如何使用OpenCV在纸上检测注册标记？

本文介绍了如何使用OpenCV在纸上检测注册标记？的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述 我一直在寻找最好的方法来检测纸上的4个黑色方块，并用它们在自己的图像中分离纸张。I've been trying to find the best way to detect the 4 black squares on the paper and use them to isolate the paper in its own image. 推荐答案似乎在您的图片上只有4个黑色方块，所以您必须做的是：It seems that on your image there are only 4 black squares so what you have to do is: 将图片转换为灰色 执行阈值 查找黑色轮廓（在OpenCV中执行此操作之前，必须反转图像，因为默认情况下，OpenCV会找到白色轮廓） li> 循环浏览这些轮廓并找到边界矩形。 执行检查：Convert image to graysDo thresholdFind black contours (before doing this in OpenCV you have to invert your image, because by default OpenCV finds white contours)Cycle through these contours and find bounding rectangle.Do the check: A）Rectangle的面积大于一些常数（在我的解决方案中 100 ）A) Rectangle's area is bigger that some constant (in my solution it was 100) B）矩形的宽度/高度接近1.0在我的心灵中是 [0.9,1.1] 范围）B) Rectangle's width/height is near 1.0 (in my soultion it was [0.9, 1.1] range) / p> The code:Mat img = imread("test.jpg"), gray;vector<Vec4i> hierarchy;vector<vector<Point2i> > contours;cvtColor(img, gray, CV_BGR2GRAY);threshold(gray, gray, 100, 255, THRESH_BINARY);bitwise_not(gray, gray);findContours(gray, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);for(size_t i=0; i<contours.size(); i++){ Rect rect = boundingRect(contours[i]); double k = (rect.height+0.0)/rect.width; if (0.9<k && k<1.1 && rect.area()>100) { drawContours(img, contours, i, Scalar(0,0,255)); }}imshow("result", img);waitKey();结果： 另请参阅这个讨论 - 你不需要4个方块来检测纸。Also read this SO discussion - you don't need that 4 squares to detect paper. 这篇关于如何使用OpenCV在纸上检测注册标记？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！