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python 与 C++ dlib人脸检测结果对比,供大家参考,具体内容如下
说明:
由于项目需求发现Linux下c++使用dlib进行人脸检测和python使用dlib检测,得到的结果出入比较大,于是写了测试用例,发现影响结果的原因有但不限于:
1.dlib版本不同(影响不大,几个像素的差别)
2.dlib 人脸检测中detector()第二个参数的设置测试结果如下:
python
PDlib.py:
# -*- coding: utf-8 -*- import sys import cv2 import dlib from skimage import io detector = dlib.get_frontal_face_detector() win = dlib.image_window() for f in sys.argv[1:]: img = io.imread(f) dets = detector(img,1) #使用detector进行人脸检测 for i, d in enumerate(dets): x = d.left() y = d.top() w = d.right() h = d.bottom() cv2.rectangle(img, (x, y), (w, h), (0, 255, 0)) print("({},{},{},{})".format( x, y, (w-x), (h-y))) win.set_image(img) io.imsave('./P_Dlib_test.jpg',img) #等待点击 dlib.hit_enter_to_continue()
C++
CDlib.cpp:
#include <dlib/image_processing/frontal_face_detector.h> #include <dlib/opencv.h> #include "opencv2/opencv.hpp" #include <iostream> using namespace dlib; using namespace std; cv::Rect Detect(cv::Mat im) { cv::Rect R; frontal_face_detector detector = get_frontal_face_detector(); array2d<bgr_pixel> img; assign_image(img, cv_image<uchar>(im)); std::vector<rectangle> dets = detector(img);//检测人脸 //查找最大脸 if (dets.size() != 0) { int Max = 0; int area = 0; for (unsigned long t = 0; t < dets.size(); ++t) { if (area < dets[t].width()*dets[t].height()) { area = dets[t].width()*dets[t].height(); Max = t; } } R.x = dets[Max].left(); R.y = dets[Max].top(); R.width = dets[Max].width(); R.height = dets[Max].height(); cout<<"("<<R.x<<","<<R.y<<","<<R.width<<","<<R.height<<")"<<endl; } return R; } int main(int argc, char** argv) { if (argc != 2) { fprintf(stderr, "请输入正确参数\n"); return 1; } string path = argv[1]; try { cv::Mat src, dec; src = cv::imread(path); src.copyTo(dec); cv::cvtColor(dec, dec, CV_BGR2GRAY); cv::Rect box; box = Detect(dec); cv::rectangle(src, box, cv::Scalar(0, 0, 255), 1, 1, 0); cv::imshow("frame", src); cv::imwrite("./C_Dlib_test.jpg", src); cv::waitKey(0);//等待建入 } catch (exception& e) { cout << e.what() << endl; } }
项目编译及运行
python
运行脚本 python PDlib.py G:\DlibTest\data\bush.jpg
C++
- 创建编译文件夹 mkdir cbuild
- 切换到编译目录 cd cbuild
- 生成makefile文件 cmake ..
- 编译项目 make
- 运行可执行文件 ./DlibTest G:\DlibTest\data\bush.jpg
Demo:点击下载
本文Linux下python与C++使用dlib实现人脸检测到此结束。过去的时间会永远流入无边的黑洞,永不再回来,所以要珍惜当下的每一秒。小编再次感谢大家对我们的支持!