自学教程：Python-OpenCV实现图像缺陷检测的实例

import cv2import numpyfrom PIL import Image, ImageDraw, ImageFont#用于给图片添加中文字符def ImgText_CN(img, text, left, top, textColor=(0, 255, 0), textSize=20):    if (isinstance(img, numpy.ndarray)):  #判断是否为OpenCV图片类型        img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))    draw = ImageDraw.Draw(img)    fontText = ImageFont.truetype("font/simhei.ttf", textSize, encoding="utf-8")    draw.text((left, top), text, textColor, font=fontText)return cv2.cvtColor(numpy.asarray(img), cv2.COLOR_RGB2BGR)#读取原图片image0=cv2.imread("0.bmp")cv2.imshow("image0", image0)#灰度转换gray0 = cv2.cvtColor(image0, cv2.COLOR_RGB2GRAY)cv2.imshow("gray0", gray0)for i in range(1,6):    img0=cv2.imread(str(i)+".bmp")#原图片    img=cv2.cvtColor(cv2.imread(str(i)+".bmp"),cv2.COLOR_RGB2GRAY)#灰度图    #使用calcHist()函数计算直方图，反映灰度值的分布情况    hist = cv2.calcHist([gray0], [0], None, [256], [0.0,255.0])    h1 = cv2.calcHist([img], [0], None, [256], [0.0,255.0])     #计算图片相似度    result = cv2.compareHist(hist,h1,method=cv2.HISTCMP_BHATTACHARYYA)#巴氏距离比较，值越小相关度越高，最大值为1，最小值为0    #print(result)    #设定阈值为0.1，若相似度小于0.1则为合格，否则不合格    if result <0.1:        detect=ImgText_CN(img0, '合格', 10, 10, textColor=(255, 0, 0), textSize=30)    else:        detect=ImgText_CN(img0, '不合格', 10, 10, textColor=(255, 0, 0), textSize=30)    cv2.imshow("Detect_" +str(i),detect)cv2.waitKey(0)