在不使用 resize() 的情况下调整 python 中的图像大小 - 最近邻

Question

For an assignment I want to resize a.jpg image with a python code, but without using the pil.image.resize() function or another similar function. I want to write the code myself but I can't figure out how. The image is RGB. I have found this can be solved by nearest neighbor interpolation (as well as other methods but this one is fine for my specific assignment). The height and the width should both be able to be made bigger or smaller. So far I only have this:

import numpy as np
import scipy as sc
import matplotlib as plt
import math
import PIL
from PIL import Image

img = np.array(Image.open("foto1.jpg"))          

height = img.shape[0]
width = img.shape[1]
dim = img.shape[2]  

new_h = int(input("New height: "))  
new_w = int(input("New width: "))  

imgR = img[:,:,0] #red pixels
imgG = img[:,:,1] #green pixels
imgB = img[:,:,2] #blue pixels

newR = np.empty([new_h, new_w])             
newG = np.empty([new_h, new_w])            
newB = np.empty([new_h, new_w]) 

So now all three colours have a new array of the right dimensions. Unfortunately on the web I can only find people who use resize() functions... Does anyone know?

Thank in advance!

Answer 1

The key to doing any image transformation like resizing is to have a mapping from output coordinates to input coordinates. Then you can simply iterate over the entire output and grab a pixel from the input. Nearest neighbor makes this particularly easy, because there's never a need to interpolate a pixel that doesn't lie exactly on integer coordinates - you simply round the coordinates to the nearest integer.

for new_y in range(new_h):
    old_y = int(round(new_y * (new_h - 1) / (height - 1)))
    if old_y < 0: old_y = 0
    if old_y >= height: old_y = height - 1
    for new_x in range(new_w):
        old_x = int(round(new_x * (new_w - 1) / (width - 1)))
        if old_x < 0: old_x = 0
        if old_x >= width: old_x = width - 1
        newR[new_y,new_x] = imgR[old_y,old_x]
        newG[new_y,new_x] = imgG[old_y,old_x]
        newB[new_y,new_x] = imgB[old_y,old_x]

Answer 2

This can only be used to resize to a dimension of (x * n or x//n, y * n or y//n, 3) , where x, y, 3 are the width, height and channels of the image and n is any natural number.

def resize_img(image, resize_width, resize_height):
    """
    :params
        image: shape -> (width, height, channels)
        
        resize_width: The resize width dimension. 
                      It could be something like x*n or x//n,
                      where x is the width of the image and n
                      could be any natural number.
        
        resize_height: The resize width dimension. 
                      It could be something like y*n or y//n,
                      where x is the width of the image and n
                      could be any natural number.
    
    :returns
        array of shape -> (resized_width, resized_height, channels)
    """

    original_width, original_height, channel = image.shape
    
    red_channel = image[:, :, 0]
    green_channel = image[:, :, 1]
    blue_channel = image[:, :, 2]
    
    resized_image = np.zeros((resize_width, resize_height, channel), dtype=np.uint8)
    resized_image[:, :, 0] = red_channel[0::original_width//resize_width, 0::original_height//resize_height]
    resized_image[:, :, 1] = green_channel[0::original_width//resize_width, 0::original_height//resize_height]
    resized_image[:, :, 2] = blue_channel[0::original_width//resize_width, 0::original_height//resize_height]
    
    return resized_image