How to Optimize HSV Boundaries for Accurate Color Detection in OpenCV?

Selecting Optimal HSV Boundaries for Color Detection Using cv::inRange (OpenCV)

In image processing, the HSV color space is frequently employed for color detection. Selecting appropriate upper and lower HSV boundaries is crucial to accurately identify target colors. This question explores the selection process for an image containing an orange lid on a coffee can.

Despite providing an estimated HSV center value of (22, 59, 100) for the lid, an initial attempt using boundaries (18, 40, 90) and (27, 255, 255) yielded unsatisfactory results. To address this, we must consider potential issues in HSV scale and image format.

Problem 1: HSV Scale Variance

Different applications may use distinct HSV scales. GIMP utilizes H = 0-360, S = 0-100, V = 0-100, while OpenCV adopts H: 0-179, S: 0-255, V: 0-255. In this case, the GIMP hue value (22) should be halved to match OpenCV's scale, resulting in a range of (5, 50, 50) - (15, 255, 255).

Problem 2: Image Format Conversion

OpenCV operates on images in BGR format, not RGB. Thus, it is necessary to modify the color conversion line to cv.CvtColor(frame, frameHSV, cv.CV_BGR2HSV). This ensures that the image is correctly converted before HSV boundary detection.

By incorporating these adjustments, we obtain a more promising result:

[Image of improved detection]

Although the output is not perfect, it presents improved detection of the orange lid. False detections may be minimized by choosing the largest contour that corresponds to the lid.

Conclusion

Selecting appropriate HSV boundaries involves consideration of scale variances and proper image format conversion. By addressing these issues, we can enhance the accuracy of color detection using cv::inRange in OpenCV.

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