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【opnecv】检测桌子上多余的物品

backend 2025/6/29 8:36:08

效果展示

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实现原理

1、转化成灰度图

 Mat image = imread("C:\\Users\\chenxinyu\\Desktop\\物品.jpg",IMREAD_COLOR);if (image.empty())return -1;Mat outImage;cvtColor(image, outImage, COLOR_BGR2GRAY);

2、绘制轮廓

在绘制轮廓之前，我们需要进行高斯模糊降噪。

 Mat outBlur,outCanny;GaussianBlur(outImage, outBlur,Size(3,3),1.5);Canny(outBlur, outCanny,100,150);

减少无关噪声对轮廓的影响。
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3、二值化操作

对原图像进行二值化操作，再与轮廓图相加，丰富饱满轮廓图。

 Mat outThreshold;threshold(outImage, outThreshold, 120, 255, THRESH_BINARY_INV);Mat outThreshold1;threshold(outImage, outThreshold1, 220, 255, THRESH_BINARY);Mat outOr;bitwise_or(outCanny, outThreshold, outOr);bitwise_or(outOr, outThreshold1, outOr);

注：第二次二值化是为针对图像中白色蛋壳。

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4、膨胀和腐蚀

去掉多余的线条，让图像更加饱满。

Mat outElement;
outElement=getStructuringElement(MORPH_ELLIPSE, Size(3, 3));
morphologyEx(outOr, outOr, MORPH_CLOSE, outElement);
morphologyEx(outOr, outOr, MORPH_OPEN, outElement);

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5、找轮廓、标识

vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(outOr, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
for (size_t i = 0; i < contours.size(); i++) {// 计算面积double area = contourArea(contours[i]); // 标注瑕疵Rect bbox = boundingRect(contours[i]);rectangle(image, bbox, Scalar(0, 0, 255), 2);  // 红色边界框
}

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整体代码

int demo4()
{Mat image = imread("C:\\Users\\chenxinyu\\Desktop\\物品.jpg",IMREAD_COLOR);if (image.empty())return -1;imshow("111.jpg", image);Mat outImage;cvtColor(image, outImage, COLOR_BGR2GRAY);imshow("222.jpg", outImage);画轮廓Mat outBlur;GaussianBlur(outImage, outBlur,Size(3,3),1.5);imshow("333.jpg", outBlur);Mat outCanny;Canny(outBlur, outCanny,100,150);imshow("444.jpg", outCanny);Mat outCanny1;Canny(outImage, outCanny1, 100, 150);imshow("text1.jpg", outCanny1);//二值分化Mat outThreshold;threshold(outImage, outThreshold, 120, 255, THRESH_BINARY_INV);imshow("555.jpg", outThreshold);Mat outThreshold1;threshold(outImage, outThreshold1, 220, 255, THRESH_BINARY);imshow("text.jpg", outThreshold1);Mat outOr;bitwise_or(outCanny, outThreshold, outOr);bitwise_or(outOr, outThreshold1, outOr);imshow("666.jpg", outOr);Mat outElement;outElement=getStructuringElement(MORPH_ELLIPSE, Size(3, 3));morphologyEx(outOr, outOr, MORPH_CLOSE, outElement);morphologyEx(outOr, outOr, MORPH_OPEN, outElement);imshow("777.jpg", outOr);vector<vector<Point>> contours;vector<Vec4i> hierarchy;findContours(outOr, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);for (size_t i = 0; i < contours.size(); i++) {// 忽略小面积区域（噪声）double area = contourArea(contours[i]);// 标注瑕疵Rect bbox = boundingRect(contours[i]);rectangle(image, bbox, Scalar(0, 0, 255), 2);  // 红色边界框}imshow("888.jpg", image);waitKey(0);
}