我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用cv2.CHAIN_APPROX_TC89_KCOS。
def polygonize_cv(mask, epsilon=1., min_area=10.): contours, hierarchy = cv2.findContours(mask, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS) # create approximate contours to have reasonable submission size approx_contours = [cv2.approxPolyDP(cnt, epsilon, True) for cnt in contours] approx_contours = contours 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(approx_contours[idx]) # create actual polygons filtering by area (removes artifacts) all_polygons = [] for idx, cnt in enumerate(approx_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) # approximating polygons might have created invalid ones, fix them all_polygons = MultiPolygon(all_polygons) if not all_polygons.is_valid: all_polygons = all_polygons.buffer(0) # Sometimes buffer() converts a simple Multipolygon to just a Polygon, # need to keep it a Multi throughout if all_polygons.type == 'Polygon': all_polygons = MultiPolygon([all_polygons]) return all_polygons
def mask_to_polygons(mask, epsilon=1, min_area=1.): """ Create a Multipolygon from a mask of 0-1 pixels. """ # find contours of mask of pixels image, contours, hierarchy = cv2.findContours( ((mask == 1) * 255).astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS) # create approximate contours to have reasonable submission size approx_contours = [cv2.approxPolyDP(cnt, epsilon, True) for cnt in contours] 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(approx_contours[idx]) # create actual polygons filtering by area (removes artifacts) all_polygons = [] for idx, cnt in enumerate(approx_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) # approximating polygons might have created invalid ones, fix them all_polygons = MultiPolygon(all_polygons) if not all_polygons.is_valid: all_polygons = all_polygons.buffer(0) # Sometimes buffer() converts a simple Multipolygon to just a Polygon, # need to keep it a Multi throughout if all_polygons.type == 'Polygon': all_polygons = MultiPolygon([all_polygons]) return all_polygons
def find_contours(image): # Attempt to locate the goals by finding all contours not enclosed within another. _, contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_KCOS) return contours
def contours(self): if self._contours is None: self._contours = [] try: contours, hierarchy = cv2.findContours(self.ndarray, cv2.RETR_TREE, cv2.CHAIN_APPROX_TC89_KCOS) for contour in contours: self._contours.append(sharkcv.Contour(contour)) except: pass return self._contours # Filter contours by any sharkcv.Contour property
def detect_contours(self): blurred = cv2.GaussianBlur(self.src, (self.kernel_size, self.kernel_size), self.sigma) # apply canny detector detected_edges = cv2.Canny(blurred, self.threshold, self.threshold * self.ratio, apertureSize=self.apertureSize, L2gradient=True) if self.use_dilate: kernel = np.ones((3, 3), np.uint8) detected_edges = cv2.morphologyEx(detected_edges, cv2.MORPH_CLOSE, kernel) self.contours_img, self.simple_contours, self.hierarchy = cv2.findContours(detected_edges.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS) # pdb.gimp_message(self.hierarchy) _, self.full_contours, _ = cv2.findContours(detected_edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
def mask_to_polygons(mask: np.ndarray, epsilon=5., min_area=10., fix=False) -> MultiPolygon: if fix: epsilon *= 4 image, contours, hierarchy = cv2.findContours( ((mask == 1) * 255).astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS) approx_contours = [cv2.approxPolyDP(cnt, epsilon, True) for cnt in contours] if not contours: return MultiPolygon() 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(approx_contours[idx]) all_polygons = [] for idx, cnt in enumerate(approx_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 = to_multipolygon(MultiPolygon(all_polygons).buffer(0)) # return all_polygons - this was used to generate the final merges if fix: all_polygons = all_polygons.buffer(-1e-7) all_polygons = all_polygons.buffer(-1e-7) # FIXME - a great idea, but should be done after conversion to final coordinates all_polygons = shapely.wkt.loads( shapely.wkt.dumps(all_polygons, rounding_precision=8)) while not all_polygons.is_valid: all_polygons = to_multipolygon(all_polygons.buffer(0)) all_polygons = shapely.wkt.loads( shapely.wkt.dumps(all_polygons, rounding_precision=8)) return all_polygons
def mask_to_poly(mask, xymax, epsilon=2, min_area=1., threshold=0.5): # __author__ = Konstantin Lopuhin # https://www.kaggle.com/lopuhin/dstl-satellite-imagery-feature-detection/full-pipeline-demo-poly-pixels-ml-poly # first, find contours with cv2: it's much faster than shapely contours, hierarchy = cv2.findContours(((mask >= threshold) * 255).astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS) # create approximate contours to have reasonable submission size approx_contours = [cv2.approxPolyDP(cnt, epsilon, True) for cnt in contours] 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(approx_contours[idx]) # create actual polygons filtering by area (removes artifacts) all_polygons = [] for idx, cnt in enumerate(approx_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) # approximating polygons might have created invalid ones, fix them all_polygons = MultiPolygon(all_polygons).buffer(0) # Sometimes buffer() converts a simple Multipolygon to just a Polygon, # need to keep it a Multi throughout if all_polygons.type == 'Polygon': all_polygons = MultiPolygon([all_polygons]) return convert_poly_to_geo_coords(all_polygons, mask.shape, xymax)
