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New approach using a roi and drawContours method

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This commit is contained in:
Tim Zeuner 2022-11-23 17:10:04 +01:00
parent 55dc2a9b9a
commit 977f531f72
1 changed files with 55 additions and 11 deletions
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66
AutonomousMode/Spielwiese/spielwiese.cpp
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@ -2,6 +2,7 @@
#include <opencv2/opencv.hpp>
#include <iostream>
#include <direct.h>
#include <input.h>
#include <processing.h>
@ -34,20 +35,63 @@ void sweep_em_all(int thresholdBinary, int videoHeight, int videoWidth, int gaus
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++;
}
}