How to use the skimage.measure function in skimage

def doTest(file_name,model):
    probs,img_arr=seg(opt.img_dir+file_name,model)
    probs=probs>opt.prob_threshould 
    probs=morphology.dilation(probs,np.ones([3,3,3]))
    probs=morphology.dilation(probs,np.ones([3,3,3]))
    probs=morphology.erosion(probs,np.ones([3,3,3]))
    #np.save("probs.npy",probs)
    labels = measure.label(probs,connectivity=2)
    #label_vals = np.unique(labels)
    regions = measure.regionprops(labels)
    centers = []
    crops = []
    bboxes = []
    spans=[]
    for prop in regions:
        B = prop.bbox
        if B[3]-B[0]>2 and B[4]-B[1]>4 and B[5]-B[2]>4:
            z=int((B[3]+B[0])/2.0)
            y=int((B[4]+B[1])/2.0)
            x=int((B[5]+B[2])/2.0)
            span=np.array([int(B[3]-B[0]),int(B[4]-B[1]),int(B[5]-B[2])])
            spans.append(span)
            centers.append(np.array([z,y,x]))
            bboxes.append(B)

bestScore = 0
            bestLayoutIndex = 4
            for layoutIndex in xrange(3, 6):
                score = np.logical_and(mask, layout_gt == layoutIndex).sum()
                if score > bestScore:
                    bestLayoutIndex = layoutIndex
                    bestScore = score
                continue
            layout_pred[mask] = bestLayoutIndex
        emptyMask = layout_pred == 0
        if emptyMask.sum() > 0:
            print(index, emptyMask.sum())
            masks = measure.label(emptyMask, background=0)
            for maskIndex in xrange(masks.min() + 1, masks.max() + 1):
                mask = masks == maskIndex
                bestScore = 0
                bestLayoutIndex = 4
                for layoutIndex in xrange(1, 6):
                    score = np.logical_and(mask, layout_gt == layoutIndex).sum()
                    if score > bestScore:
                        bestLayoutIndex = layoutIndex
                        bestScore = score
                    continue
                layout_pred[mask] = bestLayoutIndex
                continue
        layout_pred_img = drawSegmentationImage(layout_pred, blackIndex=0)

def binary_mask_to_polygon(binary_mask, tolerance=0):
        """Converts a binary mask to COCO polygon representation
        Args:
            binary_mask: a 2D binary numpy array where '1's represent the object
            tolerance: Maximum distance from original points of polygon to approximated
                polygonal chain. If tolerance is 0, the original coordinate array is returned.
        polygons = []
        # pad mask to close contours of shapes which start and end at an edge
        padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)
        contours = measure.find_contours(padded_binary_mask, 0.5)
        contours = np.subtract(contours, 1)
        for contour in contours:
            contour = PycocoCreatorTools.close_contour(contour)
            contour = measure.approximate_polygon(contour, tolerance)
            if len(contour) < 3:
                continue
            contour = np.flip(contour, axis=1)
            segmentation = contour.ravel().tolist()
            # after padding and subtracting 1 we may get -0.5 points in our segmentation 
            segmentation = [0 if i < 0 else i for i in segmentation]
            polygons.append(segmentation)
        return polygons

def find_centroids(label_image):
        input_props = skimage.measure.regionprops(
            label_image, intensity_image=None, cache=True
        input_centroids = [numpy.int_(obj["centroid"]) for obj in input_props]
        output_segmented = numpy.zeros_like(label_image)
        for ind, arr in enumerate(input_centroids):
            output_segmented[tuple(arr)] = ind + 1
        return output_segmented

def xy(self):
        if self._xy is None:
            if self.remove_background:
                img = self.img_corr
            else:
                img = self.img
            bg = ndimage.gaussian_filter(img, self.sigmin) - ndimage.gaussian_filter(img, self.sigmax)
            labels, numlabels = ndimage.label(bg > self.threshold)
            labels = morphology.remove_small_objects(labels, self.nmin)
            props = measure.regionprops(labels, img)
            self._xy = np.array([prop.centroid for prop in props])
        return self._xy

# dilate sink image
    Dilated = mp.binary_dilation(SeedImage, mp.disk(Radius))
    # generate new labels for merged seeds, define memberships
    Labels = ms.label(Dilated)
    New = Labels[Sinks[:, 1].astype(np.int), Sinks[:, 0].astype(np.int)]
    # get unique list of seed clusters
    Unique = np.arange(1, New.max()+1)
    # generate new seed list
    Merged = np.zeros(Label.shape)
    # get pixel list for each sink object
    Props = ms.regionprops(Label.astype(np.int))
    # fill in new values
    for i in Unique:
        Indices = np.nonzero(New == i)[0]
        for j in Indices:
            Coords = Props[j].coords
            Merged[Coords[:, 0], Coords[:, 1]] = i
    return Merged

new_height, new_width = int(image.shape[0] * resize_factor), int(image.shape[1] * resize_factor)
                        image = ia.imresize_single_image(image, (new_height, new_width), interpolation=self.interpolation)
                #image_sp = np.random.randint(0, 255, size=image.shape).astype(np.uint8)
                image_sp = np.copy(image)
                #time_start = time.time()
                segments = segmentation.slic(image, n_segments=n_segments_samples[i], compactness=10)
                #print("seg", np.min(segments), np.max(segments), n_segments_samples[i])
                #print("segmented in %.4fs" % (time.time() - time_start))
                #print(np.bincount(segments.flatten()))
                #time_start = time.time()
                nb_channels = image.shape[2]
                for c in sm.xrange(nb_channels):
                    # segments+1 here because otherwise regionprops always misses
                    # the last label
                    regions = measure.regionprops(segments+1, intensity_image=image[..., c])
                    for ridx, region in enumerate(regions):
                        # with mod here, because slic can sometimes create more superpixel
                        # than requested. replace_samples then does not have enough
                        # values, so we just start over with the first one again.
                        if replace_samples[ridx % len(replace_samples)] == 1:
                            #print("changing region %d of %d, channel %d, #indices %d" % (ridx, np.max(segments), c, len(np.where(segments == ridx)[0])))
                            mean_intensity = region.mean_intensity
                            image_sp_c = image_sp[..., c]
                            image_sp_c[segments == ridx] = mean_intensity
                #print("colored in %.4fs" % (time.time() - time_start))
                if orig_shape != image.shape:
                    image_sp = ia.imresize_single_image(image_sp, orig_shape[0:2], interpolation=self.interpolation)
                images[i] = image_sp
        return images

# Estimated glint diameter in pixels
    glint_d = int(cfg.getfloat('PUPILSEG','glintdiameterperc') * nx / 100.0)
    # Glint diameter should be >= 1 pixel
    if glint_d < 1:
        glint_d = 1
    # Reasonable upper and lower bounds on glint area (x3, /3)
    glint_A = np.pi * (glint_d / 2.0)**2
    A_min, A_max = glint_A / 3.0, glint_A * 3.0
    # Find bright pixels in full scale uint8 image (ie value > 250)
    bright = np.uint8(roi > 250)
    # Label connected regions (blobs)
    bright_labels = measure.label(bright)
    # Get region properties for all bright blobs in mask
    bright_props = measure.regionprops(bright_labels)
    # Init glint parameters
    r_min = np.Inf
    glint_label = -1
    glint = (np.nan, np.nan)
    glint_mask = np.zeros_like(roi, dtype="uint8")
    roi_noglint = roi.copy()
    # Find closest blob to ROI center within glint area range
    for props in bright_props:
        # Blob area in pixels^2
        A = props.area

def compare_measure(val_gt, output, args):
    y_output = skimage.color.rgb2ycbcr(output)
    y_gt = skimage.color.rgb2ycbcr(val_gt)
    y_output = y_output / 255.0
    y_gt = y_gt / 255.0
    y_psnr = skimage.measure.compare_psnr(y_output[args.scale:-args.scale, args.scale:-args.scale, :1], y_gt[args.scale:-args.scale, args.scale:-args.scale, :1], data_range = 1.0)
    y_ssim = compare_ssim(y_output[args.scale:-args.scale, args.scale:-args.scale, 0], y_gt[args.scale:-args.scale, args.scale:-args.scale, 0], gaussian_weights=True, use_sample_covariance=False, data_range = 1.0)
    return y_psnr, y_ssim

def marching_cubes(self, tsdf = None, step_size = 0):
        if step_size < 1:
            step_size = self._marching_cubes_step_size
        if tsdf is not None:
            return measure.marching_cubes_lewiner(tsdf,
                                                  step_size = step_size,
                                                  allow_degenerate=False)
        self._vertices, self._faces, self._normals, values = measure.marching_cubes_lewiner(self._tsdf,
                                                                                            step_size = step_size,
                                                                                            allow_degenerate=False)
        if self._verbose:
            print("Marching Cubes result: number of extracted vertices is %d" % (len(self._vertices)))