Python+OpenCV：基于分水岭算法的图像分割(Image Segmentation with Watershed Algorithm)

以你之姓@ 2022-12-22 14:30 278阅读 0赞

Python+OpenCV：基于分水岭算法的图像分割(Image Segmentation with Watershed Algorithm)

  1. ####################################################################################################
  2. # 基于分水岭算法的图像分割(Image Segmentation with Watershed Algorithm)
  3. def lmc_cv_image_segmentation_watershed():
  4.     """
  5.         函数功能: 基于分水岭算法的图像分割(Image Segmentation with Watershed Algorithm)。
  6.     """
  8.     # 读取图像
  9.     image = lmc_cv.imread('D:/99-Research/Python/Image/water_coins.jpg', flags=lmc_cv.IMREAD_UNCHANGED)
  10.     rgb_image = lmc_cv.cvtColor(image, lmc_cv.COLOR_BGR2RGB)
  11.     rgb_image_result = rgb_image.copy()
  12.     gray_image = lmc_cv.cvtColor(rgb_image, lmc_cv.COLOR_RGB2GRAY)
  14.     # 基于分水岭算法的图像分割(Image Segmentation with Watershed Algorithm)
  15.     ret, thresh_image = lmc_cv.threshold(gray_image, 0, 255, lmc_cv.THRESH_BINARY_INV + lmc_cv.THRESH_OTSU)
  17.     # noise removal
  18.     kernel = np.ones((3, 3), np.uint8)
  19.     opening_image = lmc_cv.morphologyEx(thresh_image, lmc_cv.MORPH_OPEN, kernel, iterations=2)
  20.     # sure background area
  21.     sure_bg_image = lmc_cv.dilate(opening_image, kernel, iterations=3)
  22.     # Finding sure foreground area
  23.     dist_transform = lmc_cv.distanceTransform(opening_image, lmc_cv.DIST_L2, 5)
  24.     ret, sure_fg_image = lmc_cv.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0)
  25.     # Finding unknown region
  26.     sure_fg_image = np.uint8(sure_fg_image)
  27.     unknown = lmc_cv.subtract(sure_bg_image, sure_fg_image)
  29.     # Marker labelling
  30.     ret, markers_image = lmc_cv.connectedComponents(sure_fg_image)
  31.     # Add one to all labels so that sure background is not 0, but 1
  32.     markers_image = markers_image + 1
  33.     # Now, mark the region of unknown with zero
  34.     markers_image[unknown == 255] = 0
  36.     markers_image = lmc_cv.watershed(rgb_image_result, markers_image)
  37.     rgb_image_result[markers_image == -1] = [255, 0, 0]
  39.     # 显示图像
  40.     pyplot.figure('Image Display')
  41.     titles = ['Original Image', 'Thresh Image', 'ForeGround Image', 'BackGround Image', 'Distance Transform Image',
  42.               'Markers Image', 'Result Image']
  43.     images = [rgb_image, thresh_image, sure_fg_image, sure_bg_image, dist_transform, markers_image, rgb_image_result]
  44.     for i in range(7):
  45.         pyplot.subplot(2, 4, i + 1)
  46.         pyplot.imshow(images[i], 'gray')
  47.         pyplot.title(titles[i])
  48.         pyplot.xticks([])
  49.         pyplot.yticks([])
  50.     pyplot.show()
  52.     # 根据用户输入保存图像
  53.     if ord("q") == (lmc_cv.waitKey(0) & 0xFF):
  54.         # 销毁窗口
  55.         pyplot.close('all')
  56.     return

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