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Here you will learn how to display and save images and videos, control mouse events and create trackbar. In this section you will learn basic operations on image like pixel editing, geometric transformations, code optimization, some mathematical tools etc.

Goal¶ In this chapter, We will understand the concepts of optical flow and its estimation using Lucas-Kanade method. We will use functions like cv2.calcOpticalFlowPyrLK() to track feature points in a video. Optical Flow¶ Optical flow is the pattern of apparent motion of image objects between two consecutive frames caused by the movemement of object or camera. It is 2D vector field where each vecto

Goals¶ In this chapter, you will learn To find objects in an image using Template Matching You will see these functions : cv2.matchTemplate(), cv2.minMaxLoc() Theory¶ Template Matching is a method for searching and finding the location of a template image in a larger image. OpenCV comes with a function cv2.matchTemplate() for this purpose. It simply slides the template image over the input image (

Learn to read video, display video and save video. Learn to capture from Camera and display it. You will learn these functions : cv2.VideoCapture(), cv2.VideoWriter() Capture Video from Camera¶ Often, we have to capture live stream with camera. OpenCV provides a very simple interface to this. Let’s capture a video from the camera (I am using the in-built webcam of my laptop), convert it into grays

Goal¶ In this chapter, We will learn about the concepts of SIFT algorithm We will learn to find SIFT Keypoints and Descriptors. Theory¶ In last couple of chapters, we saw some corner detectors like Harris etc. They are rotation-invariant, which means, even if the image is rotated, we can find the same corners. It is obvious because corners remain corners in rotated image also. But what about scali

This project is abandoned¶ The project that was previously hosted at this URL has been removed from Read the Docs. We recommend searching on the web to see if it has a new home.

In this tutorial, you will learn Simple thresholding, Adaptive thresholding, Otsu’s thresholding etc. You will learn these functions : cv2.threshold, cv2.adaptiveThreshold etc. Simple Thresholding¶ Here, the matter is straight forward. If pixel value is greater than a threshold value, it is assigned one value (may be white), else it is assigned another value (may be black). The function used is cv

This project is abandoned¶ The project that was previously hosted at this URL has been removed from Read the Docs. We recommend searching on the web to see if it has a new home.

This project is abandoned¶ The project that was previously hosted at this URL has been removed from Read the Docs. We recommend searching on the web to see if it has a new home.