolerance: Maximum distance from original points of polygon to approximated
            polygonal chain. If tolerance is 0, the original coordinate array is returned.

    """
    polygons = []
    # pad mask to close contours of shapes which start and end at an edge
    padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)
    contours = measure.find_contours(padded_binary_mask, 0.5)
    contours = np.subtract(contours, 1)
    for contour in contours:
        contour = close_contour(contour)
        contour = measure.approximate_polygon(contour, tolerance)
        if len(contour) < 3:
            continue
        contour = np.flip(contour, axis=1)
        segmentation = contour.ravel().tolist()
        # after padding and subtracting 1 we may get -0.5 points in our segmentation 
        segmentation = [0 if i < 0 else i for i in segmentation]
        polygons.append(segmentation)

    return polygons 

# 需要導入模塊: from skimage import measure [as 別名]
# 或者: from skimage.measure import approximate_polygon [as 別名]
def binary_mask_to_polygon(binary_mask, tolerance=0):
        """Converts a binary mask to COCO polygon representation

         :param binary_mask: a 2D binary numpy array where '1's represent the object
         :param tolerance: Maximum distance from original points of polygon to approximated polygonal chain. If tolerance is 0, the original coordinate array is returned.
        """
        polygons = []
        # pad mask to close contours of shapes which start and end at an edge
        padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)
        contours = np.array(measure.find_contours(padded_binary_mask, 0.5))
        # Reverse padding
        contours = contours - 1
        for contour in contours:
            # Make sure contour is closed
            contour = CocoUtility.close_contour(contour)
            # Approximate contour by polygon
            polygon = measure.approximate_polygon(contour, tolerance)
            # Skip invalid polygons
            if len(polygon) < 3:
                continue
            # Flip xy to yx point representation
            polygon = np.flip(polygon, axis=1)
            # Flatten
            polygon = polygon.ravel()
            # after padding and subtracting 1 we may get -0.5 points in our segmentation
            polygon[polygon < 0] = 0
            polygons.append(polygon.tolist())

        return polygons 

# 需要導入模塊: from skimage import measure [as 別名]
# 或者: from skimage.measure import approximate_polygon [as 別名]
def _interpolate_lines(clusters, elongation_offset, extent, st_map, end_map):
    """
    Interpolates the baseline clusters and sets the correct line direction.
    """
    logger.debug('Reticulating splines')
    lines = []
    extent = geom.Polygon([(0, 0), (extent[1]-1, 0), (extent[1]-1, extent[0]-1), (0, extent[0]-1), (0, 0)])
    f_st_map = maximum_filter(st_map, size=20)
    f_end_map = maximum_filter(end_map, size=20)
    for cluster in clusters[1:]:
        # find start-end point
        points = [point for edge in cluster for point in edge]
        dists = squareform(pdist(points))
        i, j = np.unravel_index(dists.argmax(), dists.shape)
        # build adjacency matrix for shortest path algo
        adj_mat = np.full_like(dists, np.inf)
        for l, r in cluster:
            idx_l = points.index(l)
            idx_r = points.index(r)
            adj_mat[idx_l, idx_r] = dists[idx_l, idx_r]
        # shortest path
        _, pr = shortest_path(adj_mat, directed=False, return_predecessors=True, indices=i)
        k = j
        line = [points[j]]
        while pr[k] != -9999:
            k = pr[k]
            line.append(points[k])
        # smooth line
        line = np.array(line[::-1])
        line = approximate_polygon(line[:,[1,0]], 1)
        lr_dir = line[0] - line[1]
        lr_dir = (lr_dir.T  / np.sqrt(np.sum(lr_dir**2,axis=-1))) * elongation_offset/2
        line[0] = line[0] + lr_dir
        rr_dir = line[-1] - line[-2]
        rr_dir = (rr_dir.T  / np.sqrt(np.sum(rr_dir**2,axis=-1))) * elongation_offset/2
        line[-1] = line[-1] + rr_dir
        ins = geom.LineString(line).intersection(extent)
        if ins.type == 'MultiLineString':
            ins = linemerge(ins)
            # skip lines that don't merge cleanly
            if ins.type != 'LineString':
                continue
        line = np.array(ins, dtype='uint')
        l_end = tuple(line[0])[::-1]
        r_end = tuple(line[-1])[::-1]
        if f_st_map[l_end] - f_end_map[l_end] > 0.2 and f_st_map[r_end] - f_end_map[r_end] < -0.2:
            pass
        elif f_st_map[l_end] - f_end_map[l_end] < -0.2 and f_st_map[r_end] - f_end_map[r_end] > 0.2:
            line = line[::-1]
        else:
            logger.debug('Insufficient marker confidences in output. Defaulting to upright line.')
            if line[0][0] > line[-1][0]:
                line = line[::-1]
        lines.append(line.tolist())
    return lines