How can I improve red color detection in OpenCV using HSV color space?

Enhanced Red Color Detection Using OpenCV

Introduction

When dealing with the detection of red color using OpenCV and HSV color space, it can be challenging to obtain satisfactory results. However, by exploring various approaches and parameter adjustments, significant improvements can be made.

Problem

To enhance the detection of a red rectangle within an image, the following code has been utilized:

#include <opencv2/opencv.hpp>

int main()
{
    // Image initialization
    Mat input = imread("path_to_image");

    // HSV conversion
    Mat imageHSV;
    cvtColor(input, imageHSV, COLOR_BGR2HSV);

    // HSV parameter ranges
    int H_MIN = 0;
    int H_MAX = 10;
    int S_MIN = 70;
    int S_MAX = 255;
    int V_MIN = 50;
    int V_MAX = 255;

    // Red color range in HSV
    cv::inRange(imageHSV, cv::Scalar(H_MIN, S_MIN, V_MIN),
                cv::Scalar(H_MAX, S_MAX, V_MAX), imgThreshold0);
}

Despite adjusting the HSV values using dynamic trackbars, optimal results remain elusive.

Solutions

1. Expanding Hue Value Range:

In HSV space, the red color wraps around 180. Therefore, to fully capture the entire range of red, the hue value (H) must consider both [0,10] and [170, 180].

inRange(hsv, Scalar(0, 70, 50), Scalar(10, 255, 255), mask1);
inRange(hsv, Scalar(170, 70, 50), Scalar(180, 255, 255), mask2);

2. Inverting Image and Detecting Cyan:

Alternatively, an intriguing approach is to:

• Invert the original BGR image.
• Convert the inverted image to HSV.
• Detect cyan color (around HSV 90) instead of red.

This method effectively detects the complement of red (cyan) with only a single range in HSV.

// Invert original image
Mat3b bgr_inv = ~bgr;

// Convert to HSV
Mat3b hsv_inv;
cvtColor(bgr_inv, hsv_inv, COLOR_BGR2HSV);

// Detect cyan range
inRange(hsv_inv, Scalar(90 - 10, 70, 50), Scalar(90 + 10, 255, 255), mask);

Conclusion

By incorporating these enhanced techniques, OpenCV can effectively detect red color with greater precision. These approaches provide a solid foundation for further optimizations and applications in various image processing scenarios.

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