Multiple Color Detection in Real-Time using Python-OpenCV

Last Updated : 21 Nov, 2024

For a robot to visualize the environment, along with the object detection, detection of its color in real-time is also very important.

Why this is important? : Some Real-world Applications

In self-driving car, to detect the traffic signals.
Multiple color detection is used in some industrial robots, to performing pick-and-place task in separating different colored objects.

This is an implementation of detecting multiple colors (here, only

red

green

and

blue

colors have been considered) in real-time using Python programming language.

Python Libraries Used:

Work Flow Description:

Step 1:
Input: Capture video through webcam.
Step 2:
Read the video stream in image frames.
Step 3:
Convert the imageFrame in BGR(RGB color space represented as three matrices of red, green and blue with integer values from 0 to 255) to HSV(hue-saturation-value) color space.
Hue
describes a color in terms of
saturation
, represents the amount of gray color in that color and
value
describes the brightness or intensity of the color. 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 color 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 mask is performed to specifically detect that particular color and discrad others.
Step 7:
Create contour for the individual colors to display the detected colored region distinguishly.
Step 8:
Output: Detection of the colors in real-time.

Below is the implementation.

Python

# 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 
	kernel = np.ones((5, 5), "uint8") 
	
	# For red color 
	red_mask = cv2.dilate(red_mask, kernel) 
	res_red = cv2.bitwise_and(imageFrame, imageFrame, 
							mask = red_mask) 
	
	# For green color 
	green_mask = cv2.dilate(green_mask, kernel) 
	res_green = cv2.bitwise_and(imageFrame, imageFrame, 
								mask = green_mask) 
	
	# For blue color 
	blue_mask = cv2.dilate(blue_mask, kernel) 
	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'): 
		webcam.release() 
		cv2.destroyAllWindows() 
		break