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