end-to-end super resolution toolkit
Project description
ddddsr
A python library for end-to-end image super-resolution.
Install
pip install ddddsr
Features
-
End-to-end super resolution in one package (currently support
waifu2x
models). -
(experimental) OCR for better text super resolution quality.
Usage
import ddddsr
# read input.jpg and write to output.png
# default is 2x upscaling on CPU using waifu2x cunet.
ddddsr.SR()('input.jpg', 'output.png')
API reference:
sr = ddddsr.SR(
# models: currently supports ['waifu2x_art', 'waifu2x_photo']
model: str = 'waifu2x_art',
# scale
scale: Optional[float] = None,
# output size (omitted if scale is set, if both scale and size are None, use scale = 2)
size: Optional[Union[int, List[int], Tuple[int]]] = None,
# denoise level: range in [-1, 3], -1 means no denoising.
denoise_level: int = 2,
# whether to use gpu
use_gpu: bool = False,
# if use gpu, set the device id
device_id: int = 0,
# (experimental) use OCR for better text quality
ocr_text: bool = False,
# (experimental) OCR related configurations
ocr_font_size: int = 28,
ocr_font_color: Union[Tuple[int], List[int]] = (0, 0, 0),
ocr_background_color: Union[Tuple[int], List[int]] = (255, 255, 255),
ocr_font_ttf: Optional[str] = None, # path for ttf font file
# verbose
verbose: bool = False,
)
sr(
# image, support array of [H, W, 3] or [H, W, 4], or the file path.
image: Union[np.ndarray, str],
# output_path, output file path, if is None, will return the ndarray.
output_path: str = None,
# slide window size, -1 means no slide window.
window: int = 256,
)
References
-
The original waifu2x.
-
ONNX models are from waifu2x-onnx.
-
OCR models are from ppocr-onnx and PaddleOCR.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
ddddsr-0.1.6.tar.gz
(40.8 MB
view details)
File details
Details for the file ddddsr-0.1.6.tar.gz
.
File metadata
- Download URL: ddddsr-0.1.6.tar.gz
- Upload date:
- Size: 40.8 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.2 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.4
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 43e686b1368e9ddcefcecafb4905519ea0d0ec7bcc748677b556e6a96c76d3c4 |
|
MD5 | 1a4bf85e106f353a8142b831c8321e1c |
|
BLAKE2b-256 | 0dad856f87fac7f7c96fdab639b62f37e5409af7ff4c2421ed9e26af5b9c6185 |