Upload folder using huggingface_hub

.github/workflows/sync_to_hub.yml

@@ -0,0 +1,25 @@
+name: Sync with Hugging Face Hub
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+
+      - name: Install Hugging Face CLI
+        run: pip install -U "huggingface_hub[cli]"
+
+      - name: Upload to Hugging Face Space
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          hf upload Spring-0/face-censor . . \
+            --repo-type space \
+            --token "$HF_TOKEN"

.gitignore

@@ -1,9 +1,9 @@
-__pycache__/
-*.pyc
-*.pyo
-src/runs/*
-src/yolov8n.pt
-src/data/raw
-.env
-.venv
+__pycache__/
+*.pyc
+*.pyo
+src/runs/*
+src/yolov8n.pt
+src/data/raw
+.env
+.venv
 .gradio/

Dockerfile

@@ -2,7 +2,7 @@ FROM python:3.10
 WORKDIR /src
 COPY . /src
 
-RUN apt-get update && apt-get install -y libgl1-mesa-glx && rm -rf /var/lib/apt/lists/*
+RUN apt-get update && apt-get install -y libgl1 libglib2.0-0 && rm -rf /var/lib/apt/lists/*
 
 RUN pip install --no-cache-dir -r requirements.txt
 

README.md

@@ -1,170 +1,181 @@
----
-title: face-censor
-sdk: docker
-pinned: false
----
-# Face Detection and Censoring System
-
-A Python-based system for detecting faces in images and videos using YOLOv8, with the ability to censor detected faces. The system is designed to be modular and extensible.
-
-## Features
-
-- Face detection using YOLOv8
-- Support for both image and video processing
-- Easy to use User Interface
-- Modular censoring system
-- Trained on the WIDER FACE dataset via Roboflow
-- Multiple masking methods including: blur, emoji, and text (see [demo](#demo))
-
-## Local Installation
-
-1. Clone the repository:
-```bash
-git clone https://github.com/Spring-0/face-censor.git
-cd face-censor
-```
-
-2. Create a virtual environment and activate it:
-```bash
-python -m venv .venv
-source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
-```
-
-3. Install the required packages:
-```bash
-pip install -r requirements.txt
-```
-
-4. Run:
-```bash
-python src/main.py
-```
-
-## Training the Model - Optional
-
-The project uses the WIDER FACE dataset from Roboflow for training. I have included a pre-trained model, so there is no need to re-train it unless you want to. Here is how:
-
-1. Update this line in `training/training.py` if required:
-```python
-device="0"  # Set to "0" to utilize GPU, otherwise set to "cpu" to utilize CPU
-```
-
-2. Create a `.env` file in the project root with your Roboflow API key:
-```bash
-ROBOFLOW_API_KEY=your_api_key_here
-```
-
-3. Run the training script:
-```bash
-cd training
-python3 training.py
-```
-
-## Usage
-
-### API Usage
-```python
-# Face detection model
-from models.yolo_detector import YOLOFaceDetector
-
-# Masking methods (no need to import all, just what you want to use)
-from masking.text import TextCensor
-from masking.emoji import EmojiCensor
-from masking.blur import BlurCensor
-
-# Media processor
-from processor import MediaProcessor
-
-# Initialize face detector model
-detector = YOLOFaceDetector()
-```
-### Creating Masking Object
-This is what determines what effect will be applied to mask the faces.
-
-#### Using Text Masking
-```python
-text_censor = TextCensor(
-    text="HELLO", # The text to draw on faces
-    draw_background=True, # Control whether to draw solid background behind text
-    background_color="white", # The color of the solid background
-    text_color="black", # The color of the text
-    scale_factor=0.2 # The text size scaling factor, default to 0.5
-)
-```
-#### Using Emoji Masking
-```python
-emoji_censor = EmojiCensor(
-    emoji="😁", # The emoji you want to use to mask faces
-    font="seguiemj.ttf", # The path to the emoji font file, by default uses "seguiemj.ttf"
-    scale_factor=1.0 # The emoji size scaling factor in percentage, default to 1.0
-)
-```
-#### Using Blur Masking
-```python
-blur_censor = BlurCensor(
-    blur_factor=71 # The strength of the blur effect, defaults to 99
-)
-```
-
-### Create Media Processor
-After creating the masking method object(s), you need to pass it to the `MediaProcessor` constructor like so:
-```python
-processor = MediaProcessor(detector, blur_censor)
-```
-
-### Processing Images
-```python
-# Process an image
-processor.process_image("input.jpg", "output.jpg")
-```
-
-### Processing Videos
-```python
-# Process a video
-processor.process_video("input.mp4", "output.mp4")
-```
-
-## Demo
-
-### Input Image/Video
-![Input Image/Video](assets/input.jpg)
-
-### Output Image/Video
-#### Blur Masking
-![Output Blur Image/Video](assets/output_blur.jpg)
-
-#### Emoji Masking
-![Output Emoji Image/Video](assets/output_emoji.jpg)
-
-#### Text Masking
-![Output Text Image/Video](assets/output_text.jpg)
-
-## Requirements
-
-- Python 3.8+
-- PyTorch
-- OpenCV
-- Ultralytics YOLOv8
-- Roboflow
-
-See `requirements.txt` for complete list.
-
-## License
-
-GPU General Public License - see LICENSE file for details.
-
-## Contributing
-
-1. Fork the repository
-2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
-3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
-4. Push to the branch (`git push origin feature/AmazingFeature`)
-5. Open a Pull Request
-
-## TODO
-
-- [x] Add emoji face masking
-- [ ] Add support for real time streams
-- [x] Add GUI interface
-- [ ] Add partial face censoring (eyes)
-
+![Sync with HF Status](https://github.com/Spring-0/face-censor/actions/workflows/sync_to_hub.yml/badge.svg?branch=main)
+
+# Face Detection and Censoring System
+
+A Python-based system for detecting faces in images and videos using YOLOv8, with the ability to censor detected faces. The system is designed to be modular and extensible.
+
+## Features
+
+- Face detection using YOLOv8
+- Support for both image and video processing
+- Easy to use User Interface
+- Modular censoring system
+- Trained on the WIDER FACE dataset via Roboflow
+- Multiple masking methods including: blur, emoji, and text (see [demo](#demo))
+
+## Local Installation
+
+### With Docker 🐋
+1. Pull the image:
+```bash
+docker pull spring93/face-censor
+```
+
+2. Run:
+```bash
+docker run -p 3000:7860 -it --gpus all spring93/face-censor
+```
+
+3. Navigate to `http://localhost:3000` from your browser.
+
+### Without Docker
+1. Clone the repository:
+```bash
+git clone https://github.com/Spring-0/face-censor.git
+cd face-censor
+```
+
+2. Create a virtual environment and activate it:
+```bash
+python -m venv .venv
+source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
+```
+
+3. Install the required packages:
+```bash
+pip install -r requirements.txt
+```
+
+4. Run:
+```bash
+python src/main.py
+```
+
+## Training the Model - Optional
+
+The project uses the WIDER FACE dataset from Roboflow for training. I have included a pre-trained model, so there is no need to re-train it unless you want to. Here is how:
+
+1. Update this line in `training/training.py` if required:
+```python
+device="0"  # Set to "0" to utilize GPU, otherwise set to "cpu" to utilize CPU
+```
+
+2. Create a `.env` file in the project root with your Roboflow API key:
+```bash
+ROBOFLOW_API_KEY=your_api_key_here
+```
+
+3. Run the training script:
+```bash
+cd training
+python3 training.py
+```
+
+## Usage
+
+### API Usage
+```python
+# Face detection model
+from models.yolo_detector import YOLOFaceDetector
+
+# Masking methods (no need to import all, just what you want to use)
+from masking.text import TextCensor
+from masking.emoji import EmojiCensor
+from masking.blur import BlurCensor
+
+# Media processor
+from processor import MediaProcessor
+
+# Initialize face detector model
+detector = YOLOFaceDetector()
+```
+### Creating Masking Object
+This is what determines what effect will be applied to mask the faces.
+
+#### Using Text Masking
+```python
+text_censor = TextCensor(
+    text="HELLO", # The text to draw on faces
+    draw_background=True, # Control whether to draw solid background behind text
+    background_color="white", # The color of the solid background
+    text_color="black", # The color of the text
+    scale_factor=0.2 # The text size scaling factor, default to 0.5
+)
+```
+#### Using Emoji Masking
+```python
+emoji_censor = EmojiCensor(
+    emoji="😁", # The emoji you want to use to mask faces
+    font="seguiemj.ttf", # The path to the emoji font file, by default uses "seguiemj.ttf"
+    scale_factor=1.0 # The emoji size scaling factor in percentage, default to 1.0
+)
+```
+#### Using Blur Masking
+```python
+blur_censor = BlurCensor(
+    blur_factor=71 # The strength of the blur effect, defaults to 99
+)
+```
+
+### Create Media Processor
+After creating the masking method object(s), you need to pass it to the `MediaProcessor` constructor like so:
+```python
+processor = MediaProcessor(detector, blur_censor)
+```
+
+### Processing Images
+```python
+# Process an image
+processor.process_image("input.jpg", "output.jpg")
+```
+
+### Processing Videos
+```python
+# Process a video
+processor.process_video("input.mp4", "output.mp4")
+```
+
+## Demo
+
+### Input Image/Video
+![Input Image/Video](assets/input.jpg)
+
+### Output Image/Video
+#### Blur Masking
+![Output Blur Image/Video](assets/output_blur.jpg)
+
+#### Emoji Masking
+![Output Emoji Image/Video](assets/output_emoji.jpg)
+
+#### Text Masking
+![Output Text Image/Video](assets/output_text.jpg)
+
+## Requirements
+
+- Python 3.8+
+- PyTorch
+- OpenCV
+- Ultralytics YOLOv8
+- Roboflow
+
+See `requirements.txt` for complete list.
+
+## License
+
+GPU General Public License - see LICENSE file for details.
+
+## Contributing
+
+1. Fork the repository
+2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
+3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
+4. Push to the branch (`git push origin feature/AmazingFeature`)
+5. Open a Pull Request
+
+## TODO
+
+- [x] Add emoji face masking
+- [ ] Add support for real time streams
+- [x] Add GUI interface
+- [ ] Add partial face censoring (eyes)
+