feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
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wangzhengyang
146
3rdparty/opencv-4.5.4/samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp
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146
3rdparty/opencv-4.5.4/samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp
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/**
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* @file introduction_to_pca.cpp
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* @brief This program demonstrates how to use OpenCV PCA to extract the orientation of an object
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* @author OpenCV team
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*/
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#include "opencv2/core.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/highgui.hpp"
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#include <iostream>
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using namespace std;
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using namespace cv;
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// Function declarations
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void drawAxis(Mat&, Point, Point, Scalar, const float);
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double getOrientation(const vector<Point> &, Mat&);
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/**
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* @function drawAxis
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*/
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void drawAxis(Mat& img, Point p, Point q, Scalar colour, const float scale = 0.2)
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{
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//! [visualization1]
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double angle = atan2( (double) p.y - q.y, (double) p.x - q.x ); // angle in radians
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double hypotenuse = sqrt( (double) (p.y - q.y) * (p.y - q.y) + (p.x - q.x) * (p.x - q.x));
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// Here we lengthen the arrow by a factor of scale
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q.x = (int) (p.x - scale * hypotenuse * cos(angle));
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q.y = (int) (p.y - scale * hypotenuse * sin(angle));
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line(img, p, q, colour, 1, LINE_AA);
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// create the arrow hooks
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p.x = (int) (q.x + 9 * cos(angle + CV_PI / 4));
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p.y = (int) (q.y + 9 * sin(angle + CV_PI / 4));
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line(img, p, q, colour, 1, LINE_AA);
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p.x = (int) (q.x + 9 * cos(angle - CV_PI / 4));
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p.y = (int) (q.y + 9 * sin(angle - CV_PI / 4));
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line(img, p, q, colour, 1, LINE_AA);
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//! [visualization1]
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}
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/**
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* @function getOrientation
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*/
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double getOrientation(const vector<Point> &pts, Mat &img)
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{
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//! [pca]
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//Construct a buffer used by the pca analysis
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int sz = static_cast<int>(pts.size());
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Mat data_pts = Mat(sz, 2, CV_64F);
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for (int i = 0; i < data_pts.rows; i++)
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{
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data_pts.at<double>(i, 0) = pts[i].x;
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data_pts.at<double>(i, 1) = pts[i].y;
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}
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//Perform PCA analysis
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PCA pca_analysis(data_pts, Mat(), PCA::DATA_AS_ROW);
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//Store the center of the object
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Point cntr = Point(static_cast<int>(pca_analysis.mean.at<double>(0, 0)),
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static_cast<int>(pca_analysis.mean.at<double>(0, 1)));
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//Store the eigenvalues and eigenvectors
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vector<Point2d> eigen_vecs(2);
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vector<double> eigen_val(2);
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for (int i = 0; i < 2; i++)
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{
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eigen_vecs[i] = Point2d(pca_analysis.eigenvectors.at<double>(i, 0),
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pca_analysis.eigenvectors.at<double>(i, 1));
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eigen_val[i] = pca_analysis.eigenvalues.at<double>(i);
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}
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//! [pca]
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//! [visualization]
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// Draw the principal components
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circle(img, cntr, 3, Scalar(255, 0, 255), 2);
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Point p1 = cntr + 0.02 * Point(static_cast<int>(eigen_vecs[0].x * eigen_val[0]), static_cast<int>(eigen_vecs[0].y * eigen_val[0]));
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Point p2 = cntr - 0.02 * Point(static_cast<int>(eigen_vecs[1].x * eigen_val[1]), static_cast<int>(eigen_vecs[1].y * eigen_val[1]));
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drawAxis(img, cntr, p1, Scalar(0, 255, 0), 1);
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drawAxis(img, cntr, p2, Scalar(255, 255, 0), 5);
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double angle = atan2(eigen_vecs[0].y, eigen_vecs[0].x); // orientation in radians
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//! [visualization]
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return angle;
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}
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/**
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* @function main
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*/
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int main(int argc, char** argv)
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{
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//! [pre-process]
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// Load image
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CommandLineParser parser(argc, argv, "{@input | pca_test1.jpg | input image}");
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parser.about( "This program demonstrates how to use OpenCV PCA to extract the orientation of an object.\n" );
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parser.printMessage();
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Mat src = imread( samples::findFile( parser.get<String>("@input") ) );
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// Check if image is loaded successfully
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if(src.empty())
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{
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cout << "Problem loading image!!!" << endl;
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return EXIT_FAILURE;
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}
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imshow("src", src);
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// Convert image to grayscale
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Mat gray;
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cvtColor(src, gray, COLOR_BGR2GRAY);
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// Convert image to binary
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Mat bw;
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threshold(gray, bw, 50, 255, THRESH_BINARY | THRESH_OTSU);
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//! [pre-process]
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//! [contours]
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// Find all the contours in the thresholded image
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vector<vector<Point> > contours;
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findContours(bw, contours, RETR_LIST, CHAIN_APPROX_NONE);
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for (size_t i = 0; i < contours.size(); i++)
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{
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// Calculate the area of each contour
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double area = contourArea(contours[i]);
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// Ignore contours that are too small or too large
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if (area < 1e2 || 1e5 < area) continue;
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// Draw each contour only for visualisation purposes
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drawContours(src, contours, static_cast<int>(i), Scalar(0, 0, 255), 2);
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// Find the orientation of each shape
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getOrientation(contours[i], src);
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}
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//! [contours]
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imshow("output", src);
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waitKey();
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return EXIT_SUCCESS;
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}
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81
3rdparty/opencv-4.5.4/samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp
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3rdparty/opencv-4.5.4/samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp
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#include <opencv2/core.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/imgcodecs.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/ml.hpp>
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using namespace cv;
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using namespace cv::ml;
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int main(int, char**)
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{
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// Set up training data
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//! [setup1]
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int labels[4] = {1, -1, -1, -1};
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float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
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//! [setup1]
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//! [setup2]
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Mat trainingDataMat(4, 2, CV_32F, trainingData);
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Mat labelsMat(4, 1, CV_32SC1, labels);
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//! [setup2]
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// Train the SVM
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//! [init]
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Ptr<SVM> svm = SVM::create();
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svm->setType(SVM::C_SVC);
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svm->setKernel(SVM::LINEAR);
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svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));
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//! [init]
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//! [train]
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svm->train(trainingDataMat, ROW_SAMPLE, labelsMat);
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//! [train]
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// Data for visual representation
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int width = 512, height = 512;
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Mat image = Mat::zeros(height, width, CV_8UC3);
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// Show the decision regions given by the SVM
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//! [show]
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Vec3b green(0,255,0), blue(255,0,0);
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for (int i = 0; i < image.rows; i++)
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{
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for (int j = 0; j < image.cols; j++)
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{
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Mat sampleMat = (Mat_<float>(1,2) << j,i);
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float response = svm->predict(sampleMat);
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if (response == 1)
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image.at<Vec3b>(i,j) = green;
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else if (response == -1)
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image.at<Vec3b>(i,j) = blue;
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}
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}
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//! [show]
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// Show the training data
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//! [show_data]
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int thickness = -1;
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circle( image, Point(501, 10), 5, Scalar( 0, 0, 0), thickness );
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circle( image, Point(255, 10), 5, Scalar(255, 255, 255), thickness );
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circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness );
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circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness );
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//! [show_data]
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// Show support vectors
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//! [show_vectors]
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thickness = 2;
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Mat sv = svm->getUncompressedSupportVectors();
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for (int i = 0; i < sv.rows; i++)
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{
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const float* v = sv.ptr<float>(i);
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circle(image, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thickness);
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}
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//! [show_vectors]
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imwrite("result.png", image); // save the image
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imshow("SVM Simple Example", image); // show it to the user
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waitKey();
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return 0;
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}
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144
3rdparty/opencv-4.5.4/samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp
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3rdparty/opencv-4.5.4/samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp
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#include <iostream>
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#include <opencv2/core.hpp>
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#include <opencv2/imgproc.hpp>
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#include "opencv2/imgcodecs.hpp"
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#include <opencv2/highgui.hpp>
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#include <opencv2/ml.hpp>
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using namespace cv;
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using namespace cv::ml;
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using namespace std;
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static void help()
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{
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cout<< "\n--------------------------------------------------------------------------" << endl
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<< "This program shows Support Vector Machines for Non-Linearly Separable Data. " << endl
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<< "--------------------------------------------------------------------------" << endl
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<< endl;
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}
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int main()
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{
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help();
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const int NTRAINING_SAMPLES = 100; // Number of training samples per class
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const float FRAC_LINEAR_SEP = 0.9f; // Fraction of samples which compose the linear separable part
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// Data for visual representation
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const int WIDTH = 512, HEIGHT = 512;
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Mat I = Mat::zeros(HEIGHT, WIDTH, CV_8UC3);
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//--------------------- 1. Set up training data randomly ---------------------------------------
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Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32F);
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Mat labels (2*NTRAINING_SAMPLES, 1, CV_32S);
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RNG rng(100); // Random value generation class
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// Set up the linearly separable part of the training data
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int nLinearSamples = (int) (FRAC_LINEAR_SEP * NTRAINING_SAMPLES);
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//! [setup1]
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// Generate random points for the class 1
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Mat trainClass = trainData.rowRange(0, nLinearSamples);
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// The x coordinate of the points is in [0, 0.4)
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Mat c = trainClass.colRange(0, 1);
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rng.fill(c, RNG::UNIFORM, Scalar(0), Scalar(0.4 * WIDTH));
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// The y coordinate of the points is in [0, 1)
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c = trainClass.colRange(1,2);
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rng.fill(c, RNG::UNIFORM, Scalar(0), Scalar(HEIGHT));
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// Generate random points for the class 2
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trainClass = trainData.rowRange(2*NTRAINING_SAMPLES-nLinearSamples, 2*NTRAINING_SAMPLES);
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// The x coordinate of the points is in [0.6, 1]
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c = trainClass.colRange(0 , 1);
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rng.fill(c, RNG::UNIFORM, Scalar(0.6*WIDTH), Scalar(WIDTH));
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// The y coordinate of the points is in [0, 1)
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c = trainClass.colRange(1,2);
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rng.fill(c, RNG::UNIFORM, Scalar(0), Scalar(HEIGHT));
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//! [setup1]
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//------------------ Set up the non-linearly separable part of the training data ---------------
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//! [setup2]
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// Generate random points for the classes 1 and 2
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trainClass = trainData.rowRange(nLinearSamples, 2*NTRAINING_SAMPLES-nLinearSamples);
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// The x coordinate of the points is in [0.4, 0.6)
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c = trainClass.colRange(0,1);
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rng.fill(c, RNG::UNIFORM, Scalar(0.4*WIDTH), Scalar(0.6*WIDTH));
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// The y coordinate of the points is in [0, 1)
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c = trainClass.colRange(1,2);
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rng.fill(c, RNG::UNIFORM, Scalar(0), Scalar(HEIGHT));
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//! [setup2]
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//------------------------- Set up the labels for the classes ---------------------------------
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labels.rowRange( 0, NTRAINING_SAMPLES).setTo(1); // Class 1
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labels.rowRange(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES).setTo(2); // Class 2
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//------------------------ 2. Set up the support vector machines parameters --------------------
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cout << "Starting training process" << endl;
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//! [init]
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Ptr<SVM> svm = SVM::create();
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svm->setType(SVM::C_SVC);
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svm->setC(0.1);
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svm->setKernel(SVM::LINEAR);
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svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6));
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//! [init]
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//------------------------ 3. Train the svm ----------------------------------------------------
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//! [train]
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svm->train(trainData, ROW_SAMPLE, labels);
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//! [train]
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cout << "Finished training process" << endl;
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//------------------------ 4. Show the decision regions ----------------------------------------
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//! [show]
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Vec3b green(0,100,0), blue(100,0,0);
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for (int i = 0; i < I.rows; i++)
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{
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for (int j = 0; j < I.cols; j++)
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{
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Mat sampleMat = (Mat_<float>(1,2) << j, i);
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float response = svm->predict(sampleMat);
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if (response == 1) I.at<Vec3b>(i,j) = green;
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else if (response == 2) I.at<Vec3b>(i,j) = blue;
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}
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}
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//! [show]
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//----------------------- 5. Show the training data --------------------------------------------
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//! [show_data]
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int thick = -1;
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float px, py;
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// Class 1
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for (int i = 0; i < NTRAINING_SAMPLES; i++)
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{
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px = trainData.at<float>(i,0);
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py = trainData.at<float>(i,1);
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circle(I, Point( (int) px, (int) py ), 3, Scalar(0, 255, 0), thick);
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}
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// Class 2
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for (int i = NTRAINING_SAMPLES; i <2*NTRAINING_SAMPLES; i++)
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{
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px = trainData.at<float>(i,0);
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py = trainData.at<float>(i,1);
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circle(I, Point( (int) px, (int) py ), 3, Scalar(255, 0, 0), thick);
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}
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//! [show_data]
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//------------------------- 6. Show support vectors --------------------------------------------
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//! [show_vectors]
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thick = 2;
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Mat sv = svm->getUncompressedSupportVectors();
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for (int i = 0; i < sv.rows; i++)
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{
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const float* v = sv.ptr<float>(i);
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circle(I, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thick);
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}
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//! [show_vectors]
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imwrite("result.png", I); // save the Image
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imshow("SVM for Non-Linear Training Data", I); // show it to the user
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waitKey();
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return 0;
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}
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