Compare commits
5 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Kenneth Estanislao
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1d41a20abf | ||
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Kenneth Estanislao
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df940ccc3d | ||
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Kenneth Estanislao
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834f39ec0c | ||
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Kenneth Estanislao
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56cddde87c | ||
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Kenneth Estanislao
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0dbed2883a |
+1
-1
@@ -25,4 +25,4 @@ models/DMDNet.pth
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faceswap/
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.vscode/
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switch_states.json
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/models
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venv.rar
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@@ -1,4 +1,4 @@
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<h1 align="center">Deep-Live-Cam 2.0.4c</h1>
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<h1 align="center">Deep-Live-Cam</h1>
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<p align="center">
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Real-time face swap and video deepfake with a single click and only a single image.
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@@ -30,13 +30,14 @@ By using this software, you agree to these terms and commit to using it in a man
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Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online. We are not responsible for end-user actions.
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## Exclusive v2.6d Quick Start - Pre-built (Windows/Mac Silicon)
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<a href="https://deeplivecam.net/index.php/quickstart"> <img src="media/Download.png" width="285" height="77" />
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## Quick Start - Pre-built (Windows / Nvidia)
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##### This is the fastest build you can get if you have a discrete NVIDIA or AMD GPU or Mac Silicon, And you'll receive special priority support.
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<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" />
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##### This is the fastest build you can get if you have a discrete NVIDIA GPU.
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###### These Pre-builts are perfect for non-technical users or those who don't have time to, or can't manually install all the requirements. Just a heads-up: this is an open-source project, so you can also install it manually.
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###### These Pre-builts are perfect for non-technical users or those who don't have time to, or can't manually install all the requirements. Just a heads-up: this is an open-source project, so you can also install it manually.
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## TLDR; Live Deepfake in just 3 Clicks
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@@ -98,7 +99,7 @@ Users are expected to use this software responsibly and legally. If using a real
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## Installation (Manual)
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**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the quickstart version.**
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**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the prebuilt version.**
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<details>
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<summary>Click to see the process</summary>
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@@ -109,7 +110,7 @@ This is more likely to work on your computer but will be slower as it utilizes t
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**1. Set up Your Platform**
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- Python (3.11 recommended)
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- Python (3.10 recommended)
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- pip
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- git
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- [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) - ```iex (irm ffmpeg.tc.ht)```
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@@ -133,34 +134,26 @@ Place these files in the "**models**" folder.
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We highly recommend using a `venv` to avoid issues.
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For Windows:
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```bash
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python -m venv venv
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venv\Scripts\activate
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pip install -r requirements.txt
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```
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For Linux:
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```bash
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# Ensure you use the installed Python 3.10
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python3 -m venv venv
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source venv/bin/activate
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pip install -r requirements.txt
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```
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**For macOS:**
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Apple Silicon (M1/M2/M3) requires specific setup:
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```bash
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# Install Python 3.11 (specific version is important)
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brew install python@3.11
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# Install Python 3.10 (specific version is important)
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brew install python@3.10
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# Install tkinter package (required for the GUI)
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brew install python-tk@3.10
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# Create and activate virtual environment with Python 3.11
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python3.11 -m venv venv
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# Create and activate virtual environment with Python 3.10
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python3.10 -m venv venv
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source venv/bin/activate
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# Install dependencies
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@@ -179,11 +172,6 @@ source venv/bin/activate
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# install the dependencies again
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pip install -r requirements.txt
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# gfpgan and basicsrs issue fix
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pip install git+https://github.com/xinntao/BasicSR.git@master
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pip uninstall gfpgan -y
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pip install git+https://github.com/TencentARC/GFPGAN.git@master
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```
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**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).
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@@ -192,16 +180,12 @@ pip install git+https://github.com/TencentARC/GFPGAN.git@master
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**CUDA Execution Provider (Nvidia)**
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1. Install [CUDA Toolkit 12.8.0](https://developer.nvidia.com/cuda-12-8-0-download-archive)
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2. Install [cuDNN v8.9.7 for CUDA 12.x](https://developer.nvidia.com/rdp/cudnn-archive) (required for onnxruntime-gpu):
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- Download cuDNN v8.9.7 for CUDA 12.x
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- Make sure the cuDNN bin directory is in your system PATH
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3. Install dependencies:
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1. Install [CUDA Toolkit 11.8.0](https://developer.nvidia.com/cuda-11-8-0-download-archive)
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2. Install dependencies:
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```bash
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pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
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pip uninstall onnxruntime onnxruntime-gpu
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pip install onnxruntime-gpu==1.21.0
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pip install onnxruntime-gpu==1.16.3
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```
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3. Usage:
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||||
@@ -241,7 +225,7 @@ python3.10 run.py --execution-provider coreml
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# Uninstall conflicting versions if needed
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brew uninstall --ignore-dependencies python@3.11 python@3.13
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# Keep only Python 3.11
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# Keep only Python 3.10
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brew cleanup
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```
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@@ -251,7 +235,7 @@ python3.10 run.py --execution-provider coreml
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```bash
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pip uninstall onnxruntime onnxruntime-coreml
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pip install onnxruntime-coreml==1.21.0
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pip install onnxruntime-coreml==1.13.1
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```
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2. Usage:
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@@ -266,7 +250,7 @@ python run.py --execution-provider coreml
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```bash
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pip uninstall onnxruntime onnxruntime-directml
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pip install onnxruntime-directml==1.21.0
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pip install onnxruntime-directml==1.15.1
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```
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2. Usage:
|
||||
@@ -281,7 +265,7 @@ python run.py --execution-provider directml
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```bash
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pip uninstall onnxruntime onnxruntime-openvino
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pip install onnxruntime-openvino==1.21.0
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pip install onnxruntime-openvino==1.15.0
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```
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2. Usage:
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||||
@@ -309,6 +293,19 @@ python run.py --execution-provider openvino
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- Use a screen capture tool like OBS to stream.
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- To change the face, select a new source image.
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|
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## Tips and Tricks
|
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|
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Check out these helpful guides to get the most out of Deep-Live-Cam:
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- [Unlocking the Secrets to the Perfect Deepfake Image](https://deeplivecam.net/index.php/blog/tips-and-tricks/unlocking-the-secrets-to-the-perfect-deepfake-image) - Learn how to create the best deepfake with full head coverage
|
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- [Video Call with DeepLiveCam](https://deeplivecam.net/index.php/blog/tips-and-tricks/video-call-with-deeplivecam) - Make your meetings livelier by using DeepLiveCam with OBS and meeting software
|
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- [Have a Special Guest!](https://deeplivecam.net/index.php/blog/tips-and-tricks/have-a-special-guest) - Tutorial on how to use face mapping to add special guests to your stream
|
||||
- [Watch Deepfake Movies in Realtime](https://deeplivecam.net/index.php/blog/tips-and-tricks/watch-deepfake-movies-in-realtime) - See yourself star in any video without processing the video
|
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- [Better Quality without Sacrificing Speed](https://deeplivecam.net/index.php/blog/tips-and-tricks/better-quality-without-sacrificing-speed) - Tips for achieving better results without impacting performance
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- [Instant Vtuber!](https://deeplivecam.net/index.php/blog/tips-and-tricks/instant-vtuber) - Create a new persona/vtuber easily using Metahuman Creator
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||||
|
||||
Visit our [official blog](https://deeplivecam.net/index.php/blog/tips-and-tricks) for more tips and tutorials.
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||||
|
||||
## Command Line Arguments (Unmaintained)
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||||
|
||||
```
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@@ -352,15 +349,10 @@ Looking for a CLI mode? Using the -s/--source argument will make the run program
|
||||
- [*"This real-time webcam deepfake tool raises alarms about the future of identity theft"*](https://www.diyphotography.net/this-real-time-webcam-deepfake-tool-raises-alarms-about-the-future-of-identity-theft/) - DIYPhotography
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- [*"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
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- [*"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
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- [*"They do a pretty good job matching poses, expression and even the lighting"*](https://www.youtube.com/watch?v=wnCghLjqv3s&t=551s) - TechLinked (LTT)
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||||
- [*"Als Sean Connery an der Redaktionskonferenz teilnahm"*](https://www.golem.de/news/deepfakes-als-sean-connery-an-der-redaktionskonferenz-teilnahm-2408-188172.html) - Golem.de (German)
|
||||
- [*"What the F***! Why do I look like Vinny Jr? I look exactly like Vinny Jr!? No, this shit is crazy! Bro This is F*** Crazy! "*](https://youtu.be/JbUPRmXRUtE?t=3964) - IShowSpeed
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||||
|
||||
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||||
## Credits
|
||||
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||||
- [ffmpeg](https://ffmpeg.org/): for making video-related operations easy
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||||
- [Henry](https://github.com/henryruhs): One of the major contributor in this repo
|
||||
- [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
|
||||
@@ -386,4 +378,4 @@ Looking for a CLI mode? Using the -s/--source argument will make the run program
|
||||
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date" />
|
||||
<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date" />
|
||||
</picture>
|
||||
</a>
|
||||
</a>
|
||||
@@ -1,46 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "Quelle x Ziel Zuordnung",
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||||
"select a source image": "Wähle ein Quellbild",
|
||||
"Preview": "Vorschau",
|
||||
"select a target image or video": "Wähle ein Zielbild oder Video",
|
||||
"save image output file": "Bildausgabedatei speichern",
|
||||
"save video output file": "Videoausgabedatei speichern",
|
||||
"select a target image": "Wähle ein Zielbild",
|
||||
"source": "Quelle",
|
||||
"Select a target": "Wähle ein Ziel",
|
||||
"Select a face": "Wähle ein Gesicht",
|
||||
"Keep audio": "Audio beibehalten",
|
||||
"Face Enhancer": "Gesichtsverbesserung",
|
||||
"Many faces": "Mehrere Gesichter",
|
||||
"Show FPS": "FPS anzeigen",
|
||||
"Keep fps": "FPS beibehalten",
|
||||
"Keep frames": "Frames beibehalten",
|
||||
"Fix Blueish Cam": "Bläuliche Kamera korrigieren",
|
||||
"Mouth Mask": "Mundmaske",
|
||||
"Show Mouth Mask Box": "Mundmaskenrahmen anzeigen",
|
||||
"Start": "Starten",
|
||||
"Live": "Live",
|
||||
"Destroy": "Beenden",
|
||||
"Map faces": "Gesichter zuordnen",
|
||||
"Processing...": "Verarbeitung läuft...",
|
||||
"Processing succeed!": "Verarbeitung erfolgreich!",
|
||||
"Processing ignored!": "Verarbeitung ignoriert!",
|
||||
"Failed to start camera": "Kamera konnte nicht gestartet werden",
|
||||
"Please complete pop-up or close it.": "Bitte das Pop-up komplettieren oder schließen.",
|
||||
"Getting unique faces": "Einzigartige Gesichter erfassen",
|
||||
"Please select a source image first": "Bitte zuerst ein Quellbild auswählen",
|
||||
"No faces found in target": "Keine Gesichter im Zielbild gefunden",
|
||||
"Add": "Hinzufügen",
|
||||
"Clear": "Löschen",
|
||||
"Submit": "Absenden",
|
||||
"Select source image": "Quellbild auswählen",
|
||||
"Select target image": "Zielbild auswählen",
|
||||
"Please provide mapping!": "Bitte eine Zuordnung angeben!",
|
||||
"At least 1 source with target is required!": "Mindestens eine Quelle mit einem Ziel ist erforderlich!",
|
||||
"At least 1 source with target is required!": "Mindestens eine Quelle mit einem Ziel ist erforderlich!",
|
||||
"Face could not be detected in last upload!": "Im letzten Upload konnte kein Gesicht erkannt werden!",
|
||||
"Select Camera:": "Kamera auswählen:",
|
||||
"All mappings cleared!": "Alle Zuordnungen gelöscht!",
|
||||
"Mappings successfully submitted!": "Zuordnungen erfolgreich übermittelt!",
|
||||
"Source x Target Mapper is already open.": "Quell-zu-Ziel-Zuordnung ist bereits geöffnet."
|
||||
}
|
||||
@@ -1,46 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "Mapeador de fuente x destino",
|
||||
"select a source image": "Seleccionar imagen fuente",
|
||||
"Preview": "Vista previa",
|
||||
"select a target image or video": "elegir un video o una imagen fuente",
|
||||
"save image output file": "guardar imagen final",
|
||||
"save video output file": "guardar video final",
|
||||
"select a target image": "elegir una imagen objetiva",
|
||||
"source": "fuente",
|
||||
"Select a target": "Elegir un destino",
|
||||
"Select a face": "Elegir una cara",
|
||||
"Keep audio": "Mantener audio original",
|
||||
"Face Enhancer": "Potenciador de caras",
|
||||
"Many faces": "Varias caras",
|
||||
"Show FPS": "Mostrar fps",
|
||||
"Keep fps": "Mantener fps",
|
||||
"Keep frames": "Mantener frames",
|
||||
"Fix Blueish Cam": "Corregir tono azul de video",
|
||||
"Mouth Mask": "Máscara de boca",
|
||||
"Show Mouth Mask Box": "Mostrar área de la máscara de boca",
|
||||
"Start": "Iniciar",
|
||||
"Live": "En vivo",
|
||||
"Destroy": "Borrar",
|
||||
"Map faces": "Mapear caras",
|
||||
"Processing...": "Procesando...",
|
||||
"Processing succeed!": "¡Proceso terminado con éxito!",
|
||||
"Processing ignored!": "¡Procesamiento omitido!",
|
||||
"Failed to start camera": "No se pudo iniciar la cámara",
|
||||
"Please complete pop-up or close it.": "Complete o cierre el pop-up",
|
||||
"Getting unique faces": "Buscando caras únicas",
|
||||
"Please select a source image first": "Primero, seleccione una imagen fuente",
|
||||
"No faces found in target": "No se encontró una cara en el destino",
|
||||
"Add": "Agregar",
|
||||
"Clear": "Limpiar",
|
||||
"Submit": "Enviar",
|
||||
"Select source image": "Seleccionar imagen fuente",
|
||||
"Select target image": "Seleccionar imagen destino",
|
||||
"Please provide mapping!": "Por favor, proporcione un mapeo",
|
||||
"At least 1 source with target is required!": "Se requiere al menos una fuente con un destino.",
|
||||
"At least 1 source with target is required!": "Se requiere al menos una fuente con un destino.",
|
||||
"Face could not be detected in last upload!": "¡No se pudo encontrar una cara en el último video o imagen!",
|
||||
"Select Camera:": "Elegir cámara:",
|
||||
"All mappings cleared!": "¡Todos los mapeos fueron borrados!",
|
||||
"Mappings successfully submitted!": "Mapeos enviados con éxito!",
|
||||
"Source x Target Mapper is already open.": "El mapeador de fuente x destino ya está abierto."
|
||||
}
|
||||
@@ -1,46 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "Source x Target Kartoitin",
|
||||
"select an source image": "Valitse lähde kuva",
|
||||
"Preview": "Esikatsele",
|
||||
"select an target image or video": "Valitse kohde kuva tai video",
|
||||
"save image output file": "tallenna kuva",
|
||||
"save video output file": "tallenna video",
|
||||
"select an target image": "Valitse kohde kuva",
|
||||
"source": "lähde",
|
||||
"Select a target": "Valitse kohde",
|
||||
"Select a face": "Valitse kasvot",
|
||||
"Keep audio": "Säilytä ääni",
|
||||
"Face Enhancer": "Kasvojen Parantaja",
|
||||
"Many faces": "Useampia kasvoja",
|
||||
"Show FPS": "Näytä FPS",
|
||||
"Keep fps": "Säilytä FPS",
|
||||
"Keep frames": "Säilytä ruudut",
|
||||
"Fix Blueish Cam": "Korjaa Sinertävä Kamera",
|
||||
"Mouth Mask": "Suu Maski",
|
||||
"Show Mouth Mask Box": "Näytä Suu Maski Laatiko",
|
||||
"Start": "Aloita",
|
||||
"Live": "Live",
|
||||
"Destroy": "Tuhoa",
|
||||
"Map faces": "Kartoita kasvot",
|
||||
"Processing...": "Prosessoi...",
|
||||
"Processing succeed!": "Prosessointi onnistui!",
|
||||
"Processing ignored!": "Prosessointi lopetettu!",
|
||||
"Failed to start camera": "Kameran käynnistäminen epäonnistui",
|
||||
"Please complete pop-up or close it.": "Viimeistele tai sulje ponnahdusikkuna",
|
||||
"Getting unique faces": "Hankitaan uniikkeja kasvoja",
|
||||
"Please select a source image first": "Valitse ensin lähde kuva",
|
||||
"No faces found in target": "Kasvoja ei löydetty kohteessa",
|
||||
"Add": "Lisää",
|
||||
"Clear": "Tyhjennä",
|
||||
"Submit": "Lähetä",
|
||||
"Select source image": "Valitse lähde kuva",
|
||||
"Select target image": "Valitse kohde kuva",
|
||||
"Please provide mapping!": "Tarjoa kartoitus!",
|
||||
"Atleast 1 source with target is required!": "Vähintään 1 lähde kohteen kanssa on vaadittu!",
|
||||
"At least 1 source with target is required!": "Vähintään 1 lähde kohteen kanssa on vaadittu!",
|
||||
"Face could not be detected in last upload!": "Kasvoja ei voitu tunnistaa edellisessä latauksessa!",
|
||||
"Select Camera:": "Valitse Kamera:",
|
||||
"All mappings cleared!": "Kaikki kartoitukset tyhjennetty!",
|
||||
"Mappings successfully submitted!": "Kartoitukset lähetety onnistuneesti!",
|
||||
"Source x Target Mapper is already open.": "Lähde x Kohde Kartoittaja on jo auki."
|
||||
}
|
||||
@@ -1,45 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "Pemetaan Sumber x Target",
|
||||
"select a source image": "Pilih gambar sumber",
|
||||
"Preview": "Pratinjau",
|
||||
"select a target image or video": "Pilih gambar atau video target",
|
||||
"save image output file": "Simpan file keluaran gambar",
|
||||
"save video output file": "Simpan file keluaran video",
|
||||
"select a target image": "Pilih gambar target",
|
||||
"source": "Sumber",
|
||||
"Select a target": "Pilih target",
|
||||
"Select a face": "Pilih wajah",
|
||||
"Keep audio": "Pertahankan audio",
|
||||
"Face Enhancer": "Peningkat wajah",
|
||||
"Many faces": "Banyak wajah",
|
||||
"Show FPS": "Tampilkan FPS",
|
||||
"Keep fps": "Pertahankan FPS",
|
||||
"Keep frames": "Pertahankan frame",
|
||||
"Fix Blueish Cam": "Perbaiki kamera kebiruan",
|
||||
"Mouth Mask": "Masker mulut",
|
||||
"Show Mouth Mask Box": "Tampilkan kotak masker mulut",
|
||||
"Start": "Mulai",
|
||||
"Live": "Langsung",
|
||||
"Destroy": "Hentikan",
|
||||
"Map faces": "Petakan wajah",
|
||||
"Processing...": "Sedang memproses...",
|
||||
"Processing succeed!": "Pemrosesan berhasil!",
|
||||
"Processing ignored!": "Pemrosesan diabaikan!",
|
||||
"Failed to start camera": "Gagal memulai kamera",
|
||||
"Please complete pop-up or close it.": "Harap selesaikan atau tutup pop-up.",
|
||||
"Getting unique faces": "Mengambil wajah unik",
|
||||
"Please select a source image first": "Silakan pilih gambar sumber terlebih dahulu",
|
||||
"No faces found in target": "Tidak ada wajah ditemukan pada target",
|
||||
"Add": "Tambah",
|
||||
"Clear": "Bersihkan",
|
||||
"Submit": "Kirim",
|
||||
"Select source image": "Pilih gambar sumber",
|
||||
"Select target image": "Pilih gambar target",
|
||||
"Please provide mapping!": "Harap tentukan pemetaan!",
|
||||
"At least 1 source with target is required!": "Minimal 1 sumber dengan target diperlukan!",
|
||||
"Face could not be detected in last upload!": "Wajah tidak dapat terdeteksi pada unggahan terakhir!",
|
||||
"Select Camera:": "Pilih Kamera:",
|
||||
"All mappings cleared!": "Semua pemetaan telah dibersihkan!",
|
||||
"Mappings successfully submitted!": "Pemetaan berhasil dikirim!",
|
||||
"Source x Target Mapper is already open.": "Pemetaan Sumber x Target sudah terbuka."
|
||||
}
|
||||
@@ -1,45 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "ប្រភប x បន្ថែម Mapper",
|
||||
"select a source image": "ជ្រើសរើសប្រភពរូបភាព",
|
||||
"Preview": "បង្ហាញ",
|
||||
"select a target image or video": "ជ្រើសរើសគោលដៅរូបភាពឬវីដេអូ",
|
||||
"save image output file": "រក្សាទុកលទ្ធផលឯកសាររូបភាព",
|
||||
"save video output file": "រក្សាទុកលទ្ធផលឯកសារវីដេអូ",
|
||||
"select a target image": "ជ្រើសរើសគោលដៅរូបភាព",
|
||||
"source": "ប្រភព",
|
||||
"Select a target": "ជ្រើសរើសគោលដៅ",
|
||||
"Select a face": "ជ្រើសរើសមុខ",
|
||||
"Keep audio": "រម្លងសម្លេង",
|
||||
"Face Enhancer": "ឧបករណ៍ពង្រឹងមុខ",
|
||||
"Many faces": "ទម្រង់មុខច្រើន",
|
||||
"Show FPS": "បង្ហាញ FPS",
|
||||
"Keep fps": "រម្លង fps",
|
||||
"Keep frames": "រម្លងទម្រង់",
|
||||
"Fix Blueish Cam": "ជួសជុល Cam Blueish",
|
||||
"Mouth Mask": "របាំងមាត់",
|
||||
"Show Mouth Mask Box": "បង្ហាញប្រអប់របាំងមាត់",
|
||||
"Start": "ចាប់ផ្ដើម",
|
||||
"Live": "ផ្សាយផ្ទាល់",
|
||||
"Destroy": "លុប",
|
||||
"Map faces": "ផែនទីមុខ",
|
||||
"Processing...": "កំពុងដំណើរការ...",
|
||||
"Processing succeed!": "ការដំណើរការទទួលបានជោគជ័យ!",
|
||||
"Processing ignored!": "ការដំណើរការមិនទទួលបានជោគជ័យ!",
|
||||
"Failed to start camera": "បរាជ័យដើម្បីចាប់ផ្ដើមបើកកាមេរ៉ា",
|
||||
"Please complete pop-up or close it.": "សូមបញ្ចប់ផ្ទាំងផុស ឬបិទវា.",
|
||||
"Getting unique faces": "ការចាប់ផ្ដើមទម្រង់មុខប្លែក",
|
||||
"Please select a source image first": "សូមជ្រើសរើសប្រភពរូបភាពដំបូង",
|
||||
"No faces found in target": "រកអត់ឃើញមុខនៅក្នុងគោលដៅ",
|
||||
"Add": "បន្ថែម",
|
||||
"Clear": "សម្អាត",
|
||||
"Submit": "បញ្ចូន",
|
||||
"Select source image": "ជ្រើសរើសប្រភពរូបភាព",
|
||||
"Select target image": "ជ្រើសរើសគោលដៅរូបភាព",
|
||||
"Please provide mapping!": "សូមផ្ដល់នៅផែនទី",
|
||||
"At least 1 source with target is required!": "ត្រូវការប្រភពយ៉ាងហោចណាស់ ១ ដែលមានគោលដៅ!",
|
||||
"Face could not be detected in last upload!": "មុខមិនអាចភ្ជាប់នៅក្នុងការបង្ហេាះចុងក្រោយ!",
|
||||
"Select Camera:": "ជ្រើសរើសកាមេរ៉ា",
|
||||
"All mappings cleared!": "ផែនទីទាំងអស់ត្រូវបានសម្អាត!",
|
||||
"Mappings successfully submitted!": "ផែនទីត្រូវបានបញ្ជូនជោគជ័យ!",
|
||||
"Source x Target Mapper is already open.": "ប្រភព x Target Mapper បានបើករួចហើយ។"
|
||||
}
|
||||
@@ -1,45 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "소스 x 타겟 매퍼",
|
||||
"select a source image": "소스 이미지 선택",
|
||||
"Preview": "미리보기",
|
||||
"select a target image or video": "타겟 이미지 또는 영상 선택",
|
||||
"save image output file": "이미지 출력 파일 저장",
|
||||
"save video output file": "영상 출력 파일 저장",
|
||||
"select a target image": "타겟 이미지 선택",
|
||||
"source": "소스",
|
||||
"Select a target": "타겟 선택",
|
||||
"Select a face": "얼굴 선택",
|
||||
"Keep audio": "오디오 유지",
|
||||
"Face Enhancer": "얼굴 향상",
|
||||
"Many faces": "여러 얼굴",
|
||||
"Show FPS": "FPS 표시",
|
||||
"Keep fps": "FPS 유지",
|
||||
"Keep frames": "프레임 유지",
|
||||
"Fix Blueish Cam": "푸른빛 카메라 보정",
|
||||
"Mouth Mask": "입 마스크",
|
||||
"Show Mouth Mask Box": "입 마스크 박스 표시",
|
||||
"Start": "시작",
|
||||
"Live": "라이브",
|
||||
"Destroy": "종료",
|
||||
"Map faces": "얼굴 매핑",
|
||||
"Processing...": "처리 중...",
|
||||
"Processing succeed!": "처리 성공!",
|
||||
"Processing ignored!": "처리 무시됨!",
|
||||
"Failed to start camera": "카메라 시작 실패",
|
||||
"Please complete pop-up or close it.": "팝업을 완료하거나 닫아주세요.",
|
||||
"Getting unique faces": "고유 얼굴 가져오는 중",
|
||||
"Please select a source image first": "먼저 소스 이미지를 선택해주세요",
|
||||
"No faces found in target": "타겟에서 얼굴을 찾을 수 없음",
|
||||
"Add": "추가",
|
||||
"Clear": "지우기",
|
||||
"Submit": "제출",
|
||||
"Select source image": "소스 이미지 선택",
|
||||
"Select target image": "타겟 이미지 선택",
|
||||
"Please provide mapping!": "매핑을 입력해주세요!",
|
||||
"At least 1 source with target is required!": "최소 하나의 소스와 타겟이 필요합니다!",
|
||||
"Face could not be detected in last upload!": "최근 업로드에서 얼굴을 감지할 수 없습니다!",
|
||||
"Select Camera:": "카메라 선택:",
|
||||
"All mappings cleared!": "모든 매핑이 삭제되었습니다!",
|
||||
"Mappings successfully submitted!": "매핑이 성공적으로 제출되었습니다!",
|
||||
"Source x Target Mapper is already open.": "소스 x 타겟 매퍼가 이미 열려 있습니다."
|
||||
}
|
||||
@@ -1,46 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "Mapeador de Origem x Destino",
|
||||
"select an source image": "Escolha uma imagem de origem",
|
||||
"Preview": "Prévia",
|
||||
"select an target image or video": "Escolha uma imagem ou vídeo de destino",
|
||||
"save image output file": "Salvar imagem final",
|
||||
"save video output file": "Salvar vídeo final",
|
||||
"select an target image": "Escolha uma imagem de destino",
|
||||
"source": "Origem",
|
||||
"Select a target": "Escolha o destino",
|
||||
"Select a face": "Escolha um rosto",
|
||||
"Keep audio": "Manter o áudio original",
|
||||
"Face Enhancer": "Melhorar rosto",
|
||||
"Many faces": "Vários rostos",
|
||||
"Show FPS": "Mostrar FPS",
|
||||
"Keep fps": "Manter FPS",
|
||||
"Keep frames": "Manter frames",
|
||||
"Fix Blueish Cam": "Corrigir tom azulado da câmera",
|
||||
"Mouth Mask": "Máscara da boca",
|
||||
"Show Mouth Mask Box": "Mostrar área da máscara da boca",
|
||||
"Start": "Começar",
|
||||
"Live": "Ao vivo",
|
||||
"Destroy": "Destruir",
|
||||
"Map faces": "Mapear rostos",
|
||||
"Processing...": "Processando...",
|
||||
"Processing succeed!": "Tudo certo!",
|
||||
"Processing ignored!": "Processamento ignorado!",
|
||||
"Failed to start camera": "Não foi possível iniciar a câmera",
|
||||
"Please complete pop-up or close it.": "Finalize ou feche o pop-up",
|
||||
"Getting unique faces": "Buscando rostos diferentes",
|
||||
"Please select a source image first": "Selecione primeiro uma imagem de origem",
|
||||
"No faces found in target": "Nenhum rosto encontrado na imagem de destino",
|
||||
"Add": "Adicionar",
|
||||
"Clear": "Limpar",
|
||||
"Submit": "Enviar",
|
||||
"Select source image": "Escolha a imagem de origem",
|
||||
"Select target image": "Escolha a imagem de destino",
|
||||
"Please provide mapping!": "Você precisa realizar o mapeamento!",
|
||||
"Atleast 1 source with target is required!": "É necessária pelo menos uma origem com um destino!",
|
||||
"At least 1 source with target is required!": "É necessária pelo menos uma origem com um destino!",
|
||||
"Face could not be detected in last upload!": "Não conseguimos detectar o rosto na última imagem!",
|
||||
"Select Camera:": "Escolher câmera:",
|
||||
"All mappings cleared!": "Todos os mapeamentos foram removidos!",
|
||||
"Mappings successfully submitted!": "Mapeamentos enviados com sucesso!",
|
||||
"Source x Target Mapper is already open.": "O Mapeador de Origem x Destino já está aberto."
|
||||
}
|
||||
@@ -1,45 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "Сопоставитель Источник x Цель",
|
||||
"select a source image": "выберите исходное изображение",
|
||||
"Preview": "Предпросмотр",
|
||||
"select a target image or video": "выберите целевое изображение или видео",
|
||||
"save image output file": "сохранить выходной файл изображения",
|
||||
"save video output file": "сохранить выходной файл видео",
|
||||
"select a target image": "выберите целевое изображение",
|
||||
"source": "источник",
|
||||
"Select a target": "Выберите целевое изображение",
|
||||
"Select a face": "Выберите лицо",
|
||||
"Keep audio": "Сохранить аудио",
|
||||
"Face Enhancer": "Улучшение лица",
|
||||
"Many faces": "Несколько лиц",
|
||||
"Show FPS": "Показать FPS",
|
||||
"Keep fps": "Сохранить FPS",
|
||||
"Keep frames": "Сохранить кадры",
|
||||
"Fix Blueish Cam": "Исправить синеву камеры",
|
||||
"Mouth Mask": "Маска рта",
|
||||
"Show Mouth Mask Box": "Показать рамку маски рта",
|
||||
"Start": "Старт",
|
||||
"Live": "В реальном времени",
|
||||
"Destroy": "Остановить",
|
||||
"Map faces": "Сопоставить лица",
|
||||
"Processing...": "Обработка...",
|
||||
"Processing succeed!": "Обработка успешна!",
|
||||
"Processing ignored!": "Обработка проигнорирована!",
|
||||
"Failed to start camera": "Не удалось запустить камеру",
|
||||
"Please complete pop-up or close it.": "Пожалуйста, заполните всплывающее окно или закройте его.",
|
||||
"Getting unique faces": "Получение уникальных лиц",
|
||||
"Please select a source image first": "Сначала выберите исходное изображение, пожалуйста",
|
||||
"No faces found in target": "В целевом изображении не найдено лиц",
|
||||
"Add": "Добавить",
|
||||
"Clear": "Очистить",
|
||||
"Submit": "Отправить",
|
||||
"Select source image": "Выбрать исходное изображение",
|
||||
"Select target image": "Выбрать целевое изображение",
|
||||
"Please provide mapping!": "Пожалуйста, укажите сопоставление!",
|
||||
"At least 1 source with target is required!": "Требуется хотя бы 1 источник с целью!",
|
||||
"Face could not be detected in last upload!": "Лицо не обнаружено в последнем загруженном изображении!",
|
||||
"Select Camera:": "Выберите камеру:",
|
||||
"All mappings cleared!": "Все сопоставления очищены!",
|
||||
"Mappings successfully submitted!": "Сопоставления успешно отправлены!",
|
||||
"Source x Target Mapper is already open.": "Сопоставитель Источник-Цель уже открыт."
|
||||
}
|
||||
@@ -1,45 +0,0 @@
|
||||
{
|
||||
"Source x Target Mapper": "ตัวจับคู่ต้นทาง x ปลายทาง",
|
||||
"select a source image": "เลือกรูปภาพต้นฉบับ",
|
||||
"Preview": "ตัวอย่าง",
|
||||
"select a target image or video": "เลือกรูปภาพหรือวิดีโอเป้าหมาย",
|
||||
"save image output file": "บันทึกไฟล์รูปภาพ",
|
||||
"save video output file": "บันทึกไฟล์วิดีโอ",
|
||||
"select a target image": "เลือกรูปภาพเป้าหมาย",
|
||||
"source": "ต้นฉบับ",
|
||||
"Select a target": "เลือกเป้าหมาย",
|
||||
"Select a face": "เลือกใบหน้า",
|
||||
"Keep audio": "เก็บเสียง",
|
||||
"Face Enhancer": "ปรับปรุงใบหน้า",
|
||||
"Many faces": "หลายใบหน้า",
|
||||
"Show FPS": "แสดง FPS",
|
||||
"Keep fps": "คงค่า FPS",
|
||||
"Keep frames": "คงค่าเฟรม",
|
||||
"Fix Blueish Cam": "แก้ไขภาพอมฟ้าจากกล้อง",
|
||||
"Mouth Mask": "มาสก์ปาก",
|
||||
"Show Mouth Mask Box": "แสดงกรอบมาสก์ปาก",
|
||||
"Start": "เริ่ม",
|
||||
"Live": "สด",
|
||||
"Destroy": "หยุด",
|
||||
"Map faces": "จับคู่ใบหน้า",
|
||||
"Processing...": "กำลังประมวลผล...",
|
||||
"Processing succeed!": "ประมวลผลสำเร็จแล้ว!",
|
||||
"Processing ignored!": "การประมวลผลถูกละเว้น",
|
||||
"Failed to start camera": "ไม่สามารถเริ่มกล้องได้",
|
||||
"Please complete pop-up or close it.": "โปรดดำเนินการในป๊อปอัปให้เสร็จสิ้น หรือปิด",
|
||||
"Getting unique faces": "กำลังค้นหาใบหน้าที่ไม่ซ้ำกัน",
|
||||
"Please select a source image first": "โปรดเลือกภาพต้นฉบับก่อน",
|
||||
"No faces found in target": "ไม่พบใบหน้าในภาพเป้าหมาย",
|
||||
"Add": "เพิ่ม",
|
||||
"Clear": "ล้าง",
|
||||
"Submit": "ส่ง",
|
||||
"Select source image": "เลือกภาพต้นฉบับ",
|
||||
"Select target image": "เลือกภาพเป้าหมาย",
|
||||
"Please provide mapping!": "โปรดระบุการจับคู่!",
|
||||
"At least 1 source with target is required!": "ต้องมีการจับคู่ต้นฉบับกับเป้าหมายอย่างน้อย 1 คู่!",
|
||||
"Face could not be detected in last upload!": "ไม่สามารถตรวจพบใบหน้าในไฟล์อัปโหลดล่าสุด!",
|
||||
"Select Camera:": "เลือกกล้อง:",
|
||||
"All mappings cleared!": "ล้างการจับคู่ทั้งหมดแล้ว!",
|
||||
"Mappings successfully submitted!": "ส่งการจับคู่สำเร็จแล้ว!",
|
||||
"Source x Target Mapper is already open.": "ตัวจับคู่ต้นทาง x ปลายทาง เปิดอยู่แล้ว"
|
||||
}
|
||||
+5
-5
@@ -1,11 +1,11 @@
|
||||
{
|
||||
"Source x Target Mapper": "Source x Target Mapper",
|
||||
"select a source image": "选择一个源图像",
|
||||
"select an source image": "选择一个源图像",
|
||||
"Preview": "预览",
|
||||
"select a target image or video": "选择一个目标图像或视频",
|
||||
"select an target image or video": "选择一个目标图像或视频",
|
||||
"save image output file": "保存图像输出文件",
|
||||
"save video output file": "保存视频输出文件",
|
||||
"select a target image": "选择一个目标图像",
|
||||
"select an target image": "选择一个目标图像",
|
||||
"source": "源",
|
||||
"Select a target": "选择一个目标",
|
||||
"Select a face": "选择一张脸",
|
||||
@@ -36,11 +36,11 @@
|
||||
"Select source image": "请选取源图像",
|
||||
"Select target image": "请选取目标图像",
|
||||
"Please provide mapping!": "请提供映射",
|
||||
"At least 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
|
||||
"Atleast 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
|
||||
"At least 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
|
||||
"Face could not be detected in last upload!": "最近上传的图像中没有检测到人脸!",
|
||||
"Select Camera:": "选择摄像头",
|
||||
"All mappings cleared!": "所有映射均已清除!",
|
||||
"Mappings successfully submitted!": "成功提交映射！",
|
||||
"Source x Target Mapper is already open.": "源 x 目标映射器已打开。"
|
||||
}
|
||||
}
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 9.6 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 9.0 KiB |
@@ -1,18 +0,0 @@
|
||||
import os
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
# Utility function to support unicode characters in file paths for reading
|
||||
def imread_unicode(path, flags=cv2.IMREAD_COLOR):
|
||||
return cv2.imdecode(np.fromfile(path, dtype=np.uint8), flags)
|
||||
|
||||
# Utility function to support unicode characters in file paths for writing
|
||||
def imwrite_unicode(path, img, params=None):
|
||||
root, ext = os.path.splitext(path)
|
||||
if not ext:
|
||||
ext = ".png"
|
||||
result, encoded_img = cv2.imencode(ext, img, params if params else [])
|
||||
result, encoded_img = cv2.imencode(f".{ext}", img, params if params is not None else [])
|
||||
encoded_img.tofile(path)
|
||||
return True
|
||||
return False
|
||||
+1
-2
@@ -1,7 +1,6 @@
|
||||
from typing import Any
|
||||
import cv2
|
||||
import modules.globals # Import the globals to check the color correction toggle
|
||||
from modules.gpu_processing import gpu_cvt_color
|
||||
|
||||
|
||||
def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
|
||||
@@ -20,7 +19,7 @@ def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
|
||||
|
||||
if has_frame and modules.globals.color_correction:
|
||||
# Convert the frame color if necessary
|
||||
frame = gpu_cvt_color(frame, cv2.COLOR_BGR2RGB)
|
||||
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
capture.release()
|
||||
return frame if has_frame else None
|
||||
|
||||
+873
-197
File diff suppressed because it is too large
Load Diff
@@ -1,7 +0,0 @@
|
||||
from typing import Any
|
||||
|
||||
from insightface.app.common import Face
|
||||
import numpy
|
||||
|
||||
Face = Face
|
||||
Frame = numpy.ndarray[Any, Any]
|
||||
@@ -2,7 +2,6 @@ import os
|
||||
import shutil
|
||||
from typing import Any
|
||||
import insightface
|
||||
import threading
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
@@ -14,23 +13,14 @@ from modules.utilities import get_temp_directory_path, create_temp, extract_fram
|
||||
from pathlib import Path
|
||||
|
||||
FACE_ANALYSER = None
|
||||
FACE_ANALYSER_LOCK = threading.Lock()
|
||||
|
||||
|
||||
def get_face_analyser() -> Any:
|
||||
"""Get face analyser with thread-safe initialization."""
|
||||
global FACE_ANALYSER
|
||||
|
||||
if FACE_ANALYSER is None:
|
||||
with FACE_ANALYSER_LOCK:
|
||||
# Double-check after acquiring lock
|
||||
if FACE_ANALYSER is None:
|
||||
FACE_ANALYSER = insightface.app.FaceAnalysis(
|
||||
name='buffalo_l',
|
||||
providers=modules.globals.execution_providers,
|
||||
allowed_modules=['detection', 'recognition']
|
||||
)
|
||||
FACE_ANALYSER.prepare(ctx_id=0, det_size=(320, 320))
|
||||
FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=modules.globals.execution_providers)
|
||||
FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
|
||||
return FACE_ANALYSER
|
||||
|
||||
|
||||
|
||||
+29
-58
@@ -1,5 +1,3 @@
|
||||
# --- START OF FILE globals.py ---
|
||||
|
||||
import os
|
||||
from typing import List, Dict, Any
|
||||
|
||||
@@ -11,62 +9,35 @@ file_types = [
|
||||
("Video", ("*.mp4", "*.mkv")),
|
||||
]
|
||||
|
||||
# Face Mapping Data
|
||||
source_target_map: List[Dict[str, Any]] = [] # Stores detailed map for image/video processing
|
||||
simple_map: Dict[str, Any] = {} # Stores simplified map (embeddings/faces) for live/simple mode
|
||||
source_target_map = []
|
||||
simple_map = {}
|
||||
|
||||
# Paths
|
||||
source_path: str | None = None
|
||||
target_path: str | None = None
|
||||
output_path: str | None = None
|
||||
|
||||
# Processing Options
|
||||
source_path = None
|
||||
target_path = None
|
||||
output_path = None
|
||||
frame_processors: List[str] = []
|
||||
keep_fps: bool = True
|
||||
keep_audio: bool = True
|
||||
keep_frames: bool = False
|
||||
many_faces: bool = False # Process all detected faces with default source
|
||||
map_faces: bool = False # Use source_target_map or simple_map for specific swaps
|
||||
poisson_blend: bool = False # Enable Poisson Blending for smoother face swaps
|
||||
color_correction: bool = False # Enable color correction (implementation specific)
|
||||
nsfw_filter: bool = False
|
||||
|
||||
# Video Output Options
|
||||
video_encoder: str | None = None
|
||||
video_quality: int | None = None # Typically a CRF value or bitrate
|
||||
|
||||
# Live Mode Options
|
||||
live_mirror: bool = False
|
||||
live_resizable: bool = True
|
||||
camera_input_combobox: Any | None = None # Placeholder for UI element if needed
|
||||
webcam_preview_running: bool = False
|
||||
show_fps: bool = False
|
||||
|
||||
# System Configuration
|
||||
max_memory: int | None = None # Memory limit in GB? (Needs clarification)
|
||||
execution_providers: List[str] = [] # e.g., ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
execution_threads: int | None = None # Number of threads for CPU execution
|
||||
headless: bool | None = None # Run without UI?
|
||||
log_level: str = "error" # Logging level (e.g., 'debug', 'info', 'warning', 'error')
|
||||
|
||||
# Face Processor UI Toggles (Example)
|
||||
keep_fps = True
|
||||
keep_audio = True
|
||||
keep_frames = False
|
||||
many_faces = False
|
||||
map_faces = False
|
||||
color_correction = False # New global variable for color correction toggle
|
||||
nsfw_filter = False
|
||||
video_encoder = None
|
||||
video_quality = None
|
||||
live_mirror = False
|
||||
live_resizable = True
|
||||
max_memory = None
|
||||
execution_providers: List[str] = []
|
||||
execution_threads = None
|
||||
headless = None
|
||||
log_level = "error"
|
||||
fp_ui: Dict[str, bool] = {"face_enhancer": False}
|
||||
|
||||
# Face Swapper Specific Options
|
||||
face_swapper_enabled: bool = True # General toggle for the swapper processor
|
||||
opacity: float = 1.0 # Blend factor for the swapped face (0.0-1.0)
|
||||
sharpness: float = 0.0 # Sharpness enhancement for swapped face (0.0-1.0+)
|
||||
|
||||
# Mouth Mask Options
|
||||
mouth_mask: bool = False # Enable mouth area masking/pasting
|
||||
show_mouth_mask_box: bool = False # Visualize the mouth mask area (for debugging)
|
||||
mask_feather_ratio: int = 12 # Denominator for feathering calculation (higher = smaller feather)
|
||||
mask_down_size: float = 0.1 # Expansion factor for lower lip mask (relative)
|
||||
mask_size: float = 1.0 # Expansion factor for upper lip mask (relative)
|
||||
|
||||
# --- START: Added for Frame Interpolation ---
|
||||
enable_interpolation: bool = True # Toggle temporal smoothing
|
||||
interpolation_weight: float = 0 # Blend weight for current frame (0.0-1.0). Lower=smoother.
|
||||
# --- END: Added for Frame Interpolation ---
|
||||
|
||||
# --- END OF FILE globals.py ---
|
||||
camera_input_combobox = None
|
||||
webcam_preview_running = False
|
||||
show_fps = False
|
||||
mouth_mask = False
|
||||
show_mouth_mask_box = False
|
||||
mask_feather_ratio = 8
|
||||
mask_down_size = 0.50
|
||||
mask_size = 1
|
||||
|
||||
@@ -1,286 +0,0 @@
|
||||
# --- START OF FILE gpu_processing.py ---
|
||||
"""
|
||||
GPU-accelerated image processing using OpenCV CUDA (cv2.cuda.GpuMat).
|
||||
|
||||
Provides drop-in replacements for common cv2 functions. When OpenCV is built
|
||||
with CUDA support the functions transparently upload → process → download via
|
||||
GpuMat; otherwise they fall back to the regular CPU path so the rest of the
|
||||
codebase never has to care whether CUDA is available.
|
||||
|
||||
Usage
|
||||
-----
|
||||
from modules.gpu_processing import (
|
||||
gpu_gaussian_blur, gpu_sharpen, gpu_add_weighted,
|
||||
gpu_resize, gpu_cvt_color, gpu_flip,
|
||||
is_gpu_accelerated,
|
||||
)
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from typing import Tuple, Optional
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# CUDA availability detection (evaluated once at import time)
|
||||
# ---------------------------------------------------------------------------
|
||||
CUDA_AVAILABLE: bool = False
|
||||
|
||||
try:
|
||||
# cv2.cuda.GpuMat is only present when OpenCV is compiled with CUDA
|
||||
_test_mat = cv2.cuda.GpuMat()
|
||||
# Verify we have the required filter / image-processing functions
|
||||
_has_gauss = hasattr(cv2.cuda, "createGaussianFilter")
|
||||
_has_resize = hasattr(cv2.cuda, "resize")
|
||||
_has_cvt = hasattr(cv2.cuda, "cvtColor")
|
||||
if _has_gauss and _has_resize and _has_cvt:
|
||||
CUDA_AVAILABLE = True
|
||||
print("[gpu_processing] OpenCV CUDA support detected – GPU-accelerated processing enabled.")
|
||||
else:
|
||||
missing = []
|
||||
if not _has_gauss:
|
||||
missing.append("createGaussianFilter")
|
||||
if not _has_resize:
|
||||
missing.append("resize")
|
||||
if not _has_cvt:
|
||||
missing.append("cvtColor")
|
||||
print(f"[gpu_processing] cv2.cuda.GpuMat exists but missing: {', '.join(missing)} – falling back to CPU.")
|
||||
except Exception:
|
||||
print("[gpu_processing] OpenCV CUDA not available – using CPU fallback for all operations.")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Internal helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _ensure_uint8(img: np.ndarray) -> np.ndarray:
|
||||
"""Clip and convert to uint8 if necessary."""
|
||||
if img.dtype != np.uint8:
|
||||
return np.clip(img, 0, 255).astype(np.uint8)
|
||||
return img
|
||||
|
||||
|
||||
def _ksize_odd(ksize: Tuple[int, int]) -> Tuple[int, int]:
|
||||
"""Ensure kernel dimensions are positive and odd (required by GaussianBlur)."""
|
||||
kw = max(1, ksize[0] // 2 * 2 + 1) if ksize[0] > 0 else 0
|
||||
kh = max(1, ksize[1] // 2 * 2 + 1) if ksize[1] > 0 else 0
|
||||
return (kw, kh)
|
||||
|
||||
|
||||
def _cv_type_for(img: np.ndarray) -> int:
|
||||
"""Return the OpenCV type constant matching *img* (uint8 only)."""
|
||||
channels = 1 if img.ndim == 2 else img.shape[2]
|
||||
if channels == 1:
|
||||
return cv2.CV_8UC1
|
||||
elif channels == 3:
|
||||
return cv2.CV_8UC3
|
||||
elif channels == 4:
|
||||
return cv2.CV_8UC4
|
||||
return cv2.CV_8UC3 # fallback
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Public API – Gaussian Blur
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def gpu_gaussian_blur(
|
||||
src: np.ndarray,
|
||||
ksize: Tuple[int, int],
|
||||
sigma_x: float,
|
||||
sigma_y: float = 0,
|
||||
) -> np.ndarray:
|
||||
"""Drop-in replacement for ``cv2.GaussianBlur`` with CUDA acceleration.
|
||||
|
||||
Parameters match ``cv2.GaussianBlur(src, ksize, sigmaX, sigmaY)``.
|
||||
When *ksize* is ``(0, 0)`` OpenCV computes the kernel size from *sigma_x*.
|
||||
"""
|
||||
if CUDA_AVAILABLE:
|
||||
try:
|
||||
src_u8 = _ensure_uint8(src)
|
||||
cv_type = _cv_type_for(src_u8)
|
||||
ks = _ksize_odd(ksize) if ksize != (0, 0) else ksize
|
||||
|
||||
gauss = cv2.cuda.createGaussianFilter(cv_type, cv_type, ks, sigma_x, sigma_y)
|
||||
gpu_src = cv2.cuda.GpuMat()
|
||||
gpu_src.upload(src_u8)
|
||||
gpu_dst = gauss.apply(gpu_src)
|
||||
return gpu_dst.download()
|
||||
except cv2.error:
|
||||
pass
|
||||
|
||||
return cv2.GaussianBlur(src, ksize, sigma_x, sigmaY=sigma_y)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Public API – addWeighted
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def gpu_add_weighted(
|
||||
src1: np.ndarray,
|
||||
alpha: float,
|
||||
src2: np.ndarray,
|
||||
beta: float,
|
||||
gamma: float,
|
||||
) -> np.ndarray:
|
||||
"""Drop-in replacement for ``cv2.addWeighted`` with CUDA acceleration."""
|
||||
if CUDA_AVAILABLE:
|
||||
try:
|
||||
s1 = _ensure_uint8(src1)
|
||||
s2 = _ensure_uint8(src2)
|
||||
g1 = cv2.cuda.GpuMat()
|
||||
g2 = cv2.cuda.GpuMat()
|
||||
g1.upload(s1)
|
||||
g2.upload(s2)
|
||||
gpu_dst = cv2.cuda.addWeighted(g1, alpha, g2, beta, gamma)
|
||||
return gpu_dst.download()
|
||||
except cv2.error:
|
||||
pass
|
||||
|
||||
return cv2.addWeighted(src1, alpha, src2, beta, gamma)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Public API – Unsharp-mask sharpening
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def gpu_sharpen(
|
||||
src: np.ndarray,
|
||||
strength: float,
|
||||
sigma: float = 3,
|
||||
) -> np.ndarray:
|
||||
"""Unsharp-mask sharpening, optionally GPU-accelerated.
|
||||
|
||||
Equivalent to::
|
||||
|
||||
blurred = GaussianBlur(src, (0,0), sigma)
|
||||
result = addWeighted(src, 1+strength, blurred, -strength, 0)
|
||||
"""
|
||||
if strength <= 0:
|
||||
return src
|
||||
|
||||
if CUDA_AVAILABLE:
|
||||
try:
|
||||
src_u8 = _ensure_uint8(src)
|
||||
cv_type = _cv_type_for(src_u8)
|
||||
|
||||
gauss = cv2.cuda.createGaussianFilter(cv_type, cv_type, (0, 0), sigma)
|
||||
gpu_src = cv2.cuda.GpuMat()
|
||||
gpu_src.upload(src_u8)
|
||||
gpu_blurred = gauss.apply(gpu_src)
|
||||
gpu_sharp = cv2.cuda.addWeighted(gpu_src, 1.0 + strength, gpu_blurred, -strength, 0)
|
||||
result = gpu_sharp.download()
|
||||
return np.clip(result, 0, 255).astype(np.uint8)
|
||||
except cv2.error:
|
||||
pass
|
||||
|
||||
blurred = cv2.GaussianBlur(src, (0, 0), sigma)
|
||||
sharpened = cv2.addWeighted(src, 1.0 + strength, blurred, -strength, 0)
|
||||
return np.clip(sharpened, 0, 255).astype(np.uint8)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Public API – Resize
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
# Map common cv2 interpolation flags to their CUDA equivalents
|
||||
_INTERP_MAP = {
|
||||
cv2.INTER_NEAREST: cv2.INTER_NEAREST,
|
||||
cv2.INTER_LINEAR: cv2.INTER_LINEAR,
|
||||
cv2.INTER_CUBIC: cv2.INTER_CUBIC,
|
||||
cv2.INTER_AREA: cv2.INTER_AREA,
|
||||
cv2.INTER_LANCZOS4: cv2.INTER_LANCZOS4,
|
||||
}
|
||||
|
||||
|
||||
def gpu_resize(
|
||||
src: np.ndarray,
|
||||
dsize: Tuple[int, int],
|
||||
fx: float = 0,
|
||||
fy: float = 0,
|
||||
interpolation: int = cv2.INTER_LINEAR,
|
||||
) -> np.ndarray:
|
||||
"""Drop-in replacement for ``cv2.resize`` with CUDA acceleration.
|
||||
|
||||
Parameters match ``cv2.resize(src, dsize, fx=fx, fy=fy, interpolation=...)``.
|
||||
"""
|
||||
if CUDA_AVAILABLE:
|
||||
try:
|
||||
src_u8 = _ensure_uint8(src)
|
||||
gpu_src = cv2.cuda.GpuMat()
|
||||
gpu_src.upload(src_u8)
|
||||
|
||||
interp = _INTERP_MAP.get(interpolation, cv2.INTER_LINEAR)
|
||||
|
||||
if dsize and dsize[0] > 0 and dsize[1] > 0:
|
||||
gpu_dst = cv2.cuda.resize(gpu_src, dsize, interpolation=interp)
|
||||
else:
|
||||
gpu_dst = cv2.cuda.resize(gpu_src, (0, 0), fx=fx, fy=fy, interpolation=interp)
|
||||
|
||||
return gpu_dst.download()
|
||||
except cv2.error:
|
||||
pass
|
||||
|
||||
return cv2.resize(src, dsize, fx=fx, fy=fy, interpolation=interpolation)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Public API – Color conversion
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def gpu_cvt_color(
|
||||
src: np.ndarray,
|
||||
code: int,
|
||||
) -> np.ndarray:
|
||||
"""Drop-in replacement for ``cv2.cvtColor`` with CUDA acceleration.
|
||||
|
||||
Parameters match ``cv2.cvtColor(src, code)``.
|
||||
"""
|
||||
if CUDA_AVAILABLE:
|
||||
try:
|
||||
src_u8 = _ensure_uint8(src)
|
||||
gpu_src = cv2.cuda.GpuMat()
|
||||
gpu_src.upload(src_u8)
|
||||
gpu_dst = cv2.cuda.cvtColor(gpu_src, code)
|
||||
return gpu_dst.download()
|
||||
except cv2.error:
|
||||
pass
|
||||
|
||||
return cv2.cvtColor(src, code)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Public API – Flip
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def gpu_flip(
|
||||
src: np.ndarray,
|
||||
flip_code: int,
|
||||
) -> np.ndarray:
|
||||
"""Drop-in replacement for ``cv2.flip`` with CUDA acceleration.
|
||||
|
||||
Parameters match ``cv2.flip(src, flipCode)``.
|
||||
*flip_code*: 0 = vertical, 1 = horizontal, -1 = both.
|
||||
"""
|
||||
if CUDA_AVAILABLE:
|
||||
try:
|
||||
src_u8 = _ensure_uint8(src)
|
||||
gpu_src = cv2.cuda.GpuMat()
|
||||
gpu_src.upload(src_u8)
|
||||
gpu_dst = cv2.cuda.flip(gpu_src, flip_code)
|
||||
return gpu_dst.download()
|
||||
except cv2.error:
|
||||
pass
|
||||
|
||||
return cv2.flip(src, flip_code)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Convenience: check at runtime whether GPU path is active
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def is_gpu_accelerated() -> bool:
|
||||
"""Return ``True`` when the CUDA path will be used."""
|
||||
return CUDA_AVAILABLE
|
||||
|
||||
# --- END OF FILE gpu_processing.py ---
|
||||
+2
-2
@@ -1,3 +1,3 @@
|
||||
name = 'Deep-Live-Cam'
|
||||
version = '2.0.3c'
|
||||
edition = 'GitHub Edition'
|
||||
version = '1.8'
|
||||
edition = 'GitHub Edition'
|
||||
|
||||
@@ -3,7 +3,6 @@ import opennsfw2
|
||||
from PIL import Image
|
||||
import cv2 # Add OpenCV import
|
||||
import modules.globals # Import globals to access the color correction toggle
|
||||
from modules.gpu_processing import gpu_cvt_color
|
||||
|
||||
from modules.typing import Frame
|
||||
|
||||
@@ -15,7 +14,7 @@ model = None
|
||||
def predict_frame(target_frame: Frame) -> bool:
|
||||
# Convert the frame to RGB before processing if color correction is enabled
|
||||
if modules.globals.color_correction:
|
||||
target_frame = gpu_cvt_color(target_frame, cv2.COLOR_BGR2RGB)
|
||||
target_frame = cv2.cvtColor(target_frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
image = Image.fromarray(target_frame)
|
||||
image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)
|
||||
|
||||
@@ -42,54 +42,27 @@ def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType
|
||||
|
||||
def set_frame_processors_modules_from_ui(frame_processors: List[str]) -> None:
|
||||
global FRAME_PROCESSORS_MODULES
|
||||
current_processor_names = [proc.__name__.split('.')[-1] for proc in FRAME_PROCESSORS_MODULES]
|
||||
|
||||
for frame_processor, state in modules.globals.fp_ui.items():
|
||||
if state == True and frame_processor not in current_processor_names:
|
||||
if state == True and frame_processor not in frame_processors:
|
||||
frame_processor_module = load_frame_processor_module(frame_processor)
|
||||
FRAME_PROCESSORS_MODULES.append(frame_processor_module)
|
||||
modules.globals.frame_processors.append(frame_processor)
|
||||
if state == False:
|
||||
try:
|
||||
frame_processor_module = load_frame_processor_module(frame_processor)
|
||||
FRAME_PROCESSORS_MODULES.append(frame_processor_module)
|
||||
if frame_processor not in modules.globals.frame_processors:
|
||||
modules.globals.frame_processors.append(frame_processor)
|
||||
except SystemExit:
|
||||
print(f"Warning: Failed to load frame processor {frame_processor} requested by UI state.")
|
||||
except Exception as e:
|
||||
print(f"Warning: Error loading frame processor {frame_processor} requested by UI state: {e}")
|
||||
|
||||
elif state == False and frame_processor in current_processor_names:
|
||||
try:
|
||||
module_to_remove = next((mod for mod in FRAME_PROCESSORS_MODULES if mod.__name__.endswith(f'.{frame_processor}')), None)
|
||||
if module_to_remove:
|
||||
FRAME_PROCESSORS_MODULES.remove(module_to_remove)
|
||||
if frame_processor in modules.globals.frame_processors:
|
||||
modules.globals.frame_processors.remove(frame_processor)
|
||||
except Exception as e:
|
||||
print(f"Warning: Error removing frame processor {frame_processor}: {e}")
|
||||
FRAME_PROCESSORS_MODULES.remove(frame_processor_module)
|
||||
modules.globals.frame_processors.remove(frame_processor)
|
||||
except:
|
||||
pass
|
||||
|
||||
def multi_process_frame(source_path: str, temp_frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None], progress: Any = None) -> None:
|
||||
"""Process frames in parallel with optimized batching and memory management."""
|
||||
max_workers = modules.globals.execution_threads
|
||||
|
||||
# Determine optimal batch size based on available memory and thread count
|
||||
# Process frames in batches to avoid memory overflow
|
||||
batch_size = max(1, min(32, len(temp_frame_paths) // max(1, max_workers)))
|
||||
|
||||
with ThreadPoolExecutor(max_workers=max_workers) as executor:
|
||||
# Process in batches to manage memory better
|
||||
for i in range(0, len(temp_frame_paths), batch_size):
|
||||
batch = temp_frame_paths[i:i + batch_size]
|
||||
futures = []
|
||||
|
||||
for path in batch:
|
||||
future = executor.submit(process_frames, source_path, [path], progress)
|
||||
futures.append(future)
|
||||
|
||||
# Wait for batch to complete before starting next batch
|
||||
for future in futures:
|
||||
try:
|
||||
future.result()
|
||||
except Exception as e:
|
||||
print(f"Error processing frame: {e}")
|
||||
with ThreadPoolExecutor(max_workers=modules.globals.execution_threads) as executor:
|
||||
futures = []
|
||||
for path in temp_frame_paths:
|
||||
future = executor.submit(process_frames, source_path, [path], progress)
|
||||
futures.append(future)
|
||||
for future in futures:
|
||||
future.result()
|
||||
|
||||
|
||||
def process_video(source_path: str, frame_paths: list[str], process_frames: Callable[[str, List[str], Any], None]) -> None:
|
||||
|
||||
@@ -1,20 +1,18 @@
|
||||
# --- START OF FILE face_enhancer.py ---
|
||||
# Uses ONNX Runtime for GFPGAN face enhancement (no torch/gfpgan dependency)
|
||||
|
||||
from typing import Any, List
|
||||
import cv2
|
||||
import threading
|
||||
import numpy as np
|
||||
import gfpgan
|
||||
import os
|
||||
|
||||
import onnxruntime
|
||||
|
||||
import modules.globals
|
||||
import modules.processors.frame.core
|
||||
from modules.core import update_status
|
||||
from modules.face_analyser import get_one_face, get_many_faces
|
||||
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,
|
||||
)
|
||||
@@ -29,29 +27,15 @@ models_dir = os.path.join(
|
||||
os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
|
||||
)
|
||||
|
||||
# Standard FFHQ 5-point face template for 512x512 resolution
|
||||
# Points: left_eye, right_eye, nose, left_mouth, right_mouth
|
||||
FFHQ_TEMPLATE_512 = np.array(
|
||||
[
|
||||
[192.98138, 239.94708],
|
||||
[318.90277, 240.19366],
|
||||
[256.63416, 314.01935],
|
||||
[201.26117, 371.41043],
|
||||
[313.08905, 371.15118],
|
||||
],
|
||||
dtype=np.float32,
|
||||
)
|
||||
|
||||
|
||||
def pre_check() -> bool:
|
||||
model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
|
||||
if not os.path.exists(model_path):
|
||||
update_status(
|
||||
f"GFPGAN ONNX model not found at {model_path}. "
|
||||
"Please place gfpgan-1024.onnx in the models folder.",
|
||||
NAME,
|
||||
)
|
||||
return False
|
||||
download_directory_path = models_dir
|
||||
conditional_download(
|
||||
download_directory_path,
|
||||
[
|
||||
"https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth"
|
||||
],
|
||||
)
|
||||
return True
|
||||
|
||||
|
||||
@@ -64,309 +48,62 @@ def pre_start() -> bool:
|
||||
return True
|
||||
|
||||
|
||||
def get_face_enhancer() -> onnxruntime.InferenceSession:
|
||||
"""
|
||||
Initializes and returns the GFPGAN ONNX Runtime inference session,
|
||||
using the execution providers configured in modules.globals.
|
||||
"""
|
||||
def get_face_enhancer() -> Any:
|
||||
global FACE_ENHANCER
|
||||
|
||||
with THREAD_LOCK:
|
||||
if FACE_ENHANCER is None:
|
||||
model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
|
||||
|
||||
if not os.path.exists(model_path):
|
||||
raise FileNotFoundError(
|
||||
f"{NAME}: Model not found at {model_path}"
|
||||
)
|
||||
|
||||
try:
|
||||
providers = modules.globals.execution_providers
|
||||
|
||||
session_options = onnxruntime.SessionOptions()
|
||||
session_options.graph_optimization_level = (
|
||||
onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
|
||||
)
|
||||
|
||||
FACE_ENHANCER = onnxruntime.InferenceSession(
|
||||
model_path,
|
||||
sess_options=session_options,
|
||||
providers=providers,
|
||||
)
|
||||
|
||||
input_info = FACE_ENHANCER.get_inputs()[0]
|
||||
output_info = FACE_ENHANCER.get_outputs()[0]
|
||||
active_providers = FACE_ENHANCER.get_providers()
|
||||
print(
|
||||
f"{NAME}: GFPGAN ONNX model loaded successfully."
|
||||
)
|
||||
print(
|
||||
f"{NAME}: Input: {input_info.name}, "
|
||||
f"shape: {input_info.shape}, type: {input_info.type}"
|
||||
)
|
||||
print(
|
||||
f"{NAME}: Output: {output_info.name}, "
|
||||
f"shape: {output_info.shape}, type: {output_info.type}"
|
||||
)
|
||||
print(f"{NAME}: Active providers: {active_providers}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"{NAME}: Error loading GFPGAN ONNX model: {e}")
|
||||
FACE_ENHANCER = None
|
||||
raise RuntimeError(
|
||||
f"{NAME}: Failed to load GFPGAN ONNX model: {e}"
|
||||
)
|
||||
|
||||
if FACE_ENHANCER is None:
|
||||
raise RuntimeError(
|
||||
f"{NAME}: Failed to initialize GFPGAN ONNX session. Check logs."
|
||||
)
|
||||
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]
|
||||
|
||||
return FACE_ENHANCER
|
||||
|
||||
|
||||
def _align_face(
|
||||
frame: Frame, landmarks_5: np.ndarray, output_size: int
|
||||
) -> tuple:
|
||||
"""
|
||||
Align and crop a face from the frame using 5-point landmarks and the
|
||||
standard FFHQ template.
|
||||
|
||||
Returns:
|
||||
(aligned_face, affine_matrix) or (None, None) on failure.
|
||||
"""
|
||||
# Scale the 512-base template to the desired output size
|
||||
scale = output_size / 512.0
|
||||
template = FFHQ_TEMPLATE_512 * scale
|
||||
|
||||
# Estimate a similarity transform (4 DOF: rotation, scale, tx, ty)
|
||||
affine_matrix, _ = cv2.estimateAffinePartial2D(
|
||||
landmarks_5, template, method=cv2.LMEDS
|
||||
)
|
||||
if affine_matrix is None:
|
||||
return None, None
|
||||
|
||||
# Warp the face to the aligned position
|
||||
aligned_face = cv2.warpAffine(
|
||||
frame,
|
||||
affine_matrix,
|
||||
(output_size, output_size),
|
||||
borderMode=cv2.BORDER_CONSTANT,
|
||||
borderValue=(135, 133, 132),
|
||||
)
|
||||
|
||||
return aligned_face, affine_matrix
|
||||
|
||||
|
||||
def _paste_back(
|
||||
frame: Frame,
|
||||
enhanced_face: np.ndarray,
|
||||
affine_matrix: np.ndarray,
|
||||
output_size: int,
|
||||
) -> Frame:
|
||||
"""
|
||||
Paste an enhanced (aligned) face back onto the original frame using the
|
||||
inverse affine transform with feathered-edge blending.
|
||||
"""
|
||||
h, w = frame.shape[:2]
|
||||
|
||||
# Inverse the affine warp
|
||||
inv_matrix = cv2.invertAffineTransform(affine_matrix)
|
||||
inv_restored = cv2.warpAffine(
|
||||
enhanced_face,
|
||||
inv_matrix,
|
||||
(w, h),
|
||||
borderMode=cv2.BORDER_CONSTANT,
|
||||
borderValue=(0, 0, 0),
|
||||
)
|
||||
|
||||
# Build a soft feathered mask in aligned space for edge blending
|
||||
face_mask = np.ones((output_size, output_size), dtype=np.float32)
|
||||
|
||||
# Feather the border (5 % of the size on each edge)
|
||||
border = max(1, int(output_size * 0.05))
|
||||
ramp_up = np.linspace(0.0, 1.0, border, dtype=np.float32)
|
||||
ramp_down = np.linspace(1.0, 0.0, border, dtype=np.float32)
|
||||
|
||||
# Top / bottom rows
|
||||
face_mask[:border, :] *= ramp_up[:, None]
|
||||
face_mask[-border:, :] *= ramp_down[:, None]
|
||||
# Left / right columns
|
||||
face_mask[:, :border] *= ramp_up[None, :]
|
||||
face_mask[:, -border:] *= ramp_down[None, :]
|
||||
|
||||
# Expand to 3-channel
|
||||
face_mask_3c = np.stack([face_mask] * 3, axis=-1)
|
||||
|
||||
# Warp mask back to original frame space
|
||||
inv_mask = cv2.warpAffine(
|
||||
face_mask_3c,
|
||||
inv_matrix,
|
||||
(w, h),
|
||||
borderMode=cv2.BORDER_CONSTANT,
|
||||
borderValue=(0, 0, 0),
|
||||
)
|
||||
inv_mask = np.clip(inv_mask, 0.0, 1.0)
|
||||
|
||||
# Alpha-blend
|
||||
result = (
|
||||
frame.astype(np.float32) * (1.0 - inv_mask)
|
||||
+ inv_restored.astype(np.float32) * inv_mask
|
||||
)
|
||||
return np.clip(result, 0, 255).astype(np.uint8)
|
||||
|
||||
|
||||
def _preprocess_face(aligned_face: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Convert an aligned BGR uint8 face image to the ONNX model input tensor.
|
||||
Format: NCHW float32, normalised to [-1, 1].
|
||||
"""
|
||||
# BGR -> RGB
|
||||
rgb = cv2.cvtColor(aligned_face, cv2.COLOR_BGR2RGB).astype(np.float32)
|
||||
# [0, 255] -> [0, 1] -> [-1, 1]
|
||||
rgb = rgb / 255.0
|
||||
rgb = (rgb - 0.5) / 0.5
|
||||
# HWC -> CHW, add batch dim
|
||||
chw = np.transpose(rgb, (2, 0, 1))
|
||||
return np.expand_dims(chw, axis=0) # shape: (1, 3, H, W)
|
||||
|
||||
|
||||
def _postprocess_face(output: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Convert the ONNX model output tensor back to a BGR uint8 image.
|
||||
Expects input in NCHW format with values in [-1, 1].
|
||||
"""
|
||||
face = np.squeeze(output) # remove batch dim -> (3, H, W)
|
||||
face = np.transpose(face, (1, 2, 0)) # CHW -> HWC
|
||||
# [-1, 1] -> [0, 1] -> [0, 255]
|
||||
face = (face + 1.0) / 2.0
|
||||
face = np.clip(face * 255.0, 0, 255).astype(np.uint8)
|
||||
# RGB -> BGR
|
||||
return cv2.cvtColor(face, cv2.COLOR_RGB2BGR)
|
||||
|
||||
|
||||
def enhance_face(temp_frame: Frame) -> Frame:
|
||||
"""Enhances all faces in a frame using the GFPGAN ONNX model."""
|
||||
session = get_face_enhancer()
|
||||
|
||||
# Determine model input resolution from the session metadata
|
||||
input_info = session.get_inputs()[0]
|
||||
input_name = input_info.name
|
||||
input_shape = input_info.shape # e.g. [1, 3, 512, 512]
|
||||
# Safely extract input size (handle dynamic / symbolic dimensions)
|
||||
try:
|
||||
align_size = int(input_shape[2])
|
||||
if align_size <= 0:
|
||||
align_size = 512
|
||||
except (ValueError, TypeError, IndexError):
|
||||
align_size = 512
|
||||
|
||||
# Detect faces using InsightFace (already a project dependency)
|
||||
faces = get_many_faces(temp_frame)
|
||||
if not faces:
|
||||
return temp_frame
|
||||
|
||||
result_frame = temp_frame.copy()
|
||||
|
||||
for face in faces:
|
||||
# Need the 5-point key-points for alignment
|
||||
if not hasattr(face, "kps") or face.kps is None:
|
||||
continue
|
||||
|
||||
landmarks_5 = face.kps.astype(np.float32)
|
||||
if landmarks_5.shape[0] < 5:
|
||||
continue
|
||||
|
||||
# Align / crop the face at the model's INPUT resolution
|
||||
aligned_face, affine_matrix = _align_face(
|
||||
temp_frame, landmarks_5, output_size=align_size
|
||||
)
|
||||
if aligned_face is None or affine_matrix is None:
|
||||
continue
|
||||
|
||||
try:
|
||||
with THREAD_SEMAPHORE:
|
||||
input_tensor = _preprocess_face(aligned_face)
|
||||
output_tensor = session.run(None, {input_name: input_tensor})[0]
|
||||
enhanced_bgr = _postprocess_face(output_tensor)
|
||||
|
||||
# The model may output at a different resolution than its input
|
||||
# (e.g. input 512x512 → output 1024x1024). Resize the enhanced
|
||||
# face back to the alignment size so the inverse affine maps
|
||||
# correctly.
|
||||
eh, ew = enhanced_bgr.shape[:2]
|
||||
if eh != align_size or ew != align_size:
|
||||
enhanced_bgr = cv2.resize(
|
||||
enhanced_bgr,
|
||||
(align_size, align_size),
|
||||
interpolation=cv2.INTER_LANCZOS4,
|
||||
)
|
||||
|
||||
# Paste enhanced face back onto the frame
|
||||
result_frame = _paste_back(
|
||||
result_frame, enhanced_bgr, affine_matrix, output_size=align_size
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"{NAME}: Error enhancing a face: {e}")
|
||||
continue
|
||||
|
||||
return result_frame
|
||||
with THREAD_SEMAPHORE:
|
||||
_, _, temp_frame = get_face_enhancer().enhance(temp_frame, paste_back=True)
|
||||
return temp_frame
|
||||
|
||||
|
||||
def process_frame(source_face: Face | None, temp_frame: Frame) -> Frame:
|
||||
"""Processes a frame: enhances face if detected."""
|
||||
temp_frame = enhance_face(temp_frame)
|
||||
def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
|
||||
target_face = get_one_face(temp_frame)
|
||||
if target_face:
|
||||
temp_frame = enhance_face(temp_frame)
|
||||
return temp_frame
|
||||
|
||||
|
||||
def process_frames(
|
||||
source_path: str | None, temp_frame_paths: List[str], progress: Any = None
|
||||
source_path: str, temp_frame_paths: List[str], progress: Any = None
|
||||
) -> None:
|
||||
"""Processes multiple frames from file paths."""
|
||||
for temp_frame_path in temp_frame_paths:
|
||||
if not os.path.exists(temp_frame_path):
|
||||
print(
|
||||
f"{NAME}: Warning: Frame path not found {temp_frame_path}, skipping."
|
||||
)
|
||||
if progress:
|
||||
progress.update(1)
|
||||
continue
|
||||
|
||||
temp_frame = cv2.imread(temp_frame_path)
|
||||
if temp_frame is None:
|
||||
print(
|
||||
f"{NAME}: Warning: Failed to read frame {temp_frame_path}, skipping."
|
||||
)
|
||||
if progress:
|
||||
progress.update(1)
|
||||
continue
|
||||
|
||||
result_frame = process_frame(None, temp_frame)
|
||||
cv2.imwrite(temp_frame_path, result_frame)
|
||||
result = process_frame(None, temp_frame)
|
||||
cv2.imwrite(temp_frame_path, result)
|
||||
if progress:
|
||||
progress.update(1)
|
||||
|
||||
|
||||
def process_image(
|
||||
source_path: str | None, target_path: str, output_path: str
|
||||
) -> None:
|
||||
"""Processes a single image file."""
|
||||
def process_image(source_path: str, target_path: str, output_path: str) -> None:
|
||||
target_frame = cv2.imread(target_path)
|
||||
if target_frame is None:
|
||||
print(f"{NAME}: Error: Failed to read target image {target_path}")
|
||||
return
|
||||
result_frame = process_frame(None, target_frame)
|
||||
cv2.imwrite(output_path, result_frame)
|
||||
print(f"{NAME}: Enhanced image saved to {output_path}")
|
||||
result = process_frame(None, target_frame)
|
||||
cv2.imwrite(output_path, result)
|
||||
|
||||
|
||||
def process_video(
|
||||
source_path: str | None, temp_frame_paths: List[str]
|
||||
) -> None:
|
||||
"""Processes video frames using the frame processor core."""
|
||||
modules.processors.frame.core.process_video(
|
||||
source_path, temp_frame_paths, process_frames
|
||||
)
|
||||
def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
|
||||
modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
|
||||
|
||||
|
||||
# --- END OF FILE face_enhancer.py ---
|
||||
def process_frame_v2(temp_frame: Frame) -> Frame:
|
||||
target_face = get_one_face(temp_frame)
|
||||
if target_face:
|
||||
temp_frame = enhance_face(temp_frame)
|
||||
return temp_frame
|
||||
|
||||
@@ -1,567 +0,0 @@
|
||||
import cv2
|
||||
import numpy as np
|
||||
from modules.typing import Face, Frame
|
||||
import modules.globals
|
||||
from modules.gpu_processing import gpu_gaussian_blur, gpu_resize, gpu_cvt_color
|
||||
|
||||
def apply_color_transfer(source, target):
|
||||
"""
|
||||
Apply color transfer from target to source image
|
||||
"""
|
||||
source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32")
|
||||
target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32")
|
||||
|
||||
source_mean, source_std = cv2.meanStdDev(source)
|
||||
target_mean, target_std = cv2.meanStdDev(target)
|
||||
|
||||
# Reshape mean and std to be broadcastable
|
||||
source_mean = source_mean.reshape(1, 1, 3)
|
||||
source_std = source_std.reshape(1, 1, 3)
|
||||
target_mean = target_mean.reshape(1, 1, 3)
|
||||
target_std = target_std.reshape(1, 1, 3)
|
||||
|
||||
# Perform the color transfer
|
||||
source = (source - source_mean) * (target_std / source_std) + target_mean
|
||||
|
||||
return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)
|
||||
|
||||
def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
|
||||
mask = np.zeros(frame.shape[:2], dtype=np.uint8)
|
||||
landmarks = face.landmark_2d_106
|
||||
if landmarks is not None:
|
||||
# Convert landmarks to int32
|
||||
landmarks = landmarks.astype(np.int32)
|
||||
|
||||
# Extract facial features
|
||||
right_side_face = landmarks[0:16]
|
||||
left_side_face = landmarks[17:32]
|
||||
right_eye = landmarks[33:42]
|
||||
right_eye_brow = landmarks[43:51]
|
||||
left_eye = landmarks[87:96]
|
||||
left_eye_brow = landmarks[97:105]
|
||||
|
||||
# Calculate padding
|
||||
padding = int(
|
||||
np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05
|
||||
) # 5% of face width
|
||||
|
||||
# Create a slightly larger convex hull for padding
|
||||
face_outline = landmarks[0:33]
|
||||
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 (GPU-accelerated when available)
|
||||
mask = gpu_gaussian_blur(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
|
||||
lower_lip_polygon = None
|
||||
mouth_box = (0,0,0,0)
|
||||
|
||||
landmarks = face.landmark_2d_106
|
||||
if landmarks is not None:
|
||||
# Use outer mouth landmarks (52-63) to capture the lips only
|
||||
lower_lip_order = list(range(52, 64))
|
||||
|
||||
if max(lower_lip_order) >= landmarks.shape[0]:
|
||||
return mask, mouth_cutout, mouth_box, lower_lip_polygon
|
||||
|
||||
lower_lip_landmarks = landmarks[lower_lip_order].astype(np.float32)
|
||||
|
||||
# Calculate the center of the landmarks
|
||||
center = np.mean(lower_lip_landmarks, axis=0)
|
||||
|
||||
# Expand the landmarks outward using the mouth_mask_size
|
||||
# Use a more conservative expansion to avoid affecting face shape
|
||||
expansion_factor = (
|
||||
1 + modules.globals.mask_down_size * modules.globals.mouth_mask_size
|
||||
)
|
||||
expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
|
||||
|
||||
# Removed specific top/chin extensions to preserve face shape
|
||||
|
||||
# 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)
|
||||
# Shift polygon coordinates relative to the ROI's top-left corner
|
||||
polygon_relative_to_roi = expanded_landmarks - [min_x, min_y]
|
||||
cv2.fillPoly(mask_roi, [polygon_relative_to_roi], 255)
|
||||
|
||||
# Apply Gaussian blur to soften the mask edges (GPU-accelerated when available)
|
||||
mask_roi = gpu_gaussian_blur(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
|
||||
mouth_box = (min_x, min_y, max_x, max_y)
|
||||
|
||||
return mask, mouth_cutout, mouth_box, 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 (GPU-accelerated when available)
|
||||
mask_roi = gpu_gaussian_blur(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 (GPU-accelerated when available)
|
||||
# First, strong Gaussian blur for initial softening
|
||||
mask_roi = gpu_gaussian_blur(mask_roi, (21, 21), 7)
|
||||
|
||||
# Second, medium blur for transition areas
|
||||
mask_roi = gpu_gaussian_blur(mask_roi, (11, 11), 3)
|
||||
|
||||
# Finally, light blur for fine details
|
||||
mask_roi = gpu_gaussian_blur(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 = gpu_gaussian_blur(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 = gpu_resize(cutout, (box_width, box_height))
|
||||
roi = frame[min_y:max_y, min_x:max_x]
|
||||
|
||||
if roi.shape != resized_cutout.shape:
|
||||
resized_cutout = gpu_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 (GPU-accelerated when available)
|
||||
polygon_mask = gpu_gaussian_blur(polygon_mask, (21, 21), 7)
|
||||
|
||||
# Apply additional feathering
|
||||
feather_amount = min(
|
||||
30,
|
||||
box_width // modules.globals.mask_feather_ratio,
|
||||
box_height // modules.globals.mask_feather_ratio,
|
||||
)
|
||||
feathered_mask = cv2.GaussianBlur(
|
||||
polygon_mask.astype(float), (0, 0), feather_amount
|
||||
)
|
||||
feathered_mask = feathered_mask / feathered_mask.max()
|
||||
|
||||
# Apply additional smoothing to the mask edges
|
||||
feathered_mask = cv2.GaussianBlur(feathered_mask, (5, 5), 1)
|
||||
|
||||
face_mask_roi = face_mask[min_y:max_y, min_x:max_x]
|
||||
combined_mask = feathered_mask * (face_mask_roi / 255.0)
|
||||
|
||||
combined_mask = combined_mask[:, :, np.newaxis]
|
||||
blended = (
|
||||
color_corrected_area * combined_mask + roi * (1 - combined_mask)
|
||||
).astype(np.uint8)
|
||||
|
||||
# Apply face mask to blended result
|
||||
face_mask_3channel = (
|
||||
np.repeat(face_mask_roi[:, :, np.newaxis], 3, axis=2) / 255.0
|
||||
)
|
||||
final_blend = blended * face_mask_3channel + roi * (1 - face_mask_3channel)
|
||||
|
||||
frame[min_y:max_y, min_x:max_x] = final_blend.astype(np.uint8)
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
return frame
|
||||
|
||||
def draw_mask_visualization(
|
||||
frame: Frame,
|
||||
mask_data: tuple,
|
||||
label: str,
|
||||
draw_method: str = "polygon"
|
||||
) -> Frame:
|
||||
mask, cutout, (min_x, min_y, max_x, max_y), polygon = mask_data
|
||||
|
||||
vis_frame = frame.copy()
|
||||
|
||||
# Ensure coordinates are within frame bounds
|
||||
height, width = vis_frame.shape[:2]
|
||||
min_x, min_y = max(0, min_x), max(0, min_y)
|
||||
max_x, max_y = min(width, max_x), min(height, max_y)
|
||||
|
||||
if draw_method == "ellipse" and len(polygon) > 50: # For eyes
|
||||
# Split points for left and right parts
|
||||
mid_point = len(polygon) // 2
|
||||
left_points = polygon[:mid_point]
|
||||
right_points = polygon[mid_point:]
|
||||
|
||||
try:
|
||||
# Fit ellipses to points - need at least 5 points
|
||||
if len(left_points) >= 5 and len(right_points) >= 5:
|
||||
# Convert points to the correct format for ellipse fitting
|
||||
left_points = left_points.astype(np.float32)
|
||||
right_points = right_points.astype(np.float32)
|
||||
|
||||
# Fit ellipses
|
||||
left_ellipse = cv2.fitEllipse(left_points)
|
||||
right_ellipse = cv2.fitEllipse(right_points)
|
||||
|
||||
# Draw the ellipses
|
||||
cv2.ellipse(vis_frame, left_ellipse, (0, 255, 0), 2)
|
||||
cv2.ellipse(vis_frame, right_ellipse, (0, 255, 0), 2)
|
||||
except Exception as e:
|
||||
# If ellipse fitting fails, draw simple rectangles as fallback
|
||||
left_rect = cv2.boundingRect(left_points)
|
||||
right_rect = cv2.boundingRect(right_points)
|
||||
cv2.rectangle(vis_frame,
|
||||
(left_rect[0], left_rect[1]),
|
||||
(left_rect[0] + left_rect[2], left_rect[1] + left_rect[3]),
|
||||
(0, 255, 0), 2)
|
||||
cv2.rectangle(vis_frame,
|
||||
(right_rect[0], right_rect[1]),
|
||||
(right_rect[0] + right_rect[2], right_rect[1] + right_rect[3]),
|
||||
(0, 255, 0), 2)
|
||||
else: # For mouth and eyebrows
|
||||
# Draw the polygon
|
||||
if len(polygon) > 50: # If we have multiple parts
|
||||
mid_point = len(polygon) // 2
|
||||
left_points = polygon[:mid_point]
|
||||
right_points = polygon[mid_point:]
|
||||
cv2.polylines(vis_frame, [left_points], True, (0, 255, 0), 2, cv2.LINE_AA)
|
||||
cv2.polylines(vis_frame, [right_points], True, (0, 255, 0), 2, cv2.LINE_AA)
|
||||
else:
|
||||
cv2.polylines(vis_frame, [polygon], True, (0, 255, 0), 2, cv2.LINE_AA)
|
||||
|
||||
# Add label
|
||||
cv2.putText(
|
||||
vis_frame,
|
||||
label,
|
||||
(min_x, min_y - 10),
|
||||
cv2.FONT_HERSHEY_SIMPLEX,
|
||||
0.5,
|
||||
(255, 255, 255),
|
||||
1,
|
||||
)
|
||||
|
||||
return vis_frame
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,9 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
# Import the tkinter fix to patch the ScreenChanged error
|
||||
import tkinter_fix
|
||||
|
||||
import core
|
||||
|
||||
if __name__ == '__main__':
|
||||
core.run()
|
||||
@@ -1,26 +0,0 @@
|
||||
import tkinter
|
||||
|
||||
# Only needs to be imported once at the beginning of the application
|
||||
def apply_patch():
|
||||
# Create a monkey patch for the internal _tkinter module
|
||||
original_init = tkinter.Tk.__init__
|
||||
|
||||
def patched_init(self, *args, **kwargs):
|
||||
# Call the original init
|
||||
original_init(self, *args, **kwargs)
|
||||
|
||||
# Define the missing ::tk::ScreenChanged procedure
|
||||
self.tk.eval("""
|
||||
if {[info commands ::tk::ScreenChanged] == ""} {
|
||||
proc ::tk::ScreenChanged {args} {
|
||||
# Do nothing
|
||||
return
|
||||
}
|
||||
}
|
||||
""")
|
||||
|
||||
# Apply the monkey patch
|
||||
tkinter.Tk.__init__ = patched_init
|
||||
|
||||
# Apply the patch automatically when this module is imported
|
||||
apply_patch()
|
||||
+69
-266
@@ -4,18 +4,13 @@ import customtkinter as ctk
|
||||
from typing import Callable, Tuple
|
||||
import cv2
|
||||
from cv2_enumerate_cameras import enumerate_cameras # Add this import
|
||||
from modules.gpu_processing import gpu_cvt_color, gpu_resize, gpu_flip
|
||||
from PIL import Image, ImageOps
|
||||
import time
|
||||
import json
|
||||
import queue
|
||||
import threading
|
||||
import numpy as np
|
||||
import modules.globals
|
||||
import modules.metadata
|
||||
from modules.face_analyser import (
|
||||
get_one_face,
|
||||
get_many_faces,
|
||||
get_unique_faces_from_target_image,
|
||||
get_unique_faces_from_target_video,
|
||||
add_blank_map,
|
||||
@@ -32,7 +27,6 @@ from modules.utilities import (
|
||||
)
|
||||
from modules.video_capture import VideoCapturer
|
||||
from modules.gettext import LanguageManager
|
||||
from modules import globals
|
||||
import platform
|
||||
|
||||
if platform.system() == "Windows":
|
||||
@@ -41,7 +35,7 @@ if platform.system() == "Windows":
|
||||
ROOT = None
|
||||
POPUP = None
|
||||
POPUP_LIVE = None
|
||||
ROOT_HEIGHT = 800
|
||||
ROOT_HEIGHT = 700
|
||||
ROOT_WIDTH = 600
|
||||
|
||||
PREVIEW = None
|
||||
@@ -103,7 +97,6 @@ def save_switch_states():
|
||||
"keep_frames": modules.globals.keep_frames,
|
||||
"many_faces": modules.globals.many_faces,
|
||||
"map_faces": modules.globals.map_faces,
|
||||
"poisson_blend": modules.globals.poisson_blend,
|
||||
"color_correction": modules.globals.color_correction,
|
||||
"nsfw_filter": modules.globals.nsfw_filter,
|
||||
"live_mirror": modules.globals.live_mirror,
|
||||
@@ -126,7 +119,6 @@ def load_switch_states():
|
||||
modules.globals.keep_frames = switch_states.get("keep_frames", False)
|
||||
modules.globals.many_faces = switch_states.get("many_faces", False)
|
||||
modules.globals.map_faces = switch_states.get("map_faces", False)
|
||||
modules.globals.poisson_blend = switch_states.get("poisson_blend", False)
|
||||
modules.globals.color_correction = switch_states.get("color_correction", False)
|
||||
modules.globals.nsfw_filter = switch_states.get("nsfw_filter", False)
|
||||
modules.globals.live_mirror = switch_states.get("live_mirror", False)
|
||||
@@ -160,20 +152,20 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
root.protocol("WM_DELETE_WINDOW", lambda: destroy())
|
||||
|
||||
source_label = ctk.CTkLabel(root, text=None)
|
||||
source_label.place(relx=0.1, rely=0.05, relwidth=0.275, relheight=0.225)
|
||||
source_label.place(relx=0.1, rely=0.1, relwidth=0.3, relheight=0.25)
|
||||
|
||||
target_label = ctk.CTkLabel(root, text=None)
|
||||
target_label.place(relx=0.6, rely=0.05, relwidth=0.275, relheight=0.225)
|
||||
target_label.place(relx=0.6, rely=0.1, relwidth=0.3, relheight=0.25)
|
||||
|
||||
select_face_button = ctk.CTkButton(
|
||||
root, text=_("Select a face"), cursor="hand2", command=lambda: select_source_path()
|
||||
)
|
||||
select_face_button.place(relx=0.1, rely=0.30, relwidth=0.3, relheight=0.1)
|
||||
select_face_button.place(relx=0.1, rely=0.4, relwidth=0.3, relheight=0.1)
|
||||
|
||||
swap_faces_button = ctk.CTkButton(
|
||||
root, text="↔", cursor="hand2", command=lambda: swap_faces_paths()
|
||||
)
|
||||
swap_faces_button.place(relx=0.45, rely=0.30, relwidth=0.1, relheight=0.1)
|
||||
swap_faces_button.place(relx=0.45, rely=0.4, relwidth=0.1, relheight=0.1)
|
||||
|
||||
select_target_button = ctk.CTkButton(
|
||||
root,
|
||||
@@ -181,7 +173,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
cursor="hand2",
|
||||
command=lambda: select_target_path(),
|
||||
)
|
||||
select_target_button.place(relx=0.6, rely=0.30, relwidth=0.3, relheight=0.1)
|
||||
select_target_button.place(relx=0.6, rely=0.4, relwidth=0.3, relheight=0.1)
|
||||
|
||||
keep_fps_value = ctk.BooleanVar(value=modules.globals.keep_fps)
|
||||
keep_fps_checkbox = ctk.CTkSwitch(
|
||||
@@ -194,7 +186,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
keep_fps_checkbox.place(relx=0.1, rely=0.5)
|
||||
keep_fps_checkbox.place(relx=0.1, rely=0.6)
|
||||
|
||||
keep_frames_value = ctk.BooleanVar(value=modules.globals.keep_frames)
|
||||
keep_frames_switch = ctk.CTkSwitch(
|
||||
@@ -207,7 +199,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
keep_frames_switch.place(relx=0.1, rely=0.55)
|
||||
keep_frames_switch.place(relx=0.1, rely=0.65)
|
||||
|
||||
enhancer_value = ctk.BooleanVar(value=modules.globals.fp_ui["face_enhancer"])
|
||||
enhancer_switch = ctk.CTkSwitch(
|
||||
@@ -220,7 +212,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
enhancer_switch.place(relx=0.1, rely=0.6)
|
||||
enhancer_switch.place(relx=0.1, rely=0.7)
|
||||
|
||||
keep_audio_value = ctk.BooleanVar(value=modules.globals.keep_audio)
|
||||
keep_audio_switch = ctk.CTkSwitch(
|
||||
@@ -233,7 +225,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
keep_audio_switch.place(relx=0.6, rely=0.5)
|
||||
keep_audio_switch.place(relx=0.6, rely=0.6)
|
||||
|
||||
many_faces_value = ctk.BooleanVar(value=modules.globals.many_faces)
|
||||
many_faces_switch = ctk.CTkSwitch(
|
||||
@@ -246,7 +238,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
many_faces_switch.place(relx=0.6, rely=0.55)
|
||||
many_faces_switch.place(relx=0.6, rely=0.65)
|
||||
|
||||
color_correction_value = ctk.BooleanVar(value=modules.globals.color_correction)
|
||||
color_correction_switch = ctk.CTkSwitch(
|
||||
@@ -259,7 +251,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
color_correction_switch.place(relx=0.6, rely=0.6)
|
||||
color_correction_switch.place(relx=0.6, rely=0.70)
|
||||
|
||||
# nsfw_value = ctk.BooleanVar(value=modules.globals.nsfw_filter)
|
||||
# nsfw_switch = ctk.CTkSwitch(root, text='NSFW filter', variable=nsfw_value, cursor='hand2', command=lambda: setattr(modules.globals, 'nsfw_filter', nsfw_value.get()))
|
||||
@@ -277,20 +269,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
close_mapper_window() if not map_faces.get() else None
|
||||
),
|
||||
)
|
||||
map_faces_switch.place(relx=0.1, rely=0.65)
|
||||
|
||||
poisson_blend_value = ctk.BooleanVar(value=modules.globals.poisson_blend)
|
||||
poisson_blend_switch = ctk.CTkSwitch(
|
||||
root,
|
||||
text=_("Poisson Blend"),
|
||||
variable=poisson_blend_value,
|
||||
cursor="hand2",
|
||||
command=lambda: (
|
||||
setattr(modules.globals, "poisson_blend", poisson_blend_value.get()),
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
poisson_blend_switch.place(relx=0.1, rely=0.7)
|
||||
map_faces_switch.place(relx=0.1, rely=0.75)
|
||||
|
||||
show_fps_value = ctk.BooleanVar(value=modules.globals.show_fps)
|
||||
show_fps_switch = ctk.CTkSwitch(
|
||||
@@ -303,7 +282,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
save_switch_states(),
|
||||
),
|
||||
)
|
||||
show_fps_switch.place(relx=0.6, rely=0.65)
|
||||
show_fps_switch.place(relx=0.6, rely=0.75)
|
||||
|
||||
mouth_mask_var = ctk.BooleanVar(value=modules.globals.mouth_mask)
|
||||
mouth_mask_switch = ctk.CTkSwitch(
|
||||
@@ -313,7 +292,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
cursor="hand2",
|
||||
command=lambda: setattr(modules.globals, "mouth_mask", mouth_mask_var.get()),
|
||||
)
|
||||
mouth_mask_switch.place(relx=0.1, rely=0.45)
|
||||
mouth_mask_switch.place(relx=0.1, rely=0.55)
|
||||
|
||||
show_mouth_mask_box_var = ctk.BooleanVar(value=modules.globals.show_mouth_mask_box)
|
||||
show_mouth_mask_box_switch = ctk.CTkSwitch(
|
||||
@@ -325,26 +304,26 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
modules.globals, "show_mouth_mask_box", show_mouth_mask_box_var.get()
|
||||
),
|
||||
)
|
||||
show_mouth_mask_box_switch.place(relx=0.6, rely=0.45)
|
||||
show_mouth_mask_box_switch.place(relx=0.6, rely=0.55)
|
||||
|
||||
start_button = ctk.CTkButton(
|
||||
root, text=_("Start"), cursor="hand2", command=lambda: analyze_target(start, root)
|
||||
)
|
||||
start_button.place(relx=0.15, rely=0.86, relwidth=0.2, relheight=0.05)
|
||||
start_button.place(relx=0.15, rely=0.80, relwidth=0.2, relheight=0.05)
|
||||
|
||||
stop_button = ctk.CTkButton(
|
||||
root, text=_("Destroy"), cursor="hand2", command=lambda: destroy()
|
||||
)
|
||||
stop_button.place(relx=0.4, rely=0.86, relwidth=0.2, relheight=0.05)
|
||||
stop_button.place(relx=0.4, rely=0.80, relwidth=0.2, relheight=0.05)
|
||||
|
||||
preview_button = ctk.CTkButton(
|
||||
root, text=_("Preview"), cursor="hand2", command=lambda: toggle_preview()
|
||||
)
|
||||
preview_button.place(relx=0.65, rely=0.86, relwidth=0.2, relheight=0.05)
|
||||
preview_button.place(relx=0.65, rely=0.80, relwidth=0.2, relheight=0.05)
|
||||
|
||||
# --- Camera Selection ---
|
||||
camera_label = ctk.CTkLabel(root, text=_("Select Camera:"))
|
||||
camera_label.place(relx=0.1, rely=0.92, relwidth=0.2, relheight=0.05)
|
||||
camera_label.place(relx=0.1, rely=0.86, relwidth=0.2, relheight=0.05)
|
||||
|
||||
available_cameras = get_available_cameras()
|
||||
camera_indices, camera_names = available_cameras
|
||||
@@ -363,7 +342,7 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
root, variable=camera_variable, values=camera_names
|
||||
)
|
||||
|
||||
camera_optionmenu.place(relx=0.35, rely=0.92, relwidth=0.25, relheight=0.05)
|
||||
camera_optionmenu.place(relx=0.35, rely=0.86, relwidth=0.25, relheight=0.05)
|
||||
|
||||
live_button = ctk.CTkButton(
|
||||
root,
|
||||
@@ -383,82 +362,16 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
else "disabled"
|
||||
),
|
||||
)
|
||||
live_button.place(relx=0.65, rely=0.92, relwidth=0.2, relheight=0.05)
|
||||
live_button.place(relx=0.65, rely=0.86, relwidth=0.2, relheight=0.05)
|
||||
# --- End Camera Selection ---
|
||||
|
||||
# 1) Define a DoubleVar for transparency (0 = fully transparent, 1 = fully opaque)
|
||||
transparency_var = ctk.DoubleVar(value=1.0)
|
||||
|
||||
def on_transparency_change(value: float):
|
||||
# Convert slider value to float
|
||||
val = float(value)
|
||||
modules.globals.opacity = val # Set global opacity
|
||||
percentage = int(val * 100)
|
||||
|
||||
if percentage == 0:
|
||||
modules.globals.fp_ui["face_enhancer"] = False
|
||||
update_status("Transparency set to 0% - Face swapping disabled.")
|
||||
elif percentage == 100:
|
||||
modules.globals.face_swapper_enabled = True
|
||||
update_status("Transparency set to 100%.")
|
||||
else:
|
||||
modules.globals.face_swapper_enabled = True
|
||||
update_status(f"Transparency set to {percentage}%")
|
||||
|
||||
# 2) Transparency label and slider (placed ABOVE sharpness)
|
||||
transparency_label = ctk.CTkLabel(root, text="Transparency:")
|
||||
transparency_label.place(relx=0.15, rely=0.75, relwidth=0.2, relheight=0.05)
|
||||
|
||||
transparency_slider = ctk.CTkSlider(
|
||||
root,
|
||||
from_=0.0,
|
||||
to=1.0,
|
||||
variable=transparency_var,
|
||||
command=on_transparency_change,
|
||||
fg_color="#E0E0E0",
|
||||
progress_color="#007BFF",
|
||||
button_color="#FFFFFF",
|
||||
button_hover_color="#CCCCCC",
|
||||
height=5,
|
||||
border_width=1,
|
||||
corner_radius=3,
|
||||
)
|
||||
transparency_slider.place(relx=0.35, rely=0.77, relwidth=0.5, relheight=0.02)
|
||||
|
||||
# 3) Sharpness label & slider
|
||||
sharpness_var = ctk.DoubleVar(value=0.0) # start at 0.0
|
||||
def on_sharpness_change(value: float):
|
||||
modules.globals.sharpness = float(value)
|
||||
update_status(f"Sharpness set to {value:.1f}")
|
||||
|
||||
sharpness_label = ctk.CTkLabel(root, text="Sharpness:")
|
||||
sharpness_label.place(relx=0.15, rely=0.80, relwidth=0.2, relheight=0.05)
|
||||
|
||||
sharpness_slider = ctk.CTkSlider(
|
||||
root,
|
||||
from_=0.0,
|
||||
to=5.0,
|
||||
variable=sharpness_var,
|
||||
command=on_sharpness_change,
|
||||
fg_color="#E0E0E0",
|
||||
progress_color="#007BFF",
|
||||
button_color="#FFFFFF",
|
||||
button_hover_color="#CCCCCC",
|
||||
height=5,
|
||||
border_width=1,
|
||||
corner_radius=3,
|
||||
)
|
||||
sharpness_slider.place(relx=0.35, rely=0.82, relwidth=0.5, relheight=0.02)
|
||||
|
||||
# Status and link at the bottom
|
||||
global status_label
|
||||
status_label = ctk.CTkLabel(root, text=None, justify="center")
|
||||
status_label.place(relx=0.1, rely=0.96, relwidth=0.8)
|
||||
status_label.place(relx=0.1, rely=0.9, relwidth=0.8)
|
||||
|
||||
donate_label = ctk.CTkLabel(
|
||||
root, text="Deep Live Cam", justify="center", cursor="hand2"
|
||||
)
|
||||
donate_label.place(relx=0.1, rely=0.98, relwidth=0.8)
|
||||
donate_label.place(relx=0.1, rely=0.95, relwidth=0.8)
|
||||
donate_label.configure(
|
||||
text_color=ctk.ThemeManager.theme.get("URL").get("text_color")
|
||||
)
|
||||
@@ -468,7 +381,6 @@ def create_root(start: Callable[[], None], destroy: Callable[[], None]) -> ctk.C
|
||||
|
||||
return root
|
||||
|
||||
|
||||
def close_mapper_window():
|
||||
global POPUP, POPUP_LIVE
|
||||
if POPUP and POPUP.winfo_exists():
|
||||
@@ -547,7 +459,7 @@ def create_source_target_popup(
|
||||
)
|
||||
x_label.grid(row=id, column=2, padx=10, pady=10)
|
||||
|
||||
image = Image.fromarray(gpu_cvt_color(item["target"]["cv2"], cv2.COLOR_BGR2RGB))
|
||||
image = Image.fromarray(cv2.cvtColor(item["target"]["cv2"], cv2.COLOR_BGR2RGB))
|
||||
image = image.resize(
|
||||
(MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
|
||||
)
|
||||
@@ -602,7 +514,7 @@ def update_popup_source(
|
||||
}
|
||||
|
||||
image = Image.fromarray(
|
||||
gpu_cvt_color(map[button_num]["source"]["cv2"], cv2.COLOR_BGR2RGB)
|
||||
cv2.cvtColor(map[button_num]["source"]["cv2"], cv2.COLOR_BGR2RGB)
|
||||
)
|
||||
image = image.resize(
|
||||
(MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
|
||||
@@ -784,18 +696,22 @@ def check_and_ignore_nsfw(target, destroy: Callable = None) -> bool:
|
||||
|
||||
|
||||
def fit_image_to_size(image, width: int, height: int):
|
||||
if width is None and height is None:
|
||||
if width is None or height is None or width <= 0 or height <= 0:
|
||||
return image
|
||||
h, w, _ = image.shape
|
||||
ratio_h = 0.0
|
||||
ratio_w = 0.0
|
||||
if width > height:
|
||||
ratio_h = height / h
|
||||
else:
|
||||
ratio_w = width / w
|
||||
ratio = max(ratio_w, ratio_h)
|
||||
new_size = (int(ratio * w), int(ratio * h))
|
||||
return gpu_resize(image, dsize=new_size)
|
||||
ratio_w = width / w
|
||||
ratio_h = height / h
|
||||
# Use the smaller ratio to ensure the image fits within the given dimensions
|
||||
ratio = min(ratio_w, ratio_h)
|
||||
|
||||
# Compute new dimensions, ensuring they're at least 1 pixel
|
||||
new_width = max(1, int(ratio * w))
|
||||
new_height = max(1, int(ratio * h))
|
||||
new_size = (new_width, new_height)
|
||||
|
||||
return cv2.resize(image, dsize=new_size)
|
||||
|
||||
|
||||
def render_image_preview(image_path: str, size: Tuple[int, int]) -> ctk.CTkImage:
|
||||
@@ -813,7 +729,7 @@ def render_video_preview(
|
||||
capture.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
|
||||
has_frame, frame = capture.read()
|
||||
if has_frame:
|
||||
image = Image.fromarray(gpu_cvt_color(frame, cv2.COLOR_BGR2RGB))
|
||||
image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
|
||||
if size:
|
||||
image = ImageOps.fit(image, size, Image.LANCZOS)
|
||||
return ctk.CTkImage(image, size=image.size)
|
||||
@@ -851,7 +767,7 @@ def update_preview(frame_number: int = 0) -> None:
|
||||
temp_frame = frame_processor.process_frame(
|
||||
get_one_face(cv2.imread(modules.globals.source_path)), temp_frame
|
||||
)
|
||||
image = Image.fromarray(gpu_cvt_color(temp_frame, cv2.COLOR_BGR2RGB))
|
||||
image = Image.fromarray(cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB))
|
||||
image = ImageOps.contain(
|
||||
image, (PREVIEW_MAX_WIDTH, PREVIEW_MAX_HEIGHT), Image.LANCZOS
|
||||
)
|
||||
@@ -952,97 +868,52 @@ def get_available_cameras():
|
||||
return camera_indices, camera_names
|
||||
|
||||
|
||||
def _capture_thread_func(cap, capture_queue, stop_event):
|
||||
"""Capture thread: reads frames from camera and puts them into the queue.
|
||||
Drops frames when the queue is full to avoid backpressure on the camera."""
|
||||
while not stop_event.is_set():
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
stop_event.set()
|
||||
break
|
||||
try:
|
||||
capture_queue.put_nowait(frame)
|
||||
except queue.Full:
|
||||
# Drop the oldest frame and enqueue the new one
|
||||
try:
|
||||
capture_queue.get_nowait()
|
||||
except queue.Empty:
|
||||
pass
|
||||
try:
|
||||
capture_queue.put_nowait(frame)
|
||||
except queue.Full:
|
||||
pass
|
||||
def create_webcam_preview(camera_index: int):
|
||||
global preview_label, PREVIEW
|
||||
|
||||
cap = VideoCapturer(camera_index)
|
||||
if not cap.start(PREVIEW_DEFAULT_WIDTH, PREVIEW_DEFAULT_HEIGHT, 60):
|
||||
update_status("Failed to start camera")
|
||||
return
|
||||
|
||||
# How often to run full face detection. On intermediate frames the last
|
||||
# detected face positions are reused, which significantly reduces the
|
||||
# per-frame cost of the processing thread.
|
||||
DETECT_EVERY_N = 2
|
||||
preview_label.configure(width=PREVIEW_DEFAULT_WIDTH, height=PREVIEW_DEFAULT_HEIGHT)
|
||||
PREVIEW.deiconify()
|
||||
|
||||
|
||||
def _processing_thread_func(capture_queue, processed_queue, stop_event):
|
||||
"""Processing thread: takes raw frames from capture_queue, applies face
|
||||
processing, and puts results into processed_queue. Drops processed frames
|
||||
when the output queue is full so the UI always gets the latest result.
|
||||
|
||||
Uses DETECT_EVERY_N to skip expensive face detection on intermediate
|
||||
frames, reusing cached face positions instead."""
|
||||
frame_processors = get_frame_processors_modules(modules.globals.frame_processors)
|
||||
source_image = None
|
||||
prev_time = time.time()
|
||||
fps_update_interval = 0.5
|
||||
frame_count = 0
|
||||
fps = 0
|
||||
proc_frame_index = 0
|
||||
cached_target_face = None # cached single-face result
|
||||
cached_many_faces = None # cached many-faces result
|
||||
|
||||
while not stop_event.is_set():
|
||||
try:
|
||||
frame = capture_queue.get(timeout=0.05)
|
||||
except queue.Empty:
|
||||
continue
|
||||
while True:
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
break
|
||||
|
||||
temp_frame = frame.copy()
|
||||
run_detection = (proc_frame_index % DETECT_EVERY_N == 0)
|
||||
proc_frame_index += 1
|
||||
|
||||
if modules.globals.live_mirror:
|
||||
temp_frame = gpu_flip(temp_frame, 1)
|
||||
temp_frame = cv2.flip(temp_frame, 1)
|
||||
|
||||
if modules.globals.live_resizable:
|
||||
temp_frame = fit_image_to_size(
|
||||
temp_frame, PREVIEW.winfo_width(), PREVIEW.winfo_height()
|
||||
)
|
||||
|
||||
else:
|
||||
temp_frame = fit_image_to_size(
|
||||
temp_frame, PREVIEW.winfo_width(), PREVIEW.winfo_height()
|
||||
)
|
||||
|
||||
if not modules.globals.map_faces:
|
||||
if source_image is None and modules.globals.source_path:
|
||||
source_image = get_one_face(cv2.imread(modules.globals.source_path))
|
||||
|
||||
# Update face detection cache on detection frames
|
||||
if run_detection or (cached_target_face is None and cached_many_faces is None):
|
||||
if modules.globals.many_faces:
|
||||
cached_many_faces = get_many_faces(temp_frame)
|
||||
cached_target_face = None
|
||||
else:
|
||||
cached_target_face = get_one_face(temp_frame)
|
||||
cached_many_faces = None
|
||||
|
||||
for frame_processor in frame_processors:
|
||||
if frame_processor.NAME == "DLC.FACE-ENHANCER":
|
||||
if modules.globals.fp_ui["face_enhancer"]:
|
||||
temp_frame = frame_processor.process_frame(None, temp_frame)
|
||||
elif frame_processor.NAME == "DLC.FACE-SWAPPER":
|
||||
# Use cached face positions to skip redundant detection
|
||||
swapped_bboxes = []
|
||||
if modules.globals.many_faces and cached_many_faces:
|
||||
result = temp_frame.copy()
|
||||
for t_face in cached_many_faces:
|
||||
result = frame_processor.swap_face(source_image, t_face, result)
|
||||
if hasattr(t_face, 'bbox') and t_face.bbox is not None:
|
||||
swapped_bboxes.append(t_face.bbox.astype(int))
|
||||
temp_frame = result
|
||||
elif cached_target_face is not None:
|
||||
temp_frame = frame_processor.swap_face(source_image, cached_target_face, temp_frame)
|
||||
if hasattr(cached_target_face, 'bbox') and cached_target_face.bbox is not None:
|
||||
swapped_bboxes.append(cached_target_face.bbox.astype(int))
|
||||
# Apply post-processing (sharpening, interpolation)
|
||||
temp_frame = frame_processor.apply_post_processing(temp_frame, swapped_bboxes)
|
||||
else:
|
||||
temp_frame = frame_processor.process_frame(source_image, temp_frame)
|
||||
else:
|
||||
@@ -1073,71 +944,7 @@ def _processing_thread_func(capture_queue, processed_queue, stop_event):
|
||||
2,
|
||||
)
|
||||
|
||||
# Put processed frame into output queue, dropping old frames if full
|
||||
try:
|
||||
processed_queue.put_nowait(temp_frame)
|
||||
except queue.Full:
|
||||
try:
|
||||
processed_queue.get_nowait()
|
||||
except queue.Empty:
|
||||
pass
|
||||
try:
|
||||
processed_queue.put_nowait(temp_frame)
|
||||
except queue.Full:
|
||||
pass
|
||||
|
||||
|
||||
def create_webcam_preview(camera_index: int):
|
||||
global preview_label, PREVIEW
|
||||
|
||||
cap = VideoCapturer(camera_index)
|
||||
if not cap.start(PREVIEW_DEFAULT_WIDTH, PREVIEW_DEFAULT_HEIGHT, 60):
|
||||
update_status("Failed to start camera")
|
||||
return
|
||||
|
||||
preview_label.configure(width=PREVIEW_DEFAULT_WIDTH, height=PREVIEW_DEFAULT_HEIGHT)
|
||||
PREVIEW.deiconify()
|
||||
|
||||
# Queues for decoupling capture from processing and processing from display.
|
||||
# Small maxsize ensures we always work on recent frames and drop stale ones.
|
||||
capture_queue = queue.Queue(maxsize=2)
|
||||
processed_queue = queue.Queue(maxsize=2)
|
||||
stop_event = threading.Event()
|
||||
|
||||
# Start capture thread
|
||||
cap_thread = threading.Thread(
|
||||
target=_capture_thread_func,
|
||||
args=(cap, capture_queue, stop_event),
|
||||
daemon=True,
|
||||
)
|
||||
cap_thread.start()
|
||||
|
||||
# Start processing thread
|
||||
proc_thread = threading.Thread(
|
||||
target=_processing_thread_func,
|
||||
args=(capture_queue, processed_queue, stop_event),
|
||||
daemon=True,
|
||||
)
|
||||
proc_thread.start()
|
||||
|
||||
# Main (UI) thread: pull processed frames and update the display
|
||||
while not stop_event.is_set():
|
||||
try:
|
||||
temp_frame = processed_queue.get(timeout=0.03)
|
||||
except queue.Empty:
|
||||
ROOT.update()
|
||||
continue
|
||||
|
||||
if modules.globals.live_resizable:
|
||||
temp_frame = fit_image_to_size(
|
||||
temp_frame, PREVIEW.winfo_width(), PREVIEW.winfo_height()
|
||||
)
|
||||
else:
|
||||
temp_frame = fit_image_to_size(
|
||||
temp_frame, PREVIEW.winfo_width(), PREVIEW.winfo_height()
|
||||
)
|
||||
|
||||
image = gpu_cvt_color(temp_frame, cv2.COLOR_BGR2RGB)
|
||||
image = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
|
||||
image = Image.fromarray(image)
|
||||
image = ImageOps.contain(
|
||||
image, (temp_frame.shape[1], temp_frame.shape[0]), Image.LANCZOS
|
||||
@@ -1149,10 +956,6 @@ def create_webcam_preview(camera_index: int):
|
||||
if PREVIEW.state() == "withdrawn":
|
||||
break
|
||||
|
||||
# Signal threads to stop and wait for them
|
||||
stop_event.set()
|
||||
cap_thread.join(timeout=2.0)
|
||||
proc_thread.join(timeout=2.0)
|
||||
cap.release()
|
||||
PREVIEW.withdraw()
|
||||
|
||||
@@ -1264,7 +1067,7 @@ def refresh_data(map: list):
|
||||
|
||||
if "source" in item:
|
||||
image = Image.fromarray(
|
||||
gpu_cvt_color(item["source"]["cv2"], cv2.COLOR_BGR2RGB)
|
||||
cv2.cvtColor(item["source"]["cv2"], cv2.COLOR_BGR2RGB)
|
||||
)
|
||||
image = image.resize(
|
||||
(MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
|
||||
@@ -1282,7 +1085,7 @@ def refresh_data(map: list):
|
||||
|
||||
if "target" in item:
|
||||
image = Image.fromarray(
|
||||
gpu_cvt_color(item["target"]["cv2"], cv2.COLOR_BGR2RGB)
|
||||
cv2.cvtColor(item["target"]["cv2"], cv2.COLOR_BGR2RGB)
|
||||
)
|
||||
image = image.resize(
|
||||
(MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
|
||||
@@ -1330,7 +1133,7 @@ def update_webcam_source(
|
||||
}
|
||||
|
||||
image = Image.fromarray(
|
||||
gpu_cvt_color(map[button_num]["source"]["cv2"], cv2.COLOR_BGR2RGB)
|
||||
cv2.cvtColor(map[button_num]["source"]["cv2"], cv2.COLOR_BGR2RGB)
|
||||
)
|
||||
image = image.resize(
|
||||
(MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
|
||||
@@ -1382,7 +1185,7 @@ def update_webcam_target(
|
||||
}
|
||||
|
||||
image = Image.fromarray(
|
||||
gpu_cvt_color(map[button_num]["target"]["cv2"], cv2.COLOR_BGR2RGB)
|
||||
cv2.cvtColor(map[button_num]["target"]["cv2"], cv2.COLOR_BGR2RGB)
|
||||
)
|
||||
image = image.resize(
|
||||
(MAPPER_PREVIEW_MAX_WIDTH, MAPPER_PREVIEW_MAX_HEIGHT), Image.LANCZOS
|
||||
@@ -1400,4 +1203,4 @@ def update_webcam_target(
|
||||
target_label_dict_live[button_num] = target_image
|
||||
else:
|
||||
update_pop_live_status("Face could not be detected in last upload!")
|
||||
return map
|
||||
return map
|
||||
|
||||
+23
-116
@@ -21,14 +21,13 @@ if platform.system().lower() == "darwin":
|
||||
|
||||
|
||||
def run_ffmpeg(args: List[str]) -> bool:
|
||||
"""Run ffmpeg with hardware acceleration and optimized settings."""
|
||||
commands = [
|
||||
"ffmpeg",
|
||||
"-hide_banner",
|
||||
"-hwaccel", "auto", # Auto-detect hardware acceleration
|
||||
"-hwaccel_output_format", "auto", # Use hardware format when possible
|
||||
"-threads", str(modules.globals.execution_threads or 0), # 0 = auto-detect optimal thread count
|
||||
"-loglevel", modules.globals.log_level,
|
||||
"-hwaccel",
|
||||
"auto",
|
||||
"-loglevel",
|
||||
modules.globals.log_level,
|
||||
]
|
||||
commands.extend(args)
|
||||
try:
|
||||
@@ -62,131 +61,39 @@ def detect_fps(target_path: str) -> float:
|
||||
|
||||
|
||||
def extract_frames(target_path: str) -> None:
|
||||
"""Extract frames with hardware acceleration and optimized settings."""
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
|
||||
# Use hardware-accelerated decoding and optimized pixel format
|
||||
run_ffmpeg(
|
||||
[
|
||||
"-i", target_path,
|
||||
"-vf", "format=rgb24", # Use video filter for format conversion (faster)
|
||||
"-vsync", "0", # Prevent frame duplication
|
||||
"-frame_pts", "1", # Preserve frame timing
|
||||
"-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:
|
||||
"""Create video with hardware-accelerated encoding and optimized settings."""
|
||||
temp_output_path = get_temp_output_path(target_path)
|
||||
temp_directory_path = get_temp_directory_path(target_path)
|
||||
|
||||
# Determine optimal encoder based on available hardware
|
||||
encoder = modules.globals.video_encoder
|
||||
encoder_options = []
|
||||
|
||||
# GPU-accelerated encoding options
|
||||
if 'CUDAExecutionProvider' in modules.globals.execution_providers:
|
||||
# NVIDIA GPU encoding
|
||||
if encoder == 'libx264':
|
||||
encoder = 'h264_nvenc'
|
||||
encoder_options = [
|
||||
"-preset", "p7", # Highest quality preset for NVENC
|
||||
"-tune", "hq", # High quality tuning
|
||||
"-rc", "vbr", # Variable bitrate
|
||||
"-cq", str(modules.globals.video_quality), # Quality level
|
||||
"-b:v", "0", # Let CQ control bitrate
|
||||
"-multipass", "fullres", # Two-pass encoding for better quality
|
||||
]
|
||||
elif encoder == 'libx265':
|
||||
encoder = 'hevc_nvenc'
|
||||
encoder_options = [
|
||||
"-preset", "p7",
|
||||
"-tune", "hq",
|
||||
"-rc", "vbr",
|
||||
"-cq", str(modules.globals.video_quality),
|
||||
"-b:v", "0",
|
||||
]
|
||||
elif 'DmlExecutionProvider' in modules.globals.execution_providers:
|
||||
# AMD/Intel GPU encoding (DirectML on Windows)
|
||||
if encoder == 'libx264':
|
||||
# Try AMD AMF encoder
|
||||
encoder = 'h264_amf'
|
||||
encoder_options = [
|
||||
"-quality", "quality", # Quality mode
|
||||
"-rc", "vbr_latency",
|
||||
"-qp_i", str(modules.globals.video_quality),
|
||||
"-qp_p", str(modules.globals.video_quality),
|
||||
]
|
||||
elif encoder == 'libx265':
|
||||
encoder = 'hevc_amf'
|
||||
encoder_options = [
|
||||
"-quality", "quality",
|
||||
"-rc", "vbr_latency",
|
||||
"-qp_i", str(modules.globals.video_quality),
|
||||
"-qp_p", str(modules.globals.video_quality),
|
||||
]
|
||||
else:
|
||||
# CPU encoding with optimized settings
|
||||
if encoder == 'libx264':
|
||||
encoder_options = [
|
||||
"-preset", "medium", # Balance speed/quality
|
||||
"-crf", str(modules.globals.video_quality),
|
||||
"-tune", "film", # Optimize for film content
|
||||
]
|
||||
elif encoder == 'libx265':
|
||||
encoder_options = [
|
||||
"-preset", "medium",
|
||||
"-crf", str(modules.globals.video_quality),
|
||||
"-x265-params", "log-level=error",
|
||||
]
|
||||
elif encoder == 'libvpx-vp9':
|
||||
encoder_options = [
|
||||
"-crf", str(modules.globals.video_quality),
|
||||
"-b:v", "0", # Constant quality mode
|
||||
"-cpu-used", "2", # Speed vs quality (0-5, lower=slower/better)
|
||||
]
|
||||
|
||||
# Build ffmpeg command
|
||||
ffmpeg_args = [
|
||||
"-r", str(fps),
|
||||
"-i", os.path.join(temp_directory_path, "%04d.png"),
|
||||
"-c:v", encoder,
|
||||
]
|
||||
|
||||
# Add encoder-specific options
|
||||
ffmpeg_args.extend(encoder_options)
|
||||
|
||||
# Add common options
|
||||
ffmpeg_args.extend([
|
||||
"-pix_fmt", "yuv420p",
|
||||
"-movflags", "+faststart", # Enable fast start for web playback
|
||||
"-vf", "colorspace=bt709:iall=bt601-6-625:fast=1",
|
||||
"-y",
|
||||
temp_output_path,
|
||||
])
|
||||
|
||||
# Try with hardware encoder first, fallback to software if it fails
|
||||
success = run_ffmpeg(ffmpeg_args)
|
||||
|
||||
if not success and encoder in ['h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf']:
|
||||
# Fallback to software encoding
|
||||
print(f"Hardware encoding with {encoder} failed, falling back to software encoding...")
|
||||
fallback_encoder = 'libx264' if 'h264' in encoder else 'libx265'
|
||||
ffmpeg_args_fallback = [
|
||||
"-r", str(fps),
|
||||
"-i", os.path.join(temp_directory_path, "%04d.png"),
|
||||
"-c:v", fallback_encoder,
|
||||
"-preset", "medium",
|
||||
"-crf", str(modules.globals.video_quality),
|
||||
"-pix_fmt", "yuv420p",
|
||||
"-movflags", "+faststart",
|
||||
"-vf", "colorspace=bt709:iall=bt601-6-625:fast=1",
|
||||
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(ffmpeg_args_fallback)
|
||||
)
|
||||
|
||||
|
||||
def restore_audio(target_path: str, output_path: str) -> None:
|
||||
|
||||
+17
-8
@@ -1,16 +1,25 @@
|
||||
--extra-index-url https://download.pytorch.org/whl/nightly/cu128
|
||||
|
||||
numpy>=1.23.5,<2
|
||||
typing-extensions>=4.8.0
|
||||
opencv-python==4.10.0.84
|
||||
cv2_enumerate_cameras==1.1.15
|
||||
onnx==1.18.0
|
||||
opencv-python==4.11.0.86
|
||||
onnx==1.17.0
|
||||
cv2_enumerate_cameras==1.1.18.3
|
||||
insightface==0.7.3
|
||||
psutil==5.9.8
|
||||
tk==0.1.0
|
||||
customtkinter==5.2.2
|
||||
pillow==12.1.1
|
||||
onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
|
||||
onnxruntime-gpu==1.24.2; sys_platform != 'darwin'
|
||||
pillow==11.1.0
|
||||
torch; sys_platform != 'darwin'
|
||||
torch; sys_platform == 'darwin'
|
||||
torchvision; sys_platform != 'darwin'
|
||||
torchvision; sys_platform == 'darwin'
|
||||
onnxruntime-silicon==1.21; sys_platform == 'darwin' and platform_machine == 'arm64'
|
||||
onnxruntime-gpu==1.21; sys_platform != 'darwin'
|
||||
tensorflow; sys_platform != 'darwin'
|
||||
opennsfw2==0.10.2
|
||||
protobuf==5.29.6
|
||||
pygrabber
|
||||
protobuf==4.23.2
|
||||
tqdm==4.66.4
|
||||
gfpgan==1.3.8
|
||||
tkinterdnd2==0.4.2
|
||||
pygrabber==0.2
|
||||
|
||||
@@ -1,8 +1,5 @@
|
||||
#!/usr/bin/env python3
|
||||
|
||||
# Import the tkinter fix to patch the ScreenChanged error
|
||||
import tkinter_fix
|
||||
|
||||
from modules import core
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@@ -1,26 +0,0 @@
|
||||
import tkinter
|
||||
|
||||
# Only needs to be imported once at the beginning of the application
|
||||
def apply_patch():
|
||||
# Create a monkey patch for the internal _tkinter module
|
||||
original_init = tkinter.Tk.__init__
|
||||
|
||||
def patched_init(self, *args, **kwargs):
|
||||
# Call the original init
|
||||
original_init(self, *args, **kwargs)
|
||||
|
||||
# Define the missing ::tk::ScreenChanged procedure
|
||||
self.tk.eval("""
|
||||
if {[info commands ::tk::ScreenChanged] == ""} {
|
||||
proc ::tk::ScreenChanged {args} {
|
||||
# Do nothing
|
||||
return
|
||||
}
|
||||
}
|
||||
""")
|
||||
|
||||
# Apply the monkey patch
|
||||
tkinter.Tk.__init__ = patched_init
|
||||
|
||||
# Apply the patch automatically when this module is imported
|
||||
apply_patch()
|
||||
Reference in New Issue
Block a user