自学教程：Python opencv缺陷检测的实现及问题解决

# -*- coding: utf-8 -*-'''作者 : 丁毅开发时间 : 2021/4/21 15:30'''import cv2import numpy as npfrom PIL import Image, ImageDraw, ImageFontimport matplotlib.pyplot as plt#用于给图片添加中文字符的函数def cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20):    # 判断是否OpenCV图片类型    if (isinstance(img, np.ndarray)):        img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))    # 创建一个可以在给定图像上绘图的对象    draw = ImageDraw.Draw(img)    # 字体的格式    fontStyle=ImageFont.truetype("font/simsun.ttc",textSize, encoding="utf-8")    # 绘制文本    draw.text((left, top), text, textColor, font=fontStyle)    # 转换回OpenCV格式    return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)# plt绘图显示中文plt.rcParams['font.family'] = 'SimHei'img0 = cv2.imread(r"C:/Users/pc/Desktop/0.bmp")cv2.imshow('img', img0)# 彩色图转灰度图img1 = cv2.cvtColor(img0, cv2.COLOR_BGR2GRAY)# 缺陷检测for i in range(1, 6):    defect_img0 = cv2.imread(r"C:/Users/pc/Desktop/%d.bmp"%i)    # 获取灰度图像    defect_img1 = cv2.cvtColor(defect_img0, cv2.COLOR_BGR2GRAY)    # 获取原图像的灰度直方图    hist0 = cv2.calcHist([img1], [0], None, [256], [0.0, 255.0])    # 获取待检测图像的灰度直方图    hist1 = cv2.calcHist([defect_img1], [0], None, [256], [0.0, 255.0])    # 为图像添加标题    plt.title("原图与待检测img%d对比"%i)    # 添加图例    plt.plot(hist0, label='原图')    plt.plot(hist1, label='待检测img%d'%i)    # 相似度比较    rst = cv2.compareHist(hist0, hist1, method=cv2.HISTCMP_CORREL)    # res >= 0.95即认为合格    cv2.imshow(str(i) + ".img", cv2ImgAddText(defect_img0, "合格" if rst >= 0.95 else "不合格", 20, 20, (255, 0, 0), 25))    # 设置x轴的数值范围    plt.xlim([0, 256])    plt.legend(loc='upper left')    plt.show()cv2.waitKey(0)