OpenCV检测钻孔

Question

我正在一个项目中，我必须检测表面上的钻孔。 （顶部两个孔，仅用于定向目的）

在检测到孔之后，图案将判断孔的位置并给出结果。 我已经创建了一个覆盖网格布局，并将其放置在camera2api预览上，以便用户可以对齐孔并进行扫描（真正的测试不会是LCD屏幕上的图片，如屏幕截图所示）

目前，我正在基于网格裁剪图像并将其大小调整为1920x2560，以具有用于模式判断的一致帧，这使得单个网格大约为300px。 我无法检测到斑点，是否有人可以建议我应该为这项工作选择哪种过滤方式，以及是否有比使用网格布局更好的方法，而不是使用网格布局，因为孔相对于定向孔的位置很重要最终结果（x和y轴）

这是我的代码：

Mat srcMat = resizeAndCropMatToGrid(mats[0]);
        if (srcMat == null) {
            exception = new Exception("Cropping Failed");
            errorMessage = "Unable to crop image based on grid";
            return null;
        }
        matProgressTask = srcMat;
        Mat processedMat = new Mat();
        Imgproc.cvtColor(srcMat, processedMat, Imgproc.COLOR_BGR2GRAY);
    Imgproc.GaussianBlur(processedMat, processedMat, new org.opencv.core.Size(5, 5), 5);
    Imgproc.threshold(processedMat, processedMat, 115, 255, Imgproc.THRESH_BINARY);


        matProgressTask = processedMat;


        FeatureDetector featureDetector = FeatureDetector.create(FeatureDetector.SIMPLEBLOB);
        featureDetector.read(Environment.getExternalStorageDirectory() + "/Android/blob.xml");
        MatOfKeyPoint matOfKeyPoint = new MatOfKeyPoint();
        featureDetector.detect(processedMat, matOfKeyPoint);
        KeyPoint[] keyPointsArray = matOfKeyPoint.toArray();
        Log.e("keypoints", "" + Arrays.toString(keyPointsArray));
            if (keyPointsArray.length < 1) {
            exception = new Exception("Blobs Missing");
            errorMessage = "Error: Unable to filter blobs";
        } else {
            try {
                MatOfKeyPoint matOfKeyPointFilteredBlobs = new MatOfKeyPoint(keyPointsArray);
                Features2d.drawKeypoints(srcMat, matOfKeyPointFilteredBlobs, srcMat, new Scalar(255, 0, 0), Features2d.DRAW_OVER_OUTIMG);
            } catch (Exception e) {
                e.printStackTrace();
                exception = e;
                errorMessage = "Error: Unable to draw Blobs";
                return null;
            }
            matProgressTask = srcMat;
            onProgressUpdate();
            patterData = pinpointBlobsToGetData(keyPointsArray);
            if (patterData == null) {
                exception = new Exception("Unable to establish pattern");
                errorMessage = "Error: Key points array is null";
            }
        }

这是我正在使用的大型文件配置：

 <?xml version="1.0"?> <opencv_storage> <format>3</format> <thresholdStep>10.</thresholdStep> <minThreshold>50.</minThreshold> <maxThreshold>120.</maxThreshold> <minRepeatability>2</minRepeatability> <minDistBetweenBlobs>20.</minDistBetweenBlobs> <filterByColor>1</filterByColor> <blobColor>0</blobColor> <filterByArea>1</filterByArea> <minArea>2300.</minArea> <maxArea>4500.</maxArea> <filterByCircularity>1</filterByCircularity> <minCircularity>0.2</minCircularity> <maxCircularity>1.0</maxCircularity> <filterByInertia>1</filterByInertia> <minInertiaRatio>0.2</minInertiaRatio> <maxInertiaRatio>1.0</maxInertiaRatio> <filterByConvexity>1</filterByConvexity> <minConvexity>0.2</minConvexity> <maxConvexity>1.0</maxConvexity> </opencv_storage> 

Answer 1

我正在使用Python。

对于您提供的第二张图片，我成功检测到了孔...

...使用此代码...

import cv2
import numpy as np

img = cv2.imread("C:\\Users\\Link\\Desktop\\2.jpg")
# cv2.imshow("original", img)

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# cv2.imshow("gray", gray)

blur = cv2.medianBlur(gray, 31)
# cv2.imshow("blur", blur)

ret, thresh = cv2.threshold(blur, 127, 255, cv2.THRESH_OTSU)
# cv2.imshow("thresh", thresh)

canny = cv2.Canny(thresh, 75, 200)
# cv2.imshow('canny', canny)

im2, contours, hierarchy = cv2.findContours(canny, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

contour_list = []
for contour in contours:
    approx = cv2.approxPolyDP(contour, 0.01 * cv2.arcLength(contour, True), True)
    area = cv2.contourArea(contour)
    if 5000 < area < 15000:
        contour_list.append(contour)

msg = "Total holes: {}".format(len(approx)//2)
cv2.putText(img, msg, (20, 40), cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 2, cv2.LINE_AA)

cv2.drawContours(img, contour_list, -1, (0, 255, 0), 2)
cv2.imshow('Objects Detected', img)

cv2.imwrite("detected_holes.png", img)

cv2.waitKey(0)

现在，第一个有点不同。 相同的代码将无法检测正确数量的孔。 该程序还将继续检测明显缺少孔（缺少一些主要孔的孔）。

这是我正在谈论的示例：

不仅计数器在这种情况下是错误的，而且主要的问题是无法检测到右下角的孔。

Answer 2

因此，我已经设法通过将垫子直接传递给FeatureDetector类来解决它，而无需任何事先处理...

            Mat srcMat = mats[0];
        if (srcMat == null) {
            exception = new Exception("Cropping Failed");
            errorMessage = "Unable to crop image based on grid";
            return null;
        }
        matProgressTask = srcMat;

        FeatureDetector featureDetector = FeatureDetector.create(FeatureDetector.SIMPLEBLOB);
        featureDetector.read(Environment.getExternalStorageDirectory() + "/Android/blob.xml");
        Log.e("LoadingBlob", "wqfqfwq");
        MatOfKeyPoint matOfKeyPoint = new MatOfKeyPoint();
        featureDetector.detect(srcMat, matOfKeyPoint);
        KeyPoint[] keyPointsArray = matOfKeyPoint.toArray();
        Log.e("keypoints", "" + Arrays.toString(keyPointsArray));
            if (keyPointsArray.length < 1) {
            exception = new Exception("Blobs Missing");
            errorMessage = "Error: Unable to filter blobs";
        } else {
            try {
                MatOfKeyPoint matOfKeyPointFilteredBlobs = new MatOfKeyPoint(keyPointsArray);
                Features2d.drawKeypoints(srcMat, matOfKeyPointFilteredBlobs, srcMat, new Scalar(0, 255, 0), Features2d.DRAW_OVER_OUTIMG);
            } catch (Exception e) {
                e.printStackTrace();
                exception = e;
                errorMessage = "Error: Unable to draw Blobs";
                return null;
            }
            matProgressTask = srcMat;
            onProgressUpdate();
            patterData = pinpointBlobsToGetData(keyPointsArray);
            if (patterData == null) {
                exception = new Exception("Unable to establish pattern");
                errorMessage = "Error: Key points array is null";
            }
        }

我的特征检测器参数文件是：

 <?xml version="1.0"?> <opencv_storage> <format>3</format> <thresholdStep>10.</thresholdStep> <minThreshold>50.</minThreshold> <maxThreshold>120.</maxThreshold> <minRepeatability>2</minRepeatability> <minDistBetweenBlobs>20.</minDistBetweenBlobs> <filterByColor>0</filterByColor> <blobColor>0</blobColor> <filterByArea>1</filterByArea> <minArea>3000.</minArea> <maxArea>10000.</maxArea> <filterByCircularity>1</filterByCircularity> <minCircularity>0.3</minCircularity> <maxCircularity>1.0</maxCircularity> <filterByInertia>1</filterByInertia> <minInertiaRatio>0.3</minInertiaRatio> <maxInertiaRatio>1.0</maxInertiaRatio> <filterByConvexity>1</filterByConvexity> <minConvexity>0.3</minConvexity> <maxConvexity>1.0</maxConvexity> </opencv_storage> 

结果图像：