Compiles with opencv4

Signed-off-by: xavier <micaelaverucchi@gmail.com>

CMakeLists.txt

@@ -39,6 +39,8 @@ file(GLOB tkdnn_SRC "src/*.cpp")
 set(tkdnn_LIBS kernels ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} -lcudnn -lnvinfer ${OpenCV_LIBS} -lgdal)
 
 file(GLOB class_SRC "src/class_src/*.cpp")
+list(REMOVE_ITEM class_SRC "src/class_src/boxDetection.cpp")
+
 set(class_LIBS ${OpenCV_LIBS} -lgdal yaml-cpp python2.7)
 
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -std=c++11 -O3")

demo/demo/demo.cpp

@@ -68,7 +68,7 @@ void *readVideoCapture(void *x_void_ptr)
 
     // compute fps and find camera's clock
     double shift, mean_time = 0;
-    std::cout << "Frames per second using video.get(CV_CAP_PROP_FPS) : " << cap.get(CV_CAP_PROP_FPS) << std::endl;
+    std::cout << "Frames per second using video.get(cv::CAP_PROP_FPS) : " << cap.get(cv::CAP_PROP_FPS) << std::endl;
     std::cout << "readVideoCapture computes frame rate...\n";
     // //compute frame rate
     int i = 0;
@@ -128,7 +128,7 @@ void *readVideoCapture(void *x_void_ptr)
 
         // std::cout<< "CV_CAP_PROP_POS_MSEC:   "<< cap.get( cv::CAP_PROP_POS_MSEC) <<std::endl;
         // std::cout<< "CV_CAP_PROP_POS_FRAMES:  "<< cap.get( cv::CAP_PROP_POS_FRAMES) <<std::endl;  // <-- the v4l2 'sequence' field
-        // std::cout<< "CV_CAP_PROP_FPS:  "<< cap.get( cv::CAP_PROP_FPS)<<std::endl;
+        // std::cout<< "cv::CAP_PROP_FPS:  "<< cap.get( cv::CAP_PROP_FPS)<<std::endl;
         // std::cout << "Format: " << cap.get(CV_CAP_PROP_FORMAT) << "\n";
         // CAP_PROP_POS_MSEC Current position of the video file in milliseconds or video capture timestamp.
         std::cout << "id: " << cap.get(cv::CAP_PROP_POS_MSEC) << std::endl;
@@ -368,8 +368,8 @@ void *computationTask(void *x_void_ptr)
         // if(!first_iteration)
         // {
         //     // backtorgb = cv::cvtColor(pre_canny,cv::COLOR_GRAY2RGB)
-        //     cv::cvtColor(pre_canny, pre_canny_RGB, CV_GRAY2RGB);
+        //     cv::cvtColor(pre_canny, pre_canny_RGB, cv::COLOR_GRAY2RGB);
-        //     cv::cvtColor(canny, canny_RGB, CV_GRAY2RGB);
+        //     cv::cvtColor(canny, canny_RGB, cv::COLOR_GRAY2RGB);
         //     disparity_frame = frame_disparity(pre_canny_RGB, canny_RGB, frame_nbr, 999, 0);
         //     std::cout<<"size: "<<disparity_frame.rows<<" - "<<disparity_frame.cols<<std::endl;
         //     if (disparity_frame.rows == 0 || disparity_frame.cols == 0)
@@ -394,8 +394,8 @@ void *computationTask(void *x_void_ptr)
         //     // step_t_segmentation = end_t_segmentation;
 
         //     // //frame box disparity on the preprocessed image
-        //     // cv::cvtColor(pre_canny, pre_canny_RGB, CV_GRAY2RGB);
+        //     // cv::cvtColor(pre_canny, pre_canny_RGB, cv::COLOR_GRAY2RGB);
-        //     // cv::cvtColor(canny, canny_RGB, CV_GRAY2RGB);
+        //     // cv::cvtColor(canny, canny_RGB, cv::COLOR_GRAY2RGB);
         //     // frame_box_disparity(pre_canny_RGB, canny_RGB, pre_rois, frame_nbr);
         //     // // reset pre_rois for the new roi of the current frame
         //     // pre_rois.erase(pre_rois.begin(), pre_rois.end());

include/boxDetection.h

@@ -19,7 +19,7 @@
 
 //saliency
 #include <opencv2/core/utility.hpp>
-#include <opencv2/saliency.hpp>
+//#include <opencv2/saliency.hpp>
 #include <opencv2/highgui.hpp>
 
 #define SAVE false

include/visualization.h

@@ -7,7 +7,7 @@
 
 //saliency
 #include <opencv2/core/utility.hpp>
-#include <opencv2/saliency.hpp>
+//#include <opencv2/saliency.hpp>
 #include <opencv2/highgui.hpp>
 
 #include <chrono>

src/class_src/boxDetection.cpp

@@ -1,678 +0,0 @@
-#include "boxDetection.h"
-#include <string.h>
-char buf_frame_crop_name[200];
-
-cv::Mat img_threshold(cv::Mat frame_crop)
-{
-    // Image Threshold Example
-    // https://docs.opencv.org/3.4/d7/d1c/tutorial_js_watershed.html
-    cv::Mat f = frame_crop.clone();
-    cv::Mat dst, gray;
-    // gray and threshold image
-    cv::cvtColor(f, gray, cv::COLOR_RGBA2GRAY, 0);
-    cv::threshold(gray, gray, 0, 255, cv::THRESH_BINARY_INV + cv::THRESH_OTSU);
-    return gray;
-}
-
-cv::Mat img_background(cv::Mat frame_crop)
-{
-    // Image Background Example
-    // https://docs.opencv.org/3.4/d7/d1c/tutorial_js_watershed.html
-    cv::Mat f = frame_crop.clone();
-    cv::Mat dst, gray, opening, coinsBg;
-    // gray and threshold image
-    cv::cvtColor(f, gray, cv::COLOR_RGBA2GRAY, 0);
-    cv::threshold(gray, gray, 0, 255, cv::THRESH_BINARY_INV + cv::THRESH_OTSU);
-    // get background
-    cv::Mat M = cv::Mat(3, 3, CV_8U, cv::Scalar(1, 1, 1, 1));
-    cv::erode(gray, opening, M);
-    cv::dilate(gray, opening, M);
-    cv::Point p = cv::Point(-1, -1);
-    cv::dilate(opening, coinsBg, M, p, 3);
-    return coinsBg;
-}
-
-cv::Mat img_dist_transform(cv::Mat frame_crop)
-{
-    // Distance Transform Example
-    // https://docs.opencv.org/3.4/d7/d1c/tutorial_js_watershed.html
-    cv::Mat f = frame_crop.clone();
-    cv::Mat dst, gray, opening, coinsBg, coinsFg, distTrans;
-    // gray and threshold image
-    cv::cvtColor(f, gray, cv::COLOR_RGBA2GRAY, 0);
-    cv::threshold(gray, gray, 0, 255, cv::THRESH_BINARY_INV + cv::THRESH_OTSU);
-    // cv::Mat::ones M(3,3,cv::CV_8U);
-    // get background
-    cv::Mat M = cv::Mat(3, 3, CV_8U, cv::Scalar(1, 1, 1, 1));
-    cv::erode(gray, opening, M);
-    cv::dilate(gray, opening, M);
-    cv::Point p = cv::Point(-1, -1);
-    cv::dilate(opening, coinsBg, M, p, 3);
-    // distance transorm
-    cv::distanceTransform(opening, distTrans, cv::DIST_L2, 5);
-    cv::normalize(distTrans, distTrans, 1, 0, cv::NORM_INF);
-    return distTrans;
-}
-
-// cv::Mat img_watershed(cv::Mat frame_crop)
-// {
-//     // Image Watershed Example
-//     // https://docs.opencv.org/3.4/d7/d1c/tutorial_js_watershed.html
-//     cv::Mat f = frame_crop.clone();
-//     cv::Mat dst, gray, opening, coinsBg, coinsFg, distTrans, unknown, markers;
-//     // gray and threshold image
-//     cv::cvtColor(f, gray, cv::COLOR_RGBA2GRAY, 0);
-//     cv::threshold(gray, gray, 0, 255, cv::THRESH_BINARY_INV + cv::THRESH_OTSU);
-//     // get background
-//     cv::Mat M = cv::Mat(3, 3, CV_8U, cv::Scalar(1,1,1,1));
-//     cv::erode(gray, opening, M);
-//     cv::dilate(gray, opening, M);
-//     cv::Point p = cv::Point(-1,-1);
-//     cv::dilate(opening, coinsBg, M, p, 3);
-//     // distance transorm
-//     cv::distanceTransform(opening, distTrans, cv::DIST_L2, 5);
-//     cv::normalize(distTrans, distTrans, 1, 0, cv::NORM_INF);
-
-//     // get foreground
-//     cv::threshold(distTrans, coinsFg, 0.7 * 1, 255, cv::THRESH_BINARY);
-//     coinsFg.convertTo(coinsFg, CV_8U, 1, 0);
-//     cv::subtract(coinsBg, coinsFg, unknown);
-//     // get connected components networks
-//     cv::connectedComponents(coinsFg, markers);
-//     // intptr_t n = NULL;
-//     for(int i = 0; i< markers.rows; i++)
-//     {
-//         for (int j = 0; j< markers.cols; j++)
-//         {
-//             M.at<uchar>(0, 0);
-//             markers.intPtr(i,j)[0] = markers.ucharPtr(i,j)[0] +1;
-//             if(unknown.ucharPtr(i,j)[0] == 255)
-//             {
-//                 markers.intPtr(i,j)[0] = 0;
-//             }
-//         }
-//     }
-//     cv::cvtColor(f, f, cv::COLOR_RGBA2RGB, 0);
-//     cv::watershed(f, markers);
-//     //draw barriers
-//     for(int i = 0; i< markers.rows; i++)
-//     {
-//         for (int j = 0; j< markers.cols; j++)
-//         {
-//             if(markers.IntPtr(i,j)[0] == -1)
-//             {
-//                 f.ucharPtr(i,j)[0] = 255; // R
-//                 f.ucharPtr(i,j)[1] = 0; // G
-//                 f.ucharPtr(i,j)[2] = 0; // B
-//             }
-//         }
-//     }
-// }
-
-//////
-
-cv::Mat img_sobel_abssobel(cv::Mat frame_crop, int ret = 0)
-{
-    //ret = 0 --> dstx
-    //ret = 1 --> dsty
-    //ret = 2 --> absDstx
-    //ret = 3 --> absDsty
-    // Image Sobel and Image AbsSobel
-    // https://docs.opencv.org/trunk/da/d85/tutorial_js_gradients.html
-    // compute image gradient on two different directions
-
-    cv::Mat f = frame_crop.clone();
-    int x, y;
-    (ret == 0 || ret == 2) ? x = 1, y = 0 : NULL;
-    (ret == 1 || ret == 3) ? x = 0, y = 1 : NULL;
-    cv::Mat dst;
-    cv::cvtColor(f, f, cv::COLOR_RGB2GRAY, 0);
-    // You can try more different parameters
-    cv::Sobel(f, dst, CV_8U, x, y, 3, 1, 0, cv::BORDER_DEFAULT);
-    // for absSobel
-    if (ret == 2 || ret == 3)
-        cv::convertScaleAbs(dst, dst, 1, 0);
-    // next 3 rows to be checked
-    //// ??cv::Mat f2 = frame_crop.clone();
-    //// cv.Scharr(?(f,f2), dstx, cv.CV_8U, 1, 0, 1, 0, cv.BORDER_DEFAULT);
-    //// cv.Scharr(?(f,f2), dsty, cv.CV_8U, 0, 1, 1, 0, cv.BORDER_DEFAULT);
-    return dst;
-}
-
-cv::Mat img_laplacian(cv::Mat frame_crop, int ret = 1)
-{
-    //ret = 0 --> src_gray
-    //ret = 1 --> dst
-    // Image Laplacian
-    // compute image gradient with laplacian
-    cv::Mat f = frame_crop.clone();
-    cv::Mat src_gray, dst;
-    int kernel_size = 3;
-    int scale = 1;
-    int delta = 0;
-    int ddepth = CV_16S;
-    cv::GaussianBlur(f, f, cv::Size(3, 3), 0, 0, cv::BORDER_DEFAULT);
-    /// Convert the image to grayscale
-    cv::cvtColor(f, src_gray, CV_RGB2GRAY);
-    if (ret == 0)
-        return src_gray;
-
-    // else: Apply Laplace function
-    cv::Mat abs_dst;
-    cv::Laplacian(src_gray, dst, ddepth, kernel_size, scale, delta, cv::BORDER_DEFAULT);
-    // //compute sharpness
-    // float sharpnessValue = cv::mean(dst);
-    return dst;
-}
-
-cv::Mat find_contours(cv::Mat frame_crop, cv::Mat img, cv::Mat canny_output, int n_lines = 1)
-{
-    // n_line: number of line to plot on image
-    cv::Mat img_line = frame_crop.clone();
-    cv::Mat ret_thresh;
-    std::vector<std::vector<cv::Point>> contours;
-    double thresh = 127;
-    double maxValue = 255;
-    cv::threshold(img, ret_thresh, thresh, maxValue, 0); //0); // = cv2.threshold(img,127,255,0)
-    cv::findContours(canny_output, contours, 1, 2);      //cv::CHAIN_APPROX_SIMPLE );//1, 2); //contours,hierarchy = cv2.findContours(thresh, 1, 2)
-    // cv::threshold(img2, ret2, thresh, maxValue, 0);
-    // cv::findContours(canny_output2, contours2, 1, 2);
-    // cv::threshold(img3a, ret3a, thresh, maxValue, 0);
-    // cv::findContours(canny_output3a, contours3a, 1, 2);
-    // cv::threshold(img3b, ret3b, thresh, maxValue, 0);
-    // cv::findContours(canny_output3b, contours3b, 1, 2);
-
-    cv::Vec4f line;
-    float vx, vy, x, y;
-    int lefty, righty;
-    for (int i = 0; i < n_lines; i++)
-    {
-        cv::fitLine(contours[i], line, CV_DIST_L2, 0, 0.01, 0.01);
-        vx = line(0);
-        vy = line(1);
-        x = line(2);
-        y = line(3);
-        lefty = int((-x * vy / vx) + y);
-        righty = int(((img.cols - x) * vy / vx) + y);
-        cv::line(img_line, cv::Point(img.cols - 1, righty), cv::Point(0, lefty), (255, 0, 0), 2);
-    }
-
-    // cv::imshow("bla", img);
-    // cv::waitKey(1000);
-    return img_line;
-}
-
-// cv::Mat fit_rectangular(cv::Mat frame_crop, cv::Mat img, cv::Mat canny_output)
-// {
-//     cv::Mat img_clone = frame_crop.clone();
-//     cv::Mat ret_thresh;
-//     std::vector<std::vector<cv::Point> > contours;
-//     double thresh = 127;
-//     double maxValue = 255;
-//     cv::threshold(img, ret_thresh, thresh, maxValue, 0);//0); // = cv2.threshold(img,127,255,0)
-//     cv::findContours(canny_output, contours, 1, 2);//cv::CHAIN_APPROX_SIMPLE );//1, 2); //contours,hierarchy = cv2.findContours(thresh, 1, 2)
-
-//     cv::RotatedRect rect = cv::minAreaRect(contours[0]);
-//     cv::Mat boxPts1;
-//     std::vector<std::vector<cv::Point> > boxPts2;
-//     cv::boxPoints(rect, boxPts1);
-//     // boxPts = np.int0(boxPts);
-//     for (int x = 0; x < img.cols; x++)
-//         for (int y = 0; y < img.rows; y++)
-//             boxPts2.at(x).push_back(cv::Point(boxPts1.at<int>(x, y)));
-
-//     cv::drawContours(img_clone, boxPts2,0,(0,0,255),2);
-//     // drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
-//     return img_clone;
-// }
-
-cv::Mat compute_saliency(cv::Mat frame_crop, cv::Ptr<cv::saliency::Saliency> saliencyAlgorithm, int const_molt_mat, int ret = 0)
-{
-    //ret=0 --> saliencyMap
-    //ret=1 --> binaryMap
-    // SPECTRAL_RESIDUAL algorithm
-    cv::Mat f = frame_crop.clone();
-    cv::Mat saliencyMap;
-    cv::Mat binaryMap;
-
-    if (saliencyAlgorithm->computeSaliency(f, saliencyMap))
-    {
-        if (ret == 0)
-            return saliencyMap * const_molt_mat;
-
-        cv::saliency::StaticSaliencySpectralResidual spec;
-        spec.computeBinaryMap(saliencyMap, binaryMap);
-
-        // imshow( "Saliency Map", saliencyMap );
-        // imshow( "Original Image", image );
-        // imshow( "Binary Map", binaryMap );
-        // waitKey( 0 );
-        return binaryMap * const_molt_mat;
-    }
-    return cv::Mat(0, 0, CV_8U, cv::Scalar(0, 0, 0, 0));
-}
-
-//////
-
-void image_segmentation(cv::Mat frame_crop, int frame_nbr, int i)
-{
-    // Watershed Algorithm
-    // https://docs.opencv.org/3.4/d7/d1c/tutorial_js_watershed.html
-
-    auto step_t_segmentation = std::chrono::steady_clock::now();
-    auto end_t_segmentation = std::chrono::steady_clock::now();
-    cv::Mat ret;
-    // ret = img_threshold(frame_crop);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgthr.jpg", frame_nbr, i, img_threshold(frame_crop));
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME imgthr (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-    // ret =img_background(frame_crop);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgback.jpg", frame_nbr, i, img_background(frame_crop));
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME imgback (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-    // ret = img_dist_transform(frame_crop);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgtrans.jpg", frame_nbr, i, img_dist_transform(frame_crop));
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME imgtrans (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-    // // ret = img_watershed(frame_crop);
-    // if(SAVE) SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgwatershed.jpg", frame_nbr, i, img_watershed(frame_crop));
-}
-
-void image_gradients(cv::Mat frame_crop, int frame_nbr, int i)
-{
-    // Image Gradients
-    // https://docs.opencv.org/trunk/da/d85/tutorial_js_gradients.html
-
-    auto step_t_segmentation = std::chrono::steady_clock::now();
-    auto end_t_segmentation = std::chrono::steady_clock::now();
-    cv::Mat ret;
-    // sobel
-    // ret = img_sobel_abssobel(frame_crop, 0);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgsobel_x_8U.jpgg", frame_nbr, i, img_sobel_abssobel(frame_crop, 0));
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME sobel0 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-    // ret = img_sobel_abssobel(frame_crop, 1);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgsobel_y_8U.jpg", frame_nbr, i, img_sobel_abssobel(frame_crop, 1));
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME sobel1 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-    // ret = img_sobel_abssobel(frame_crop, 2);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgsobel_x_64F.jpg", frame_nbr, i, img_sobel_abssobel(frame_crop, 2));
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME sobel2 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-    // ret = img_sobel_abssobel(frame_crop, 3);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imgsobel_y_64F.jpg", frame_nbr, i, img_sobel_abssobel(frame_crop, 3));
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME sobel3 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-
-    // laplacian
-    // ret = img_laplacian(frame_crop, 0);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imglaplacian_gr.jpg", frame_nbr, i, img_laplacian(frame_crop, 0));
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME laplacian0 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-    // ret = img_laplacian(frame_crop, 1);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_imglaplacian_dst.jpg", frame_nbr, i, img_laplacian(frame_crop, 1));
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME laplacian1 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-    //////
-}
-
-void image_find_contours(cv::Mat frame_crop, int frame_nbr, int i)
-{
-    // Finding contours in your image
-    // https://docs.opencv.org/3.4/df/d0d/tutorial_find_contours.html
-
-    auto step_t_segmentation = std::chrono::steady_clock::now();
-    auto end_t_segmentation = std::chrono::steady_clock::now();
-    // plot lines on figure. 3 ways:
-    // 1 - use gray image (no more operations) to get contours (one line)
-    // 2 - use laplacian image (one line)
-    // 3 - use sobel (1st dir) image and sobel (2nd dir) image to plot two different lines
-    cv::Mat canny_output1, canny_output2, canny_output3a, canny_output3b;
-    cv::Mat contours;
-    // src_gray
-    cv::Mat img1 = img_laplacian(frame_crop, 0);
-    cv::Canny(img1, canny_output1, 100, 100 * 2);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_canny1.jpg", frame_nbr, i, canny_output1);
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME canny1 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-    // // dst
-    // cv::Mat img2 = img_laplacian(frame_crop, 2);
-    // cv::Canny(img2, canny_output2, 100, 100*2 );
-    cv::Canny(img1, canny_output2, 100, 100 * 2);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_canny2.jpg", frame_nbr, i, canny_output2);
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME canny2 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-    // dstx
-    cv::Mat img3a = img_sobel_abssobel(frame_crop, 0);
-    cv::Canny(img3a, canny_output3a, 100, 100 * 2);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_canny3a.jpg", frame_nbr, i, canny_output3a);
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME canny3a (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-    // dsty
-    cv::Mat img3b = img_sobel_abssobel(frame_crop, 1);
-    cv::Canny(img3b, canny_output3b, 100, 100 * 2);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_canny3b.jpg", frame_nbr, i, canny_output3b);
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME canny3b (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-
-    // 1 line
-    // contours = find_contours(frame_crop, img1, canny_output1, 1);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_line1.jpg", frame_nbr, i, find_contours(frame_crop, img1, canny_output1, 1));
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME line1 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-    // 3 line
-    // contours = find_contours(frame_crop, img1, canny_output2, 1);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_line2.jpg", frame_nbr, i, find_contours(frame_crop, img1, canny_output2, 1));
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME line2 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-    // mix 1 line of image with 1 line of another
-    cv::Mat img_line = frame_crop.clone();
-    img_line = find_contours(img_line, img3a, canny_output3a, 1);
-    img_line = find_contours(img_line, img3b, canny_output3b, 1);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_line3.jpg", frame_nbr, i, img_line);
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME line3 (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-
-    // img_line = frame_crop.clone();
-    // img_line = find_contours(img_line, img3a, canny_output3a, 2);
-    // img_line = find_contours(img_line, img3b, canny_output3b, 2);
-    // sprintf(buf_frame_crop_name,"../demo/demo/data/img_crop/%d_%d_line3bis.jpg",frame_nbr, i);
-    // cv::imwrite(buf_frame_crop_name, img_line);
-
-    // cv::Mat canny_output4;
-    // cv::Mat img4 = img_laplacian(frame_crop, 0);
-    // cv::Canny(img4, canny_output4, 100, 100*2 );
-    // printf(buf_frame_crop_name,"../demo/demo/data/img_crop/%d_%d_rect.jpg",frame_nbr, i);
-    // cv::imwrite(buf_frame_crop_name, fit_rectangular(frame_crop, img4, canny_output4));
-}
-
-void image_saliency(cv::Mat frame_crop, int frame_nbr, int i)
-{
-    // https://github.com/opencv/opencv_contrib/blob/master/modules/saliency/samples/computeSaliency.cpp
-    cv::Ptr<cv::saliency::Saliency> saliencyAlgorithm;
-
-    int const_molt_mat = 0;
-    auto step_t_segmentation = std::chrono::steady_clock::now();
-    auto end_t_segmentation = std::chrono::steady_clock::now();
-
-    // SPECTRAL_RESIDUAL
-    const_molt_mat = 255;
-    saliencyAlgorithm = cv::saliency::StaticSaliencySpectralResidual::create();
-    cv::Mat spect_res = compute_saliency(frame_crop, saliencyAlgorithm, const_molt_mat, 0);
-    if (!spect_res.empty())
-    {
-        if (SAVE)
-            SAVE_TO("../demo/demo/data/img_crop/%d_%d_saliency_SpectralResidual.jpg", frame_nbr, i, spect_res);
-    }
-    else
-    {
-        std::cout << "something is wrond (image_saliency)" << std::endl;
-    }
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME SPECTRAL_RESIDUAL (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-
-    // BINARY SPECTRAL_RESIDUAL
-    const_molt_mat = 255;
-    spect_res = compute_saliency(frame_crop, saliencyAlgorithm, const_molt_mat, 1);
-    if (!spect_res.empty())
-    {
-        if (SAVE)
-            SAVE_TO("../demo/demo/data/img_crop/%d_%d_saliency_BinarySpectralResidual.jpg", frame_nbr, i, spect_res);
-    }
-    else
-    {
-        std::cout << "something is wrond (image_saliency)" << std::endl;
-    }
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME BINARY SPECTRAL_RESIDUAL (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-
-    // FINE_GRAINED
-    const_molt_mat = 1;
-    saliencyAlgorithm = cv::saliency::StaticSaliencyFineGrained::create();
-    spect_res = compute_saliency(frame_crop, saliencyAlgorithm, const_molt_mat, 0);
-    if (!spect_res.empty())
-    {
-        if (SAVE)
-            SAVE_TO("../demo/demo/data/img_crop/%d_%d_saliency_FineGrained.jpg", frame_nbr, i, spect_res);
-    }
-    else
-    {
-        std::cout << "something is wrond (image_saliency)" << std::endl;
-    }
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME FINE_GRAINED (" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-
-    // saliencyAlgorithm = cv::saliency::ObjectnessBING::create();
-    // std::vector<cv::Vec4i> saliencyMap1;
-    // saliencyAlgorithm.dynamicCast<cv::saliency::ObjectnessBING>()->setTrainingPath( "" );
-    // saliencyAlgorithm.dynamicCast<cv::saliency::ObjectnessBING>()->setBBResDir( "Results" );
-    // std::cout<<"mmm"<<std::endl;
-    // saliencyAlgorithm->computeSaliency( frame_crop, saliencyMap1 );
-    // int ndet = int(saliencyMap1.size());
-    // std::cout << "Objectness done " << ndet << std::endl;
-    // // // The result are sorted by objectness. We only use the first maxd boxes here.
-    // // int maxd = 7, step = 255 / maxd, jitter=9; // jitter to seperate single rects
-    // // cv::Mat draw = frame_crop.clone();
-    // // for (int i = 0; i < std::min(maxd, ndet); i++)
-    // // {
-    // //     cv::Vec4i bb = saliencyMap1[i];
-    // //     cv::Scalar col = cv::Scalar(((i*step)%255), 100, 255-((i*step)%255));
-    // //     cv::Point off(cv::theRNG().uniform(-jitter,jitter), cv::theRNG().uniform(-jitter,jitter));
-    // //     cv::rectangle(draw, cv::Point(bb[0]+off.x, bb[1]+off.y), cv::Point(bb[2]+off.x, bb[3]+off.y), col, 2);
-    // //     cv::rectangle(draw, cv::Rect(20, 20+i*10, 10,10), col, -1); // mini temperature scale
-    // // }
-    // // imshow("BING", draw);
-    // // waitKey();
-    // printf(buf_frame_crop_name,"../demo/demo/data/img_crop/%d_%d_saliency_BING.jpg",frame_nbr, i);
-    // cv::imwrite(buf_frame_crop_name, saliencyMap1);
-
-    ////
-
-    // BING WANG APR 2014
-    cv::Mat saliencyMap;
-    cv::Mat frame_sal = frame_crop.clone();
-    saliencyAlgorithm = cv::saliency::MotionSaliencyBinWangApr2014::create();
-    saliencyAlgorithm.dynamicCast<cv::saliency::MotionSaliencyBinWangApr2014>()->setImagesize(frame_sal.cols, frame_sal.rows);
-    saliencyAlgorithm.dynamicCast<cv::saliency::MotionSaliencyBinWangApr2014>()->init();
-    cvtColor(frame_sal, frame_sal, cv::COLOR_BGR2GRAY);
-    saliencyAlgorithm->computeSaliency(frame_sal, saliencyMap);
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d_saliency_BinWangApr.jpg", frame_nbr, i, saliencyMap);
-
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " - TIME BING WANG APR 2014(" << frame_nbr << "-" << i << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-}
-
-cv::Mat frame_disparity(cv::Mat pre_frame, cv::Mat frame, int frame_nbr, int i, int ret = 0)
-{
-    // https://stackoverflow.com/questions/27035672/cv-extract-differences-between-two-images
-    cv::Mat backgroundImage = pre_frame.clone();
-    cv::Mat currentImage = frame.clone();
-    cv::Mat diffImage;
-    // pass to HSV color
-    if (ret)
-    {
-        cv::cvtColor(backgroundImage, backgroundImage, CV_BGR2HSV);
-        cv::cvtColor(currentImage, currentImage, CV_BGR2HSV);
-    }
-    cv::absdiff(backgroundImage, currentImage, diffImage);
-
-    cv::Mat foregroundMask = cv::Mat::zeros(diffImage.rows, diffImage.cols, CV_8UC1);
-    // std::cout<<"diffImage: "<<diffImage.cols<<" - "<<diffImage.rows<<std::endl;
-    // if(SAVE) SAVE_TO("../demo/demo/data/img_disparity/%d_%d_pc1.jpg", frame_nbr, i, backgroundImage);
-    // if(SAVE) SAVE_TO("../demo/demo/data/img_disparity/%d_%d_c1.jpg", frame_nbr, i, currentImage);
-    float threshold = 30.0f;
-    float dist;
-
-    for (int j = 0; j < diffImage.rows; ++j)
-    {
-        for (int k = 0; k < diffImage.cols; ++k)
-        {
-            cv::Vec3b pix = diffImage.at<cv::Vec3b>(j, k);
-
-            dist = (pix[0] * pix[0] + pix[1] * pix[1] + pix[2] * pix[2]);
-            dist = sqrt(dist);
-
-            if (dist > threshold)
-            {
-                foregroundMask.at<unsigned char>(j, k) = 255;
-            }
-        }
-    }
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_disparity/%d_%d_dif.jpg", frame_nbr, i, foregroundMask);
-
-    return foregroundMask;
-}
-
-void frame_box_disparity(cv::Mat pre_frame, cv::Mat frame, std::vector<cv::Rect> pre_rois, int frame_nbr)
-{
-
-    int roi_tollerance = 10;
-    cv::Mat pre_frame_crop, frame_crop;
-    int dx, dy;
-    int id = 1;
-    auto step_t_segmentation = std::chrono::steady_clock::now();
-    auto end_t_segmentation = std::chrono::steady_clock::now();
-
-    for (auto r : pre_rois)
-    {
-        if (SAVE)
-            SAVE_TO("../demo/demo/data/img_disparity/%d_%d_orig.jpg", frame_nbr, id, pre_frame(r));
-
-        //resize last roi with a tollerance
-        dx = r.width / roi_tollerance;
-        dy = r.height / roi_tollerance;
-        r.x = (r.x - dx > 0) ? (r.x - dx) : 0;
-        r.y = (r.y - dy > 0) ? (r.y - dy) : 0;
-        // std::cout<<"disp: x "<<r.x<<" - y "<<r.y<<std::endl;
-        r.width = ((r.x + r.width + dx + dx) >= frame.cols) ? (frame.cols - 1 - r.x) : (r.width + dx + dx);
-        r.height = ((r.y + r.height + dy + dy) >= frame.rows) ? (frame.rows - 1 - r.y) : (r.height + dy + dy);
-        // std::cout<<"disp: w "<<r.width<<" - h "<<r.height<<std::endl;
-        // std::cout<<"disp: wf "<<frame.cols<<" - hf "<<frame.rows<<std::endl;
-        // std::cout<<"---"<<std::endl;
-        // std::cout<<"disp: x "<<r.x<<" to "<<r.width+r.x<<" wf "<<frame.cols<<std::endl;
-        // std::cout<<"disp: y "<<r.y<<" to "<<r.height+r.y<<" hf "<<frame.rows<<std::endl;
-
-        //crop pre_frame and current frame
-        pre_frame_crop = pre_frame(r);
-        frame_crop = frame(r);
-        if (SAVE)
-            SAVE_TO("../demo/demo/data/img_disparity/%d_%d_cur.jpg", frame_nbr, id, frame_crop);
-        if (SAVE)
-            SAVE_TO("../demo/demo/data/img_disparity/%d_%d_pre.jpg", frame_nbr, id, pre_frame_crop);
-
-        // difference from two consecutive frame
-        step_t_segmentation = std::chrono::steady_clock::now();
-        frame_disparity(pre_frame_crop, frame_crop, frame_nbr, id, 0);
-        end_t_segmentation = std::chrono::steady_clock::now();
-        std::cout << " TIME frame_disparity (" << frame_nbr << "-" << id << ") : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-        step_t_segmentation = end_t_segmentation;
-        id++;
-    }
-}
-
-void segmentation(cv::Mat pre_frame, cv::Mat frame_crop, int frame_nbr, int i, int mode)
-{
-    //mode=0 (for whole frame), it computes the frame disparity
-    //mode=1 (for single box), it doesn't compute the frame disparity (it has already been done-see frame_box_disparity())
-    // whole figure
-    char buf_str[15];
-    if (!mode)
-        sprintf(buf_str, "whole frame");
-    else
-        sprintf(buf_str, "a box frame");
-
-    if (SAVE)
-        SAVE_TO("../demo/demo/data/img_crop/%d_%d.jpg", frame_nbr, i, frame_crop);
-
-    auto step_t_segmentation = std::chrono::steady_clock::now();
-    auto end_t_segmentation = std::chrono::steady_clock::now();
-
-    // Watershed Algorithm
-    std::cout << "image segmentation:" << std::endl;
-    image_segmentation(frame_crop, frame_nbr, i);
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " TIME " << buf_str << ": image_segmentation : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-
-    // Image Gradients
-    std::cout << "image gradients:" << std::endl;
-    image_gradients(frame_crop, frame_nbr, i);
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " TIME " << buf_str << ": image_gradients : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-
-    // Find contours
-    std::cout << "image find contours:" << std::endl;
-    image_find_contours(frame_crop, frame_nbr, i);
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " TIME " << buf_str << ": image_find_contours : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-
-    //saliency map
-    std::cout << "image saliency:" << std::endl;
-    image_saliency(frame_crop, frame_nbr, i);
-    end_t_segmentation = std::chrono::steady_clock::now();
-    std::cout << " TIME " << buf_str << ": image_saliency : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-    step_t_segmentation = end_t_segmentation;
-
-    //frame disparity
-    if (!mode && frame_nbr != 0)
-    {
-        std::cout << "frame disparity:" << std::endl;
-        frame_disparity(pre_frame, frame_crop, frame_nbr, i, 0);
-        end_t_segmentation = std::chrono::steady_clock::now();
-        std::cout << " TIME " << buf_str << ": frame_disparity : " << std::chrono::duration_cast<std::chrono::milliseconds>(end_t_segmentation - step_t_segmentation).count() << " ms" << std::endl;
-        step_t_segmentation = end_t_segmentation;
-    }
-}

src/class_src/visualization.cpp

@@ -200,7 +200,7 @@ void *detectionFrame(void *x_void_ptr)
                 // assert (camera_p[0].x < original_frame_loc.cols);
                 // assert (camera_p[0].y < original_frame_loc.rows);
                 if (camera_p[0].x < original_frame_loc.cols && camera_p[0].y < original_frame_loc.rows && camera_p[0].x >= 0 && camera_p[0].y >= 0)
-                    cv::circle(original_frame_loc, cv::Point(camera_p[0].x, camera_p[0].y), 3.0, cv::Scalar(t.r_, t.g_, t.b_), CV_FILLED, 8, 0);
+                    cv::circle(original_frame_loc, cv::Point(camera_p[0].x, camera_p[0].y), 3.0, cv::Scalar(t.r_, t.g_, t.b_), cv::FILLED, 8, 0);
             }
         }
 
@@ -259,7 +259,7 @@ void *topviewFrame(void *x_void_ptr)
                 gc.enu2Geodetic(t.pred_list_[p].x_, t.pred_list_[p].y_, 0, &lat, &lon, &alt);
                 coord2pixel(lat, lon, pix_x, pix_y, adfGeoTransform);
                 if (pix_x < frame_top.cols && pix_y < frame_top.rows && pix_x >= 0 && pix_y >= 0)
-                    cv::circle(frame_top, cv::Point(pix_x, pix_y), 7.0, cv::Scalar(t.r_, t.g_, t.b_), CV_FILLED, 8, 0);
+                    cv::circle(frame_top, cv::Point(pix_x, pix_y), 7.0, cv::Scalar(t.r_, t.g_, t.b_), cv::FILLED, 8, 0);
             }
         }
         //outputVideo<< frame_top;
@@ -312,7 +312,7 @@ void *disparityFrame(void *x_void_ptr)
             //preprocessing frame
             step_t = std::chrono::steady_clock::now();
             // src_gray
-            canny_img = img_laplacian(frame_loc, 0);
+            //canny_img = img_laplacian(frame_loc, 0);
             cv::Canny(canny_img, canny, 100, 100 * 2);
             // sprintf(buf_frame_crop_name,"../demo/demo/data/img_disparity/%d_%d_canny.jpg",frame_nbr_loc, 999);
             // cv::imwrite(buf_frame_crop_name, canny);
@@ -325,9 +325,9 @@ void *disparityFrame(void *x_void_ptr)
             if (!first_iteration)
             {
                 // backtorgb = cv::cvtColor(pre_canny,cv::COLOR_GRAY2RGB)
-                cv::cvtColor(pre_canny, pre_canny_RGB, CV_GRAY2RGB);
+                cv::cvtColor(pre_canny, pre_canny_RGB, cv::COLOR_GRAY2RGB);
-                cv::cvtColor(canny, canny_RGB, CV_GRAY2RGB);
+                cv::cvtColor(canny, canny_RGB, cv::COLOR_GRAY2RGB);
-                disparity_frame = frame_disparity(pre_canny_RGB, canny_RGB, frame_nbr_loc, 999, 0);
+                //disparity_frame = frame_disparity(pre_canny_RGB, canny_RGB, frame_nbr_loc, 999, 0);
                 // std::cout<<"size: "<<disparity_frame.rows<<" - "<<disparity_frame.cols<<std::endl;
                 // if (disparity_frame.rows == 0 || disparity_frame.cols == 0)
                 //     return -1;
@@ -351,8 +351,8 @@ void *disparityFrame(void *x_void_ptr)
                 // step_t_segmentation = end_t_segmentation;
 
                 // //frame box disparity on the preprocessed image
-                // cv::cvtColor(pre_canny, pre_canny_RGB, CV_GRAY2RGB);
+                // cv::cvtColor(pre_canny, pre_canny_RGB, cv::COLOR_GRAY2RGB);
-                // cv::cvtColor(canny, canny_RGB, CV_GRAY2RGB);
+                // cv::cvtColor(canny, canny_RGB, cv::COLOR_GRAY2RGB);
                 // frame_box_disparity(pre_canny_RGB, canny_RGB, pre_rois, frame_nbr_loc);
                 // // reset pre_rois for the new roi of the current frame
                 // pre_rois.erase(pre_rois.begin(), pre_rois.end());