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import numpy as np
import torch
from tqdm import tqdm
import math
def img_psnr_cuda(img1, img2):
# [0,1]
# compute mse
# mse = np.mean((img1-img2)**2)
mse = torch.mean((img1 / 1.0 - img2 / 1.0) ** 2)
# compute psnr
if mse < 1e-10:
return 100
psnr = 20 * torch.log10(1 / torch.sqrt(mse))
return psnr
def img_mse(img1, img2):
# [0,1]
# compute mse
# mse = np.mean((img1-img2)**2)
mse = np.sqrt(np.mean((img1*255/ 1.0 - img2*255 / 1.0) ** 2))
return mse
def trans(x):
return x
def calculate_mse(videos1, videos2):
print("calculate_mse...")
# videos [batch_size, timestamps, channel, h, w]
assert videos1.shape == videos2.shape
videos1 = trans(videos1)
videos2 = trans(videos2)
mse_results = []
for video_num in tqdm(range(videos1.shape[0])):
# get a video
# video [timestamps, channel, h, w]
video1 = videos1[video_num]
video2 = videos2[video_num]
mse_results_of_a_video = []
for clip_timestamp in range(len(video1)):
# get a img
# img [timestamps[x], channel, h, w]
# img [channel, h, w] numpy
img1 = video1[clip_timestamp].numpy()
img2 = video2[clip_timestamp].numpy()
# calculate psnr of a video
mse_results_of_a_video.append(img_mse(img1, img2))
mse_results.append(mse_results_of_a_video)
mse_results = np.array(mse_results) # [batch_size, num_frames]
mse = {}
mse_std = {}
for clip_timestamp in range(len(video1)):
mse[clip_timestamp] = np.mean(mse_results[:,clip_timestamp])
mse_std[clip_timestamp] = np.std(mse_results[:,clip_timestamp])
result = {
"value": mse,
"value_std": mse_std,
"video_setting": video1.shape,
"video_setting_name": "time, channel, heigth, width",
}
return result
# test code / using example
def main():
NUMBER_OF_VIDEOS = 8
VIDEO_LENGTH = 50
CHANNEL = 3
SIZE = 64
videos1 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
videos2 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
import json
result = calculate_psnr(videos1, videos2)
print(json.dumps(result, indent=4))
if __name__ == "__main__":
main() |