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Image processing is popularly known as digital image processing. It involves the use of a computer system to manipulate a digital image. While image processing is a branch of signals and systems, it is a concept that focuses wholly on images. Digital image processing also involves the creation of a computer system that can process an image. This system takes a digital image as input, uses various algorithms to manipulate it, and then gives out an image as output. Digital image processing is usually done using OpenCV to remove noise from the image i.e. unwanted features on the image.
OpenCV allows you to perform operations on your video frame by frame. A video might have several frames which are just specific instances of the video at a point in time. Also, a single second of a video can have multiple frames. So what techniques can we use to process a video?
This method involves applying thresholding on small sections of the frame.
Video smoothing is the process of getting rid of the sharpness of a video by introducing some blurriness. Cv2.Gaussianblur() and cv2.bilateralFilter() are some of the methods used in video smoothing.
this technique is used to identify surface and object edges in a video
Camera calibration is the process of measuring the parameters and coefficients of a camera. This parameter information is vital in estimating the correct relationship between an image's 2D projection and its corresponding real-world 3D point. Two essential parameters should be recovered in camera calibration. They are:
The internal parameters
These parameters include the optical center and focal length of the camera lens.
They are parameters that are associated with the rotation and orientation of the camera concerning a physical coordinate system.
In OpenCV, you must be well-versed in image formation geometry before you can understand the process of camera calibration.