forked from TheAlgorithms/Python
-
Notifications
You must be signed in to change notification settings - Fork 0
/
peak_signal_to_noise_ratio.py
46 lines (33 loc) · 1.31 KB
/
peak_signal_to_noise_ratio.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
"""
Peak signal-to-noise ratio - PSNR
https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
Source:
https://tutorials.techonical.com/how-to-calculate-psnr-value-of-two-images-using-python
"""
import math
import os
import cv2
import numpy as np
PIXEL_MAX = 255.0
def peak_signal_to_noise_ratio(original: float, contrast: float) -> float:
mse = np.mean((original - contrast) ** 2)
if mse == 0:
return 100
return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
def main() -> None:
dir_path = os.path.dirname(os.path.realpath(__file__))
# Loading images (original image and compressed image)
original = cv2.imread(os.path.join(dir_path, "image_data/original_image.png"))
contrast = cv2.imread(os.path.join(dir_path, "image_data/compressed_image.png"), 1)
original2 = cv2.imread(os.path.join(dir_path, "image_data/PSNR-example-base.png"))
contrast2 = cv2.imread(
os.path.join(dir_path, "image_data/PSNR-example-comp-10.jpg"), 1
)
# Value expected: 29.73dB
print("-- First Test --")
print(f"PSNR value is {peak_signal_to_noise_ratio(original, contrast)} dB")
# # Value expected: 31.53dB (Wikipedia Example)
print("\n-- Second Test --")
print(f"PSNR value is {peak_signal_to_noise_ratio(original2, contrast2)} dB")
if __name__ == "__main__":
main()