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https://github.com/terminaldweller/magni
upscales all the images in a webpage and re-serves the images locally
https://github.com/terminaldweller/magni
manga socks5-proxy upscaler
Last synced: 8 days ago
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upscales all the images in a webpage and re-serves the images locally
- Host: GitHub
- URL: https://github.com/terminaldweller/magni
- Owner: terminaldweller
- License: gpl-3.0
- Created: 2022-11-19T18:53:10.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-09-02T18:59:39.000Z (over 1 year ago)
- Last Synced: 2024-12-24T00:13:30.565Z (10 days ago)
- Topics: manga, socks5-proxy, upscaler
- Language: Python
- Homepage:
- Size: 81.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# magni
magni takes a url and upscales all the images inside and returns a simple html page with the images embedded.
```sh
usage: magni.py [-h] [--url URL] [--method METHOD] [--port PORT]
options:
-h, --help show this help message and exit
--url URL, -u URL the url to the page containing the images
--method METHOD, -m METHOD
the method to use. either fsrcnn or espcn
--port PORT, -p PORT the port to serve the images over
```
## Install and Run
### Install
```sh
poetry install
```
### Run
```sh
poetry shell && HTTPS_PROXY=socks5h://127.0.0.1:9094 ./magni.py --url https://chapmanganato.com/manga-dt980702/chapter-184
```
you can obviously use `poetry run` as well:
```sh
HTTPS_PROXY=socks5h://127.0.0.1:9094 poetry run ./magni.py --url https://chapmanganato.com/manga-dt980702/chapter-184
```
### Docker
#### Build
```sh
docker build -t magni:latest --load .
```
#### Run
```sh
docker run -p 8086:8086 \
-e HTTPS_PROXY=socks5h://192.168.1.100:9050 \
-e MAGNI_MODEL_PATH=/opt/magni_models \
-e MAGNI_IMAGE_PATH=/opt/magni_images \
-v ./models:/opts/magni_models \
magni:latest --url https://chapmanganato.com/manga-dt980702/chapter-184
```
## Env Vars
magni recognizes three environment variables:
### HTTP_PROXY/HTTPS_PROXY
You can also specify a socks5 proxy here since magni uses `pysocks` to make the connections.
If the env vars are not defined or are empty magni will not use any proxy.
### MAGNI_MODEL_PATH
Path to the directory where magni will store the models.
If the env var is not defined or is empty, magni will use `./models` as a default value.
### MAGNI_IMAGE_PATH
Path to the directory where magni will store the upscaled images.
If the env var is not defined or is empty magni will use `./images` as a default value.
### MAGNI_USER_AGENT
The user agent magni will use the download the images.
If the env var is not defined or is empty, magni will use a default user agent you can see below:
```
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36
```
## TODO
* currently the models we are using are not as effective. I should either fine ones that are specifically trained on greyscale images or just train some myself.