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You can easily calculate FVD, PSNR, SSIM, LPIPS for evaluating the quality of generated or predicted videos.
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# common_metrics_on_video_quality
## Introduction
You can easily calculate the following video quality metrics for video generation and video preiction tasks:
- **FVD**: Frechét Video Distance
- **SSIM**: Structural Similarity Index Measure
- **LPIPS**: Learned Perceptual Image Patch Similarity
- **PSNR**: Peak-Signal-to-Noise Ratio
## Installation
Version reference: numpy-1.26.4 opencv-python-4.10.0.84 scipy-1.13.1 tqdm-4.67.1 einops-0.8.0
```
conda create -n test python=3.9
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
conda install "numpy<2.0"
pip install opencv-python tqdm einops
git clone https://github.com/JunyaoHu/common_metrics_on_video_quality
```
## Evaluation Example
For example, we evaluated 8 pairs of videos, each with 30 frames, 3 channels, and a size of 64x64.
```
original video: [8x30x3x64x64] pixel in [0,1]
generated video: [8x30x3x64x64] pixel in [0,1]
```
Run the following code `demo.py` to calculate the four metrics.
```
cd common_metrics_on_video_quality
python demo.py
```
The key content of `demo.py` is:
```
calculate_fvd(videos1, videos2, device, method='styleganv', only_final=True)
calculate_ssim(videos1, videos2, only_final=True)
calculate_psnr(videos1, videos2, only_final=True)
calculate_lpips(videos1, videos2, device, only_final=True)
```
In the example, a all-zero matrix of [8x30x3x64x64] and a all-one matrix of [8x30x3x64x64], their $\mathrm{FVD}(\mathit{frames_A}, \mathit{frames_B})$ is about 151 (Due to different pytorch versions, the number may fluctuate around 1).
```
{
"fvd": {"value": [151.25648496845326]},
"ssim": {"value": [9.999000099990664e-05],"value_std": [0.0]},
"psnr": {"value": [0.0],"value_std": [0.0]},
"lpips": {"value": [0.8140090703964233],"value_std": [0.0]}
}
```
If we set `only_final=False`,
```
calculate_fvd(videos1, videos2, device, method='styleganv', only_final=False)
calculate_ssim(videos1, videos2, only_final=False)
calculate_psnr(videos1, videos2, only_final=False)
calculate_lpips(videos1, videos2, device, only_final=False)
```
We can calculate:
- FVD: $\mathrm{FVD}_n(\mathit{frames_A}, \mathit{frames_B}) = \mathrm{FVD}(\mathit{frames_A}[:n], \mathit{frames_B}[:n]), 10 \le n \le T $
- PSNR/SSIM/LPIPS: $\mathrm{F}_n(\mathit{frames_A}, \mathit{frames_B}) = \mathrm{avg}(\mathrm{F}(\mathit{frames_A}[i], \mathit{frames_B}[i])), \mathrm{F}=\{\mathrm{PSNR}, \mathrm{SSIM}, \mathrm{LPIPS}\}, 0 \le i \le n - 1, 1 \le n \le T $
```
{
"fvd": {
"value": [
569.2296293622766,
486.3584254441098,
551.9610501807822,
146.36638178542628,
172.85453222258292,
133.70311962583372,
152.91750309134142,
357.7402855012116,
382.4668646785276,
306.73840379649727,
338.4151811780684,
78.17255931098194,
82.33446642508818,
64.5885972265882,
66.06281151704604,
314.4803706985065,
316.6870909853734,
288.97196946254184,
287.7805184515251,
152.1524775765185,
151.2564750365302
]
},
"ssim": {"value": [9.999000099990664e-05,...,],"value_std": [0.0,...]},
"psnr": {"value": [0.0,...],...},
"lpips": {"value": [0.8140090703964233,...],...}
}
```
## Comparison with orginal Tensorflow FVD metric
If you want to use the original version of FVD which comes from Tensorflow, and compare FVD result with this repo's implementation:
You should create a tensorflow-1.0 envrironment:
```
# https://github.com/universome/fvd-comparison/blob/master/requirements.txt
conda create -n tf1 python=3.7
pip install tensorflow==1.15.0 tensorflow-gan==1.0.0.dev0 tensorflow-hub==0.12.0 scipy==1.7.3 tqdm
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
calculate_fvd_tensorflow.py
# calculate_fvd(videos1, videos2, only_final=True)
# output:
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matched fp:011d1ef2317bdf0c, topic:video-generation, readme:video generation
matched fp:011d1ef2317bdf0c, topic:pytorch