图像处理（二）之基于OpenCV的水泥裂缝检测（区域延申）

一、基本逻辑思路

一、前期图像处理

1. 读取图形、修改尺寸、灰度图转换

    # 读取图片origin = PreProcess.read_image("test_img/1.jpg", color_code=cv.IMREAD_ANYCOLOR) # 裁剪图片origin = PreProcess.resize_img(origin)# 彩色图片->灰度图img = PreProcess.convert_color(origin)

2. 直方图均衡化

原理：

直方图均衡化通常是对图像灰度值进行归一化的方法，并且可以增强图像的对比度；

图像中像素个数多的灰度级进行展宽，而对图像中像素个数少的灰度进行压缩，从而扩展像原取值的动态范围，提高了对比度和灰度色调的变化，使图像更加清晰；

equalizeHist（）函数中输入的图像必须是8-bit的单通道图像。

代码：

img = PreProcess.equalize_hist(img, flag=False)   # 均值化

def equalize_hist(img, flag=False):hist_img = np.zeros(shape=img.shape)hist_img = cv.equalizeHist(img, hist_img)if flag:plt.rcParams['font.sans-serif'] = ['SimHei']   # 正确打印中文plt.subplot(2, 2, 1)plt.imshow(img, cmap="gray")plt.title("原图")plt.subplot(2, 2, 2)plt.hist(img)  #plt.title("原图直方图")plt.subplot(2, 2, 3)plt.imshow(hist_img, cmap="gray")  #plt.title("均衡化结果")plt.subplot(2, 2, 4)plt.hist(hist_img) #plt.title("均衡化结果直方图")plt.show()return hist_img

结果：

3. 手动滤波：使得图像整体的明暗变化均衡，直方图呈现中间多两边少的趋势

逻辑：

随机选取5块小区域、计算5块区域的灰度均值avg
图片按照10*10的小矩阵Matrix遍历修改：
1. 计算Matrix的均值Mavg
2. Matrix = Matrix*avg/Mavg

代码：

img = PreProcess.center_avg_imp(img, ksize=20, flag=False)

def center_avg_imp(img, ksize=10, flag=False):new_img = np.copy(img)dw = int(img.shape[1] / 7)   # 获取原图像长宽的1/7dh = int(img.shape[0] / 7)# 选取图像中的5个区域region_1 = new_img[dh * 1: dh * 2, dw * 1: dw * 2]region_2 = new_img[dh * 1: dh * 2, dw * 5: dw * 6]region_3 = new_img[dh * 5: dh * 6, dw * 5: dw * 6]region_4 = new_img[dh * 1: dh * 2, dw * 5: dw * 6]region_5 = new_img[dh * 3: dh * 4, dw * 3: dw * 4]# 计算5个区域的均值avg1 = np.average(region_1)avg2 = np.average(region_2)avg3 = np.average(region_3)avg4 = np.average(region_4)avg5 = np.average(region_5)# 5个区域均值的均值avg = (avg1 + avg2 + avg3 + avg4 + avg5) / 5for x in range(0, img.shape[0], ksize):for y in range(0, img.shape[1], ksize):# 每次运算选取10*10的像素方块进行操作    *均值/方块均值new_img[x:x + ksize, y:y + ksize] = \img[x:x + ksize, y:y + ksize] * (avg / np.average(img[x:x + ksize, y:y + ksize]))# new_img = cv.medianBlur(img, 15)# 中值滤波（Median filter）是一种典型的非线性滤波技术，基本思想是用像素点邻域灰度值的中值来代替该像素点的灰度值# 2.ksize：核大小，必须是比1大的奇数【举个例子：3，5，7…】if flag:plt.subplot(2, 2, 1)plt.imshow(img, cmap='gray')plt.subplot(2, 2, 2)plt.imshow(new_img, cmap='gray')plt.subplot(2, 2, 4)plt.hist(new_img)plt.subplot(2, 2, 3)plt.hist(img)plt.show()return new_img

结果：

4. 中值滤波

原理：

中值滤波（Median filter）是一种典型的非线性滤波技术，基本思想是用像素点邻域灰度值的中值来代替该像素点的灰度值
ksize：核大小，必须是比1大的奇数【举个例子：3，5，7…】

代码：

img = PreProcess.med_blur(img, ksize=5, flag=False)

def med_blur(img, ksize=3, flag=False):"""Median filter for input image:param img: input image:param ksize: size of filter:return: image after median filter"""if img.dtype is not np.uint8:img = img.astype(np.uint8)new_img = cv.medianBlur(img, ksize)if flag:plt.rcParams['font.sans-serif'] = ['SimHei']  # 正确打印中文plt.subplot(2, 2, 1)plt.imshow(img, cmap="gray")plt.title("原图")plt.subplot(2, 2, 2)plt.hist(img)plt.title("原图直方图")plt.subplot(2, 2, 3)plt.imshow(new_img, cmap="gray")plt.title("中值滤波结果")plt.subplot(2, 2, 4)plt.hist(new_img)plt.title("中值滤波结果直方图")plt.show()return new_img

结果：

二、裂缝延伸

rg = RegionGrow.RegionGrow(img) #创建RegionGrow类实例 ，完整代码见博客最后面
rg.img_cut()
rg.min_pos()
img = rg.region_grow()  # 延伸

1. 将原图像切割成3*3小矩阵

    def img_cut(self):"""cut the image into 9 parts:return: list of image"""# determine the size of pre blockself.block_w = int(self.imw / 3)self.block_h = int(self.imh / 3)for i in range(3):for j in range(3):# 以3*3小矩形进行遍历self.imlist.append(self.image[i * self.block_h:(i + 1) * self.block_h,j * self.block_w:(j + 1) * self.block_w])#返回矩阵列表len= 9return self.imlist

2. 暗度调节至1/4，寻找小矩阵图像中距离中心点最暗的点（黑点）

min_pos : [(188, 138), None, None, (214, 183), (290, 388), (337, 681), None, (479, 342), None

    def min_pos(self):"""to find out the darkness point in each block:return: a list of position in each block"""min_val = np.min(np.min(self.image))print("min_val",min_val)  # 0block_index = 0# 遍历小矩阵列表 (200, 266)for block in self.imlist:block = np.floor(block / 4)    # 对输入的多维数组逐元素进行向下取整：变暗block = block.astype(np.uint8)   # 转化为图像数据0-255posarr = np.where(block == min_val)  ## check is is this block contains min value# 如果没找到就继续，从下一个小方块找if len(posarr[0]) <= 0:self.poslist.append(None)block_index += 1continue# todo using a more useful method to chose the seed# No.1 chose the point which is closest to center point# pick a point randomly and convert to global positioncenter = (int(self.block_h / 2), int(self.block_w / 2))  # center pointpt = DataTool.min_distace(posarr, center)  # 在posarr中找距离center最近的坐标posw = int(block_index % 3) * self.block_w + pt[1]posh = int(block_index / 3) * self.block_h + pt[0]# posw，posh为 posarr_min 在大图中的绝对坐标self.poslist.append((posh, posw))block_index += 1print("min_pos : ",self.poslist)return self.poslist

3.延申

a. min_pos开始，寻找临近的黑点，并扩展出一块完整的区域:timblock

b. 每个区域timblock相加，形成完整的多区域图像

    def region_grow(self, mode=8):"""image segmentation using region grow:type img: image:param img: input image:type mode: int:param mode: 4 or 8 only(8 as default):return: new image after segmentation"""for x in range(9):point = self.poslist[x]timblock = np.zeros(shape=self.image.shape)if point is None:print("原图没有黑点！")continue# the position of the seedstart_point = (point[0], point[1])   # 从距离中心点最近的黑像素点开始# print("retimg:",np.where(self.retimg > 0))cv2.imshow("11 ",self.retimg)cv2.waitKey()if self.retimg[start_point[0], start_point[1]] != 0:# 如果此点的像素不为0：证明已经被延伸到了print("已经被延伸到了！")continue# the stack of point which need to be visitedpoint_list = [start_point]print("point_list:",point_list)# the dict of visited pointvisited_point = dict()visited_point[start_point] = start_pointprint("visited_point : ",visited_point)while len(point_list) > 0:# pop the top point and grow around this pointpoint = point_list.pop()# 延伸！！！！for i in range(-1, 2):for j in range(-1, 2):# the point that is going to grownew_point = point[0] + i, point[1] + j# is the point visited, if visited pass the point# print("延伸后的点：",new_point)if visited_point.get(new_point) is not None:continuetry:# 如果延伸点在图像范围内、两个点的灰度值相差在40之内：在t imblock将此点赋为255、并将此点放入p oint_list， v isited_point[new_point] = new_pointif 0 <= new_point[0] < self.imh and 0 <= new_point[1] < self.imw and np.abs(int(self.image[new_point[0], new_point[1]]) -int(self.image[start_point[0], start_point[1]])) < 40:timblock[new_point[0], new_point[1]] = 255point_list.append(new_point)visited_point[new_point] = new_pointexcept:print(new_point)# print("point_list:",point_list)self.im_merge2(timblock)  #  timblock, self.retimg相加赋给self.retimgself.retimg = self.retimg != 0# cv2.imshow("retimg",self.retimg)  打印不出来return self.retimg

结果：图1、图2分别为两次延申的timblock，图3为重叠后的最终结果

三、裂缝识别（框选）

暂无解析

四、代码中的其他函数

1. 连通域标记图像ConnectedComponents 详解见： https://blog.csdn.net/jia20003/article/details/80662396

cv.connectedComponents(img)

2. array寻值（项目中用来寻找黑点、找到的点将以Xarray，Yarray的形式返回，（ Xarray[i],Yarray[i] ）表示其中的某一点）

3. 图像重叠

self.retimg = np.add(temp_img, self.retimg)

五、项目源码

1. main.py

# coding:utf-8
import time
import cv2 as cv
from matplotlib import pyplot as plt
import numpy as np
import Feature
import PreProcess
import RegionGrowif __name__ == "__main__":# 读取图片origin = PreProcess.read_image("test_img/1.jpg", color_code=cv.IMREAD_ANYCOLOR)   # 彩色图片# 裁剪图片origin = PreProcess.resize_img(origin)# 彩色图片->灰度图img = PreProcess.convert_color(origin)if img is not None:img = PreProcess.equalize_hist(img, flag=False)   # 均值化# cv.imshow("equalize_hist",img)img = PreProcess.center_avg_imp(img, ksize=20, flag=False)# cv.imshow("center_avg_imp2", img)img = PreProcess.med_blur(img, ksize=5, flag=False)start_time = time.time()print(start_time)rg = RegionGrow.RegionGrow(img)rg.img_cut()rg.min_pos()img = rg.region_grow()  # 衍生！！end_time = time.time()print("run in %.2f" % (end_time - start_time))# img = rg.im_merge()plt.imshow(img, cmap="gray")plt.show()# img = PreProcess.binary_image(img, 100, True)img = PreProcess.med_blur(img, ksize=3, flag=False)   # 中值滤波plt.imsave("a.jpg", img, cmap='gray')# img = cv.dilate(img, np.array([[1,1,1],[1,1,1],[1,1,1]]))result, imgs = Feature.connected_region_label(img, flag=True)# plt.imshow(imgs)plt.imshow(imgs[0])plt.imshow(imgs[1])plt.imshow(imgs[2])print(result,imgs[0].shape,imgs[1].shape,imgs[2].shape)for img in imgs[1:]:area_result = Feature.get_area_pos(img, flag=False)print(area_result)for r in area_result:origin = cv.rectangle(origin,(r[1], r[2]),(r[1] + r[3], r[2] + r[4]),(0, 0, 0),thickness=2)origin = cv.putText(origin,'area:' + str(r[0]),color=(0, 0, 255),org=(r[1], r[2] + 30),fontFace=cv.FONT_ITALIC,fontScale=0.5)origin = PreProcess.convert_color(origin, cv.COLOR_BGR2RGB)plt.imshow(origin)plt.rcParams['font.sans-serif'] = ['KaiTi']plt.title("识别结果")plt.show()plt.imsave("b.jpg", origin)

2. PreProcess.py 前期图像处理

import cv2 as cv
import numpy as np
from matplotlib import cm
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3Ddef read_image(path, color_code=cv.IMREAD_GRAYSCALE):"""read a picture from path:type path: string:param path: the location of a picture:type color_code: opencv color code:param color_code: which type the image should be read, cv.IMREAD_GRAYSCALE as default:return: the picture read from the path, None if there is an error"""return cv.imread(path, color_code)def convert_color_gray(image):"""convert a bgr image to gray:type image: opencv image:param image: the image need to convert:return: an image in gray color"""return cv.cvtColor(image, cv.COLOR_BGR2GRAY)def resize_img(img, width=800):"""resize image:type img: image:param img: input image:type width: int:param width: width after resize,800 as default:return: image after resize"""return cv.resize(img, (width, int(width * img.shape[0] / img.shape[1])))def convert_color(image, code=cv.COLOR_BGR2GRAY):"""convert color space of an image:type image: image:param image: input image:type code: opencv convert code:param code: opencv color convert , COLOR_BGR2GRAY as default:return: image after convert"""return cv.cvtColor(image, code)def center_avg_imp(img, ksize=10, flag=False):"""improve the image pixels by image center pixel average:type img: image:param img: the image need to be improved:type ksize: int:param ksize: the filter size, 10 as default:type flag: Boolean:param flag: show the result or not:return: the result after deal"""new_img = np.copy(img)dw = int(img.shape[1] / 7)   # 获取原图像长宽的1/7dh = int(img.shape[0] / 7)# 选取图像中的5个区域region_1 = new_img[dh * 1: dh * 2, dw * 1: dw * 2]region_2 = new_img[dh * 1: dh * 2, dw * 5: dw * 6]region_3 = new_img[dh * 5: dh * 6, dw * 5: dw * 6]region_4 = new_img[dh * 1: dh * 2, dw * 5: dw * 6]region_5 = new_img[dh * 3: dh * 4, dw * 3: dw * 4]# 计算5个区域的均值avg1 = np.average(region_1)avg2 = np.average(region_2)avg3 = np.average(region_3)avg4 = np.average(region_4)avg5 = np.average(region_5)# 5个区域均值的均值avg = (avg1 + avg2 + avg3 + avg4 + avg5) / 5for x in range(0, img.shape[0], ksize):for y in range(0, img.shape[1], ksize):# 每次运算选取10*10的像素方块进行操作    *均值/方块均值new_img[x:x + ksize, y:y + ksize] = \img[x:x + ksize, y:y + ksize] * (avg / np.average(img[x:x + ksize, y:y + ksize]))# new_img = cv.medianBlur(img, 15)# 中值滤波（Median filter）是一种典型的非线性滤波技术，基本思想是用像素点邻域灰度值的中值来代替该像素点的灰度值# 2.ksize：核大小，必须是比1大的奇数【举个例子：3，5，7…】if flag:plt.subplot(2, 2, 1)plt.imshow(img, cmap='gray')plt.subplot(2, 2, 2)plt.imshow(new_img, cmap='gray')plt.subplot(2, 2, 4)plt.hist(new_img)plt.subplot(2, 2, 3)plt.hist(img)plt.show()return new_imgdef equalize_hist(img, flag=False):"""equalize hist to improve image:type img: image:param img: input image:type flag: Boolean:param flag: show the result if is True, False as default:return: the image after equalize hist"""hist_img = np.zeros(shape=img.shape)hist_img = cv.equalizeHist(img, hist_img)if flag:plt.rcParams['font.sans-serif'] = ['SimHei']   # 正确打印中文plt.subplot(2, 2, 1)plt.imshow(img, cmap="gray")plt.title("原图")plt.subplot(2, 2, 2)plt.hist(img)  #plt.title("原图直方图")plt.subplot(2, 2, 3)plt.imshow(hist_img, cmap="gray")  #plt.title("均衡化结果")plt.subplot(2, 2, 4)plt.hist(hist_img) #plt.title("均衡化结果直方图")plt.show()return hist_imgdef med_blur(img, ksize=3, flag=False):"""Median filter for input image:param img: input image:param ksize: size of filter:return: image after median filter"""if img.dtype is not np.uint8:img = img.astype(np.uint8)new_img = cv.medianBlur(img, ksize)if flag:plt.rcParams['font.sans-serif'] = ['SimHei']  # 正确打印中文plt.subplot(2, 2, 1)plt.imshow(img, cmap="gray")plt.title("原图")plt.subplot(2, 2, 2)plt.hist(img)plt.title("原图直方图")plt.subplot(2, 2, 3)plt.imshow(new_img, cmap="gray")plt.title("中值滤波结果")plt.subplot(2, 2, 4)plt.hist(new_img)plt.title("中值滤波结果直方图")plt.show()return new_imgdef gauss_blur(img, ksize=[3, 3]):cv.GaussianBlur(img, ksize=ksize)def adj_gamma(img, flag=False):"""对图像进行归一化处理:param img: 输入图像:param flag: 是否显示归一化之后的图像:return: 归一化之后的图像"""new_image = imgnew_image = new_image - np.min(np.min(new_image))new_image = new_image / np.max(np.max(new_image))if flag:x = np.arange(0, new_image.shape[1], 1)y = np.arange(0, new_image.shape[0], 1)xg, yg = np.meshgrid(x, y)fig = plt.figure()ax = Axes3D(fig)ax.plot_surface(xg, yg, new_image, rstride=1, cstride=1, cmap=cm.viridis)plt.show()return new_imagedef binary_image(img, thresh=0.15, flag=False):"""对图形进行二值化:param img: 输入图形:param thresh: 阈值:param flag: 是否显示结果:return: 二值化之后的图形"""t = np.reshape(img, img.shape[1] * img.shape[0])pixel = np.bincount(t)xdata = np.linspace(1, pixel.shape[0], pixel.shape[0])index = np.argwhere(pixel == np.max(pixel))thresh = index[0][0] / 3plt.plot(pixel)plt.show()ret, new_img = cv.threshold(img, thresh, 255, cv.THRESH_BINARY)new_img = np.abs(new_img - 255)if flag:plt.subplot(2, 1, 1)plt.imshow(img, cmap="gray")plt.subplot(2, 1, 2)plt.imshow(new_img, cmap="gray")plt.show()return new_imgdef hist_segmentation(img):"""do image segmentation using hist:type img: gray image:param img: origin image:return: image after segmentation"""hist = cv.calcHist([img], [0], None, [256], [0, 255])max_index = np.where(hist == max(hist))mask = hist[0:max_index[0][0]]min_index = np.where(mask == min(mask))ret, new_im = cv.threshold(img, min_index[0][0], 255, cv.THRESH_BINARY)return new_im

3. RegionGrow.py 区域生长实现类

"""
to do image segmentation using region grow method
"""
import numpy as np
import cv2
import DataToolclass RegionGrow:def __init__(self, image):"""init class:param image: the image need to process"""self.image = imageself.imlist = []self.poslist = []self.retimg = np.zeros(shape=self.image.shape)self.imh, self.imw = image.shapeself.block_w = 0self.block_h = 0def region_grow(self, mode=8):"""image segmentation using region grow:type img: image:param img: input image:type mode: int:param mode: 4 or 8 only(8 as default):return: new image after segmentation"""for x in range(9):point = self.poslist[x]timblock = np.zeros(shape=self.image.shape)if point is None:print("原图没有黑点！")continue# the position of the seedstart_point = (point[0], point[1])   # 从距离中心点最近的黑像素点开始# print("retimg:",np.where(self.retimg > 0))cv2.imshow("11 ",self.retimg)cv2.waitKey()if self.retimg[start_point[0], start_point[1]] != 0:# 如果此点的像素不为0：证明已经被延伸到了print("已经被延伸到了！")continue# the stack of point which need to be visitedpoint_list = [start_point]print("point_list:",point_list)# the dict of visited pointvisited_point = dict()visited_point[start_point] = start_pointprint("visited_point : ",visited_point)while len(point_list) > 0:# pop the top point and grow around this pointpoint = point_list.pop()# 延伸！！！！for i in range(-1, 2):for j in range(-1, 2):# the point that is going to grownew_point = point[0] + i, point[1] + j# is the point visited, if visited pass the point# print("延伸后的点：",new_point)if visited_point.get(new_point) is not None:continuetry:# 如果延伸点在图像范围内、两个点的灰度值相差在40之内：在t imblock将此点赋为255、并将此点放入p oint_list， v isited_point[new_point] = new_pointif 0 <= new_point[0] < self.imh and 0 <= new_point[1] < self.imw and np.abs(int(self.image[new_point[0], new_point[1]]) -int(self.image[start_point[0], start_point[1]])) < 40:timblock[new_point[0], new_point[1]] = 255point_list.append(new_point)visited_point[new_point] = new_pointexcept:print(new_point)# print("point_list:",point_list)self.im_merge2(timblock)  #  timblock, self.retimg相加赋给self.retimgself.retimg = self.retimg != 0# cv2.imshow("retimg",self.retimg)  打印不出来return self.retimgdef img_cut(self):"""cut the image into 9 parts:return: list of image"""# determine the size of pre blockself.block_w = int(self.imw / 3)self.block_h = int(self.imh / 3)for i in range(3):for j in range(3):# 以3*3小矩形进行遍历self.imlist.append(self.image[i * self.block_h:(i + 1) * self.block_h,j * self.block_w:(j + 1) * self.block_w])#返回矩阵列表len= 9return self.imlistdef min_pos(self):"""to find out the darkness point in each block:return: a list of position in each block"""min_val = np.min(np.min(self.image))print("min_val",min_val)  # 0block_index = 0# 遍历小矩阵列表 (200, 266)for block in self.imlist:block = np.floor(block / 4)    # 对输入的多维数组逐元素进行向下取整：变暗block = block.astype(np.uint8)   # 转化为图像数据0-255posarr = np.where(block == min_val)  ## check is is this block contains min value# 如果没找到就继续，从下一个小方块找if len(posarr[0]) <= 0:self.poslist.append(None)block_index += 1continue# todo using a more useful method to chose the seed# No.1 chose the point which is closest to center point# pick a point randomly and convert to global positioncenter = (int(self.block_h / 2), int(self.block_w / 2))  # center pointpt = DataTool.min_distace(posarr, center)  # 在posarr中找距离center最近的坐标posw = int(block_index % 3) * self.block_w + pt[1]posh = int(block_index / 3) * self.block_h + pt[0]# posw，posh为 posarr_min 在大图中的绝对坐标self.poslist.append((posh, posw))block_index += 1print("min_pos : ",self.poslist)return self.poslistdef im_merge2(self, temp_img):self.retimg = np.add(temp_img, self.retimg)print('im_merge2:',temp_img)cv2.imshow("im_merge2",temp_img)cv2.waitKey()

4. DataTool.py 工具脚本

import mathdef distance_calc(point1, point2):"""to calculate the distance of 2 points:param point1: pt1:param point2: pt2:return: the distance"""return math.sqrt((point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2)def min_distace(pointlist, center):"""given a group of points and a center point to find out the point which is closest to center point:type pointlist: tuple:param pointlist: the list of point:type center: tuple:param center: center point:return: the point closest to center"""dis_dict = dict()dis_list = []# for all the point calculate distancefor index in range(len(pointlist[0])):pt = (pointlist[0][index], pointlist[1][index])dis = distance_calc(pt, center)dis_dict[dis] = pt    # 距离：pointlist坐标dis_list.append(dis)dis_list.sort()return dis_dict[dis_list[0]]   # 在pointlist中找距离center最近的坐标

5. Feature.py 实现区域框选

import cv2 as cv
import numpy as np
from matplotlib import pyplot as pltdef get_area_pos(img, filter_size=1000, flag=False):"""从图形中获取区域面积及位置:param img: 输入图形:param filter_size: 过滤的面积大小:param flag: show result?:return: list(area,pos);area:int, pos（x,y,w,h）"""# 检查类型if img.dtype is not np.uint8:img = img.astype(np.uint8)# 获取边缘点contours, hierarchy = cv.findContours(img,cv.RETR_TREE,cv.CHAIN_APPROX_NONE)result_list = []# 统计面积以及位置for con in contours:image = cv.drawContours(img, con, -1, 255)area = cv.contourArea(con)if area > filter_size:x, y, w, h = cv.boundingRect(con)result_list.append((area, x, y, w, h))if w / h > 1:print("横向裂缝")print(find_min_max_width(con))else:print("纵向裂缝")print(find_min_max_width_vertical(con))if flag:temp_img = np.zeros(image.shape)temp_img = cv.drawContours(temp_img, con, -1, 255)print('x:%d，y:%d，w:%d，h:%d' % (x, y, w, h))temp_img = cv.rectangle(temp_img, (x, y), (x + w, y + h), 180)cv.imshow("result", temp_img)cv.waitKey()return result_listdef connected_region_label(img, flag=False):"""对二值图像进行连通性分析:param img: 输入图像:param flag: 是否显示结果:return: 连通区域总数，标记的每个连通区域"""img = img.astype(np.uint8)result, labels = cv.connectedComponents(img)if flag:temp = labels * 10plt.imshow(temp, cmap="gray")plt.show()print(result)labels_imgs = []for i in range(result):ret = np.asarray((labels == i), np.bool)labels_imgs.append(ret)return result, labels_imgsdef find_min_max_width(contour):min_dict = dict()max_dict = dict()max_width = 0min_width = 999min_pos = 0max_pos = 0for pt in contour:if min_dict.get(pt[0][0]) is None or max_dict.get(pt[0][0]) is None:min_dict[pt[0][0]] = max_dict[pt[0][0]] = pt[0][1]elif min_dict[pt[0][0]] > pt[0][1]:min_dict[pt[0][0]] = pt[0][1]if max_width < (max_dict[pt[0][0]] - min_dict[pt[0][0]]):max_width = max_dict[pt[0][0]] - min_dict[pt[0][0]]max_pos = pt[0][0]if min_width > (max_dict[pt[0][0]] - min_dict[pt[0][0]]):min_width = max_dict[pt[0][0]] - min_dict[pt[0][0]]min_pos = pt[0][0]elif max_dict[pt[0][0]] < pt[0][1]:max_dict[pt[0][0]] = pt[0][1]if max_width < (max_dict[pt[0][0]] - min_dict[pt[0][0]]):max_width = max_dict[pt[0][0]] - min_dict[pt[0][0]]max_pos = pt[0][0]if min_width > (max_dict[pt[0][0]] - min_dict[pt[0][0]]):min_width = max_dict[pt[0][0]] - min_dict[pt[0][0]]min_pos = pt[0][0]return max_width, min_widthdef find_min_max_width_vertical(contour):min_dict = dict()max_dict = dict()max_width = 0min_width = 999min_pos = 0max_pos = 0for pt in contour:if min_dict.get(pt[0][1]) is None or max_dict.get(pt[0][1]) is None:min_dict[pt[0][1]] = max_dict[pt[0][1]] = pt[0][0]elif min_dict[pt[0][1]] > pt[0][0]:min_dict[pt[0][1]] = pt[0][0]if max_width < (max_dict[pt[0][1]] - min_dict[pt[0][1]]):max_width = max_dict[pt[0][1]] - min_dict[pt[0][1]]max_pos = pt[0][0]if min_width > (max_dict[pt[0][1]] - min_dict[pt[0][1]]):min_width = max_dict[pt[0][1]] - min_dict[pt[0][1]]min_pos = pt[0][0]elif max_dict[pt[0][1]] < pt[0][0]:max_dict[pt[0][1]] = pt[0][0]if max_width < (max_dict[pt[0][1]] - min_dict[pt[0][1]]):max_width = max_dict[pt[0][1]] - min_dict[pt[0][1]]max_pos = pt[0][0]if min_width > (max_dict[pt[0][1]] - min_dict[pt[0][1]]):min_width = max_dict[pt[0][1]] - min_dict[pt[0][1]]min_pos = pt[0][0]return max_width, min_width