LLIC 0.1.13
Lightweight Learned Image Compression
Requires Python: >=3.6

# Lightweight Learned Image Compression (LLIC)

## Installation

1. Follow the installation instructions for [torch](https://pytorch.org/get-started/locally/) and [compressai](https://interdigitalinc.github.io/CompressAI/installation.html)
2. Install LLIC via pip: `pip install LLIC`

## Pre-trained checkpoints

An imagenet-trained checkpoint for RGB images is available on huggingface: [LLIC_rgb_v0.0.1.pth](https://huggingface.co/danjacobellis/LLIC/resolve/main/LLIC_rgb_v0.0.1.pth)

[Request access to other checkpoints (grayscale, hyperspectral, microscopy, etc)](mailto:danjacobellis@utexas.edu)

## Usage example


```python
import torch
import zlib
import numpy as np
import compressai
from io import BytesIO
from IPython.display import display
from PIL import Image
from LLIC import LLIC
from torchvision.transforms import ToPILImage, PILToTensor
```

Load the model


```python
checkpoint = torch.load("LLIC_rgb_v0.0.1.pth",map_location="cpu")
codec = LLIC.RateDistortionAutoEncoder()
codec.load_state_dict(checkpoint['model_state_dict'])
```




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Download example image


```python
!wget https://r0k.us/graphics/kodak/kodak/kodim05.png
```


```python
original_image = Image.open("kodim05.png")
original_image
```




    
![png](README_files/README_6_0.png)
    



The analysis and synthesis transforms expect dimensions to be multiples of of 16. Zero padding can be applied otherwise.


```python
def pad(x, p=2**5):
    h, w = x.size(2), x.size(3)
    pad, _ = compressai.ops.compute_padding(h, w, min_div=p)
    return torch.nn.functional.pad(x, pad, mode="constant", value=0)

def preprocess(pil_image):
    tensor = PILToTensor()(pil_image)
    tensor = tensor.unsqueeze(0)
    tensor = tensor.to(torch.float)
    tensor = tensor/255
    tensor = tensor - 0.5
    return pad(tensor)
```

Compress the image and save file


```python
padded_image = preprocess(original_image)
original_size = padded_image.shape
compressed_image, compressed_shape = LLIC.compress(padded_image, codec)
with open("kodim05.llic", 'wb') as f:
    f.write(compressed_image)
```

Decompress and view the image


```python
def crop(x, size):
    H, W = x.size(2), x.size(3)
    h, w = size
    _, unpad = compressai.ops.compute_padding(h, w, out_h=H, out_w=W)
    return torch.nn.functional.pad(x, unpad, mode="constant", value=0)

def postprocess(tensor):
    tensor = tensor[0] + 0.5
    tensor = 255*tensor
    tensor = tensor.clamp(0,255)
    tensor = tensor.to(torch.uint8)
    pil_image = ToPILImage()(tensor)
    return pil_image
```


```python
with open("kodim05.llic", 'rb') as f:
    compressed_image = f.read()
    tensor = LLIC.decompress(compressed_image, compressed_shape, codec)
    recovered_image = postprocess(crop(tensor, (512,768)))
```


```python
recovered_image
```




    
![png](README_files/README_14_0.png)
    




```python
!jupyter nbconvert --to markdown README.ipynb
```

    [NbConvertApp] Converting notebook README.ipynb to markdown
    [NbConvertApp] Support files will be in README_files/
    [NbConvertApp] Making directory README_files
    [NbConvertApp] Writing 2873 bytes to README.md