问如何正确计算图像中每个单元的相邻单元数？
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Stack Overflow用户

提问于 2020-04-09 16:32:47

回答 1查看 162关注 0票数 0

我想计算一个二进制图像中六边形单元的百分比(例如，具有6个相邻单元的单元数量/总单元数量)，并生成如下所示的彩色编码图像。

我已经尝试了下面的python代码，但是我没有得到正确的输出。例如，如您所见，二进制图像中的红色单元格有4、5、6或7个相邻单元格，但计算错误，因为它们有3、4和6个相邻单元格。

附上了一个二进制图像及其输出的示例。

import sys
import json
import cv2
import os
import scipy.io
import numpy as np
from scipy.ndimage import measurements, morphology
from skimage import measure
import time


def cells_measurements(path, orig_image, color="yellow", size=3, 
    pixel_sz=0.00104167):
    size = int(size)
    im = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
    _, im = cv2.threshold(im, 127, 255, cv2.THRESH_BINARY)
    labeled_image, num_of_cells = measurements.label(255 - im)
    props = measure.regionprops(labeled_image)
     number_of_cells = len(props)

  colored_image = np.pad(cv2.cvtColor(~im, cv2.COLOR_GRAY2BGR), ((1, 1),(1, 1), (0, 0,)), mode='constant',constant_values=0)

  colors = [[0, 0, 128], [0, 0, 255], [0, 128, 255], [0, 255, 255],[128,255, 128], [255, 255, 0], [255, 128, 0],[255, 0, 0]]

   count_hex = 0
   labels = np.unique(labeled_image)
   for l in labels[1:]:
       i_temp = (labeled_image == l).astype(float) * 255
       i_temp = cv2.dilate(i_temp, np.ones((3, 3)), iterations=2) - i_temp
       i_temp2 = np.copy(labeled_image)
       i_temp2[i_temp == 0.] = 0
       adjacent = len(np.unique(i_temp2)) - 1
       if adjacent == 6:
          count_hex += 1
     cv2.floodFill(colored_image, None, (int(cell_center_all[l - 1][1]), 
     int(cell_center_all[l - 1][0])), colors[min(adjacent, 7)])

       hexagonal_cells = (count_hex / num_of_cells) * 100

     colored_image = np.pad(colored_image, ((0, 0), (0, 75), (0, 0)), 
    'constant', constant_values=255)
       for i in range(8):
           step = colored_image.shape[0] // 8
           colored_image[i * step:+(i + 1) * step, -60:-35] = colors[7 - i]
           colored_image[i * step, -60:-35] = 0
           colored_image[(i + 1) * step, -60:-35] = 0
           colored_image[i * step:(i + 1) * step, -60] = 0
           colored_image[i * step:(i + 1) * step, -35] = 0
           cv2.putText(colored_image, str(7 - i), (colored_image.shape[1] - 
           30, 5 + i * step + step // 2), cv2.FONT_HERSHEY_DUPLEX, 0.5, 0)

   color_path = 'Labeled Images/' + fn + "_color.png"
   cv2.imwrite(color_path, colored_image)

  Auto_Cells_Pleomorphism = []

  Mask_Path = 'generated_samples_masks/'
  Original_img_path = 'TestingSet/enhanced_imgs/'
  # please note that the "mask" image is the result of the segmentation 
  algorithm which I will provide today

  # Loop over images
  for i in range(1, 640):
      filename = str(i) + '.png'
      print(filename)
      Masks_Path = os.path.join(Mask_Path, filename)
      Original_image = os.path.join(Original_img_path, filename)
      [hexagonal_cells] = cells_measurements(Masks_Path, Original_image, 
      color="yellow", size=3) 

  Auto_Cells_Pleomorphism.append(round(hexagonal_cells))