I wrote a script that draws some images, usually not bigger than 50x50px. Then I want to display that image in a Tkinter window. But first I need to enlarge the image because 30x30px are too small for a user to see every single pixel generated by my script. So I wrote this:
multiplier = 4 image = np.full((height * multiplier, width * multiplier, 3), 0, dtype=np.uint8) for r in range(height): for c in range(width): for i in range(multiplier): for j in range(multiplier): image[r * multiplier + i][c * multiplier + j] = original[r][c]
P.S. original was initialized the same way as image
Also I tried:
resize((width * multiplier ,height * multiplier), Image.ANTIALIAS)
but it’s not an option, because it makes an image look blurry. So what would be the better solution?
You could use PIL.Image/openCV and PIL.ImageFilter modules:
from PIL import Image, ImageFilter import cv2 image = cv2.resize(image, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA) image = Image.fromarray(image)
fy are the values you have to set yourself. Hope this helps 🙂
I would suggest resizing with Nearest Neighbour resampling so you don’t introduce any new, blurred colours – just ones already existing in your image:
import numpy as np from PIL import Image im = Image.open("snake.png").convert('RGB') im = im.resize((200,200),resample=Image.NEAREST) im.save("result.png")
You can go from a Pillow image to a numpy array with:
numpy_array = np.array(pillowImage)
numpy array to a Pillow image with:
pillow_image = Image.fromarray(numpyArray)
You can use deep learning Image Super-Resolution (ISR) or (SR3) to enlarge images with good quality.
Here’s the unofficial implementation of Image Super-Resolution via Iterative Refinement using Pytorch
The paper: https://iterative-refinement.github.io/
Here’s a video explanation by Two Minutes Paper https://www.youtube.com/watch?v=WCAF3PNEc_c
Here’s a solution using just PIL:
from PIL import Image multiplier = 4 original = Image.open("snake.png") width, height = original.size resized = original.resize((width * multiplier, height * multiplier), resample=0) resized.save("resized.png")
Using resample=0 is the key.