Metadata-Version: 2.1
Name: srmd-ncnn-py
Version: 2.0.0
Author-Email: Tohrusky <65994850+Tohrusky@users.noreply.github.com>
License: BSD-3-Clause
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: C++
Project-URL: Homepage, https://github.com/Final2x/srmd-ncnn-py
Project-URL: Repository, https://github.com/Final2x/srmd-ncnn-py
Requires-Python: >=3.8
Requires-Dist: opencv-python
Requires-Dist: pillow
Description-Content-Type: text/markdown

# srmd-ncnn-py

Python Binding for srmd-ncnn-py with PyBind11

[![PyPI version](https://badge.fury.io/py/srmd-ncnn-py.svg?123456)](https://badge.fury.io/py/srmd-ncnn-py?123456)
[![Release](https://github.com/Tohrusky/srmd-ncnn-py/actions/workflows/Release.yml/badge.svg)](https://github.com/Tohrusky/srmd-ncnn-py/actions/workflows/Release.yml)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/srmd-ncnn-py)

SRMD - Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR, 2018).
This wrapper provides an easy-to-use interface for running the pre-trained SRMD model.

### Current building status matrix

|    System     |                                                                                                          Status                                                                                                           | CPU (32bit) | CPU (64bit) | GPU (32bit) |    GPU (64bit)     |
| :-----------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------: | :---------: | :---------: | :----------------: |
| Linux (Clang) |          [![CI-Linux-x64-Clang](https://github.com/Tohrusky/srmd-ncnn-py/actions/workflows/CI-Linux-x64-Clang.yml/badge.svg)](https://github.com/Tohrusky/srmd-ncnn-py/actions/workflows/CI-Linux-x64-Clang.yml)          |      —      |      —      |      —      | :white_check_mark: |
|  Linux (GCC)  |             [![CI-Linux-x64-GCC](https://github.com/Tohrusky/srmd-ncnn-py/actions/workflows/CI-Linux-x64-GCC.yml/badge.svg)](https://github.com/Tohrusky/srmd-ncnn-py/actions/workflows/CI-Linux-x64-GCC.yml)             |      —      |      —      |      —      | :white_check_mark: |
|    Windows    |        [![CI-Windows-x64-MSVC](https://github.com/Tohrusky/srmd-ncnn-py/actions/workflows/CI-Windows-x64-MSVC.yml/badge.svg)](https://github.com/Tohrusky/srmd-ncnn-py/actions/workflows/CI-Windows-x64-MSVC.yml)         |      —      |      —      |      —      | :white_check_mark: |
|     MacOS     | [![CI-MacOS-Universal-Clang](https://github.com/Tohrusky/srmd-ncnn-py/actions/workflows/CI-MacOS-Universal-Clang.yml/badge.svg)](https://github.com/Tohrusky/srmd-ncnn-py/actions/workflows/CI-MacOS-Universal-Clang.yml) |      —      |      —      |      —      | :white_check_mark: |
|  MacOS (ARM)  | [![CI-MacOS-Universal-Clang](https://github.com/Tohrusky/srmd-ncnn-py/actions/workflows/CI-MacOS-Universal-Clang.yml/badge.svg)](https://github.com/Tohrusky/srmd-ncnn-py/actions/workflows/CI-MacOS-Universal-Clang.yml) |      —      |      —      |      —      | :white_check_mark: |

# Usage

`Python >= 3.6 (>= 3.9 in MacOS arm)`

To use this package, simply install it via pip:

```sh
pip install srmd-ncnn-py
```

For Linux user:

```sh
apt install -y libomp5 libvulkan-dev
```

Then, import the SRMD class from the package:

```python
from srmd_ncnn_py import SRMD
```

To initialize the model:

```python
srmd = SRMD(gpuid: int = 0, tta_mode: bool = False, noise: int = 3, scale: int = 2, tilesize: int = 0, model: int = 0)
# model can be "models-srmd" or an absolute path to a model folder
```

Here, gpuid specifies the GPU device to use, tta_mode enables test-time augmentation, noise specifies the level of noise to apply to the image (-1 to 10), scale is the scaling factor for super-resolution (2 to 4), tilesize specifies the tile size for processing (0 or >= 32), and model specifies the pre-trained model to use.

Once the model is initialized, you can use the upscale method to super-resolve your images:

### Pillow

```python
from PIL import Image
srmd = SRMD(gpuid=0)
with Image.open("input.jpg") as image:
    image = srmd.process_pil(image)
    image.save("output.jpg", quality=95)
```

### opencv-python

```python
import cv2
srmd = SRMD(gpuid=0)
image = cv2.imdecode(np.fromfile("input.jpg", dtype=np.uint8), cv2.IMREAD_COLOR)
image = srmd.process_cv2(image)
cv2.imencode(".jpg", image)[1].tofile("output_cv2.jpg")
```

### ffmpeg

```python
import subprocess as sp
# your ffmpeg parameters
command_out = [FFMPEG_BIN,........]
command_in = [FFMPEG_BIN,........]
pipe_out = sp.Popen(command_out, stdout=sp.PIPE, bufsize=10 ** 8)
pipe_in = sp.Popen(command_in, stdin=sp.PIPE)
srmd = SRMD(gpuid=0)
while True:
    raw_image = pipe_out.stdout.read(src_width * src_height * 3)
    if not raw_image:
        break
    raw_image = srmd.process_bytes(raw_image, src_width, src_height, 3)
    pipe_in.stdin.write(raw_image)
```

# Build

[here](https://github.com/Tohrusky/srmd-ncnn-py/blob/main/.github/workflows/Release.yml)

_The project just only been tested in Ubuntu 18+ and Debian 9+ environments on Linux, so if the project does not work on your system, please try building it._

# References

The following references were used in the development of this project:

[nihui/srmd-ncnn-vulkan](https://github.com/nihui/srmd-ncnn-vulkan) - This project was the main inspiration for our work. It provided the core implementation of the SRMD algorithm using the ncnn and Vulkan libraries.

[cszn/SRMD](https://github.com/cszn/SRMD) - Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR, 2018) (Matlab)

[media2x/srmd-ncnn-vulkan-python](https://github.com/media2x/srmd-ncnn-vulkan-python) - This project was used as a reference for implementing the wrapper. _Special thanks_ to the original author for sharing the code.

[ncnn](https://github.com/Tencent/ncnn) - ncnn is a high-performance neural network inference framework developed by Tencent AI Lab.

# License

This project is licensed under the BSD 3-Clause - see the [LICENSE file](https://github.com/Tohrusky/srmd-ncnn-py/blob/main/LICENSE) for details.
