https://github.com/tohrusky/realesrgan-ncnn-py
Python Binding for realesrgan-ncnn-vulkan with PyBind11
https://github.com/tohrusky/realesrgan-ncnn-py
cpp ncnn pybind11 python3 realesrgan super-resolution
Last synced: 4 months ago
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Python Binding for realesrgan-ncnn-vulkan with PyBind11
- Host: GitHub
- URL: https://github.com/tohrusky/realesrgan-ncnn-py
- Owner: Tohrusky
- License: bsd-3-clause
- Created: 2023-04-19T09:38:51.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-30T11:05:44.000Z (almost 2 years ago)
- Last Synced: 2025-04-08T20:49:04.151Z (7 months ago)
- Topics: cpp, ncnn, pybind11, python3, realesrgan, super-resolution
- Language: C++
- Homepage:
- Size: 39 MB
- Stars: 117
- Watchers: 4
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# realesrgan-ncnn-py
Python Binding for realesrgan-ncnn-py with PyBind11
[](https://badge.fury.io/py/realesrgan-ncnn-py?123456)
[](https://github.com/Final2x/realesrgan-ncnn-py/actions/workflows/test_pip.yml)
[](https://github.com/Tohrusky/realesrgan-ncnn-py/actions/workflows/Release.yml)
Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure
synthetic data.
This wrapper provides an easy-to-use interface for running the pre-trained Real-ESRGAN model.
### Current building status matrix
| System | Status | CPU (32bit) | CPU (64bit) | GPU (32bit) | GPU (64bit) |
| :-----------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------: | :----------------: | :---------: | :----------------: |
| Linux (Clang) | [](https://github.com/Tohrusky/realesrgan-ncnn-py/actions/workflows/CI-Linux-x64-Clang.yml) | — | :white_check_mark: | — | :white_check_mark: |
| Linux (GCC) | [](https://github.com/Tohrusky/realesrgan-ncnn-py/actions/workflows/CI-Linux-x64-GCC.yml) | — | :white_check_mark: | — | :white_check_mark: |
| Windows | [](https://github.com/Tohrusky/realesrgan-ncnn-py/actions/workflows/CI-Windows-x64-MSVC.yml) | — | :white_check_mark: | — | :white_check_mark: |
| MacOS | [](https://github.com/Tohrusky/realcugan-ncnn-py/actions/workflows/CI-MacOS-Universal-Clang.yml) | — | :white_check_mark: | — | :white_check_mark: |
| MacOS (ARM) | [](https://github.com/Tohrusky/realcugan-ncnn-py/actions/workflows/CI-MacOS-Universal-Clang.yml) | — | :white_check_mark: | — | :white_check_mark: |
# Usage
`Python >= 3.6 (>= 3.9 in MacOS arm)`
To use this package, simply install it via pip:
```sh
pip install realesrgan-ncnn-py
```
For Linux user:
```sh
apt install -y libomp5 libvulkan-dev
```
Then, import the Realesrgan class from the package:
```python
from realesrgan_ncnn_py import Realesrgan
```
To initialize the model:
```python
realesrgan = Realesrgan(gpuid: int = 0, tta_mode: bool = False, tilesize: int = 0, model: int = 0)
# model can be -1, 0, 1, 2, 3, 4; 0 for default, -1 for custom load
# 0: {"param": "realesr-animevideov3-x2.param", "bin": "realesr-animevideov3-x2.bin", "scale": 2},
# 1: {"param": "realesr-animevideov3-x3.param", "bin": "realesr-animevideov3-x3.bin", "scale": 3},
# 2: {"param": "realesr-animevideov3-x4.param", "bin": "realesr-animevideov3-x4.bin", "scale": 4},
# 3: {"param": "realesrgan-x4plus-anime.param", "bin": "realesrgan-x4plus-anime.bin", "scale": 4},
# 4: {"param": "realesrgan-x4plus.param", "bin": "realesrgan-x4plus.bin", "scale": 4}
```
Here, gpuid specifies the GPU device to use, tta_mode enables test-time augmentation, tilesize specifies the tile size
for processing (0 or >= 32), and model specifies the num of 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
realesrgan = Realesrgan(gpuid=0)
with Image.open("input.jpg") as image:
image = realesrgan.process_pil(image)
image.save("output.jpg", quality=95)
```
### opencv-python
```python
import cv2
realesrgan = Realesrgan(gpuid=0)
image = cv2.imdecode(np.fromfile("input.jpg", dtype=np.uint8), cv2.IMREAD_COLOR)
image = realesrgan.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)
realesrgan = Realesrgan(gpuid=0)
while True:
raw_image = pipe_out.stdout.read(src_width * src_height * 3)
if not raw_image:
break
raw_image = realesrgan.process_bytes(raw_image, src_width, src_height, 3)
pipe_in.stdin.write(raw_image)
```
# Build
[here](https://github.com/Tohrusky/realesrgan-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:
[xinntao/Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan) - This project was the main
inspiration for our work. It provided the core implementation of the Real-ESRGAN algorithm using the ncnn and Vulkan
libraries.
[Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) - Real-ESRGAN is an AI super resolution model, aims at developing
Practical Algorithms for General Image/Video Restoration.
[media2x/realsr-ncnn-vulkan-python](https://github.com/media2x/realsr-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/realesrgan-ncnn-py/blob/main/LICENSE) for details.