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/*
*
* This file is part of the open-source SeetaFace engine, which includes three modules:
* SeetaFace Detection, SeetaFace Alignment, and SeetaFace Identification.
*
* This file is an example of how to use SeetaFace engine for face detection, the
* face detection method described in the following paper:
*
*
* Funnel-structured cascade for multi-view face detection with alignment awareness,
* Shuzhe Wu, Meina Kan, Zhenliang He, Shiguang Shan, Xilin Chen.
* In Neurocomputing (under review)
*
*
* Copyright (C) 2016, Visual Information Processing and Learning (VIPL) group,
* Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
*
* The codes are mainly developed by Shuzhe Wu (a Ph.D supervised by Prof. Shiguang Shan)
*
* As an open-source face recognition engine: you can redistribute SeetaFace source codes
* and/or modify it under the terms of the BSD 2-Clause License.
*
* You should have received a copy of the BSD 2-Clause License along with the software.
* If not, see < https://opensource.org/licenses/BSD-2-Clause>.
*
* Contact Info: you can send an email to SeetaFace@vipl.ict.ac.cn for any problems.
*
* Note: the above information must be kept whenever or wherever the codes are used.
*
*/
#include <cstdint>
#include <fstream>
#include <iostream>
#include <string>
#include<conio.h>
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "face_detection.h"
using namespace std;
int main(int argc, char** argv) {
if (argc < 3) {
cout << "Usage: " << argv[0]
<< " image_path model_path"
<< endl;
return -1;
}
const char* img_path = argv[1];
seeta::FaceDetection detector(argv[2]);
detector.SetMinFaceSize(40);
detector.SetScoreThresh(2.f);
detector.SetImagePyramidScaleFactor(0.8f);
detector.SetWindowStep(4, 4);
cv::Mat img = cv::imread(img_path, cv::IMREAD_UNCHANGED);
cv::Mat img_gray;
if (img.channels() != 1)
cv::cvtColor(img, img_gray, cv::COLOR_BGR2GRAY);
else
img_gray = img;
seeta::ImageData img_data;
img_data.data = img_gray.data;
img_data.width = img_gray.cols;
img_data.height = img_gray.rows;
img_data.num_channels = 1;
long t0 = cv::getTickCount();
std::vector<seeta::FaceInfo> faces = detector.Detect(img_data);
long t1 = cv::getTickCount();
double secs = (t1 - t0)/cv::getTickFrequency();
cout << "Detections takes " << secs << " seconds " << endl;
#ifdef USE_OPENMP
cout << "OpenMP is used." << endl;
#else
cout << "OpenMP is not used. " << endl;
#endif
#ifdef USE_SSE
cout << "SSE is used." << endl;
#else
cout << "SSE is not used." << endl;
#endif
cout << "Image size (wxh): " << img_data.width << "x"
<< img_data.height << endl;
cv::Rect face_rect;
int32_t num_face = static_cast<int32_t>(faces.size());
for (int32_t i = 0; i < num_face; i++) {
face_rect.x = faces[i].bbox.x;
face_rect.y = faces[i].bbox.y;
face_rect.width = faces[i].bbox.width;
face_rect.height = faces[i].bbox.height;
cv::rectangle(img, face_rect, CV_RGB(0, 0, 255), 4, 8, 0);
}
cv::namedWindow("Test", cv::WINDOW_AUTOSIZE);
cv::imshow("Test", img);
cv::waitKey(0);
cv::destroyAllWindows();
} |
