Python skimage 模块，filters() 实例源码

def match_template_mask(image, template, mask=None, method=None, sigma=0.33):
    """Match template against image applying mask to template using method.
    Method can be either of (None, 'laplacian', 'sobel', 'scharr', 'prewitt',
    'roberts', 'canny').
    Returns locations to look for max values."""
    if mask is not None:
        if method:
            kernel = np.ones((3, 3), np.uint8)
            mask = cv2.erode(mask, kernel)
            if method == 'laplacian':
                # use CV_64F to not loose edges, convert to uint8 afterwards
                edge_image = np.uint8(np.absolute(
                    cv2.Laplacian(image, cv2.CV_64F)))
                edge_template = np.uint8(np.absolute(
                    cv2.Laplacian(template, cv2.CV_64F)
                ))
            elif method in ('sobel', 'scharr', 'prewitt', 'roberts'):
                filter_func = getattr(skfilters, method)
                edge_image = filter_func(image)
                edge_template = filter_func(template)
                edge_image = convert(edge_image)
                edge_template = convert(edge_template)
            else:  # method == 'canny'
                values = np.hstack([image.ravel(), template.ravel()])
                median = np.median(values)
                lower = int(max(0, (1.0 - sigma) * median))
                upper = int(min(255, (1.0 + sigma) * median))
                edge_image = cv2.Canny(image, lower, upper)
                edge_template = cv2.Canny(template, lower, upper)
            results = cv2.matchTemplate(edge_image, edge_template & mask,
                                        cv2.TM_CCOEFF_NORMED)
        else:
            results = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED,
                                        mask)
    else:
        results = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED)
    return results

def threshold(self, t):
        self.__threshold = t
        self.threshold_changed()
        elevation_map = skimage.filters.sobel(self.morphology)

        markers = numpy.zeros_like(self.morphology)
        markers[self.morphology < self.threshold] = 1
        markers[self.morphology > self.threshold * 1.1] = 2
        segmentation = skimage.morphology.watershed(elevation_map, markers)

        self.overlay = segmentation == 2