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@@ -1,26 +1,38 @@
|
||||
***[Remove this]The issue would be closed without notice and be considered spam if the template is not followed.***
|
||||
---
|
||||
name: Bug report
|
||||
about: Create a report to help us improve
|
||||
title: ''
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
**Describe the bug**
|
||||
A clear and concise description of what the bug is.
|
||||
|
||||
**To Reproduce**
|
||||
Steps to reproduce the behavior:
|
||||
1. Go to '...'
|
||||
2. Click on '....'
|
||||
3. Scroll down to '....'
|
||||
4. See error
|
||||
|
||||
**Expected behavior**
|
||||
A clear and concise description of what you expected to happen.
|
||||
|
||||
**Screenshots**
|
||||
If applicable, add screenshots to help explain your problem.
|
||||
|
||||
**Error Message**
|
||||
|
||||
`<The error message in terminal>`
|
||||
|
||||
**Desktop (please complete the following information):**
|
||||
- OS: [e.g. Windows]
|
||||
- OS: [e.g. iOS]
|
||||
- Browser [e.g. chrome, safari]
|
||||
- Version [e.g. 22]
|
||||
|
||||
**Smartphone (please complete the following information):**
|
||||
- Device: [e.g. iPhone6]
|
||||
- OS: [e.g. iOS8.1]
|
||||
- Browser [e.g. stock browser, safari]
|
||||
- Version [e.g. 22]
|
||||
- GPU
|
||||
- CPU
|
||||
|
||||
**Additional context**
|
||||
Add any other context about the problem here.
|
||||
|
||||
**Confirmation (Mandatory)**
|
||||
- [ ] I have followed the template
|
||||
- [ ] This is not a query about how to increase performance
|
||||
- [ ] I have checked the issues page, and this is not a duplicate
|
||||
|
||||
|
||||
@@ -24,4 +24,3 @@ models/GFPGANv1.4.pth
|
||||
models/DMDNet.pth
|
||||
faceswap/
|
||||
.vscode/
|
||||
switch_states.json
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
3.10.0
|
||||
+1
-38
@@ -1,38 +1 @@
|
||||
# Collaboration Guidelines and Codebase Quality Standards
|
||||
|
||||
To ensure smooth collaboration and maintain the high quality of our codebase, please adhere to the following guidelines:
|
||||
|
||||
## Branching Strategy
|
||||
|
||||
* **`premain`**:
|
||||
* Always push your changes to the `premain` branch initially.
|
||||
* This safeguards the `main` branch from unintentional disruptions.
|
||||
* All tests will be performed on the `premain` branch.
|
||||
* Changes will only be merged into `main` after several hours or days of rigorous testing.
|
||||
* **`experimental`**:
|
||||
* For large or potentially disruptive changes, use the `experimental` branch.
|
||||
* This allows for thorough discussion and review before considering a merge into `main`.
|
||||
|
||||
## Pre-Pull Request Checklist
|
||||
|
||||
Before creating a Pull Request (PR), ensure you have completed the following tests:
|
||||
|
||||
### Functionality
|
||||
|
||||
* **Realtime Faceswap**:
|
||||
* Test with face enhancer **enabled** and **disabled**.
|
||||
* **Map Faces**:
|
||||
* Test with both options (**enabled** and **disabled**).
|
||||
* **Camera Listing**:
|
||||
* Verify that all cameras are listed accurately.
|
||||
|
||||
### Stability
|
||||
|
||||
* **Realtime FPS**:
|
||||
* Confirm that there is no drop in real-time frames per second (FPS).
|
||||
* **Boot Time**:
|
||||
* Changes should not negatively impact the boot time of either the application or the real-time faceswap feature.
|
||||
* **GPU Overloading**:
|
||||
* Test for a minimum of 15 minutes to guarantee no GPU overloading, which could lead to crashes.
|
||||
* **App Performance**:
|
||||
* The application should remain responsive and not exhibit any lag.
|
||||
Please always push on the experimental to ensure we don't mess with the main branch. All the test will be done on the experimental and will be pushed to the main branch after few days of testing.
|
||||
|
||||
@@ -5,124 +5,49 @@
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<a href="https://trendshift.io/repositories/11395" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11395" alt="hacksider%2FDeep-Live-Cam | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
|
||||
<img src="media/demo.gif" alt="Demo GIF">
|
||||
<img src="media/avgpcperformancedemo.gif" alt="Performance Demo GIF">
|
||||
</p>
|
||||
|
||||
<p align="center">
|
||||
<img src="media/demo.gif" alt="Demo GIF" width="800">
|
||||
</p>
|
||||
## Disclaimer
|
||||
|
||||
## Disclaimer
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
- 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.
|
||||
|
||||
- Content Restrictions: The software includes built-in checks to prevent processing inappropriate media, such as nudity, graphic content, or sensitive material.
|
||||
|
||||
- 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.
|
||||
|
||||
- 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.
|
||||
|
||||
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.
|
||||
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 law and ethics. We may shut down the project or add watermarks if legally required.
|
||||
|
||||
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.
|
||||
|
||||
|
||||
## Quick Start - Pre-built (Windows / Nvidia)
|
||||
## Quick Start (Windows / Nvidia)
|
||||
|
||||
<a href="https://hacksider.gumroad.com/l/vccdmm"> <img src="https://github.com/user-attachments/assets/7d993b32-e3e8-4cd3-bbfb-a549152ebdd5" width="285" height="77" />
|
||||
|
||||
##### This is the fastest build you can get if you have a discrete NVIDIA GPU.
|
||||
|
||||
## Quick Start - Pre-built (Mac / Silicon)
|
||||
|
||||
<a href="https://krshh.gumroad.com/l/Deep-Live-Cam-Mac"> <img src="https://github.com/user-attachments/assets/d5d913b5-a7de-4609-96b9-979a5749a703" width="285" height="77" />
|
||||
|
||||
###### 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.
|
||||
|
||||
## TLDR; Live Deepfake in just 3 Clicks
|
||||
|
||||
1. Select a face
|
||||
2. Select which camera to use
|
||||
3. Press live!
|
||||
|
||||
## Features & Uses - Everything is real-time
|
||||
|
||||
### Mouth Mask
|
||||
|
||||
**Retain your original mouth for accurate movement using Mouth Mask**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/ludwig.gif" alt="resizable-gif">
|
||||
</p>
|
||||
|
||||
### Face Mapping
|
||||
|
||||
**Use different faces on multiple subjects simultaneously**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/streamers.gif" alt="face_mapping_source">
|
||||
</p>
|
||||
|
||||
### Your Movie, Your Face
|
||||
|
||||
**Watch movies with any face in real-time**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/movie.gif" alt="movie">
|
||||
</p>
|
||||
|
||||
### Live Show
|
||||
|
||||
**Run Live shows and performances**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/live_show.gif" alt="show">
|
||||
</p>
|
||||
|
||||
### Memes
|
||||
|
||||
**Create Your most viral meme yet**
|
||||
|
||||
<p align="center">
|
||||
<img src="media/meme.gif" alt="show" width="450">
|
||||
<br>
|
||||
<sub>Created using Many Faces feature in Deep-Live-Cam</sub>
|
||||
</p>
|
||||
[](https://hacksider.gumroad.com/l/vccdmm)
|
||||
|
||||
[Download latest pre-built version with CUDA support](https://hacksider.gumroad.com/l/vccdmm) - No Manual Installation/Downloading required.
|
||||
|
||||
## Installation (Manual)
|
||||
|
||||
**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the prebuilt version.**
|
||||
|
||||
<details>
|
||||
<summary>Click to see the process</summary>
|
||||
|
||||
### Installation
|
||||
**Please be aware that the installation needs technical skills and is NOT for beginners, consider downloading the prebuilt. Please do NOT open platform and installation related issues on GitHub before discussing it on the discord server.**
|
||||
### Basic Installation (CPU)
|
||||
|
||||
This is more likely to work on your computer but will be slower as it utilizes the CPU.
|
||||
|
||||
**1. Set up Your Platform**
|
||||
**1. Setup Your Platform**
|
||||
|
||||
- Python (3.10 recommended)
|
||||
- pip
|
||||
- git
|
||||
- [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) - ```iex (irm ffmpeg.tc.ht)```
|
||||
- [Visual Studio 2022 Runtimes (Windows)](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
|
||||
- Python (3.10 recommended)
|
||||
- pip
|
||||
- git
|
||||
- [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA)
|
||||
- [Visual Studio 2022 Runtimes (Windows)](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
|
||||
|
||||
**2. Clone the Repository**
|
||||
**2. Clone Repository**
|
||||
|
||||
```bash
|
||||
https://github.com/hacksider/Deep-Live-Cam.git
|
||||
```
|
||||
|
||||
**3. Download the Models**
|
||||
**3. Download Models**
|
||||
|
||||
1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.pth)
|
||||
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.
|
||||
|
||||
@@ -142,20 +67,21 @@ brew install python-tk@3.10
|
||||
|
||||
**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).
|
||||
|
||||
### GPU Acceleration
|
||||
|
||||
### GPU Acceleration (Optional)
|
||||
|
||||
<details>
|
||||
<summary>Click to see the details</summary>
|
||||
|
||||
**CUDA Execution Provider (Nvidia)**
|
||||
|
||||
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)
|
||||
1. Install [CUDA Toolkit 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive)
|
||||
2. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-gpu
|
||||
pip install onnxruntime-gpu==1.16.3
|
||||
```
|
||||
|
||||
3. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider cuda
|
||||
```
|
||||
@@ -163,14 +89,11 @@ python run.py --execution-provider cuda
|
||||
**CoreML Execution Provider (Apple Silicon)**
|
||||
|
||||
1. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-silicon
|
||||
pip install onnxruntime-silicon==1.13.1
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider coreml
|
||||
```
|
||||
@@ -178,14 +101,11 @@ python run.py --execution-provider coreml
|
||||
**CoreML Execution Provider (Apple Legacy)**
|
||||
|
||||
1. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-coreml
|
||||
pip install onnxruntime-coreml==1.13.1
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider coreml
|
||||
```
|
||||
@@ -193,14 +113,11 @@ python run.py --execution-provider coreml
|
||||
**DirectML Execution Provider (Windows)**
|
||||
|
||||
1. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-directml
|
||||
pip install onnxruntime-directml==1.15.1
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider directml
|
||||
```
|
||||
@@ -208,39 +125,75 @@ python run.py --execution-provider directml
|
||||
**OpenVINO™ Execution Provider (Intel)**
|
||||
|
||||
1. Install dependencies:
|
||||
|
||||
```bash
|
||||
pip uninstall onnxruntime onnxruntime-openvino
|
||||
pip install onnxruntime-openvino==1.15.0
|
||||
```
|
||||
|
||||
2. Usage:
|
||||
|
||||
```bash
|
||||
python run.py --execution-provider openvino
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
**1. Image/Video Mode**
|
||||
|
||||
- Execute `python run.py`.
|
||||
- Choose a source face image and a target image/video.
|
||||
- Click "Start".
|
||||
- The output will be saved in a directory named after the target video.
|
||||
- Execute `python run.py`.
|
||||
- Choose a source face image and a target image/video.
|
||||
- Click "Start".
|
||||
- The output will be saved in a directory named after the target video.
|
||||
|
||||
**2. Webcam Mode**
|
||||
|
||||
- Execute `python run.py`.
|
||||
- Select a source face image.
|
||||
- Click "Live".
|
||||
- Wait for the preview to appear (10-30 seconds).
|
||||
- Use a screen capture tool like OBS to stream.
|
||||
- To change the face, select a new source image.
|
||||
- Execute `python run.py`.
|
||||
- Select a source face image.
|
||||
- Click "Live".
|
||||
- Wait for the preview to appear (10-30 seconds).
|
||||
- Use a screen capture tool like OBS to stream.
|
||||
- To change the face, select a new source image.
|
||||
|
||||
## Command Line Arguments (Unmaintained)
|
||||
|
||||
|
||||
## Features
|
||||
|
||||
### Resizable Preview Window
|
||||
|
||||
Dynamically improve performance using the `--live-resizable` parameter.
|
||||
|
||||
|
||||
|
||||
### Face Mapping
|
||||
|
||||
Track and change faces on the fly.
|
||||
|
||||
|
||||
|
||||
**Source Video:**
|
||||
|
||||
|
||||
|
||||
**Enable Face Mapping:**
|
||||
|
||||
|
||||
|
||||
**Map the Faces:**
|
||||
|
||||
|
||||
|
||||
**See the Magic!**
|
||||
|
||||
|
||||
|
||||
**Watch movies in realtime:**
|
||||
|
||||
It's as simple as opening a movie on the screen, and selecting OBS as your camera!
|
||||
|
||||
|
||||
|
||||
## Command Line Arguments
|
||||
|
||||
```
|
||||
options:
|
||||
@@ -254,7 +207,7 @@ options:
|
||||
--keep-frames keep temporary frames
|
||||
--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
|
||||
@@ -267,44 +220,188 @@ options:
|
||||
|
||||
Looking for a CLI mode? Using the -s/--source argument will make the run program in cli mode.
|
||||
|
||||
## Press
|
||||
|
||||
**We are always open to criticism and are ready to improve, that's why we didn't cherry-pick anything.**
|
||||
## Webcam Mode on WSL2 Ubuntu (Optional)
|
||||
|
||||
<details>
|
||||
<summary>Click to see the details</summary>
|
||||
|
||||
If you want to use WSL2 on Windows 11 you will notice, that Ubuntu WSL2 doesn't come with USB-Webcam support in the Kernel. You need to do two things: Compile the Kernel with the right modules integrated and forward your USB Webcam from Windows to Ubuntu with the usbipd app. Here are detailed Steps:
|
||||
|
||||
This tutorial will guide you through the process of setting up WSL2 Ubuntu with USB webcam support, rebuilding the kernel, and preparing the environment for the Deep-Live-Cam project.
|
||||
|
||||
**1. Install WSL2 Ubuntu**
|
||||
|
||||
Install WSL2 Ubuntu from the Microsoft Store or using PowerShell:
|
||||
|
||||
**2. Enable USB Support in WSL2**
|
||||
|
||||
1. Install the USB/IP tool for Windows:
|
||||
[https://learn.microsoft.com/en-us/windows/wsl/connect-usb](https://learn.microsoft.com/en-us/windows/wsl/connect-usb)
|
||||
|
||||
2. In Windows PowerShell (as Administrator), connect your webcam to WSL:
|
||||
|
||||
```powershell
|
||||
usbipd list
|
||||
usbipd bind --busid x-x # Replace x-x with your webcam's bus ID
|
||||
usbipd attach --wsl --busid x-x # Replace x-x with your webcam's bus ID
|
||||
```
|
||||
You need to redo the above every time you reboot wsl or re-connect your webcam/usb device.
|
||||
|
||||
**3. Rebuild WSL2 Ubuntu Kernel with USB and Webcam Modules**
|
||||
|
||||
Follow these steps to rebuild the kernel:
|
||||
|
||||
1. Start with this guide: [https://github.com/PINTO0309/wsl2_linux_kernel_usbcam_enable_conf](https://github.com/PINTO0309/wsl2_linux_kernel_usbcam_enable_conf)
|
||||
|
||||
2. When you reach the `sudo wget [github.com](http://github.com/)...PINTO0309` step, which won't work for newer kernel versions, follow this video instead or alternatively follow the video tutorial from the beginning:
|
||||
[https://www.youtube.com/watch?v=t_YnACEPmrM](https://www.youtube.com/watch?v=t_YnACEPmrM)
|
||||
|
||||
Additional info: [https://askubuntu.com/questions/1413377/camera-not-working-in-cheese-in-wsl2](https://askubuntu.com/questions/1413377/camera-not-working-in-cheese-in-wsl2)
|
||||
|
||||
3. After rebuilding, restart WSL with the new kernel.
|
||||
|
||||
**4. Set Up Deep-Live-Cam Project**
|
||||
Within Ubuntu:
|
||||
1. Clone the repository:
|
||||
|
||||
```bash
|
||||
git clone [https://github.com/hacksider/Deep-Live-Cam](https://github.com/hacksider/Deep-Live-Cam)
|
||||
```
|
||||
|
||||
2. Follow the installation instructions in the repository, including cuda toolkit 11.8, make 100% sure it's not cuda toolkit 12.x.
|
||||
|
||||
**5. Verify and Load Kernel Modules**
|
||||
|
||||
1. Check if USB and webcam modules are built into the kernel:
|
||||
|
||||
```bash
|
||||
zcat /proc/config.gz | grep -i "CONFIG_USB_VIDEO_CLASS"
|
||||
```
|
||||
|
||||
2. If modules are loadable (m), not built-in (y), check if the file exists:
|
||||
|
||||
```bash
|
||||
ls /lib/modules/$(uname -r)/kernel/drivers/media/usb/uvc/
|
||||
```
|
||||
|
||||
3. Load the module and check for errors (optional if built-in):
|
||||
|
||||
```bash
|
||||
sudo modprobe uvcvideo
|
||||
dmesg | tail
|
||||
```
|
||||
|
||||
4. Verify video devices:
|
||||
|
||||
```bash
|
||||
sudo ls -al /dev/video*
|
||||
```
|
||||
|
||||
**6. Set Up Permissions**
|
||||
|
||||
1. Add user to video group and set permissions:
|
||||
|
||||
```bash
|
||||
sudo usermod -a -G video $USER
|
||||
sudo chgrp video /dev/video0 /dev/video1
|
||||
sudo chmod 660 /dev/video0 /dev/video1
|
||||
```
|
||||
|
||||
2. Create a udev rule for permanent permissions:
|
||||
|
||||
```bash
|
||||
sudo nano /etc/udev/rules.d/81-webcam.rules
|
||||
```
|
||||
|
||||
Add this content:
|
||||
|
||||
```
|
||||
KERNEL=="video[0-9]*", GROUP="video", MODE="0660"
|
||||
```
|
||||
|
||||
3. Reload udev rules:
|
||||
|
||||
```bash
|
||||
sudo udevadm control --reload-rules && sudo udevadm trigger
|
||||
```
|
||||
|
||||
4. Log out and log back into your WSL session.
|
||||
|
||||
5. Start Deep-Live-Cam with `python run.py --execution-provider cuda --max-memory 8` where 8 can be changed to the number of GB VRAM of your GPU has, minus 1-2GB. If you have a RTX3080 with 10GB I suggest adding 8GB. Leave some left for Windows.
|
||||
|
||||
**Final Notes**
|
||||
|
||||
- Steps 6 and 7 may be optional if the modules are built into the kernel and permissions are already set correctly.
|
||||
- Always ensure you're using compatible versions of CUDA, ONNX, and other dependencies.
|
||||
- If issues persist, consider checking the Deep-Live-Cam project's specific requirements and troubleshooting steps.
|
||||
|
||||
By following these steps, you should have a WSL2 Ubuntu environment with USB webcam support ready for the Deep-Live-Cam project. If you encounter any issues, refer back to the specific error messages and troubleshooting steps provided.
|
||||
|
||||
**Troubleshooting CUDA Issues**
|
||||
|
||||
If you encounter this error:
|
||||
|
||||
```
|
||||
[ONNXRuntimeError] : 1 : FAIL : Failed to load library [libonnxruntime_providers_cuda.so](http://libonnxruntime_providers_cuda.so/) with error: libcufft.so.10: cannot open shared object file: No such file or directory
|
||||
```
|
||||
|
||||
Follow these steps:
|
||||
|
||||
1. Install CUDA Toolkit 11.8 (ONNX 1.16.3 requires CUDA 11.x, not 12.x):
|
||||
[https://developer.nvidia.com/cuda-11-8-0-download-archive](https://developer.nvidia.com/cuda-11-8-0-download-archive)
|
||||
select: Linux, x86_64, WSL-Ubuntu, 2.0, deb (local)
|
||||
2. Check CUDA version:
|
||||
|
||||
```bash
|
||||
/usr/local/cuda/bin/nvcc --version
|
||||
```
|
||||
|
||||
3. If the wrong version is installed, remove it completely:
|
||||
[https://askubuntu.com/questions/530043/removing-nvidia-cuda-toolkit-and-installing-new-one](https://askubuntu.com/questions/530043/removing-nvidia-cuda-toolkit-and-installing-new-one)
|
||||
|
||||
4. Install CUDA Toolkit 11.8 again [https://developer.nvidia.com/cuda-11-8-0-download-archive](https://developer.nvidia.com/cuda-11-8-0-download-archive), select: Linux, x86_64, WSL-Ubuntu, 2.0, deb (local)
|
||||
|
||||
```bash
|
||||
sudo apt-get -y install cuda-toolkit-11-8
|
||||
```
|
||||
</details>
|
||||
|
||||
|
||||
## Future Updates & Roadmap
|
||||
|
||||
For the latest experimental builds and features, see the [experimental branch](https://github.com/hacksider/Deep-Live-Cam/tree/experimental).
|
||||
|
||||
**TODO:**
|
||||
|
||||
- [ ] Develop a version for web app/service
|
||||
- [ ] Speed up model loading
|
||||
- [ ] Speed up real-time face swapping
|
||||
- [x] Support multiple faces
|
||||
- [x] UI/UX enhancements for desktop app
|
||||
|
||||
This is an open-source project developed in our free time. Updates may be delayed.
|
||||
|
||||
**Tips and Links:**
|
||||
- [How to make the most of Deep-Live-Cam](https://hacksider.gumroad.com/p/how-to-make-the-most-on-deep-live-cam)
|
||||
- Face enhancer is good, but still very slow for any live streaming purpose.
|
||||
|
||||
- [*"Deep-Live-Cam goes viral, allowing anyone to become a digital doppelganger"*](https://arstechnica.com/information-technology/2024/08/new-ai-tool-enables-real-time-face-swapping-on-webcams-raising-fraud-concerns/) - Ars Technica
|
||||
- [*"Thanks Deep Live Cam, shapeshifters are among us now"*](https://dataconomy.com/2024/08/15/what-is-deep-live-cam-github-deepfake/) - Dataconomy
|
||||
- [*"This free AI tool lets you become anyone during video-calls"*](https://www.newsbytesapp.com/news/science/deep-live-cam-ai-impersonation-tool-goes-viral/story) - NewsBytes
|
||||
- [*"OK, this viral AI live stream software is truly terrifying"*](https://www.creativebloq.com/ai/ok-this-viral-ai-live-stream-software-is-truly-terrifying) - Creative Bloq
|
||||
- [*"Deepfake AI Tool Lets You Become Anyone in a Video Call With Single Photo"*](https://petapixel.com/2024/08/14/deep-live-cam-deepfake-ai-tool-lets-you-become-anyone-in-a-video-call-with-single-photo-mark-zuckerberg-jd-vance-elon-musk/) - PetaPixel
|
||||
- [*"Deep-Live-Cam Uses AI to Transform Your Face in Real-Time, Celebrities Included"*](https://www.techeblog.com/deep-live-cam-ai-transform-face/) - TechEBlog
|
||||
- [*"An AI tool that "makes you look like anyone" during a video call is going viral online"*](https://telegrafi.com/en/a-tool-that-makes-you-look-like-anyone-during-a-video-call-is-going-viral-on-the-Internet/) - Telegrafi
|
||||
- [*"This Deepfake Tool Turning Images Into Livestreams is Topping the GitHub Charts"*](https://decrypt.co/244565/this-deepfake-tool-turning-images-into-livestreams-is-topping-the-github-charts) - Emerge
|
||||
- [*"New Real-Time Face-Swapping AI Allows Anyone to Mimic Famous Faces"*](https://www.digitalmusicnews.com/2024/08/15/face-swapping-ai-real-time-mimic/) - Digital Music News
|
||||
- [*"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
|
||||
|
||||
## Credits
|
||||
|
||||
- [ffmpeg](https://ffmpeg.org/): for making video-related operations easy
|
||||
- [deepinsight](https://github.com/deepinsight): for their [insightface](https://github.com/deepinsight/insightface) project which provided a well-made library and models. Please be reminded that the [use of the model is for non-commercial research purposes only](https://github.com/deepinsight/insightface?tab=readme-ov-file#license).
|
||||
- [havok2-htwo](https://github.com/havok2-htwo): for sharing the code for webcam
|
||||
- [GosuDRM](https://github.com/GosuDRM): for the open version of roop
|
||||
- [pereiraroland26](https://github.com/pereiraroland26): Multiple faces support
|
||||
- [vic4key](https://github.com/vic4key): For supporting/contributing to this project
|
||||
- [kier007](https://github.com/kier007): for improving the user experience
|
||||
- [qitianai](https://github.com/qitianai): for multi-lingual support
|
||||
- and [all developers](https://github.com/hacksider/Deep-Live-Cam/graphs/contributors) behind libraries used in this project.
|
||||
- Footnote: Please be informed that the base author of the code is [s0md3v](https://github.com/s0md3v/roop)
|
||||
- All the wonderful users who helped make this project go viral by starring the repo ❤️
|
||||
|
||||
[](https://github.com/hacksider/Deep-Live-Cam/stargazers)
|
||||
- [ffmpeg](https://ffmpeg.org/): for making video related operations easy
|
||||
- [deepinsight](https://github.com/deepinsight): for their [insightface](https://github.com/deepinsight/insightface) project which provided a well-made library and models. Please be reminded that the [use of the model is for non-commercial research purposes only](https://github.com/deepinsight/insightface?tab=readme-ov-file#license).
|
||||
- [havok2-htwo](https://github.com/havok2-htwo) : for sharing the code for webcam
|
||||
- [GosuDRM](https://github.com/GosuDRM) : for open version of roop
|
||||
- [pereiraroland26](https://github.com/pereiraroland26) : Multiple faces support
|
||||
- [vic4key](https://github.com/vic4key) : For supporting/contributing on this project
|
||||
- [KRSHH](https://github.com/KRSHH) : For updating the UI
|
||||
- and [all developers](https://github.com/hacksider/Deep-Live-Cam/graphs/contributors) behind libraries used in this project.
|
||||
- Foot Note: [This is originally roop-cam, see the full history of the code here.](https://github.com/hacksider/roop-cam) Please be informed that the base author of the code is [s0md3v](https://github.com/s0md3v/roop)
|
||||
|
||||
## Contributions
|
||||
|
||||
|
||||
|
||||
## Stars to the Moon 🚀
|
||||
## Star History
|
||||
|
||||
<a href="https://star-history.com/#hacksider/deep-live-cam&Date">
|
||||
<picture>
|
||||
@@ -313,5 +410,3 @@ Looking for a CLI mode? Using the -s/--source argument will make the run program
|
||||
<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date" />
|
||||
</picture>
|
||||
</a>
|
||||
|
||||
|
||||
|
||||
@@ -1,46 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "Source x Target Mapper",
|
||||
"select an source image": "选择一个源图像",
|
||||
"Preview": "预览",
|
||||
"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 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",
|
||||
"Keep audio": "保留音频",
|
||||
"Face Enhancer": "面纹增强器",
|
||||
"Many faces": "多脸",
|
||||
"Show FPS": "显示帧率",
|
||||
"Keep fps": "保持帧率",
|
||||
"Keep frames": "保持帧数",
|
||||
"Fix Blueish Cam": "修复偏蓝的摄像头",
|
||||
"Mouth Mask": "口罩",
|
||||
"Show Mouth Mask Box": "显示口罩盒",
|
||||
"Start": "开始",
|
||||
"Live": "直播",
|
||||
"Destroy": "结束",
|
||||
"Map faces": "识别人脸",
|
||||
"Processing...": "处理中...",
|
||||
"Processing succeed!": "处理成功!",
|
||||
"Processing ignored!": "处理被忽略!",
|
||||
"Failed to start camera": "启动相机失败",
|
||||
"Please complete pop-up or close it.": "请先完成弹出窗口或者关闭它",
|
||||
"Getting unique faces": "获取独特面部",
|
||||
"Please select a source image first": "请先选择一个源图像",
|
||||
"No faces found in target": "目标图像中没有人脸",
|
||||
"Add": "添加",
|
||||
"Clear": "清除",
|
||||
"Submit": "确认",
|
||||
"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",
|
||||
"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。"
|
||||
}
|
||||
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|
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@@ -1,4 +1,4 @@
|
||||
just put the models in this folder -
|
||||
|
||||
https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx?download=true
|
||||
https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth
|
||||
https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth
|
||||
+5
-9
@@ -20,7 +20,6 @@ import modules.metadata
|
||||
import modules.ui as ui
|
||||
from modules.processors.frame.core import get_frame_processors_modules
|
||||
from modules.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
|
||||
from modules.fake_face_handler import cleanup_fake_face
|
||||
|
||||
if 'ROCMExecutionProvider' in modules.globals.execution_providers:
|
||||
del torch
|
||||
@@ -36,14 +35,14 @@ def parse_args() -> None:
|
||||
program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
|
||||
program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
|
||||
program.add_argument('--frame-processor', help='pipeline of frame processors', dest='frame_processor', default=['face_swapper'], choices=['face_swapper', 'face_enhancer'], nargs='+')
|
||||
program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)
|
||||
program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
|
||||
program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
|
||||
program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)
|
||||
program.add_argument('--nsfw-filter', help='filter the NSFW image or video', dest='nsfw_filter', action='store_true', default=False)
|
||||
program.add_argument('--map-faces', help='map source target faces', dest='map_faces', action='store_true', default=False)
|
||||
program.add_argument('--mouth-mask', help='mask the mouth region', dest='mouth_mask', action='store_true', default=False)
|
||||
program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
|
||||
program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')
|
||||
program.add_argument('-l', '--lang', help='Ui language', default="en")
|
||||
program.add_argument('--live-mirror', help='The live camera display as you see it in the front-facing camera frame', dest='live_mirror', action='store_true', default=False)
|
||||
program.add_argument('--live-resizable', help='The live camera frame is resizable', dest='live_resizable', action='store_true', default=False)
|
||||
program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
|
||||
@@ -64,11 +63,10 @@ def parse_args() -> None:
|
||||
modules.globals.output_path = normalize_output_path(modules.globals.source_path, modules.globals.target_path, args.output_path)
|
||||
modules.globals.frame_processors = args.frame_processor
|
||||
modules.globals.headless = args.source_path or args.target_path or args.output_path
|
||||
modules.globals.keep_fps = True
|
||||
modules.globals.keep_frames = True
|
||||
modules.globals.keep_fps = args.keep_fps
|
||||
modules.globals.keep_audio = args.keep_audio
|
||||
modules.globals.keep_frames = args.keep_frames
|
||||
modules.globals.many_faces = args.many_faces
|
||||
modules.globals.mouth_mask = args.mouth_mask
|
||||
modules.globals.nsfw_filter = args.nsfw_filter
|
||||
modules.globals.map_faces = args.map_faces
|
||||
modules.globals.video_encoder = args.video_encoder
|
||||
@@ -78,7 +76,6 @@ def parse_args() -> None:
|
||||
modules.globals.max_memory = args.max_memory
|
||||
modules.globals.execution_providers = decode_execution_providers(args.execution_provider)
|
||||
modules.globals.execution_threads = args.execution_threads
|
||||
modules.globals.lang = args.lang
|
||||
|
||||
#for ENHANCER tumbler:
|
||||
if 'face_enhancer' in args.frame_processor:
|
||||
@@ -240,7 +237,6 @@ def start() -> None:
|
||||
def destroy(to_quit=True) -> None:
|
||||
if modules.globals.target_path:
|
||||
clean_temp(modules.globals.target_path)
|
||||
cleanup_fake_face()
|
||||
if to_quit: quit()
|
||||
|
||||
|
||||
@@ -255,5 +251,5 @@ def run() -> None:
|
||||
if modules.globals.headless:
|
||||
start()
|
||||
else:
|
||||
window = ui.init(start, destroy, modules.globals.lang)
|
||||
window = ui.init(start, destroy)
|
||||
window.mainloop()
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 264 KiB |
@@ -1,120 +0,0 @@
|
||||
import os
|
||||
import requests
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
import cv2
|
||||
import numpy as np
|
||||
import modules.globals
|
||||
|
||||
def add_padding_to_face(image, padding_ratio=0.3):
|
||||
"""Add padding around the face image
|
||||
|
||||
Args:
|
||||
image: The input face image
|
||||
padding_ratio: Amount of padding to add as a ratio of image dimensions
|
||||
|
||||
Returns:
|
||||
Padded image with background padding added
|
||||
"""
|
||||
if image is None:
|
||||
return None
|
||||
|
||||
height, width = image.shape[:2]
|
||||
pad_x = int(width * padding_ratio)
|
||||
pad_y = int(height * padding_ratio)
|
||||
|
||||
# Create larger image with padding
|
||||
padded_height = height + 2 * pad_y
|
||||
padded_width = width + 2 * pad_x
|
||||
padded_image = np.zeros((padded_height, padded_width, 3), dtype=np.uint8)
|
||||
|
||||
# Fill padded area with blurred and darkened edge pixels
|
||||
edge_color = cv2.blur(image, (15, 15))
|
||||
edge_color = (edge_color * 0.6).astype(np.uint8) # Darken the padding
|
||||
|
||||
# Fill the padded image with original face
|
||||
padded_image[pad_y:pad_y+height, pad_x:pad_x+width] = image
|
||||
|
||||
# Fill padding areas with edge color
|
||||
# Top padding - repeat first row
|
||||
top_edge = edge_color[0, :, :]
|
||||
for i in range(pad_y):
|
||||
padded_image[i, pad_x:pad_x+width] = top_edge
|
||||
|
||||
# Bottom padding - repeat last row
|
||||
bottom_edge = edge_color[-1, :, :]
|
||||
for i in range(pad_y):
|
||||
padded_image[pad_y+height+i, pad_x:pad_x+width] = bottom_edge
|
||||
|
||||
# Left padding - repeat first column
|
||||
left_edge = edge_color[:, 0, :]
|
||||
for i in range(pad_x):
|
||||
padded_image[pad_y:pad_y+height, i] = left_edge
|
||||
|
||||
# Right padding - repeat last column
|
||||
right_edge = edge_color[:, -1, :]
|
||||
for i in range(pad_x):
|
||||
padded_image[pad_y:pad_y+height, pad_x+width+i] = right_edge
|
||||
|
||||
# Fill corners with nearest edge colors
|
||||
# Top-left corner
|
||||
padded_image[:pad_y, :pad_x] = edge_color[0, 0, :]
|
||||
# Top-right corner
|
||||
padded_image[:pad_y, pad_x+width:] = edge_color[0, -1, :]
|
||||
# Bottom-left corner
|
||||
padded_image[pad_y+height:, :pad_x] = edge_color[-1, 0, :]
|
||||
# Bottom-right corner
|
||||
padded_image[pad_y+height:, pad_x+width:] = edge_color[-1, -1, :]
|
||||
|
||||
return padded_image
|
||||
|
||||
def get_fake_face() -> str:
|
||||
"""Fetch a face from thispersondoesnotexist.com and save it temporarily"""
|
||||
try:
|
||||
# Create temp directory if it doesn't exist
|
||||
temp_dir = Path(tempfile.gettempdir()) / "deep-live-cam"
|
||||
temp_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Generate temp file path
|
||||
temp_file = temp_dir / "fake_face.jpg"
|
||||
|
||||
# Basic headers to mimic a browser request
|
||||
headers = {
|
||||
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
|
||||
}
|
||||
|
||||
# Fetch the image
|
||||
response = requests.get('https://thispersondoesnotexist.com', headers=headers)
|
||||
|
||||
if response.status_code == 200:
|
||||
# Read image from response
|
||||
image_array = np.asarray(bytearray(response.content), dtype=np.uint8)
|
||||
image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
|
||||
|
||||
# Add padding around the face
|
||||
padded_image = add_padding_to_face(image)
|
||||
|
||||
# Save the padded image
|
||||
cv2.imwrite(str(temp_file), padded_image)
|
||||
return str(temp_file)
|
||||
else:
|
||||
print(f"Failed to fetch fake face: {response.status_code}")
|
||||
return None
|
||||
except Exception as e:
|
||||
print(f"Error fetching fake face: {str(e)}")
|
||||
return None
|
||||
|
||||
def cleanup_fake_face():
|
||||
"""Clean up the temporary fake face image"""
|
||||
try:
|
||||
if modules.globals.fake_face_path and os.path.exists(modules.globals.fake_face_path):
|
||||
os.remove(modules.globals.fake_face_path)
|
||||
modules.globals.fake_face_path = None
|
||||
except Exception as e:
|
||||
print(f"Error cleaning up fake face: {str(e)}")
|
||||
|
||||
def refresh_fake_face():
|
||||
"""Refresh the fake face image"""
|
||||
cleanup_fake_face()
|
||||
modules.globals.fake_face_path = get_fake_face()
|
||||
return modules.globals.fake_face_path is not None
|
||||
@@ -1,26 +0,0 @@
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
class LanguageManager:
|
||||
def __init__(self, default_language="en"):
|
||||
self.current_language = default_language
|
||||
self.translations = {}
|
||||
self.load_language(default_language)
|
||||
|
||||
def load_language(self, language_code) -> bool:
|
||||
"""load language file"""
|
||||
if language_code == "en":
|
||||
return True
|
||||
try:
|
||||
file_path = Path(__file__).parent.parent / f"locales/{language_code}.json"
|
||||
with open(file_path, "r", encoding="utf-8") as file:
|
||||
self.translations = json.load(file)
|
||||
self.current_language = language_code
|
||||
return True
|
||||
except FileNotFoundError:
|
||||
print(f"Language file not found: {language_code}")
|
||||
return False
|
||||
|
||||
def _(self, key, default=None) -> str:
|
||||
"""get translate text"""
|
||||
return self.translations.get(key, default if default else key)
|
||||
+16
-22
@@ -16,37 +16,31 @@ source_path = None
|
||||
target_path = None
|
||||
output_path = None
|
||||
frame_processors: List[str] = []
|
||||
keep_fps = True
|
||||
keep_audio = True
|
||||
keep_frames = False
|
||||
many_faces = False
|
||||
map_faces = False
|
||||
color_correction = False
|
||||
nsfw_filter = False
|
||||
keep_fps = True # Initialize with default value
|
||||
keep_audio = True # Initialize with default value
|
||||
keep_frames = False # Initialize with default value
|
||||
many_faces = False # Initialize with default value
|
||||
map_faces = False # Initialize with default value
|
||||
color_correction = False # Initialize with default value
|
||||
nsfw_filter = False # Initialize with default value
|
||||
video_encoder = None
|
||||
video_quality = None
|
||||
live_mirror = False
|
||||
live_resizable = True
|
||||
live_mirror = False # Initialize with default value
|
||||
live_resizable = False # Initialize with default value
|
||||
max_memory = None
|
||||
execution_providers: List[str] = []
|
||||
execution_threads = None
|
||||
headless = None
|
||||
log_level = "error"
|
||||
fp_ui: Dict[str, bool] = {"face_enhancer": False}
|
||||
fp_ui: Dict[str, bool] = {"face_enhancer": False} # Initialize with default value
|
||||
camera_input_combobox = None
|
||||
webcam_preview_running = False
|
||||
show_fps = False
|
||||
show_fps = False # Initialize with default value
|
||||
mouth_mask = False
|
||||
show_mouth_mask_box = False
|
||||
mask_down_size = 0.5
|
||||
mask_size = 1.0
|
||||
mask_feather_ratio = 8
|
||||
mask_down_size = 0.50
|
||||
mask_size = 1
|
||||
mouth_mask_size = 1.0
|
||||
eyes_mask = False
|
||||
show_eyes_mask_box = False
|
||||
eyebrows_mask = False
|
||||
show_eyebrows_mask_box = False
|
||||
eyes_mask_size = 1.0
|
||||
eyebrows_mask_size = 1.0
|
||||
use_fake_face = False
|
||||
fake_face_path = None
|
||||
opacity_switch = False
|
||||
face_opacity = 100
|
||||
selected_camera = None
|
||||
|
||||
+3
-3
@@ -1,3 +1,3 @@
|
||||
name = 'Deep-Live-Cam'
|
||||
version = '1.8'
|
||||
edition = 'GitHub Edition'
|
||||
name = 'Deep Live Cam'
|
||||
version = '1.6.0'
|
||||
edition = 'Portable'
|
||||
|
||||
@@ -9,41 +9,23 @@ 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,
|
||||
is_video,
|
||||
)
|
||||
from modules.utilities import conditional_download, resolve_relative_path, is_image, is_video
|
||||
|
||||
FACE_ENHANCER = None
|
||||
THREAD_SEMAPHORE = threading.Semaphore()
|
||||
THREAD_LOCK = threading.Lock()
|
||||
NAME = "DLC.FACE-ENHANCER"
|
||||
|
||||
abs_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
models_dir = os.path.join(
|
||||
os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
|
||||
)
|
||||
NAME = 'DLC.FACE-ENHANCER'
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
download_directory_path = models_dir
|
||||
conditional_download(
|
||||
download_directory_path,
|
||||
[
|
||||
"https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth"
|
||||
],
|
||||
)
|
||||
download_directory_path = resolve_relative_path('..\models')
|
||||
conditional_download(download_directory_path, ['https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth'])
|
||||
return True
|
||||
|
||||
|
||||
def pre_start() -> bool:
|
||||
if not is_image(modules.globals.target_path) and not is_video(
|
||||
modules.globals.target_path
|
||||
):
|
||||
update_status("Select an image or video for target path.", NAME)
|
||||
if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
|
||||
update_status('Select an image or video for target path.', NAME)
|
||||
return False
|
||||
return True
|
||||
|
||||
@@ -53,24 +35,21 @@ def get_face_enhancer() -> Any:
|
||||
|
||||
with THREAD_LOCK:
|
||||
if FACE_ENHANCER is None:
|
||||
model_path = os.path.join(models_dir, "GFPGANv1.4.pth")
|
||||
|
||||
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]
|
||||
|
||||
if os.name == 'nt':
|
||||
model_path = resolve_relative_path('..\models\GFPGANv1.4.pth')
|
||||
# todo: set models path https://github.com/TencentARC/GFPGAN/issues/399
|
||||
else:
|
||||
model_path = resolve_relative_path('../models/GFPGANv1.4.pth')
|
||||
FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1) # type: ignore[attr-defined]
|
||||
return FACE_ENHANCER
|
||||
|
||||
|
||||
def enhance_face(temp_frame: Frame) -> Frame:
|
||||
with THREAD_SEMAPHORE:
|
||||
_, _, temp_frame = get_face_enhancer().enhance(temp_frame, paste_back=True)
|
||||
_, _, temp_frame = get_face_enhancer().enhance(
|
||||
temp_frame,
|
||||
paste_back=True
|
||||
)
|
||||
return temp_frame
|
||||
|
||||
|
||||
@@ -81,9 +60,7 @@ def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
|
||||
return temp_frame
|
||||
|
||||
|
||||
def process_frames(
|
||||
source_path: str, temp_frame_paths: List[str], progress: Any = None
|
||||
) -> None:
|
||||
def process_frames(source_path: str, temp_frame_paths: List[str], progress: Any = None) -> None:
|
||||
for temp_frame_path in temp_frame_paths:
|
||||
temp_frame = cv2.imread(temp_frame_path)
|
||||
result = process_frame(None, temp_frame)
|
||||
@@ -100,10 +77,3 @@ def process_image(source_path: str, target_path: str, output_path: str) -> None:
|
||||
|
||||
def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
|
||||
modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
|
||||
|
||||
|
||||
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
|
||||
|
||||
@@ -1,634 +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 forehead extension
|
||||
right_eyebrow_top = np.min(right_eye_brow[:, 1])
|
||||
left_eyebrow_top = np.min(left_eye_brow[:, 1])
|
||||
eyebrow_top = min(right_eyebrow_top, left_eyebrow_top)
|
||||
|
||||
face_top = np.min([right_side_face[0, 1], left_side_face[-1, 1]])
|
||||
forehead_height = face_top - eyebrow_top
|
||||
extended_forehead_height = int(forehead_height * 5.0) # Extend by 50%
|
||||
|
||||
# Create forehead points
|
||||
forehead_left = right_side_face[0].copy()
|
||||
forehead_right = left_side_face[-1].copy()
|
||||
forehead_left[1] -= extended_forehead_height
|
||||
forehead_right[1] -= extended_forehead_height
|
||||
|
||||
# Combine all points to create the face outline
|
||||
face_outline = np.vstack(
|
||||
[
|
||||
[forehead_left],
|
||||
right_side_face,
|
||||
left_side_face[::-1], # Reverse left side to create a continuous outline
|
||||
[forehead_right],
|
||||
]
|
||||
)
|
||||
|
||||
# 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
|
||||
@@ -10,32 +10,19 @@ from modules.face_analyser import get_one_face, get_many_faces, default_source_f
|
||||
from modules.typing import Face, Frame
|
||||
from modules.utilities import (
|
||||
conditional_download,
|
||||
resolve_relative_path,
|
||||
is_image,
|
||||
is_video,
|
||||
)
|
||||
from modules.cluster_analysis import find_closest_centroid
|
||||
from modules.processors.frame.face_masking import (
|
||||
create_face_mask,
|
||||
create_lower_mouth_mask,
|
||||
create_eyes_mask,
|
||||
create_eyebrows_mask,
|
||||
apply_mask_area,
|
||||
draw_mask_visualization
|
||||
)
|
||||
import os
|
||||
|
||||
FACE_SWAPPER = None
|
||||
THREAD_LOCK = threading.Lock()
|
||||
NAME = "DLC.FACE-SWAPPER"
|
||||
|
||||
abs_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
models_dir = os.path.join(
|
||||
os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
|
||||
)
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
download_directory_path = abs_dir
|
||||
download_directory_path = resolve_relative_path("../models")
|
||||
conditional_download(
|
||||
download_directory_path,
|
||||
[
|
||||
@@ -67,7 +54,7 @@ def get_face_swapper() -> Any:
|
||||
|
||||
with THREAD_LOCK:
|
||||
if FACE_SWAPPER is None:
|
||||
model_path = os.path.join(models_dir, "inswapper_128_fp16.onnx")
|
||||
model_path = resolve_relative_path("../models/inswapper_128_fp16.onnx")
|
||||
FACE_SWAPPER = insightface.model_zoo.get_model(
|
||||
model_path, providers=modules.globals.execution_providers
|
||||
)
|
||||
@@ -75,75 +62,38 @@ def get_face_swapper() -> Any:
|
||||
|
||||
|
||||
def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
|
||||
face_swapper = get_face_swapper()
|
||||
|
||||
# Apply the face swap
|
||||
swapped_frame = face_swapper.get(
|
||||
swapped_frame = get_face_swapper().get(
|
||||
temp_frame, target_face, source_face, paste_back=True
|
||||
)
|
||||
|
||||
# Create face mask for both mouth and eyes masking
|
||||
face_mask = create_face_mask(target_face, temp_frame)
|
||||
# Apply opacity if enabled
|
||||
if modules.globals.opacity_switch:
|
||||
opacity = modules.globals.face_opacity / 100
|
||||
swapped_frame = cv2.addWeighted(
|
||||
swapped_frame, opacity, temp_frame, 1 - opacity, 0
|
||||
)
|
||||
|
||||
# Apply mouth mask if enabled
|
||||
if modules.globals.mouth_mask:
|
||||
# Create and apply mouth mask
|
||||
face_mask = create_face_mask(target_face, temp_frame)
|
||||
mouth_mask_data = create_lower_mouth_mask(target_face, temp_frame)
|
||||
swapped_frame = apply_mask_area(
|
||||
swapped_frame,
|
||||
mouth_mask_data[1], # mouth_cutout
|
||||
mouth_mask_data[2], # mouth_box
|
||||
face_mask,
|
||||
mouth_mask_data[3] # mouth_polygon
|
||||
)
|
||||
mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = mouth_mask_data
|
||||
|
||||
if modules.globals.show_mouth_mask_box:
|
||||
swapped_frame = draw_mask_visualization(
|
||||
swapped_frame,
|
||||
mouth_mask_data,
|
||||
"Lower Mouth Mask"
|
||||
if mouth_box is not None:
|
||||
swapped_frame = apply_mouth_area(
|
||||
swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
|
||||
)
|
||||
|
||||
if modules.globals.eyes_mask:
|
||||
# Create and apply eyes mask
|
||||
eyes_mask_data = create_eyes_mask(target_face, temp_frame)
|
||||
swapped_frame = apply_mask_area(
|
||||
swapped_frame,
|
||||
eyes_mask_data[1], # eyes_cutout
|
||||
eyes_mask_data[2], # eyes_box
|
||||
face_mask,
|
||||
eyes_mask_data[3] # eyes_polygon
|
||||
)
|
||||
|
||||
if modules.globals.show_eyes_mask_box:
|
||||
swapped_frame = draw_mask_visualization(
|
||||
swapped_frame,
|
||||
eyes_mask_data,
|
||||
"Eyes Mask",
|
||||
draw_method="ellipse"
|
||||
)
|
||||
|
||||
if modules.globals.eyebrows_mask:
|
||||
# Create and apply eyebrows mask
|
||||
eyebrows_mask_data = create_eyebrows_mask(target_face, temp_frame)
|
||||
swapped_frame = apply_mask_area(
|
||||
swapped_frame,
|
||||
eyebrows_mask_data[1], # eyebrows_cutout
|
||||
eyebrows_mask_data[2], # eyebrows_box
|
||||
face_mask,
|
||||
eyebrows_mask_data[3] # eyebrows_polygon
|
||||
)
|
||||
|
||||
if modules.globals.show_eyebrows_mask_box:
|
||||
swapped_frame = draw_mask_visualization(
|
||||
swapped_frame,
|
||||
eyebrows_mask_data,
|
||||
"Eyebrows Mask"
|
||||
)
|
||||
if modules.globals.show_mouth_mask_box:
|
||||
swapped_frame = draw_mouth_mask_visualization(
|
||||
swapped_frame, target_face, mouth_mask_data
|
||||
)
|
||||
|
||||
return swapped_frame
|
||||
|
||||
|
||||
def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
|
||||
# Ensure the frame is in RGB format if color correction is enabled
|
||||
if modules.globals.color_correction:
|
||||
temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
@@ -201,7 +151,6 @@ def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
|
||||
for frame in target_frame:
|
||||
for target_face in frame["faces"]:
|
||||
temp_frame = swap_face(source_face, target_face, temp_frame)
|
||||
|
||||
else:
|
||||
detected_faces = get_many_faces(temp_frame)
|
||||
if modules.globals.many_faces:
|
||||
@@ -301,3 +250,244 @@ def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
|
||||
modules.processors.frame.core.process_video(
|
||||
source_path, temp_frame_paths, process_frames
|
||||
)
|
||||
|
||||
|
||||
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:
|
||||
landmarks = landmarks.astype(np.int32)
|
||||
|
||||
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]
|
||||
|
||||
right_eyebrow_top = np.min(right_eye_brow[:, 1])
|
||||
left_eyebrow_top = np.min(left_eye_brow[:, 1])
|
||||
eyebrow_top = min(right_eyebrow_top, left_eyebrow_top)
|
||||
|
||||
face_top = np.min([right_side_face[0, 1], left_side_face[-1, 1]])
|
||||
forehead_height = face_top - eyebrow_top
|
||||
extended_forehead_height = int(forehead_height * 5.0)
|
||||
|
||||
forehead_left = right_side_face[0].copy()
|
||||
forehead_right = left_side_face[-1].copy()
|
||||
forehead_left[1] -= extended_forehead_height
|
||||
forehead_right[1] -= extended_forehead_height
|
||||
|
||||
face_outline = np.vstack(
|
||||
[
|
||||
[forehead_left],
|
||||
right_side_face,
|
||||
left_side_face[::-1],
|
||||
[forehead_right],
|
||||
]
|
||||
)
|
||||
|
||||
padding = int(np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05)
|
||||
|
||||
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)
|
||||
cv2.fillConvexPoly(mask, hull_padded, 255)
|
||||
mask = cv2.GaussianBlur(mask, (5, 5), 3)
|
||||
|
||||
return mask
|
||||
|
||||
|
||||
def create_lower_mouth_mask(face: Face, frame: Frame) -> tuple:
|
||||
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
|
||||
mouth_cutout = None
|
||||
landmarks = face.landmark_2d_106
|
||||
if landmarks is not None:
|
||||
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)
|
||||
|
||||
center = np.mean(lower_lip_landmarks, axis=0)
|
||||
expansion_factor = 1 + modules.globals.mask_down_size
|
||||
expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
|
||||
|
||||
toplip_indices = [20, 0, 1, 2, 3, 4, 5]
|
||||
toplip_extension = modules.globals.mask_size * 0.5
|
||||
for idx in toplip_indices:
|
||||
direction = expanded_landmarks[idx] - center
|
||||
direction = direction / np.linalg.norm(direction)
|
||||
expanded_landmarks[idx] += direction * toplip_extension
|
||||
|
||||
chin_indices = [11, 12, 13, 14, 15, 16]
|
||||
chin_extension = 2 * 0.2
|
||||
for idx in chin_indices:
|
||||
expanded_landmarks[idx][1] += (
|
||||
expanded_landmarks[idx][1] - center[1]
|
||||
) * chin_extension
|
||||
|
||||
expanded_landmarks = expanded_landmarks.astype(np.int32)
|
||||
|
||||
min_x, min_y = np.min(expanded_landmarks, axis=0)
|
||||
max_x, max_y = np.max(expanded_landmarks, axis=0)
|
||||
|
||||
padding = int((max_x - min_x) * 0.1)
|
||||
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)
|
||||
|
||||
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
|
||||
|
||||
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)
|
||||
mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
|
||||
mask[min_y:max_y, min_x:max_x] = mask_roi
|
||||
mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
|
||||
|
||||
return mask, mouth_cutout, (min_x, min_y, max_x, max_y), expanded_landmarks
|
||||
|
||||
return mask, mouth_cutout, None, None
|
||||
|
||||
|
||||
def apply_mouth_area(
|
||||
frame: Frame,
|
||||
mouth_cutout: np.ndarray,
|
||||
mouth_box: tuple,
|
||||
face_mask: np.ndarray,
|
||||
mouth_polygon: np.ndarray,
|
||||
) -> Frame:
|
||||
min_x, min_y, max_x, max_y = mouth_box
|
||||
box_width = max_x - min_x
|
||||
box_height = max_y - min_y
|
||||
|
||||
if (
|
||||
mouth_cutout is None
|
||||
or box_width is None
|
||||
or box_height is None
|
||||
or face_mask is None
|
||||
or mouth_polygon is None
|
||||
):
|
||||
return frame
|
||||
|
||||
try:
|
||||
resized_mouth_cutout = cv2.resize(mouth_cutout, (box_width, box_height))
|
||||
roi = frame[min_y:max_y, min_x:max_x]
|
||||
|
||||
if roi.shape != resized_mouth_cutout.shape:
|
||||
resized_mouth_cutout = cv2.resize(
|
||||
resized_mouth_cutout, (roi.shape[1], roi.shape[0])
|
||||
)
|
||||
|
||||
color_corrected_mouth = apply_color_transfer(resized_mouth_cutout, roi)
|
||||
|
||||
polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
|
||||
adjusted_polygon = mouth_polygon - [min_x, min_y]
|
||||
cv2.fillPoly(polygon_mask, [adjusted_polygon], 255)
|
||||
|
||||
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()
|
||||
|
||||
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_mouth * combined_mask + roi * (1 - combined_mask)
|
||||
).astype(np.uint8)
|
||||
|
||||
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 apply_color_transfer(source: np.ndarray, target: np.ndarray) -> np.ndarray:
|
||||
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)
|
||||
|
||||
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)
|
||||
|
||||
source = (source - source_mean) * (target_std / source_std) + target_mean
|
||||
|
||||
return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)
|
||||
|
||||
|
||||
def draw_mouth_mask_visualization(
|
||||
frame: Frame, face: Face, mouth_mask_data: tuple
|
||||
) -> Frame:
|
||||
landmarks = face.landmark_2d_106
|
||||
if landmarks is not None and mouth_mask_data is not None:
|
||||
mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon = (
|
||||
mouth_mask_data
|
||||
)
|
||||
|
||||
vis_frame = frame.copy()
|
||||
|
||||
# Draw the lower lip polygon
|
||||
cv2.polylines(vis_frame, [lower_lip_polygon], True, (0, 255, 0), 2)
|
||||
|
||||
# Add labels
|
||||
cv2.putText(
|
||||
vis_frame,
|
||||
"Mouth Mask",
|
||||
(min_x, min_y - 10),
|
||||
cv2.FONT_HERSHEY_SIMPLEX,
|
||||
0.5,
|
||||
(255, 255, 255),
|
||||
1,
|
||||
)
|
||||
|
||||
return vis_frame
|
||||
return frame
|
||||
|
||||
+1064
-624
File diff suppressed because it is too large
Load Diff
+19
-87
@@ -12,23 +12,16 @@ from tqdm import tqdm
|
||||
|
||||
import modules.globals
|
||||
|
||||
TEMP_FILE = "temp.mp4"
|
||||
TEMP_DIRECTORY = "temp"
|
||||
TEMP_FILE = 'temp.mp4'
|
||||
TEMP_DIRECTORY = 'temp'
|
||||
|
||||
# monkey patch ssl for mac
|
||||
if platform.system().lower() == "darwin":
|
||||
if platform.system().lower() == 'darwin':
|
||||
ssl._create_default_https_context = ssl._create_unverified_context
|
||||
|
||||
|
||||
def run_ffmpeg(args: List[str]) -> bool:
|
||||
commands = [
|
||||
"ffmpeg",
|
||||
"-hide_banner",
|
||||
"-hwaccel",
|
||||
"auto",
|
||||
"-loglevel",
|
||||
modules.globals.log_level,
|
||||
]
|
||||
commands = ['ffmpeg', '-hide_banner', '-hwaccel', 'auto', '-loglevel', modules.globals.log_level]
|
||||
commands.extend(args)
|
||||
try:
|
||||
subprocess.check_output(commands, stderr=subprocess.STDOUT)
|
||||
@@ -39,19 +32,8 @@ def run_ffmpeg(args: List[str]) -> bool:
|
||||
|
||||
|
||||
def detect_fps(target_path: str) -> float:
|
||||
command = [
|
||||
"ffprobe",
|
||||
"-v",
|
||||
"error",
|
||||
"-select_streams",
|
||||
"v:0",
|
||||
"-show_entries",
|
||||
"stream=r_frame_rate",
|
||||
"-of",
|
||||
"default=noprint_wrappers=1:nokey=1",
|
||||
target_path,
|
||||
]
|
||||
output = subprocess.check_output(command).decode().strip().split("/")
|
||||
command = ['ffprobe', '-v', 'error', '-select_streams', 'v:0', '-show_entries', 'stream=r_frame_rate', '-of', 'default=noprint_wrappers=1:nokey=1', target_path]
|
||||
output = subprocess.check_output(command).decode().strip().split('/')
|
||||
try:
|
||||
numerator, denominator = map(int, output)
|
||||
return numerator / denominator
|
||||
@@ -62,65 +44,25 @@ def detect_fps(target_path: str) -> float:
|
||||
|
||||
def extract_frames(target_path: str) -> None:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
run_ffmpeg(
|
||||
[
|
||||
"-i",
|
||||
target_path,
|
||||
"-pix_fmt",
|
||||
"rgb24",
|
||||
os.path.join(temp_directory_path, "%04d.png"),
|
||||
]
|
||||
)
|
||||
run_ffmpeg(['-i', target_path, '-pix_fmt', 'rgb24', os.path.join(temp_directory_path, '%04d.png')])
|
||||
|
||||
|
||||
def create_video(target_path: str, fps: float = 30.0) -> None:
|
||||
temp_output_path = get_temp_output_path(target_path)
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
run_ffmpeg(
|
||||
[
|
||||
"-r",
|
||||
str(fps),
|
||||
"-i",
|
||||
os.path.join(temp_directory_path, "%04d.png"),
|
||||
"-c:v",
|
||||
modules.globals.video_encoder,
|
||||
"-crf",
|
||||
str(modules.globals.video_quality),
|
||||
"-pix_fmt",
|
||||
"yuv420p",
|
||||
"-vf",
|
||||
"colorspace=bt709:iall=bt601-6-625:fast=1",
|
||||
"-y",
|
||||
temp_output_path,
|
||||
]
|
||||
)
|
||||
run_ffmpeg(['-r', str(fps), '-i', os.path.join(temp_directory_path, '%04d.png'), '-c:v', modules.globals.video_encoder, '-crf', str(modules.globals.video_quality), '-pix_fmt', 'yuv420p', '-vf', 'colorspace=bt709:iall=bt601-6-625:fast=1', '-y', temp_output_path])
|
||||
|
||||
|
||||
def restore_audio(target_path: str, output_path: str) -> None:
|
||||
temp_output_path = get_temp_output_path(target_path)
|
||||
done = run_ffmpeg(
|
||||
[
|
||||
"-i",
|
||||
temp_output_path,
|
||||
"-i",
|
||||
target_path,
|
||||
"-c:v",
|
||||
"copy",
|
||||
"-map",
|
||||
"0:v:0",
|
||||
"-map",
|
||||
"1:a:0",
|
||||
"-y",
|
||||
output_path,
|
||||
]
|
||||
)
|
||||
done = run_ffmpeg(['-i', temp_output_path, '-i', target_path, '-c:v', 'copy', '-map', '0:v:0', '-map', '1:a:0', '-y', output_path])
|
||||
if not done:
|
||||
move_temp(target_path, output_path)
|
||||
|
||||
|
||||
def get_temp_frame_paths(target_path: str) -> List[str]:
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
return glob.glob((os.path.join(glob.escape(temp_directory_path), "*.png")))
|
||||
return glob.glob((os.path.join(glob.escape(temp_directory_path), '*.png')))
|
||||
|
||||
|
||||
def get_temp_directory_path(target_path: str) -> str:
|
||||
@@ -139,9 +81,7 @@ def normalize_output_path(source_path: str, target_path: str, output_path: str)
|
||||
source_name, _ = os.path.splitext(os.path.basename(source_path))
|
||||
target_name, target_extension = os.path.splitext(os.path.basename(target_path))
|
||||
if os.path.isdir(output_path):
|
||||
return os.path.join(
|
||||
output_path, source_name + "-" + target_name + target_extension
|
||||
)
|
||||
return os.path.join(output_path, source_name + '-' + target_name + target_extension)
|
||||
return output_path
|
||||
|
||||
|
||||
@@ -168,20 +108,20 @@ def clean_temp(target_path: str) -> None:
|
||||
|
||||
|
||||
def has_image_extension(image_path: str) -> bool:
|
||||
return image_path.lower().endswith(("png", "jpg", "jpeg"))
|
||||
return image_path.lower().endswith(('png', 'jpg', 'jpeg'))
|
||||
|
||||
|
||||
def is_image(image_path: str) -> bool:
|
||||
if image_path and os.path.isfile(image_path):
|
||||
mimetype, _ = mimetypes.guess_type(image_path)
|
||||
return bool(mimetype and mimetype.startswith("image/"))
|
||||
return bool(mimetype and mimetype.startswith('image/'))
|
||||
return False
|
||||
|
||||
|
||||
def is_video(video_path: str) -> bool:
|
||||
if video_path and os.path.isfile(video_path):
|
||||
mimetype, _ = mimetypes.guess_type(video_path)
|
||||
return bool(mimetype and mimetype.startswith("video/"))
|
||||
return bool(mimetype and mimetype.startswith('video/'))
|
||||
return False
|
||||
|
||||
|
||||
@@ -189,20 +129,12 @@ def conditional_download(download_directory_path: str, urls: List[str]) -> None:
|
||||
if not os.path.exists(download_directory_path):
|
||||
os.makedirs(download_directory_path)
|
||||
for url in urls:
|
||||
download_file_path = os.path.join(
|
||||
download_directory_path, os.path.basename(url)
|
||||
)
|
||||
download_file_path = os.path.join(download_directory_path, os.path.basename(url))
|
||||
if not os.path.exists(download_file_path):
|
||||
request = urllib.request.urlopen(url) # type: ignore[attr-defined]
|
||||
total = int(request.headers.get("Content-Length", 0))
|
||||
with tqdm(
|
||||
total=total,
|
||||
desc="Downloading",
|
||||
unit="B",
|
||||
unit_scale=True,
|
||||
unit_divisor=1024,
|
||||
) as progress:
|
||||
urllib.request.urlretrieve(url, download_file_path, reporthook=lambda count, block_size, total_size: progress.update(block_size)) # type: ignore[attr-defined]
|
||||
request = urllib.request.urlopen(url) # type: ignore[attr-defined]
|
||||
total = int(request.headers.get('Content-Length', 0))
|
||||
with tqdm(total=total, desc='Downloading', unit='B', unit_scale=True, unit_divisor=1024) as progress:
|
||||
urllib.request.urlretrieve(url, download_file_path, reporthook=lambda count, block_size, total_size: progress.update(block_size)) # type: ignore[attr-defined]
|
||||
|
||||
|
||||
def resolve_relative_path(path: str) -> str:
|
||||
|
||||
@@ -1,94 +0,0 @@
|
||||
import cv2
|
||||
import numpy as np
|
||||
from typing import Optional, Tuple, Callable
|
||||
import platform
|
||||
import threading
|
||||
|
||||
# Only import Windows-specific library if on Windows
|
||||
if platform.system() == "Windows":
|
||||
from pygrabber.dshow_graph import FilterGraph
|
||||
|
||||
|
||||
class VideoCapturer:
|
||||
def __init__(self, device_index: int):
|
||||
self.device_index = device_index
|
||||
self.frame_callback = None
|
||||
self._current_frame = None
|
||||
self._frame_ready = threading.Event()
|
||||
self.is_running = False
|
||||
self.cap = None
|
||||
|
||||
# Initialize Windows-specific components if on Windows
|
||||
if platform.system() == "Windows":
|
||||
self.graph = FilterGraph()
|
||||
# Verify device exists
|
||||
devices = self.graph.get_input_devices()
|
||||
if self.device_index >= len(devices):
|
||||
raise ValueError(
|
||||
f"Invalid device index {device_index}. Available devices: {len(devices)}"
|
||||
)
|
||||
|
||||
def start(self, width: int = 960, height: int = 540, fps: int = 60) -> bool:
|
||||
"""Initialize and start video capture"""
|
||||
try:
|
||||
if platform.system() == "Windows":
|
||||
# Windows-specific capture methods
|
||||
capture_methods = [
|
||||
(self.device_index, cv2.CAP_DSHOW), # Try DirectShow first
|
||||
(self.device_index, cv2.CAP_ANY), # Then try default backend
|
||||
(-1, cv2.CAP_ANY), # Try -1 as fallback
|
||||
(0, cv2.CAP_ANY), # Finally try 0 without specific backend
|
||||
]
|
||||
|
||||
for dev_id, backend in capture_methods:
|
||||
try:
|
||||
self.cap = cv2.VideoCapture(dev_id, backend)
|
||||
if self.cap.isOpened():
|
||||
break
|
||||
self.cap.release()
|
||||
except Exception:
|
||||
continue
|
||||
else:
|
||||
# Unix-like systems (Linux/Mac) capture method
|
||||
self.cap = cv2.VideoCapture(self.device_index)
|
||||
|
||||
if not self.cap or not self.cap.isOpened():
|
||||
raise RuntimeError("Failed to open camera")
|
||||
|
||||
# Configure format
|
||||
self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
|
||||
self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
|
||||
self.cap.set(cv2.CAP_PROP_FPS, fps)
|
||||
|
||||
self.is_running = True
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"Failed to start capture: {str(e)}")
|
||||
if self.cap:
|
||||
self.cap.release()
|
||||
return False
|
||||
|
||||
def read(self) -> Tuple[bool, Optional[np.ndarray]]:
|
||||
"""Read a frame from the camera"""
|
||||
if not self.is_running or self.cap is None:
|
||||
return False, None
|
||||
|
||||
ret, frame = self.cap.read()
|
||||
if ret:
|
||||
self._current_frame = frame
|
||||
if self.frame_callback:
|
||||
self.frame_callback(frame)
|
||||
return True, frame
|
||||
return False, None
|
||||
|
||||
def release(self) -> None:
|
||||
"""Stop capture and release resources"""
|
||||
if self.is_running and self.cap is not None:
|
||||
self.cap.release()
|
||||
self.is_running = False
|
||||
self.cap = None
|
||||
|
||||
def set_frame_callback(self, callback: Callable[[np.ndarray], None]) -> None:
|
||||
"""Set callback for frame processing"""
|
||||
self.frame_callback = callback
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 13 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 31 KiB |
+3
-4
@@ -1,14 +1,13 @@
|
||||
--extra-index-url https://download.pytorch.org/whl/cu118
|
||||
|
||||
numpy>=1.23.5,<2
|
||||
opencv-python==4.10.0.84
|
||||
opencv-python==4.8.1.78
|
||||
cv2_enumerate_cameras==1.1.15
|
||||
onnx==1.16.0
|
||||
insightface==0.7.3
|
||||
psutil==5.9.8
|
||||
tk==0.1.0
|
||||
customtkinter==5.2.2
|
||||
pillow==11.1.0
|
||||
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'
|
||||
@@ -21,4 +20,4 @@ protobuf==4.23.2
|
||||
tqdm==4.66.4
|
||||
gfpgan==1.3.8
|
||||
tkinterdnd2==0.4.2
|
||||
pygrabber==0.2
|
||||
customtkinter==5.2.2
|
||||
|
||||
+1
-1
@@ -1 +1 @@
|
||||
python run.py --execution-provider cuda
|
||||
python run.py --execution-provider cuda --execution-threads 60 --max-memory 60
|
||||
@@ -1 +0,0 @@
|
||||
python run.py --execution-provider dml
|
||||
@@ -0,0 +1 @@
|
||||
python run.py --execution-provider dml
|
||||
@@ -0,0 +1,13 @@
|
||||
@echo off
|
||||
:: Installing Microsoft Visual C++ Runtime - all versions 1.0.1 if it's not already installed
|
||||
choco install vcredist-all
|
||||
:: Installing CUDA if it's not already installed
|
||||
choco install cuda
|
||||
:: Inatalling ffmpeg if it's not already installed
|
||||
choco install ffmpeg
|
||||
:: Installing Python if it's not already installed
|
||||
choco install python -y
|
||||
:: Assuming successful installation, we ensure pip is upgraded
|
||||
python -m ensurepip --upgrade
|
||||
:: Use pip to install the packages listed in 'requirements.txt'
|
||||
pip install -r requirements.txt
|
||||
@@ -0,0 +1,122 @@
|
||||
@echo off
|
||||
setlocal EnableDelayedExpansion
|
||||
|
||||
:: 1. Setup your platform
|
||||
echo Setting up your platform...
|
||||
|
||||
:: Python
|
||||
where python >nul 2>&1
|
||||
if %ERRORLEVEL% neq 0 (
|
||||
echo Python is not installed. Please install Python 3.10 or later.
|
||||
pause
|
||||
exit /b
|
||||
)
|
||||
|
||||
:: Pip
|
||||
where pip >nul 2>&1
|
||||
if %ERRORLEVEL% neq 0 (
|
||||
echo Pip is not installed. Please install Pip.
|
||||
pause
|
||||
exit /b
|
||||
)
|
||||
|
||||
:: Git
|
||||
where git >nul 2>&1
|
||||
if %ERRORLEVEL% neq 0 (
|
||||
echo Git is not installed. Installing Git...
|
||||
winget install --id Git.Git -e --source winget
|
||||
)
|
||||
|
||||
:: FFMPEG
|
||||
where ffmpeg >nul 2>&1
|
||||
if %ERRORLEVEL% neq 0 (
|
||||
echo FFMPEG is not installed. Installing FFMPEG...
|
||||
winget install --id Gyan.FFmpeg -e --source winget
|
||||
)
|
||||
|
||||
:: Visual Studio 2022 Runtimes
|
||||
echo Installing Visual Studio 2022 Runtimes...
|
||||
winget install --id Microsoft.VC++2015-2022Redist-x64 -e --source winget
|
||||
|
||||
:: 2. Clone Repository
|
||||
if exist Deep-Live-Cam (
|
||||
echo Deep-Live-Cam directory already exists.
|
||||
set /p overwrite="Do you want to overwrite? (Y/N): "
|
||||
if /i "%overwrite%"=="Y" (
|
||||
rmdir /s /q Deep-Live-Cam
|
||||
git clone https://github.com/hacksider/Deep-Live-Cam.git
|
||||
) else (
|
||||
echo Skipping clone, using existing directory.
|
||||
)
|
||||
) else (
|
||||
git clone https://github.com/hacksider/Deep-Live-Cam.git
|
||||
)
|
||||
cd Deep-Live-Cam
|
||||
|
||||
:: 3. Download Models
|
||||
echo Downloading models...
|
||||
mkdir models
|
||||
curl -L -o models/GFPGANv1.4.pth https://path.to.model/GFPGANv1.4.pth
|
||||
curl -L -o models/inswapper_128_fp16.onnx https://path.to.model/inswapper_128_fp16.onnx
|
||||
|
||||
:: 4. Install dependencies
|
||||
echo Creating a virtual environment...
|
||||
python -m venv venv
|
||||
call venv\Scripts\activate
|
||||
|
||||
echo Installing required Python packages...
|
||||
pip install --upgrade pip
|
||||
pip install -r requirements.txt
|
||||
|
||||
echo Setup complete. You can now run the application.
|
||||
|
||||
:: GPU Acceleration Options
|
||||
echo.
|
||||
echo Choose the GPU Acceleration Option if applicable:
|
||||
echo 1. CUDA (Nvidia)
|
||||
echo 2. CoreML (Apple Silicon)
|
||||
echo 3. CoreML (Apple Legacy)
|
||||
echo 4. DirectML (Windows)
|
||||
echo 5. OpenVINO (Intel)
|
||||
echo 6. None
|
||||
set /p choice="Enter your choice (1-6): "
|
||||
|
||||
if "%choice%"=="1" (
|
||||
echo Installing CUDA dependencies...
|
||||
pip uninstall -y onnxruntime onnxruntime-gpu
|
||||
pip install onnxruntime-gpu==1.16.3
|
||||
set exec_provider="cuda"
|
||||
) else if "%choice%"=="2" (
|
||||
echo Installing CoreML (Apple Silicon) dependencies...
|
||||
pip uninstall -y onnxruntime onnxruntime-silicon
|
||||
pip install onnxruntime-silicon==1.13.1
|
||||
set exec_provider="coreml"
|
||||
) else if "%choice%"=="3" (
|
||||
echo Installing CoreML (Apple Legacy) dependencies...
|
||||
pip uninstall -y onnxruntime onnxruntime-coreml
|
||||
pip install onnxruntime-coreml==1.13.1
|
||||
set exec_provider="coreml"
|
||||
) else if "%choice%"=="4" (
|
||||
echo Installing DirectML dependencies...
|
||||
pip uninstall -y onnxruntime onnxruntime-directml
|
||||
pip install onnxruntime-directml==1.15.1
|
||||
set exec_provider="directml"
|
||||
) else if "%choice%"=="5" (
|
||||
echo Installing OpenVINO dependencies...
|
||||
pip uninstall -y onnxruntime onnxruntime-openvino
|
||||
pip install onnxruntime-openvino==1.15.0
|
||||
set exec_provider="openvino"
|
||||
) else (
|
||||
echo Skipping GPU acceleration setup.
|
||||
)
|
||||
|
||||
:: Run the application
|
||||
if defined exec_provider (
|
||||
echo Running the application with %exec_provider% execution provider...
|
||||
python run.py --execution-provider %exec_provider%
|
||||
) else (
|
||||
echo Running the application...
|
||||
python run.py
|
||||
)
|
||||
|
||||
pause
|
||||
Reference in New Issue
Block a user