The function is used to open a camera for video capture.
Syntax: cv2.VideoCapture(camera_id = 0)
Open CV
Extension Description
OpenCV provides a real-time optimized Computer Vision library, tools, and hardware.
Introduction
Computer Vision is a way for us to understand how computers read and interpret images and videos. It’s really important and used a lot in Artificial Intelligence! It helps with things like self-driving cars, robotics, and photo correction apps.
OpenCV is a huge open-source library for computer vision, machine learning, and image processing. It is super important for computer vision today because it helps us with real-time operations. For example, OpenCV can be used to help identify objects, faces, and even handwriting in images and videos.
OpenCV is used with libraries such as NumPy to help computers analyze and process OpenCV array structures. To understand the patterns and features of the images, vector spaces, and mathematical operations are used.
OpenCV has many applications like face recognition, automated inspection, street view image stitching, video and image search and retrieval, robot and driverless car navigation, and object recognition.
Image processing is another related field that uses signals to manipulate and improve images. It involves three steps: importing an image, analyzing and manipulating the image, and getting an output of either an altered image or a report based on the image analysis.
Computer vision and image processing are so cool, don’t you think? Try learning more about it!
The function flips a 2D array around vertical, horizontal, or both axes.
Syntax: cv2.flip(input_array, flip_code = 1)
Resizing an image means changing its dimensions of it, be it width alone, height alone, or changing both of them. Also, the aspect ratio of the original image could be preserved in the resized image. To resize an image, OpenCV provides cv2.resize() function.
Syntax: cv2.resize(image_array, dsize = (224,224))
The function is used to draw a text string on any image.
Syntax: cv2.putText(image, text, org, font, fontScale = 2, color = (255, 0, 0), thickness, lineType = cv2.LINE_AA)
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