目標檢測之一（傳統演算法和深度學習的原始碼學習）
阿新 • • 發佈：2019-01-06
#include<opencv2\opencv.hpp>
#include<opencv2\core\core.hpp>
#include<opencv2\highgui\\highgui.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int main()
{
string xmlPath = "car_model.xml"; //訓練好的分類器xml檔案
CascadeClassifier ccf; //建立分類器物件
Mat img;
if (!ccf.load(xmlPath)) //載入訓練檔案
{
cout << "不能載入指定的xml檔案" << endl;
return 0;
}
namedWindow("car");
bool stop = false;
//獲取攝像頭影象
//VideoCapture cap(0);
//獲取資料夾圖片序列
string img_path = "data";//把圖片放到當前目錄的data資料夾下即可，檔名隨便都可以
vector<cv::String> vec_img;
glob(img_path, vec_img);
if (vec_img.empty())
{
std::cout << "there is no pictures." << endl;
return -1;
}
//寫入視訊檔案，我處理的是序列圖片，所以我把檢測結果寫成視訊檔案了，便於觀察
//VideoWriter writer;
//string video_name = "car1.avi";
//Mat temp = imread(vec_img[0]);
//int frame_fps = 15;//幀率
//writer = VideoWriter(video_name, CV_FOURCC('X', 'V', 'I', 'D'), frame_fps, Size(temp.cols, temp.rows));
while (!stop)
{
int64 start = getTickCount();
for (int i = 0; i < vec_img.size(); ++i)
{
img = imread(vec_img[i]);
//if (!cap.read(img))
// break;
//cap >> img;
vector<Rect> cars; //建立一個容器儲存檢測出來的車輛
Mat gray;
cvtColor(img, gray, CV_BGR2GRAY); //轉換成灰度圖，因為harr特徵從灰度圖中提取
//equalizeHist(gray, gray); //直方圖均衡行
ccf.detectMultiScale(gray, cars, 1.1, 3, 0, Size(10, 10), Size(100, 100)); //檢測車輛
//cout << cars.size() << endl;
for (vector<Rect>::const_iterator iter = cars.begin(); iter != cars.end(); iter++)
{
rectangle(img, *iter, Scalar(0, 0, 255), 2, 8); //畫出矩形
}
imshow("car", img);
//writer.write(img);
if (waitKey(2) == 'q')//按q退出
{
stop = true;
break;
}
}
//計算執行時間
cout << (getTickCount() - start) / getTickFrequency() << endl;
break;
}
return 1;
}