运行环境
- visual studio 2017（2019也可）
- opencv3.4（410也可）
xml文件
- 从OpenCV目录里找
C:\OpenCV4.0\opencv\sources\data\haarcascades
这里也有其它目标检测的xml文件，有空可以一试
- 为方便拷贝出来放在代码工程的debug目录
代码
代码从OpenCV提供的sample提取，稍微改一改
C:\OpenCV4.0\opencv\sources\samples\cpp\facedetect.cpp
这里面其它sample也值得一学
#include "opencv2/objdetect.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
using namespace std;
using namespace cv;
static void help()
{
cout << "\nThis program demonstrates the use of cv::CascadeClassifier class to detect objects (Face + eyes). You can use Haar or LBP features.\n"
"This classifier can recognize many kinds of rigid objects, once the appropriate classifier is trained.\n"
"It's most known use is for faces.\n"
"Usage:\n"
"./facedetect [--cascade=<cascade_path> this is the primary trained classifier such as frontal face]\n"
" [--nested-cascade[=nested_cascade_path this an optional secondary classifier such as eyes]]\n"
" [--scale=<image scale greater or equal to 1, try 1.3 for example>]\n"
" [--try-flip]\n"
" [filename|camera_index]\n\n"
"see facedetect.cmd for one call:\n"
"./facedetect --cascade=\"data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"data/haarcascades/haarcascade_eye_tree_eyeglasses.xml\" --scale=1.3\n\n"
"During execution:\n\tHit any key to quit.\n"
"\tUsing OpenCV version " << CV_VERSION << "\n" << endl;
}
void detectAndDraw(Mat& img, CascadeClassifier& cascade,
CascadeClassifier& nestedCascade,
double scale, bool tryflip);
string cascadeName;
string nestedCascadeName;
int main(int argc, const char** argv)
{
VideoCapture capture;
Mat frame, image;
string inputName;
bool tryflip;
CascadeClassifier cascade, nestedCascade;
double scale;
cv::CommandLineParser parser(argc, argv,
"{help h||}"
"{cascade|data/haarcascades/haarcascade_frontalface_alt.xml|}"
"{nested-cascade|data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}"
"{scale|1|}{try-flip||}{@filename||}"
);
if (parser.has("help"))
{
help();
return 0;
}
cascadeName = parser.get<string>("cascade");
nestedCascadeName = parser.get<string>("nested-cascade");
scale = parser.get<double>("scale");
if (scale < 1)
scale = 1;
tryflip = parser.has("try-flip");
inputName = parser.get<string>("@filename");
if (!parser.check())
{
parser.printErrors();
return 0;
}
if (!nestedCascade.load(samples::findFileOrKeep(nestedCascadeName)))
cerr << "WARNING: Could not load classifier cascade for nested objects" << endl;
if (!cascade.load(samples::findFile(cascadeName)))
{
cerr << "ERROR: Could not load classifier cascade" << endl;
help();
return -1;
}
if (inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1))
{
int camera = inputName.empty() ? 0 : inputName[0] - '0';
if (!capture.open(camera))
{
cout << "Capture from camera #" << camera << " didn't work" << endl;
return 1;
}
}
else if (!inputName.empty())
{
image = imread(samples::findFileOrKeep(inputName), IMREAD_COLOR);
if (image.empty())
{
if (!capture.open(samples::findFileOrKeep(inputName)))
{
cout << "Could not read " << inputName << endl;
return 1;
}
}
}
else
{
image = imread(samples::findFile("lena.jpg"), IMREAD_COLOR);
if (image.empty())
{
cout << "Couldn't read lena.jpg" << endl;
return 1;
}
}
if (capture.isOpened())
{
cout << "Video capturing has been started ..." << endl;
for (;;)
{
capture >> frame;
if (frame.empty())
break;
Mat frame1 = frame.clone();
detectAndDraw(frame1, cascade, nestedCascade, scale, tryflip);
char c = (char)waitKey(10);
if (c == 27 || c == 'q' || c == 'Q')
break;
}
}
else
{
cout << "Detecting face(s) in " << inputName << endl;
if (!image.empty())
{
detectAndDraw(image, cascade, nestedCascade, scale, tryflip);
waitKey(0);
}
else if (!inputName.empty())
{
/* assume it is a text file containing the
list of the image filenames to be processed - one per line */
FILE* f;
fopen_s(&f, inputName.c_str(), "rt");
if (f)
{
char buf[1000 + 1];
while (fgets(buf, 1000, f))
{
int len = (int)strlen(buf);
while (len > 0 && isspace(buf[len - 1]))
len--;
buf[len] = '\0';
cout << "file " << buf << endl;
image = imread(buf, 1);
if (!image.empty())
{
detectAndDraw(image, cascade, nestedCascade, scale, tryflip);
char c = (char)waitKey(0);
if (c == 27 || c == 'q' || c == 'Q')
break;
}
else
{
cerr << "Aw snap, couldn't read image " << buf << endl;
}
}
fclose(f);
}
}
}
return 0;
}
void detectAndDraw(Mat& img, CascadeClassifier& cascade,
CascadeClassifier& nestedCascade,
double scale, bool tryflip)
{
double t = 0;
vector<Rect> faces, faces2;
const static Scalar colors[] =
{
Scalar(255,0,0),
Scalar(255,128,0),
Scalar(255,255,0),
Scalar(0,255,0),
Scalar(0,128,255),
Scalar(0,255,255),
Scalar(0,0,255),
Scalar(255,0,255)
};
Mat gray, smallImg;
cvtColor(img, gray, COLOR_BGR2GRAY);
double fx = 1 / scale;
resize(gray, smallImg, Size(), fx, fx, INTER_LINEAR_EXACT);
equalizeHist(smallImg, smallImg);
t = (double)getTickCount();
cascade.detectMultiScale(smallImg, faces,
1.1, 2, 0
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
| CASCADE_SCALE_IMAGE,
Size(30, 30));
if (tryflip)
{
flip(smallImg, smallImg, 1);
cascade.detectMultiScale(smallImg, faces2,
1.1, 2, 0
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
| CASCADE_SCALE_IMAGE,
Size(30, 30));
for (vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); ++r)
{
faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height));
}
}
t = (double)getTickCount() - t;
printf("detection time = %g ms\n", t * 1000 / getTickFrequency());
for (size_t i = 0; i < faces.size(); i++)
{
Rect r = faces[i];
Mat smallImgROI;
vector<Rect> nestedObjects;
Point center;
Scalar color = colors[i % 8];
int radius;
double aspect_ratio = (double)r.width / r.height;
//if (0.75 < aspect_ratio && aspect_ratio < 1.3)
//{
// center.x = cvRound((r.x + r.width*0.5)*scale);
// center.y = cvRound((r.y + r.height*0.5)*scale);
// radius = cvRound((r.width + r.height)*0.25*scale);
// circle(img, center, radius, color, 3, 8, 0);
//}
//else
rectangle(img, Point(cvRound(r.x*scale), cvRound(r.y*scale)),
Point(cvRound((r.x + r.width - 1)*scale), cvRound((r.y + r.height - 1)*scale)),
color, 3, 8, 0);
if (nestedCascade.empty())
continue;
smallImgROI = smallImg(r);
nestedCascade.detectMultiScale(smallImgROI, nestedObjects,
1.1, 2, 0
//|CASCADE_FIND_BIGGEST_OBJECT
//|CASCADE_DO_ROUGH_SEARCH
//|CASCADE_DO_CANNY_PRUNING
| CASCADE_SCALE_IMAGE,
Size(30, 30));
for (size_t j = 0; j < nestedObjects.size(); j++)
{
Rect nr = nestedObjects[j];
center.x = cvRound((r.x + nr.x + nr.width*0.5)*scale);
center.y = cvRound((r.y + nr.y + nr.height*0.5)*scale);
radius = cvRound((nr.width + nr.height)*0.25*scale);
circle(img, center, radius, color, 3, 8, 0);
}
}
imshow("result", img);
}
代码编译成功后编辑一个bat文件即可，例如：
XML_object_detect --cascade=./data/haarcascades/haarcascade_frontalface_alt.xml --scale=1.3 --try-flip ./img/261068.jpg
pause有个比较全面的博客值得参考https://blog.csdn.net/u012679707/article/details/80376969