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It works like a red team within your system. You describe the task in plain language — then it plans the attack itself, selects tools, and proceeds through the entire process: from reconnaissance to reporting. Without manual fiddling and endless commands.
What it can do in practice:
git clone https://github.com/GH05TCREW/ghostcrew.git
cd ghostcrew
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python main.py
#python #soft #github
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#python #soft #github
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Do you want to automate image processing without Photoshop?
A script project based on the
rembg library:pip install rembg
Example code:
from rembg import remove
def remove_background(input_path: str, output_path: str) -> None:
"""Removes the background from the image and saves the result."""
with open(input_path, 'rb') as input_file:
with open(output_path, 'wb') as output_file:
image_bytes: bytes = input_file.read()
output_bytes: bytes = remove(image_bytes)
output_file.write(output_bytes)
if __name__ == "__main__":
remove_background('input.png', 'output.png')
#python #soft #code
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Forwarded from Machine Learning with Python
SVFR — a full-fledged framework for restoring faces in videos.
It can:
Essentially, the model takes old or damaged videos and makes them "as if they were shot yesterday". And it's free and open-source.
1. Create an environment
conda create -n svfr python=3.9 -y
conda activate svfr
2. Install PyTorch (for your CUDA)
pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2
3. Install dependencies
pip install -r requirements.txt
4. Download models
conda install git-lfs
git lfs install
git clone https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt models/stable-video-diffusion-img2vid-xt
5. Start processing videos
python infer.py \
--config config/infer.yaml \
--task_ids 0 \
--input_path input.mp4 \
--output_dir results/ \
--crop_face_region
Where task_ids:
*
0 — face enhancement*
1 — colorization*
2 — redrawing damageAn ideal tool if:
#python #soft #github
https://t.me/CodeProgrammer
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