Revert "Slight Performance Improvement"

This reverts commit 9a472e2435.

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,38 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Desktop (please complete the following information):**
+ - OS: [e.g. iOS]
+ - Browser [e.g. chrome, safari]
+ - Version [e.g. 22]
+
+**Smartphone (please complete the following information):**
+ - Device: [e.g. iPhone6]
+ - OS: [e.g. iOS8.1]
+ - Browser [e.g. stock browser, safari]
+ - Version [e.g. 22]
+
+**Additional context**
+Add any other context about the problem here.

.gitignore

@@ -6,17 +6,21 @@ __pycache__/
 .todo
 *.log
 *.backup
-
+tf_env/
 *.png
 *.mp4
 *.mkv
 
 .tmp/
 temp/
+.venv/
 venv/
+env/
 workflow/
 gfpgan/
 models/inswapper_128.onnx
 models/GFPGANv1.4.pth
 *.onnx
 models/DMDNet.pth
+faceswap/
+.vscode/

CONTRIBUTING.md

@@ -0,0 +1 @@
+Please always push on the experimental to ensure we don't mess with the main branch. All the test will be done on the experimental and will be pushed to the main branch after few days of testing.

README.md

@@ -1,163 +1,217 @@
-![demo-gif](demo.gif)
+<h1 align="center">Deep-Live-Cam</h1>
 
+<p align="center">
+  Real-time face swap and video deepfake with a single click and only a single image.
+</p>
+
+<p align="center">
+  <img src="media/demo.gif" alt="Demo GIF">
+  <img src="media/avgpcperformancedemo.gif" alt="Performance Demo GIF">
+</p>
 
 ## Disclaimer
-This software is meant to be a productive contribution to the rapidly growing AI-generated media industry. It will help artists with tasks such as animating a custom character or using the character as a model for clothing etc.
 
-The developers of this software are aware of its possible unethical applications and are committed to take preventative measures against them. It has a built-in check which prevents the program from working on inappropriate media including but not limited to nudity, graphic content, sensitive material such as war footage etc. We will continue to develop this project in the positive direction while adhering to law and ethics. This project may be shut down or include watermarks on the output if requested by law.
+This software is intended as a productive contribution to the AI-generated media industry. It aims to assist artists with tasks like animating custom characters or using them as models for clothing, etc.
 
-Users of this software are expected to use this software responsibly while abiding the local law. If face of a real person is being used, users are suggested to get consent from the concerned person and clearly mention that it is a deepfake when posting content online. Developers of this software will not be responsible for actions of end-users.
+We are aware of the potential for unethical applications and are committed to preventative measures. A built-in check prevents the program from processing inappropriate media (nudity, graphic content, sensitive material like war footage, etc.). We will continue to develop this project responsibly, adhering to law and ethics. We may shut down the project or add watermarks if legally required.
 
-## How do I install it?
+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.
 
 
-### Basic: It is more likely to work on your computer but it will also be very slow. You can follow instructions for the basic install (This usually runs via **CPU**)
-#### 1.Setup your platform
--   python (3.10 recommended)
+## Quick Start (Windows / Nvidia)
+
+[![Download](media/download.png)](https://hacksider.gumroad.com/l/vccdmm) 
+
+[Download latest pre-built version with CUDA support](https://hacksider.gumroad.com/l/vccdmm) - No Manual Installation/Downloading required.
+
+## Installation (Manual)
+**Please be aware that the installation needs technical skills and is NOT for beginners, consider downloading the prebuilt. Please do NOT open platform and installation related issues on GitHub before discussing it on the discord server.**
+### Basic Installation (CPU)
+
+This is more likely to work on your computer but will be slower as it utilizes the CPU.
+
+**1. Setup Your Platform**
+
+- Python (3.10 recommended)
 - pip
 - git
 - [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) 
--   [visual studio 2022 runtimes (windows)](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
-#### 2. Clone Repository
-    https://github.com/hacksider/Deep-Live-Cam.git
+- [Visual Studio 2022 Runtimes (Windows)](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
 
-#### 3. Download Models
+**2. Clone Repository**
+
+```bash
+https://github.com/hacksider/Deep-Live-Cam.git
+```
+
+**3. Download Models**
 
 1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.pth)
- 2. [inswapper_128_fp16.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx)
+2. [inswapper_128_fp16.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128.onnx) (Note: Use this [replacement version](https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx) if you encounter issues)
 
-Then put those 2 files on the "**models**" folder
+Place these files in the "**models**" folder.
 
-#### 4. Install dependency
-We highly recommend to work with a  `venv`  to avoid issues.
-```
+**4. Install Dependencies**
+
+We highly recommend using a `venv` to avoid issues.
+
+```bash
 pip install -r requirements.txt
 ```
-##### DONE!!! If you dont have any GPU, You should be able to run roop using `python run.py` command. Keep in mind that while running the program for first time, it will download some models which can take time depending on your network connection.
 
-### *Proceed if you want to use GPU Acceleration
-### CUDA Execution Provider (Nvidia)*
+**For macOS:** Install or upgrade the `python-tk` package:
+
+```bash
+brew install python-tk@3.10
+```
+
+**Run:** If you don't have a GPU, you can run Deep-Live-Cam using `python run.py`. Note that initial execution will download models (~300MB).
+
+
+### GPU Acceleration (Optional)
+
+<details>
+<summary>Click to see the details</summary>
+
+**CUDA Execution Provider (Nvidia)**
 
 1. Install [CUDA Toolkit 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive)
-    
 2. Install dependencies:
-    
-
-```
+```bash
 pip uninstall onnxruntime onnxruntime-gpu
 pip install onnxruntime-gpu==1.16.3
-
-```
-
-3.  Usage in case the provider is available:
-
 ```
+3. Usage:
+```bash
 python run.py --execution-provider cuda
-
 ```
 
-### [](https://github.com/s0md3v/roop/wiki/2.-Acceleration#coreml-execution-provider-apple-silicon)CoreML Execution Provider (Apple Silicon)
+**CoreML Execution Provider (Apple Silicon)**
 
 1. Install dependencies:
-
-```
+```bash
 pip uninstall onnxruntime onnxruntime-silicon
 pip install onnxruntime-silicon==1.13.1
-
-```
-
-2.  Usage in case the provider is available:
-
 ```
+2. Usage:
+```bash
 python run.py --execution-provider coreml
-
 ```
 
-### [](https://github.com/s0md3v/roop/wiki/2.-Acceleration#coreml-execution-provider-apple-legacy)CoreML Execution Provider (Apple Legacy)
+**CoreML Execution Provider (Apple Legacy)**
 
 1. Install dependencies:
-
-```
+```bash
 pip uninstall onnxruntime onnxruntime-coreml
 pip install onnxruntime-coreml==1.13.1
-
-```
-
-2.  Usage in case the provider is available:
-
 ```
+2. Usage:
+```bash
 python run.py --execution-provider coreml
-
 ```
 
-### [](https://github.com/s0md3v/roop/wiki/2.-Acceleration#directml-execution-provider-windows)DirectML Execution Provider (Windows)
+**DirectML Execution Provider (Windows)**
 
 1. Install dependencies:
-
-```
+```bash
 pip uninstall onnxruntime onnxruntime-directml
 pip install onnxruntime-directml==1.15.1
-
-```
-
-2.  Usage in case the provider is available:
-
 ```
+2. Usage:
+```bash
 python run.py --execution-provider directml
-
 ```
 
-### [](https://github.com/s0md3v/roop/wiki/2.-Acceleration#openvino-execution-provider-intel)OpenVINO™ Execution Provider (Intel)
+**OpenVINO™ Execution Provider (Intel)**
 
 1. Install dependencies:
-
-```
+```bash
 pip uninstall onnxruntime onnxruntime-openvino
 pip install onnxruntime-openvino==1.15.0
-
-```
-
-2.  Usage in case the provider is available:
-
 ```
+2. Usage:
+```bash
 python run.py --execution-provider openvino
 ```
 
-## How do I use it?
-> Note: When you run this program for the first time, it will download some models ~300MB in size.
-
-Executing `python run.py` command will launch this window:
-![gui-demo](instruction.png)
-
-Choose a face (image with desired face) and the target image/video (image/video in which you want to replace the face) and click on `Start`. Open file explorer and navigate to the directory you select your output to be in. You will find a directory named `<video_title>` where you can see the frames being swapped in realtime. Once the processing is done, it will create the output file. That's it.
-
-## For the webcam mode
-Just follow the clicks on the screenshot
-1. Select a face
-2. Click live
-3. Wait for a few second (it takes a longer time, usually 10 to 30 seconds before the preview shows up)
-
-![demo-gif](demo.gif)
-
-Just use your favorite screencapture to stream like OBS
-> Note: In case you want to change your face, just select another picture, the preview mode will then restart (so just wait a bit).
+</details>
 
 
-Additional command line arguments are given below. To learn out what they do, check [this guide](https://github.com/s0md3v/roop/wiki/Advanced-Options).
+## Usage
+
+**1. Image/Video Mode**
+
+- Execute `python run.py`. 
+- Choose a source face image and a target image/video.
+- Click "Start".
+- The output will be saved in a directory named after the target video.
+
+**2. Webcam Mode**
+
+- Execute `python run.py`.
+- Select a source face image.
+- Click "Live".
+- Wait for the preview to appear (10-30 seconds).
+- Use a screen capture tool like OBS to stream.
+- To change the face, select a new source image.
+
+![demo-gif](media/demo.gif)
+
+## Features
+
+### Resizable Preview Window
+
+Dynamically improve performance using the `--live-resizable` parameter.
+
+![resizable-gif](media/resizable.gif)
+
+### Face Mapping
+
+Track and change faces on the fly.
+
+![face_mapping_source](media/face_mapping_source.gif)
+
+**Source Video:**
+
+![face-mapping](media/face_mapping.png)
+
+**Enable Face Mapping:**
+
+![face-mapping2](media/face_mapping2.png)
+
+**Map the Faces:**
+
+![face_mapping_result](media/face_mapping_result.gif)
+
+**See the Magic!**
+
+![movie](media/movie.gif)
+
+**Watch movies in realtime:**
+
+It's as simple as opening a movie on the screen, and selecting OBS as your camera!
+![image](media/movie_img.png)
+
+
+## Command Line Arguments
 
 ```
 options:
   -h, --help                                               show this help message and exit
-  -s SOURCE_PATH, --source SOURCE_PATH                     select an source image
-  -t TARGET_PATH, --target TARGET_PATH                     select an target image or video
+  -s SOURCE_PATH, --source SOURCE_PATH                     select a source image
+  -t TARGET_PATH, --target TARGET_PATH                     select a target image or video
   -o OUTPUT_PATH, --output OUTPUT_PATH                     select output file or directory
   --frame-processor FRAME_PROCESSOR [FRAME_PROCESSOR ...]  frame processors (choices: face_swapper, face_enhancer, ...)
   --keep-fps                                               keep original fps
   --keep-audio                                             keep original audio
   --keep-frames                                            keep temporary frames
   --many-faces                                             process every face
+  --map-faces                                              map source target faces
+  --nsfw-filter                                            filter the NSFW image or video
   --video-encoder {libx264,libx265,libvpx-vp9}             adjust output video encoder
   --video-quality [0-51]                                   adjust output video quality
+  --live-mirror                                            the live camera display as you see it in the front-facing camera frame
+  --live-resizable                                         the live camera frame is resizable
   --max-memory MAX_MEMORY                                  maximum amount of RAM in GB
   --execution-provider {cpu} [{cpu} ...]                   available execution provider (choices: cpu, ...)
   --execution-threads EXECUTION_THREADS                    number of execution threads
@@ -166,10 +220,193 @@ options:
 
 Looking for a CLI mode? Using the -s/--source argument will make the run program in cli mode.
 
+
+## Webcam Mode on WSL2 Ubuntu (Optional)
+
+<details>
+<summary>Click to see the details</summary>
+
+If you want to use WSL2 on Windows 11 you will notice, that Ubuntu WSL2 doesn't come with USB-Webcam support in the Kernel. You need to do two things: Compile the Kernel with the right modules integrated and forward your USB Webcam from Windows to Ubuntu with the usbipd app. Here are detailed Steps:
+
+This tutorial will guide you through the process of setting up WSL2 Ubuntu with USB webcam support, rebuilding the kernel, and preparing the environment for the Deep-Live-Cam project.  
+  
+**1. Install WSL2 Ubuntu**  
+  
+Install WSL2 Ubuntu from the Microsoft Store or using PowerShell:  
+  
+**2. Enable USB Support in WSL2**  
+  
+1. Install the USB/IP tool for Windows:  
+[https://learn.microsoft.com/en-us/windows/wsl/connect-usb](https://learn.microsoft.com/en-us/windows/wsl/connect-usb)  
+  
+2. In Windows PowerShell (as Administrator), connect your webcam to WSL:  
+  
+```powershell  
+usbipd list  
+usbipd bind --busid x-x # Replace x-x with your webcam's bus ID  
+usbipd attach --wsl --busid x-x # Replace x-x with your webcam's bus ID  
+```  
+ You need to redo the above every time you reboot wsl or re-connect your webcam/usb device.
+ 
+**3. Rebuild WSL2 Ubuntu Kernel with USB and Webcam Modules**  
+  
+Follow these steps to rebuild the kernel:  
+  
+1. Start with this guide: [https://github.com/PINTO0309/wsl2_linux_kernel_usbcam_enable_conf](https://github.com/PINTO0309/wsl2_linux_kernel_usbcam_enable_conf)  
+  
+2. When you reach the `sudo wget [github.com](http://github.com/)...PINTO0309` step, which won't work for newer kernel versions, follow this video instead or alternatively follow the video tutorial from the beginning: 
+[https://www.youtube.com/watch?v=t_YnACEPmrM](https://www.youtube.com/watch?v=t_YnACEPmrM)  
+  
+Additional info: [https://askubuntu.com/questions/1413377/camera-not-working-in-cheese-in-wsl2](https://askubuntu.com/questions/1413377/camera-not-working-in-cheese-in-wsl2)  
+  
+3. After rebuilding, restart WSL with the new kernel. 
+  
+**4. Set Up Deep-Live-Cam Project**  
+ Within Ubuntu:
+1. Clone the repository:  
+  
+```bash  
+git clone [https://github.com/hacksider/Deep-Live-Cam](https://github.com/hacksider/Deep-Live-Cam)  
+```  
+  
+2. Follow the installation instructions in the repository, including cuda toolkit 11.8, make 100% sure it's not cuda toolkit 12.x.  
+   
+**5. Verify and Load Kernel Modules**  
+  
+1. Check if USB and webcam modules are built into the kernel:  
+  
+```bash  
+zcat /proc/config.gz | grep -i "CONFIG_USB_VIDEO_CLASS"  
+```  
+  
+2. If modules are loadable (m), not built-in (y), check if the file exists:  
+  
+```bash  
+ls /lib/modules/$(uname -r)/kernel/drivers/media/usb/uvc/  
+```  
+  
+3. Load the module and check for errors (optional if built-in):  
+  
+```bash  
+sudo modprobe uvcvideo  
+dmesg | tail  
+```  
+  
+4. Verify video devices:  
+  
+```bash  
+sudo ls -al /dev/video*  
+```  
+  
+**6. Set Up Permissions**  
+  
+1. Add user to video group and set permissions:  
+  
+```bash  
+sudo usermod -a -G video $USER  
+sudo chgrp video /dev/video0 /dev/video1  
+sudo chmod 660 /dev/video0 /dev/video1  
+```  
+  
+2. Create a udev rule for permanent permissions:  
+  
+```bash  
+sudo nano /etc/udev/rules.d/81-webcam.rules  
+```  
+  
+Add this content:  
+  
+```  
+KERNEL=="video[0-9]*", GROUP="video", MODE="0660"  
+```  
+  
+3. Reload udev rules:  
+  
+```bash  
+sudo udevadm control --reload-rules && sudo udevadm trigger  
+```  
+  
+4. Log out and log back into your WSL session.  
+  
+5. Start Deep-Live-Cam with `python run.py --execution-provider cuda --max-memory 8` where 8 can be changed to the number of GB VRAM of your GPU has, minus 1-2GB. If you have a RTX3080 with 10GB I suggest adding 8GB. Leave some left for Windows.
+
+**Final Notes**  
+  
+- Steps 6 and 7 may be optional if the modules are built into the kernel and permissions are already set correctly.  
+- Always ensure you're using compatible versions of CUDA, ONNX, and other dependencies.  
+- If issues persist, consider checking the Deep-Live-Cam project's specific requirements and troubleshooting steps.  
+  
+By following these steps, you should have a WSL2 Ubuntu environment with USB webcam support ready for the Deep-Live-Cam project. If you encounter any issues, refer back to the specific error messages and troubleshooting steps provided.
+
+**Troubleshooting CUDA Issues** 
+
+If you encounter this error:  
+  
+```  
+[ONNXRuntimeError] : 1 : FAIL : Failed to load library [libonnxruntime_providers_cuda.so](http://libonnxruntime_providers_cuda.so/) with error: libcufft.so.10: cannot open shared object file: No such file or directory  
+```  
+  
+Follow these steps:  
+  
+1. Install CUDA Toolkit 11.8 (ONNX 1.16.3 requires CUDA 11.x, not 12.x):  
+[https://developer.nvidia.com/cuda-11-8-0-download-archive](https://developer.nvidia.com/cuda-11-8-0-download-archive)  
+  select: Linux, x86_64, WSL-Ubuntu, 2.0, deb (local)
+2. Check CUDA version:  
+  
+```bash  
+/usr/local/cuda/bin/nvcc --version  
+```  
+  
+3. If the wrong version is installed, remove it completely:  
+[https://askubuntu.com/questions/530043/removing-nvidia-cuda-toolkit-and-installing-new-one](https://askubuntu.com/questions/530043/removing-nvidia-cuda-toolkit-and-installing-new-one)  
+  
+4. Install CUDA Toolkit 11.8 again [https://developer.nvidia.com/cuda-11-8-0-download-archive](https://developer.nvidia.com/cuda-11-8-0-download-archive), select: Linux, x86_64, WSL-Ubuntu, 2.0, deb (local)
+  
+```bash  
+sudo apt-get -y install cuda-toolkit-11-8  
+```
+</details>
+
+
+## Future Updates & Roadmap
+
+For the latest experimental builds and features, see the [experimental branch](https://github.com/hacksider/Deep-Live-Cam/tree/experimental).
+
+**TODO:**
+
+- [ ] Develop a version for web app/service
+- [ ] Speed up model loading
+- [ ] Speed up real-time face swapping
+- [x] Support multiple faces 
+- [x] UI/UX enhancements for desktop app
+
+This is an open-source project developed in our free time. Updates may be delayed.
+
+**Tips and Links:**
+- [How to make the most of Deep-Live-Cam](https://hacksider.gumroad.com/p/how-to-make-the-most-on-deep-live-cam)
+- Face enhancer is good, but still very slow for any live streaming purpose.
+
+
 ## Credits
-- [henryruhs](https://github.com/henryruhs): for being an irreplaceable contributor to the project
+
 - [ffmpeg](https://ffmpeg.org/): for making video related operations easy
-- [deepinsight](https://github.com/deepinsight): for their [insightface](https://github.com/deepinsight/insightface) project which provided a well-made library and models.
+- [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/nsfw-roop) : for uncensoring roop
-- and all developers behind libraries used in this project.
+- [GosuDRM](https://github.com/GosuDRM) : for open version of roop
+- [pereiraroland26](https://github.com/pereiraroland26) : Multiple faces support
+- [vic4key](https://github.com/vic4key) : For supporting/contributing on this project
+- [KRSHH](https://github.com/KRSHH) : For updating the UI
+- and [all developers](https://github.com/hacksider/Deep-Live-Cam/graphs/contributors) behind libraries used in this project.
+- Foot Note: [This is originally roop-cam, see the full history of the code here.](https://github.com/hacksider/roop-cam) Please be informed that the base author of the code is [s0md3v](https://github.com/s0md3v/roop)
+
+## Contributions
+![Alt](https://repobeats.axiom.co/api/embed/fec8e29c45dfdb9c5916f3a7830e1249308d20e1.svg "Repobeats analytics image")
+## Star History
+
+<a href="https://star-history.com/#hacksider/deep-live-cam&Date">
+ <picture>
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date&theme=dark" />
+   <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>

models/instructions.txt

@@ -1 +1,4 @@
-just put the models in this folder
+just put the models in this folder -
+
+https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx?download=true
+https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth

modules/capturer.py

@@ -1,16 +1,28 @@
 from typing import Any
 import cv2
+import modules.globals  # Import the globals to check the color correction toggle
 
 
 def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
     capture = cv2.VideoCapture(video_path)
+
+    # Set MJPEG format to ensure correct color space handling
+    capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
+    
+    # Only force RGB conversion if color correction is enabled
+    if modules.globals.color_correction:
+        capture.set(cv2.CAP_PROP_CONVERT_RGB, 1)
+    
     frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
     capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
     has_frame, frame = capture.read()
+
+    if has_frame and modules.globals.color_correction:
+        # Convert the frame color if necessary
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
     capture.release()
-    if has_frame:
-        return frame
-    return None
+    return frame if has_frame else None
 
 
 def get_video_frame_total(video_path: str) -> int:

modules/cluster_analysis.py

@@ -0,0 +1,32 @@
+import numpy as np
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score
+from typing import Any
+
+
+def find_cluster_centroids(embeddings, max_k=10) -> Any:
+    inertia = []
+    cluster_centroids = []
+    K = range(1, max_k+1)
+
+    for k in K:
+        kmeans = KMeans(n_clusters=k, random_state=0)
+        kmeans.fit(embeddings)
+        inertia.append(kmeans.inertia_)
+        cluster_centroids.append({"k": k, "centroids": kmeans.cluster_centers_})
+
+    diffs = [inertia[i] - inertia[i+1] for i in range(len(inertia)-1)]
+    optimal_centroids = cluster_centroids[diffs.index(max(diffs)) + 1]['centroids']
+
+    return optimal_centroids
+
+def find_closest_centroid(centroids: list, normed_face_embedding) -> list:
+    try:
+        centroids = np.array(centroids)
+        normed_face_embedding = np.array(normed_face_embedding)
+        similarities = np.dot(centroids, normed_face_embedding)
+        closest_centroid_index = np.argmax(similarities)
+        
+        return closest_centroid_index, centroids[closest_centroid_index]
+    except ValueError:
+        return None

modules/core.py

@@ -39,8 +39,12 @@ def parse_args() -> None:
     program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
     program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
     program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)
+    program.add_argument('--nsfw-filter', help='filter the NSFW image or video', dest='nsfw_filter', action='store_true', default=False)
+    program.add_argument('--map-faces', help='map source target faces', dest='map_faces', action='store_true', default=False)
     program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
     program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')
+    program.add_argument('--live-mirror', help='The live camera display as you see it in the front-facing camera frame', dest='live_mirror', action='store_true', default=False)
+    program.add_argument('--live-resizable', help='The live camera frame is resizable', dest='live_resizable', action='store_true', default=False)
     program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
     program.add_argument('--execution-provider', help='execution provider', dest='execution_provider', default=['cpu'], choices=suggest_execution_providers(), nargs='+')
     program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())
@@ -63,8 +67,12 @@ def parse_args() -> None:
     modules.globals.keep_audio = args.keep_audio
     modules.globals.keep_frames = args.keep_frames
     modules.globals.many_faces = args.many_faces
+    modules.globals.nsfw_filter = args.nsfw_filter
+    modules.globals.map_faces = args.map_faces
     modules.globals.video_encoder = args.video_encoder
     modules.globals.video_quality = args.video_quality
+    modules.globals.live_mirror = args.live_mirror
+    modules.globals.live_resizable = args.live_resizable
     modules.globals.max_memory = args.max_memory
     modules.globals.execution_providers = decode_execution_providers(args.execution_provider)
     modules.globals.execution_threads = args.execution_threads
@@ -75,8 +83,6 @@ def parse_args() -> None:
     else:
         modules.globals.fp_ui['face_enhancer'] = False
 
-    modules.globals.nsfw = False
-
     # translate deprecated args
     if args.source_path_deprecated:
         print('\033[33mArgument -f and --face are deprecated. Use -s and --source instead.\033[0m')
@@ -165,18 +171,19 @@ def update_status(message: str, scope: str = 'DLC.CORE') -> None:
     if not modules.globals.headless:
         ui.update_status(message)
 
-
 def start() -> None:
     for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
         if not frame_processor.pre_start():
             return
+    update_status('Processing...')
     # process image to image
     if has_image_extension(modules.globals.target_path):
-        if modules.globals.nsfw == False:
-            from modules.predicter import predict_image
-            if predict_image(modules.globals.target_path):
-                destroy()
+        if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
+            return
+        try:
             shutil.copy2(modules.globals.target_path, modules.globals.output_path)
+        except Exception as e:
+            print("Error copying file:", str(e))
         for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
             update_status('Progressing...', frame_processor.NAME)
             frame_processor.process_image(modules.globals.source_path, modules.globals.output_path, modules.globals.output_path)
@@ -187,14 +194,15 @@ def start() -> None:
             update_status('Processing to image failed!')
         return
     # process image to videos
-    if modules.globals.nsfw == False:
-        from modules.predicter import predict_video
-        if predict_video(modules.globals.target_path):
-            destroy()
+    if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
+        return
+
+    if not modules.globals.map_faces:
         update_status('Creating temp resources...')
         create_temp(modules.globals.target_path)
         update_status('Extracting frames...')
         extract_frames(modules.globals.target_path)
+
     temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
     for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
         update_status('Progressing...', frame_processor.NAME)
@@ -226,10 +234,10 @@ def start() -> None:
         update_status('Processing to video failed!')
 
 
-def destroy() -> None:
+def destroy(to_quit=True) -> None:
     if modules.globals.target_path:
         clean_temp(modules.globals.target_path)
-    quit()
+    if to_quit: quit()
 
 
 def run() -> None:

modules/face_analyser.py

@@ -1,8 +1,16 @@
+import os
+import shutil
 from typing import Any
 import insightface
 
+import cv2
+import numpy as np
 import modules.globals
+from tqdm import tqdm
 from modules.typing import Frame
+from modules.cluster_analysis import find_cluster_centroids, find_closest_centroid
+from modules.utilities import get_temp_directory_path, create_temp, extract_frames, clean_temp, get_temp_frame_paths
+from pathlib import Path
 
 FACE_ANALYSER = None
 
@@ -29,3 +37,153 @@ def get_many_faces(frame: Frame) -> Any:
         return get_face_analyser().get(frame)
     except IndexError:
         return None
+
+def has_valid_map() -> bool:
+    for map in modules.globals.souce_target_map:
+        if "source" in map and "target" in map:
+            return True
+    return False
+
+def default_source_face() -> Any:
+    for map in modules.globals.souce_target_map:
+        if "source" in map:
+            return map['source']['face']
+    return None
+
+def simplify_maps() -> Any:
+    centroids = []
+    faces = []
+    for map in modules.globals.souce_target_map:
+        if "source" in map and "target" in map:
+            centroids.append(map['target']['face'].normed_embedding)
+            faces.append(map['source']['face'])
+
+    modules.globals.simple_map = {'source_faces': faces, 'target_embeddings': centroids}
+    return None
+
+def add_blank_map() -> Any:
+    try:
+        max_id = -1
+        if len(modules.globals.souce_target_map) > 0:
+            max_id = max(modules.globals.souce_target_map, key=lambda x: x['id'])['id']
+
+        modules.globals.souce_target_map.append({
+                'id' : max_id + 1
+                })
+    except ValueError:
+        return None
+    
+def get_unique_faces_from_target_image() -> Any:
+    try:
+        modules.globals.souce_target_map = []
+        target_frame = cv2.imread(modules.globals.target_path)
+        many_faces = get_many_faces(target_frame)
+        i = 0
+
+        for face in many_faces:
+            x_min, y_min, x_max, y_max = face['bbox']
+            modules.globals.souce_target_map.append({
+                'id' : i, 
+                'target' : {
+                            'cv2' : target_frame[int(y_min):int(y_max), int(x_min):int(x_max)],
+                            'face' : face
+                            }
+                })
+            i = i + 1
+    except ValueError:
+        return None
+    
+    
+def get_unique_faces_from_target_video() -> Any:
+    try:
+        modules.globals.souce_target_map = []
+        frame_face_embeddings = []
+        face_embeddings = []
+    
+        print('Creating temp resources...')
+        clean_temp(modules.globals.target_path)
+        create_temp(modules.globals.target_path)
+        print('Extracting frames...')
+        extract_frames(modules.globals.target_path)
+
+        temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
+
+        i = 0
+        for temp_frame_path in tqdm(temp_frame_paths, desc="Extracting face embeddings from frames"):
+            temp_frame = cv2.imread(temp_frame_path)
+            many_faces = get_many_faces(temp_frame)
+
+            for face in many_faces:
+                face_embeddings.append(face.normed_embedding)
+            
+            frame_face_embeddings.append({'frame': i, 'faces': many_faces, 'location': temp_frame_path})
+            i += 1
+
+        centroids = find_cluster_centroids(face_embeddings)
+
+        for frame in frame_face_embeddings:
+            for face in frame['faces']:
+                closest_centroid_index, _ = find_closest_centroid(centroids, face.normed_embedding)
+                face['target_centroid'] = closest_centroid_index
+
+        for i in range(len(centroids)):
+            modules.globals.souce_target_map.append({
+                'id' : i
+            })
+
+            temp = []
+            for frame in tqdm(frame_face_embeddings, desc=f"Mapping frame embeddings to centroids-{i}"):
+                temp.append({'frame': frame['frame'], 'faces': [face for face in frame['faces'] if face['target_centroid'] == i], 'location': frame['location']})
+
+            modules.globals.souce_target_map[i]['target_faces_in_frame'] = temp
+
+        # dump_faces(centroids, frame_face_embeddings)
+        default_target_face()
+    except ValueError:
+        return None
+    
+
+def default_target_face():
+    for map in modules.globals.souce_target_map:
+        best_face = None
+        best_frame = None
+        for frame in map['target_faces_in_frame']:
+            if len(frame['faces']) > 0:
+                best_face = frame['faces'][0]
+                best_frame = frame
+                break
+
+        for frame in map['target_faces_in_frame']:
+            for face in frame['faces']:
+                if face['det_score'] > best_face['det_score']:
+                    best_face = face
+                    best_frame = frame
+
+        x_min, y_min, x_max, y_max = best_face['bbox']
+
+        target_frame = cv2.imread(best_frame['location'])
+        map['target'] = {
+                        'cv2' : target_frame[int(y_min):int(y_max), int(x_min):int(x_max)],
+                        'face' : best_face
+                        }
+
+
+def dump_faces(centroids: Any, frame_face_embeddings: list):
+    temp_directory_path = get_temp_directory_path(modules.globals.target_path)
+
+    for i in range(len(centroids)):
+        if os.path.exists(temp_directory_path + f"/{i}") and os.path.isdir(temp_directory_path + f"/{i}"):
+            shutil.rmtree(temp_directory_path + f"/{i}")
+        Path(temp_directory_path + f"/{i}").mkdir(parents=True, exist_ok=True)
+
+        for frame in tqdm(frame_face_embeddings, desc=f"Copying faces to temp/./{i}"):
+            temp_frame = cv2.imread(frame['location'])
+
+            j = 0
+            for face in frame['faces']:
+                if face['target_centroid'] == i:
+                    x_min, y_min, x_max, y_max = face['bbox']
+
+                    if temp_frame[int(y_min):int(y_max), int(x_min):int(x_max)].size > 0:
+                        cv2.imwrite(temp_directory_path + f"/{i}/{frame['frame']}_{j}.png", temp_frame[int(y_min):int(y_max), int(x_min):int(x_max)])
+                j += 1

modules/globals.py

@@ -1,30 +1,46 @@
 import os
-from typing import List, Dict
+from typing import List, Dict, Any
 
 ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
-WORKFLOW_DIR = os.path.join(ROOT_DIR, 'workflow')
+WORKFLOW_DIR = os.path.join(ROOT_DIR, "workflow")
 
 file_types = [
-    ('Image', ('*.png','*.jpg','*.jpeg','*.gif','*.bmp')),
-    ('Video', ('*.mp4','*.mkv'))
+    ("Image", ("*.png", "*.jpg", "*.jpeg", "*.gif", "*.bmp")),
+    ("Video", ("*.mp4", "*.mkv")),
 ]
 
+souce_target_map = []
+simple_map = {}
+
 source_path = None
 target_path = None
 output_path = None
 frame_processors: List[str] = []
-keep_fps = None
-keep_audio = None
-keep_frames = None
-many_faces = None
+keep_fps = True  # Initialize with default value
+keep_audio = True  # Initialize with default value
+keep_frames = False  # Initialize with default value
+many_faces = False  # Initialize with default value
+map_faces = False  # Initialize with default value
+color_correction = False  # Initialize with default value
+nsfw_filter = False  # Initialize with default value
 video_encoder = None
 video_quality = None
+live_mirror = False  # Initialize with default value
+live_resizable = False  # Initialize with default value
 max_memory = None
 execution_providers: List[str] = []
 execution_threads = None
 headless = None
-log_level = 'error'
-fp_ui: Dict[str, bool] = {}
-nsfw = None
+log_level = "error"
+fp_ui: Dict[str, bool] = {"face_enhancer": False}  # Initialize with default value
 camera_input_combobox = None
 webcam_preview_running = False
+show_fps = False  # Initialize with default value
+mouth_mask = False
+show_mouth_mask_box = False
+mask_down_size = 0.5
+mask_size = 1.0
+mask_feather_ratio = 8
+opacity_switch = False
+face_opacity = 100
+selected_camera = None

modules/metadata.py

@@ -1,3 +1,3 @@
 name = 'Deep Live Cam'
-version = '1.3.0'
+version = '1.6.0'
 edition = 'Portable'

modules/predicter.py

@@ -1,16 +1,27 @@
 import numpy
 import opennsfw2
 from PIL import Image
+import cv2  # Add OpenCV import
+import modules.globals  # Import globals to access the color correction toggle
 
 from modules.typing import Frame
 
 MAX_PROBABILITY = 0.85
 
+# Preload the model once for efficiency
+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 = cv2.cvtColor(target_frame, cv2.COLOR_BGR2RGB)
+        
     image = Image.fromarray(target_frame)
     image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)
+    global model
+    if model is None: 
         model = opennsfw2.make_open_nsfw_model()
+        
     views = numpy.expand_dims(image, axis=0)
     _, probability = model.predict(views)[0]
     return probability > MAX_PROBABILITY

modules/processors/frame/face_swapper.py

@@ -2,34 +2,49 @@ from typing import Any, List
 import cv2
 import insightface
 import threading
-
+import numpy as np
 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, get_many_faces, default_source_face
 from modules.typing import Face, Frame
-from modules.utilities import conditional_download, resolve_relative_path, is_image, is_video
+from modules.utilities import (
+    conditional_download,
+    resolve_relative_path,
+    is_image,
+    is_video,
+)
+from modules.cluster_analysis import find_closest_centroid
 
 FACE_SWAPPER = None
 THREAD_LOCK = threading.Lock()
-NAME = 'DLC.FACE-SWAPPER'
+NAME = "DLC.FACE-SWAPPER"
 
 
 def pre_check() -> bool:
-    download_directory_path = resolve_relative_path('../models')
-    conditional_download(download_directory_path, ['https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx'])
+    download_directory_path = resolve_relative_path("../models")
+    conditional_download(
+        download_directory_path,
+        [
+            "https://huggingface.co/hacksider/deep-live-cam/blob/main/inswapper_128_fp16.onnx"
+        ],
+    )
     return True
 
 
 def pre_start() -> bool:
-    if not is_image(modules.globals.source_path):
-        update_status('Select an image for source path.', NAME)
+    if not modules.globals.map_faces and not is_image(modules.globals.source_path):
+        update_status("Select an image for source path.", NAME)
         return False
-    elif not get_one_face(cv2.imread(modules.globals.source_path)):
-        update_status('No face in source path detected.', NAME)
+    elif not modules.globals.map_faces and not get_one_face(
+        cv2.imread(modules.globals.source_path)
+    ):
+        update_status("No face in source path detected.", NAME)
         return False
-    if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
-        update_status('Select an image or video for target path.', NAME)
+    if not is_image(modules.globals.target_path) and not is_video(
+        modules.globals.target_path
+    ):
+        update_status("Select an image or video for target path.", NAME)
         return False
     return True
 
@@ -39,16 +54,49 @@ def get_face_swapper() -> Any:
 
     with THREAD_LOCK:
         if FACE_SWAPPER is None:
-            model_path = resolve_relative_path('../models/inswapper_128_fp16.onnx')
-            FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=modules.globals.execution_providers)
+            model_path = resolve_relative_path("../models/inswapper_128_fp16.onnx")
+            FACE_SWAPPER = insightface.model_zoo.get_model(
+                model_path, providers=modules.globals.execution_providers
+            )
     return FACE_SWAPPER
 
 
 def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
-    return get_face_swapper().get(temp_frame, target_face, source_face, paste_back=True)
+    swapped_frame = get_face_swapper().get(
+        temp_frame, target_face, source_face, paste_back=True
+    )
+
+    # Apply opacity if enabled
+    if modules.globals.opacity_switch:
+        opacity = modules.globals.face_opacity / 100
+        swapped_frame = cv2.addWeighted(
+            swapped_frame, opacity, temp_frame, 1 - opacity, 0
+        )
+
+    # Apply mouth mask if enabled
+    if modules.globals.mouth_mask:
+        face_mask = create_face_mask(target_face, temp_frame)
+        mouth_mask_data = create_lower_mouth_mask(target_face, temp_frame)
+        mouth_mask, mouth_cutout, mouth_box, lower_lip_polygon = mouth_mask_data
+
+        if mouth_box is not None:
+            swapped_frame = apply_mouth_area(
+                swapped_frame, mouth_cutout, mouth_box, face_mask, lower_lip_polygon
+            )
+
+            if modules.globals.show_mouth_mask_box:
+                swapped_frame = draw_mouth_mask_visualization(
+                    swapped_frame, target_face, mouth_mask_data
+                )
+
+    return swapped_frame
 
 
 def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
+    # Ensure the frame is in RGB format if color correction is enabled
+    if modules.globals.color_correction:
+        temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB)
+
     if modules.globals.many_faces:
         many_faces = get_many_faces(temp_frame)
         if many_faces:
@@ -61,7 +109,99 @@ def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
     return temp_frame
 
 
-def process_frames(source_path: str, temp_frame_paths: List[str], progress: Any = None) -> None:
+def process_frame_v2(temp_frame: Frame, temp_frame_path: str = "") -> Frame:
+    if is_image(modules.globals.target_path):
+        if modules.globals.many_faces:
+            source_face = default_source_face()
+            for map in modules.globals.souce_target_map:
+                target_face = map["target"]["face"]
+                temp_frame = swap_face(source_face, target_face, temp_frame)
+
+        elif not modules.globals.many_faces:
+            for map in modules.globals.souce_target_map:
+                if "source" in map:
+                    source_face = map["source"]["face"]
+                    target_face = map["target"]["face"]
+                    temp_frame = swap_face(source_face, target_face, temp_frame)
+
+    elif is_video(modules.globals.target_path):
+        if modules.globals.many_faces:
+            source_face = default_source_face()
+            for map in modules.globals.souce_target_map:
+                target_frame = [
+                    f
+                    for f in map["target_faces_in_frame"]
+                    if f["location"] == temp_frame_path
+                ]
+
+                for frame in target_frame:
+                    for target_face in frame["faces"]:
+                        temp_frame = swap_face(source_face, target_face, temp_frame)
+
+        elif not modules.globals.many_faces:
+            for map in modules.globals.souce_target_map:
+                if "source" in map:
+                    target_frame = [
+                        f
+                        for f in map["target_faces_in_frame"]
+                        if f["location"] == temp_frame_path
+                    ]
+                    source_face = map["source"]["face"]
+
+                    for frame in target_frame:
+                        for target_face in frame["faces"]:
+                            temp_frame = swap_face(source_face, target_face, temp_frame)
+    else:
+        detected_faces = get_many_faces(temp_frame)
+        if modules.globals.many_faces:
+            if detected_faces:
+                source_face = default_source_face()
+                for target_face in detected_faces:
+                    temp_frame = swap_face(source_face, target_face, temp_frame)
+
+        elif not modules.globals.many_faces:
+            if detected_faces:
+                if len(detected_faces) <= len(
+                    modules.globals.simple_map["target_embeddings"]
+                ):
+                    for detected_face in detected_faces:
+                        closest_centroid_index, _ = find_closest_centroid(
+                            modules.globals.simple_map["target_embeddings"],
+                            detected_face.normed_embedding,
+                        )
+
+                        temp_frame = swap_face(
+                            modules.globals.simple_map["source_faces"][
+                                closest_centroid_index
+                            ],
+                            detected_face,
+                            temp_frame,
+                        )
+                else:
+                    detected_faces_centroids = []
+                    for face in detected_faces:
+                        detected_faces_centroids.append(face.normed_embedding)
+                    i = 0
+                    for target_embedding in modules.globals.simple_map[
+                        "target_embeddings"
+                    ]:
+                        closest_centroid_index, _ = find_closest_centroid(
+                            detected_faces_centroids, target_embedding
+                        )
+
+                        temp_frame = swap_face(
+                            modules.globals.simple_map["source_faces"][i],
+                            detected_faces[closest_centroid_index],
+                            temp_frame,
+                        )
+                        i += 1
+    return temp_frame
+
+
+def process_frames(
+    source_path: str, temp_frame_paths: List[str], progress: Any = None
+) -> None:
+    if not modules.globals.map_faces:
         source_face = get_one_face(cv2.imread(source_path))
         for temp_frame_path in temp_frame_paths:
             temp_frame = cv2.imread(temp_frame_path)
@@ -73,14 +213,281 @@ def process_frames(source_path: str, temp_frame_paths: List[str], progress: Any
                 pass
             if progress:
                 progress.update(1)
+    else:
+        for temp_frame_path in temp_frame_paths:
+            temp_frame = cv2.imread(temp_frame_path)
+            try:
+                result = process_frame_v2(temp_frame, temp_frame_path)
+                cv2.imwrite(temp_frame_path, result)
+            except Exception as exception:
+                print(exception)
+                pass
+            if progress:
+                progress.update(1)
 
 
 def process_image(source_path: str, target_path: str, output_path: str) -> None:
+    if not modules.globals.map_faces:
         source_face = get_one_face(cv2.imread(source_path))
         target_frame = cv2.imread(target_path)
         result = process_frame(source_face, target_frame)
         cv2.imwrite(output_path, result)
+    else:
+        if modules.globals.many_faces:
+            update_status(
+                "Many faces enabled. Using first source image. Progressing...", NAME
+            )
+        target_frame = cv2.imread(output_path)
+        result = process_frame_v2(target_frame)
+        cv2.imwrite(output_path, result)
 
 
 def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
-    modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)
+    if modules.globals.map_faces and modules.globals.many_faces:
+        update_status(
+            "Many faces enabled. Using first source image. Progressing...", NAME
+        )
+    modules.processors.frame.core.process_video(
+        source_path, temp_frame_paths, process_frames
+    )
+
+
+def create_face_mask(face: Face, frame: Frame) -> np.ndarray:
+    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
+    landmarks = face.landmark_2d_106
+    if landmarks is not None:
+        landmarks = landmarks.astype(np.int32)
+
+        right_side_face = landmarks[0:16]
+        left_side_face = landmarks[17:32]
+        right_eye = landmarks[33:42]
+        right_eye_brow = landmarks[43:51]
+        left_eye = landmarks[87:96]
+        left_eye_brow = landmarks[97:105]
+
+        right_eyebrow_top = np.min(right_eye_brow[:, 1])
+        left_eyebrow_top = np.min(left_eye_brow[:, 1])
+        eyebrow_top = min(right_eyebrow_top, left_eyebrow_top)
+
+        face_top = np.min([right_side_face[0, 1], left_side_face[-1, 1]])
+        forehead_height = face_top - eyebrow_top
+        extended_forehead_height = int(forehead_height * 5.0)
+
+        forehead_left = right_side_face[0].copy()
+        forehead_right = left_side_face[-1].copy()
+        forehead_left[1] -= extended_forehead_height
+        forehead_right[1] -= extended_forehead_height
+
+        face_outline = np.vstack(
+            [
+                [forehead_left],
+                right_side_face,
+                left_side_face[::-1],
+                [forehead_right],
+            ]
+        )
+
+        padding = int(np.linalg.norm(right_side_face[0] - left_side_face[-1]) * 0.05)
+
+        hull = cv2.convexHull(face_outline)
+        hull_padded = []
+        for point in hull:
+            x, y = point[0]
+            center = np.mean(face_outline, axis=0)
+            direction = np.array([x, y]) - center
+            direction = direction / np.linalg.norm(direction)
+            padded_point = np.array([x, y]) + direction * padding
+            hull_padded.append(padded_point)
+
+        hull_padded = np.array(hull_padded, dtype=np.int32)
+        cv2.fillConvexPoly(mask, hull_padded, 255)
+        mask = cv2.GaussianBlur(mask, (5, 5), 3)
+
+    return mask
+
+
+def create_lower_mouth_mask(face: Face, frame: Frame) -> tuple:
+    mask = np.zeros(frame.shape[:2], dtype=np.uint8)
+    mouth_cutout = None
+    landmarks = face.landmark_2d_106
+    if landmarks is not None:
+        lower_lip_order = [
+            65,
+            66,
+            62,
+            70,
+            69,
+            18,
+            19,
+            20,
+            21,
+            22,
+            23,
+            24,
+            0,
+            8,
+            7,
+            6,
+            5,
+            4,
+            3,
+            2,
+            65,
+        ]
+        lower_lip_landmarks = landmarks[lower_lip_order].astype(np.float32)
+
+        center = np.mean(lower_lip_landmarks, axis=0)
+        expansion_factor = 1 + modules.globals.mask_down_size
+        expanded_landmarks = (lower_lip_landmarks - center) * expansion_factor + center
+
+        toplip_indices = [20, 0, 1, 2, 3, 4, 5]
+        toplip_extension = modules.globals.mask_size * 0.5
+        for idx in toplip_indices:
+            direction = expanded_landmarks[idx] - center
+            direction = direction / np.linalg.norm(direction)
+            expanded_landmarks[idx] += direction * toplip_extension
+
+        chin_indices = [11, 12, 13, 14, 15, 16]
+        chin_extension = 2 * 0.2
+        for idx in chin_indices:
+            expanded_landmarks[idx][1] += (
+                expanded_landmarks[idx][1] - center[1]
+            ) * chin_extension
+
+        expanded_landmarks = expanded_landmarks.astype(np.int32)
+
+        min_x, min_y = np.min(expanded_landmarks, axis=0)
+        max_x, max_y = np.max(expanded_landmarks, axis=0)
+
+        padding = int((max_x - min_x) * 0.1)
+        min_x = max(0, min_x - padding)
+        min_y = max(0, min_y - padding)
+        max_x = min(frame.shape[1], max_x + padding)
+        max_y = min(frame.shape[0], max_y + padding)
+
+        if max_x <= min_x or max_y <= min_y:
+            if (max_x - min_x) <= 1:
+                max_x = min_x + 1
+            if (max_y - min_y) <= 1:
+                max_y = min_y + 1
+
+        mask_roi = np.zeros((max_y - min_y, max_x - min_x), dtype=np.uint8)
+        cv2.fillPoly(mask_roi, [expanded_landmarks - [min_x, min_y]], 255)
+        mask_roi = cv2.GaussianBlur(mask_roi, (15, 15), 5)
+        mask[min_y:max_y, min_x:max_x] = mask_roi
+        mouth_cutout = frame[min_y:max_y, min_x:max_x].copy()
+
+        return mask, mouth_cutout, (min_x, min_y, max_x, max_y), expanded_landmarks
+
+    return mask, mouth_cutout, None, None
+
+
+def apply_mouth_area(
+    frame: Frame,
+    mouth_cutout: np.ndarray,
+    mouth_box: tuple,
+    face_mask: np.ndarray,
+    mouth_polygon: np.ndarray,
+) -> Frame:
+    min_x, min_y, max_x, max_y = mouth_box
+    box_width = max_x - min_x
+    box_height = max_y - min_y
+
+    if (
+        mouth_cutout is None
+        or box_width is None
+        or box_height is None
+        or face_mask is None
+        or mouth_polygon is None
+    ):
+        return frame
+
+    try:
+        resized_mouth_cutout = cv2.resize(mouth_cutout, (box_width, box_height))
+        roi = frame[min_y:max_y, min_x:max_x]
+
+        if roi.shape != resized_mouth_cutout.shape:
+            resized_mouth_cutout = cv2.resize(
+                resized_mouth_cutout, (roi.shape[1], roi.shape[0])
+            )
+
+        color_corrected_mouth = apply_color_transfer(resized_mouth_cutout, roi)
+
+        polygon_mask = np.zeros(roi.shape[:2], dtype=np.uint8)
+        adjusted_polygon = mouth_polygon - [min_x, min_y]
+        cv2.fillPoly(polygon_mask, [adjusted_polygon], 255)
+
+        feather_amount = min(
+            30,
+            box_width // modules.globals.mask_feather_ratio,
+            box_height // modules.globals.mask_feather_ratio,
+        )
+        feathered_mask = cv2.GaussianBlur(
+            polygon_mask.astype(float), (0, 0), feather_amount
+        )
+        feathered_mask = feathered_mask / feathered_mask.max()
+
+        face_mask_roi = face_mask[min_y:max_y, min_x:max_x]
+        combined_mask = feathered_mask * (face_mask_roi / 255.0)
+
+        combined_mask = combined_mask[:, :, np.newaxis]
+        blended = (
+            color_corrected_mouth * combined_mask + roi * (1 - combined_mask)
+        ).astype(np.uint8)
+
+        face_mask_3channel = (
+            np.repeat(face_mask_roi[:, :, np.newaxis], 3, axis=2) / 255.0
+        )
+        final_blend = blended * face_mask_3channel + roi * (1 - face_mask_3channel)
+
+        frame[min_y:max_y, min_x:max_x] = final_blend.astype(np.uint8)
+    except Exception as e:
+        pass
+
+    return frame
+
+
+def apply_color_transfer(source: np.ndarray, target: np.ndarray) -> np.ndarray:
+    source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32")
+    target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32")
+
+    source_mean, source_std = cv2.meanStdDev(source)
+    target_mean, target_std = cv2.meanStdDev(target)
+
+    source_mean = source_mean.reshape(1, 1, 3)
+    source_std = source_std.reshape(1, 1, 3)
+    target_mean = target_mean.reshape(1, 1, 3)
+    target_std = target_std.reshape(1, 1, 3)
+
+    source = (source - source_mean) * (target_std / source_std) + target_mean
+
+    return cv2.cvtColor(np.clip(source, 0, 255).astype("uint8"), cv2.COLOR_LAB2BGR)
+
+
+def draw_mouth_mask_visualization(
+    frame: Frame, face: Face, mouth_mask_data: tuple
+) -> Frame:
+    landmarks = face.landmark_2d_106
+    if landmarks is not None and mouth_mask_data is not None:
+        mask, mouth_cutout, (min_x, min_y, max_x, max_y), lower_lip_polygon = (
+            mouth_mask_data
+        )
+
+        vis_frame = frame.copy()
+
+        # Draw the lower lip polygon
+        cv2.polylines(vis_frame, [lower_lip_polygon], True, (0, 255, 0), 2)
+
+        # Add labels
+        cv2.putText(
+            vis_frame,
+            "Mouth Mask",
+            (min_x, min_y - 10),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.5,
+            (255, 255, 255),
+            1,
+        )
+
+        return vis_frame
+    return frame

requirements.txt

@@ -1,23 +1,23 @@
 --extra-index-url https://download.pytorch.org/whl/cu118
 
-numpy==1.23.5
+numpy>=1.23.5,<2
 opencv-python==4.8.1.78
+cv2_enumerate_cameras==1.1.15
 onnx==1.16.0
 insightface==0.7.3
 psutil==5.9.8
 tk==0.1.0
-customtkinter==5.2.2
 pillow==9.5.0
 torch==2.0.1+cu118; sys_platform != 'darwin'
 torch==2.0.1; sys_platform == 'darwin'
 torchvision==0.15.2+cu118; sys_platform != 'darwin'
 torchvision==0.15.2; sys_platform == 'darwin'
-onnxruntime==1.18.0; sys_platform == 'darwin' and platform_machine != 'arm64'
 onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
-onnxruntime-gpu==1.18.0; sys_platform != 'darwin'
-tensorflow==2.13.0rc1; sys_platform == 'darwin'
+onnxruntime-gpu==1.16.3; sys_platform != 'darwin'
 tensorflow==2.12.1; sys_platform != 'darwin'
 opennsfw2==0.10.2
 protobuf==4.23.2
 tqdm==4.66.4
 gfpgan==1.3.8
+tkinterdnd2==0.4.2
+customtkinter==5.2.2