Opencv机器视觉缺陷检测，印刷检测

用巴氏距离，surf特征点匹配检测

代码片段

#巴氏距离
 hist1 = create_hist(imgName)  # 给标准样品绘制直方图
            for i in range(1, 2):
                print(i)  # 打印图片序号
                img = cv2.imread(imgName1)
                hist2 = create_hist(imgName1)  # 给测试样品绘制直方图
                match1 = cv2.compareHist(hist1, hist2, cv2.HISTCMP_BHATTACHARYYA)  # 返回巴氏距离
                match2 = cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL)  # 返回相关性
                print("巴氏距离：%s, 相关性：%s" % (match1, match2))
                print("\n")
                if match2 >= 0.99999999999999:
                    jpg = QtGui.QPixmap("hege.png").scaled(self.label_3.width(), self.label_3.height())
                    self.label_3.setPixmap(jpg)
                else:
                    jpg = QtGui.QPixmap("buhege.png").scaled(self.label_3.width(), self.label_3.height())
                    self.label_3.setPixmap(jpg)
                print("\n")
               

  #采用surf特征点集寻找图片缺陷
                std = cv2.imread(imgName1)
                test = cv2.imread(imgName)

                # 统一尺寸
                std = cv2.resize(std, (int(test.shape[1]), int(test.shape[0])))

                # 标准图透视变换成待测图角度
                std_wrap = rectify(test, std, is_debug=True)
                show = cv2.resize(std_wrap, (740, 580))
                cv2.imwrite("toushi.jpeg", show)
                #os.remove("toushi.jpeg")

                # 调整后的标准图与待测图作差
                diff = cv2.absdiff(test, std_wrap)
                diff = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
                cv2.imwrite("diff.jpeg", diff)

                # 阈值化
                diff_thr = cv2.threshold(diff, 50, 255, cv2.THRESH_TOZERO)[1]
                cv2.imwrite("d_thr.jpeg", diff_thr)

                # 均值滤波
                diff_thr_blur = cv2.blur(diff_thr, (3, 3))
                cv2.imwrite("dt_blur.jpeg", diff_thr_blur)

                # 闭运算后阈值化
                kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
                diff_thr_mor = cv2.morphologyEx(diff_thr, cv2.MORPH_CLOSE, kernel)
                cv2.imwrite("dtb_mor.jpeg", diff_thr_mor)

                # 标记缺陷
                dst = diff_thr_mor
                _, contours, hierarchy1 = cv2.findContours(dst, mode=cv2.RETR_TREE, method=cv2.CHAIN_APPROX_NONE)
                ex = test.copy()
                for c in contours:
                    if len(c) < 10:
                        continue
                    x, y, w, h = cv2.boundingRect(c)
                    cv2.rectangle(ex, (x, y), (x + w, y + h), (255, 255, 0), 2)
                ex = cv2.resize(ex, (640, 480))
                print(ex)
                cv2.imwrite("final.jpeg", ex)
                cv2.waitKey(0)
                compare(match2, img, i)  # 比较并绘制

Pyqt制作简单界面

运行效果图

巴士距离和surf特征点匹配。

缺陷检测（图片作差）

缺陷标定（两张测试图）（蓝色）

surf特征点匹配

巴氏距离直方图

下载链接

1、已生成可执行exe文件。无需任何环境搭建和软件下载，打开即可使用
链接：https://download.csdn.net/download/m0_51381592/20086159

2、源码，环境需要
python3.6（必须）
pyqt
链接：https://download.csdn.net/download/m0_51381592/20086249
源码