File size: 2,155 Bytes
8b78124 | 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | ## PredNet denoising (black-box SavedModel)
This repository packages a TensorFlow **SavedModel** for video-frame prediction/denoising **without publishing any model architecture source code**. Inference calls the SavedModel signature as a black box and produces predicted frames.
### Files
- `savedmodel/`: TensorFlow SavedModel directory (weights + graph)
- `infer/`: minimal inference helpers (black-box loader + IO)
- `predict.py`: CLI that outputs prediction images
- `anomaly.py`: CLI that outputs per-frame anomaly scores (ahat_error + 1-SSIM vectors)
### Install
```bash
pip install -r requirements.txt
```
### Run (from a directory of frames)
`frames_dir` should contain ordered image frames (e.g. `0001.png`, `0002.png`, ...).
```bash
python predict.py --model_dir savedmodel --frames_dir /path/to/frames --out_dir outputs --save_sequence_grid
```
Outputs:
- `outputs/pred_last.png`
- `outputs/pred_sequence_grid.png` (optional)
### Run (from npy/npz)
Accepts `frames` stored as `[T,H,W,C]` or `[B,T,H,W,C]`.
```bash
python predict.py --array frames.npy --out_dir outputs
```
### Anomaly scores (4-frame window)
For each window of 4 frames ending at time `t`, this computes two vectors of length 4:
- `ahat_error[1..4]`: mean absolute error between `pred_frame_k` and `GT_frame_4`
- `dissim_1mssim[1..4]`: `1 - SSIM(pred_frame_k, GT_frame_4)`
```bash
python anomaly.py --model_dir savedmodel --frames_dir /path/to/frames --out_json anomaly.json --out_csv anomaly.csv
```
### Hugging Face Hub usage
After you upload this repo to the Hub, users can download it via `huggingface_hub` and point `--model_dir` at the downloaded `savedmodel/` directory.
### HuggingFace Pipeline Usage
```python
from transformers import pipeline
import numpy as np
pipe = pipeline(
"video-frame-prediction",
model="dvdface/denoising-prednet",
trust_remote_code=True,
)
# frames: [T, H, W, C] numpy array, uint8 (0-255) or float (0-1)
frames = np.random.randint(0, 255, (4, 128, 128, 3), dtype=np.uint8)
result = pipe(frames)
print(result["sequence"].shape) # (4, 128, 128, 3)
print(result["last_frame"].shape) # (128, 128, 3)
```
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