How can OpenCV's inRange function be optimized for accurate red color detection in images?

Using OpenCV to Enhance Red Color Detection

Accurate color detection is essential in various computer vision tasks. This article addresses the specific challenge of detecting red objects using the OpenCV library. By exploring the HSV color space and refining thresholding parameters, we aim to improve the detection of a red rectangle within an image.

Problem Statement

Given an image with a red rectangle, the goal is to isolate and detect the red object using OpenCV's inRange function and the HSV color space. However, the initial attempts using the provided parameter ranges have not yielded satisfactory results.

Proposed Solution: HSV Color Space

In HSV space, the red hue wraps around the 180-degree value. Therefore, to effectively detect red, we need to consider values from 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);

Mat1b mask = mask1 | mask2;

By combining these two masks, we capture the red color range more accurately, as seen in the improved results.

Alternative Approach: Inverted Image HSV

Another perspective on this problem is to invert the original BGR image before converting it to HSV. In the inverted image, the red color becomes cyan, making it easier to detect:

Mat3b bgr_inv = ~bgr;
cvtColor(bgr_inv, hsv_inv, COLOR_BGR2HSV);

inRange(hsv_inv, Scalar(90 - 10, 70, 50), Scalar(90 + 10, 255, 255), mask);

This approach allows us to search for a single target color (cyan) in the inverted HSV image, providing a valid alternative to the dual-range approach.

Conclusion

By refining the color detection parameters and utilizing specific properties of the HSV color space, we can significantly enhance the detection of red objects using OpenCV. The provided solutions illustrate the versatility and effectiveness of OpenCV in handling challenging color detection scenarios.

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