OpenCV

I used OpenCV’s built-in face recognizer and trained it using all of the images I downloaded from Google in these two folders. There are three modules we need to import – os, cv2 and numpy. In the Function: Now that we are inside each folder, we are going to loop through all the images in […]

Face Detection VS Face Recognition Face detection only detects the presence of a face in an image while fact recognition involves identifying whose face it is. Face detection is performed using a classifier, it is essentially an algorithm that decides whether a given image is positive or negative meaning whether a face is present or […]

The gradients are edge-like regions that are present in the image. It is similar to edge detection. Laplacian # Laplacianlap = cv.Laplacian(gray, ddepth=cv.CV_64F)lap = np.uint8(np.absolute(lap))cv.imshow(‘Laplacian’, lap) Sobel Canny

Select an image to mask on, and create a blank image to create the shape of the mask. Create a circle and rectangle mask respectively. Use bit operation to combine the mask and the image.

First create a blank image using numpy to work on, and draw a circle and a rectangle on separate blank images. Bitwise AND Bitwise OR Bitwise XOR Bitwise NOT

Choose an image to work with: Averaging Gaussian Blur Median Blur Bilateral Blur

Choose an image to work with: Split Color Channel Merge Color Channel

Read in an image using OpenCV. BGR -> Gray BGR -> HSV BGR -> LAB BGR -> RGB

Choose an image: Method 1: Canny Edges Method 2: Threshold & Contours