Computer vision is a technique for understanding how images and videos are stored, as well as modifying and retrieving data from them. Artificial intelligence is primarily dependent on computer vision. Self-driving cars, robotics, and photo editing apps all rely heavily on computer vision.
OpenCV is a big open-source library for computer vision, machine learning, and image processing, and it presently plays a significant part in real-time operations, which are crucial in today's systems. It can distinguish objects, faces, and even human handwriting in photographs and movies. Python can process the OpenCV array structure for analysis when it is combined with other modules such as NumPy. We employ vector space and execute mathematical operations on these features to identify visual patterns and their various features.
pip install opencv-python
cv2.imread(â€˜img name with pathâ€™, flag)
eg: img = cv2.imread('example.png', cv2.IMREAD_COLOR)
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
img = cv2.imread('tomatoes.jpg', 0)
** in both situation, you will get a grayscalled image.
OpenCv has a large variety of functions that help you to manupulate images and achieve all the above listed goals. It is not possible to conclude such topics in a short piece of article. So, This article will continue further.