Overview: Virtual Painter is an OpenCV-based C++ project that allows you to draw on a canvas using colored objects in front of a webcam. This project detects predefined colors (in this case, green and orange) and tracks their movement, enabling you to paint on a digital canvas in real-time.
Usage:
- Clone or download the project repository.
- Make sure you have OpenCV installed and configured in your C++ development environment.
- Compile the project using a C++ compiler (Make sure to configure it using CMake).
- Run the compiled executable.
Code Overview:
// Include necessary libraries
#include <opencv2/imgcodecs.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/objdetect.hpp>
#include <iostream>
// Define color ranges for detection
const vector<vector<int>> colors = {
{0, 179, 224, 255, 255, 255}, // orange (kinda)
{40, 91, 139, 255, 255, 255} // green
};
// Initialize a video capture object
VideoCapture cam(0);
Mat img;
// Main loop for real-time painting
signed main() {
while (true) {
cam.read(img);
newPoints = get_colors(img);
draw_on_canvas(img, newPoints, colorValue);
imshow("Webcam", img);
waitKey(1);
}
}How it Works:
- The project captures video from the webcam in real-time.
- It detects and tracks objects of predefined colors (green and orange) using OpenCV. However you can add more colors using the HSV values.
- The detected objects' positions are used to draw on the digital canvas.
Adding more Colors:
// values from the function go to colors vector
void color_picker() {
VideoCapture cap(0);
Mat image;
Mat image_HSV, mask;
int hue_min = 0; int sat_min = 0; int val_min = 0;
int hue_max = 19; int sat_max = 240; int val_max = 255;
namedWindow("Trackbars", (640, 200));
createTrackbar("Hue Min", "Trackbars", &hue_min, 179);
createTrackbar("Sat Min", "Trackbars", &sat_min, 179);
createTrackbar("Val Min", "Trackbars", &val_min, 255);
createTrackbar("Hue Max", "Trackbars", &hue_max, 255);
createTrackbar("Sat Max", "Trackbars", &sat_max, 255);
createTrackbar("Val Max", "Trackbars", &val_max, 255);
while (true) {
cap.read(image);
cvtColor(image, image_HSV, COLOR_BGR2HSV);
inRange(image_HSV, Scalar(hue_min, sat_min, val_min), Scalar(hue_max, sat_max, val_max), mask);
cap.read(image);
cout << "{" << hue_min << ", " << sat_min << ", " << val_min << ", " << hue_max << ", " << sat_max << ", " << val_max << "}" << endl;
imshow("Original", image);
imshow("Original HSV", image_HSV);
imshow("Masked", mask);
waitKey(1);
}
}
// convert the hsv into bgr format and put into colorValue vector
Scalar hsvToBgr(int hueMin, int satMin, int valMin) {
Mat hsv(1, 1, CV_8UC3, Scalar(hueMin, satMin, valMin));
Mat bgr;
cvtColor(hsv, bgr, COLOR_HSV2BGR);
Vec3b bgrScalar = bgr.at<Vec3b>(0, 0);
return Scalar(bgrScalar[0], bgrScalar[1], bgrScalar[2]);
}Color Detection:
- The
get_colorsfunction converts the captured video frames to the HSV color space and then applies predefined color ranges to create masks for the specified colors. - It finds contours in the masks to detect the objects and their positions.
Drawing on Canvas:
- The
draw_on_canvasfunction takes the detected object positions and draws circles on the canvas using the corresponding color.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent