本文实例为大家分享了OpenCV实现人脸检测功能的具体代码，供大家参考，具体内容如下
1、HAAR级联检测
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
|
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
#include <iostream>
#include <cstdlib>
using namespace std;
int main(int artc, char** argv) {
face_detect_haar();
waitKey(0);
return 0;
}
void face_detect_haar() {
CascadeClassifier faceDetector;
std::string haar_data_file = "./models/haarcascades/haarcascade_frontalface_alt_tree.xml";
faceDetector.load(haar_data_file);
vector<Rect> faces;
//VideoCapture capture(0);
VideoCapture capture("./video/test.mp4");
Mat frame, gray;
int count=0;
while (capture.read(frame)) {
int64 start = getTickCount();
if (frame.empty())
{
break;
}
// 水平镜像调整
// flip(frame, frame, 1);
imshow("input", frame);
if (frame.channels() == 4)
cvtColor(frame, frame, COLOR_BGRA2BGR);
cvtColor(frame, gray, COLOR_BGR2GRAY);
equalizeHist(gray, gray);
faceDetector.detectMultiScale(gray, faces, 1.2, 1, 0, Size(30, 30), Size(400, 400));
for (size_t t = 0; t < faces.size(); t++) {
count++;
rectangle(frame, faces[t], Scalar(0, 255, 0), 2, 8, 0);
}
float fps = getTickFrequency() / (getTickCount() - start);
ostringstream ss;ss.str("");
ss << "FPS: " << fps << " ; inference time: " << time << " ms";
putText(frame, ss.str(), Point(20, 20), 0, 0.75, Scalar(0, 0, 255), 2, 8);
imshow("haar_face_detection", frame);
if (waitKey(1) >= 0) break;
}
printf("total face: %d\n", count);
}
|
2、 DNN人脸检测
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
|
#include <opencv2/dnn.hpp>
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace cv::dnn;
#include <iostream>
#include <cstdlib>
using namespace std;
const size_t inWidth = 300;
const size_t inHeight = 300;
const double inScaleFactor = 1.0;
const Scalar meanVal(104.0, 177.0, 123.0);
const float confidenceThreshold = 0.7;
void face_detect_dnn();
void mtcnn_demo();
int main(int argc, char** argv)
{
face_detect_dnn();
waitKey(0);
return 0;
}
void face_detect_dnn() {
//这里采用tensorflow模型
std::string modelBinary = "./models/dnn/face_detector/opencv_face_detector_uint8.pb";
std::string modelDesc = "./models/dnn/face_detector/opencv_face_detector.pbtxt";
// 初始化网络
dnn::Net net = readNetFromTensorflow(modelBinary, modelDesc);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(DNN_TARGET_CPU);
if (net.empty())
{
printf("Load models fail...\n");
return;
}
// 打开摄像头
// VideoCapture capture(0);
VideoCapture capture("./video/test.mp4");
if (!capture.isOpened()) {
printf("Don't find video...\n");
return;
}
Mat frame;
int count=0;
while (capture.read(frame)) {
int64 start = getTickCount();
if (frame.empty())
{
break;
}
// 水平镜像调整
// flip(frame, frame, 1);
imshow("input", frame);
if (frame.channels() == 4)
cvtColor(frame, frame, COLOR_BGRA2BGR);
// 输入数据调整
Mat inputBlob = blobFromImage(frame, inScaleFactor,
Size(inWidth, inHeight), meanVal, false, false);
net.setInput(inputBlob, "data");
// 人脸检测
Mat detection = net.forward("detection_out");
vector<double> layersTimings;
double freq = getTickFrequency() / 1000;
double time = net.getPerfProfile(layersTimings) / freq;
Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
ostringstream ss;
for (int i = 0; i < detectionMat.rows; i++)
{
// 置信度 0～1之间
float confidence = detectionMat.at<float>(i, 2);
if (confidence > confidenceThreshold)
{
count++;
int xLeftBottom = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
int yLeftBottom = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
Rect object((int)xLeftBottom, (int)yLeftBottom,
(int)(xRightTop - xLeftBottom),
(int)(yRightTop - yLeftBottom));
rectangle(frame, object, Scalar(0, 255, 0));
ss << confidence;
std::string conf(ss.str());
std::string label = "Face: " + conf;
int baseLine = 0;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
Size(labelSize.width, labelSize.height + baseLine)),
Scalar(255, 255, 255), FILLED);
putText(frame, label, Point(xLeftBottom, yLeftBottom),
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
}
}
float fps = getTickFrequency() / (getTickCount() - start);
ss.str("");
ss << "FPS: " << fps << " ; inference time: " << time << " ms";
putText(frame, ss.str(), Point(20, 20), 0, 0.75, Scalar(0, 0, 255), 2, 8);
imshow("dnn_face_detection", frame);
if (waitKey(1) >= 0) break;
}
printf("total face: %d\n", count);
}
|
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/u012156872/article/details/104298472