Python Binding for srmd-ncnn-py with PyBind11

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.

System	Status	CPU (32bit)	CPU (64bit)	GPU (32bit)	GPU (64bit)
Linux (Clang)		—	—	—	✅
Linux (GCC)		—	—	—	✅
Windows		—	—	—	✅
MacOS		—	—	—	✅
MacOS (ARM)		—	—	—	✅

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

To use this package, simply install it via pip:

pip install srmd-ncnn-py

For Linux user:

apt install -y libomp5 libvulkan-dev

Then, import the SRMD class from the package:

from srmd_ncnn_py import SRMD

To initialize the model:

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:

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)

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")

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)

here

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.

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

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 - Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR, 2018) (Matlab)

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 - ncnn is a high-performance neural network inference framework developed by Tencent AI Lab.

This project is licensed under the BSD 3-Clause - see the LICENSE file for details.