119 lines
3.8 KiB
C++
119 lines
3.8 KiB
C++
/*
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* one_way_sample.cpp
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* outlet_detection
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*
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* Created by Victor Eruhimov on 8/5/09.
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* Copyright 2009 Argus Corp. All rights reserved.
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*
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*/
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#include "opencv2/imgproc/imgproc.hpp"
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#include "opencv2/features2d/features2d.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/imgproc/imgproc_c.h"
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#include "opencv2/nonfree/nonfree.hpp"
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#include "opencv2/legacy/legacy.hpp"
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#include "opencv2/legacy/compat.hpp"
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#include <string>
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#include <stdio.h>
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static void help()
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{
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printf("\nThis program demonstrates the one way interest point descriptor found in features2d.hpp\n"
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"Correspondences are drawn\n");
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printf("Format: \n./one_way_sample <path_to_samples> <image1> <image2>\n");
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printf("For example: ./one_way_sample . ../c/scene_l.bmp ../c/scene_r.bmp\n");
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}
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using namespace cv;
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Mat DrawCorrespondences(const Mat& img1, const vector<KeyPoint>& features1, const Mat& img2,
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const vector<KeyPoint>& features2, const vector<int>& desc_idx);
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int main(int argc, char** argv)
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{
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const char images_list[] = "one_way_train_images.txt";
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const CvSize patch_size = cvSize(24, 24);
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const int pose_count = 50;
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if (argc != 4)
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{
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help();
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return 0;
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}
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std::string path_name = argv[1];
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std::string img1_name = path_name + "/" + std::string(argv[2]);
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std::string img2_name = path_name + "/" + std::string(argv[3]);
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printf("Reading the images...\n");
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Mat img1 = imread(img1_name, CV_LOAD_IMAGE_GRAYSCALE);
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Mat img2 = imread(img2_name, CV_LOAD_IMAGE_GRAYSCALE);
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// extract keypoints from the first image
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SURF surf_extractor(5.0e3);
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vector<KeyPoint> keypoints1;
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// printf("Extracting keypoints\n");
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surf_extractor(img1, Mat(), keypoints1);
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printf("Extracted %d keypoints...\n", (int)keypoints1.size());
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printf("Training one way descriptors... \n");
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// create descriptors
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OneWayDescriptorBase descriptors(patch_size, pose_count, OneWayDescriptorBase::GetPCAFilename(), path_name,
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images_list);
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IplImage img1_c = img1;
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IplImage img2_c = img2;
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descriptors.CreateDescriptorsFromImage(&img1_c, keypoints1);
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printf("done\n");
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// extract keypoints from the second image
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vector<KeyPoint> keypoints2;
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surf_extractor(img2, Mat(), keypoints2);
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printf("Extracted %d keypoints from the second image...\n", (int)keypoints2.size());
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printf("Finding nearest neighbors...");
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// find NN for each of keypoints2 in keypoints1
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vector<int> desc_idx;
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desc_idx.resize(keypoints2.size());
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for (size_t i = 0; i < keypoints2.size(); i++)
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{
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int pose_idx = 0;
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float distance = 0;
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descriptors.FindDescriptor(&img2_c, keypoints2[i].pt, desc_idx[i], pose_idx, distance);
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}
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printf("done\n");
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Mat img_corr = DrawCorrespondences(img1, keypoints1, img2, keypoints2, desc_idx);
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imshow("correspondences", img_corr);
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waitKey(0);
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}
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Mat DrawCorrespondences(const Mat& img1, const vector<KeyPoint>& features1, const Mat& img2,
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const vector<KeyPoint>& features2, const vector<int>& desc_idx)
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{
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Mat part, img_corr(Size(img1.cols + img2.cols, MAX(img1.rows, img2.rows)), CV_8UC3);
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img_corr = Scalar::all(0);
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part = img_corr(Rect(0, 0, img1.cols, img1.rows));
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cvtColor(img1, part, COLOR_GRAY2RGB);
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part = img_corr(Rect(img1.cols, 0, img2.cols, img2.rows));
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cvtColor(img1, part, COLOR_GRAY2RGB);
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for (size_t i = 0; i < features1.size(); i++)
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{
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circle(img_corr, features1[i].pt, 3, CV_RGB(255, 0, 0));
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}
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for (size_t i = 0; i < features2.size(); i++)
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{
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Point pt((int)features2[i].pt.x + img1.cols, (int)features2[i].pt.y);
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circle(img_corr, pt, 3, Scalar(0, 0, 255));
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line(img_corr, features1[desc_idx[i]].pt, pt, Scalar(0, 255, 0));
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}
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return img_corr;
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}
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