![]() The difference in the sizes can be seen if we observe each image's x and y axes. Let us plot the images using matplotlib subplots for a better comparison of results. Res = cv2.resize(image, (120,120), interpolation=cv2.INTER_LINEAR) Smaller = cv2.resize(image, None, interpolation=cv2.INTER_AREA, fx=0.5, fy=0.5) cv2.INTER_LANCZOS4: Lanczos interpolation technique.īigger = cv2.resize(image, None, interpolation=cv2.INTER_CUBIC, fx=2, fy=2,).cv2.INTER_CUBIC: Bicubic interpolation technique.Call cv2.resize () function with the new dimensions Here is a quick cheat sheet for resizing images in four ways: Read the image img cv2.imread('image.jpeg') Scale down to 25 p 0.25 w int(img. Multiply the width and the height by the scaling factor. This is Extensively used to shrink the image To do this: Read an image into your program. cv2.INTER_AREA: Performs resampling using the pixel-area relationship.This is the default interpolation technique. cv2.INTER_LINEAR: Bilinear interpolation technique.cv2.INTER_NEAREST: Interpolation by using the nearest neighbor technique.There are various interpolation methods available in OpenCV. interpolation: (Optional) The interpolation method which is to be used.fy : (Optional) Scaling factor along the y axis of the image.fx : (Optional) Scaling factor along the x-axis of the image.dsize: The output dimension of the image.The cv2.resize() function takes the following parameters Another way is by mentioning a scaling factor. One way is by mentioning the output dimension directly. We can easily resize the image in two ways using the cv2.resize() function. The image that we are using here is the one shown below. Let us first import the necessary libraries and read the images. Step 1: Import the libraries and read the image ![]()
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