Quantitatively check the quality of a compressed image by calculating the Peak Signal-to-Noise Ratio (PSNR) between two images. More info at https://code.adonline.id.au/peak-signal-to-noise-ratio-python/

psnr.py

#!/usr/bin/env python

from math import log10, sqrt 
import cv2 
import numpy as np
import argparse

# Based on https://www.geeksforgeeks.org/python-peak-signal-to-noise-ratio-psnr/

def options():
	parser = argparse.ArgumentParser(description="Read image metadata")
	parser.add_argument("-o", "--original", help="Input image file.", required=True)
	parser.add_argument("-c", "--copy", help="Input image file.", required=True)
	args = parser.parse_args()
	return args

def PSNR(original, compressed):
	mse = np.mean((original - compressed) ** 2)
	if(mse == 0):
		return 100
	max_pixel = 255.0
	psnr = 20 * log10(max_pixel / sqrt(mse))
	return psnr 
  
def main(): 

	# Get options
	args = options()

	# Import images
	original = cv2.imread(args.original)
	compressed = cv2.imread(args.copy, 1)

	# Check for same size and ratio and report accordingly
	ho, wo, _ = original.shape
	hc, wc, _ = compressed.shape
	ratio_orig = ho/wo
	ratio_comp = hc/wc
	dim = (wc, hc)

	if round(ratio_orig, 2) != round(ratio_comp, 2):
		print("\nImages not of the same dimension. Check input.")
		exit()

	# Resize original if the compressed image is smaller
	elif ho > hc and wo > wc:
		print("\nResizing original image for analysis...")
		original = cv2.resize(original, dim)

	elif ho < hc and wo < wc:
		print("\nCompressed image has a larger dimension than the original. Check input.")
		exit()

	value = PSNR(original, compressed)
	print("\nPeak Signal-to-Noise Ratio (PSNR) value is", value, "dB")

if __name__ == '__main__':
	main()