Color Spaces
Color spaces are a way to represent the color channels present in the image that gives the image that particular hue. There are several different color spaces and each has its own significance. Some of the popular color spaces are RGB (Red, Green, Blue), CMYK (Cyan, Magenta, Yellow, Black), HSV (Hue, Saturation, Value), etc.
cv2.cvtColor() method is used to convert an image from one color space to another. There are more than 150 color-space conversion methods available in OpenCV.
Example: Python OpenCV Color Spaces
Python3
# Python program to explain cv2.cvtColor() method # importing cv2 import cv2 # path path = 'geeks.png' # Reading an image in default mode src = cv2.imread(path) # Window name in which image is displayed window_name = 'w3wiki' # Using cv2.cvtColor() method # Using cv2.COLOR_BGR2GRAY color space # conversion code image = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY ) # Displaying the image cv2.imshow(window_name, image) cv2.waitKey( 0 ) cv2.destroyAllWindows() |
Output:
Essential OpenCV Functions to Get Started into Computer Vision
Computer vision is a process by which we can understand the images and videos how they are stored and how we can manipulate and retrieve data from them. Computer Vision is the base or mostly used for Artificial Intelligence. Computer-Vision is playing a major role in self-driving cars, robotics as well as in photo correction apps.
OpenCV is the huge open-source library for computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today’s systems. By using it, one can process images and videos to identify objects, faces, or even handwriting of a human. When it is integrated with various libraries, such as NumPy, python is capable of processing the OpenCV array structure for analysis. To Identify image patterns and their various features we use vector space and perform mathematical operations on these features.
In this article, we will discuss some commonly used functions in OpenCV along with their applications.
Note: The functions used in this article are common for different languages supported by OpenCV.