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 cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import 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)