The problem is quite simple: I want to take a very large image (3448x4592) and transform it into, for example,(200x200) pixels, without distorting it. How to make it?

import os
import time
import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

f = 'tv_laptop.jpg'
path = 'DATA/'+f
img = cv2.imread(path)
# Correcting coloring for MatPlotLib
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
i = plt.imshow(img, cmap='gray')
img.shape

(3448, 4592, 3)

resized = cv2.resize(img, (200,200), cv2.INTER_AREA)
# Correcting coloring for MatPlotLib
img = cv2.cvtColor(resized, cv2.COLOR_RGB2BGR)
i = plt.imshow(img, cmap='gray')
img.shape

(200, 200, 3)

# reload everything
f = 'tv_laptop.jpg'
path = 'DATA/'+f
img = cv2.imread(path)
img = img.copy()
# Correcting coloring for MatPlotLib
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
i = plt.imshow(img, cmap='gray')
img.shape
# Saving height(h) for variable h, and length for w
# Use Tuple unpacking tecnique
h, w = img.shape[:2]
print(h, w)

3448 4592

size = (200,200)
sh, sw = size
print(sh, sw)

200 200

# interpolation method
if h > sh or w > sw: # shrinking image
interp = cv2.INTER_AREA
else: # stretching image
interp = cv2.INTER_CUBIC

# aspect ratio of image
aspect = w/h
print(f"The original aspect ratio is {aspect:.3}")

# compute scaling and pad sizing
if aspect > 1: # horizontal image
  new_w = sw
  new_h = np.round(new_w/aspect).astype(int)
  pad_vert = (sh-new_h)/2
  pad_top, pad_bot = np.floor(pad_vert).astype(int),   
       np.ceil(pad_vert).astype(int)
  pad_left, pad_right = 0, 0

print(f"h={new_h}, new_w={new_w}")
# Recalc the aspect ratio
aspect = w/h
print(f"The new aspect ratio is still {aspect:.3}")
print("With this math we proportionally convert the image to 200 x 200")

The original aspect ratio is 1.33
h=150, new_w=200
The new aspect ratio is still 1.33
With this math we proportionally convert the image to 200 x 200

scaled_img = cv2.resize(img, (new_w, new_h), interpolation=interp)

scaled_img = cv2.copyMakeBorder(scaled_img, pad_top, pad_bot, pad_left, pad_right, borderType=cv2.BORDER_CONSTANT, value=127)

i = plt.imshow(scaled_img, cmap='gray')

scaled_img.shape

(200, 200, 3)

import cv2
import numpy as np
def resizeAndPad(img, size, padColor=0):
  h, w = img.shape[:2]
  sh, sw = size
  # interpolation method
  if h > sh or w > sw: # shrinking image
     interp = cv2.INTER_AREA

  else: # stretching image
    interp = cv2.INTER_CUBIC

# aspect ratio of image
aspect = w/h

# compute scaling and pad sizing
if aspect > 1: # horizontal image
  new_w = sw
  new_h = np.round(new_w/aspect).astype(int)
  pad_vert = (sh-new_h)/2
  pad_top, pad_bot = np.floor(pad_vert).astype(int),   
     np.ceil(pad_vert).astype(int)
  pad_left, pad_right = 0, 0

elif aspect < 1: # vertical image
  new_h = sh
  new_w = np.round(new_h*aspect).astype(int)
  pad_horz = (sw-new_w)/2
  pad_left, pad_right = np.floor(pad_horz).astype(int), 
     np.ceil(pad_horz).astype(int)

  pad_top, pad_bot = 0, 0

else: # square image
  new_h, new_w = sh, sw
  pad_left, pad_right, pad_top, pad_bot = 0, 0, 0, 0

# set pad color
if len(img.shape) == 3 and not isinstance(padColor, (list, tuple, 
     np.ndarray)): # color image but only one color provided
  padColor = [padColor]*3

# scale and pad
scaled_img = cv2.resize(img, (new_w, new_h), interpolation=interp)
scaled_img = cv2.copyMakeBorder(scaled_img, pad_top, pad_bot, 
     pad_left, pad_right, borderType=cv2.BORDER_CONSTANT,  
     value=padColor)

return scaled_img

new_img = resizeAndPad(img, (200,200), 127)
i = plt.imshow(new_img, cmap='gray')
new_img.shape

# Saving the new image inside DATA folder
filename = 'tv_laptop_resized.jpg'  
# Using cv2.imwrite() method
# Saving the image
cv2.imwrite(filename, new_img)

(200, 200, 3)

print("That´s it! Thank you once again!\nI hope will be helpful.")

That´s it! Thank you once again!
I hope will be helpful.