spatial_image_analysis.py 文件源码

def wall_voxels_between_two_cells(self, label_1, label_2, bbox=None, verbose=False):
        """
        Return the voxels coordinates defining the contact wall between two labels.

        Args:
           image: (ndarray of ints) - Array containing objects defined by labels
           label_1: (int) - object id #1
           label_2: (int) - object id #2
           bbox: (dict, optional) - If given, contain a dict of slices

        Returns:
         - xyz 3xN array.
        """

        if bbox is not None:
            if isinstance(bbox, dict):
                label_1, label_2 = sort_boundingbox(bbox, label_1, label_2)
                boundingbox = bbox[label_1]
            elif isinstance(bbox, tuple) and len(bbox)==3:
                boundingbox = bbox
            else:
                try:
                    boundingbox = find_smallest_boundingbox(self.image, label_1, label_2)
                except:
                    print "Could neither use the provided value of `bbox`, nor gess it!"
                    boundingbox = tuple([(0,s-1,None) for s in self.image.shape])
            dilated_bbox = dilation( boundingbox )
            dilated_bbox_img = self.image[dilated_bbox]
        else:
            try:
                boundingbox = find_smallest_boundingbox(self.image, label_1, label_2)
            except:
                dilated_bbox_img = self.image

        mask_img_1 = (dilated_bbox_img == label_1)
        mask_img_2 = (dilated_bbox_img == label_2)

        struct = nd.generate_binary_structure(3, 2)
        dil_1 = nd.binary_dilation(mask_img_1, structure=struct)
        dil_2 = nd.binary_dilation(mask_img_2, structure=struct)
        x,y,z = np.where( ( (dil_1 & mask_img_2) | (dil_2 & mask_img_1) ) == 1 )

        if bbox is not None:
            return np.array( (x+dilated_bbox[0].start, y+dilated_bbox[1].start, z+dilated_bbox[2].start) )
        else:
            return np.array( (x, y, z) )