前言

傳統(tǒng)的邊緣檢測對一些內(nèi)容，色彩比較豐富的圖像，提取出來的邊緣并不理想，ECCV2014來自于微軟研究院的Piotr等人的《Edge Boxes: Locating Object Proposals from Edges》這個文章，采用的是純圖像的方法實現(xiàn)了目標(biāo)檢測的算法，也是基于物體的邊緣分割。這個算法對邊緣的提取要好過傳統(tǒng)的canny算法。如果想要深入了解可以看大神的論文。

Edge Boxes

1.檢測代碼

void edgebox(Mat &src,Mat &dst, modelInit &model, paraClass &o) {Mat I = src.clone();assert(I.rows != 0 && I.cols != 0);clock_t begin = clock();model.opts.nms = 1;Mat I_resize;float shrink = 4;resize(I, I_resize, Size(), 1 / shrink, 1 / shrink);tuple<Mat, Mat, Mat, Mat> detect = edgesDetect(I_resize, model, 4);Mat E, O, unuse1, unuse2;tie(E, O, unuse1, unuse2) = detect;E = edgesNms(E, O, 2, 0, 1, model.opts.nThreads);Mat bbs;cout << 1 << endl;bbs = edgebox_main(E, O, o) * shrink;cout << "time:" << ((double)clock() - begin) / CLOCKS_PER_SEC << "s" << endl;I.copyTo(dst);//for top10 box scoresfor (int i = 0; i < model.opts.showboxnum; i++) {//draw the bboxPoint2f p1(bbs.at<float>(i, 0), bbs.at<float>(i, 1));Point2f p2(bbs.at<float>(i, 0) + bbs.at<float>(i, 2), bbs.at<float>(i, 1) + bbs.at<float>(i, 3));Point2f p3(bbs.at<float>(i, 0), bbs.at<float>(i, 1) + bbs.at<float>(i, 3));Point2f p4(bbs.at<float>(i, 0) + bbs.at<float>(i, 2), bbs.at<float>(i, 1));int tlx = (int)bbs.at<float>(i, 0);int tly = (int)bbs.at<float>(i, 1);//brx may be bigger than I.cols-1//bry may be bigger than I.rows-1int brx = std::min((int)(bbs.at<float>(i, 0) + bbs.at<float>(i, 2)), I.cols - 1);int bry = std::min((int)(bbs.at<float>(i, 1) + bbs.at<float>(i, 3)), I.rows - 1);Mat box;box = I.colRange(tlx, brx).rowRange(tly, bry);rectangle(dst, p1, p2, Scalar(0, 255, 0), 1);Point2f ptext(bbs.at<float>(i, 0), bbs.at<float>(i, 1) - 3);putText(dst, to_string(bbs.at<float>(i, 4)), ptext, FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 1);} }void edgeDetection(Mat &src, Mat &dst, modelInit &model, paraClass &o) {Mat I = src.clone();assert(I.rows != 0 && I.cols != 0);///clock_t begin = clock();model.opts.nms = 1;Mat I_resize;float shrink = 4;tuple<Mat, Mat, Mat, Mat> detect = edgesDetect(I, model, 4);Mat E, O, unuse1, unuse2;tie(E, O, unuse1, unuse2) = detect;E = edgesNms(E, O, 2, 0, 1, model.opts.nThreads);Mat bbs;bbs = edgebox_main(E, O, o) * shrink;double E_min, E_max;cv::minMaxLoc(E, &E_min, &E_max);dst = (E - E_min) / (E_max - E_min) * 255;dst.convertTo(dst, CV_8U); }

2.運行效果

總結(jié)

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