I'm working with TEM tomograms and need to resize the images from 657x717 px to 478x438 px (about 2/3 of the original size). However, I don’t want to simply crop the edges, but rather shrink the entire content of the image while preserving all key details. What would be the best approach to achieve this downscaling without losing important information? Are there any specific techniques or libraries in Python or other languages that handle this kind of resizing particularly well? Any advice or code suggestions would be much appreciated. Thank you! This is what I got from Chatgpt, as a starting point (I am an inexperienced programmer). import cv2 import numpy as np #Load your TEM image (replace 'your_image.tif' with your actual image file) image = cv2.imread('your_image.tif', cv2.IMREAD_UNCHANGED) #Define the new dimensions (478x438) new_size = (478, 438) #Resize the image using bicubic interpolation resized_image = cv2.resize(image, new_size, interpolation=cv2.INTER_CUBIC) #Save or display the resized image cv2.imwrite('resized_image.tif', resized_image) cv2.imshow('Resized Image', resized_image) cv2.waitKey(0) cv2.destroyAllWindows() Continue reading...
