C++에서 OpenCV를 사용하여 색상을 감지하는 방법은 무엇입니까?

특정 색상을 감지하고 색상을 기반으로 개체를 추적하는 방법을 이해합니다. 색상 감지 및 색상 감지 기반 추적 시스템의 성능은 환경에 따라 다릅니다.

방의 조명을 바꾸거나 배경색을 바꾸면 색상 감지에 상당한 영향을 미칩니다.

다음 프로그램은 C++에서 OpenCV를 사용하여 색상을 감지하는 방법을 보여줍니다.

예

#include<iostream>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
using namespace std;
using namespace cv;
int main(int argc, char** argv) {
   VideoCapture video_load(0);//capturing video from default camera//
   namedWindow("Adjust");//declaring window to show the image//
   int Hue_Lower_Value = 0;//initial hue value(lower)//
   int Hue_Lower_Upper_Value = 22;//initial hue value(upper)//
   int Saturation_Lower_Value = 0;//initial saturation(lower)//
   int Saturation_Upper_Value = 255;//initial saturation(upper)//
   int Value_Lower = 0;//initial value (lower)//
   int Value_Upper = 255;//initial saturation(upper)//
   createTrackbar("Hue_Lower", "Adjust", &Hue_Lower_Value, 179);//track-bar for lower hue//
   createTrackbar("Hue_Upper", "Adjust", &Hue_Lower_Upper_Value, 179);//track-bar for lower-upper hue//
   createTrackbar("Sat_Lower", "Adjust", &Saturation_Lower_Value, 255);//track-bar for lower saturation//
   createTrackbar("Sat_Upper", "Adjust", &Saturation_Upper_Value, 255);//track-bar for higher saturation//
   createTrackbar("Val_Lower", "Adjust", &Value_Lower, 255);//track-bar for lower value//
   createTrackbar("Val_Upper", "Adjust", &Value_Upper, 255);//track-bar for upper value//
   while (1) {
      Mat actual_Image;//matrix to load actual image//
      bool temp = video_load.read(actual_Image);//loading actual image to matrix from video stream//
      Mat convert_to_HSV;//declaring a matrix to store converted image//
      cvtColor(actual_Image, convert_to_HSV, COLOR_BGR2HSV);//converting BGR image to HSV and storing it in convert_to_HSV matrix//
      Mat detection_screen;//declaring matrix for window where object will be detected//
      inRange(convert_to_HSV,Scalar(Hue_Lower_Value,Saturation_Lower_Value, Value_Lower),Scalar(Hue_Lower_Upper_Value,Saturation_Upper_Value, Value_Upper), detection_screen);//applying track-bar modified value of track-bar//
      erode(detection_screen, detection_screen, getStructuringElement(MORPH_ELLIPSE, Size(5, 5)));//morphological opening for removing small objects from foreground//
      dilate(detection_screen, detection_screen, getStructuringElement(MORPH_ELLIPSE, Size(5, 5)));//morphological opening for removing small object from foreground//
      dilate(detection_screen, detection_screen, getStructuringElement(MORPH_ELLIPSE, Size(5, 5)));//morphological closing for filling up small holes in foreground//
      erode(detection_screen, detection_screen, getStructuringElement(MORPH_ELLIPSE, Size(5, 5)));//morphological closing for filling up small holes in foreground//
      imshow("Threesholded Image", detection_screen);//showing detected object//
      imshow("Original", actual_Image);//showing actual image//
      if (waitKey(30) == 27){ //if esc is press break the loop//
         break;
      }
   }
   return 0;
}

출력