Colour detection is the process of detecting the name of any colour. Simple isn’t it? Well, for humans this is an extremely easy task but for computers, it is not straightforward. Human eyes and brains work together to translate light into colour. Light receptors that are present in our eyes transmit the signal to the brain. Our brain then recognizes the colour. Since childhood, we have mapped certain lights with their colour names. We will be using the somewhat same strategy to detect colour names.
● For a robot to visualize the environment, along with the object detection, detection of its colour in real-time is also very important.

Why this is important? : Some Real-world Applications
●In a self-driving car, to detect the traffic signals.
Multiple colour detection is used in some industrial robots, to performing pick-and-place task in separating different coloured objects.
●This is an implementation of detecting multiple colours (here, only red, green and blue colours have been considered) in real-time using Python programming language.

Python Libraries Used:
●NumPy
●OpenCV-Python

Work Flow Description:
●Step 1:
Input: Capture video through webcam.
●Step 2:
Read the video stream in image frames.
●Step 3:
Convert the image frame in BGR(RGB colour space represented as three matrices of red, green and blue with integer values from 0 to 255) to HSV(hue-saturation-value) colour space.
Hue
describes a colour in terms of
saturation
, represents the amount of grey colour in that colour and
value
describes the brightness or intensity of the colour. This can be represented as three matrices in the range of 0-179, 0-255 and 0-255 respectively.
●Step 4:
Define the range of each colour and create the corresponding mask.
●Step 5:
Morphological Transform: Dilation, to remove noises from the images.
●Step 6:
bitwise_and between the image frame and the mask is performed to specifically detect that particular colour and discard others.
●Step 7:
Create contour for the individual colours to display the detected coloured region distinguish.
●Step 8:
Output: Detection of the colours in real-time.

# Python code for Multiple Color Detection 

import numpy as np 
import cv2 


# Capturing video through webcam 
webcam = cv2.VideoCapture(0) 

# Start a while loop 
while(1): 
	
	# Reading the video from the 
	# webcam in image frames 
	_, imageFrame = webcam.read() 

	# Convert the imageFrame in 
	# BGR(RGB color space) to 
	# HSV(hue-saturation-value) 
	# color space 
	hsvFrame = cv2.cvtColor(imageFrame, cv2.COLOR_BGR2HSV) 

	# Set range for red color and 
	# define mask 
	red_lower = np.array([136, 87, 111], np.uint8) 
	red_upper = np.array([180, 255, 255], np.uint8) 
	red_mask = cv2.inRange(hsvFrame, red_lower, red_upper) 

	# Set range for green color and 
	# define mask 
	green_lower = np.array([25, 52, 72], np.uint8) 
	green_upper = np.array([102, 255, 255], np.uint8) 
	green_mask = cv2.inRange(hsvFrame, green_lower, green_upper) 

	# Set range for blue color and 
	# define mask 
	blue_lower = np.array([94, 80, 2], np.uint8) 
	blue_upper = np.array([120, 255, 255], np.uint8) 
	blue_mask = cv2.inRange(hsvFrame, blue_lower, blue_upper) 
	
	# Morphological Transform, Dilation 
	# for each color and bitwise_and operator 
	# between imageFrame and mask determines 

# to detect only that particular color 
	kernal = np.ones((5, 5), "uint8") 
	
	# For red color 
	red_mask = cv2.dilate(red_mask, kernal) 
	res_red = cv2.bitwise_and(imageFrame, imageFrame, 
							mask = red_mask) 
	
	# For green color 
	green_mask = cv2.dilate(green_mask, kernal) 
	res_green = cv2.bitwise_and(imageFrame, imageFrame, 
								mask = green_mask) 
	
	# For blue color 
	blue_mask = cv2.dilate(blue_mask, kernal) 
	res_blue = cv2.bitwise_and(imageFrame, imageFrame, 
							mask = blue_mask) 

	# Creating contour to track red color 
	contours, hierarchy = cv2.findContours(red_mask, 
										cv2.RETR_TREE, 
										cv2.CHAIN_APPROX_SIMPLE) 
	
	for pic, contour in enumerate(contours): 
		area = cv2.contourArea(contour) 
		if(area > 300): 
			x, y, w, h = cv2.boundingRect(contour) 
			imageFrame = cv2.rectangle(imageFrame, (x, y), 
									(x + w, y + h), 
									(0, 0, 255), 2) 
			
			cv2.putText(imageFrame, "Red Colour", (x, y), 
						cv2.FONT_HERSHEY_SIMPLEX, 1.0, 
						(0, 0, 255))	 

	# Creating contour to track green color 
	contours, hierarchy = cv2.findContours(green_mask, 
										cv2.RETR_TREE, 
										cv2.CHAIN_APPROX_SIMPLE) 
	
	for pic, contour in enumerate(contours): 

		area = cv2.contourArea(contour) 
		if(area > 300): 
			x, y, w, h = cv2.boundingRect(contour) 
			imageFrame = cv2.rectangle(imageFrame, (x, y), 
									(x + w, y + h), 
									(0, 255, 0), 2) 
			
			cv2.putText(imageFrame, "Green Colour", (x, y), 
						cv2.FONT_HERSHEY_SIMPLEX, 
						1.0, (0, 255, 0)) 

	# Creating contour to track blue color 
	contours, hierarchy = cv2.findContours(blue_mask, 
										cv2.RETR_TREE, 
										cv2.CHAIN_APPROX_SIMPLE) 
	for pic, contour in enumerate(contours): 
		area = cv2.contourArea(contour) 
		if(area > 300): 
			x, y, w, h = cv2.boundingRect(contour) 
			imageFrame = cv2.rectangle(imageFrame, (x, y), 
									(x + w, y + h), 
									(255, 0, 0), 2) 
			
			cv2.putText(imageFrame, "Blue Colour", (x, y), 
						cv2.FONT_HERSHEY_SIMPLEX, 
						1.0, (255, 0, 0)) 
			
	# Program Termination 
	cv2.imshow("Multiple Color Detection in Real-TIme", imageFrame) 
	if cv2.waitKey(10) & 0xFF == ord('q'): 
		cap.release() 
		cv2.destroyAllWindows() 
		break

Source Code & Link:
Link - https://drive.google.com/drive/folders/1nU2WJfIErrzsA_BspmShIQmuLHf_w6bn?usp=sharing 

#python #opencv