In this lesson, we will analyze 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, we can implement a basic object recognition based on colors. In this tutorial, I will explain in a few steps how to create a mask to balance the recognition of our object in real-time.

We import the libraries Opencv and Numpy, then load the cap to get the frames from the webcam. After that we start a while Loop where we get the frames and we do the detection.

```import cv2
import numpy as np

cap = cv2.VideoCapture(0)

while True:
hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)```

Inside the while loop we define the HSV ranges (low_red, high_red), we create the mask and we show only the object with the red color.

```    # Red color
low_red = np.array([161, 155, 84])
high_red = np.array([179, 255, 255])

Same for the other colors:

```    # Blue color
low_blue = np.array([94, 80, 2])
high_blue = np.array([126, 255, 255])

# Green color
low_green = np.array([25, 52, 72])
high_green = np.array([102, 255, 255])

# Every color except white
low = np.array([0, 42, 0])
high = np.array([179, 255, 255])

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```

## Object recognition based on colors

In this article you have learned, I hope without too many worries, the important concept of OpenCV for basic object recognition in computer vision.
For a deeper understanding of the topic and to have a greater mastery of the subject, I suggest you evaluate the purchase of my Object Detection course.