New approach using a roi and drawContours method

AutonomousMode/Spielwiese/spielwiese.cpp

 #include <opencv2/opencv.hpp>
 
 #include <iostream>
+#include <direct.h>
 
 #include <input.h>
 #include <processing.h>
 
 void in_depth_processing_chain(int thresholdBinary, int videoHeight, int videoWidth, int gaussKernelSize, int thresholdCanny1, int thresholdCanny2, int apertureSizeCanny)
 {
-    std::string outputFolder = "C:\\Users\\User\\Desktop\\temp";
     Input input(videoHeight, videoWidth);
 
-    Mat image = input.readFile("C:\\Users\\User\\Desktop\\Studium\\02_Master_MSY\\2. Semester  Winter 22 23\\Projekt\\Line-Following-Robot\\Test_data");
-    imwrite(outputFolder + "\\01_input.jpg", image);
-    cvtColor(image, image, COLOR_BGR2GRAY);
-    imwrite(outputFolder + "\\02_color_convert.jpg", image);
-    GaussianBlur(image, image, Size(gaussKernelSize, gaussKernelSize), 0); 
-    imwrite(outputFolder + "\\03_gauss.jpg", image);
-    threshold(image, image, thresholdBinary, 255, THRESH_BINARY);
-    imwrite(outputFolder + "\\04_threshold.jpg", image);
-    Canny(image, image, thresholdCanny1, thresholdCanny2, apertureSizeCanny);
-    imwrite(outputFolder + "\\05_canny.jpg", image);
+    std::string outputFolder = "C:\\Users\\User\\Desktop\\temp";
+    std::string inputFolder = "C:\\Users\\User\\Desktop\\Studium\\02_Master_MSY\\2. Semester  Winter 22 23\\Projekt\\Line-Following-Robot\\AutonomousMode\\Test_data";
+
+    std::vector<std::string> filenames;
+    cv::glob(inputFolder, filenames);
+
+    //filenames.begin(), filenames.end()
+    int i = 0;
+    for(std::vector<std::string>::iterator it = filenames.begin(); it != filenames.end(); it++)
+    {
+        std::string current_output = outputFolder + "\\" + to_string(i);
+        std::cout << current_output << std::endl;
+        const char* current_output_char = current_output.c_str();
+        _mkdir(current_output_char);
+
+        std::string inputFile = inputFolder + "\\image" + to_string(i+1) + ".jpeg";
+        Mat original_image = input.readFile(inputFile);
+        imwrite(current_output + "\\00_input.jpg", original_image);
+
+        Rect roi = Rect(0, original_image.rows*(7.5/12.0), original_image.cols, original_image.rows/12);
+
+        Mat image = original_image(roi);
 
+        imwrite(current_output + "\\01_roi.jpg", image);
+        cvtColor(image, image, COLOR_BGR2GRAY);
+        imwrite(current_output + "\\02_color_convert.jpg", image);
+        GaussianBlur(image, image, Size(gaussKernelSize, gaussKernelSize), 0); 
+        imwrite(current_output + "\\03_gauss.jpg", image);
+        threshold(image, image, thresholdBinary, 255, THRESH_BINARY);
+        imwrite(current_output + "\\04_threshold.jpg", image);
+
+        // Opening (reduces noise)
+        Mat kernel(5,5, CV_8UC1,1);
+        morphologyEx(image, image, 2, kernel);
+        imwrite(current_output + "\\05_opening.jpg", image);
+
+        //Canny(image, image, thresholdCanny1, thresholdCanny2, apertureSizeCanny);
+        //imwrite(outputFolder + "\\06_canny.jpg", image);
+
+        vector<vector<Point> > contours;
+        vector<Vec4i> hierarchy;
+
+        findContours(image,contours, hierarchy, RETR_LIST, CHAIN_APPROX_SIMPLE);
+
+        for( int i = 0; i< contours.size(); i++ ) // iterate through each contour. 
+        {
+                double a = contourArea( contours[i],false);  //  Find the area of contour
+                if(a > 3500)
+                {
+                    drawContours(original_image, contours, i, Scalar(0,255,255), 1, 8, hierarchy, 0, Point(0,original_image.rows*(7.5/12.0)));
+                }
+        }
+        imwrite(current_output + "\\06_contours.jpg", original_image);
+        i++;
+    }
 }