# Abstract This publication presents a simple and efficient approach for detecting the dominant RGB color (Red, Green, or Blue) from a live webcam feed using OpenCV and Python. The method leverages OpenCV for real-time video capture and NumPy for pixel-wise computation of color intensity means. The application demonstrates a practical use case of computer vision in real-time video analysis. # Introduction Color detection is a fundamental task in computer vision with applications in object tracking, image segmentation, and interactive systems. This project, "Detecting RGB Colors using OpenCV," aims to identify the dominant RGB color in real-time video frames captured through a webcam. By calculating the mean intensity of each channel, the dominant color is determined and displayed. # Related work Previous studies and applications in color detection often involve: .Thresholding techniques for color segmentation. . Machine learning models for complex color recognition tasks. . Real-time color detection in robotics and augmented reality. This project simplifies the task by focusing on the direct computation of channel means, suitable for quick prototyping and educational purposes. # Methodology Tools and Libraries . OpenCV: For video capture and frame manipulation. . NumPy: For efficient numerical operations on frame data. #Code Implementation The project captures frames from the webcam and computes the mean pixel intensity for the Red, Green, and Blue channels. The dominant color is identified by comparing these mean values. #Code import cv2 import numpy as np vid = cv2.VideoCapture(0) while True: _, frame = vid.read() cv2.imshow("frame", frame) # Setting values for base colors b = frame[:,:,0] g = frame[:,:,1] r = frame[:,:,2] # Computing the mean b_mean = np.mean(b) g_mean = np.mean(g) r_mean = np.mean(r) print("Blue" if b_mean > g_mean and b_mean > r_mean else "Green" if g_mean > r_mean and g_mean > b_mean else "Red") # Breaking the loop if "q" is pressed if cv2.waitKey(1) & 0xFF == ord("q"): break vid.release() cv2.destroyAllWindows() ![blue.png](blue.png) ![green.png](green.png) ![red.png](red.png) # Experiments The program was tested in varying lighting conditions and with objects of distinct primary colors placed in the webcam's view. The dominant color detected was displayed in the console, providing immediate feedback. #Testing Conditions 1. Uniformly colored objects (red, green, or blue). 2. Multicolored scenes with varying lighting intensities. # Results The program reliably identified the dominant RGB color in real-time: . In uniformly colored scenarios, the detection was accurate with minimal delay. . In mixed-color scenarios, the result corresponded to the average intensity of the visible dominant color. # Discussion The approach is straightforward but has limitations: :) Lighting Sensitivity: Variations in lighting can affect the perceived intensity of colors. :) Mixed Colors: Scenes with multiple colors close in intensity may lead to less decisive results. :) Camera Quality: The accuracy depends on the resolution and color fidelity of the webcam. Improvements can involve advanced techniques like color clustering or leveraging machine learning for more nuanced color detection. # Conclusion This project demonstrates an intuitive method for detecting dominant RGB colors in real-time video using OpenCV. It serves as a practical introduction to color detection, with potential applications in education and rapid prototyping of computer vision systems. # References 1. Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library. 2. OpenCV Documentation: https://docs.opencv.org 3. NumPy Documentation: https://numpy.org # Acknowledgements Special thanks to the OpenCV and NumPy communities for providing robust tools that simplify computer vision tasks. This project is inspired by the growing interest in practical computer vision applications and aims to contribute to the learning journey of enthusiasts and professionals alike. # Appendix #Technical Details . Environment: Python 3.9.13, OpenCV 4.5, NumPy 1.21 . Hardware: Tested on a standard laptop webcam. . Execution Steps: 1. Install the required libraries: pip install opencv-python numpy. 2. Run the provided Python script. 3. Observe the dominant color in the console output. #Challenges Faced 1. Ambient Lighting: Adjusting for variable lighting conditions to improve color detection accuracy. 2. Frame Rate: Ensuring real-time processing on average hardware. 3. Color Bias: Handling scenarios where colors are mixed or reflections alter the perceived dominant color. #Future Work . Integrate advanced techniques like clustering for more complex scenes. . Extend the project to support detection of secondary colors or patterns. . Develop a GUI overlay to display the detected color in real-time.