mirror of
https://git.datalinker.icu/ali-vilab/TeaCache
synced 2025-12-09 21:04:25 +08:00
91 lines
2.1 KiB
Python
91 lines
2.1 KiB
Python
import math
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import numpy as np
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import torch
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def img_psnr(img1, img2):
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# [0,1]
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# compute mse
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# mse = np.mean((img1-img2)**2)
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mse = np.mean((img1 / 1.0 - img2 / 1.0) ** 2)
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# compute psnr
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if mse < 1e-10:
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return 100
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psnr = 20 * math.log10(1 / math.sqrt(mse))
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return psnr
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def trans(x):
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return x
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def calculate_psnr(videos1, videos2):
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# videos [batch_size, timestamps, channel, h, w]
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assert videos1.shape == videos2.shape
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videos1 = trans(videos1)
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videos2 = trans(videos2)
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psnr_results = []
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for video_num in range(videos1.shape[0]):
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# get a video
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# video [timestamps, channel, h, w]
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video1 = videos1[video_num]
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video2 = videos2[video_num]
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psnr_results_of_a_video = []
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for clip_timestamp in range(len(video1)):
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# get a img
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# img [timestamps[x], channel, h, w]
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# img [channel, h, w] numpy
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img1 = video1[clip_timestamp].numpy()
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img2 = video2[clip_timestamp].numpy()
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# calculate psnr of a video
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psnr_results_of_a_video.append(img_psnr(img1, img2))
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psnr_results.append(psnr_results_of_a_video)
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psnr_results = np.array(psnr_results)
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psnr = {}
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psnr_std = {}
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for clip_timestamp in range(len(video1)):
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psnr[clip_timestamp] = np.mean(psnr_results[:, clip_timestamp])
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psnr_std[clip_timestamp] = np.std(psnr_results[:, clip_timestamp])
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result = {
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"value": psnr,
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"value_std": psnr_std,
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"video_setting": video1.shape,
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"video_setting_name": "time, channel, heigth, width",
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}
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return result
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# test code / using example
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def main():
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NUMBER_OF_VIDEOS = 8
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VIDEO_LENGTH = 50
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CHANNEL = 3
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SIZE = 64
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videos1 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
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videos2 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
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import json
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result = calculate_psnr(videos1, videos2)
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print(json.dumps(result, indent=4))
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if __name__ == "__main__":
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main()
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