Top answer in this link How to pixelate a square image to 256 big pixels with python? uses PIL to pixelate image. Converting image from PIL to cv2.Mat is possible but I'm not allowed to use other library, and I couldn't find any good method using opencv.
Is there any way to pixelate image using OpenCV library only in Python? Any sample image is fine. Solution with pixel size parameter that I can control for later adjustment would be very appreciated.
With the help of himself, I moved Mark Setchell's answer, which is the above mentioned top answer, to plain OpenCV Python code. (Have a look at the revision history of my answer to see the old version using a loop.)
import cv2
# Input image
input = cv2.imread('images/paddington.png')
# Get input size
height, width = input.shape[:2]
# Desired "pixelated" size
w, h = (16, 16)
# Resize input to "pixelated" size
temp = cv2.resize(input, (w, h), interpolation=cv2.INTER_LINEAR)
# Initialize output image
output = cv2.resize(temp, (width, height), interpolation=cv2.INTER_NEAREST)
cv2.imshow('Input', input)
cv2.imshow('Output', output)
cv2.waitKey(0)
Input (from linked question):
Output:
Disclaimer: I'm new to Python in general, and specially to the Python API of OpenCV (C++ for the win). Comments, improvements, highlighting Python no-gos are highly welcome!
cv2.resize(...INTER_NEAREST...)?
cv2.resize(...) or the "second" (in your code from the linked answer)? For the latter: I tried that, but I got some interpolated image, and not that "pixelated" kind of. There seem to be differences in PIL and OpenCV regarding this issue - which I assume is the reason, the questioner posted this question in first place."Initialize" with this big = cv2.resize(temp, (w, h), 0, 0, cv2.INTER_NEAREST) Don't worry, you already have my vote anyway ;-)For anyone looking for alternate solution,
import cv2
import numpy as np
img = cv2.imread('input.png')
height, width, channels = img.shape
pixel_size = 50
# Pad image
pad_x = (pixel_size - width % pixel_size) % pixel_size
pad_y = (pixel_size - height % pixel_size) % pixel_size
img = np.pad(img, ((0, pad_y), (0, pad_x), (0, 0)), mode='reflect')
# Reshape image into blocks and compute average color of each block
h, w, c = img.shape
blocks = np.mean(img.reshape(h//pixel_size, pixel_size, -1, pixel_size, c), axis=(1, 3))
# Repeat average color of each block to fill corresponding region in the image
output = np.repeat(np.repeat(blocks, pixel_size, axis=1), pixel_size, axis=0)
# Remove padding
output = output[:height, :width].astype("uint8")
cv2.imshow('Output', output)
cv2.waitKey(0)
