如何将从OpenCV获得的轮廓转换为SHP文件多边形？

Question

我已经使用OpenCV从蒙版图像中成功提取轮廓：

image = cv2.imread(file)

lower = np.array([240, 240, 240])
upper = np.array([255, 255, 255])
shape_mask = cv2.inRange(image, lower, upper)

contours, hierarchy = cv2.findContours(shape_mask.copy(), cv2.RETR_CCOMP,
                                       cv2.CHAIN_APPROX_SIMPLE)

现在如何继续将此轮廓映射到多边形（带有孔）的列表中，并以形状将其导出为SHP文件？

此处给出部分答案：How to convert NumPy arrays obtained from cv2.findContours to Shapely polygons?

但是，这忽略了在多边形内部有孔的情况。我将如何继续获取所有多边形？

Answer 1

找到答案：https://michhar.github.io/masks_to_polygons_and_back/

从蒙版图像以numpy数组创建MultiPolygons的助手：

def mask_to_polygons(mask, epsilon=10., min_area=10.):
    """Convert a mask ndarray (binarized image) to Multipolygons"""
    # first, find contours with cv2: it's much faster than shapely
    image, contours, hierarchy = cv2.findContours(mask,
                                  cv2.RETR_CCOMP,
                                  cv2.CHAIN_APPROX_NONE)
    if not contours:
        return MultiPolygon()
    # now messy stuff to associate parent and child contours
    cnt_children = defaultdict(list)
    child_contours = set()
    assert hierarchy.shape[0] == 1
    # http://docs.opencv.org/3.1.0/d9/d8b/tutorial_py_contours_hierarchy.html
    for idx, (_, _, _, parent_idx) in enumerate(hierarchy[0]):
        if parent_idx != -1:
            child_contours.add(idx)
            cnt_children[parent_idx].append(contours[idx])
    # create actual polygons filtering by area (removes artifacts)
    all_polygons = []
    for idx, cnt in enumerate(contours):
        if idx not in child_contours and cv2.contourArea(cnt) >= min_area:
            assert cnt.shape[1] == 1
            poly = Polygon(
                shell=cnt[:, 0, :],
                holes=[c[:, 0, :] for c in cnt_children.get(idx, [])
                       if cv2.contourArea(c) >= min_area])
            all_polygons.append(poly)
    all_polygons = MultiPolygon(all_polygons)

    return all_polygons

创建这些辅助功能的信用：

这些帮助者的原始来源是Konstantin发表的Kaggle帖子Lopuhin here-您需要登录Kaggle才能看到它