系统搭建
- 系统搭建
OpenCV的安装
cmake
sudo apt-get install cmake
其他环境以来
sudo apt-get install build-essential libgtk2.0-dev libavcodec-dev libavformat-dev libjpeg.dev libtiff5.dev libswscale-dev libjasper-dev
- 不安装会报这个错误
OpenCV(4.6.0) /home/dell/下载/opencv-4.6.0/modules/highgui/src/window.cpp:1250: error: (-2:Unspecified error) The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Cocoa support. If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script in function 'cvNamedWindow'
error
- E: 无法定位软件包 libjasper-dev
解决（换源重新安装）
- http://mirrors.ustc.edu.cn/help/ubuntu.html
sudo add-apt-repository "deb http://security.ubuntu.com/ubuntu xenial-security main"
sudo apt update
- 由于没有公钥,无法验证下列签名: NO_PUBKEY 40976EAF437D05B5 NO_PUBKEY 3B4FE6AC
sudo apt-key adv --recv-keys --keyserver keyserver.ubuntu.com 40976EAF437D05B5 3B4FE6ACC0B21F32
下载源码
https://github.com/opencv/opencv/releases
增强模块 https://github.com/opencv/opencv_contrib/tags
安装
unzip opencv-4.6.0.zipunzip opencv_contrib-4.6.0.zipcd opencv-4.6.0/sudo mkdir buildcd buildsudo cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local
或sudo cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local -D OPENCV_EXTRA_MODULES_PATH= **/opencv_contrib-4.6.0/modules/ ..
error
- 如果报错
CMake Error: The source directory "/home/dell/下载/opencv-4.6.0/build" does not appear to contain CMakeLists.txt.使用sudo cmake /home/dell/下载/opencv-4.6.0/ -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr/local
sudo make或sudo make -j4j为变异是使用的核心数量,这一步非常满sudo make install
-- Installing: /usr/local/share/opencv4/lbpcascades/lbpcascade_profileface.xml
-- Installing: /usr/local/share/opencv4/lbpcascades/lbpcascade_silverware.xml
-- Installing: /usr/local/bin/opencv_annotation
-- Set runtime path of "/usr/local/bin/opencv_annotation" to "/usr/local/lib"
-- Installing: /usr/local/bin/opencv_visualisation
-- Set runtime path of "/usr/local/bin/opencv_visualisation" to "/usr/local/lib"
-- Installing: /usr/local/bin/opencv_interactive-calibration
-- Set runtime path of "/usr/local/bin/opencv_interactive-calibration" to "/usr/local/lib"
-- Installing: /usr/local/bin/opencv_version
-- Set runtime path of "/usr/local/bin/opencv_version" to "/usr/local/lib"
-- Installing: /usr/local/bin/opencv_model_diagnostics
-- Set runtime path of "/usr/local/bin/opencv_model_diagnostics" to "/usr/local/lib"
环境变量
sudo vim /etc/ld.so.conf.d/opencv.confi/usr/local/lib+ esc + :wq + enter
sudo ldconfigsudo vim /etc/bash.bashrc
PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig
export PKG_CONFIG_PATH
sudo updatedb
或者按照这种方式配置
测试
cmake_minimum_required(VERSION 3.15)
project(untitled)
set(CMAKE_CXX_STANDARD 14)
MESSAGE(STATUS "Project: untitled") #打印相关消息消息
find_package(OpenCV REQUIRED)# 通过find_package引入非官方的库（该方式只对支持cmake编译安装的库有效）
set(SOURCE_FILES main.cpp)
include_directories(${OpenCV_INCLUDE_DIRS})
add_executable(untitled main.cpp)
target_link_libraries(untitled ${OpenCV_LIBS})
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main() {
string path = "/home/dell/下载/a.png";
Mat src = imread(path);
namedWindow("img");
imshow("img",src);
waitKey(0);
return 0;
}
- 重启一下就ok了
- 结果
onnx
效果风格转换
网络的论文和ONNX下载
- 下载地址
blobFromImages(blobFromImage) + imagesFromBlob
处理图像到blob,[0, 255] ->[0, 1],大小，RGB->BGR和转换forward的blob到图像。
网络传播整体代码
cmake_minimum_required(VERSION 3.15)
project(untitled)
set(CMAKE_CXX_STANDARD 14)
MESSAGE(STATUS "Project: untitled")
find_package(OpenCV REQUIRED)
set(SOURCE_FILES main.cpp)
include_directories(${OpenCV_INCLUDE_DIRS})
add_executable(untitled main.cpp)
target_link_libraries(untitled ${OpenCV_LIBS})
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>
#include <fstream>
using namespace cv;
using namespace cv::dnn;
using namespace std;
// 图像处理 标准化处理
void PreProcess(const Mat& image, Mat& image_blob)
{
Mat input;
image.copyTo(input);
//数据处理 标准化
std::vector<Mat> channels, channel_p;
split(input, channels);
Mat R, G, B;
B = channels.at(0);
G = channels.at(1);
R = channels.at(2);
B = (B / 255. - 0.406) / 0.225;
G = (G / 255. - 0.456) / 0.224;
R = (R / 255. - 0.485) / 0.229;
channel_p.push_back(R);
channel_p.push_back(G);
channel_p.push_back(B);
Mat outt;
merge(channel_p, outt);
image_blob = outt;
}
String bin_model = "/home/dell/CLionProjects/untitled/mosaic-9.onnx";
int main(int argc, char** argv) {
//数据处理
Mat test = Mat::zeros(10,10, CV_64FC1 );
Mat image1 = imread("/home/dell/下载/a.png");
resize(image1, image1, Size(256, 256), INTER_AREA);
image1.convertTo(image1, CV_32FC3);// convertTo()数据类型CV_32FC3
PreProcess(image1, image1);
vector<Mat> images;
images.push_back(image1);images.push_back(image1);
int w = 224;int h = 224;
// 加载网络
cv::dnn::Net net = cv::dnn::readNetFromONNX(bin_model); // 加载训练好的识别模型 net = cv2.dnn.readNetFromONNX('**.onnx')
net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
if (net.empty()) {
printf("read onnx model data failure...\n");
return -1;
}
Mat inputBlob = blobFromImages(images, 1.0, Size(w, h), Scalar(0, 0, 0), false, true);
net.setInput(inputBlob);
cv::Mat prob = net.forward(); // 推理出结果 cols,rows 矩阵的行数，列数【注意，在图像中行数对应的是高度，列数对应的是宽度】，当维数大于2时，均为-1;
std::vector<cv::Mat> predTmp;
cv::dnn::imagesFromBlob(prob, predTmp);
imshow("show Image", images[0]);
cv::waitKey(0);
imshow("Image mosaic", predTmp[0]);
cv::waitKey(0);
vector<double> times;
double time = net.getPerfProfile(times);
float ms = (time * 1000) / getTickFrequency();
printf("current inference time : %.2f ms \n", ms);
return 0;
}
CG
- clion中debug不生效
代码 onnx+ opdncv+vgg16
// https://blog.csdn.net/qq_44747572/article/details/121467657
// /home/dell/下载/opencv-4.6.0/samples/data/dnn/classification_classes_ILSVRC2012.txt
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>
#include <fstream>
using namespace cv;
using namespace cv::dnn;
using namespace std;
// 图像处理 标准化处理
void PreProcess(const Mat& image, Mat& image_blob)
{
Mat input;
image.copyTo(input);
//数据处理 标准化
std::vector<Mat> channels, channel_p;
split(input, channels);
Mat R, G, B;
B = channels.at(0);
G = channels.at(1);
R = channels.at(2);
B = (B / 255. - 0.406) / 0.225;
G = (G / 255. - 0.456) / 0.224;
R = (R / 255. - 0.485) / 0.229;
channel_p.push_back(R);
channel_p.push_back(G);
channel_p.push_back(B);
Mat outt;
merge(channel_p, outt);
image_blob = outt;
}
std::vector<String> readClassNames(string labels_txt_file)
{
std::vector<String> classNames;
std::ifstream fp(labels_txt_file);
if (!fp.is_open())
{
printf("could not open file...\n");
exit(-1);
}
std::string name;
while (!fp.eof())
{
std::getline(fp, name);
if (name.length())
classNames.push_back(name);
}
fp.close();
return classNames;
}
String bin_model = "/home/dell/下载/vgg16.onnx";
String labels_txt_file = "/home/dell/下载/opencv-4.6.0/samples/data/dnn/classification_classes_ILSVRC2012.txt";
vector<String> readClassNames(); // string对象作为vector对象
int main(int argc, char** argv) {
Mat image1 = imread("/home/dell/下载/a.png");
//用于显示
vector<Mat>Showimages;
Showimages.push_back(image1);
//处理image1
resize(image1, image1, Size(256, 256), INTER_AREA);
image1.convertTo(image1, CV_32FC3);
PreProcess(image1, image1); //标准化处理
//将image1和image2合并到images
vector<Mat> images;
images.push_back(image1);
images.push_back(image1);
vector<String> labels = readClassNames(labels_txt_file);
int w = 224;
int h = 224;
// 加载网络
cv::dnn::Net net = cv::dnn::readNetFromONNX(bin_model); // 加载训练好的识别模型
if (net.empty()) {
printf("read onnx model data failure...\n");
return -1;
}
Mat inputBlob = blobFromImages(images, 1.0, Size(w, h), Scalar(0, 0, 0), false, true);
// 执行图像分类
net.setInput(inputBlob);
cv::Mat prob = net.forward(); // 推理出结果
cout << prob.cols<< endl;
vector<double> times;
double time = net.getPerfProfile(times);
float ms = (time * 1000) / getTickFrequency();
printf("current inference time : %.2f ms \n", ms);
// 得到最可能分类输出
for (int n = 0; n < prob.rows; n++) {
Point classNumber;
double classProb;
Mat probMat = prob(Rect(0, n, 1000, 1)).clone();
Mat result = probMat.reshape(1, 1);
minMaxLoc(result, NULL, &classProb, NULL, &classNumber);
int classidx = classNumber.x;
printf("\n current image classification : %s, possible : %.2f\n", labels.at(classidx).c_str(), classProb);
// 显示文本
putText(Showimages[n], labels.at(classidx), Point(10, 20), FONT_HERSHEY_SIMPLEX, 0.6, Scalar(0, 0, 255), 1, 1);
imshow("Image Classification", Showimages[n]);
waitKey(0);
}
return 0;
}
