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