import cv2

import numpy as np

for n in open('list.txt'): # list.txt为目标文件列表

    path = n[:-1] # 去除文件路径的换行符

    img = cv2.imread(path)

    gray =cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 图像转灰度

    ret, binary = cv2.threshold(gray, 75, 255, cv2.THRESH_BINARY) # 灰度转二值图像

    cv2.imwrite(path + 'abc.png', binary)

    kernel = np.ones((21,21),np.uint8) # 给图像闭运算定义核

    kernel_1 = np.ones((101,101),np.uint8) # 给图像开运算定义核

    # 图像先闭运算再开运算可以过滤孤立的物体， 将密集物体区域形成一片连通区

    closing = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)

    opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel_1)

    # 给图像的边缘像素设定为255，否则下面连通区的检测无法识别贴在图像边缘的连通区
    # 特别注意！！！，此操作会将整个图像也视为一个连通区域

    opening_x = opening.shape[0]

    opening_y = opening.shape[1]

    opening[:,0] = 255

    opening[:,opening_y-1] = 255

    opening[0,:] = 255

    opening[opening_x-1,:] = 255

    # 检测图像连通区（输入为二值化图像）

    image, contours, hierarchy = cv2.findContours(opening,1,2)

    for n in range(len(contours)):

        # 筛选面积较大的连通区，阈值为20000

        cnt = contours[n]

        area = cv2.contourArea(cnt)

        if area > 20000:

            x,y,w,h=cv2.boundingRect(cnt)

            img_ = cv2.rectangle(img ,(x,y),(x+w,y+h),(0,0,255),4) # 画框

            print('')

            img__ = img[y-h:y+h,x-w:x+w,:]

    cv2.imwrite(path + 'abc_open.png', opening)

    cv2.imwrite(path + 'abc_close.png', closing)

    cv2.imwrite(path + 'abc_close_range.png', img_)