一、创建图像
void QuickDemo::mat_creation_demo(Mat &image) {
//克隆，复制
Mat m1, m2;
m1 = image.clone();
image.copyTo(m2);
// 创建空白图像
Mat m3 = Mat::zeros(Size(88, 88), CV_8UC3);//大小8*8像素，8位3通道
m3 = Scalar(0, 0, 255);//上色，三通道分别为B,G,R，取值范围均在0~255
std::cout << "width: " << m3.cols << " height: " << m3.rows << " channels: "<<m3.channels()<< std::endl;
// std::cout << m3 << std::endl;
Mat m4;
m3.copyTo(m4);
m4 = Scalar(0, 255, 255);
imshow("图像", m3);
imshow("图像4", m4);
}二、像素读写
void QuickDemo::pixel_visit_demo(Mat &image) {
int w = image.cols;//获取图像宽度
int h = image.rows;//获取图像高度
int dims = image.channels();//获取图像通道数
/*
//图像颜色反转
for (int row = 0; row < h; row++) {
for (int col = 0; col < w; col++) {
if (dims == 1) { // 灰度图像
int pv = image.at<uchar>(row, col);
image.at<uchar>(row, col) = 255 - pv;
}
if (dims == 3) { // 彩色图像
Vec3b bgr = image.at<Vec3b>(row, col);
image.at<Vec3b>(row, col)[0] = 255 - bgr[0];
image.at<Vec3b>(row, col)[1] = 255 - bgr[1];
image.at<Vec3b>(row, col)[2] = 255 - bgr[2];
}
}
}
*/
for (int row = 0; row < h; row++) {
uchar* current_row = image.ptr<uchar>(row);
for (int col = 0; col < w; col++) {
if (dims == 1) { // 灰度图像
int pv = *current_row;
*current_row++ = 255 - pv;
}
if (dims == 3) { // 彩色图像
*current_row++ = 255 - *current_row;
*current_row++ = 255 - *current_row;
*current_row++ = 255 - *current_row;
}
}
}
imshow("像素读写演示", image);
}三、算术操作
加：add() add(image, m, dst);
减：subtract() subtract(image, m, dst);
乘：multiply() multiply(image, m, dst);
除：divide() divide(image, m, dst);
限制值的范围：saturate_cast saturate_cast<uchar>(a + b);//将a+b限制在uchar之内，结果大于255，则为255；小于0，则为0
void QuickDemo::operators_demo(Mat &image) {
Mat dst = Mat::zeros(image.size(), image.type());
Mat m = Mat::zeros(image.size(), image.type());
m = Scalar(5, 5, 5);
// 加法
/*
int w = image.cols;
int h = image.rows;
int dims = image.channels();
for (int row = 0; row < h; row++) {
for (int col = 0; col < w; col++) {
Vec3b p1 = image.at<Vec3b>(row, col);
Vec3b p2 = m.at<Vec3b>(row, col);
dst.at<Vec3b>(row, col)[0] = saturate_cast<uchar>(p1[0] + p2[0]);
dst.at<Vec3b>(row, col)[1] = saturate_cast<uchar>(p1[1] + p2[1]);
dst.at<Vec3b>(row, col)[2] = saturate_cast<uchar>(p1[2] + p2[2]);
}
}
*/
divide(image, m, dst);
imshow("加法操作", dst);
}四、滚动条
static void on_lightness(int b, void* userdata) {//亮度回调函数
Mat image = *((Mat*)userdata);
Mat dst = Mat::zeros(image.size(), image.type());
Mat m = Mat::zeros(image.size(), image.type());
addWeighted(image, 1.0, m, 0, b, dst);
imshow("亮度与对比度调整", dst);
}
static void on_contrast(int b, void* userdata) {//对比度回调函数
Mat image = *((Mat*)userdata);
Mat dst = Mat::zeros(image.size(), image.type());
Mat m = Mat::zeros(image.size(), image.type());
double contrast = b / 100.0;
addWeighted(image, contrast, m, 0.0, 0, dst);
imshow("亮度与对比度调整", dst);
}
void QuickDemo::tracking_bar_demo(Mat &image) {
namedWindow("亮度与对比度调整", WINDOW_AUTOSIZE);
int lightness = 50;
int max_value = 100;
int contrast_value = 100;
createTrackbar("Value Bar:", "亮度与对比度调整", &lightness, max_value, on_lightness, (void*) (&image));
createTrackbar("Contrast Bar:", "亮度与对比度调整", &contrast_value, 200, on_contrast, (void*)(&image));
on_lightness(50, &image);
}五、键盘响应
void QuickDemo::key_demo(Mat &image) {
Mat dst = Mat::zeros(image.size(), image.type());
while (true) {
int c = waitKey(100);
if (c == 27) { // 退出
break;
}
if (c == 49) { // Key #1
std::cout << "you enter key # 1 "<< std::endl;
cvtColor(image, dst, COLOR_BGR2GRAY);
}
if (c == 50) { // Key #2
std::cout << "you enter key # 2 " << std::endl;
cvtColor(image, dst, COLOR_BGR2HSV);
}
if (c == 51) { // Key #3
std::cout << "you enter key # 3 " << std::endl;
dst = Scalar(50, 50, 50);
add(image, dst, dst);
}
imshow("键盘响应", dst);
}
}六、颜色表操作
void QuickDemo::color_style_demo(Mat &image) {
int colormap[] = {
COLORMAP_AUTUMN,
COLORMAP_BONE,
COLORMAP_JET,
COLORMAP_WINTER,
COLORMAP_RAINBOW,
COLORMAP_OCEAN,
COLORMAP_SUMMER,
COLORMAP_SPRING,
COLORMAP_COOL,
COLORMAP_PINK,
COLORMAP_HOT,
COLORMAP_PARULA,
COLORMAP_MAGMA,
COLORMAP_INFERNO,
COLORMAP_PLASMA,
COLORMAP_VIRIDIS,
COLORMAP_CIVIDIS,
COLORMAP_TWILIGHT,
COLORMAP_TWILIGHT_SHIFTED
};
Mat dst;
int index = 0;
while (true) {
int c = waitKey(500);
if (c == 27) { // 退出
break;
}
applyColorMap(image, dst, colormap[index%19]);
index++;
imshow("颜色风格", dst);
}
}七、像素逻辑操作
与：bitwise_and(m1,m2,dst);
或：bitwise_or(m1,m2,dst);
非：bitwise_not(image,dst); 图像取反操作，dst = ~image;
异或：bitwise_xor(m1,m2,dst)
void QuickDemo::bitwise_demo(Mat &image) {
Mat m1 = Mat::zeros(Size(256, 256), CV_8UC3);
Mat m2 = Mat::zeros(Size(256, 256), CV_8UC3);
rectangle(m1, Rect(100, 100, 80, 80), Scalar(255, 255, 0), -1, LINE_8, 0);//画出一个矩形（名，位置及大小，颜色，线宽（>0绘制，<0填充），？，？）
rectangle(m2, Rect(150, 150, 80, 80), Scalar(0, 255, 255), -1, LINE_8, 0);
imshow("m1", m1);
imshow("m2", m2);
Mat dst;
bitwise_xor(m1, m2, dst);//逻辑操作函数
imshow("像素位操作", dst);
}八、通道合并与分离
void QuickDemo::channels_demo(Mat &image) {
std::vector<Mat> mv;
split(image, mv);//分离图像
imshow("蓝色", mv[0]);
imshow("绿色", mv[1]);
imshow("红色", mv[2]);//分离出三个单通道，显示为灰度图像
Mat dst;
mv[0] = 0;
mv[1] = 0;
merge(mv, dst);//合并图像
imshow("红色", dst);
int from_to[] = { 0, 2, 1,1, 2, 0 }; //0，2交换，1，1交换，2，0交换
mixChannels(&image, 1, &dst, 1, from_to, 3);//通道交换函数（图片地址，图片数量，生成图片地址，图片数量，交换方式，交换通道数量）
imshow("通道混合", dst);
}九、色彩空间转换
void QuickDemo::inrange_demo(Mat &image) {
Mat hsv;
cvtColor(image, hsv, COLOR_BGR2HSV);//色彩空间转换
Mat mask;
inRange(hsv, Scalar(35, 43, 46), Scalar(77, 255, 255), mask);//提取图片中绿色，具体hsv色彩见最上面
Mat redback = Mat::zeros(image.size(), image.type());
redback = Scalar(40, 40, 200);
bitwise_not(mask, mask);//非运算，留下出绿色外的
imshow("mask", mask);
image.copyTo(redback, mask);//将绿色外的图片复制到红色背景上
imshow("roi区域提取", redback);
}十、像素值统计
void QuickDemo::pixel_statistic_demo(Mat &image) {
double minv, maxv;
Point minLoc, maxLoc;
std::vector<Mat> mv;
split(image, mv);
for (int i = 0; i < mv.size(); i++) {
minMaxLoc(mv[i], &minv, &maxv, &minLoc, &maxLoc, Mat());
std::cout <<"No. channels:"<< i << " min value:" << minv << " max value:" << maxv << std::endl;
}
Mat mean, stddev;
Mat redback = Mat::zeros(image.size(), image.type());
redback = Scalar(40, 40, 200);
meanStdDev(redback, mean, stddev);
imshow("redback", redback);
std::cout << "means:" << mean << std::endl;
std::cout<< " stddev:" << stddev << std::endl;
}十一、几何图形绘制
void QuickDemo::drawing_demo(Mat &image) {
Rect rect;
rect.x = 100;//确定x位置
rect.y = 100;//确定y位置
rect.width = 250;//宽
rect.height = 300;//高
Mat bg = Mat::zeros(image.size(), image.type());//创建Mat
rectangle(bg, rect, Scalar(0, 0, 255), -1, 8, 0);//绘制方形
circle(bg, Point(350, 400), 15, Scalar(255, 0, 0), -1, 8, 0);//绘制圆形（图像，圆心，半径，颜色，线宽，？，？）
line(bg, Point(100, 100), Point(350, 400), Scalar(0, 255, 0), 4, LINE_AA, 0);//绘制线（图像，点，点，颜色，宽度，？，？）
RotatedRect rrt;
rrt.center = Point(200, 200);//中心点
rrt.size = Size(100, 200);//大小
rrt.angle = 90.0;//旋转角度
ellipse(bg, rrt, Scalar(0, 255, 255), 2, 8);//绘制椭圆（图像，位置大小，颜色，线宽，？）
Mat dst;
addWeighted(image, 0.7, bg, 0.3, 0, dst);
imshow("绘制演示", bg);
}十二、随机数与随机颜色
void QuickDemo::random_drawing() {
Mat canvas = Mat::zeros(Size(512, 512), CV_8UC3);
int w = canvas.cols;
int h = canvas.rows;
RNG rng(12345);//产生随机数函数
while (true) {
int c = waitKey(10);
if (c == 27) { // 退出
break;
}
int x1 = rng.uniform(0, w);//产生(0,w)的随机数
int y1 = rng.uniform(0, h);//产生(0,h)的随机数
int x2 = rng.uniform(0, w);//产生(0,w)的随机数
int y2 = rng.uniform(0, h);//产生(0,h)的随机数
int b = rng.uniform(0, 255);//产生(0,255)的随机数
int g = rng.uniform(0, 255);//产生(0,255)的随机数
int r = rng.uniform(0, 255);//产生(0,255)的随机数
// canvas = Scalar(0, 0, 0);
line(canvas, Point(x1, y1), Point(x2, y2), Scalar(b, g, r), 1, LINE_AA, 0);
imshow("随机绘制演示", canvas);
}
}十三、多边形绘制与填充
void QuickDemo::polyline_drawing_demo() {
Mat canvas = Mat::zeros(Size(512, 512), CV_8UC3);//新建画布
int w = canvas.cols;//获取宽
int h = canvas.rows;//获取高
Point p1(100, 100);
Point p2(300, 150);
Point p3(300, 350);
Point p4(250, 450);
Point p5(50, 450);
std::vector<Point> pts;
pts.push_back(p1);
pts.push_back(p2);
pts.push_back(p3);
pts.push_back(p3);
pts.push_back(p4);
pts.push_back(p5);
//fillPoly(canvas, pts, Scalar(255, 0, 255), 8, 0);//填充图形
// polylines(canvas, pts, true, Scalar(0, 255, 0), -1, 8, 0);//绘制边框
std::vector<std::vector<Point>> contours;//构造多个点集的集合
contours.push_back(pts);
drawContours(canvas, contours, 0, Scalar(0, 0, 255), -1, 8);//绘制/填充多边形（图像，点集，绘制第几个(全部:-1)，颜色，线宽，？）
imshow("绘制多边形", canvas);
}十四、鼠标操作与响应
Point sp(-1, -1);
Point ep(-1, -1);
Mat temp;
static void on_draw(int event, int x, int y, int flags, void *userdata) {
Mat image = *((Mat*)userdata);
if (event == EVENT_LBUTTONDOWN) {
sp.x = x;
sp.y = y;
std::cout <<"start point:" << sp << std::endl;
}
else if (event == EVENT_LBUTTONUP) {
ep.x = x;
ep.y = y;
int dx = ep.x - sp.x;
int dy = ep.y - sp.y;
if (dx > 0 && dy > 0) {
Rect box(sp.x, sp.y, dx, dy);
temp.copyTo(image);
imshow("ROI区域", image(box));
rectangle(image, box, Scalar(0, 0, 255), 2, 8, 0);
imshow("鼠标绘制", image);
// ready for next drawing
sp.x = -1;
sp.y = -1;
}
}
else if (event == EVENT_MOUSEMOVE) {
if (sp.x > 0 && sp.y > 0) {
ep.x = x;
ep.y = y;
int dx = ep.x - sp.x;
int dy = ep.y - sp.y;
if (dx > 0 && dy > 0) {
Rect box(sp.x, sp.y, dx, dy);
temp.copyTo(image);
rectangle(image, box, Scalar(0, 0, 255), 2, 8, 0);
imshow("鼠标绘制", image);
}
}
}
}
void QuickDemo::mouse_drawing_demo(Mat &image) {
namedWindow("鼠标绘制", WINDOW_AUTOSIZE);
setMouseCallback("鼠标绘制", on_draw, (void*)(&image));
imshow("鼠标绘制", image);
temp = image.clone();
}十五、图像像素类型转换与归一化
四种归一化：
NORM_MINMAX;（最常用）
NORM_INF;
NORM_L1;
NORM_L2;
void QuickDemo::norm_demo(Mat &image) {
Mat dst;
std::cout << image.type() << std::endl;
image.convertTo(image, CV_32F);//数据类型转换，转换为浮点数类型
std::cout << image.type() << std::endl;
normalize(image, dst, 1.0, 0, NORM_MINMAX);//归一化函数
std::cout << dst.type() << std::endl;
imshow("图像数据归一化", dst);
// CV_8UC3, CV_32FC3
}十六、图像放缩与插值
void QuickDemo::resize_demo(Mat &image) {
Mat zoomin, zoomout;
int h = image.rows;
int w = image.cols;
resize(image, zoomin, Size(w / 2, h / 2), 0, 0, INTER_LINEAR);//图像缩放（输入图像，输出图像，缩放大小，拉伸x轴(0-1)，拉伸y轴(0-1)，插值方法(线性插值)） **放缩大小和拉伸二选其一**
imshow("zoomout", zoomout);//缩小
resize(image, zoomout, Size(w*1.5, h*1.5), 0, 0, INTER_LINEAR);
imshow("zoomin", zoomin);//放大
}十七、图像翻转
void QuickDemo::flip_demo(Mat &image) {
Mat dst;
// flip(image, dst, 0); // 上下翻转，x轴对称
// flip(image, dst, 1); // 左右翻转，y轴对称
flip(image, dst, -1); // 180°旋转，中心对称
imshow("图像翻转", dst);
}十八、图像旋转
void QuickDemo::rotate_demo(Mat &image) {
Mat dst, M;
int w = image.cols;
int h = image.rows;
M = getRotationMatrix2D(Point2f(w / 2, h / 2), 45, 1.0);//获取图像数组（原来图像中心位置，旋转角度，缩放倍数）
//计算新的宽高
double cos = abs(M.at<double>(0, 0));
double sin = abs(M.at<double>(0, 1));
int nw = cos*w + sin*h;//新宽
int nh = sin*w + cos*h;//新高
M.at<double>(0, 2) += (nw / 2 - w / 2);//新中心位置
M.at<double>(1,2) += (nh / 2 - h / 2);//新中心位置
warpAffine(image, dst, M, Size(nw, nh), INTER_LINEAR, 0, Scalar(255, 255, 0));//旋转图片（输入，输出，图像数组，输出大小，插值方法，？，颜色）
imshow("旋转演示", dst);
}十九、视频文件处理与保存
void QuickDemo::video_demo(Mat &image) {
VideoCapture capture("D:/images/video/example_dsh.mp4");//初始化对象
int frame_width = capture.get(CAP_PROP_FRAME_WIDTH);//获取每帧宽度
int frame_height = capture.get(CAP_PROP_FRAME_HEIGHT);//获取每帧高度
int count = capture.get(CAP_PROP_FRAME_COUNT);//获取总帧数
double fps = capture.get(CAP_PROP_FPS);//获取帧率
std::cout << "frame width:" << frame_width << std::endl;
std::cout << "frame height:" << frame_height << std::endl;
std::cout << "FPS:" << fps << std::endl;
std::cout << "Number of Frames:" << count << std::endl;
VideoWriter writer("D:/test.mp4", capture.get(CAP_PROP_FOURCC), fps, Size(frame_width, frame_height), true);//保持视频（视频名，视频编码格式(直接获取如该代码)，帧率，视频宽高，？）
Mat frame;
while (true) {
capture.read(frame);
flip(frame, frame, 1);
if (frame.empty()) {
break;
}
imshow("frame", frame);
colorSpace_Demo(frame);
writer.write(frame);//保持视频
// TODO: do something....
int c = waitKey(1);
if (c == 27) { // 退出
break;
}
}
// release
capture.release();
writer.release();
}二十、图像直方图
void QuickDemo::histogram_demo(Mat &image) {
// 三通道分离
std::vector<Mat> bgr_plane;
split(image, bgr_plane);
// 定义参数变量
const int channels[1] = { 0 };
const int bins[1] = { 256 };
float hranges[2] = { 0,255 };
const float* ranges[1] = { hranges };
Mat b_hist;
Mat g_hist;
Mat r_hist;
// 计算Blue, Green, Red通道的直方图
calcHist(&bgr_plane[0], 1, 0, Mat(), b_hist, 1, bins, ranges);//计算直方图（图像，图像张数，通道，蒙版，输出，维度(1维)，？，范围？）
calcHist(&bgr_plane[1], 1, 0, Mat(), g_hist, 1, bins, ranges);
calcHist(&bgr_plane[2], 1, 0, Mat(), r_hist, 1, bins, ranges);
// 显示直方图
int hist_w = 512;
int hist_h = 400;
int bin_w = cvRound((double)hist_w / bins[0]);
Mat histImage = Mat::zeros(hist_h, hist_w, CV_8UC3);
// 归一化直方图数据
normalize(b_hist, b_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());
normalize(g_hist, g_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());
normalize(r_hist, r_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());
// 绘制直方图曲线
for (int i = 1; i < bins[0]; i++) {
line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(b_hist.at<float>(i - 1))),
Point(bin_w*(i), hist_h - cvRound(b_hist.at<float>(i))), Scalar(255, 0, 0), 2, 8, 0);
line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(g_hist.at<float>(i - 1))),
Point(bin_w*(i), hist_h - cvRound(g_hist.at<float>(i))), Scalar(0, 255, 0), 2, 8, 0);
line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(r_hist.at<float>(i - 1))),
Point(bin_w*(i), hist_h - cvRound(r_hist.at<float>(i))), Scalar(0, 0, 255), 2, 8, 0);
}
// 显示直方图
namedWindow("Histogram Demo", WINDOW_AUTOSIZE);
imshow("Histogram Demo", histImage);
}二十一、二维直方图
void QuickDemo::histogram_2d_demo(Mat &image) {
// 2D 直方图
Mat hsv, hs_hist;
cvtColor(image, hsv, COLOR_BGR2HSV);
int hbins = 30, sbins = 32;
int hist_bins[] = { hbins, sbins };
float h_range[] = { 0, 180 };
float s_range[] = { 0, 256 };
const float* hs_ranges[] = { h_range, s_range };
int hs_channels[] = { 0, 1 };
calcHist(&hsv, 1, hs_channels, Mat(), hs_hist, 2, hist_bins, hs_ranges, true, false);
double maxVal = 0;
minMaxLoc(hs_hist, 0, &maxVal, 0, 0);
int scale = 10;
Mat hist2d_image = Mat::zeros(sbins*scale, hbins * scale, CV_8UC3);
for (int h = 0; h < hbins; h++) {
for (int s = 0; s < sbins; s++)
{
float binVal = hs_hist.at<float>(h, s);
int intensity = cvRound(binVal * 255 / maxVal);
rectangle(hist2d_image, Point(h*scale, s*scale),
Point((h + 1)*scale - 1, (s + 1)*scale - 1),
Scalar::all(intensity),
-1);
}
}
applyColorMap(hist2d_image, hist2d_image, COLORMAP_JET);
imshow("H-S Histogram", hist2d_image);
imwrite("D:/hist_2d.png", hist2d_image);
}二十二、直方图均衡化
void QuickDemo::histogram_eq_demo(Mat &image) {
Mat gray;
cvtColor(image, gray, COLOR_BGR2GRAY);
imshow("灰度图像", gray);
Mat dst;
equalizeHist(gray, dst);
imshow("直方图均衡化演示", dst);
}二十三、图像卷积操作
void QuickDemo::blur_demo(Mat &image) {
Mat dst;
blur(image, dst, Size(15, 15), Point(-1, -1));
imshow("图像模糊", dst);
}二十四、高斯模糊
void QuickDemo::gaussian_blur_demo(Mat &image) {
Mat dst;
GaussianBlur(image, dst, Size(0, 0), 15);//（输入，输出，卷积核大小，σ参数） **size和σ只需要一个，一般设置σ**
imshow("高斯模糊", dst);
}二十五、高斯双边模糊
void QuickDemo::bifilter_demo(Mat &image) {
Mat dst;
bilateralFilter(image, dst, 0, 100, 10);//（输入，输出）
imshow("双边模糊", dst);
}二十六、人脸识别实例
*注意更改自己文件位置
void QuickDemo::face_detection_demo() {
std::string root_dir = "D:/opencv-4.4.0/opencv/sources/samples/dnn/face_detector/";
dnn::Net net = dnn::readNetFromTensorflow(root_dir+ "opencv_face_detector_uint8.pb", root_dir+"opencv_face_detector.pbtxt");
VideoCapture capture("D:/images/video/example_dsh.mp4");
Mat frame;
while (true) {
capture.read(frame);
if (frame.empty()) {
break;
}
Mat blob = dnn::blobFromImage(frame, 1.0, Size(300, 300), Scalar(104, 177, 123), false, false);
net.setInput(blob);// NCHW
Mat probs = net.forward(); //
Mat detectionMat(probs.size[2], probs.size[3], CV_32F, probs.ptr<float>());
// 解析结果
for (int i = 0; i < detectionMat.rows; i++) {
float confidence = detectionMat.at<float>(i, 2);
if (confidence > 0.5) {
int x1 = static_cast<int>(detectionMat.at<float>(i, 3)*frame.cols);
int y1 = static_cast<int>(detectionMat.at<float>(i, 4)*frame.rows);
int x2 = static_cast<int>(detectionMat.at<float>(i, 5)*frame.cols);
int y2 = static_cast<int>(detectionMat.at<float>(i, 6)*frame.rows);
Rect box(x1, y1, x2 - x1, y2 - y1);
rectangle(frame, box, Scalar(0, 0, 255), 2, 8, 0);
}
}
imshow("人脸检测演示", frame);
int c = waitKey(1);
if (c == 27) { // 退出
break;
}
}
}本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
