Python+OpenCV实现图像缺陷检测

import cv2
import numpy
from 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)