text: import cv2
  import numpy as np
  import math
  import Polygon as plg
  
  
  def watershed1(image, viz=False):
      boxes = []
      if len(image.shape) == 3:
          gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
      else:
          gray = image
      if viz:
          cv2.imshow("gray", gray)
          cv2.waitKey()
      ret, binary = cv2.threshold(gray, 0.6 * np.max(gray), 255, cv2.THRESH_BINARY)
      if viz:
          cv2.imshow("binary", binary)
          cv2.waitKey()
      # 形态学操作，进一步消除图像中噪点
      kernel = np.ones((3, 3), np.uint8)
      # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
      mb = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel, iterations=2)  # iterations连续两次开操作
      sure_bg = cv2.dilate(mb, kernel, iterations=3)  # 3次膨胀,可以获取到大部分都是背景的区域
      if viz:
          cv2.imshow("sure_bg", sure_bg)
          cv2.waitKey()
  
      # 距离变换
      dist = cv2.distanceTransform(mb, cv2.DIST_L2, 5)
      if viz:
          cv2.imshow("dist", dist)
          cv2.waitKey()
      ret, sure_fg = cv2.threshold(dist, 0.2 * np.max(dist), 255, cv2.THRESH_BINARY)
      surface_fg = np.uint8(sure_fg)  # 保持色彩空间一致才能进行运算，现在是背景空间为整型空间，前景为浮点型空间，所以进行转换
      if viz:
          cv2.imshow("surface_fg", surface_fg)
          cv2.waitKey()
      unknown = cv2.subtract(sure_bg, surface_fg)
      # 获取maskers,在markers中含有种子区域
      ret, markers = cv2.connectedComponents(surface_fg)
  
      # 分水岭变换
      markers = markers + 1
      markers[unknown == 255] = 0
  
      if viz:
          color_markers = np.uint8(markers)
          color_markers = cv2.applyColorMap(color_markers, cv2.COLORMAP_JET)
          cv2.imshow("color_markers", color_markers)
          cv2.waitKey()
  
      markers = cv2.watershed(image, markers=markers)
      image[markers == -1] = [0, 0, 255]
      if viz:
          cv2.imshow("image", image)
          cv2.waitKey()
      for i in range(2, np.max(markers) + 1):
          np_contours = np.roll(np.array(np.where(markers == i)), 1, axis=0).transpose().reshape(-1, 2)
          # print(np_contours.shape)
          rectangle = cv2.minAreaRect(np_contours)
          box = cv2.boxPoints(rectangle)
          w, h = np.linalg.norm(box[0] - box[1]), np.linalg.norm(box[1] - box[2])
          box_ratio = max(w, h) / (min(w, h) + 1e-5)
          if abs(1 - box_ratio) <= 0.1:
              l, r = min(np_contours[:, 0]), max(np_contours[:, 0])
              t, b = min(np_contours[:, 1]), max(np_contours[:, 1])
              box = np.array([[l, t], [r, t], [r, b], [l, b]], dtype=np.float32)
  
          # make clock-wise order
          startidx = box.sum(axis=1).argmin()
          box = np.roll(box, 4 - startidx, 0)
          box = np.array(box)
          boxes.append(box)
      return np.array(boxes)
  
  
  def getDetCharBoxes_core(textmap, text_threshold=0.5, low_text=0.4):
      # prepare data
      textmap = textmap.copy()
      img_h, img_w = textmap.shape
  
      """ labeling method """
      ret, text_score = cv2.threshold(textmap, low_text, 1, 0)
      nLabels, labels, stats, centroids = cv2.connectedComponentsWithStats(text_score.astype(np.uint8),
                                                                           connectivity=4)
  
      det = []
      mapper = []
      for k in range(1, nLabels):
          # size filtering
          size = stats[k, cv2.CC_STAT_AREA]
          if size < 10: continue
  
          # thresholding
          if np.max(textmap[labels == k]) < text_threshold: continue
  
          # make segmentation map
          segmap = np.zeros(textmap.shape, dtype=np.uint8)
          segmap[labels == k] = 255
          # segmap[np.logical_and(link_score == 1, text_score == 0)] = 0  # remove link area
          x, y = stats[k, cv2.CC_STAT_LEFT], stats[k, cv2.CC_STAT_TOP]
          w, h = stats[k, cv2.CC_STAT_WIDTH], stats[k, cv2.CC_STAT_HEIGHT]
          niter = int(math.sqrt(size * min(w, h) / (w * h)) * 2)
          sx, ex, sy, ey = x - niter, x + w + niter + 1, y - niter, y + h + niter + 1
          # boundary check
          if sx < 0: sx = 0
          if sy < 0: sy = 0
          if ex >= img_w: ex = img_w
          if ey >= img_h: ey = img_h
          kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1 + niter, 1 + niter))
          segmap[sy:ey, sx:ex] = cv2.dilate(segmap[sy:ey, sx:ex], kernel)
  
          # make box
          np_contours = np.roll(np.array(np.where(segmap != 0)), 1, axis=0).transpose().reshape(-1, 2)
          rectangle = cv2.minAreaRect(np_contours)
          box = cv2.boxPoints(rectangle)
  
          # align diamond-shape
          w, h = np.linalg.norm(box[0] - box[1]), np.linalg.norm(box[1] - box[2])
          box_ratio = max(w, h) / (min(w, h) + 1e-5)
          if abs(1 - box_ratio) <= 0.1:
              l, r = min(np_contours[:, 0]), max(np_contours[:, 0])
              t, b = min(np_contours[:, 1]), max(np_contours[:, 1])
              box = np.array([[l, t], [r, t], [r, b], [l, b]], dtype=np.float32)
  
          # make clock-wise order
          startidx = box.sum(axis=1).argmin()
          box = np.roll(box, 4 - startidx, 0)
          box = np.array(box)
  
          det.append(box)
          mapper.append(k)
  
      return det, labels, mapper
  
  
  def watershed2(image, viz=False):
      gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
      gray = np.float32(gray) / 255.0
      boxes, _, _ = getDetCharBoxes_core(gray)
      return np.array(boxes)
  
  
  def watershed(oriimage, image, viz=False):
      # viz = True
      boxes = []
      if len(image.shape) == 3:
          gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
      else:
          gray = image
      if viz:
          cv2.imshow("gray", gray)
          cv2.waitKey()
      ret, binary = cv2.threshold(gray, 0.2 * np.max(gray), 255, cv2.THRESH_BINARY)
      if viz:
          cv2.imshow("binary", binary)
          cv2.waitKey()
      # 形态学操作，进一步消除图像中噪点
      kernel = np.ones((3, 3), np.uint8)
      # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
      mb = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel, iterations=2)  # iterations连续两次开操作
      sure_bg = cv2.dilate(mb, kernel, iterations=3)  # 3次膨胀,可以获取到大部分都是背景的区域
      sure_bg = mb
      if viz:
          cv2.imshow("sure_bg", mb)
          cv2.waitKey()
      # 距离变换
      # dist = cv2.distanceTransform(mb, cv2.DIST_L2, 5)
      # if viz:
      #     cv2.imshow("dist", dist)
      #     cv2.waitKey()
      ret, sure_fg = cv2.threshold(gray, 0.6 * gray.max(), 255, cv2.THRESH_BINARY)
      surface_fg = np.uint8(sure_fg)  # 保持色彩空间一致才能进行运算，现在是背景空间为整型空间，前景为浮点型空间，所以进行转换
      if viz:
          cv2.imshow("surface_fg", surface_fg)
          cv2.waitKey()
      unknown = cv2.subtract(sure_bg, surface_fg)
      # 获取maskers,在markers中含有种子区域
      ret, markers = cv2.connectedComponents(surface_fg)
  
      nLabels, labels, stats, centroids = cv2.connectedComponentsWithStats(surface_fg,
                                                                           connectivity=4)
      # 分水岭变换
      markers = labels.copy() + 1
      # markers = markers+1
      markers[unknown == 255] = 0
  
      if viz:
          color_markers = np.uint8(markers)
          color_markers = color_markers / (color_markers.max() / 255)
          color_markers = np.uint8(color_markers)
          color_markers = cv2.applyColorMap(color_markers, cv2.COLORMAP_JET)
          cv2.imshow("color_markers", color_markers)
          cv2.waitKey()
      # a = cv2.applyColorMap(gray, cv2.COLORMAP_JET)
      markers = cv2.watershed(oriimage, markers=markers)
      oriimage[markers == -1] = [0, 0, 255]
  
      if viz:
          color_markers = np.uint8(markers + 1)
          color_markers = color_markers / (color_markers.max() / 255)
          color_markers = np.uint8(color_markers)
          color_markers = cv2.applyColorMap(color_markers, cv2.COLORMAP_JET)
          cv2.imshow("color_markers1", color_markers)
          cv2.waitKey()
  
      if viz:
          cv2.imshow("image", oriimage)
          cv2.waitKey()
      for i in range(2, np.max(markers) + 1):
          np_contours = np.roll(np.array(np.where(markers == i)), 1, axis=0).transpose().reshape(-1, 2)
          # segmap = np.zeros(gray.shape, dtype=np.uint8)
          # segmap[markers == i] = 255
          # size = np_contours.shape[0]
          # x, y, w, h = cv2.boundingRect(np_contours)
          # if w == 0 or h == 0:
          #     continue
          #
          # niter = int(math.sqrt(size * min(w, h) / (w * h)) * 2)
          # sx, ex, sy, ey = x - niter, x + w + niter + 1, y - niter, y + h + niter + 1
          # # boundary check
          # if sx < 0: sx = 0
          # if sy < 0: sy = 0
          # if ex >= gray.shape[1]: ex = gray.shape[1]
          # if ey >= gray.shape[0]: ey = gray.shape[0]
          # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1 + niter, 1 + niter))
          # segmap[sy:ey, sx:ex] = cv2.dilate(segmap[sy:ey, sx:ex], kernel)
          # np_contours = np.roll(np.array(np.where(segmap != 0)), 1, axis=0).transpose().reshape(-1, 2)
          rectangle = cv2.minAreaRect(np_contours)
          box = cv2.boxPoints(rectangle)
  
          startidx = box.sum(axis=1).argmin()
          box = np.roll(box, 4 - startidx, 0)
          poly = plg.Polygon(box)
          area = poly.area()
          if area < 10:
              continue
          box = np.array(box)
          boxes.append(box)
      return np.array(boxes)
  
  
  if __name__ == '__main__':
      image = cv2.imread('images/standard.jpg', cv2.IMREAD_COLOR)
      boxes = watershed(image, True)
      print(boxes)