spatial_image_analysis.py 文件源码

def fuse_labels_in_image(self, labels, verbose = True):
        """ Modify the image so the given labels are fused (to the min value)."""
        assert isinstance(labels, list) and len(labels) >= 2
        assert self.background() not in labels

        min_lab = min(labels)
        labels.remove(min_lab)
        N=len(labels); percent = 0
        if verbose: print "Fusing the following {} labels: {} to value '{}'.".format(N, labels, min_lab)
        for n, label in enumerate(labels):
            if verbose and n*100/float(N) >= percent: print "{}%...".format(percent),; percent += 5
            if verbose and n+1==N: print "100%"
            try:
                bbox = self.boundingbox(label)
                xyz = np.where( (self.image[bbox]) == label )
                self.image[tuple((xyz[0]+bbox[0].start, xyz[1]+bbox[1].start, xyz[2]+bbox[2].start))]=min_lab
            except:
                print "No boundingbox found for cell id #{}, skipping...".format(label)
                continue
        print "Done!"
        return None