我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用skimage.filters.threshold_adaptive()。
def to_binary_adaptive(img): sigma = 1.0 m = max(img.shape) if m > 2000: block_size = 80 elif m > 1500: block_size = 50 elif m > 1000: block_size = 35 else: block_size = 20 bimg = gaussian_filter(img, sigma=sigma) bimg = threshold_adaptive(bimg, block_size, offset=2 / 255) bimg = np.array(bimg, 'uint8') * 255 return bimg
def binarisation(src_image): if len(src_image.shape) == 3: image = (src_image.sum(axis=2) / 3).astype('ubyte') else: image = src_image thresh = threshold_otsu(image) binary = (image > thresh).astype('ubyte') binary1 = 1 - binary im = 255 - np.multiply(255 - image, binary1) block_size = 35 binary = threshold_adaptive(image, block_size, offset=20) binary = binary.astype('ubyte') return binary
def binarisation(src_image): if len(src_image.shape) == 3: image = (src_image.sum(axis=2) / 3).astype('ubyte') else: image = src_image thresh = threshold_otsu(image) binary = (image > thresh).astype('ubyte') binary1 = 1 - binary im = 255 - np.multiply(255 - image, binary1) block_size = 35 binary = threshold_adaptive(image, block_size, offset=20) binary = binary.astype('ubyte') return binary # 100
def binarisation(src_image): if len(src_image.shape) == 3: image = (src_image.sum(axis=2) / 3).astype('ubyte') else: image = src_image thresh = threshold_otsu(image) binary = (image > thresh).astype('ubyte') binary1 = 1 - binary im = 255 - np.multiply(255 - image, binary1) block_size = 35 binary = threshold_adaptive(im, block_size, offset=20) binary = binary.astype('ubyte') return binary
def binarisation1(src_image): if len(src_image.shape) == 3: image = (src_image.sum(axis=2) / 3).astype('ubyte') else: image = src_image block_size = 35 binary = threshold_adaptive(image, block_size, offset=20) binary = binary.astype('ubyte') return binary
def cropped_image(image, contours, bw=True, show=False): ratio = image.shape[0] / float(scale_factor) warped = four_point_transform(image, contours.reshape(4, 2) * ratio) if bw: # convert the warped image to grayscale, then threshold it to give it that 'black and white' paper effect warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY) warped = threshold_adaptive(warped, 251, offset = 10) warped = warped.astype("uint8") * 255 if show: #this is for debugging puposes cv2.imshow("Payload", warped) cv2.waitKey(0) cv2.destroyAllWindows() return warped
def iter_blob_extremes(image, n=5): original_shape = image.shape[::-1] if max(original_shape) < 2000: size = (500, 500) y_scale = original_shape[0] / 500 x_scale = original_shape[1] / 500 else: size = (1000, 1000) y_scale = original_shape[0] / 1000 x_scale = original_shape[1] / 1000 img = resize(image, size) bimg = gaussian_filter(img, sigma=1.0) bimg = threshold_adaptive(bimg, 20, offset=2/255) bimg = -bimg bimg = ndi.binary_fill_holes(bimg) label_image = label(bimg, background=False) label_image += 1 regions = regionprops(label_image) regions.sort(key=attrgetter('area'), reverse=True) iter_n = 0 for region in regions: try: iter_n += 1 if iter_n > n: break # Skip small images if region.area < int(np.prod(size) * 0.05): continue coords = get_contours(add_border(label_image == region.label, size=label_image.shape, border_size=1, background_value=False))[0] coords = np.fliplr(coords) top_left = sorted(coords, key=lambda x: np.linalg.norm(np.array(x)))[0] top_right = sorted(coords, key=lambda x: np.linalg.norm(np.array(x) - [img.shape[1], 0]))[0] bottom_left = sorted(coords, key=lambda x: np.linalg.norm(np.array(x) - [0, img.shape[0]]))[0] bottom_right = sorted(coords, key=lambda x: np.linalg.norm(np.array(x) - [img.shape[1], img.shape[0]]))[0] scaled_extremes = [(int(x[0] * y_scale), int(x[1]*x_scale)) for x in (top_left, top_right, bottom_left, bottom_right)] yield scaled_extremes except Exception: pass raise SudokuExtractError("No suitable blob could be found.")
