我们从Python开源项目中，提取了以下4个代码示例，用于说明如何使用skimage.measure()。
def get_masks(img, n_seg=250): logger.debug('SLIC segmentation initialised') segments = skimage.segmentation.slic(img, n_segments=n_seg, compactness=10, sigma=1) logger.debug('SLIC segmentation complete') logger.debug('contour extraction...') masks = [[numpy.zeros((img.shape[0], img.shape[1]), dtype=numpy.uint8), None]] for region in skimage.measure.regionprops(segments): masks.append([masks[0][0].copy(), region.bbox]) x_min, y_min, x_max, y_max = region.bbox masks[-1][0][x_min:x_max, y_min:y_max] = skimage.img_as_ubyte(region.convex_image) logger.debug('contours extracted') return masks[1:]
def generate_markers(image): #Creation of the internal Marker marker_internal = image < -400 marker_internal = segmentation.clear_border(marker_internal) marker_internal_labels = measure.label(marker_internal) areas = [r.area for r in measure.regionprops(marker_internal_labels)] areas.sort() if len(areas) > 2: for region in measure.regionprops(marker_internal_labels): if region.area < areas[-2]: for coordinates in region.coords: marker_internal_labels[coordinates[0], coordinates[1]] = 0 marker_internal = marker_internal_labels > 0 #Creation of the external Marker external_a = ndimage.binary_dilation(marker_internal, iterations=10) external_b = ndimage.binary_dilation(marker_internal, iterations=55) marker_external = external_b ^ external_a #Creation of the Watershed Marker matrix marker_watershed = np.zeros(image.shape, dtype=np.int) marker_watershed += marker_internal * 255 marker_watershed += marker_external * 128 return marker_internal, marker_external, marker_watershed
