夜间车辆检测
// ConsoleApplication36.cpp : 定义控制台应用程序的入口点。
// ConsoleApplication29.cpp : 定义控制台应用程序的入口点。
//
#include "stdafx.h"
#include
#include
#include
#include
using namespace cv;
using namespace std;
using namespace cv::xfeatures2d;
int hmin = 0;
int hmin_Max = 255;
int hmax = 255;
int hmax_Max = 255;
//饱和度 or g
int smin = 0;
int smin_Max = 255;
int smax = 255;
int smax_Max = 255;
//亮度 or r
int vmin = 0;
int vmin_Max = 255;
int vmax = 255;
int vmax_Max = 255;
//显示原图的窗口
string windowName = "src";
//输出图像的显示窗口
string dstName = "dst";
//输出图像
Mat bgr, hsv, dst;
//回调函数
void callBack(int, void*)
{
//输出图像分配内存
//Mat dst1 = Mat::zeros(dst.size(), CV_32FC3);
//Mat dst1 = Mat::zeros(dst.size(), CV_32FC3);
Mat dst1 = Mat::zeros(dst.size(), CV_8UC3);
//掩码
Mat mask;
//inRange(img, Scalar(hmin, smin, vmin), Scalar(hmax, smax, vmax), mask);
//inRange(hsv, Scalar(hmin, smin / float(smin_Max), vmin / float(vmin_Max)), Scalar(hmax, smax / float(smax_Max), vmax / float(vmax_Max)), mask);
inRange(dst, Scalar(hmin, smin, vmin), Scalar(hmax, smax, vmax), mask);
//只保留
for (int r = 0; r < dst.rows; r++)
{
for (int c = 0; c < dst.cols; c++)
{
if (mask.at
{
dst1.at
}
}
}
//输出图像
imshow(dstName, dst1);
//保存图像
/*dst.convertTo(dst, CV_8UC3, 255.0, 0);
imwrite("HSV_inRange.jpg", dst);*/
}
int main()
{
VideoCapture cap("C:\\Users\\lbn\\Desktop\\夜间车辆\\6.mp4");
while (1) {
Mat srcImage;//= imread("C:\\Users\\lbn\\Desktop\\夜间车辆\\01.bmp");
cap >> srcImage;
resize(srcImage, srcImage, Size(srcImage.cols , srcImage.rows ), (0, 0), (0, 0), 3);
Mat asd;
srcImage.copyTo(asd);
imshow("【原图】", srcImage);
for (int i = 0; i < srcImage.rows; i++) {
for (int j = 0; j < srcImage.cols; j++) {
//检测红色区域
if ((srcImage.at
(srcImage.at
{
}
else
{
srcImage.at
/*srcImage.at
srcImage.at
srcImage.at
}
}
}
//imshow("【原图1】", srcImage);
/*Mat grayImage;
cvtColor(srcImage, grayImage, CV_BGR2GRAY);
Mat cannyImage;
Canny(grayImage, cannyImage, 0, 255, 3);
imshow("【canny1】", cannyImage);*/
int g_nStructElementSize = 2;
Mat element = getStructuringElement(MORPH_RECT, Size(2 * g_nStructElementSize + 1, 2 * g_nStructElementSize + 1), Point(g_nStructElementSize, g_nStructElementSize));
dilate(srcImage, srcImage, element); dilate(srcImage, srcImage, element);// erode(srcImage, srcImage, element);
//imshow("【原图2】", srcImage);
for (int i = 0; i < srcImage.rows; i++) {
for (int j = 0; j < srcImage.cols; j++) {
//检测红色区域
if (srcImage.at
srcImage.at
}
}
//imshow("【原图3】", srcImage);
Mat grayImage;
cvtColor(srcImage, grayImage, CV_BGR2GRAY);
Mat cannyImage;
threshold(grayImage, cannyImage, 0, 255, THRESH_BINARY);
//Canny(grayImage, cannyImage, 0, 255, 3);
imshow("【原图3】", cannyImage);
//在二值图像中提取轮廓
vector
vector
findContours(cannyImage, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0));
//对每个轮廓的点集 找逼近多边形
// vector
Mat drawImage = Mat::zeros(srcImage.size(), CV_8UC3);
/*for (int i = 0; i < (int)contours.size(); i++)
{
approxPolyDP(contours[i], approxPoint[i], 3, true);
}*/
drawContours(drawImage, contours, -1, Scalar(255, 255, 255), 1);
/******************************************绘制曲线的方式********************************************/
//用绘制轮廓的函数 绘制曲线
//for (int i = 0; i < (int)approxPoint.size(); i++)
//{
//drawContours(drawImage, contours, i, Scalar(255, 255, 255), 1);
// drawContours(drawImage, approxPoint, i, Scalar(255, 255, 255), 1);
//}
imshow("【原图4】", drawImage);
vector
//vector
for (int i = 0; i < (int)contours.size(); i++)
{
Point2f center;
float radius;
boundRect[i]= boundingRect(Mat(contours[i]));
rectangle(asd, boundRect[i].tl(), boundRect[i].br(), Scalar(0, 255, 255), 1, 8, 0);
minEnclosingCircle(contours[i], center, radius);
//根据得到的圆形和半径 绘制圆形
//circle(asd, static_cast
// , Scalar(0, 255, 255), 1);
// CvRect rect = cvBoundingRect(contours[i], 0);
//cvRectangle(drawImage, cvPoint(rect.x, rect.y), cvPoint(rect.x + rect.width, rect.y + rect.height), CV_RGB(0, 255, 0), 1, 8, 0);
}
/*for (int i = 0; i < (int)approxPoint.size(); i++)
{
rectangle(asd, boundRect[i].tl(), boundRect[i].br(), Scalar(0, 255, 255), 1, 8, 0);
}*/
imshow("【原图5】", asd);
waitKey(1);
}
return 0;
}
//先对图像进行空间的转换（为了之后要提取二值图像）
//Mat grayImage;
//cvtColor(srcImage, grayImage, CV_BGR2GRAY);
////对图像进行滤波，达到较好的效果
//GaussianBlur(grayImage, grayImage, Size(3, 3), 0, 0);
//imshow("【滤波后的图像】", grayImage);
//int g_nStructElementSize = 2;
//Mat element = getStructuringElement(MORPH_ELLIPSE, Size(2 * g_nStructElementSize + 1, 2 * g_nStructElementSize + 1), Point(g_nStructElementSize, g_nStructElementSize));
//erode(grayImage, grayImage, element); erode(grayImage, grayImage, element);
//dilate(grayImage, grayImage, element); dilate(grayImage, grayImage, element); dilate(grayImage, grayImage, element);
////用边缘检测的方式获取二值图像
//Mat cannyImage;
//Canny(grayImage, cannyImage, 0, 255, 3);
////在二值图像中提取轮廓
//vector
//vector
//findContours(cannyImage, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));
////对每个轮廓的点集 找逼近多边形
//vector
//for (int i = 0; i < (int)contours.size(); i++)
//{
// approxPolyDP(contours[i], approxPoint[i], 3, true);
//}
///******************************************绘制曲线的方式********************************************/
////用绘制轮廓的函数 绘制曲线
//Mat drawImage = Mat::zeros(srcImage.size(), CV_8UC3);
//for (int i = 0; i < (int)contours.size(); i++)
//{
// //drawContours(drawImage, contours, i, Scalar(255, 255, 255), 1);
// drawContours(drawImage, contours, i, Scalar(255, 255, 255), 1);
//}
////Mat dst = Mat::zeros(srcImage.size(), CV_8UC3);
//dst = Mat::zeros(srcImage.size(), CV_8UC3);
//for (int r = 0; r < srcImage.rows; r++)
//{
// for (int c = 0; c < srcImage.cols; c++)
// {
// if (drawImage.at
// {
// dst.at
// }
// }
//}
//Point2f center;
//float radius;
//for (int i = 0; i < (int)contours.size(); i++)
//{
// //approxPolyDP(contours[i], approxPoint[i], 3, true);
// minEnclosingCircle(contours[i], center, radius);
// //根据得到的圆形和半径 绘制圆形
// circle(dst, static_cast
// , Scalar(0, 255, 255), 3);
//}
//imshow("【绘制后的图像drawImage】", drawImage);
//imshow("【绘制后的图像dst】", dst);
////dst.convertTo(bgr, CV_32FC3, 1.0 / 255, 0);
////颜色空间转换
////cvtColor(bgr, hsv, COLOR_BGR2HSV);
////定义输出图像的显示窗口
//namedWindow(dstName, 0);
////调节色相 H or b
//createTrackbar("hmin", dstName, &hmin, hmin_Max, callBack);
//createTrackbar("hmax", dstName, &hmax, hmax_Max, callBack);
////调节饱和度 S or g
//createTrackbar("smin", dstName, &smin, smin_Max, callBack);
//createTrackbar("smax", dstName, &smax, smax_Max, callBack);
////调节亮度 V or r
//createTrackbar("vmin", dstName, &vmin, vmin_Max, callBack);
//createTrackbar("vmax", dstName, &vmax, vmax_Max, callBack);
//callBack(0, 0);
//SimpleBlobDetector::Params params;
//size_t minRepeatability; //2 重复的最小次数，只有属于灰度图像斑点的那些二值图像斑点数量大于该值时，该灰度图像斑点才被认为是特征点
//float minDistBetweenBlobs; //10 最小的斑点距离，不同二值图像的斑点间距离小于该值时，被认为是同一个位置的斑点，否则是不同位置上的斑点
//params.filterByColor = true; //斑点颜色的限制变量
//params.blobColor = 255; //表示只提取黑色斑点；如果该变量为255，表示只提取白色斑点
// //阈值控制
//params.minThreshold = 50; //二值化的起始阈值 40
//params.maxThreshold = 255; //二值化的终止阈值 160
//params.thresholdStep = 2; //二值化的阈值步长
// //像素面积大小控制
//params.filterByArea = true;
//params.minArea = 50; //斑点的最小面积
//params.maxArea = 18000; //斑点的最大面积
// //形状（凸）
//params.filterByCircularity = true;
//params.minCircularity = 0.5; //斑点的最小圆度
//params.maxCircularity = std::numeric_limits
// //形状凹
//params.filterByConvexity = false;
//params.minConvexity = 0.5; //0.05f//斑点的最小凹度
//params.maxConvexity = std::numeric_limits
// //形状圆
//params.filterByInertia = false;
//params.minInertiaRatio = 0.5f; //斑点的最小惯性率 圆的值为1，直线的值为0 //.05f
//params.maxInertiaRatio = std::numeric_limits
//Ptr
//vector
//detector1->detect(srcImage, keyPoints);
//Mat image_Star_Blob;
////drawKeypoints(image, keyPoints, image_with_keyPoints, Scalar(0, 0, 255), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
//drawKeypoints(dst, keyPoints, image_Star_Blob, Scalar(255, 0, 0), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
//imshow("image_Star_Blob", image_Star_Blob);
////cout << "Blob_size 点个数：" << keyPoints.size() << endl;
////cout << "Blob_size 直径：" << keyPoints[0].size << endl;
////cout << "Blob_response 点强度:" << keyPoints[0].response << endl;
////cout << "Blob_pt 点位置：" << keyPoints[0].pt << endl;
//////cout << "keypoints _angle" << keyPoints.angle << endl;
//////cout << "keypoints_octave" << keyPoints.octave << endl;
////Mat Star_RGBrect_Blob;
////drawKeypoints(Star_RGBrect, keyPoints, Star_RGBrect_Blob, Scalar(255, 0, 0), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
////imshow("Star_RGBrect_Blob", Star_RGBrect_Blob);
////Mat Star_HSVrect_Blob;
////drawKeypoints(Star_HSVrect, keyPoints, Star_HSVrect_Blob, Scalar(255, 0, 0), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
////imshow("Star_HSVrect_Blob", Star_HSVrect_Blob);
////Mat Blob_contour;
////Mat back(image.size(), CV_8UC3, Scalar(0, 0, 0));
////drawKeypoints(back, keyPoints, Blob_contour, Scalar(255, 255, 255), DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
////imshow("Blob_contour", Blob_contour);
//////圆圈的大小表示特征的重要性大小