前言
人脸检测标准库FDDB详细介绍了数据库和使用方法。对于训练的模型，如何评估模型的效果呢，本文对此进行介绍。说实话，参考了很多博客，但是感觉都不是很明白(当然本文也会有瑕疵)，故在此记录！
测试环境
1.安装Perl；
2.安装Gnuplot；
操作步骤
1.根据训练好的模型测试数据库的人脸检测结果，并将结果输出，输出格式与要求一致即可，即out-fold-**.txt和results.txt；
检测结果格式如下：
... <image name i> <number of faces in this image =im> <face i1> <face i2> ... <face im> ...
shapes format:
4 a. Rectangular regions Each face region is represented as: <left_x top_y width height detection_score> 4 b. Elliptical regions Each face region is represented as: <major_axis_radius minor_axis_radius angle center_x center_y detection_score>.
这里需要得到detection_score这个参数，如何得到这个参数是一个好问题，可以使用opencv自带的函数获取，也可以使用其他方法获取(fddb_faq);
cascade.detectMultiScale(img, objs, reject_levels, level_weights, scale_factor, min_neighbors, 0, cv::Size(), cv::Size(), true);
fddb_faq:
Q. How do you compute the detection score that is to be included in the face detection output file? The score included in the output file of a face detection system should be obtained by the system itself. The evaluation code included in the benchmark varies the threshold on this score to obtain different points on the desired ROC curve. Q. What range should the detection score be? The range of scores can lie anywhere on the real line (-infinity to infinity). In other words, the scores are used to order the detections, and their absolute values do not matter.
本文使用的是opencv自带函数和IOU判断结合来获取的。
//re:https://blog.csdn.net/xukaiwen_2016/article/details/52318476?locationNum=6 /************************************************************************ * File: genResult.cpp * Coder: AMY * Email:happyamyhope@163.com * Date: 2018/10/15 * ChLog: score max. * Re: http://haoxiang.org/2013/11/opencv-detectmultiscale-output-detection-score/ ************************************************************************/ #include "opencv2/objdetect/objdetect.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include <cctype> #include <iostream> #include <fstream> #include <iterator> #include <stdio.h> std::vector<cv::Rect> detectAndScore(cv::Mat& img, cv::CascadeClassifier& cascade, double scale, double* score); std::vector<cv::Rect> detectAndScoreMax(cv::Mat& img, cv::CascadeClassifier& cascade, double scale, double* score); std::string cascadeName = "..//src//haar_roboman_ff_alt2.xml"; //compute iou. float compute_iou(cv::Rect boxA, cv::Rect boxB) { int xA = std::max(boxA.x, boxB.x); int yA = std::max(boxA.y, boxB.y); int xB = std::min(boxA.x+boxA.width, boxB.x+boxB.width); int yB = std::min(boxA.y+boxA.height, boxB.y+boxB.height); float inter_area = std::max(0, xB-xA+1) * std::max(0, yB-yA+1); float boxA_area = boxA.width * boxA.height; float boxB_area = boxB.width * boxB.height; float iou = inter_area / (boxA_area + boxB_area - inter_area); return iou; } int main(int argc, const char** argv) { cv::Mat frame, frameCopy, image; std::string inputName; std::string dir; cv::CascadeClassifier cascade; double scale = 1; cascade.load(cascadeName); std::ofstream out_all_txt("result_all.txt"); for (unsigned int file_num=1; file_num<11;file_num++) { std::string str = std::to_string(file_num); if (str.size() < 2) str = "0" + str; std::string out_name = "out-fold-" + str + ".txt"; std::cout << "start file " << out_name << std::endl; std::ifstream in_txt("..//FDDB-folds//FDDB-fold-" + str + ".txt"); std::ofstream out_txt(out_name); std::string dir1 = "..//FDDB-originalPics//"; while (!in_txt.eof()) { getline(in_txt, inputName); if (in_txt.eof()) break; dir = dir1 + inputName; dir += ".jpg"; //dir = "..//FDDB-originalPics//2002//08//25//big//img_674.jpg"; //std::cout << dir << std::endl; image = cv::imread(dir, CV_LOAD_IMAGE_COLOR); if (!image.empty()) { std::ofstream out_txt(out_name, std::ios::app); double scoreBuffer[50]; std::vector<cv::Rect> faces = detectAndScoreMax(image, cascade, scale, scoreBuffer); out_txt << inputName << std::endl << faces.size() << std::endl; out_all_txt << inputName << std::endl << faces.size() << std::endl; //std::cout << faces.size() << std::endl; for (unsigned int i = 0; i<faces.size(); i++) { cv::rectangle(image, faces[i], cv::Scalar(0, 0, 255), 1, 1, 0); out_txt << faces[i].x << " " << faces[i].y << " " << faces[i].width << " " << faces[i].height << " " << scoreBuffer[i] << std::endl; out_all_txt << faces[i].x << " " << faces[i].y << " " << faces[i].width << " " << faces[i].height << " " << scoreBuffer[i] << std::endl; } faces.clear(); } //cv::imshow("src", image); //cv::waitKey(100); } //if (in_txt.eof()) std::cout << "[EOF reached]" << std::endl; //else std::cout << "[EOF reading]" << std::endl; in_txt.close(); out_txt.close(); } out_all_txt.close(); cv::waitKey(1); return 0; } std::vector<cv::Rect> detectAndScoreMax(cv::Mat& color, cv::CascadeClassifier& cascade, double scale, double* scoreBuffer) { cv::Mat gray; cv::Mat img(cvRound(color.rows / scale), cvRound(color.cols / scale), CV_8UC1); cv::cvtColor(color, gray, CV_BGR2GRAY); cv::resize(gray, img, img.size(), 0, 0, CV_INTER_LINEAR); cv::equalizeHist(img, img); const float scale_factor(1.2f); const int min_neighbors(3); std::vector<cv::Rect> faces; std::vector<int> reject_levels; std::vector<double> level_weights; cascade.detectMultiScale(img, faces, reject_levels, level_weights, scale_factor, min_neighbors, 0, cv::Size(), cv::Size(), true); //std::cout << "faces.size(): " << faces.size() << "---level_weights.size(): " << level_weights.size() << std::endl; for (unsigned int n = 0; n < faces.size(); n++) { scoreBuffer[n] = level_weights[n]; //std::cout << "level_weight: " << level_weights[n] << std::endl; } return faces; } std::vector<cv::Rect> detectAndScore(cv::Mat& color, cv::CascadeClassifier& cascade, double scale, double* scoreBuffer) { cv::Mat gray; cv::Mat img(cvRound(color.rows / scale), cvRound(color.cols / scale), CV_8UC1); cv::cvtColor(color, gray, CV_BGR2GRAY); cv::resize(gray, img, img.size(), 0, 0, CV_INTER_LINEAR); cv::equalizeHist(img, img); const float scale_factor(1.2f); const int min_neighbors(3); //long t0 = cv::getTickCount(); std::vector<cv::Rect> faces; cascade.detectMultiScale(img, faces, scale_factor, min_neighbors, 0, cv::Size(), cv::Size()); //long t1 = cv::getTickCount(); //double secs = (t1 - t0)/cv::getTickFrequency(); //std::cout << "Detections takes " << secs << " seconds " << std::endl; std::vector<cv::Rect> objs; std::vector<int> reject_levels; std::vector<double> level_weights; cascade.detectMultiScale(img, objs, reject_levels, level_weights, scale_factor, min_neighbors, 0, cv::Size(), cv::Size(), true); //std::cout << "faces.size(): " << faces.size() << "---objs.size(): " << objs.size() << std::endl; for (unsigned int n = 0; n < faces.size(); n++) { int iou_max_idx = 0; float iou_max = 0.0; for (unsigned int k=0; k < objs.size(); k++) { float iou = compute_iou(faces[n], objs[k]); if ( (iou>0.5) && (reject_levels[k]>=15) && (iou>iou_max) ) { iou_max = iou; iou_max_idx = k; //std::cout << "iou: " << iou << "---reject_levels[k]: " << reject_levels[k] << std::endl; } } scoreBuffer[n] = level_weights[iou_max_idx]; } return faces; }