spatial_image_analysis.py 文件源码

def cells_voxel_layer(self, labels, region_boundingbox = False, single_frame = False):
        """
        This function extract the first layer of voxel surrounding a cell defined by `label`
        Args:
           label: (int|list) - cell-label for which we want to extract the first layer of voxel;
           region_boundingbox: (bool) - if True, consider a boundingbox surrounding all labels, instead of each label alone.
           single_frame: (bool) - if True, return only one array with all voxels position defining cell walls.
        :Output:
         returns a binary image: 1 where the cell-label of interest is, 0 elsewhere
        """
        if isinstance(labels,int):
            labels = [labels]
        if single_frame:
            region_boundingbox=True

        if not isinstance(region_boundingbox,bool):
            if sum([isinstance(s,slice) for s in region_boundingbox])==3:
                bbox = region_boundingbox
            else:
                print "TypeError: Wong type for 'region_boundingbox', should either be bool or la tuple of slices"
                return None
        elif isinstance(region_boundingbox,bool) and region_boundingbox:
            bbox = self.region_boundingbox(labels)
        else:
            bboxes = self.boundingbox(labels, real=False)

        # Generate the smaller eroding structure possible:
        struct = nd.generate_binary_structure(3, 2)
        if single_frame:
            vox_layer = np.zeros_like(self.image[bbox], dtype=int)
        else:
            vox_layer = {}
        for clabel in labels:
            if region_boundingbox:
                bbox_im = self.image[bbox]
            else:
                bbox_im = self.image[bboxes[clabel]]
            # Creating a mask (1 where the cell-label of interest is, 0 elsewhere):
            mask_bbox_im = (bbox_im == clabel)
            # Erode the cell using the structure:
            eroded_mask_bbox_im = nd.binary_erosion(mask_bbox_im, structure=struct)
            if single_frame:
                vox_layer += np.array(mask_bbox_im - eroded_mask_bbox_im, dtype=int)
            else:
                vox_layer[clabel] = np.array(mask_bbox_im - eroded_mask_bbox_im, dtype=int)

        if len(labels)==1:
            return vox_layer[clabel]
        else:
            return vox_layer