Compare commits
3 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Makaru
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5dd6d1fe64 | ||
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Makaru
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9af216e819 | ||
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Makaru
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59d64d4b6a |
@@ -9,96 +9,56 @@
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</p>
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<p align="center">
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<img src="media/demo.gif" alt="Demo GIF" width="800">
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<img src="media/demo.gif" alt="Demo GIF">
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<img src="media/avgpcperformancedemo.gif" alt="Performance Demo GIF">
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</p>
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|
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## Disclaimer
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###### This software is intended as a productive contribution to the AI-generated media industry. It aims to assist artists with tasks like animating custom characters or using them as models for clothing, etc.
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This deepfake software is designed to be a productive tool for the AI-generated media industry. It can assist artists in animating custom characters, creating engaging content, and even using models for clothing design.
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###### We are aware of the potential for unethical applications and are committed to preventative measures. A built-in check prevents the program from processing inappropriate media (nudity, graphic content, sensitive material like war footage, etc.). We will continue to develop this project responsibly, adhering to the law and ethics. We may shut down the project or add watermarks if legally required.
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|
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We are aware of the potential for unethical applications and are committed to preventative measures. A built-in check prevents the program from processing inappropriate media (nudity, graphic content, sensitive material like war footage, etc.). We will continue to develop this project responsibly, adhering to the law and ethics. We may shut down the project or add watermarks if legally required.
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###### Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online. We are not responsible for end-user actions.
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- Ethical Use: Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online.
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## Quick Start - Pre-built
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<div align="center">
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<a href="https://hacksider.gumroad.com/l/vccdmm">
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<img src="https://github.com/user-attachments/assets/7d993b32-e3e8-4cd3-bbfb-a549152ebdd5" width="285" height="77" />
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</a>
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<a href="https://krshh.gumroad.com/l/Deep-Live-Cam-Mac">
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<img src="https://github.com/user-attachments/assets/d5d913b5-a7de-4609-96b9-979a5749a703" width="285" height="77" />
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</a>
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</div>
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- Content Restrictions: The software includes built-in checks to prevent processing inappropriate media, such as nudity, graphic content, or sensitive material.
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|
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- Legal Compliance: We adhere to all relevant laws and ethical guidelines. If legally required, we may shut down the project or add watermarks to the output.
|
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|
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- User Responsibility: We are not responsible for end-user actions. Users must ensure their use of the software aligns with ethical standards and legal requirements.
|
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|
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By using this software, you agree to these terms and commit to using it in a manner that respects the rights and dignity of others.
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|
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Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online. We are not responsible for end-user actions.
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## Exclusive v2.3 Quick Start - Pre-built (Windows/Mac Silicon)
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<a href="https://deeplivecam.net/index.php/quickstart"> <img src="media/Download.png" width="285" height="77" />
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##### This is the fastest build you can get if you have a discrete NVIDIA or AMD GPU or Mac Silicon, And you'll receive special priority support.
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###### These Pre-builts are perfect for non-technical users or those who don't have time to, or can't manually install all the requirements. Just a heads-up: this is an open-source project, so you can also install it manually.
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## TLDR; Live Deepfake in just 3 Clicks
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1. Select a face
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2. Select which camera to use
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3. Press live!
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## Features & Uses - Everything is in real-time
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## Features - Everything is real-time
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### Mouth Mask
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**Retain your original mouth for accurate movement using Mouth Mask**
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**Retain your original mouth using Mouth Mask**
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<p align="center">
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<img src="media/ludwig.gif" alt="resizable-gif">
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</p>
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|
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|
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### Face Mapping
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**Use different faces on multiple subjects simultaneously**
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**Use different faces on multiple subjects**
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<p align="center">
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<img src="media/streamers.gif" alt="face_mapping_source">
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</p>
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### Your Movie, Your Face
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**Watch movies with any face in real-time**
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<p align="center">
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<img src="media/movie.gif" alt="movie">
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</p>
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|
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### Live Show
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## Benchmarks
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**Run Live shows and performances**
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**Nearly 0% detection!**
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<p align="center">
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<img src="media/live_show.gif" alt="show">
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</p>
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### Memes
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**Create Your Most Viral Meme Yet**
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<p align="center">
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<img src="media/meme.gif" alt="show" width="450">
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<br>
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<sub>Created using Many Faces feature in Deep-Live-Cam</sub>
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</p>
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### Omegle
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**Surprise people on Omegle**
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|
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<p align="center">
|
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<video src="https://github.com/user-attachments/assets/2e9b9b82-fa04-4b70-9f56-b1f68e7672d0" width="450" controls></video>
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</p>
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|
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## Installation (Manual)
|
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**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the quickstart version.**
|
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**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the prebuilt version.**
|
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|
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<details>
|
||||
<summary>Click to see the process</summary>
|
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@@ -109,23 +69,22 @@ This is more likely to work on your computer but will be slower as it utilizes t
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|
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**1. Set up Your Platform**
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- Python (3.11 recommended)
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- Python (3.10 recommended)
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- pip
|
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- git
|
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- [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) - ```iex (irm ffmpeg.tc.ht)```
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- [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA)
|
||||
- [Visual Studio 2022 Runtimes (Windows)](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
|
||||
|
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**2. Clone the Repository**
|
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|
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```bash
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git clone https://github.com/hacksider/Deep-Live-Cam.git
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cd Deep-Live-Cam
|
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https://github.com/hacksider/Deep-Live-Cam.git
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```
|
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**3. Download the Models**
|
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|
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1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.pth)
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2. [inswapper\_128\_fp16.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx)
|
||||
2. [inswapper\_128\_fp16.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128.onnx) (Note: Use this [replacement version](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx) if you encounter issues)
|
||||
|
||||
Place these files in the "**models**" folder.
|
||||
|
||||
@@ -133,57 +92,14 @@ Place these files in the "**models**" folder.
|
||||
|
||||
We highly recommend using a `venv` to avoid issues.
|
||||
|
||||
|
||||
For Windows:
|
||||
```bash
|
||||
python -m venv venv
|
||||
venv\Scripts\activate
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
For Linux:
|
||||
```bash
|
||||
# Ensure you use the installed Python 3.10
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
**For macOS:**
|
||||
|
||||
Apple Silicon (M1/M2/M3) requires specific setup:
|
||||
**For macOS:** Install or upgrade the `python-tk` package:
|
||||
|
||||
```bash
|
||||
# Install Python 3.11 (specific version is important)
|
||||
brew install python@3.11
|
||||
|
||||
# Install tkinter package (required for the GUI)
|
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brew install python-tk@3.10
|
||||
|
||||
# Create and activate virtual environment with Python 3.11
|
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python3.11 -m venv venv
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source venv/bin/activate
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||||
|
||||
# Install dependencies
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
** In case something goes wrong and you need to reinstall the virtual environment **
|
||||
|
||||
```bash
|
||||
# Deactivate the virtual environment
|
||||
rm -rf venv
|
||||
|
||||
# Reinstall the virtual environment
|
||||
python -m venv venv
|
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source venv/bin/activate
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|
||||
# install the dependencies again
|
||||
pip install -r requirements.txt
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||||
|
||||
# gfpgan and basicsrs issue fix
|
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pip install git+https://github.com/xinntao/BasicSR.git@master
|
||||
pip uninstall gfpgan -y
|
||||
pip install git+https://github.com/TencentARC/GFPGAN.git@master
|
||||
```
|
||||
|
||||
**Run:** If you don't have a GPU, you can run Deep-Live-Cam using `python run.py`. Note that initial execution will download models (~300MB).
|
||||
@@ -192,16 +108,12 @@ pip install git+https://github.com/TencentARC/GFPGAN.git@master
|
||||
|
||||
**CUDA Execution Provider (Nvidia)**
|
||||
|
||||
1. Install [CUDA Toolkit 12.8.0](https://developer.nvidia.com/cuda-12-8-0-download-archive)
|
||||
2. Install [cuDNN v8.9.7 for CUDA 12.x](https://developer.nvidia.com/rdp/cudnn-archive) (required for onnxruntime-gpu):
|
||||
- Download cuDNN v8.9.7 for CUDA 12.x
|
||||
- Make sure the cuDNN bin directory is in your system PATH
|
||||
3. Install dependencies:
|
||||
1. Install [CUDA Toolkit 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive) or [CUDA Toolkit 12.1.1](https://developer.nvidia.com/cuda-12-1-1-download-archive)
|
||||
2. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
|
||||
pip uninstall onnxruntime onnxruntime-gpu
|
||||
pip install onnxruntime-gpu==1.21.0
|
||||
pip install onnxruntime-gpu==1.16.3
|
||||
```
|
||||
|
||||
3. Usage:
|
||||
@@ -212,46 +124,26 @@ python run.py --execution-provider cuda
|
||||
|
||||
**CoreML Execution Provider (Apple Silicon)**
|
||||
|
||||
Apple Silicon (M1/M2/M3) specific installation:
|
||||
|
||||
1. Make sure you've completed the macOS setup above using Python 3.10.
|
||||
2. Install dependencies:
|
||||
1. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-silicon
|
||||
pip install onnxruntime-silicon==1.13.1
|
||||
```
|
||||
|
||||
3. Usage (important: specify Python 3.10):
|
||||
2. Usage:
|
||||
|
||||
```bash
|
||||
python3.10 run.py --execution-provider coreml
|
||||
python run.py --execution-provider coreml
|
||||
```
|
||||
|
||||
**Important Notes for macOS:**
|
||||
- You **must** use Python 3.10, not newer versions like 3.11 or 3.13
|
||||
- Always run with `python3.10` command not just `python` if you have multiple Python versions installed
|
||||
- If you get error about `_tkinter` missing, reinstall the tkinter package: `brew reinstall python-tk@3.10`
|
||||
- If you get model loading errors, check that your models are in the correct folder
|
||||
- If you encounter conflicts with other Python versions, consider uninstalling them:
|
||||
```bash
|
||||
# List all installed Python versions
|
||||
brew list | grep python
|
||||
|
||||
# Uninstall conflicting versions if needed
|
||||
brew uninstall --ignore-dependencies python@3.11 python@3.13
|
||||
|
||||
# Keep only Python 3.11
|
||||
brew cleanup
|
||||
```
|
||||
|
||||
**CoreML Execution Provider (Apple Legacy)**
|
||||
|
||||
1. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-coreml
|
||||
pip install onnxruntime-coreml==1.21.0
|
||||
pip install onnxruntime-coreml==1.13.1
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
@@ -266,7 +158,7 @@ python run.py --execution-provider coreml
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-directml
|
||||
pip install onnxruntime-directml==1.21.0
|
||||
pip install onnxruntime-directml==1.15.1
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
@@ -281,7 +173,7 @@ python run.py --execution-provider directml
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-openvino
|
||||
pip install onnxruntime-openvino==1.21.0
|
||||
pip install onnxruntime-openvino==1.15.0
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
@@ -289,6 +181,7 @@ pip install onnxruntime-openvino==1.21.0
|
||||
```bash
|
||||
python run.py --execution-provider openvino
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## Usage
|
||||
@@ -324,6 +217,7 @@ options:
|
||||
--many-faces process every face
|
||||
--map-faces map source target faces
|
||||
--mouth-mask mask the mouth region
|
||||
--nsfw-filter filter the NSFW image or video
|
||||
--video-encoder {libx264,libx265,libvpx-vp9} adjust output video encoder
|
||||
--video-quality [0-51] adjust output video quality
|
||||
--live-mirror the live camera display as you see it in the front-facing camera frame
|
||||
@@ -352,9 +246,6 @@ Looking for a CLI mode? Using the -s/--source argument will make the run program
|
||||
- [*"This real-time webcam deepfake tool raises alarms about the future of identity theft"*](https://www.diyphotography.net/this-real-time-webcam-deepfake-tool-raises-alarms-about-the-future-of-identity-theft/) - DIYPhotography
|
||||
- [*"That's Crazy, Oh God. That's Fucking Freaky Dude... That's So Wild Dude"*](https://www.youtube.com/watch?time_continue=1074&v=py4Tc-Y8BcY) - SomeOrdinaryGamers
|
||||
- [*"Alright look look look, now look chat, we can do any face we want to look like chat"*](https://www.youtube.com/live/mFsCe7AIxq8?feature=shared&t=2686) - IShowSpeed
|
||||
- [*"They do a pretty good job matching poses, expression and even the lighting"*](https://www.youtube.com/watch?v=wnCghLjqv3s&t=551s) - TechLinked (LTT)
|
||||
- [*"Als Sean Connery an der Redaktionskonferenz teilnahm"*](https://www.golem.de/news/deepfakes-als-sean-connery-an-der-redaktionskonferenz-teilnahm-2408-188172.html) - Golem.de (German)
|
||||
|
||||
|
||||
## Credits
|
||||
|
||||
|
||||
@@ -1,46 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "Quelle x Ziel Zuordnung",
|
||||
"select a source image": "Wähle ein Quellbild",
|
||||
"Preview": "Vorschau",
|
||||
"select a target image or video": "Wähle ein Zielbild oder Video",
|
||||
"save image output file": "Bildausgabedatei speichern",
|
||||
"save video output file": "Videoausgabedatei speichern",
|
||||
"select a target image": "Wähle ein Zielbild",
|
||||
"source": "Quelle",
|
||||
"Select a target": "Wähle ein Ziel",
|
||||
"Select a face": "Wähle ein Gesicht",
|
||||
"Keep audio": "Audio beibehalten",
|
||||
"Face Enhancer": "Gesichtsverbesserung",
|
||||
"Many faces": "Mehrere Gesichter",
|
||||
"Show FPS": "FPS anzeigen",
|
||||
"Keep fps": "FPS beibehalten",
|
||||
"Keep frames": "Frames beibehalten",
|
||||
"Fix Blueish Cam": "Bläuliche Kamera korrigieren",
|
||||
"Mouth Mask": "Mundmaske",
|
||||
"Show Mouth Mask Box": "Mundmaskenrahmen anzeigen",
|
||||
"Start": "Starten",
|
||||
"Live": "Live",
|
||||
"Destroy": "Beenden",
|
||||
"Map faces": "Gesichter zuordnen",
|
||||
"Processing...": "Verarbeitung läuft...",
|
||||
"Processing succeed!": "Verarbeitung erfolgreich!",
|
||||
"Processing ignored!": "Verarbeitung ignoriert!",
|
||||
"Failed to start camera": "Kamera konnte nicht gestartet werden",
|
||||
"Please complete pop-up or close it.": "Bitte das Pop-up komplettieren oder schließen.",
|
||||
"Getting unique faces": "Einzigartige Gesichter erfassen",
|
||||
"Please select a source image first": "Bitte zuerst ein Quellbild auswählen",
|
||||
"No faces found in target": "Keine Gesichter im Zielbild gefunden",
|
||||
"Add": "Hinzufügen",
|
||||
"Clear": "Löschen",
|
||||
"Submit": "Absenden",
|
||||
"Select source image": "Quellbild auswählen",
|
||||
"Select target image": "Zielbild auswählen",
|
||||
"Please provide mapping!": "Bitte eine Zuordnung angeben!",
|
||||
"At least 1 source with target is required!": "Mindestens eine Quelle mit einem Ziel ist erforderlich!",
|
||||
"At least 1 source with target is required!": "Mindestens eine Quelle mit einem Ziel ist erforderlich!",
|
||||
"Face could not be detected in last upload!": "Im letzten Upload konnte kein Gesicht erkannt werden!",
|
||||
"Select Camera:": "Kamera auswählen:",
|
||||
"All mappings cleared!": "Alle Zuordnungen gelöscht!",
|
||||
"Mappings successfully submitted!": "Zuordnungen erfolgreich übermittelt!",
|
||||
"Source x Target Mapper is already open.": "Quell-zu-Ziel-Zuordnung ist bereits geöffnet."
|
||||
}
|
||||
@@ -1,46 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "Mapeador de fuente x destino",
|
||||
"select a source image": "Seleccionar imagen fuente",
|
||||
"Preview": "Vista previa",
|
||||
"select a target image or video": "elegir un video o una imagen fuente",
|
||||
"save image output file": "guardar imagen final",
|
||||
"save video output file": "guardar video final",
|
||||
"select a target image": "elegir una imagen objetiva",
|
||||
"source": "fuente",
|
||||
"Select a target": "Elegir un destino",
|
||||
"Select a face": "Elegir una cara",
|
||||
"Keep audio": "Mantener audio original",
|
||||
"Face Enhancer": "Potenciador de caras",
|
||||
"Many faces": "Varias caras",
|
||||
"Show FPS": "Mostrar fps",
|
||||
"Keep fps": "Mantener fps",
|
||||
"Keep frames": "Mantener frames",
|
||||
"Fix Blueish Cam": "Corregir tono azul de video",
|
||||
"Mouth Mask": "Máscara de boca",
|
||||
"Show Mouth Mask Box": "Mostrar área de la máscara de boca",
|
||||
"Start": "Iniciar",
|
||||
"Live": "En vivo",
|
||||
"Destroy": "Borrar",
|
||||
"Map faces": "Mapear caras",
|
||||
"Processing...": "Procesando...",
|
||||
"Processing succeed!": "¡Proceso terminado con éxito!",
|
||||
"Processing ignored!": "¡Procesamiento omitido!",
|
||||
"Failed to start camera": "No se pudo iniciar la cámara",
|
||||
"Please complete pop-up or close it.": "Complete o cierre el pop-up",
|
||||
"Getting unique faces": "Buscando caras únicas",
|
||||
"Please select a source image first": "Primero, seleccione una imagen fuente",
|
||||
"No faces found in target": "No se encontró una cara en el destino",
|
||||
"Add": "Agregar",
|
||||
"Clear": "Limpiar",
|
||||
"Submit": "Enviar",
|
||||
"Select source image": "Seleccionar imagen fuente",
|
||||
"Select target image": "Seleccionar imagen destino",
|
||||
"Please provide mapping!": "Por favor, proporcione un mapeo",
|
||||
"At least 1 source with target is required!": "Se requiere al menos una fuente con un destino.",
|
||||
"At least 1 source with target is required!": "Se requiere al menos una fuente con un destino.",
|
||||
"Face could not be detected in last upload!": "¡No se pudo encontrar una cara en el último video o imagen!",
|
||||
"Select Camera:": "Elegir cámara:",
|
||||
"All mappings cleared!": "¡Todos los mapeos fueron borrados!",
|
||||
"Mappings successfully submitted!": "Mapeos enviados con éxito!",
|
||||
"Source x Target Mapper is already open.": "El mapeador de fuente x destino ya está abierto."
|
||||
}
|
||||
@@ -1,46 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "Source x Target Kartoitin",
|
||||
"select an source image": "Valitse lähde kuva",
|
||||
"Preview": "Esikatsele",
|
||||
"select an target image or video": "Valitse kohde kuva tai video",
|
||||
"save image output file": "tallenna kuva",
|
||||
"save video output file": "tallenna video",
|
||||
"select an target image": "Valitse kohde kuva",
|
||||
"source": "lähde",
|
||||
"Select a target": "Valitse kohde",
|
||||
"Select a face": "Valitse kasvot",
|
||||
"Keep audio": "Säilytä ääni",
|
||||
"Face Enhancer": "Kasvojen Parantaja",
|
||||
"Many faces": "Useampia kasvoja",
|
||||
"Show FPS": "Näytä FPS",
|
||||
"Keep fps": "Säilytä FPS",
|
||||
"Keep frames": "Säilytä ruudut",
|
||||
"Fix Blueish Cam": "Korjaa Sinertävä Kamera",
|
||||
"Mouth Mask": "Suu Maski",
|
||||
"Show Mouth Mask Box": "Näytä Suu Maski Laatiko",
|
||||
"Start": "Aloita",
|
||||
"Live": "Live",
|
||||
"Destroy": "Tuhoa",
|
||||
"Map faces": "Kartoita kasvot",
|
||||
"Processing...": "Prosessoi...",
|
||||
"Processing succeed!": "Prosessointi onnistui!",
|
||||
"Processing ignored!": "Prosessointi lopetettu!",
|
||||
"Failed to start camera": "Kameran käynnistäminen epäonnistui",
|
||||
"Please complete pop-up or close it.": "Viimeistele tai sulje ponnahdusikkuna",
|
||||
"Getting unique faces": "Hankitaan uniikkeja kasvoja",
|
||||
"Please select a source image first": "Valitse ensin lähde kuva",
|
||||
"No faces found in target": "Kasvoja ei löydetty kohteessa",
|
||||
"Add": "Lisää",
|
||||
"Clear": "Tyhjennä",
|
||||
"Submit": "Lähetä",
|
||||
"Select source image": "Valitse lähde kuva",
|
||||
"Select target image": "Valitse kohde kuva",
|
||||
"Please provide mapping!": "Tarjoa kartoitus!",
|
||||
"Atleast 1 source with target is required!": "Vähintään 1 lähde kohteen kanssa on vaadittu!",
|
||||
"At least 1 source with target is required!": "Vähintään 1 lähde kohteen kanssa on vaadittu!",
|
||||
"Face could not be detected in last upload!": "Kasvoja ei voitu tunnistaa edellisessä latauksessa!",
|
||||
"Select Camera:": "Valitse Kamera:",
|
||||
"All mappings cleared!": "Kaikki kartoitukset tyhjennetty!",
|
||||
"Mappings successfully submitted!": "Kartoitukset lähetety onnistuneesti!",
|
||||
"Source x Target Mapper is already open.": "Lähde x Kohde Kartoittaja on jo auki."
|
||||
}
|
||||
@@ -1,45 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "ប្រភប x បន្ថែម Mapper",
|
||||
"select a source image": "ជ្រើសរើសប្រភពរូបភាព",
|
||||
"Preview": "បង្ហាញ",
|
||||
"select a target image or video": "ជ្រើសរើសគោលដៅរូបភាពឬវីដេអូ",
|
||||
"save image output file": "រក្សាទុកលទ្ធផលឯកសាររូបភាព",
|
||||
"save video output file": "រក្សាទុកលទ្ធផលឯកសារវីដេអូ",
|
||||
"select a target image": "ជ្រើសរើសគោលដៅរូបភាព",
|
||||
"source": "ប្រភព",
|
||||
"Select a target": "ជ្រើសរើសគោលដៅ",
|
||||
"Select a face": "ជ្រើសរើសមុខ",
|
||||
"Keep audio": "រម្លងសម្លេង",
|
||||
"Face Enhancer": "ឧបករណ៍ពង្រឹងមុខ",
|
||||
"Many faces": "ទម្រង់មុខច្រើន",
|
||||
"Show FPS": "បង្ហាញ FPS",
|
||||
"Keep fps": "រម្លង fps",
|
||||
"Keep frames": "រម្លងទម្រង់",
|
||||
"Fix Blueish Cam": "ជួសជុល Cam Blueish",
|
||||
"Mouth Mask": "របាំងមាត់",
|
||||
"Show Mouth Mask Box": "បង្ហាញប្រអប់របាំងមាត់",
|
||||
"Start": "ចាប់ផ្ដើម",
|
||||
"Live": "ផ្សាយផ្ទាល់",
|
||||
"Destroy": "លុប",
|
||||
"Map faces": "ផែនទីមុខ",
|
||||
"Processing...": "កំពុងដំណើរការ...",
|
||||
"Processing succeed!": "ការដំណើរការទទួលបានជោគជ័យ!",
|
||||
"Processing ignored!": "ការដំណើរការមិនទទួលបានជោគជ័យ!",
|
||||
"Failed to start camera": "បរាជ័យដើម្បីចាប់ផ្ដើមបើកកាមេរ៉ា",
|
||||
"Please complete pop-up or close it.": "សូមបញ្ចប់ផ្ទាំងផុស ឬបិទវា.",
|
||||
"Getting unique faces": "ការចាប់ផ្ដើមទម្រង់មុខប្លែក",
|
||||
"Please select a source image first": "សូមជ្រើសរើសប្រភពរូបភាពដំបូង",
|
||||
"No faces found in target": "រកអត់ឃើញមុខនៅក្នុងគោលដៅ",
|
||||
"Add": "បន្ថែម",
|
||||
"Clear": "សម្អាត",
|
||||
"Submit": "បញ្ចូន",
|
||||
"Select source image": "ជ្រើសរើសប្រភពរូបភាព",
|
||||
"Select target image": "ជ្រើសរើសគោលដៅរូបភាព",
|
||||
"Please provide mapping!": "សូមផ្ដល់នៅផែនទី",
|
||||
"At least 1 source with target is required!": "ត្រូវការប្រភពយ៉ាងហោចណាស់ ១ ដែលមានគោលដៅ!",
|
||||
"Face could not be detected in last upload!": "មុខមិនអាចភ្ជាប់នៅក្នុងការបង្ហេាះចុងក្រោយ!",
|
||||
"Select Camera:": "ជ្រើសរើសកាមេរ៉ា",
|
||||
"All mappings cleared!": "ផែនទីទាំងអស់ត្រូវបានសម្អាត!",
|
||||
"Mappings successfully submitted!": "ផែនទីត្រូវបានបញ្ជូនជោគជ័យ!",
|
||||
"Source x Target Mapper is already open.": "ប្រភព x Target Mapper បានបើករួចហើយ។"
|
||||
}
|
||||
@@ -1,45 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "소스 x 타겟 매퍼",
|
||||
"select a source image": "소스 이미지 선택",
|
||||
"Preview": "미리보기",
|
||||
"select a target image or video": "타겟 이미지 또는 영상 선택",
|
||||
"save image output file": "이미지 출력 파일 저장",
|
||||
"save video output file": "영상 출력 파일 저장",
|
||||
"select a target image": "타겟 이미지 선택",
|
||||
"source": "소스",
|
||||
"Select a target": "타겟 선택",
|
||||
"Select a face": "얼굴 선택",
|
||||
"Keep audio": "오디오 유지",
|
||||
"Face Enhancer": "얼굴 향상",
|
||||
"Many faces": "여러 얼굴",
|
||||
"Show FPS": "FPS 표시",
|
||||
"Keep fps": "FPS 유지",
|
||||
"Keep frames": "프레임 유지",
|
||||
"Fix Blueish Cam": "푸른빛 카메라 보정",
|
||||
"Mouth Mask": "입 마스크",
|
||||
"Show Mouth Mask Box": "입 마스크 박스 표시",
|
||||
"Start": "시작",
|
||||
"Live": "라이브",
|
||||
"Destroy": "종료",
|
||||
"Map faces": "얼굴 매핑",
|
||||
"Processing...": "처리 중...",
|
||||
"Processing succeed!": "처리 성공!",
|
||||
"Processing ignored!": "처리 무시됨!",
|
||||
"Failed to start camera": "카메라 시작 실패",
|
||||
"Please complete pop-up or close it.": "팝업을 완료하거나 닫아주세요.",
|
||||
"Getting unique faces": "고유 얼굴 가져오는 중",
|
||||
"Please select a source image first": "먼저 소스 이미지를 선택해주세요",
|
||||
"No faces found in target": "타겟에서 얼굴을 찾을 수 없음",
|
||||
"Add": "추가",
|
||||
"Clear": "지우기",
|
||||
"Submit": "제출",
|
||||
"Select source image": "소스 이미지 선택",
|
||||
"Select target image": "타겟 이미지 선택",
|
||||
"Please provide mapping!": "매핑을 입력해주세요!",
|
||||
"At least 1 source with target is required!": "최소 하나의 소스와 타겟이 필요합니다!",
|
||||
"Face could not be detected in last upload!": "최근 업로드에서 얼굴을 감지할 수 없습니다!",
|
||||
"Select Camera:": "카메라 선택:",
|
||||
"All mappings cleared!": "모든 매핑이 삭제되었습니다!",
|
||||
"Mappings successfully submitted!": "매핑이 성공적으로 제출되었습니다!",
|
||||
"Source x Target Mapper is already open.": "소스 x 타겟 매퍼가 이미 열려 있습니다."
|
||||
}
|
||||
@@ -1,46 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "Mapeador de Origem x Destino",
|
||||
"select an source image": "Escolha uma imagem de origem",
|
||||
"Preview": "Prévia",
|
||||
"select an target image or video": "Escolha uma imagem ou vídeo de destino",
|
||||
"save image output file": "Salvar imagem final",
|
||||
"save video output file": "Salvar vídeo final",
|
||||
"select an target image": "Escolha uma imagem de destino",
|
||||
"source": "Origem",
|
||||
"Select a target": "Escolha o destino",
|
||||
"Select a face": "Escolha um rosto",
|
||||
"Keep audio": "Manter o áudio original",
|
||||
"Face Enhancer": "Melhorar rosto",
|
||||
"Many faces": "Vários rostos",
|
||||
"Show FPS": "Mostrar FPS",
|
||||
"Keep fps": "Manter FPS",
|
||||
"Keep frames": "Manter frames",
|
||||
"Fix Blueish Cam": "Corrigir tom azulado da câmera",
|
||||
"Mouth Mask": "Máscara da boca",
|
||||
"Show Mouth Mask Box": "Mostrar área da máscara da boca",
|
||||
"Start": "Começar",
|
||||
"Live": "Ao vivo",
|
||||
"Destroy": "Destruir",
|
||||
"Map faces": "Mapear rostos",
|
||||
"Processing...": "Processando...",
|
||||
"Processing succeed!": "Tudo certo!",
|
||||
"Processing ignored!": "Processamento ignorado!",
|
||||
"Failed to start camera": "Não foi possível iniciar a câmera",
|
||||
"Please complete pop-up or close it.": "Finalize ou feche o pop-up",
|
||||
"Getting unique faces": "Buscando rostos diferentes",
|
||||
"Please select a source image first": "Selecione primeiro uma imagem de origem",
|
||||
"No faces found in target": "Nenhum rosto encontrado na imagem de destino",
|
||||
"Add": "Adicionar",
|
||||
"Clear": "Limpar",
|
||||
"Submit": "Enviar",
|
||||
"Select source image": "Escolha a imagem de origem",
|
||||
"Select target image": "Escolha a imagem de destino",
|
||||
"Please provide mapping!": "Você precisa realizar o mapeamento!",
|
||||
"Atleast 1 source with target is required!": "É necessária pelo menos uma origem com um destino!",
|
||||
"At least 1 source with target is required!": "É necessária pelo menos uma origem com um destino!",
|
||||
"Face could not be detected in last upload!": "Não conseguimos detectar o rosto na última imagem!",
|
||||
"Select Camera:": "Escolher câmera:",
|
||||
"All mappings cleared!": "Todos os mapeamentos foram removidos!",
|
||||
"Mappings successfully submitted!": "Mapeamentos enviados com sucesso!",
|
||||
"Source x Target Mapper is already open.": "O Mapeador de Origem x Destino já está aberto."
|
||||
}
|
||||
@@ -1,45 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "Сопоставитель Источник x Цель",
|
||||
"select a source image": "выберите исходное изображение",
|
||||
"Preview": "Предпросмотр",
|
||||
"select a target image or video": "выберите целевое изображение или видео",
|
||||
"save image output file": "сохранить выходной файл изображения",
|
||||
"save video output file": "сохранить выходной файл видео",
|
||||
"select a target image": "выберите целевое изображение",
|
||||
"source": "источник",
|
||||
"Select a target": "Выберите целевое изображение",
|
||||
"Select a face": "Выберите лицо",
|
||||
"Keep audio": "Сохранить аудио",
|
||||
"Face Enhancer": "Улучшение лица",
|
||||
"Many faces": "Несколько лиц",
|
||||
"Show FPS": "Показать FPS",
|
||||
"Keep fps": "Сохранить FPS",
|
||||
"Keep frames": "Сохранить кадры",
|
||||
"Fix Blueish Cam": "Исправить синеву камеры",
|
||||
"Mouth Mask": "Маска рта",
|
||||
"Show Mouth Mask Box": "Показать рамку маски рта",
|
||||
"Start": "Старт",
|
||||
"Live": "В реальном времени",
|
||||
"Destroy": "Остановить",
|
||||
"Map faces": "Сопоставить лица",
|
||||
"Processing...": "Обработка...",
|
||||
"Processing succeed!": "Обработка успешна!",
|
||||
"Processing ignored!": "Обработка проигнорирована!",
|
||||
"Failed to start camera": "Не удалось запустить камеру",
|
||||
"Please complete pop-up or close it.": "Пожалуйста, заполните всплывающее окно или закройте его.",
|
||||
"Getting unique faces": "Получение уникальных лиц",
|
||||
"Please select a source image first": "Сначала выберите исходное изображение, пожалуйста",
|
||||
"No faces found in target": "В целевом изображении не найдено лиц",
|
||||
"Add": "Добавить",
|
||||
"Clear": "Очистить",
|
||||
"Submit": "Отправить",
|
||||
"Select source image": "Выбрать исходное изображение",
|
||||
"Select target image": "Выбрать целевое изображение",
|
||||
"Please provide mapping!": "Пожалуйста, укажите сопоставление!",
|
||||
"At least 1 source with target is required!": "Требуется хотя бы 1 источник с целью!",
|
||||
"Face could not be detected in last upload!": "Лицо не обнаружено в последнем загруженном изображении!",
|
||||
"Select Camera:": "Выберите камеру:",
|
||||
"All mappings cleared!": "Все сопоставления очищены!",
|
||||
"Mappings successfully submitted!": "Сопоставления успешно отправлены!",
|
||||
"Source x Target Mapper is already open.": "Сопоставитель Источник-Цель уже открыт."
|
||||
}
|
||||
@@ -1,45 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "ตัวจับคู่ต้นทาง x ปลายทาง",
|
||||
"select a source image": "เลือกรูปภาพต้นฉบับ",
|
||||
"Preview": "ตัวอย่าง",
|
||||
"select a target image or video": "เลือกรูปภาพหรือวิดีโอเป้าหมาย",
|
||||
"save image output file": "บันทึกไฟล์รูปภาพ",
|
||||
"save video output file": "บันทึกไฟล์วิดีโอ",
|
||||
"select a target image": "เลือกรูปภาพเป้าหมาย",
|
||||
"source": "ต้นฉบับ",
|
||||
"Select a target": "เลือกเป้าหมาย",
|
||||
"Select a face": "เลือกใบหน้า",
|
||||
"Keep audio": "เก็บเสียง",
|
||||
"Face Enhancer": "ปรับปรุงใบหน้า",
|
||||
"Many faces": "หลายใบหน้า",
|
||||
"Show FPS": "แสดง FPS",
|
||||
"Keep fps": "คงค่า FPS",
|
||||
"Keep frames": "คงค่าเฟรม",
|
||||
"Fix Blueish Cam": "แก้ไขภาพอมฟ้าจากกล้อง",
|
||||
"Mouth Mask": "มาสก์ปาก",
|
||||
"Show Mouth Mask Box": "แสดงกรอบมาสก์ปาก",
|
||||
"Start": "เริ่ม",
|
||||
"Live": "สด",
|
||||
"Destroy": "หยุด",
|
||||
"Map faces": "จับคู่ใบหน้า",
|
||||
"Processing...": "กำลังประมวลผล...",
|
||||
"Processing succeed!": "ประมวลผลสำเร็จแล้ว!",
|
||||
"Processing ignored!": "การประมวลผลถูกละเว้น",
|
||||
"Failed to start camera": "ไม่สามารถเริ่มกล้องได้",
|
||||
"Please complete pop-up or close it.": "โปรดดำเนินการในป๊อปอัปให้เสร็จสิ้น หรือปิด",
|
||||
"Getting unique faces": "กำลังค้นหาใบหน้าที่ไม่ซ้ำกัน",
|
||||
"Please select a source image first": "โปรดเลือกภาพต้นฉบับก่อน",
|
||||
"No faces found in target": "ไม่พบใบหน้าในภาพเป้าหมาย",
|
||||
"Add": "เพิ่ม",
|
||||
"Clear": "ล้าง",
|
||||
"Submit": "ส่ง",
|
||||
"Select source image": "เลือกภาพต้นฉบับ",
|
||||
"Select target image": "เลือกภาพเป้าหมาย",
|
||||
"Please provide mapping!": "โปรดระบุการจับคู่!",
|
||||
"At least 1 source with target is required!": "ต้องมีการจับคู่ต้นฉบับกับเป้าหมายอย่างน้อย 1 คู่!",
|
||||
"Face could not be detected in last upload!": "ไม่สามารถตรวจพบใบหน้าในไฟล์อัปโหลดล่าสุด!",
|
||||
"Select Camera:": "เลือกกล้อง:",
|
||||
"All mappings cleared!": "ล้างการจับคู่ทั้งหมดแล้ว!",
|
||||
"Mappings successfully submitted!": "ส่งการจับคู่สำเร็จแล้ว!",
|
||||
"Source x Target Mapper is already open.": "ตัวจับคู่ต้นทาง x ปลายทาง เปิดอยู่แล้ว"
|
||||
}
|
||||
+5
-5
@@ -1,11 +1,11 @@
|
||||
{
|
||||
"Source x Target Mapper": "Source x Target Mapper",
|
||||
"select a source image": "选择一个源图像",
|
||||
"select an source image": "选择一个源图像",
|
||||
"Preview": "预览",
|
||||
"select a target image or video": "选择一个目标图像或视频",
|
||||
"select an target image or video": "选择一个目标图像或视频",
|
||||
"save image output file": "保存图像输出文件",
|
||||
"save video output file": "保存视频输出文件",
|
||||
"select a target image": "选择一个目标图像",
|
||||
"select an target image": "选择一个目标图像",
|
||||
"source": "源",
|
||||
"Select a target": "选择一个目标",
|
||||
"Select a face": "选择一张脸",
|
||||
@@ -36,11 +36,11 @@
|
||||
"Select source image": "请选取源图像",
|
||||
"Select target image": "请选取目标图像",
|
||||
"Please provide mapping!": "请提供映射",
|
||||
"At least 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
|
||||
"Atleast 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
|
||||
"At least 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
|
||||
"Face could not be detected in last upload!": "最近上传的图像中没有检测到人脸!",
|
||||
"Select Camera:": "选择摄像头",
|
||||
"All mappings cleared!": "所有映射均已清除!",
|
||||
"Mappings successfully submitted!": "成功提交映射！",
|
||||
"Source x Target Mapper is already open.": "源 x 目标映射器已打开。"
|
||||
}
|
||||
}
|
||||
Binary file not shown.
|
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Binary file not shown.
|
After Width: | Height: | Size: 9.0 KiB |
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|
Before Width: | Height: | Size: 8.2 MiB |
Binary file not shown.
|
Before Width: | Height: | Size: 5.0 MiB |
@@ -1,18 +0,0 @@
|
||||
import os
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
# Utility function to support unicode characters in file paths for reading
|
||||
def imread_unicode(path, flags=cv2.IMREAD_COLOR):
|
||||
return cv2.imdecode(np.fromfile(path, dtype=np.uint8), flags)
|
||||
|
||||
# Utility function to support unicode characters in file paths for writing
|
||||
def imwrite_unicode(path, img, params=None):
|
||||
root, ext = os.path.splitext(path)
|
||||
if not ext:
|
||||
ext = ".png"
|
||||
result, encoded_img = cv2.imencode(ext, img, params if params else [])
|
||||
result, encoded_img = cv2.imencode(f".{ext}", img, params if params is not None else [])
|
||||
encoded_img.tofile(path)
|
||||
return True
|
||||
return False
|
||||
@@ -1,7 +0,0 @@
|
||||
from typing import Any
|
||||
|
||||
from insightface.app.common import Face
|
||||
import numpy
|
||||
|
||||
Face = Face
|
||||
Frame = numpy.ndarray[Any, Any]
|
||||
+12
-12
@@ -39,13 +39,13 @@ def get_many_faces(frame: Frame) -> Any:
|
||||
return None
|
||||
|
||||
def has_valid_map() -> bool:
|
||||
for map in modules.globals.source_target_map:
|
||||
for map in modules.globals.souce_target_map:
|
||||
if "source" in map and "target" in map:
|
||||
return True
|
||||
return False
|
||||
|
||||
def default_source_face() -> Any:
|
||||
for map in modules.globals.source_target_map:
|
||||
for map in modules.globals.souce_target_map:
|
||||
if "source" in map:
|
||||
return map['source']['face']
|
||||
return None
|
||||
@@ -53,7 +53,7 @@ def default_source_face() -> Any:
|
||||
def simplify_maps() -> Any:
|
||||
centroids = []
|
||||
faces = []
|
||||
for map in modules.globals.source_target_map:
|
||||
for map in modules.globals.souce_target_map:
|
||||
if "source" in map and "target" in map:
|
||||
centroids.append(map['target']['face'].normed_embedding)
|
||||
faces.append(map['source']['face'])
|
||||
@@ -64,10 +64,10 @@ def simplify_maps() -> Any:
|
||||
def add_blank_map() -> Any:
|
||||
try:
|
||||
max_id = -1
|
||||
if len(modules.globals.source_target_map) > 0:
|
||||
max_id = max(modules.globals.source_target_map, key=lambda x: x['id'])['id']
|
||||
if len(modules.globals.souce_target_map) > 0:
|
||||
max_id = max(modules.globals.souce_target_map, key=lambda x: x['id'])['id']
|
||||
|
||||
modules.globals.source_target_map.append({
|
||||
modules.globals.souce_target_map.append({
|
||||
'id' : max_id + 1
|
||||
})
|
||||
except ValueError:
|
||||
@@ -75,14 +75,14 @@ def add_blank_map() -> Any:
|
||||
|
||||
def get_unique_faces_from_target_image() -> Any:
|
||||
try:
|
||||
modules.globals.source_target_map = []
|
||||
modules.globals.souce_target_map = []
|
||||
target_frame = cv2.imread(modules.globals.target_path)
|
||||
many_faces = get_many_faces(target_frame)
|
||||
i = 0
|
||||
|
||||
for face in many_faces:
|
||||
x_min, y_min, x_max, y_max = face['bbox']
|
||||
modules.globals.source_target_map.append({
|
||||
modules.globals.souce_target_map.append({
|
||||
'id' : i,
|
||||
'target' : {
|
||||
'cv2' : target_frame[int(y_min):int(y_max), int(x_min):int(x_max)],
|
||||
@@ -96,7 +96,7 @@ def get_unique_faces_from_target_image() -> Any:
|
||||
|
||||
def get_unique_faces_from_target_video() -> Any:
|
||||
try:
|
||||
modules.globals.source_target_map = []
|
||||
modules.globals.souce_target_map = []
|
||||
frame_face_embeddings = []
|
||||
face_embeddings = []
|
||||
|
||||
@@ -127,7 +127,7 @@ def get_unique_faces_from_target_video() -> Any:
|
||||
face['target_centroid'] = closest_centroid_index
|
||||
|
||||
for i in range(len(centroids)):
|
||||
modules.globals.source_target_map.append({
|
||||
modules.globals.souce_target_map.append({
|
||||
'id' : i
|
||||
})
|
||||
|
||||
@@ -135,7 +135,7 @@ def get_unique_faces_from_target_video() -> Any:
|
||||
for frame in tqdm(frame_face_embeddings, desc=f"Mapping frame embeddings to centroids-{i}"):
|
||||
temp.append({'frame': frame['frame'], 'faces': [face for face in frame['faces'] if face['target_centroid'] == i], 'location': frame['location']})
|
||||
|
||||
modules.globals.source_target_map[i]['target_faces_in_frame'] = temp
|
||||
modules.globals.souce_target_map[i]['target_faces_in_frame'] = temp
|
||||
|
||||
# dump_faces(centroids, frame_face_embeddings)
|
||||
default_target_face()
|
||||
@@ -144,7 +144,7 @@ def get_unique_faces_from_target_video() -> Any:
|
||||
|
||||
|
||||
def default_target_face():
|
||||
for map in modules.globals.source_target_map:
|
||||
for map in modules.globals.souce_target_map:
|
||||
best_face = None
|
||||
best_frame = None
|
||||
for frame in map['target_faces_in_frame']:
|
||||
|
||||
+31
-57
@@ -1,5 +1,3 @@
|
||||
# --- START OF FILE globals.py ---
|
||||
|
||||
import os
|
||||
from typing import List, Dict, Any
|
||||
|
||||
@@ -11,61 +9,37 @@ file_types = [
|
||||
("Video", ("*.mp4", "*.mkv")),
|
||||
]
|
||||
|
||||
# Face Mapping Data
|
||||
souce_target_map: List[Dict[str, Any]] = [] # Stores detailed map for image/video processing
|
||||
simple_map: Dict[str, Any] = {} # Stores simplified map (embeddings/faces) for live/simple mode
|
||||
souce_target_map = []
|
||||
simple_map = {}
|
||||
|
||||
# Paths
|
||||
source_path: str | None = None
|
||||
target_path: str | None = None
|
||||
output_path: str | None = None
|
||||
|
||||
# Processing Options
|
||||
source_path = None
|
||||
target_path = None
|
||||
output_path = None
|
||||
frame_processors: List[str] = []
|
||||
keep_fps: bool = True
|
||||
keep_audio: bool = True
|
||||
keep_frames: bool = False
|
||||
many_faces: bool = False # Process all detected faces with default source
|
||||
map_faces: bool = False # Use souce_target_map or simple_map for specific swaps
|
||||
color_correction: bool = False # Enable color correction (implementation specific)
|
||||
nsfw_filter: bool = False
|
||||
|
||||
# Video Output Options
|
||||
video_encoder: str | None = None
|
||||
video_quality: int | None = None # Typically a CRF value or bitrate
|
||||
|
||||
# Live Mode Options
|
||||
live_mirror: bool = False
|
||||
live_resizable: bool = True
|
||||
camera_input_combobox: Any | None = None # Placeholder for UI element if needed
|
||||
webcam_preview_running: bool = False
|
||||
show_fps: bool = False
|
||||
|
||||
# System Configuration
|
||||
max_memory: int | None = None # Memory limit in GB? (Needs clarification)
|
||||
execution_providers: List[str] = [] # e.g., ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
execution_threads: int | None = None # Number of threads for CPU execution
|
||||
headless: bool | None = None # Run without UI?
|
||||
log_level: str = "error" # Logging level (e.g., 'debug', 'info', 'warning', 'error')
|
||||
|
||||
# Face Processor UI Toggles (Example)
|
||||
keep_fps = True
|
||||
keep_audio = True
|
||||
keep_frames = False
|
||||
many_faces = False
|
||||
map_faces = False
|
||||
color_correction = False # New global variable for color correction toggle
|
||||
nsfw_filter = False
|
||||
video_encoder = None
|
||||
video_quality = None
|
||||
live_mirror = False
|
||||
live_resizable = True
|
||||
max_memory = None
|
||||
execution_providers: List[str] = []
|
||||
execution_threads = None
|
||||
headless = None
|
||||
log_level = "error"
|
||||
fp_ui: Dict[str, bool] = {"face_enhancer": False}
|
||||
|
||||
# Face Swapper Specific Options
|
||||
face_swapper_enabled: bool = True # General toggle for the swapper processor
|
||||
opacity: float = 1.0 # Blend factor for the swapped face (0.0-1.0)
|
||||
sharpness: float = 0.0 # Sharpness enhancement for swapped face (0.0-1.0+)
|
||||
|
||||
# Mouth Mask Options
|
||||
mouth_mask: bool = False # Enable mouth area masking/pasting
|
||||
show_mouth_mask_box: bool = False # Visualize the mouth mask area (for debugging)
|
||||
mask_feather_ratio: int = 12 # Denominator for feathering calculation (higher = smaller feather)
|
||||
mask_down_size: float = 0.1 # Expansion factor for lower lip mask (relative)
|
||||
mask_size: float = 1.0 # Expansion factor for upper lip mask (relative)
|
||||
|
||||
# --- START: Added for Frame Interpolation ---
|
||||
enable_interpolation: bool = True # Toggle temporal smoothing
|
||||
interpolation_weight: float = 0 # Blend weight for current frame (0.0-1.0). Lower=smoother.
|
||||
# --- END: Added for Frame Interpolation ---
|
||||
|
||||
# --- END OF FILE globals.py ---
|
||||
camera_input_combobox = None
|
||||
webcam_preview_running = False
|
||||
show_fps = False
|
||||
mouth_mask = False
|
||||
show_mouth_mask_box = False
|
||||
mask_feather_ratio = 8
|
||||
mask_down_size = 0.50
|
||||
mask_size = 1
|
||||
opacity = 1.0
|
||||
face_swapper_enabled = True
|
||||
|
||||
+1
-1
@@ -1,3 +1,3 @@
|
||||
name = 'Deep-Live-Cam'
|
||||
version = '2.0c'
|
||||
version = '1.8'
|
||||
edition = 'GitHub Edition'
|
||||
|
||||
@@ -42,29 +42,18 @@ def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType
|
||||
|
||||
def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
|
||||
global FRAME_PROCESSORS_MODULES
|
||||
current_processor_names = [proc.__name__.split('.')[-1] for proc in FRAME_PROCESSORS_MODULES]
|
||||
|
||||
for frame_processor, state in modules.globals.fp_ui.items():
|
||||
if state == True and frame_processor not in current_processor_names:
|
||||
if state == True and frame_processor not in frame_processors:
|
||||
frame_processor_module = load_frame_processor_module(frame_processor)
|
||||
FRAME_PROCESSORS_MODULES.append(frame_processor_module)
|
||||
modules.globals.frame_processors.append(frame_processor)
|
||||
if state == False:
|
||||
try:
|
||||
frame_processor_module = load_frame_processor_module(frame_processor)
|
||||
FRAME_PROCESSORS_MODULES.append(frame_processor_module)
|
||||
if frame_processor not in modules.globals.frame_processors:
|
||||
modules.globals.frame_processors.append(frame_processor)
|
||||
except SystemExit:
|
||||
print(f"Warning: Failed to load frame processor {frame_processor} requested by UI state.")
|
||||
except Exception as e:
|
||||
print(f"Warning: Error loading frame processor {frame_processor} requested by UI state: {e}")
|
||||
|
||||
elif state == False and frame_processor in current_processor_names:
|
||||
try:
|
||||
module_to_remove = next((mod for mod in FRAME_PROCESSORS_MODULES if mod.__name__.endswith(f'.{frame_processor}')), None)
|
||||
if module_to_remove:
|
||||
FRAME_PROCESSORS_MODULES.remove(module_to_remove)
|
||||
if frame_processor in modules.globals.frame_processors:
|
||||
modules.globals.frame_processors.remove(frame_processor)
|
||||
except Exception as e:
|
||||
print(f"Warning: Error removing frame processor {frame_processor}: {e}")
|
||||
FRAME_PROCESSORS_MODULES.remove(frame_processor_module)
|
||||
modules.globals.frame_processors.remove(frame_processor)
|
||||
except:
|
||||
pass
|
||||
|
||||
def multi_process_frame(source_path: str, temp_frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None], progress: Any = None) -> None:
|
||||
with ThreadPoolExecutor(max_workers=modules.globals.execution_threads) as executor:
|
||||
|
||||
@@ -1,18 +1,16 @@
|
||||
# --- START OF FILE face_enhancer.py ---
|
||||
|
||||
from typing import Any, List
|
||||
import cv2
|
||||
import threading
|
||||
import gfpgan
|
||||
import os
|
||||
import platform
|
||||
import torch # Make sure torch is imported
|
||||
|
||||
import modules.globals
|
||||
import modules.processors.frame.core
|
||||
from modules.core import update_status
|
||||
from modules.face_analyser import get_one_face
|
||||
from modules.typing import Frame, Face
|
||||
import platform
|
||||
import torch
|
||||
from modules.utilities import (
|
||||
conditional_download,
|
||||
is_image,
|
||||
@@ -51,156 +49,61 @@ def pre_start() -> bool:
|
||||
|
||||
|
||||
def get_face_enhancer() -> Any:
|
||||
"""
|
||||
Initializes and returns the GFPGAN face enhancer instance,
|
||||
prioritizing CUDA, then MPS (Mac), then CPU.
|
||||
"""
|
||||
global FACE_ENHANCER
|
||||
|
||||
with THREAD_LOCK:
|
||||
if FACE_ENHANCER is None:
|
||||
model_path = os.path.join(models_dir, "GFPGANv1.4.pth")
|
||||
device = None
|
||||
try:
|
||||
# Priority 1: CUDA
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda")
|
||||
print(f"{NAME}: Using CUDA device.")
|
||||
# Priority 2: MPS (Mac Silicon)
|
||||
elif platform.system() == "Darwin" and torch.backends.mps.is_available():
|
||||
device = torch.device("mps")
|
||||
print(f"{NAME}: Using MPS device.")
|
||||
# Priority 3: CPU
|
||||
else:
|
||||
device = torch.device("cpu")
|
||||
print(f"{NAME}: Using CPU device.")
|
||||
|
||||
FACE_ENHANCER = gfpgan.GFPGANer(
|
||||
model_path=model_path,
|
||||
upscale=1, # upscale=1 means enhancement only, no resizing
|
||||
arch='clean',
|
||||
channel_multiplier=2,
|
||||
bg_upsampler=None,
|
||||
device=device
|
||||
)
|
||||
print(f"{NAME}: GFPGANer initialized successfully on {device}.")
|
||||
|
||||
except Exception as e:
|
||||
print(f"{NAME}: Error initializing GFPGANer: {e}")
|
||||
# Fallback to CPU if initialization with GPU fails for some reason
|
||||
if device is not None and device.type != 'cpu':
|
||||
print(f"{NAME}: Falling back to CPU due to error.")
|
||||
try:
|
||||
device = torch.device("cpu")
|
||||
FACE_ENHANCER = gfpgan.GFPGANer(
|
||||
model_path=model_path,
|
||||
upscale=1,
|
||||
arch='clean',
|
||||
channel_multiplier=2,
|
||||
bg_upsampler=None,
|
||||
device=device
|
||||
)
|
||||
print(f"{NAME}: GFPGANer initialized successfully on CPU after fallback.")
|
||||
except Exception as fallback_e:
|
||||
print(f"{NAME}: FATAL: Could not initialize GFPGANer even on CPU: {fallback_e}")
|
||||
FACE_ENHANCER = None # Ensure it's None if totally failed
|
||||
else:
|
||||
# If it failed even on the first CPU attempt or device was already CPU
|
||||
print(f"{NAME}: FATAL: Could not initialize GFPGANer on CPU: {e}")
|
||||
FACE_ENHANCER = None # Ensure it's None if totally failed
|
||||
|
||||
|
||||
# Check if enhancer is still None after attempting initialization
|
||||
if FACE_ENHANCER is None:
|
||||
raise RuntimeError(f"{NAME}: Failed to initialize GFPGANer. Check logs for errors.")
|
||||
|
||||
match platform.system():
|
||||
case "Darwin": # Mac OS
|
||||
if torch.backends.mps.is_available():
|
||||
mps_device = torch.device("mps")
|
||||
FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=mps_device) # type: ignore[attr-defined]
|
||||
else:
|
||||
FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1) # type: ignore[attr-defined]
|
||||
case _: # Other OS
|
||||
FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1) # type: ignore[attr-defined]
|
||||
|
||||
return FACE_ENHANCER
|
||||
|
||||
|
||||
def enhance_face(temp_frame: Frame) -> Frame:
|
||||
"""Enhances faces in a single frame using the global GFPGANer instance."""
|
||||
# Ensure enhancer is ready
|
||||
enhancer = get_face_enhancer()
|
||||
try:
|
||||
with THREAD_SEMAPHORE:
|
||||
# The enhance method returns: _, restored_faces, restored_img
|
||||
_, _, restored_img = enhancer.enhance(
|
||||
temp_frame,
|
||||
has_aligned=False, # Assume faces are not pre-aligned
|
||||
only_center_face=False, # Enhance all detected faces
|
||||
paste_back=True # Paste enhanced faces back onto the original image
|
||||
)
|
||||
# GFPGAN might return None if no face is detected or an error occurs
|
||||
if restored_img is None:
|
||||
# print(f"{NAME}: Warning: GFPGAN enhancement returned None. Returning original frame.")
|
||||
return temp_frame
|
||||
return restored_img
|
||||
except Exception as e:
|
||||
print(f"{NAME}: Error during face enhancement: {e}")
|
||||
# Return the original frame in case of error during enhancement
|
||||
return temp_frame
|
||||
with THREAD_SEMAPHORE:
|
||||
_, _, temp_frame = get_face_enhancer().enhance(temp_frame, paste_back=True)
|
||||
return temp_frame
|
||||
|
||||
|
||||
def process_frame(source_face: Face | None, temp_frame: Frame) -> Frame:
|
||||
"""Processes a frame: enhances face if detected."""
|
||||
# We don't strictly need source_face for enhancement only
|
||||
# Check if any face exists to potentially save processing time, though GFPGAN also does detection.
|
||||
# For simplicity and ensuring enhancement is attempted if possible, we can rely on enhance_face.
|
||||
# target_face = get_one_face(temp_frame) # This gets only ONE face
|
||||
# If you want to enhance ONLY if a face is detected by your *own* analyser first:
|
||||
# has_face = get_one_face(temp_frame) is not None # Or use get_many_faces
|
||||
# if has_face:
|
||||
# temp_frame = enhance_face(temp_frame)
|
||||
# else: # Enhance regardless, let GFPGAN handle detection
|
||||
temp_frame = enhance_face(temp_frame)
|
||||
def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
|
||||
target_face = get_one_face(temp_frame)
|
||||
if target_face:
|
||||
temp_frame = enhance_face(temp_frame)
|
||||
return temp_frame
|
||||
|
||||
|
||||
def process_frames(
|
||||
source_path: str | None, temp_frame_paths: List[str], progress: Any = None
|
||||
source_path: str, temp_frame_paths: List[str], progress: Any = None
|
||||
) -> None:
|
||||
"""Processes multiple frames from file paths."""
|
||||
for temp_frame_path in temp_frame_paths:
|
||||
if not os.path.exists(temp_frame_path):
|
||||
print(f"{NAME}: Warning: Frame path not found {temp_frame_path}, skipping.")
|
||||
if progress:
|
||||
progress.update(1)
|
||||
continue
|
||||
|
||||
temp_frame = cv2.imread(temp_frame_path)
|
||||
if temp_frame is None:
|
||||
print(f"{NAME}: Warning: Failed to read frame {temp_frame_path}, skipping.")
|
||||
if progress:
|
||||
progress.update(1)
|
||||
continue
|
||||
|
||||
result_frame = process_frame(None, temp_frame)
|
||||
cv2.imwrite(temp_frame_path, result_frame)
|
||||
result = process_frame(None, temp_frame)
|
||||
cv2.imwrite(temp_frame_path, result)
|
||||
if progress:
|
||||
progress.update(1)
|
||||
|
||||
|
||||
def process_image(source_path: str | None, target_path: str, output_path: str) -> None:
|
||||
"""Processes a single image file."""
|
||||
def process_image(source_path: str, target_path: str, output_path: str) -> None:
|
||||
target_frame = cv2.imread(target_path)
|
||||
if target_frame is None:
|
||||
print(f"{NAME}: Error: Failed to read target image {target_path}")
|
||||
return
|
||||
result_frame = process_frame(None, target_frame)
|
||||
cv2.imwrite(output_path, result_frame)
|
||||
print(f"{NAME}: Enhanced image saved to {output_path}")
|
||||
result = process_frame(None, target_frame)
|
||||
cv2.imwrite(output_path, result)
|
||||
|
||||
|
||||
def process_video(source_path: str | None, temp_frame_paths: List[str]) -> None:
|
||||
"""Processes video frames using the frame processor core."""
|
||||
# source_path might be optional depending on how process_video is called
|
||||
modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)
|
||||
def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
|
||||
modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
|
||||
|
||||
# Optional: Keep process_frame_v2 if it's used elsewhere, otherwise it's redundant
|
||||
# def process_frame_v2(temp_frame: Frame) -> Frame:
|
||||
# target_face = get_one_face(temp_frame)
|
||||
# if target_face:
|
||||
# temp_frame = enhance_face(temp_frame)
|
||||
# return temp_frame
|
||||
|
||||
# --- END OF FILE face_enhancer.py ---
|
||||
def process_frame_v2(temp_frame: Frame) -> Frame:
|
||||
target_face = get_one_face(temp_frame)
|
||||
if target_face:
|
||||
temp_frame = enhance_face(temp_frame)
|
||||
return temp_frame
|
||||
|
||||
@@ -1,609 +0,0 @@
|
||||
import cv2
|
||||
import numpy as np
|
||||
from modules.typing import Face, Frame
|
||||
import modules.globals
|
||||
|
||||
def apply_color_transfer(source, target):
|
||||
"""
|
||||
Apply color transfer from target to source image
|
||||
"""
|
||||
source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32")
|
||||
target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32")
|
||||
|
||||
source_mean, source_std = cv2.meanStdDev(source)
|
||||
target_mean, target_std = cv2.meanStdDev(target)
|
||||
|
||||
# Reshape mean and std to be broadcastable
|
||||
source_mean = source_mean.reshape(1, 1, 3)
|
||||
source_std = source_std.reshape(1, 1, 3)
|
||||
target_mean = target_mean.reshape(1, 1, 3)
|
||||
target_std = target_std.reshape(1, 1, 3)
|
||||
|
||||
# Perform the color transfer
|
||||
source = (source - source_mean) * (target_std / source_std) + target_mean
|
||||
|
||||
return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)
|
||||
|
||||
def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
|
||||
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
|
||||
landmarks = face.landmark_2d_106
|
||||
if landmarks is not None:
|
||||
# Convert landmarks to int32
|
||||
landmarks = landmarks.astype(np.int32)
|
||||
|
||||
# Extract facial features
|
||||
right_side_face = landmarks[0:16]
|
||||
left_side_face = landmarks[17:32]
|
||||
right_eye = landmarks[33:42]
|
||||
right_eye_brow = landmarks[43:51]
|
||||
left_eye = landmarks[87:96]
|
||||
left_eye_brow = landmarks[97:105]
|
||||
|
||||
# Calculate padding
|
||||
padding = int(
|
||||
np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05
|
||||
) # 5% of face width
|
||||
|
||||
# Create a slightly larger convex hull for padding
|
||||
hull = cv2.convexHull(face_outline)
|
||||
hull_padded = []
|
||||
for point in hull:
|
||||
x, y = point[0]
|
||||
center = np.mean(face_outline, axis=0)
|
||||
direction = np.array([x, y]) - center
|
||||
direction = direction / np.linalg.norm(direction)
|
||||
padded_point = np.array([x, y]) + direction * padding
|
||||
hull_padded.append(padded_point)
|
||||
|
||||
hull_padded = np.array(hull_padded, dtype=np.int32)
|
||||
|
||||
# Fill the padded convex hull
|
||||
cv2.fillConvexPoly(mask, hull_padded, 255)
|
||||
|
||||
# Smooth the mask edges
|
||||
mask = cv2.GaussianBlur(mask, (5, 5), 3)
|
||||
|
||||
return mask
|
||||
|
||||
def create_lower_mouth_mask(
|
||||
face: Face, frame: Frame
|
||||
) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
|
||||
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
|
||||
mouth_cutout = None
|
||||
landmarks = face.landmark_2d_106
|
||||
if landmarks is not None:
|
||||
# 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
|
||||
lower_lip_order = [
|
||||
65,
|
||||
66,
|
||||
62,
|
||||
70,
|
||||
69,
|
||||
18,
|
||||
19,
|
||||
20,
|
||||
21,
|
||||
22,
|
||||
23,
|
||||
24,
|
||||
0,
|
||||
8,
|
||||
7,
|
||||
6,
|
||||
5,
|
||||
4,
|
||||
3,
|
||||
2,
|
||||
65,
|
||||
]
|
||||
lower_lip_landmarks = landmarks[lower_lip_order].astype(
|
||||
np.float32
|
||||
) # Use float for precise calculations
|
||||
|
||||
# Calculate the center of the landmarks
|
||||
center = np.mean(lower_lip_landmarks, axis=0)
|
||||
|
||||
# Expand the landmarks outward using the mouth_mask_size
|
||||
expansion_factor = (
|
||||
1 + modules.globals.mask_down_size * modules.globals.mouth_mask_size
|
||||
) # Adjust expansion based on slider
|
||||
expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
|
||||
|
||||
# Extend the top lip part
|
||||
toplip_indices = [
|
||||
20,
|
||||
0,
|
||||
1,
|
||||
2,
|
||||
3,
|
||||
4,
|
||||
5,
|
||||
] # Indices for landmarks 2, 65, 66, 62, 70, 69, 18
|
||||
toplip_extension = (
|
||||
modules.globals.mask_size * modules.globals.mouth_mask_size * 0.5
|
||||
) # Adjust extension based on slider
|
||||
for idx in toplip_indices:
|
||||
direction = expanded_landmarks[idx] - center
|
||||
direction = direction / np.linalg.norm(direction)
|
||||
expanded_landmarks[idx] += direction * toplip_extension
|
||||
|
||||
# Extend the bottom part (chin area)
|
||||
chin_indices = [
|
||||
11,
|
||||
12,
|
||||
13,
|
||||
14,
|
||||
15,
|
||||
16,
|
||||
] # Indices for landmarks 21, 22, 23, 24, 0, 8
|
||||
chin_extension = 2 * 0.2 # Adjust this factor to control the extension
|
||||
for idx in chin_indices:
|
||||
expanded_landmarks[idx][1] += (
|
||||
expanded_landmarks[idx][1] - center[1]
|
||||
) * chin_extension
|
||||
|
||||
# Convert back to integer coordinates
|
||||
expanded_landmarks = expanded_landmarks.astype(np.int32)
|
||||
|
||||
# Calculate bounding box for the expanded lower mouth
|
||||
min_x, min_y = np.min(expanded_landmarks, axis=0)
|
||||
max_x, max_y = np.max(expanded_landmarks, axis=0)
|
||||
|
||||
# Add some padding to the bounding box
|
||||
padding = int((max_x - min_x) * 0.1) # 10% padding
|
||||
min_x = max(0, min_x - padding)
|
||||
min_y = max(0, min_y - padding)
|
||||
max_x = min(frame.shape[1], max_x + padding)
|
||||
max_y = min(frame.shape[0], max_y + padding)
|
||||
|
||||
# Ensure the bounding box dimensions are valid
|
||||
if max_x <= min_x or max_y <= min_y:
|
||||
if (max_x - min_x) <= 1:
|
||||
max_x = min_x + 1
|
||||
if (max_y - min_y) <= 1:
|
||||
max_y = min_y + 1
|
||||
|
||||
# Create the mask
|
||||
mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
|
||||
cv2.fillPoly(mask_roi, [expanded_landmarks - [min_x, min_y]], 255)
|
||||
|
||||
# Apply Gaussian blur to soften the mask edges
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
|
||||
|
||||
# Place the mask ROI in the full-sized mask
|
||||
mask[min_y:max_y, min_x:max_x] = mask_roi
|
||||
|
||||
# Extract the masked area from the frame
|
||||
mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
|
||||
|
||||
# Return the expanded lower lip polygon in original frame coordinates
|
||||
lower_lip_polygon = expanded_landmarks
|
||||
|
||||
return mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon
|
||||
|
||||
def create_eyes_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
|
||||
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
|
||||
eyes_cutout = None
|
||||
landmarks = face.landmark_2d_106
|
||||
if landmarks is not None:
|
||||
# Left eye landmarks (87-96) and right eye landmarks (33-42)
|
||||
left_eye = landmarks[87:96]
|
||||
right_eye = landmarks[33:42]
|
||||
|
||||
# Calculate centers and dimensions for each eye
|
||||
left_eye_center = np.mean(left_eye, axis=0).astype(np.int32)
|
||||
right_eye_center = np.mean(right_eye, axis=0).astype(np.int32)
|
||||
|
||||
# Calculate eye dimensions with size adjustment
|
||||
def get_eye_dimensions(eye_points):
|
||||
x_coords = eye_points[:, 0]
|
||||
y_coords = eye_points[:, 1]
|
||||
width = int((np.max(x_coords) - np.min(x_coords)) * (1 + modules.globals.mask_down_size * modules.globals.eyes_mask_size))
|
||||
height = int((np.max(y_coords) - np.min(y_coords)) * (1 + modules.globals.mask_down_size * modules.globals.eyes_mask_size))
|
||||
return width, height
|
||||
|
||||
left_width, left_height = get_eye_dimensions(left_eye)
|
||||
right_width, right_height = get_eye_dimensions(right_eye)
|
||||
|
||||
# Add extra padding
|
||||
padding = int(max(left_width, right_width) * 0.2)
|
||||
|
||||
# Calculate bounding box for both eyes
|
||||
min_x = min(left_eye_center[0] - left_width//2, right_eye_center[0] - right_width//2) - padding
|
||||
max_x = max(left_eye_center[0] + left_width//2, right_eye_center[0] + right_width//2) + padding
|
||||
min_y = min(left_eye_center[1] - left_height//2, right_eye_center[1] - right_height//2) - padding
|
||||
max_y = max(left_eye_center[1] + left_height//2, right_eye_center[1] + right_height//2) + padding
|
||||
|
||||
# Ensure coordinates are within frame bounds
|
||||
min_x = max(0, min_x)
|
||||
min_y = max(0, min_y)
|
||||
max_x = min(frame.shape[1], max_x)
|
||||
max_y = min(frame.shape[0], max_y)
|
||||
|
||||
# Create mask for the eyes region
|
||||
mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
|
||||
|
||||
# Draw ellipses for both eyes
|
||||
left_center = (left_eye_center[0] - min_x, left_eye_center[1] - min_y)
|
||||
right_center = (right_eye_center[0] - min_x, right_eye_center[1] - min_y)
|
||||
|
||||
# Calculate axes lengths (half of width and height)
|
||||
left_axes = (left_width//2, left_height//2)
|
||||
right_axes = (right_width//2, right_height//2)
|
||||
|
||||
# Draw filled ellipses
|
||||
cv2.ellipse(mask_roi, left_center, left_axes, 0, 0, 360, 255, -1)
|
||||
cv2.ellipse(mask_roi, right_center, right_axes, 0, 0, 360, 255, -1)
|
||||
|
||||
# Apply Gaussian blur to soften mask edges
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
|
||||
|
||||
# Place the mask ROI in the full-sized mask
|
||||
mask[min_y:max_y, min_x:max_x] = mask_roi
|
||||
|
||||
# Extract the masked area from the frame
|
||||
eyes_cutout = frame[min_y:max_y, min_x:max_x].copy()
|
||||
|
||||
# Create polygon points for visualization
|
||||
def create_ellipse_points(center, axes):
|
||||
t = np.linspace(0, 2*np.pi, 32)
|
||||
x = center[0] + axes[0] * np.cos(t)
|
||||
y = center[1] + axes[1] * np.sin(t)
|
||||
return np.column_stack((x, y)).astype(np.int32)
|
||||
|
||||
# Generate points for both ellipses
|
||||
left_points = create_ellipse_points((left_eye_center[0], left_eye_center[1]), (left_width//2, left_height//2))
|
||||
right_points = create_ellipse_points((right_eye_center[0], right_eye_center[1]), (right_width//2, right_height//2))
|
||||
|
||||
# Combine points for both eyes
|
||||
eyes_polygon = np.vstack([left_points, right_points])
|
||||
|
||||
return mask, eyes_cutout, (min_x, min_y, max_x, max_y), eyes_polygon
|
||||
|
||||
def create_curved_eyebrow(points):
|
||||
if len(points) >= 5:
|
||||
# Sort points by x-coordinate
|
||||
sorted_idx = np.argsort(points[:, 0])
|
||||
sorted_points = points[sorted_idx]
|
||||
|
||||
# Calculate dimensions
|
||||
x_min, y_min = np.min(sorted_points, axis=0)
|
||||
x_max, y_max = np.max(sorted_points, axis=0)
|
||||
width = x_max - x_min
|
||||
height = y_max - y_min
|
||||
|
||||
# Create more points for smoother curve
|
||||
num_points = 50
|
||||
x = np.linspace(x_min, x_max, num_points)
|
||||
|
||||
# Fit quadratic curve through points for more natural arch
|
||||
coeffs = np.polyfit(sorted_points[:, 0], sorted_points[:, 1], 2)
|
||||
y = np.polyval(coeffs, x)
|
||||
|
||||
# Increased offsets to create more separation
|
||||
top_offset = height * 0.5 # Increased from 0.3 to shift up more
|
||||
bottom_offset = height * 0.2 # Increased from 0.1 to shift down more
|
||||
|
||||
# Create smooth curves
|
||||
top_curve = y - top_offset
|
||||
bottom_curve = y + bottom_offset
|
||||
|
||||
# Create curved endpoints with more pronounced taper
|
||||
end_points = 5
|
||||
start_x = np.linspace(x[0] - width * 0.15, x[0], end_points) # Increased taper
|
||||
end_x = np.linspace(x[-1], x[-1] + width * 0.15, end_points) # Increased taper
|
||||
|
||||
# Create tapered ends
|
||||
start_curve = np.column_stack((
|
||||
start_x,
|
||||
np.linspace(bottom_curve[0], top_curve[0], end_points)
|
||||
))
|
||||
end_curve = np.column_stack((
|
||||
end_x,
|
||||
np.linspace(bottom_curve[-1], top_curve[-1], end_points)
|
||||
))
|
||||
|
||||
# Combine all points to form a smooth contour
|
||||
contour_points = np.vstack([
|
||||
start_curve,
|
||||
np.column_stack((x, top_curve)),
|
||||
end_curve,
|
||||
np.column_stack((x[::-1], bottom_curve[::-1]))
|
||||
])
|
||||
|
||||
# Add slight padding for better coverage
|
||||
center = np.mean(contour_points, axis=0)
|
||||
vectors = contour_points - center
|
||||
padded_points = center + vectors * 1.2 # Increased padding slightly
|
||||
|
||||
return padded_points
|
||||
return points
|
||||
|
||||
def create_eyebrows_mask(face: Face, frame: Frame) -> (np.ndarray, np.ndarray, tuple, np.ndarray):
|
||||
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
|
||||
eyebrows_cutout = None
|
||||
landmarks = face.landmark_2d_106
|
||||
if landmarks is not None:
|
||||
# Left eyebrow landmarks (97-105) and right eyebrow landmarks (43-51)
|
||||
left_eyebrow = landmarks[97:105].astype(np.float32)
|
||||
right_eyebrow = landmarks[43:51].astype(np.float32)
|
||||
|
||||
# Calculate centers and dimensions for each eyebrow
|
||||
left_center = np.mean(left_eyebrow, axis=0)
|
||||
right_center = np.mean(right_eyebrow, axis=0)
|
||||
|
||||
# Calculate bounding box with padding adjusted by size
|
||||
all_points = np.vstack([left_eyebrow, right_eyebrow])
|
||||
padding_factor = modules.globals.eyebrows_mask_size
|
||||
min_x = np.min(all_points[:, 0]) - 25 * padding_factor
|
||||
max_x = np.max(all_points[:, 0]) + 25 * padding_factor
|
||||
min_y = np.min(all_points[:, 1]) - 20 * padding_factor
|
||||
max_y = np.max(all_points[:, 1]) + 15 * padding_factor
|
||||
|
||||
# Ensure coordinates are within frame bounds
|
||||
min_x = max(0, int(min_x))
|
||||
min_y = max(0, int(min_y))
|
||||
max_x = min(frame.shape[1], int(max_x))
|
||||
max_y = min(frame.shape[0], int(max_y))
|
||||
|
||||
# Create mask for the eyebrows region
|
||||
mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
|
||||
|
||||
try:
|
||||
# Convert points to local coordinates
|
||||
left_local = left_eyebrow - [min_x, min_y]
|
||||
right_local = right_eyebrow - [min_x, min_y]
|
||||
|
||||
def create_curved_eyebrow(points):
|
||||
if len(points) >= 5:
|
||||
# Sort points by x-coordinate
|
||||
sorted_idx = np.argsort(points[:, 0])
|
||||
sorted_points = points[sorted_idx]
|
||||
|
||||
# Calculate dimensions
|
||||
x_min, y_min = np.min(sorted_points, axis=0)
|
||||
x_max, y_max = np.max(sorted_points, axis=0)
|
||||
width = x_max - x_min
|
||||
height = y_max - y_min
|
||||
|
||||
# Create more points for smoother curve
|
||||
num_points = 50
|
||||
x = np.linspace(x_min, x_max, num_points)
|
||||
|
||||
# Fit quadratic curve through points for more natural arch
|
||||
coeffs = np.polyfit(sorted_points[:, 0], sorted_points[:, 1], 2)
|
||||
y = np.polyval(coeffs, x)
|
||||
|
||||
# Increased offsets to create more separation
|
||||
top_offset = height * 0.5 # Increased from 0.3 to shift up more
|
||||
bottom_offset = height * 0.2 # Increased from 0.1 to shift down more
|
||||
|
||||
# Create smooth curves
|
||||
top_curve = y - top_offset
|
||||
bottom_curve = y + bottom_offset
|
||||
|
||||
# Create curved endpoints with more pronounced taper
|
||||
end_points = 5
|
||||
start_x = np.linspace(x[0] - width * 0.15, x[0], end_points) # Increased taper
|
||||
end_x = np.linspace(x[-1], x[-1] + width * 0.15, end_points) # Increased taper
|
||||
|
||||
# Create tapered ends
|
||||
start_curve = np.column_stack((
|
||||
start_x,
|
||||
np.linspace(bottom_curve[0], top_curve[0], end_points)
|
||||
))
|
||||
end_curve = np.column_stack((
|
||||
end_x,
|
||||
np.linspace(bottom_curve[-1], top_curve[-1], end_points)
|
||||
))
|
||||
|
||||
# Combine all points to form a smooth contour
|
||||
contour_points = np.vstack([
|
||||
start_curve,
|
||||
np.column_stack((x, top_curve)),
|
||||
end_curve,
|
||||
np.column_stack((x[::-1], bottom_curve[::-1]))
|
||||
])
|
||||
|
||||
# Add slight padding for better coverage
|
||||
center = np.mean(contour_points, axis=0)
|
||||
vectors = contour_points - center
|
||||
padded_points = center + vectors * 1.2 # Increased padding slightly
|
||||
|
||||
return padded_points
|
||||
return points
|
||||
|
||||
# Generate and draw eyebrow shapes
|
||||
left_shape = create_curved_eyebrow(left_local)
|
||||
right_shape = create_curved_eyebrow(right_local)
|
||||
|
||||
# Apply multi-stage blurring for natural feathering
|
||||
# First, strong Gaussian blur for initial softening
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (21, 21), 7)
|
||||
|
||||
# Second, medium blur for transition areas
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (11, 11), 3)
|
||||
|
||||
# Finally, light blur for fine details
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (5, 5), 1)
|
||||
|
||||
# Normalize mask values
|
||||
mask_roi = cv2.normalize(mask_roi, None, 0, 255, cv2.NORM_MINMAX)
|
||||
|
||||
# Place the mask ROI in the full-sized mask
|
||||
mask[min_y:max_y, min_x:max_x] = mask_roi
|
||||
|
||||
# Extract the masked area from the frame
|
||||
eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
|
||||
|
||||
# Combine points for visualization
|
||||
eyebrows_polygon = np.vstack([
|
||||
left_shape + [min_x, min_y],
|
||||
right_shape + [min_x, min_y]
|
||||
]).astype(np.int32)
|
||||
|
||||
except Exception as e:
|
||||
# Fallback to simple polygons if curve fitting fails
|
||||
left_local = left_eyebrow - [min_x, min_y]
|
||||
right_local = right_eyebrow - [min_x, min_y]
|
||||
cv2.fillPoly(mask_roi, [left_local.astype(np.int32)], 255)
|
||||
cv2.fillPoly(mask_roi, [right_local.astype(np.int32)], 255)
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (21, 21), 7)
|
||||
mask[min_y:max_y, min_x:max_x] = mask_roi
|
||||
eyebrows_cutout = frame[min_y:max_y, min_x:max_x].copy()
|
||||
eyebrows_polygon = np.vstack([left_eyebrow, right_eyebrow]).astype(np.int32)
|
||||
|
||||
return mask, eyebrows_cutout, (min_x, min_y, max_x, max_y), eyebrows_polygon
|
||||
|
||||
def apply_mask_area(
|
||||
frame: np.ndarray,
|
||||
cutout: np.ndarray,
|
||||
box: tuple,
|
||||
face_mask: np.ndarray,
|
||||
polygon: np.ndarray,
|
||||
) -> np.ndarray:
|
||||
min_x, min_y, max_x, max_y = box
|
||||
box_width = max_x - min_x
|
||||
box_height = max_y - min_y
|
||||
|
||||
if (
|
||||
cutout is None
|
||||
or box_width is None
|
||||
or box_height is None
|
||||
or face_mask is None
|
||||
or polygon is None
|
||||
):
|
||||
return frame
|
||||
|
||||
try:
|
||||
resized_cutout = cv2.resize(cutout, (box_width, box_height))
|
||||
roi = frame[min_y:max_y, min_x:max_x]
|
||||
|
||||
if roi.shape != resized_cutout.shape:
|
||||
resized_cutout = cv2.resize(
|
||||
resized_cutout, (roi.shape[1], roi.shape[0])
|
||||
)
|
||||
|
||||
color_corrected_area = apply_color_transfer(resized_cutout, roi)
|
||||
|
||||
# Create mask for the area
|
||||
polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
|
||||
|
||||
# Split points for left and right parts if needed
|
||||
if len(polygon) > 50: # Arbitrary threshold to detect if we have multiple parts
|
||||
mid_point = len(polygon) // 2
|
||||
left_points = polygon[:mid_point] - [min_x, min_y]
|
||||
right_points = polygon[mid_point:] - [min_x, min_y]
|
||||
cv2.fillPoly(polygon_mask, [left_points], 255)
|
||||
cv2.fillPoly(polygon_mask, [right_points], 255)
|
||||
else:
|
||||
adjusted_polygon = polygon - [min_x, min_y]
|
||||
cv2.fillPoly(polygon_mask, [adjusted_polygon], 255)
|
||||
|
||||
# Apply strong initial feathering
|
||||
polygon_mask = cv2.GaussianBlur(polygon_mask, (21, 21), 7)
|
||||
|
||||
# Apply additional feathering
|
||||
feather_amount = min(
|
||||
30,
|
||||
box_width // modules.globals.mask_feather_ratio,
|
||||
box_height // modules.globals.mask_feather_ratio,
|
||||
)
|
||||
feathered_mask = cv2.GaussianBlur(
|
||||
polygon_mask.astype(float), (0, 0), feather_amount
|
||||
)
|
||||
feathered_mask = feathered_mask / feathered_mask.max()
|
||||
|
||||
# Apply additional smoothing to the mask edges
|
||||
feathered_mask = cv2.GaussianBlur(feathered_mask, (5, 5), 1)
|
||||
|
||||
face_mask_roi = face_mask[min_y:max_y, min_x:max_x]
|
||||
combined_mask = feathered_mask * (face_mask_roi / 255.0)
|
||||
|
||||
combined_mask = combined_mask[:, :, np.newaxis]
|
||||
blended = (
|
||||
color_corrected_area * combined_mask + roi * (1 - combined_mask)
|
||||
).astype(np.uint8)
|
||||
|
||||
# Apply face mask to blended result
|
||||
face_mask_3channel = (
|
||||
np.repeat(face_mask_roi[:, :, np.newaxis], 3, axis=2) / 255.0
|
||||
)
|
||||
final_blend = blended * face_mask_3channel + roi * (1 - face_mask_3channel)
|
||||
|
||||
frame[min_y:max_y, min_x:max_x] = final_blend.astype(np.uint8)
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
return frame
|
||||
|
||||
def draw_mask_visualization(
|
||||
frame: Frame,
|
||||
mask_data: tuple,
|
||||
label: str,
|
||||
draw_method: str = "polygon"
|
||||
) -> Frame:
|
||||
mask, cutout, (min_x, min_y, max_x, max_y), polygon = mask_data
|
||||
|
||||
vis_frame = frame.copy()
|
||||
|
||||
# Ensure coordinates are within frame bounds
|
||||
height, width = vis_frame.shape[:2]
|
||||
min_x, min_y = max(0, min_x), max(0, min_y)
|
||||
max_x, max_y = min(width, max_x), min(height, max_y)
|
||||
|
||||
if draw_method == "ellipse" and len(polygon) > 50: # For eyes
|
||||
# Split points for left and right parts
|
||||
mid_point = len(polygon) // 2
|
||||
left_points = polygon[:mid_point]
|
||||
right_points = polygon[mid_point:]
|
||||
|
||||
try:
|
||||
# Fit ellipses to points - need at least 5 points
|
||||
if len(left_points) >= 5 and len(right_points) >= 5:
|
||||
# Convert points to the correct format for ellipse fitting
|
||||
left_points = left_points.astype(np.float32)
|
||||
right_points = right_points.astype(np.float32)
|
||||
|
||||
# Fit ellipses
|
||||
left_ellipse = cv2.fitEllipse(left_points)
|
||||
right_ellipse = cv2.fitEllipse(right_points)
|
||||
|
||||
# Draw the ellipses
|
||||
cv2.ellipse(vis_frame, left_ellipse, (0, 255, 0), 2)
|
||||
cv2.ellipse(vis_frame, right_ellipse, (0, 255, 0), 2)
|
||||
except Exception as e:
|
||||
# If ellipse fitting fails, draw simple rectangles as fallback
|
||||
left_rect = cv2.boundingRect(left_points)
|
||||
right_rect = cv2.boundingRect(right_points)
|
||||
cv2.rectangle(vis_frame,
|
||||
(left_rect[0], left_rect[1]),
|
||||
(left_rect[0] + left_rect[2], left_rect[1] + left_rect[3]),
|
||||
(0, 255, 0), 2)
|
||||
cv2.rectangle(vis_frame,
|
||||
(right_rect[0], right_rect[1]),
|
||||
(right_rect[0] + right_rect[2], right_rect[1] + right_rect[3]),
|
||||
(0, 255, 0), 2)
|
||||
else: # For mouth and eyebrows
|
||||
# Draw the polygon
|
||||
if len(polygon) > 50: # If we have multiple parts
|
||||
mid_point = len(polygon) // 2
|
||||
left_points = polygon[:mid_point]
|
||||
right_points = polygon[mid_point:]
|
||||
cv2.polylines(vis_frame, [left_points], True, (0, 255, 0), 2, cv2.LINE_AA)
|
||||
cv2.polylines(vis_frame, [right_points], True, (0, 255, 0), 2, cv2.LINE_AA)
|
||||
else:
|
||||
cv2.polylines(vis_frame, [polygon], True, (0, 255, 0), 2, cv2.LINE_AA)
|
||||
|
||||
# Add label
|
||||
cv2.putText(
|
||||
vis_frame,
|
||||
label,
|
||||
(min_x, min_y - 10),
|
||||
cv2.FONT_HERSHEY_SIMPLEX,
|
||||
0.5,
|
||||
(255, 255, 255),
|
||||
1,
|
||||
)
|
||||
|
||||
return vis_frame
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,9 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
# Import the tkinter fix to patch the ScreenChanged error
|
||||
import tkinter_fix
|
||||
|
||||
import core
|
||||
|
||||
if __name__ == '__main__':
|
||||
core.run()
|
||||
@@ -1,26 +0,0 @@
|
||||
import tkinter
|
||||
|
||||
# Only needs to be imported once at the beginning of the application
|
||||
def apply_patch():
|
||||
# Create a monkey patch for the internal _tkinter module
|
||||
original_init = tkinter.Tk.__init__
|
||||
|
||||
def patched_init(self, *args, **kwargs):
|
||||
# Call the original init
|
||||
original_init(self, *args, **kwargs)
|
||||
|
||||
# Define the missing ::tk::ScreenChanged procedure
|
||||
self.tk.eval("""
|
||||
if {[info commands ::tk::ScreenChanged] == ""} {
|
||||
proc ::tk::ScreenChanged {args} {
|
||||
# Do nothing
|
||||
return
|
||||
}
|
||||
}
|
||||
""")
|
||||
|
||||
# Apply the monkey patch
|
||||
tkinter.Tk.__init__ = patched_init
|
||||
|
||||
# Apply the patch automatically when this module is imported
|
||||
apply_patch()
|
||||
+44
-83
@@ -36,7 +36,7 @@ if platform.system() == "Windows":
|
||||
ROOT = None
|
||||
POPUP = None
|
||||
POPUP_LIVE = None
|
||||
ROOT_HEIGHT = 750
|
||||
ROOT_HEIGHT = 700
|
||||
ROOT_WIDTH = 600
|
||||
|
||||
PREVIEW = None
|
||||
@@ -153,20 +153,20 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
root.protocol("WM_DELETE_WINDOW", lambda: destroy())
|
||||
|
||||
source_label = ctk.CTkLabel(root, text=None)
|
||||
source_label.place(relx=0.1, rely=0.05, relwidth=0.275, relheight=0.225)
|
||||
source_label.place(relx=0.1, rely=0.1, relwidth=0.3, relheight=0.25)
|
||||
|
||||
target_label = ctk.CTkLabel(root, text=None)
|
||||
target_label.place(relx=0.6, rely=0.05, relwidth=0.275, relheight=0.225)
|
||||
target_label.place(relx=0.6, rely=0.1, relwidth=0.3, relheight=0.25)
|
||||
|
||||
select_face_button = ctk.CTkButton(
|
||||
root, text=_("Select a face"), cursor="hand2", command=lambda: select_source_path()
|
||||
)
|
||||
select_face_button.place(relx=0.1, rely=0.30, relwidth=0.3, relheight=0.1)
|
||||
select_face_button.place(relx=0.1, rely=0.375, relwidth=0.3, relheight=0.1)
|
||||
|
||||
swap_faces_button = ctk.CTkButton(
|
||||
root, text="↔", cursor="hand2", command=lambda: swap_faces_paths()
|
||||
)
|
||||
swap_faces_button.place(relx=0.45, rely=0.30, relwidth=0.1, relheight=0.1)
|
||||
swap_faces_button.place(relx=0.45, rely=0.375, relwidth=0.1, relheight=0.1)
|
||||
|
||||
select_target_button = ctk.CTkButton(
|
||||
root,
|
||||
@@ -174,7 +174,35 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
cursor="hand2",
|
||||
command=lambda: select_target_path(),
|
||||
)
|
||||
select_target_button.place(relx=0.6, rely=0.30, relwidth=0.3, relheight=0.1)
|
||||
select_target_button.place(relx=0.6, rely=0.375, relwidth=0.3, relheight=0.1)
|
||||
|
||||
transparency_values = ["0%","25%", "50%", "75%", "100%"]
|
||||
transparency_var = ctk.StringVar(value="100%") # Default to 100%
|
||||
|
||||
def on_transparency_change(value: str):
|
||||
percentage = int(value.strip('%'))
|
||||
modules.globals.opacity = percentage / 100.0
|
||||
|
||||
if percentage == 0:
|
||||
modules.globals.fp_ui["face_enhancer"] = False
|
||||
update_status("Transparency set to 0% - Face swapping disabled.")
|
||||
elif percentage == 100:
|
||||
modules.globals.face_swapper_enabled = True
|
||||
update_status("Transparency set to 100%.")
|
||||
else:
|
||||
modules.globals.face_swapper_enabled = True
|
||||
update_status(f"Transparency set to {value}")
|
||||
|
||||
transparency_label = ctk.CTkLabel(root, text="Transparency:")
|
||||
transparency_label.place(relx=0.1, rely=0.5, relwidth=0.2, relheight=0.05)
|
||||
|
||||
transparency_dropdown = ctk.CTkOptionMenu(
|
||||
root,
|
||||
values=transparency_values,
|
||||
variable=transparency_var,
|
||||
command=on_transparency_change,
|
||||
)
|
||||
transparency_dropdown.place(relx=0.35, rely=0.5, relwidth=0.25, relheight=0.05)
|
||||
|
||||
keep_fps_value = ctk.BooleanVar(value=modules.globals.keep_fps)
|
||||
keep_fps_checkbox = ctk.CTkSwitch(
|
||||
@@ -187,7 +215,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
keep_fps_checkbox.place(relx=0.1, rely=0.5)
|
||||
keep_fps_checkbox.place(relx=0.1, rely=0.6)
|
||||
|
||||
keep_frames_value = ctk.BooleanVar(value=modules.globals.keep_frames)
|
||||
keep_frames_switch = ctk.CTkSwitch(
|
||||
@@ -200,7 +228,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
keep_frames_switch.place(relx=0.1, rely=0.55)
|
||||
keep_frames_switch.place(relx=0.1, rely=0.65)
|
||||
|
||||
enhancer_value = ctk.BooleanVar(value=modules.globals.fp_ui["face_enhancer"])
|
||||
enhancer_switch = ctk.CTkSwitch(
|
||||
@@ -213,7 +241,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
enhancer_switch.place(relx=0.1, rely=0.6)
|
||||
enhancer_switch.place(relx=0.1, rely=0.7)
|
||||
|
||||
keep_audio_value = ctk.BooleanVar(value=modules.globals.keep_audio)
|
||||
keep_audio_switch = ctk.CTkSwitch(
|
||||
@@ -226,7 +254,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
keep_audio_switch.place(relx=0.6, rely=0.5)
|
||||
keep_audio_switch.place(relx=0.6, rely=0.6)
|
||||
|
||||
many_faces_value = ctk.BooleanVar(value=modules.globals.many_faces)
|
||||
many_faces_switch = ctk.CTkSwitch(
|
||||
@@ -239,7 +267,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
many_faces_switch.place(relx=0.6, rely=0.55)
|
||||
many_faces_switch.place(relx=0.6, rely=0.65)
|
||||
|
||||
color_correction_value = ctk.BooleanVar(value=modules.globals.color_correction)
|
||||
color_correction_switch = ctk.CTkSwitch(
|
||||
@@ -252,7 +280,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
color_correction_switch.place(relx=0.6, rely=0.6)
|
||||
color_correction_switch.place(relx=0.6, rely=0.70)
|
||||
|
||||
# nsfw_value = ctk.BooleanVar(value=modules.globals.nsfw_filter)
|
||||
# nsfw_switch = ctk.CTkSwitch(root, text='NSFW filter', variable=nsfw_value, cursor='hand2', command=lambda: setattr(modules.globals, 'nsfw_filter', nsfw_value.get()))
|
||||
@@ -270,7 +298,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
close_mapper_window() if not map_faces.get() else None
|
||||
),
|
||||
)
|
||||
map_faces_switch.place(relx=0.1, rely=0.65)
|
||||
map_faces_switch.place(relx=0.1, rely=0.75)
|
||||
|
||||
show_fps_value = ctk.BooleanVar(value=modules.globals.show_fps)
|
||||
show_fps_switch = ctk.CTkSwitch(
|
||||
@@ -283,7 +311,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
show_fps_switch.place(relx=0.6, rely=0.65)
|
||||
show_fps_switch.place(relx=0.6, rely=0.75)
|
||||
|
||||
mouth_mask_var = ctk.BooleanVar(value=modules.globals.mouth_mask)
|
||||
mouth_mask_switch = ctk.CTkSwitch(
|
||||
@@ -293,7 +321,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
cursor="hand2",
|
||||
command=lambda: setattr(modules.globals, "mouth_mask", mouth_mask_var.get()),
|
||||
)
|
||||
mouth_mask_switch.place(relx=0.1, rely=0.45)
|
||||
mouth_mask_switch.place(relx=0.1, rely=0.55)
|
||||
|
||||
show_mouth_mask_box_var = ctk.BooleanVar(value=modules.globals.show_mouth_mask_box)
|
||||
show_mouth_mask_box_switch = ctk.CTkSwitch(
|
||||
@@ -305,7 +333,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
modules.globals, "show_mouth_mask_box", show_mouth_mask_box_var.get()
|
||||
),
|
||||
)
|
||||
show_mouth_mask_box_switch.place(relx=0.6, rely=0.45)
|
||||
show_mouth_mask_box_switch.place(relx=0.6, rely=0.55)
|
||||
|
||||
start_button = ctk.CTkButton(
|
||||
root, text=_("Start"), cursor="hand2", command=lambda: analyze_target(start, root)
|
||||
@@ -366,72 +394,6 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
live_button.place(relx=0.65, rely=0.86, relwidth=0.2, relheight=0.05)
|
||||
# --- End Camera Selection ---
|
||||
|
||||
# 1) Define a DoubleVar for transparency (0 = fully transparent, 1 = fully opaque)
|
||||
transparency_var = ctk.DoubleVar(value=1.0)
|
||||
|
||||
def on_transparency_change(value: float):
|
||||
# Convert slider value to float
|
||||
val = float(value)
|
||||
modules.globals.opacity = val # Set global opacity
|
||||
percentage = int(val * 100)
|
||||
|
||||
if percentage == 0:
|
||||
modules.globals.fp_ui["face_enhancer"] = False
|
||||
update_status("Transparency set to 0% - Face swapping disabled.")
|
||||
elif percentage == 100:
|
||||
modules.globals.face_swapper_enabled = True
|
||||
update_status("Transparency set to 100%.")
|
||||
else:
|
||||
modules.globals.face_swapper_enabled = True
|
||||
update_status(f"Transparency set to {percentage}%")
|
||||
|
||||
# 2) Transparency label and slider (placed ABOVE sharpness)
|
||||
transparency_label = ctk.CTkLabel(root, text="Transparency:")
|
||||
transparency_label.place(relx=0.15, rely=0.69, relwidth=0.2, relheight=0.05)
|
||||
|
||||
transparency_slider = ctk.CTkSlider(
|
||||
root,
|
||||
from_=0.0,
|
||||
to=1.0,
|
||||
variable=transparency_var,
|
||||
command=on_transparency_change,
|
||||
fg_color="#E0E0E0",
|
||||
progress_color="#007BFF",
|
||||
button_color="#FFFFFF",
|
||||
button_hover_color="#CCCCCC",
|
||||
height=5,
|
||||
border_width=1,
|
||||
corner_radius=3,
|
||||
)
|
||||
transparency_slider.place(relx=0.35, rely=0.71, relwidth=0.5, relheight=0.02)
|
||||
|
||||
# 3) Sharpness label & slider
|
||||
sharpness_var = ctk.DoubleVar(value=0.0) # start at 0.0
|
||||
def on_sharpness_change(value: float):
|
||||
modules.globals.sharpness = float(value)
|
||||
update_status(f"Sharpness set to {value:.1f}")
|
||||
|
||||
sharpness_label = ctk.CTkLabel(root, text="Sharpness:")
|
||||
sharpness_label.place(relx=0.15, rely=0.74, relwidth=0.2, relheight=0.05)
|
||||
|
||||
sharpness_slider = ctk.CTkSlider(
|
||||
root,
|
||||
from_=0.0,
|
||||
to=5.0,
|
||||
variable=sharpness_var,
|
||||
command=on_sharpness_change,
|
||||
fg_color="#E0E0E0",
|
||||
progress_color="#007BFF",
|
||||
button_color="#FFFFFF",
|
||||
button_hover_color="#CCCCCC",
|
||||
height=5,
|
||||
border_width=1,
|
||||
corner_radius=3,
|
||||
)
|
||||
sharpness_slider.place(relx=0.35, rely=0.76, relwidth=0.5, relheight=0.02)
|
||||
|
||||
# Status and link at the bottom
|
||||
global status_label
|
||||
status_label = ctk.CTkLabel(root, text=None, justify="center")
|
||||
status_label.place(relx=0.1, rely=0.9, relwidth=0.8)
|
||||
|
||||
@@ -448,7 +410,6 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
|
||||
return root
|
||||
|
||||
|
||||
def close_mapper_window():
|
||||
global POPUP, POPUP_LIVE
|
||||
if POPUP and POPUP.winfo_exists():
|
||||
|
||||
+14
-14
@@ -1,24 +1,24 @@
|
||||
--extra-index-url https://download.pytorch.org/whl/cu128
|
||||
--extra-index-url https://download.pytorch.org/whl/cu118
|
||||
|
||||
numpy>=1.23.5,<2
|
||||
typing-extensions>=4.8.0
|
||||
opencv-python==4.10.0.84
|
||||
cv2_enumerate_cameras==1.1.15
|
||||
onnx==1.18.0
|
||||
onnx==1.16.0
|
||||
insightface==0.7.3
|
||||
psutil==5.9.8
|
||||
tk==0.1.0
|
||||
customtkinter==5.2.2
|
||||
pillow==11.1.0
|
||||
torch; sys_platform != 'darwin'
|
||||
torch==2.8.0+cu128; sys_platform == 'darwin'
|
||||
torchvision; sys_platform != 'darwin'
|
||||
torchvision==0.20.1; sys_platform == 'darwin'
|
||||
pillow==9.5.0
|
||||
torch==2.0.1+cu118; sys_platform != 'darwin'
|
||||
torch==2.0.1; sys_platform == 'darwin'
|
||||
torchvision==0.15.2+cu118; sys_platform != 'darwin'
|
||||
torchvision==0.15.2; sys_platform == 'darwin'
|
||||
onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
|
||||
onnxruntime-gpu==1.22.0; sys_platform != 'darwin'
|
||||
tensorflow; sys_platform != 'darwin'
|
||||
onnxruntime-gpu==1.16.3; sys_platform != 'darwin'
|
||||
tensorflow==2.12.1; sys_platform != 'darwin'
|
||||
opennsfw2==0.10.2
|
||||
protobuf==4.25.1
|
||||
git+https://github.com/xinntao/BasicSR.git@master
|
||||
git+https://github.com/TencentARC/GFPGAN.git@master
|
||||
pygrabber
|
||||
protobuf==4.23.2
|
||||
tqdm==4.66.4
|
||||
gfpgan==1.3.8
|
||||
tkinterdnd2==0.4.2
|
||||
pygrabber==0.2
|
||||
|
||||
@@ -1,8 +1,5 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
# Import the tkinter fix to patch the ScreenChanged error
|
||||
import tkinter_fix
|
||||
|
||||
from modules import core
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@@ -1,26 +0,0 @@
|
||||
import tkinter
|
||||
|
||||
# Only needs to be imported once at the beginning of the application
|
||||
def apply_patch():
|
||||
# Create a monkey patch for the internal _tkinter module
|
||||
original_init = tkinter.Tk.__init__
|
||||
|
||||
def patched_init(self, *args, **kwargs):
|
||||
# Call the original init
|
||||
original_init(self, *args, **kwargs)
|
||||
|
||||
# Define the missing ::tk::ScreenChanged procedure
|
||||
self.tk.eval("""
|
||||
if {[info commands ::tk::ScreenChanged] == ""} {
|
||||
proc ::tk::ScreenChanged {args} {
|
||||
# Do nothing
|
||||
return
|
||||
}
|
||||
}
|
||||
""")
|
||||
|
||||
# Apply the monkey patch
|
||||
tkinter.Tk.__init__ = patched_init
|
||||
|
||||
# Apply the patch automatically when this module is imported
|
||||
apply_patch()
|
||||
Reference in New Issue
Block a user