Image Processing

Learn to use marker-based image segmentation using watershed algorithm. We cover cv.watershed() with distance transform to segment touching objects.

Learn to use GrabCut algorithm to extract foreground in images. We cover cv2.grabCut() with both rectangle and mask initialization modes.

Learn to find the Fourier Transform of images using OpenCV and NumPy. We will see cv2.dft(), cv2.idft() and frequency domain applications.

Learn to use Hough Transform to find circles in an image. We will see cv2.HoughCircles() function with practical examples.

Learn to detect lines in an image using Hough Transform. We will see cv2.HoughLines() and cv2.HoughLinesP() functions with examples.

In this article we will learn simple thresholding, adaptive thresholding, and Otsu's thresholding, and we will learn these functions: cv2.threshold, cv2.adaptiveThreshold

Template Matching is a method for searching and finding the location of a template image in a larger image. Learn cv2.matchTemplate() and cv2.minMaxLoc() with examples.

We will learn different morphological operations like Erosion, Dilation, Opening, Closing. We will see different functions like: cv2.erode(), cv2.dilate(), cv2.morphologyEx()

Blurring images with various low-pass filters. Applying custom filters to images (2D convolution)