How to detect a certain colour using Opencv¶
Firstly, analyzing a basic but important tool for identifying colors through a mask. We’re going to see in this video how to detect colors through HSV Color space on Opencv with Python.
HSV corresponds to: Hue is the color Saturation is the greyness Value is the brightness
Understanding the concepts of balancing these three elements allows us to implement a basic color-based object recognition system. Here blow is how to make a mask in a few simple steps to balance the recognition of our object in real-time. We import the Opencv and Numpy libraries, then load the cap to get the webcam frames. Following that, we begin a while loop in which we obtain the frames and perform the detection.
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
while True:
_, frame = cap.read()
hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
Within the while loop, we define the HSV ranges (low red, high red), create the mask, and display only the red objects.
# Red color
low_red = np.array([161, 155, 84])
high_red = np.array([179, 255, 255])
red_mask = cv2.inRange(hsv_frame, low_red, high_red)
red = cv2.bitwise_and(frame, frame, mask=red_mask)
Same for the other colors:
# Blue color
low_blue = np.array([94, 80, 2])
high_blue = np.array([126, 255, 255])
blue_mask = cv2.inRange(hsv_frame, low_blue, high_blue)
blue = cv2.bitwise_and(frame, frame, mask=blue_mask)
# Green color
low_green = np.array([25, 52, 72])
high_green = np.array([102, 255, 255])
green_mask = cv2.inRange(hsv_frame, low_green, high_green)
green = cv2.bitwise_and(frame, frame, mask=green_mask)
# Every color except white
low = np.array([0, 42, 0])
high = np.array([179, 255, 255])
mask = cv2.inRange(hsv_frame, low, high)
result = cv2.bitwise_and(frame, frame, mask=mask)
We finally show the result:
cv2.imshow("Frame", frame)
cv2.imshow("Red", red)
cv2.imshow("Blue", blue)
cv2.imshow("Green", green)
cv2.imshow("Result", result)
key = cv2.waitKey(1)
if key == 27:
break