add Drag and drop functionality

Update ui.py
2026-09-05 07:16:35 +02:00 · 2025-03-26 13:08:18 +05:30 · 2025-03-26 12:14:29 +05:30
50 changed files with 1881 additions and 7774 deletions
@@ -1,16 +0,0 @@
 name: ruff
 on:
  pull_request:
  push:
    branches: [main]
 jobs:
  ruff:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: astral-sh/ruff-action@v4.0.0
        with:
          version: "0.15.7"
          args: "check --output-format=github"
@@ -25,7 +25,3 @@ models/DMDNet.pth
 faceswap/
 .vscode/
 switch_states.json
 /models
 install.bat
 /.claude
 *.bat
@@ -1,4 +1,4 @@
-<h1 align="center">Deep-Live-Cam 2.1.6</h1>
+<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.
@@ -30,45 +30,14 @@ By using this software, you agree to these terms and commit to using it in a man
 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.
 ## Pre-built Deep-Live-Cam 2.7 Ultimate!
-<p align="center">
+## Quick Start - Pre-built (Windows / Nvidia)
  <a href="https://deeplivecam.net/index.php/quickstart">
    <img src="https://github.com/user-attachments/assets/fa2cdf79-c933-4b93-844a-b087192261ed" width="100%" alt="Lite / Ultimate Download Banner">
  </a>
 </p>
-<p align="center">
+  <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" />
 <a href="https://deeplivecam.net/index.php/plans/nvidia-gpu?plan_id=0&group_id=1">
  <img src="https://github.com/user-attachments/assets/56b61811-3a1e-4672-9b50-cf7f6e8e6852" width="40" alt="Windows">
 </a>
  &nbsp;&nbsp;&nbsp;
 <a href="https://deeplivecam.net/index.php/plans/nvidia-gpu?plan_id=0&group_id=2">
  <img src="https://github.com/user-attachments/assets/6538e3a6-c957-431a-b586-2d6abcf534dc" width="34" alt="Mac Silicon">
 </a>
  &nbsp;&nbsp;&nbsp;
 <a href="https://deeplivecam.net/index.php/plans/nvidia-gpu?plan_id=0&group_id=3">
  <img src="https://github.com/user-attachments/assets/ad45142e-426c-4364-a2a9-a512670cc62c" width="40" alt="CPU">
 </a>
 </p>
-<p align="center">
+##### This is the fastest build you can get if you have a discrete NVIDIA GPU.
  <strong>Windows • Mac Silicon • CPU • NVIDIA • AMD</strong>
 </p>
-<p align="center">
+###### 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.
  Builds optimized for your hardware.
 </p>
 <p align="center">
  <a href="https://deeplivecam.net/index.php/quickstart">
    <img src="media/Download.png" width="280" alt="Download">
  </a>
 </p>
 > **Ultimate** includes **30+ exclusive features**, performance optimizations, and **priority support** We only have a single official website which is https://deeplivecam.net . Please be careful on where you download other versions of this application aside from that website and this github repo.
 Perfect if you want the fastest setup with **zero manual installation**, pre-configured dependencies, and optimized builds for every supported platform.
 ## TLDR; Live Deepfake in just 3 Clicks
 ![easysteps](https://github.com/user-attachments/assets/af825228-852c-411b-b787-ffd9aac72fc6)
@@ -130,7 +99,7 @@ Perfect if you want the fastest setup with **zero manual installation**, pre-con
 ## Installation (Manual)
-**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the quickstart version.**
+**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the prebuilt version.**
 <details>
 <summary>Click to see the process</summary>
@@ -141,7 +110,7 @@ This is more likely to work on your computer but will be slower as it utilizes t
 **1. Set up Your Platform**
-   Python (3.14 recommended; 3.11-3.14 supported)
+-   Python (3.10 recommended)
 -   pip
 -   git
 -   [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) - ```iex (irm ffmpeg.tc.ht)```
@@ -150,13 +119,13 @@ This is more likely to work on your computer but will be slower as it utilizes t
 **2. Clone the Repository**
 ```bash
-git clone --depth 1 https://github.com/hacksider/Deep-Live-Cam.git
+git clone https://github.com/hacksider/Deep-Live-Cam.git
 cd Deep-Live-Cam
 ```
 **3. Download the Models**
-1. [gfpgan-1024.onnx](https://huggingface.co/hacksider/deep-live-cam/resolve/main/gfpgan-1024.onnx)
+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)
 Place these files in the "**models**" folder.
@@ -165,34 +134,26 @@ Place these files in the "**models**" folder.
 We highly recommend using a `venv` to avoid issues.
 For Windows:
 ```bash
 python -m venv venv
 venv\Scripts\activate
 pip install -r requirements.txt
 ```
 For Linux:
 ```bash
 # Ensure you use the installed Python 3.14
 python3 -m venv venv
 source venv/bin/activate
 pip install -r requirements.txt
 ```
 **For macOS:**
-Apple Silicon (M1 through M5) requires specific setup:
+Apple Silicon (M1/M2/M3) requires specific setup:
 ```bash
-# Install Python 3.14
+# Install Python 3.10 (specific version is important)
-brew install python@3.14
+brew install python@3.10
 # Install tkinter package (required for the GUI)
-brew install python-tk@3.14
+brew install python-tk@3.10
-# Create and activate virtual environment with Python 3.14
+# Create and activate virtual environment with Python 3.10
-python3.14 -m venv venv
+python3.10 -m venv venv
 source venv/bin/activate
 # Install dependencies
@@ -211,11 +172,6 @@ source venv/bin/activate
 # install the dependencies again
 pip install -r requirements.txt
 # gfpgan and basicsrs issue fix
 pip install git+https://github.com/xinntao/BasicSR.git@master
 pip uninstall gfpgan -y
 pip install git+https://github.com/TencentARC/GFPGAN.git@master
 ```
 **Run:** If you don't have a GPU, you can run Deep-Live-Cam using `python run.py`. Note that initial execution will download models (~300MB).
@@ -224,16 +180,12 @@ pip install git+https://github.com/TencentARC/GFPGAN.git@master
 **CUDA Execution Provider (Nvidia)**
-1. Install [CUDA Toolkit 12.8.0](https://developer.nvidia.com/cuda-12-8-0-download-archive)
+1. Install [CUDA Toolkit 11.8.0](https://developer.nvidia.com/cuda-11-8-0-download-archive)
-2. Install [cuDNN v8.9.7 for CUDA 12.x](https://developer.nvidia.com/rdp/cudnn-archive) (required for onnxruntime-gpu):
+2. Install dependencies:
   - Download cuDNN v8.9.7 for CUDA 12.x
   - Make sure the cuDNN bin directory is in your system PATH
 3. Install dependencies:
 ```bash
 pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
 pip uninstall onnxruntime onnxruntime-gpu
-pip install onnxruntime-gpu==1.26.0
+pip install onnxruntime-gpu==1.16.3
 ```
 3. Usage:
@@ -244,29 +196,26 @@ python run.py --execution-provider cuda
 **CoreML Execution Provider (Apple Silicon)**
-Apple Silicon (M1 through M5) specific installation:
+Apple Silicon (M1/M2/M3) specific installation:
-1. Make sure you've completed the macOS setup above using Python 3.14.
+1. Make sure you've completed the macOS setup above using Python 3.10.
-2. No extra install step is needed — `requirements.txt` pulls the official
+2. Install dependencies:
   `onnxruntime` build, whose macOS wheels ship the CoreML execution provider.
   If you previously installed the unmaintained `onnxruntime-silicon` fork,
   remove it first, as it shadows the real package:
 ```bash
-pip uninstall onnxruntime-silicon
+pip uninstall onnxruntime onnxruntime-silicon
-pip install -r requirements.txt
+pip install onnxruntime-silicon==1.13.1
 ```
-3. Usage:
+3. Usage (important: specify Python 3.10):
 ```bash
-python3.14 run.py --execution-provider coreml
+python3.10 run.py --execution-provider coreml
 ```
 **Important Notes for macOS:**
- Python 3.11 is the minimum (onnxruntime dropped 3.10); 3.14 is recommended
+- You **must** use Python 3.10, not newer versions like 3.11 or 3.13
- Always run with `python3.14` command not just `python` if you have multiple Python versions installed
+- Always run with `python3.10` command not just `python` if you have multiple Python versions installed
- If you get error about `_tkinter` missing, reinstall the tkinter package: `brew reinstall python-tk@3.14`
+- If you get error about `_tkinter` missing, reinstall the tkinter package: `brew reinstall python-tk@3.10`
 - If you get model loading errors, check that your models are in the correct folder
 - If you encounter conflicts with other Python versions, consider uninstalling them:
  ```bash
@@ -274,9 +223,9 @@ python3.14 run.py --execution-provider coreml
  brew list | grep python
  # Uninstall conflicting versions if needed
-  brew uninstall --ignore-dependencies python@3.11
+  brew uninstall --ignore-dependencies python@3.11 python@3.13
-  # Keep only Python 3.14
+  # Keep only Python 3.10
  brew cleanup
  ```
@@ -286,7 +235,7 @@ python3.14 run.py --execution-provider coreml
 ```bash
 pip uninstall onnxruntime onnxruntime-coreml
-pip install onnxruntime-coreml==1.21.0
+pip install onnxruntime-coreml==1.13.1
 ```
 2. Usage:
@@ -301,7 +250,7 @@ python run.py --execution-provider coreml
 ```bash
 pip uninstall onnxruntime onnxruntime-directml
-pip install onnxruntime-directml==1.21.0
+pip install onnxruntime-directml==1.15.1
 ```
 2. Usage:
@@ -316,25 +265,9 @@ python run.py --execution-provider directml
 ```bash
 pip uninstall onnxruntime onnxruntime-openvino
-pip install onnxruntime-openvino==1.21.0
+pip install onnxruntime-openvino==1.15.0
 ```
 **Note:** `onnxruntime-openvino` newer than 1.21.0 must be installed together with `openvino`, and the two versions must correspond one-to-one. The supported pairings are:
 | onnxruntime-openvino | OpenVINO |
 | --- | --- |
 | 1.24.1 | 2025.4.1 |
 | 1.23.0 | 2025.3 |
 | 1.22.0 | 2025.1 |
 ```bash
 # Example: onnxruntime-openvino 1.24.1 pairs with OpenVINO 2025.4.1
 pip install openvino==2025.4.1
 pip install onnxruntime-openvino==1.24.1
 ```
 See the [OpenVINO Execution Provider requirements](https://onnxruntime.ai/docs/execution-providers/OpenVINO-ExecutionProvider.html#requirements) for the full version-mapping details.
 2. Usage:
 ```bash
@@ -360,8 +293,18 @@ python run.py --execution-provider openvino
 -   Use a screen capture tool like OBS to stream.
 -   To change the face, select a new source image.
-## Download all models in this huggingface link
+## Tips and Tricks
- [**Download models here**](https://huggingface.co/hacksider/deep-live-cam/tree/main)
+
 Check out these helpful guides to get the most out of Deep-Live-Cam:
 - [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
 - [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
 - [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
 - [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
 - [Instant Vtuber!](https://deeplivecam.net/index.php/blog/tips-and-tricks/instant-vtuber) - Create a new persona/vtuber easily using Metahuman Creator
 Visit our [official blog](https://deeplivecam.net/index.php/blog/tips-and-tricks) for more tips and tutorials.
 ## Command Line Arguments (Unmaintained)
@@ -392,22 +335,24 @@ Looking for a CLI mode? Using the -s/--source argument will make the run program
 ## Press
- - [**Ars Technica**](https://arstechnica.com/information-technology/2024/08/new-ai-tool-enables-real-time-face-swapping-on-webcams-raising-fraud-concerns/) - *"Deep-Live-Cam goes viral, allowing anyone to become a digital doppelganger"*
+**We are always open to criticism and are ready to improve, that's why we didn't cherry-pick anything.**
 - [**Yahoo!**](https://www.yahoo.com/tech/ok-viral-ai-live-stream-080041056.html) - *"OK, this viral AI live stream software is truly terrifying"*
 - [**CNN Brasil**](https://www.cnnbrasil.com.br/tecnologia/ia-consegue-clonar-rostos-na-webcam-entenda-funcionamento/) - *"AI can clone faces on webcam; understand how it works"*
 - [**Bloomberg Technoz**](https://www.bloombergtechnoz.com/detail-news/71032/kenalan-dengan-teknologi-deep-live-cam-bisa-jadi-alat-menipu) - *"Get to know Deep Live Cam technology, it can be used as a tool for deception."*
 - [**TrendMicro**](https://www.trendmicro.com/vinfo/gb/security/news/cyber-attacks/ai-vs-ai-deepfakes-and-ekyc) - *"AI vs AI: DeepFakes and eKYC"*
 - [**PetaPixel**](https://petapixel.com/2024/08/14/deep-live-cam-deepfake-ai-tool-lets-you-become-anyone-in-a-video-call-with-single-photo-mark-zuckerberg-jd-vance-elon-musk/) - *"Deepfake AI Tool Lets You Become Anyone in a Video Call With Single Photo"*
 - [**SomeOrdinaryGamers**](https://www.youtube.com/watch?time_continue=1074&v=py4Tc-Y8BcY) - *"That's Crazy, Oh God. That's Fucking Freaky Dude... That's So Wild Dude"*
 - [**IShowSpeed**](https://www.youtube.com/live/mFsCe7AIxq8?feature=shared&t=2686) - *"Alright look look look, now look chat, we can do any face we want to look like chat"*
 - [**TechLinked (Linus Tech Tips)**](https://www.youtube.com/watch?v=wnCghLjqv3s&t=551s) - *"They do a pretty good job matching poses, expression and even the lighting"*
 - [**IShowSpeed**](https://youtu.be/JbUPRmXRUtE?t=3964) - *"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!"*
 - [*"Deep-Live-Cam goes viral, allowing anyone to become a digital doppelganger"*](https://arstechnica.com/information-technology/2024/08/new-ai-tool-enables-real-time-face-swapping-on-webcams-raising-fraud-concerns/) - Ars Technica
 - [*"Thanks Deep Live Cam, shapeshifters are among us now"*](https://dataconomy.com/2024/08/15/what-is-deep-live-cam-github-deepfake/) - Dataconomy
 - [*"This free AI tool lets you become anyone during video-calls"*](https://www.newsbytesapp.com/news/science/deep-live-cam-ai-impersonation-tool-goes-viral/story) - NewsBytes
 - [*"OK, this viral AI live stream software is truly terrifying"*](https://www.creativebloq.com/ai/ok-this-viral-ai-live-stream-software-is-truly-terrifying) - Creative Bloq
 - [*"Deepfake AI Tool Lets You Become Anyone in a Video Call With Single Photo"*](https://petapixel.com/2024/08/14/deep-live-cam-deepfake-ai-tool-lets-you-become-anyone-in-a-video-call-with-single-photo-mark-zuckerberg-jd-vance-elon-musk/) - PetaPixel
 - [*"Deep-Live-Cam Uses AI to Transform Your Face in Real-Time, Celebrities Included"*](https://www.techeblog.com/deep-live-cam-ai-transform-face/) - TechEBlog
 - [*"An AI tool that "makes you look like anyone" during a video call is going viral online"*](https://telegrafi.com/en/a-tool-that-makes-you-look-like-anyone-during-a-video-call-is-going-viral-on-the-Internet/) - Telegrafi
 - [*"This Deepfake Tool Turning Images Into Livestreams is Topping the GitHub Charts"*](https://decrypt.co/244565/this-deepfake-tool-turning-images-into-livestreams-is-topping-the-github-charts) - Emerge
 - [*"New Real-Time Face-Swapping AI Allows Anyone to Mimic Famous Faces"*](https://www.digitalmusicnews.com/2024/08/15/face-swapping-ai-real-time-mimic/) - Digital Music News
 - [*"This real-time webcam deepfake tool raises alarms about the future of identity theft"*](https://www.diyphotography.net/this-real-time-webcam-deepfake-tool-raises-alarms-about-the-future-of-identity-theft/) - DIYPhotography
 - [*"That's Crazy, Oh God. That's Fucking Freaky Dude... That's So Wild Dude"*](https://www.youtube.com/watch?time_continue=1074&v=py4Tc-Y8BcY) - SomeOrdinaryGamers
 - [*"Alright look look look, now look chat, we can do any face we want to look like chat"*](https://www.youtube.com/live/mFsCe7AIxq8?feature=shared&t=2686) - IShowSpeed
 ## Credits
 -   [ffmpeg](https://ffmpeg.org/): for making video-related operations easy
 -   [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
@@ -415,8 +360,6 @@ Looking for a CLI mode? Using the -s/--source argument will make the run program
 -   [vic4key](https://github.com/vic4key): For supporting/contributing to this project
 -   [kier007](https://github.com/kier007): for improving the user experience
 -   [qitianai](https://github.com/qitianai): for multi-lingual support
 -   [laurigates](https://github.com/laurigates): Decoupling stuffs to make everything faster!
 -   [maxwbuckley](https://github.com/maxwbuckley): For making the effort to optimize this for mac!
 -   and [all developers](https://github.com/hacksider/Deep-Live-Cam/graphs/contributors) behind libraries used in this project.
 -   Footnote: Please be informed that the base author of the code is [s0md3v](https://github.com/s0md3v/roop)
 -   All the wonderful users who helped make this project go viral by starring the repo ❤️
@@ -1,181 +0,0 @@
 """Standalone pipeline benchmark — no UI required.
 Captures 200 frames from the webcam and runs the full face swap pipeline,
 printing per-stage timing and effective FPS.
 """
 import os, sys, time, cv2, numpy as np, queue, threading
 # PATH fix for cuDNN (Windows only)
 if sys.platform == "win32":
    _sp = os.path.join(sys.prefix, "Lib", "site-packages")
    _torch_lib = os.path.join(_sp, "torch", "lib")
    if os.path.isdir(_torch_lib):
        os.environ["PATH"] = _torch_lib + os.pathsep + os.environ["PATH"]
 import insightface
 from insightface.app import FaceAnalysis
 from modules.processors.frame.face_swapper import _fast_paste_back
 from modules import platform_info
 platform_info.print_banner()
 # Pick providers based on what's actually available on this machine.
 if platform_info.HAS_CUDA_PROVIDER:
    _providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
 elif platform_info.HAS_COREML_PROVIDER:
    _providers = ["CoreMLExecutionProvider", "CPUExecutionProvider"]
 else:
    _providers = ["CPUExecutionProvider"]
 # --- Init models (same as the app) ---
 print(f"Loading models with providers={_providers}...")
 fa = FaceAnalysis(
    name="buffalo_l",
    providers=_providers,
    allowed_modules=["detection", "recognition", "landmark_2d_106"],
 )
 fa.prepare(ctx_id=0, det_size=(640, 640))
 swap_model = insightface.model_zoo.get_model(
    "models/inswapper_128.onnx",
    providers=_providers,
 )
 face_size = swap_model.input_size[0]
 aimg_dummy = np.empty((face_size, face_size, 3), dtype=np.uint8)
 # --- Camera setup ---
 # Windows: DirectShow explicit for MJPEG 1080p60 support.
 # macOS/Linux: default backend (AVFoundation / V4L2).
 print("Opening camera at 1080p60 MJPEG...")
 if sys.platform == "win32":
    cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
 else:
    cap = cv2.VideoCapture(0)
 cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"MJPG"))
 cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
 cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
 cap.set(cv2.CAP_PROP_FPS, 60)
 time.sleep(0.5)
 # Warmup + get source face
 for _ in range(15):
    cap.read()
 ret, src_frame = cap.read()
 faces = fa.get(src_frame)
 if not faces:
    print("ERROR: No face detected in warmup frame")
    cap.release()
    sys.exit(1)
 source_face = faces[0]
 print(f"Source face acquired. Frame: {src_frame.shape}")
 # --- Capture thread (same as app) ---
 capture_queue = queue.Queue(maxsize=2)
 stop_event = threading.Event()
 def capture_thread():
    while not stop_event.is_set():
        ret, frame = cap.read()
        if not ret:
            break
        try:
            capture_queue.put_nowait(frame)
        except queue.Full:
            try:
                capture_queue.get_nowait()
            except queue.Empty:
                pass
            try:
                capture_queue.put_nowait(frame)
            except queue.Full:
                pass
 cap_t = threading.Thread(target=capture_thread, daemon=True)
 cap_t.start()
 # --- Warmup processing ---
 print("Warming up pipeline...")
 for _ in range(20):
    try:
        frame = capture_queue.get(timeout=0.1)
    except queue.Empty:
        continue
    f = frame.copy()
    det_faces = fa.get(f)
    if det_faces:
        tgt = min(det_faces, key=lambda x: x.bbox[0])
        bgr_fake, M = swap_model.get(f, tgt, source_face, paste_back=False)
        _fast_paste_back(f, bgr_fake, aimg_dummy, M)
 # --- Benchmark ---
 N = 200
 print(f"\nBenchmarking {N} frames...")
 t_queue, t_det, t_onnx, t_paste, t_copy, t_cvt, t_total = [], [], [], [], [], [], []
 det_count = 0
 cached_face = None
 for i in range(N):
    tt = time.perf_counter()
    t0 = time.perf_counter()
    try:
        frame = capture_queue.get(timeout=0.1)
    except queue.Empty:
        continue
    t_queue.append((time.perf_counter() - t0) * 1000)
    # Detection every 3rd frame — det-only (no landmark/recognition)
    det_count += 1
    if det_count % 3 == 0:
        t0 = time.perf_counter()
        from insightface.app.common import Face as _Face
        bboxes, kpss = fa.det_model.detect(frame, max_num=0, metric='default')
        if bboxes.shape[0] > 0:
            idx = int(bboxes[:, 0].argmin())
            cached_face = _Face(bbox=bboxes[idx, :4], kps=kpss[idx], det_score=bboxes[idx, 4])
        t_det.append((time.perf_counter() - t0) * 1000)
    if cached_face is not None:
        # No frame.copy() — _fast_paste_back writes in-place, we own the frame
        t0 = time.perf_counter()
        bgr_fake, M = swap_model.get(frame, cached_face, source_face, paste_back=False)
        t_onnx.append((time.perf_counter() - t0) * 1000)
        t0 = time.perf_counter()
        result = _fast_paste_back(frame, bgr_fake, aimg_dummy, M)
        t_paste.append((time.perf_counter() - t0) * 1000)
        # Display prep — resize then flip (no cvtColor needed)
        t0 = time.perf_counter()
        small = cv2.resize(result, (640, 360))
        _ = small[:, :, ::-1]  # BGR→RGB zero-copy
        t_cvt.append((time.perf_counter() - t0) * 1000)
    t_total.append((time.perf_counter() - tt) * 1000)
 stop_event.set()
 cap.release()
 # --- Results ---
 def s(name, arr):
    if not arr:
        return
    avg = sum(arr) / len(arr)
    print(f"  {name:25s}: avg={avg:6.1f}ms  min={min(arr):5.1f}ms  max={max(arr):6.1f}ms  n={len(arr)}")
 print(f"\n{'='*55}")
 print(f"  1080p Pipeline Benchmark ({len(t_total)} frames)")
 print(f"{'='*55}")
 s("queue.get (wait for cam)", t_queue)
 s("detection (fa.get)", t_det)
 s("frame.copy()", t_copy)
 s("ONNX swap", t_onnx)
 s("_fast_paste_back", t_paste)
 s("cvtColor BGR->RGB", t_cvt)
 s("TOTAL per frame", t_total)
 avg_total = sum(t_total) / len(t_total)
 avg_queue = sum(t_queue) / len(t_queue)
 print(f"\n  Effective FPS:          {1000/avg_total:.1f}")
 print(f"  FPS (excl. cam wait):   {1000/(avg_total - avg_queue):.1f}")
 print(f"{'='*55}")
@@ -1,46 +0,0 @@
 {
    "Source x Target Mapper": "Quelle x Ziel Zuordnung",
    "select a source image": "Wähle ein Quellbild",
    "Preview": "Vorschau",
    "select a target image or video": "Wähle ein Zielbild oder Video",
    "save image output file": "Bildausgabedatei speichern",
    "save video output file": "Videoausgabedatei speichern",
    "select a target image": "Wähle ein Zielbild",
    "source": "Quelle",
    "Select a target": "Wähle ein Ziel",
    "Select a face": "Wähle ein Gesicht",
    "Keep audio": "Audio beibehalten",
    "Face Enhancer": "Gesichtsverbesserung",
    "Many faces": "Mehrere Gesichter",
    "Show FPS": "FPS anzeigen",
    "Keep fps": "FPS beibehalten",
    "Keep frames": "Frames beibehalten",
    "Fix Blueish Cam": "Bläuliche Kamera korrigieren",
    "Mouth Mask": "Mundmaske",
    "Show Mouth Mask Box": "Mundmaskenrahmen anzeigen",
    "Start": "Starten",
    "Live": "Live",
    "Destroy": "Beenden",
    "Map faces": "Gesichter zuordnen",
    "Processing...": "Verarbeitung läuft...",
    "Processing succeed!": "Verarbeitung erfolgreich!",
    "Processing ignored!": "Verarbeitung ignoriert!",
    "Failed to start camera": "Kamera konnte nicht gestartet werden",
    "Please complete pop-up or close it.": "Bitte das Pop-up komplettieren oder schließen.",
    "Getting unique faces": "Einzigartige Gesichter erfassen",
    "Please select a source image first": "Bitte zuerst ein Quellbild auswählen",
    "No faces found in target": "Keine Gesichter im Zielbild gefunden",
    "Add": "Hinzufügen",
    "Clear": "Löschen",
    "Submit": "Absenden",
    "Select source image": "Quellbild auswählen",
    "Select target image": "Zielbild auswählen",
    "Please provide mapping!": "Bitte eine Zuordnung angeben!",
    "At least 1 source with target is required!": "Mindestens eine Quelle mit einem Ziel ist erforderlich!",
    "At least 1 source with target is required!": "Mindestens eine Quelle mit einem Ziel ist erforderlich!",
    "Face could not be detected in last upload!": "Im letzten Upload konnte kein Gesicht erkannt werden!",
    "Select Camera:": "Kamera auswählen:",
    "All mappings cleared!": "Alle Zuordnungen gelöscht!",
    "Mappings successfully submitted!": "Zuordnungen erfolgreich übermittelt!",
    "Source x Target Mapper is already open.": "Quell-zu-Ziel-Zuordnung ist bereits geöffnet."
 }
@@ -1,46 +0,0 @@
 {
    "Source x Target Mapper": "Mapeador de fuente x destino",
    "select a source image": "Seleccionar imagen fuente",
    "Preview": "Vista previa",
    "select a target image or video": "elegir un video o una imagen fuente",
    "save image output file": "guardar imagen final",
    "save video output file": "guardar video final",
    "select a target image": "elegir una imagen objetiva",
    "source": "fuente",
    "Select a target": "Elegir un destino",
    "Select a face": "Elegir una cara",
    "Keep audio": "Mantener audio original",
    "Face Enhancer": "Potenciador de caras",
    "Many faces": "Varias caras",
    "Show FPS": "Mostrar fps",
    "Keep fps": "Mantener fps",
    "Keep frames": "Mantener frames",
    "Fix Blueish Cam": "Corregir tono azul de video",
    "Mouth Mask": "Máscara de boca",
    "Show Mouth Mask Box": "Mostrar área de la máscara de boca",
    "Start": "Iniciar",
    "Live": "En vivo",
    "Destroy": "Borrar",
    "Map faces": "Mapear caras",
    "Processing...": "Procesando...",
    "Processing succeed!": "¡Proceso terminado con éxito!",
    "Processing ignored!": "¡Procesamiento omitido!",
    "Failed to start camera": "No se pudo iniciar la cámara",
    "Please complete pop-up or close it.": "Complete o cierre el pop-up",
    "Getting unique faces": "Buscando caras únicas",
    "Please select a source image first": "Primero, seleccione una imagen fuente",
    "No faces found in target": "No se encontró una cara en el destino",
    "Add": "Agregar",
    "Clear": "Limpiar",
    "Submit": "Enviar",
    "Select source image": "Seleccionar imagen fuente",
    "Select target image": "Seleccionar imagen destino",
    "Please provide mapping!": "Por favor, proporcione un mapeo",
    "At least 1 source with target is required!": "Se requiere al menos una fuente con un destino.",
    "At least 1 source with target is required!": "Se requiere al menos una fuente con un destino.",
    "Face could not be detected in last upload!": "¡No se pudo encontrar una cara en el último video o imagen!",
    "Select Camera:": "Elegir cámara:",
    "All mappings cleared!": "¡Todos los mapeos fueron borrados!",
    "Mappings successfully submitted!": "Mapeos enviados con éxito!",
    "Source x Target Mapper is already open.": "El mapeador de fuente x destino ya está abierto."
 }
@@ -1,46 +0,0 @@
 {
    "Source x Target Mapper": "Source x Target Kartoitin",
    "select an source image": "Valitse lähde kuva",
    "Preview": "Esikatsele",
    "select an target image or video": "Valitse kohde kuva tai video",
    "save image output file": "tallenna kuva",
    "save video output file": "tallenna video",
    "select an target image": "Valitse kohde kuva",
    "source": "lähde",
    "Select a target": "Valitse kohde",
    "Select a face": "Valitse kasvot",
    "Keep audio": "Säilytä ääni",
    "Face Enhancer": "Kasvojen Parantaja",
    "Many faces": "Useampia kasvoja",
    "Show FPS": "Näytä FPS",
    "Keep fps": "Säilytä FPS",
    "Keep frames": "Säilytä ruudut",
    "Fix Blueish Cam": "Korjaa Sinertävä Kamera",
    "Mouth Mask": "Suu Maski",
    "Show Mouth Mask Box": "Näytä Suu Maski Laatiko",
    "Start": "Aloita",
    "Live": "Live",
    "Destroy": "Tuhoa",
    "Map faces": "Kartoita kasvot",
    "Processing...": "Prosessoi...",
    "Processing succeed!": "Prosessointi onnistui!",
    "Processing ignored!": "Prosessointi lopetettu!",
    "Failed to start camera": "Kameran käynnistäminen epäonnistui",
    "Please complete pop-up or close it.": "Viimeistele tai sulje ponnahdusikkuna",
    "Getting unique faces": "Hankitaan uniikkeja kasvoja",
    "Please select a source image first": "Valitse ensin lähde kuva",
    "No faces found in target": "Kasvoja ei löydetty kohteessa",
    "Add": "Lisää",
    "Clear": "Tyhjennä",
    "Submit": "Lähetä",
    "Select source image": "Valitse lähde kuva",
    "Select target image": "Valitse kohde kuva",
    "Please provide mapping!": "Tarjoa kartoitus!",
    "Atleast 1 source with target is required!": "Vähintään 1 lähde kohteen kanssa on vaadittu!",
    "At least 1 source with target is required!": "Vähintään 1 lähde kohteen kanssa on vaadittu!",
    "Face could not be detected in last upload!": "Kasvoja ei voitu tunnistaa edellisessä latauksessa!",
    "Select Camera:": "Valitse Kamera:",
    "All mappings cleared!": "Kaikki kartoitukset tyhjennetty!",
    "Mappings successfully submitted!": "Kartoitukset lähetety onnistuneesti!",
    "Source x Target Mapper is already open.": "Lähde x Kohde Kartoittaja on jo auki."
 }
@@ -1,45 +0,0 @@
 {
    "Source x Target Mapper": "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"
 }
@@ -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",
@@ -36,7 +36,7 @@
    "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",
@@ -1,4 +1,4 @@
 just put the models in this folder -
 https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx?download=true
-https://huggingface.co/hacksider/deep-live-cam/resolve/main/gfpgan-1024.onnx?download=true
+https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth
@@ -1,38 +0,0 @@
 import os
 import cv2
 import numpy as np
 # Utility function to support unicode characters in file paths for reading.
 # OpenCV's cv2.imread() encodes the path with the locale ANSI code page on
 # Windows, so it silently returns None for paths containing non-ASCII
 # characters (Chinese, Japanese, Cyrillic, accents, ...). Reading the bytes
 # through NumPy (which uses Python's unicode-aware file I/O) and decoding them
 # in memory sidesteps that limitation. Returns None on failure, matching
 # cv2.imread() so it stays a drop-in replacement.
 def imread_unicode(path, flags=cv2.IMREAD_COLOR):
    try:
        data = np.fromfile(path, dtype=np.uint8)
        if data.size == 0:
            return None
        return cv2.imdecode(data, flags)
    except Exception:
        return None
 # Utility function to support unicode characters in file paths for writing.
 # cv2.imwrite() has the same ANSI-path limitation, so we encode the image in
 # memory and write the bytes out with NumPy's unicode-aware file I/O. Returns
 # True/False like cv2.imwrite() so it stays a drop-in replacement.
 def imwrite_unicode(path, img, params=None):
    try:
        root, ext = os.path.splitext(path)
        if not ext:
            ext = ".png"
        result, encoded_img = cv2.imencode(ext, img, params if params is not None else [])
        if not result:
            return False
        encoded_img.tofile(path)
        return True
    except Exception:
        return False
@@ -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:
@@ -14,18 +13,13 @@ def get_video_frame(video_path: str, frame_number: int = 0) -> Any:
    if modules.globals.color_correction:
        capture.set(cv2.CAP_PROP_CONVERT_RGB, 1)
-    frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
+    frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
-    if frame_total <= 0:
+    capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
        capture.release()
        return None
    target_index = 0 if frame_number <= 1 else min(frame_total - 1, frame_number - 1)
    capture.set(cv2.CAP_PROP_POS_FRAMES, target_index)
    has_frame, frame = capture.read()
    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
@@ -1,21 +1,10 @@
 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:
    n_samples = len(embeddings)
    if n_samples == 0:
        raise ValueError("embeddings must not be empty")
    if max_k < 1:
        raise ValueError("max_k must be at least 1")
    max_k = min(max_k, n_samples)
    if max_k == 1:
        kmeans = KMeans(n_clusters=1, random_state=0)
        kmeans.fit(embeddings)
        return kmeans.cluster_centers_
    inertia = []
    cluster_centroids = []
    K = range(1, max_k+1)
@@ -2,7 +2,7 @@ import os
 import sys
 # single thread doubles cuda performance - needs to be set before torch import
 if any(arg.startswith('--execution-provider') for arg in sys.argv):
-    os.environ['OMP_NUM_THREADS'] = '6'
+    os.environ['OMP_NUM_THREADS'] = '1'
 # reduce tensorflow log level
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 import warnings
@@ -11,30 +11,21 @@ import platform
 import signal
 import shutil
 import argparse
-try:
+import torch
    import torch
    HAS_TORCH = True
 except ImportError:
    HAS_TORCH = False
 import onnxruntime
-try:
+import tensorflow
    import tensorflow
    HAS_TENSORFLOW = True
 except ImportError:
    HAS_TENSORFLOW = False
 import modules.globals
 import modules.metadata
 import modules.ui as ui
-from modules.processors.frame.core import get_frame_processors_modules, process_video_in_memory
+from modules.processors.frame.core import get_frame_processors_modules
 from modules.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
-if HAS_TORCH and 'ROCMExecutionProvider' in modules.globals.execution_providers:
+if 'ROCMExecutionProvider' in modules.globals.execution_providers:
    del torch
 warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
-if HAS_TORCH:
+warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
    warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
 def parse_args() -> None:
@@ -43,7 +34,7 @@ def parse_args() -> None:
    program.add_argument('-s', '--source', help='select an source image', dest='source_path')
    program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
    program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
-    program.add_argument('--frame-processor', help='pipeline of frame processors', dest='frame_processor', default=['face_swapper'], choices=['face_swapper', 'face_enhancer', 'face_enhancer_gpen256', 'face_enhancer_gpen512'], nargs='+')
+    program.add_argument('--frame-processor', help='pipeline of frame processors', dest='frame_processor', default=['face_swapper'], choices=['face_swapper', 'face_enhancer'], nargs='+')
    program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)
    program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
    program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
@@ -57,8 +48,8 @@ def parse_args() -> None:
    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=[suggest_default_execution_provider()], choices=suggest_execution_providers(), nargs='+')
+    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=None)
+    program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())
    program.add_argument('-v', '--version', action='version', version=f'{modules.metadata.name} {modules.metadata.version}')
    # register deprecated args
@@ -90,15 +81,11 @@ def parse_args() -> None:
    modules.globals.execution_threads = args.execution_threads
    modules.globals.lang = args.lang
-    # The argparse default (None) avoids evaluating suggest_execution_threads()
+    #for ENHANCER tumbler:
-    # before providers are decoded, and deprecated-arg overrides above may
+    if 'face_enhancer' in args.frame_processor:
-    # have already set execution_threads.
+        modules.globals.fp_ui['face_enhancer'] = True
-    if modules.globals.execution_threads is None:
+    else:
-        modules.globals.execution_threads = suggest_execution_threads()
+        modules.globals.fp_ui['face_enhancer'] = False
    #for ENHANCER tumblers:
    for enhancer_key in ('face_enhancer', 'face_enhancer_gpen256', 'face_enhancer_gpen512'):
        modules.globals.fp_ui[enhancer_key] = enhancer_key in args.frame_processor
    # translate deprecated args
    if args.source_path_deprecated:
@@ -137,52 +124,28 @@ def suggest_max_memory() -> int:
    return 16
 def suggest_default_execution_provider() -> str:
    """Pick the best available provider: cuda > rocm > coreml > openvino > dml > cpu."""
    available = encode_execution_providers(onnxruntime.get_available_providers())
    for pref in ('cuda', 'rocm', 'coreml', 'openvino', 'dml'):
        if pref in available:
            return pref
    return 'cpu'
 def suggest_execution_providers() -> List[str]:
    return encode_execution_providers(onnxruntime.get_available_providers())
 def suggest_execution_threads() -> int:
    """Suggest optimal thread count based on hardware and execution provider."""
    import os
    # Get CPU count
    cpu_count = os.cpu_count() or 4
    if 'DmlExecutionProvider' in modules.globals.execution_providers:
        return 1
    if 'ROCMExecutionProvider' in modules.globals.execution_providers:
        return 1
-    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
+    return 8
        return 2
    if 'OpenVINOExecutionProvider' in modules.globals.execution_providers:
        return 1
    # For CPU execution, use most cores but leave some for system
    return max(4, min(cpu_count - 2, 16))
 def limit_resources() -> None:
    # prevent tensorflow memory leak
    if HAS_TENSORFLOW:
    gpus = tensorflow.config.experimental.list_physical_devices('GPU')
    for gpu in gpus:
        tensorflow.config.experimental.set_memory_growth(gpu, True)
    # limit memory usage
    if modules.globals.max_memory:
        # setrlimit(RLIMIT_DATA) fails with EINVAL on macOS, crashing on launch.
        # See https://github.com/hacksider/Deep-Live-Cam/issues/1848
        if platform.system().lower() == 'darwin':
            return
        memory = modules.globals.max_memory * 1024 ** 3
        if platform.system().lower() == 'darwin':
            memory = modules.globals.max_memory * 1024 ** 6
        if platform.system().lower() == 'windows':
            import ctypes
            kernel32 = ctypes.windll.kernel32
@@ -193,7 +156,7 @@ def limit_resources() -> None:
 def release_resources() -> None:
-    if 'CUDAExecutionProvider' in modules.globals.execution_providers and HAS_TORCH:
+    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
        torch.cuda.empty_cache()
@@ -213,16 +176,10 @@ def update_status(message: str, scope: str = 'DLC.CORE') -> None:
        ui.update_status(message)
 def start() -> None:
    """Start processing with performance monitoring."""
    import time
    start_time = time.time()
    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_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
@@ -236,80 +193,34 @@ def start() -> None:
            frame_processor.process_image(modules.globals.source_path, modules.globals.output_path, modules.globals.output_path)
            release_resources()
        if is_image(modules.globals.target_path):
-            elapsed = time.time() - start_time
+            update_status('Processing to image succeed!')
            update_status(f'Processing to image succeed! (Time: {elapsed:.2f}s)')
        else:
            update_status('Processing to image failed!')
        return
    # process image to videos
    if modules.globals.nsfw_filter and ui.check_and_ignore_nsfw(modules.globals.target_path, destroy):
        return
    # Detect FPS early (needed by both pipelines)
    if modules.globals.keep_fps:
        update_status('Detecting fps...')
        fps = detect_fps(modules.globals.target_path)
    else:
        fps = 30.0
    video_created = False
    # --- In-memory pipeline (non-map_faces only) ---
    # Reads frames from FFmpeg pipe, processes in memory, encodes directly.
    # Eliminates all per-frame PNG disk I/O for a major speed-up.
    if not modules.globals.map_faces:
-        update_status(f'Processing video in-memory at {fps} fps...')
+        update_status('Creating temp resources...')
        create_temp(modules.globals.target_path)
        processing_start = time.time()
        video_created = process_video_in_memory(
            modules.globals.source_path,
            modules.globals.target_path,
            fps,
        )
        processing_time = time.time() - processing_start
        release_resources()
        if video_created:
            update_status(f'In-memory processing + encoding completed in {processing_time:.2f}s')
    # --- Disk-based fallback (required for map_faces, or if pipe failed) ---
    if not video_created:
        if not modules.globals.map_faces:
            update_status('Falling back to disk-based processing...')
        extraction_start = time.time()
        create_temp(modules.globals.target_path)
        update_status('Extracting frames...')
        extract_frames(modules.globals.target_path)
        extraction_time = time.time() - extraction_start
    temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
        total_frames = len(temp_frame_paths)
        update_status(f'Processing {total_frames} frames with {modules.globals.execution_threads} threads...')
        processing_start = time.time()
    for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
        update_status('Progressing...', frame_processor.NAME)
        frame_processor.process_video(modules.globals.source_path, temp_frame_paths)
        release_resources()
-        processing_time = time.time() - processing_start
+    # handles fps
-        fps_processing = total_frames / processing_time if processing_time > 0 else 0
+    if modules.globals.keep_fps:
-        update_status(f'Frame processing completed in {processing_time:.2f}s ({fps_processing:.2f} fps)')
+        update_status('Detecting fps...')
-
+        fps = detect_fps(modules.globals.target_path)
        encoding_start = time.time()
        update_status(f'Creating video with {fps} fps...')
-        video_created = create_video(modules.globals.target_path, fps)
+        create_video(modules.globals.target_path, fps)
-        encoding_time = time.time() - encoding_start
+    else:
-        if video_created:
+        update_status('Creating video with 30.0 fps...')
-            update_status(f'Video encoding completed in {encoding_time:.2f}s')
+        create_video(modules.globals.target_path)
    if not video_created:
        update_status('Video encoding failed. No temporary output video was created.')
        clean_temp(modules.globals.target_path)
        return
    # handle audio
    if modules.globals.keep_audio:
        if modules.globals.keep_fps:
@@ -319,13 +230,10 @@ def start() -> None:
        restore_audio(modules.globals.target_path, modules.globals.output_path)
    else:
        move_temp(modules.globals.target_path, modules.globals.output_path)
    # clean and validate
    clean_temp(modules.globals.target_path)
-    
+    if is_video(modules.globals.target_path):
-    total_time = time.time() - start_time
+        update_status('Processing to video succeed!')
    if is_video(modules.globals.target_path) and modules.globals.output_path and os.path.isfile(modules.globals.output_path):
        update_status(f'Video processing succeeded! Total time: {total_time:.2f}s')
    else:
        update_status('Processing to video failed!')
@@ -333,8 +241,7 @@ def start() -> None:
 def destroy(to_quit=True) -> None:
    if modules.globals.target_path:
        clean_temp(modules.globals.target_path)
-    if to_quit:
+    if to_quit: quit()
        quit()
 def run() -> None:
@@ -344,9 +251,6 @@ def run() -> None:
    for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
        if not frame_processor.pre_check():
            return
    # Pre-load face analyser in main thread before GUI starts
    #from modules.face_analyser import get_face_analyser
    #get_face_analyser()
    limit_resources()
    if modules.globals.headless:
        start()
@@ -1,7 +0,0 @@
 from typing import Any
 from insightface.app.common import Face
 import numpy
 Face = Face
 Frame = numpy.ndarray[Any, Any] 
@@ -2,10 +2,10 @@ import os
 import shutil
 from typing import Any
 import insightface
 import threading
 import cv2
 import numpy as np
 import modules.globals
 from modules import imread_unicode, imwrite_unicode
 from tqdm import tqdm
 from modules.typing import Frame
 from modules.cluster_analysis import find_cluster_centroids, find_closest_centroid
@@ -13,212 +13,31 @@ from modules.utilities import get_temp_directory_path, create_temp, extract_fram
 from pathlib import Path
 FACE_ANALYSER = None
 FACE_ANALYSER_LOCK = threading.Lock()
 DET_SIZE = (640, 640)
 def get_face_analyser() -> Any:
    """Get face analyser with thread-safe initialization."""
    global FACE_ANALYSER
    if FACE_ANALYSER is None:
-        with FACE_ANALYSER_LOCK:
+        FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=modules.globals.execution_providers)
-            # Double-check after acquiring lock
+        FACE_ANALYSER.prepare(ctx_id=0, det_size=(640, 640))
            if FACE_ANALYSER is None:
                from modules.processors.frame._onnx_enhancer import (
                    build_provider_config,
                )
                from modules.model_downloader import ensure_insightface_pack
                ensure_insightface_pack('buffalo_l')
                providers = build_provider_config()
                FACE_ANALYSER = insightface.app.FaceAnalysis(
                    name='buffalo_l',
                    providers=providers,
                    allowed_modules=['detection', 'recognition', 'landmark_2d_106']
                )
                FACE_ANALYSER.prepare(ctx_id=0, det_size=DET_SIZE)
                _optimize_det_model(FACE_ANALYSER, providers)
    return FACE_ANALYSER
-def _optimize_det_model(fa: Any, providers) -> None:
+def get_one_face(frame: Frame) -> Any:
-    """Replace the detection model's ONNX session with a CoreML-optimized one.
+    face = get_face_analyser().get(frame)
    Folds dynamic Shape→Gather chains into constants (the input size is
    fixed at det_size), eliminating CPU↔ANE partition boundaries in the
    RetinaFace FPN upsampling path.  21ms → 4ms on M3 Max.
    """
    from modules.onnx_optimize import optimize_for_coreml, IS_APPLE_SILICON
    if not IS_APPLE_SILICON:
        return
    det_model = fa.det_model
    model_path = getattr(det_model, 'model_file', None)
    if model_path is None or not os.path.exists(model_path):
        return
    input_shape = (1, 3, DET_SIZE[1], DET_SIZE[0])
    optimized_path = optimize_for_coreml(model_path, input_shape=input_shape)
    if optimized_path == model_path:
        return
    import onnxruntime
    session_options = onnxruntime.SessionOptions()
    session_options.graph_optimization_level = (
        onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
    )
    # Route detection to GPU shader cores (CPUAndGPU) instead of ANE.
    # This lets detection run concurrently with the swap model on the
    # ANE, overlapping the two inference calls.  Detection is fast
    # enough on GPU (~4ms) and this frees ANE for the heavier swap.
    det_providers = []
    for p in providers:
        name = p[0] if isinstance(p, tuple) else p
        if name == "CoreMLExecutionProvider":
            det_providers.append((
                "CoreMLExecutionProvider",
                {"ModelFormat": "MLProgram", "MLComputeUnits": "CPUAndGPU"},
            ))
        else:
            det_providers.append(p)
    det_model.session = onnxruntime.InferenceSession(
        optimized_path, sess_options=session_options, providers=det_providers,
    )
 def _needs_landmark() -> bool:
    """Check whether any active feature requires 106-point landmarks.
    Landmarks are needed by face enhancers and mouth masking, but not
    by the face swapper alone.
    """
    if getattr(modules.globals, "mouth_mask", False):
        return True
    processors = getattr(modules.globals, "frame_processors", [])
    return any(p in processors for p in
               ("face_enhancer", "face_enhancer_gpen256", "face_enhancer_gpen512"))
 def _is_dml() -> bool:
    return any("DmlExecutionProvider" in p for p in modules.globals.execution_providers)
 def _analyse_faces(frame: Frame) -> list:
    """Run face detection, then recognition (and optionally landmark).
    Replaces InsightFace's ``FaceAnalysis.get()`` to skip the
    landmark_2d_106 model when only face_swapper is active (saves ~1ms
    per face and avoids an unnecessary ONNX session call).
    """
    fa = get_face_analyser()
    bboxes, kpss = fa.det_model.detect(frame, max_num=0, metric="default")
    if bboxes.shape[0] == 0:
        return []
    need_landmark = _needs_landmark()
    rec_model = fa.models.get("recognition")
    lmk_model = fa.models.get("landmark_2d_106") if need_landmark else None
    from insightface.app.common import Face
    faces = []
    for i in range(bboxes.shape[0]):
        face = Face(bbox=bboxes[i, 0:4],
                    kps=kpss[i] if kpss is not None else None,
                    det_score=bboxes[i, 4])
        if rec_model is not None:
            rec_model.get(frame, face)
        if lmk_model is not None:
            lmk_model.get(frame, face)
        faces.append(face)
    return faces
 def get_one_face(frame: Frame, faces: Any = None) -> Any:
    if faces is None:
        if _is_dml():
            with modules.globals.dml_lock:
                faces = _analyse_faces(frame)
        else:
            faces = _analyse_faces(frame)
    try:
-        return min(faces, key=lambda x: x.bbox[0])
+        return min(face, key=lambda x: x.bbox[0])
    except ValueError:
        return None
 def get_many_faces(frame: Frame) -> Any:
    try:
-        if _is_dml():
+        return get_face_analyser().get(frame)
            with modules.globals.dml_lock:
                return _analyse_faces(frame)
        else:
            return _analyse_faces(frame)
    except IndexError:
        return None
 def detect_one_face_fast(frame: Frame) -> Any:
    """Detection-only — skips landmark and recognition models.
    Returns a Face with bbox, kps, det_score (enough for face swap).
    ~10ms vs ~16ms for full get_one_face() at 1080p.
    """
    from insightface.app.common import Face
    fa = get_face_analyser()
    bboxes, kpss = fa.det_model.detect(frame, max_num=0, metric='default')
    if bboxes.shape[0] == 0:
        return None
    idx = int(bboxes[:, 0].argmin())
    return Face(bbox=bboxes[idx, :4], kps=kpss[idx], det_score=bboxes[idx, 4])
 def detect_many_faces_fast(frame: Frame) -> Any:
    """Detection-only multi-face — skips landmark and recognition."""
    from insightface.app.common import Face
    fa = get_face_analyser()
    bboxes, kpss = fa.det_model.detect(frame, max_num=0, metric='default')
    if bboxes.shape[0] == 0:
        return None
    return [Face(bbox=bboxes[i, :4], kps=kpss[i], det_score=bboxes[i, 4])
            for i in range(bboxes.shape[0])]
 def ensure_landmarks(frame: Frame, faces: Any) -> None:
    """Run the 2d106 landmark model in-place on faces that lack it.
    The fast webcam path (detect_one_face_fast / detect_many_faces_fast)
    produces detection-only Face objects with no ``landmark_2d_106``.
    Mouth masking needs those landmarks, so add them on demand only when
    the feature is active — keeping the fast path fast otherwise.
    """
    if faces is None:
        return
    if not isinstance(faces, (list, tuple)):
        faces = [faces]
    fa = get_face_analyser()
    lmk_model = fa.models.get("landmark_2d_106")
    if lmk_model is None:
        return
    for face in faces:
        if face is None:
            continue
        # insightface Face is a dict; missing keys raise AttributeError,
        # so getattr(..., None) is the safe presence check.
        if getattr(face, "landmark_2d_106", None) is None:
            try:
                lmk_model.get(frame, face)
            except Exception as e:  # pragma: no cover - never break the swap
                print(f"Error computing 2d106 landmarks: {e}")
 def has_valid_map() -> bool:
    for map in modules.globals.source_target_map:
        if "source" in map and "target" in map:
@@ -257,10 +76,8 @@ def add_blank_map() -> Any:
 def get_unique_faces_from_target_image() -> Any:
    try:
        modules.globals.source_target_map = []
-        target_frame = imread_unicode(modules.globals.target_path)
+        target_frame = cv2.imread(modules.globals.target_path)
        many_faces = get_many_faces(target_frame)
        if many_faces is None:
            return None
        i = 0
        for face in many_faces:
@@ -293,10 +110,8 @@ def get_unique_faces_from_target_video() -> Any:
        i = 0
        for temp_frame_path in tqdm(temp_frame_paths, desc="Extracting face embeddings from frames"):
-            temp_frame = imread_unicode(temp_frame_path)
+            temp_frame = cv2.imread(temp_frame_path)
            many_faces = get_many_faces(temp_frame)
            if many_faces is None:
                continue
            for face in many_faces:
                face_embeddings.append(face.normed_embedding)
@@ -338,9 +153,6 @@ def default_target_face():
                best_frame = frame
                break
        if best_face is None:
            continue  # No faces detected in this cluster — skip
        for frame in map['target_faces_in_frame']:
            for face in frame['faces']:
                if face['det_score'] > best_face['det_score']:
@@ -349,7 +161,7 @@ def default_target_face():
        x_min, y_min, x_max, y_max = best_face['bbox']
-        target_frame = imread_unicode(best_frame['location'])
+        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
@@ -365,7 +177,7 @@ def dump_faces(centroids: Any, frame_face_embeddings: list):
        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 = imread_unicode(frame['location'])
+            temp_frame = cv2.imread(frame['location'])
            j = 0
            for face in frame['faces']:
@@ -373,5 +185,5 @@ def dump_faces(centroids: Any, frame_face_embeddings: list):
                    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:
-                        imwrite_unicode(temp_directory_path + f"/{i}/{frame['frame']}_{j}.png", temp_frame[int(y_min):int(y_max), int(x_min):int(x_max)])
+                        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
@@ -1,79 +1,43 @@
 # --- START OF FILE globals.py ---
 import os
 from typing import List, Dict, Any
 ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
 WORKFLOW_DIR = os.path.join(ROOT_DIR, "workflow")
-# Canonical media extensions, defined once so the file dialogs and
+file_types = [
-# has_image_extension never drift. GIF is intentionally excluded: OpenCV's
+    ("Image", ("*.png", "*.jpg", "*.jpeg", "*.gif", "*.bmp")),
-# cv2.imread/imwrite (the only image I/O this app uses) cannot decode or
+    ("Video", ("*.mp4", "*.mkv")),
-# encode GIF on 4.10 or 4.11, so offering it would silently fail. WEBP works
+]
 # via the libwebp bundled with opencv-python.
 IMAGE_EXTENSIONS = (".png", ".jpg", ".jpeg", ".bmp", ".webp")
 VIDEO_EXTENSIONS = (".mp4", ".mkv")
-# Face Mapping Data
+source_target_map = []
-source_target_map: List[Dict[str, Any]] = [] # Stores detailed map for image/video processing
+simple_map = {}
 simple_map: Dict[str, Any] = {}             # Stores simplified map (embeddings/faces) for live/simple mode
-# Paths
+source_path = None
-source_path: str | None = None
+target_path = None
-target_path: str | None = None
+output_path = None
 output_path: str | None = None
 # Processing Options
 frame_processors: List[str] = []
-keep_fps: bool = True
+keep_fps = True
-keep_audio: bool = True
+keep_audio = True
-keep_frames: bool = False
+keep_frames = False
-many_faces: bool = False         # Process all detected faces with default source
+many_faces = False
-map_faces: bool = False          # Use source_target_map or simple_map for specific swaps
+map_faces = False
-poisson_blend: bool = False      # Enable Poisson Blending for smoother face swaps
+color_correction = False  # New global variable for color correction toggle
-color_correction: bool = False   # Enable color correction (implementation specific)
+nsfw_filter = False
-nsfw_filter: bool = False
+video_encoder = None
-
+video_quality = None
-# Video Output Options
+live_mirror = False
-video_encoder: str | None = None
+live_resizable = True
-video_quality: int | None = None # Typically a CRF value or bitrate
+max_memory = None
-
+execution_providers: List[str] = []
-# Live Mode Options
+execution_threads = None
-live_mirror: bool = False
+headless = None
-live_resizable: bool = True
+log_level = "error"
-camera_input_combobox: Any | None = None # Placeholder for UI element if needed
+fp_ui: Dict[str, bool] = {"face_enhancer": False}
-webcam_preview_running: bool = False
+camera_input_combobox = None
-show_fps: bool = False
+webcam_preview_running = False
-
+show_fps = False
-# System Configuration
+mouth_mask = False
-max_memory: int | None = None        # Memory limit in GB? (Needs clarification)
+show_mouth_mask_box = False
-execution_providers: List[str] = []  # e.g., ['CUDAExecutionProvider', 'CPUExecutionProvider']
+mask_feather_ratio = 8
-execution_threads: int | None = None # Number of threads for CPU execution
+mask_down_size = 0.50
-headless: bool | None = None         # Run without UI?
+mask_size = 1
 log_level: str = "error"             # Logging level (e.g., 'debug', 'info', 'warning', 'error')
 # Face Processor UI Toggles (Example)
 fp_ui: Dict[str, bool] = {"face_enhancer": False, "face_enhancer_gpen256": False, "face_enhancer_gpen512": 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)
 mouth_mask_size: float = 0.0       # Mouth mask size (0-100; 0=off, 100=mouth to chin)
 # --- 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 ---
 import threading
 dml_lock = threading.Lock()
@@ -1,285 +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 os
 import cv2
 import numpy as np
 from typing import Tuple
 # ---------------------------------------------------------------------------
 # CUDA availability detection (evaluated once at import time)
 # ---------------------------------------------------------------------------
 CUDA_AVAILABLE: bool = False
 # OpenCV CUDA per-operation acceleration is DISABLED by default.
 # Each gpu_* call uploads to GPU, processes, then downloads back to CPU.
 # At webcam resolution (~960x540) this upload/download overhead far exceeds
 # the time saved on the actual operation, making it slower than pure CPU.
 # The heavy lifting (face detection, swap, enhancement) runs on GPU via
 # ONNX Runtime's CUDAExecutionProvider, which is where GPU matters.
 #
 # To force-enable, set OPENCV_CUDA_PROCESSING=1 in your environment.
 if os.environ.get("OPENCV_CUDA_PROCESSING") == "1":
    try:
        _test_mat = cv2.cuda.GpuMat()
        _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 processing enabled via OPENCV_CUDA_PROCESSING=1.")
    except Exception:
        pass
 # ---------------------------------------------------------------------------
 # 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 ---
@@ -1,3 +1,3 @@
 name = 'Deep-Live-Cam'
-version = '2.1.5'
+version = '1.8'
 edition = 'GitHub Edition'
@@ -1,178 +0,0 @@
 import os
 import platform
 import ssl
 import threading
 import urllib.error
 import urllib.request
 from typing import Dict, List, Optional
 from tqdm import tqdm
 from modules.paths import MODELS_DIR
 HF_REPO_ID = "hacksider/deep-live-cam"
 HF_RESOLVE_BASE = f"https://huggingface.co/{HF_REPO_ID}/resolve/main/"
 MODEL_SIZES: Dict[str, int] = {
    "inswapper_128.onnx": 554253681,
    "inswapper_128_fp16.onnx": 277680638,
    "gfpgan-1024.onnx": 365875079,
    "GPEN-BFR-256.onnx": 75715262,
    "GPEN-BFR-512.onnx": 284244491,
    "buffalo_l/buffalo_l/1k3d68.onnx": 143607619,
    "buffalo_l/buffalo_l/2d106det.onnx": 5030888,
    "buffalo_l/buffalo_l/det_10g.onnx": 16923827,
    "buffalo_l/buffalo_l/genderage.onnx": 1322532,
    "buffalo_l/buffalo_l/w600k_r50.onnx": 174383860,
 }
 _LOCKS: Dict[str, threading.Lock] = {}
 _LOCKS_GUARD = threading.Lock()
 CHUNK_SIZE = 1024 * 256
 def _ssl_context():
    if platform.system().lower() == "darwin":
        return ssl._create_unverified_context()
    return None
 def _lock_for(key: str) -> threading.Lock:
    with _LOCKS_GUARD:
        if key not in _LOCKS:
            _LOCKS[key] = threading.Lock()
        return _LOCKS[key]
 def resolve_url(name: str) -> str:
    return HF_RESOLVE_BASE + name.replace(os.sep, "/")
 def local_path(name: str, dest_dir: Optional[str] = None) -> str:
    if dest_dir is not None:
        return os.path.join(dest_dir, os.path.basename(name))
    return os.path.join(MODELS_DIR, *name.replace("/", os.sep).split(os.sep))
 def expected_size(name: str) -> Optional[int]:
    return MODEL_SIZES.get(name.replace(os.sep, "/"))
 def is_present(name: str, dest_dir: Optional[str] = None) -> bool:
    path = local_path(name, dest_dir)
    return os.path.isfile(path) and os.path.getsize(path) > 0
 def _download(name: str, url: str, target: str, size: Optional[int]) -> bool:
    os.makedirs(os.path.dirname(target) or MODELS_DIR, exist_ok=True)
    partial = target + ".part"
    resume_from = os.path.getsize(partial) if os.path.isfile(partial) else 0
    headers = {"User-Agent": "Deep-Live-Cam"}
    if resume_from:
        headers["Range"] = f"bytes={resume_from}-"
    try:
        request = urllib.request.Request(url, headers=headers)
        response = urllib.request.urlopen(request, context=_ssl_context(), timeout=60)
    except urllib.error.HTTPError as error:
        if resume_from and error.code in (416, 501):
            try:
                os.remove(partial)
            except OSError:
                pass
            return _download(name, url, target, size)
        print(f"[DLC.MODELS] Failed to download {name}: HTTP {error.code}")
        return False
    except (urllib.error.URLError, OSError) as error:
        print(f"[DLC.MODELS] Failed to download {name}: {error}")
        return False
    with response:
        if resume_from and getattr(response, "status", 200) != 206:
            resume_from = 0
        remaining = int(response.headers.get("Content-Length", 0) or 0)
        total = size or (resume_from + remaining) or None
        mode = "ab" if resume_from else "wb"
        try:
            with open(partial, mode) as handle:
                with tqdm(
                    total=total,
                    initial=resume_from,
                    desc=f"Downloading {os.path.basename(name)}",
                    unit="B",
                    unit_scale=True,
                    unit_divisor=1024,
                ) as progress:
                    while True:
                        buffer = response.read(CHUNK_SIZE)
                        if not buffer:
                            break
                        handle.write(buffer)
                        progress.update(len(buffer))
        except (urllib.error.URLError, OSError) as error:
            print(f"[DLC.MODELS] Download of {name} interrupted: {error}")
            return False
    downloaded = os.path.getsize(partial)
    if size is not None and downloaded != size:
        print(f"[DLC.MODELS] {name} is {downloaded} bytes, expected {size}. Discarding.")
        try:
            os.remove(partial)
        except OSError:
            pass
        return False
    try:
        os.replace(partial, target)
    except OSError as error:
        print(f"[DLC.MODELS] Could not finalise {name}: {error}")
        return False
    return True
 def ensure_model(
    name: str, quiet: bool = False, dest_dir: Optional[str] = None
 ) -> Optional[str]:
    name = name.replace(os.sep, "/")
    target = local_path(name, dest_dir)
    with _lock_for(target):
        if is_present(name, dest_dir):
            return target
        if not quiet:
            print(f"[DLC.MODELS] {name} not found in models folder, downloading...")
        if _download(name, resolve_url(name), target, expected_size(name)):
            return target
    return None
 def ensure_any(names: List[str]) -> Optional[str]:
    for name in names:
        if is_present(name):
            return local_path(name)
    for name in names:
        path = ensure_model(name)
        if path is not None:
            return path
    return None
 def ensure_insightface_pack(name: str = "buffalo_l") -> bool:
    members = [n for n in MODEL_SIZES if n.startswith(f"{name}/")]
    if not members:
        return False
    dest_dir = os.path.join(os.path.expanduser("~"), ".insightface", "models", name)
    if all(is_present(member, dest_dir) for member in members):
        return True
    print(f"[DLC.MODELS] insightface pack '{name}' is missing, downloading...")
    ok = True
    for member in members:
        if ensure_model(member, quiet=True, dest_dir=dest_dir) is None:
            ok = False
    if not ok:
        print(f"[DLC.MODELS] Could not pre-fill '{name}'; insightface will retry.")
    return ok
@@ -1,550 +0,0 @@
 """ONNX model optimizations for CoreML execution on Apple Silicon.
 Each pass eliminates a different CPU↔ANE round-trip that ORT's CoreML EP
 would otherwise introduce:
 1. **Shape/Gather constant folding** — Dynamic ``Shape`` → ``Gather`` chains
   (e.g. for FPN upsample target sizes in RetinaFace) force ops onto CPU even
   when the input dimensions are known at load time.  We run ONNX shape
   inference with the known input size and replace these chains with constants.
   Float32-noise-level differences only (max ~6e-6).
 2. **Pad(reflect) decomposition** — CoreML doesn't support ``Pad(mode=reflect)``.
   Models using reflect padding (e.g. inswapper_128) get split into many CoreML
   subgraphs with CPU fallbacks between each.  We rewrite each ``Pad(reflect)``
   as equivalent ``Slice`` + ``Concat`` ops that CoreML handles natively.
   Bit-for-bit identical output. (Fixed upstream in microsoft/onnxruntime#28073.)
 3. **Split → Slice decomposition** — CoreML's EP doesn't support the ONNX
   ``Split`` op, causing partition boundaries in models with channel-wise
   splits (e.g. GFPGAN's SFT modulation). Each 2-way Split becomes two Slices.
 4. **Scalar Gather widening** — ORT's CoreML EP rejects ``Gather`` nodes with
   rank-0 (scalar) indices. StyleGAN-derived models (GFPGAN) slice per-layer
   style codes using exactly this pattern. We widen each scalar index to
   ``[1]`` and squeeze the added axis on the Gather output.
   (Filed upstream as microsoft/onnxruntime#28180.)
 All passes are cached on disk with a ``_coreml`` suffix so the rewrite cost
 is paid only once per model.
 """
 import os
 import platform
 import numpy as np
 IS_APPLE_SILICON = platform.system() == "Darwin" and platform.machine() == "arm64"
 def optimize_for_coreml(model_path: str, input_shape: tuple = None) -> str:
    """Return path to a CoreML-optimized ONNX model.
    Applies all applicable optimizations and caches the result next to
    the original model (with ``_coreml`` suffix).
    Args:
        model_path: Path to the original ONNX model.
        input_shape: Optional fixed input shape (e.g. ``(1, 3, 640, 640)``).
            When provided, enables Shape/Gather constant folding.
    Returns the optimized path, or the original path if no optimizations
    apply or we're not on Apple Silicon.
    """
    if not IS_APPLE_SILICON:
        return model_path
    base, ext = os.path.splitext(model_path)
    optimized_path = f"{base}_coreml{ext}"
    if os.path.exists(optimized_path):
        if os.path.getmtime(optimized_path) >= os.path.getmtime(model_path):
            return optimized_path
    import onnx
    from onnx import numpy_helper
    model = onnx.load(model_path)
    changed = False
    if _fold_shape_gather(model, input_shape):
        changed = True
    # TODO(ort>=1.26): drop this pass. Fixed upstream by microsoft/onnxruntime#28073.
    if _decompose_reflect_pad(model):
        changed = True
    if _decompose_split(model):
        changed = True
    # TODO: drop this pass once microsoft/onnxruntime#28180 ships. The CoreML
    # Gather op builder rejects rank-0 (scalar) indices; we widen them to [1]
    # + Squeeze so StyleGAN-family models (GFPGAN) stay on ANE.
    if _rewrite_scalar_gather(model):
        changed = True
    if not changed:
        return model_path
    # Preserve insightface's emap convention: the INSwapper class reads
    # graph.initializer[-1] as the embedding map.  If the original model
    # had a (512, 512) matrix as its last initializer, keep it last.
    _preserve_emap_position(model, numpy_helper)
    onnx.save(model, optimized_path)
    return optimized_path
 # ---------------------------------------------------------------------------
 # Pass 1: Fold Shape → Gather chains into constants
 # ---------------------------------------------------------------------------
 def _fold_shape_gather(model, input_shape) -> bool:
    """Replace dynamic Shape→Gather chains with constants when input size is known.
    Only removes a Shape node when ALL of its consumers are Gather nodes
    that are also being folded.  This prevents breaking graphs where
    a Shape output feeds into other ops as well.
    """
    if input_shape is None:
        return False
    from onnx import numpy_helper, shape_inference
    graph = model.graph
    # Set fixed input dimensions for shape inference
    inp = graph.input[0]
    dims = inp.type.tensor_type.shape.dim
    for i, size in enumerate(input_shape):
        if i < len(dims):
            dims[i].dim_value = size
    try:
        model_inferred = shape_inference.infer_shapes(model)
    except Exception:
        return False
    # Extract inferred shapes
    value_shapes = {}
    for vi in list(model_inferred.graph.value_info) + list(graph.input) + list(graph.output):
        shape_dims = vi.type.tensor_type.shape.dim
        shape = []
        for d in shape_dims:
            if d.dim_value > 0:
                shape.append(d.dim_value)
            else:
                shape.append(None)
        value_shapes[vi.name] = shape
    inits = {init.name: numpy_helper.to_array(init) for init in graph.initializer}
    # Build consumer map: output_name → list of consuming nodes
    consumers = {}
    for node in graph.node:
        for i in node.input:
            consumers.setdefault(i, []).append(node)
    # Also check graph outputs — an output name consumed by the graph
    # output list must not be removed
    graph_output_names = {o.name for o in graph.output}
    # Find Shape nodes with fully-known output
    shape_constants = {}
    for node in graph.node:
        if node.op_type == "Shape":
            inp_shape = value_shapes.get(node.input[0])
            if inp_shape and all(isinstance(d, int) for d in inp_shape):
                shape_constants[node.output[0]] = np.array(inp_shape, dtype=np.int64)
    if not shape_constants:
        return False
    # Find Gather nodes consuming Shape constants
    gather_constants = {}
    for node in graph.node:
        if node.op_type == "Gather" and node.input[0] in shape_constants:
            idx_name = node.input[1]
            if idx_name in inits:
                idx = int(inits[idx_name])
                val = int(shape_constants[node.input[0]][idx])
                gather_constants[node.output[0]] = np.array(val, dtype=np.int64)
    if not gather_constants:
        return False
    # Determine which Gather nodes to fold (always safe — we replace
    # the output with a constant initializer)
    gather_remove_ids = set()
    for node in graph.node:
        if node.op_type == "Gather" and node.output[0] in gather_constants:
            gather_remove_ids.add(id(node))
    # Determine which Shape nodes are safe to remove: only if ALL
    # consumers of the Shape output are Gather nodes being folded,
    # and the output isn't a graph output.
    shape_remove_ids = set()
    for node in graph.node:
        if node.op_type == "Shape" and node.output[0] in shape_constants:
            out_name = node.output[0]
            if out_name in graph_output_names:
                continue
            node_consumers = consumers.get(out_name, [])
            if all(id(c) in gather_remove_ids for c in node_consumers):
                shape_remove_ids.add(id(node))
    remove_ids = gather_remove_ids | shape_remove_ids
    # Add Gather output constants as initializers
    existing = {i.name for i in graph.initializer}
    for name, val in gather_constants.items():
        if name not in existing:
            graph.initializer.append(numpy_helper.from_array(val, name=name))
    new_nodes = [n for n in graph.node if id(n) not in remove_ids]
    del graph.node[:]
    graph.node.extend(new_nodes)
    return True
 # ---------------------------------------------------------------------------
 # Pass 2: Decompose Pad(reflect) → Slice + Concat
 #
 # TEMPORARY: fixed upstream in microsoft/onnxruntime#28073 (merged 2026-04-20).
 # Once the ORT floor is >= 1.26.0, MLProgram handles Pad(mode=reflect) natively
 # via MIL tensor_operation.pad and this entire pass can be deleted.
 # ---------------------------------------------------------------------------
 def _decompose_reflect_pad(model) -> bool:
    """Rewrite Pad(reflect) as Slice+Concat sequences CoreML can handle."""
    from onnx import numpy_helper, helper
    graph = model.graph
    inits = {init.name: numpy_helper.to_array(init) for init in graph.initializer}
    reflect_pads = []
    for node in graph.node:
        if node.op_type == "Pad":
            mode = "constant"
            for attr in node.attribute:
                if attr.name == "mode":
                    mode = attr.s.decode()
            if mode == "reflect" and len(node.input) > 1 and node.input[1] in inits:
                reflect_pads.append(node)
    if not reflect_pads:
        return False
    existing_names = {i.name for i in graph.initializer}
    def ensure_const(name, value):
        if name not in existing_names:
            graph.initializer.append(
                numpy_helper.from_array(np.array(value, dtype=np.int64), name=name)
            )
            existing_names.add(name)
    ensure_const("_rp_ax2", [2])
    ensure_const("_rp_ax3", [3])
    max_pad = 0
    for node in reflect_pads:
        pads = inits[node.input[1]].tolist()
        max_pad = max(max_pad, int(pads[2]), int(pads[3]))
    for v in range(1, max_pad + 2):
        ensure_const(f"_rp_p{v}", [v])
        ensure_const(f"_rp_n{v}", [-v])
    _counter = [0]
    def uid():
        _counter[0] += 1
        return _counter[0]
    pad_ids = {id(n) for n in reflect_pads}
    pad_init_names = set()
    new_nodes = []
    for node in graph.node:
        if id(node) not in pad_ids:
            new_nodes.append(node)
            continue
        pads = inits[node.input[1]].tolist()
        h_pad, w_pad = int(pads[2]), int(pads[3])
        for inp in node.input[1:]:
            if inp in inits:
                pad_init_names.add(inp)
        current = node.input[0]
        if h_pad > 0:
            top = []
            for i in range(h_pad, 0, -1):
                name = f"_rp_t{uid()}"
                new_nodes.append(helper.make_node(
                    "Slice",
                    inputs=[current, f"_rp_p{i}", f"_rp_p{i+1}", "_rp_ax2"],
                    outputs=[name],
                ))
                top.append(name)
            bot = []
            for i in range(1, h_pad + 1):
                name = f"_rp_b{uid()}"
                new_nodes.append(helper.make_node(
                    "Slice",
                    inputs=[current, f"_rp_n{i+1}", f"_rp_n{i}", "_rp_ax2"],
                    outputs=[name],
                ))
                bot.append(name)
            h_out = f"_rp_h{uid()}"
            new_nodes.append(helper.make_node(
                "Concat", inputs=top + [current] + bot, outputs=[h_out], axis=2
            ))
            current = h_out
        if w_pad > 0:
            left = []
            for i in range(w_pad, 0, -1):
                name = f"_rp_l{uid()}"
                new_nodes.append(helper.make_node(
                    "Slice",
                    inputs=[current, f"_rp_p{i}", f"_rp_p{i+1}", "_rp_ax3"],
                    outputs=[name],
                ))
                left.append(name)
            right = []
            for i in range(1, w_pad + 1):
                name = f"_rp_r{uid()}"
                new_nodes.append(helper.make_node(
                    "Slice",
                    inputs=[current, f"_rp_n{i+1}", f"_rp_n{i}", "_rp_ax3"],
                    outputs=[name],
                ))
                right.append(name)
            new_nodes.append(helper.make_node(
                "Concat",
                inputs=left + [current] + right,
                outputs=[node.output[0]],
                axis=3,
            ))
        elif h_pad > 0:
            new_nodes.append(helper.make_node(
                "Identity", inputs=[current], outputs=[node.output[0]]
            ))
    # Remove old Pad initializers
    clean_inits = [i for i in graph.initializer if i.name not in pad_init_names]
    del graph.initializer[:]
    graph.initializer.extend(clean_inits)
    del graph.node[:]
    graph.node.extend(new_nodes)
    return True
 # ---------------------------------------------------------------------------
 # Pass 3: Decompose Split → Slice pairs
 # ---------------------------------------------------------------------------
 def _decompose_split(model) -> bool:
    """Rewrite Split(axis=1) as Slice pairs that CoreML can handle.
    CoreML's EP doesn't support the ONNX ``Split`` op, causing partition
    boundaries in models that use channel-wise splits (e.g. GFPGAN's SFT
    modulation layers).  Each Split with two outputs becomes two Slice ops.
    """
    from onnx import numpy_helper, helper
    graph = model.graph
    splits = []
    for node in graph.node:
        if node.op_type == "Split":
            axis = 0
            split_sizes = []
            for attr in node.attribute:
                if attr.name == "axis":
                    axis = attr.i
                if attr.name == "split":
                    split_sizes = list(attr.ints)
            if axis == 1 and len(split_sizes) == 2 and len(node.output) == 2:
                splits.append((node, split_sizes))
    if not splits:
        return False
    existing = {i.name for i in graph.initializer}
    def ensure_const(name, value):
        if name not in existing:
            graph.initializer.append(
                numpy_helper.from_array(np.array(value, dtype=np.int64), name=name)
            )
            existing.add(name)
    ensure_const("_sp_ax1", [1])
    # Collect all needed boundary constants
    for _, (a, b) in splits:
        ensure_const("_sp_s0", [0])
        ensure_const(f"_sp_s{a}", [a])
        ensure_const(f"_sp_s{a + b}", [a + b])
    split_ids = {id(node) for node, _ in splits}
    replacements = {}
    for node, (a, b) in splits:
        slice0 = helper.make_node(
            "Slice",
            inputs=[node.input[0], "_sp_s0", f"_sp_s{a}", "_sp_ax1"],
            outputs=[node.output[0]],
        )
        slice1 = helper.make_node(
            "Slice",
            inputs=[node.input[0], f"_sp_s{a}", f"_sp_s{a + b}", "_sp_ax1"],
            outputs=[node.output[1]],
        )
        replacements[id(node)] = [slice0, slice1]
    new_nodes = []
    for node in graph.node:
        if id(node) in split_ids:
            new_nodes.extend(replacements[id(node)])
        else:
            new_nodes.append(node)
    del graph.node[:]
    graph.node.extend(new_nodes)
    return True
 # ---------------------------------------------------------------------------
 # Pass 4: Widen scalar Gather indices to [1] + Squeeze
 #
 # TEMPORARY: filed upstream as microsoft/onnxruntime#28180. ORT's CoreML EP
 # GatherOpBuilder::IsOpSupportedImpl rejects rank-0 (scalar) indices with
 # `Gather does not support scalar 'indices'`. The builder's own comment
 # describes the workaround (promote to [1], squeeze the added axis) but
 # doesn't apply it. We do the same thing at the ONNX level so StyleGAN-
 # family models (GFPGAN is the hot example — 16 per-layer style-code
 # slices) don't split the CoreML subgraph. Once the upstream fix ships
 # and the ORT floor is raised, delete this pass.
 # ---------------------------------------------------------------------------
 def _rewrite_scalar_gather(model) -> bool:
    """Rewrite Gather(data, scalar_idx) as Gather(data, [scalar_idx]) + Squeeze.
    Only touches Gather nodes whose index is a rank-0 int64 constant or
    initializer; everything else passes through unchanged. The rewrite
    is semantically identical — indices get an added leading axis, the
    Squeeze removes it after the gather.
    """
    from onnx import numpy_helper, helper, TensorProto
    graph = model.graph
    # Opset 13 moved Squeeze's axes from attribute to input.
    opset = next(
        (o.version for o in model.opset_import if o.domain in ("", "ai.onnx")),
        11,
    )
    const_values = {}
    for n in graph.node:
        if n.op_type == "Constant":
            for a in n.attribute:
                if a.name == "value":
                    const_values[n.output[0]] = a.t
    init_values = {i.name: i for i in graph.initializer}
    def scalar_int64(name):
        """Return int value if `name` resolves to a rank-0 int64 constant, else None."""
        tensor = const_values.get(name) or init_values.get(name)
        if tensor is None or tensor.data_type != TensorProto.INT64:
            return None
        arr = numpy_helper.to_array(tensor)
        return int(arr) if arr.ndim == 0 else None
    rewrote = 0
    new_nodes = []
    for n in graph.node:
        if n.op_type == "Gather":
            val = scalar_int64(n.input[1])
            if val is not None:
                axis = next((a.i for a in n.attribute if a.name == "axis"), 0)
                idx_1d_name = f"{n.input[1]}_1d_{rewrote}"
                idx_const = helper.make_node(
                    "Constant",
                    inputs=[],
                    outputs=[idx_1d_name],
                    value=helper.make_tensor(idx_1d_name, TensorProto.INT64, [1], [val]),
                )
                gather_out = f"{n.output[0]}_pre_squeeze_{rewrote}"
                new_gather = helper.make_node(
                    "Gather",
                    inputs=[n.input[0], idx_1d_name],
                    outputs=[gather_out],
                    name=n.name,
                    axis=axis,
                )
                if opset < 13:
                    squeeze = helper.make_node(
                        "Squeeze",
                        inputs=[gather_out],
                        outputs=[n.output[0]],
                        name=(n.name or "gather") + "_squeeze",
                        axes=[axis],
                    )
                    new_nodes.extend([idx_const, new_gather, squeeze])
                else:
                    axes_name = f"{idx_1d_name}_sq_axes"
                    axes_const = helper.make_node(
                        "Constant",
                        inputs=[],
                        outputs=[axes_name],
                        value=helper.make_tensor(axes_name, TensorProto.INT64, [1], [axis]),
                    )
                    squeeze = helper.make_node(
                        "Squeeze",
                        inputs=[gather_out, axes_name],
                        outputs=[n.output[0]],
                        name=(n.name or "gather") + "_squeeze",
                    )
                    new_nodes.extend([idx_const, axes_const, new_gather, squeeze])
                rewrote += 1
                continue
        new_nodes.append(n)
    if rewrote == 0:
        return False
    del graph.node[:]
    graph.node.extend(new_nodes)
    return True
 # ---------------------------------------------------------------------------
 # Helpers
 # ---------------------------------------------------------------------------
 def _preserve_emap_position(model, numpy_helper):
    """Keep the insightface emap (512×512 matrix) as the last initializer."""
    graph = model.graph
    emap_init = None
    for init in graph.initializer:
        if not init.name.startswith("_rp_"):
            arr = numpy_helper.to_array(init)
            if len(arr.shape) == 2 and arr.shape[0] == 512 and arr.shape[1] == 512:
                emap_init = init
                break
    if emap_init is not None:
        inits = [i for i in graph.initializer if i.name != emap_init.name]
        del graph.initializer[:]
        graph.initializer.extend(inits)
        graph.initializer.append(emap_init)
@@ -1,6 +0,0 @@
 """Shared path constants for the Deep-Live-Cam project."""
 import os
 ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 MODELS_DIR = os.path.join(ROOT_DIR, "models")
@@ -1,91 +0,0 @@
 """Centralized platform + accelerator detection.
 Imported once at startup to expose typed flags the rest of the codebase
 can branch on without re-querying `platform`, `torch.cuda`, or
 `onnxruntime.get_available_providers()` repeatedly.
 The banner printed by :func:`print_banner` is the single user-facing
 report of which code path the app will take.
 """
 from __future__ import annotations
 import platform as _platform
 import sys
 from typing import List, Tuple
 IS_WINDOWS: bool = _platform.system() == "Windows"
 IS_MACOS: bool = _platform.system() == "Darwin"
 IS_LINUX: bool = _platform.system() == "Linux"
 IS_APPLE_SILICON: bool = IS_MACOS and _platform.machine() == "arm64"
 def _detect_torch_cuda() -> bool:
    try:
        import torch  # noqa: WPS433 — local import, avoid hard dep at module load
        return bool(torch.cuda.is_available())
    except Exception:
        return False
 def _detect_onnx_providers() -> List[str]:
    try:
        import onnxruntime
        return list(onnxruntime.get_available_providers())
    except Exception:
        return []
 HAS_TORCH_CUDA: bool = _detect_torch_cuda()
 ONNX_PROVIDERS: List[str] = _detect_onnx_providers()
 HAS_CUDA_PROVIDER: bool = "CUDAExecutionProvider" in ONNX_PROVIDERS
 HAS_COREML_PROVIDER: bool = "CoreMLExecutionProvider" in ONNX_PROVIDERS
 HAS_DML_PROVIDER: bool = "DmlExecutionProvider" in ONNX_PROVIDERS
 HAS_OPENVINO_PROVIDER: bool = "OpenVINOExecutionProvider" in ONNX_PROVIDERS
 # OpenVINO execution-provider config shared by every ONNX session builder.
 # AUTO:GPU,NPU,CPU lets OpenVINO pick the best available device in priority
 # order (Intel GPU → NPU → CPU).
 OPENVINO_PROVIDER_CONFIG = (
    "OpenVINOExecutionProvider",
    {"device_type": "AUTO:GPU,NPU,CPU"},
 )
 def camera_backends() -> List[Tuple[int, int]]:
    """Return an ordered list of ``(device_index, cv2_backend)`` attempts.
    Windows prefers MSMF (60fps capable) with DirectShow as fallback.
    macOS/Linux use the default backend (AVFoundation / V4L2).
    """
    import cv2
    if IS_WINDOWS:
        return [
            (0, cv2.CAP_MSMF),
            (0, cv2.CAP_DSHOW),
            (0, cv2.CAP_ANY),
        ]
    return [(0, cv2.CAP_ANY)]
 def accelerator_label() -> str:
    if HAS_CUDA_PROVIDER:
        return "CUDA (NVIDIA)"
    if IS_APPLE_SILICON and HAS_COREML_PROVIDER:
        return "CoreML (Apple Neural Engine)"
    if HAS_COREML_PROVIDER:
        return "CoreML"
    if HAS_OPENVINO_PROVIDER:
        return "OpenVINO (Intel)"
    if HAS_DML_PROVIDER:
        return "DirectML"
    return "CPU"
 def print_banner() -> None:
    """Print a one-line summary of the platform + accelerator selection."""
    os_label = f"{_platform.system()} {_platform.machine()}"
    print(
        f"[platform] {os_label} | python {sys.version.split()[0]} | "
        f"accelerator: {accelerator_label()} | providers: {ONNX_PROVIDERS}",
        flush=True,
    )
@@ -1,22 +1,8 @@
 import importlib.util
 import os
 import numpy
 # Keras 3 defaults to the TensorFlow backend, which has no Python 3.14 wheels.
 # opennsfw2 only runs inference, so any installed backend works; pick one that
 # is actually present before opennsfw2 imports keras.
 if "KERAS_BACKEND" not in os.environ:
    for _backend in ("torch", "tensorflow", "jax"):
        if importlib.util.find_spec(_backend) is not None:
            os.environ["KERAS_BACKEND"] = _backend
            break
 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
@@ -28,7 +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)
@@ -1,244 +0,0 @@
 """Shared ONNX-based face enhancement utilities for GPEN-BFR models.
 Provides session creation, pre/post processing, and the core
 enhance-face-via-ONNX pipeline.
 """
 import os
 import platform
 import threading
 from typing import Any
 import cv2
 import numpy as np
 import onnxruntime
 import modules.globals
 from modules.platform_info import OPENVINO_PROVIDER_CONFIG
 IS_APPLE_SILICON = platform.system() == "Darwin" and platform.machine() == "arm64"
 # Limit concurrent ONNX calls to avoid VRAM exhaustion on multi-face frames
 THREAD_SEMAPHORE = threading.Semaphore(min(max(1, (os.cpu_count() or 1)), 8))
 def build_provider_config(providers=None):
    """Wrap raw provider name strings with optimised CUDA / CoreML options.
    Providers that are already ``(name, options_dict)`` tuples are passed
    through unchanged.  Non-CUDA providers are left as bare strings.
    """
    if providers is None:
        providers = modules.globals.execution_providers
    config = []
    for p in providers:
        if isinstance(p, tuple):
            # Already configured – pass through
            config.append(p)
        elif p == "CUDAExecutionProvider":
            # Use bare provider — ONNX Runtime's defaults are fastest on
            # modern GPUs (Blackwell/sm_120).  Custom options like
            # EXHAUSTIVE cudnn_conv_algo_search hurt performance on these
            # architectures.
            config.append(p)
        elif p == "CoreMLExecutionProvider" and IS_APPLE_SILICON:
            config.append((
                "CoreMLExecutionProvider",
                {
                    "ModelFormat": "MLProgram",
                    "MLComputeUnits": "ALL",
                    "AllowLowPrecisionAccumulationOnGPU": 1,
                },
            ))
        elif p == "OpenVINOExecutionProvider":
            # AUTO lets OpenVINO select the best device
            config.append(OPENVINO_PROVIDER_CONFIG)
        else:
            config.append(p)
    return config
 def run_inference(session: onnxruntime.InferenceSession,
                  input_name: str,
                  input_tensor: "np.ndarray") -> "np.ndarray":
    """Run ONNX inference, using IO binding when a CUDA session is active.
    IO binding avoids redundant host↔device copies by transferring the
    input tensor directly to GPU memory and letting ONNX Runtime allocate
    the output on the device.  Falls back to the standard ``session.run``
    path for non-CUDA providers or if binding fails.
    """
    if "CUDAExecutionProvider" in session.get_providers():
        try:
            io_binding = session.io_binding()
            # Input: numpy → GPU
            ort_input = onnxruntime.OrtValue.ortvalue_from_numpy(
                input_tensor, "cuda", 0,
            )
            io_binding.bind_ortvalue_input(input_name, ort_input)
            # Output: allocate on GPU (avoids a CPU-side allocation)
            output_name = session.get_outputs()[0].name
            io_binding.bind_output(output_name, "cuda", 0)
            session.run_with_iobinding(io_binding)
            return io_binding.get_outputs()[0].numpy()
        except Exception:
            # Fall back to standard path (e.g. ORT version mismatch,
            # unsupported op, or VRAM pressure)
            pass
    return session.run(None, {input_name: input_tensor})[0]
 def create_onnx_session(model_path: str) -> onnxruntime.InferenceSession:
    """Create an ONNX Runtime session with optimised provider config.
    On Apple Silicon, applies CoreML graph optimizations (Pad decomposition,
    Shape/Gather folding, Split decomposition) to reduce CPU↔ANE partition
    boundaries.
    """
    if IS_APPLE_SILICON:
        from modules.onnx_optimize import optimize_for_coreml
        # Infer input shape from the model for Shape/Gather folding
        try:
            import onnx
            m = onnx.load(model_path)
            inp = m.graph.input[0]
            dims = inp.type.tensor_type.shape.dim
            shape = tuple(d.dim_value for d in dims if d.dim_value > 0)
            input_shape = shape if len(shape) == 4 else None
        except Exception:
            input_shape = None
        model_path = optimize_for_coreml(model_path, input_shape=input_shape)
    providers = build_provider_config()
    session_options = onnxruntime.SessionOptions()
    session_options.graph_optimization_level = (
        onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
    )
    session = onnxruntime.InferenceSession(
        model_path, sess_options=session_options, providers=providers,
    )
    return session
 def warmup_session(session: onnxruntime.InferenceSession) -> None:
    """Run a dummy inference pass to trigger JIT / compile caching."""
    try:
        input_feed = {
            inp.name: np.zeros(
                [d if isinstance(d, int) and d > 0 else 1 for d in inp.shape],
                dtype=np.float32,
            )
            for inp in session.get_inputs()
        }
        session.run(None, input_feed)
    except Exception as e:
        print(f"ONNX enhancer warmup skipped (non-fatal): {e}")
 def preprocess_face(face_img: np.ndarray, input_size: int) -> np.ndarray:
    """Resize, normalize, and convert a BGR face crop to ONNX input blob.
    GPEN-BFR expects [1, 3, H, W] float32 in RGB, normalized to [-1, 1].
    """
    resized = cv2.resize(face_img, (input_size, input_size), interpolation=cv2.INTER_LINEAR)
    rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
    blob = rgb.astype(np.float32) / 255.0 * 2.0 - 1.0
    blob = np.transpose(blob, (2, 0, 1))[np.newaxis, ...]
    return blob
 def postprocess_face(output: np.ndarray) -> np.ndarray:
    """Convert ONNX output [1, 3, H, W] float32 back to BGR uint8 image."""
    img = output[0].transpose(1, 2, 0)
    img = ((img + 1.0) / 2.0 * 255.0)
    img = np.clip(img, 0, 255).astype(np.uint8)
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    return img
 def _get_face_affine(face: Any, input_size: int):
    """Compute affine transform to align a face to GPEN input space.
    Returns (M, inv_M) — forward and inverse affine matrices.
    """
    template = np.array([
        [0.31556875, 0.4615741],
        [0.68262291, 0.4615741],
        [0.50009375, 0.6405054],
        [0.34947187, 0.8246919],
        [0.65343645, 0.8246919],
    ], dtype=np.float32) * input_size
    landmarks = None
    if hasattr(face, "kps") and face.kps is not None:
        landmarks = face.kps.astype(np.float32)
    elif hasattr(face, "landmark_2d_106") and face.landmark_2d_106 is not None:
        lm106 = face.landmark_2d_106
        landmarks = np.array([
            lm106[38],  # left eye
            lm106[88],  # right eye
            lm106[86],  # nose tip
            lm106[52],  # left mouth
            lm106[61],  # right mouth
        ], dtype=np.float32)
    if landmarks is None or len(landmarks) < 5:
        return None, None
    M = cv2.estimateAffinePartial2D(landmarks, template, method=cv2.LMEDS)[0]
    if M is None:
        return None, None
    inv_M = cv2.invertAffineTransform(M)
    return M, inv_M
 def enhance_face_onnx(
    frame: np.ndarray,
    face: Any,
    session: onnxruntime.InferenceSession,
    input_size: int,
 ) -> np.ndarray:
    """Enhance a single face in the frame using an ONNX face restoration model."""
    M, inv_M = _get_face_affine(face, input_size)
    if M is None:
        return frame
    face_crop = cv2.warpAffine(
        frame, M, (input_size, input_size),
        flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REPLICATE,
    )
    blob = preprocess_face(face_crop, input_size)
    with THREAD_SEMAPHORE:
        input_name = session.get_inputs()[0].name
        output = run_inference(session, input_name, blob)
    enhanced = postprocess_face(output)
    # Create mask for blending (feathered edges)
    mask = np.ones((input_size, input_size), dtype=np.float32)
    border = max(1, input_size // 16)
    mask[:border, :] = np.linspace(0, 1, border)[:, np.newaxis]
    mask[-border:, :] = np.linspace(1, 0, border)[:, np.newaxis]
    mask[:, :border] = np.minimum(mask[:, :border], np.linspace(0, 1, border)[np.newaxis, :])
    mask[:, -border:] = np.minimum(mask[:, -border:], np.linspace(1, 0, border)[np.newaxis, :])
    h, w = frame.shape[:2]
    warped_enhanced = cv2.warpAffine(
        enhanced, inv_M, (w, h),
        flags=cv2.INTER_LINEAR, borderValue=(0, 0, 0),
    )
    warped_mask = cv2.warpAffine(
        mask, inv_M, (w, h),
        flags=cv2.INTER_LINEAR, borderValue=0,
    )
    mask_3ch = warped_mask[:, :, np.newaxis]
    result = (warped_enhanced.astype(np.float32) * mask_3ch +
              frame.astype(np.float32) * (1.0 - mask_3ch))
    return np.clip(result, 0, 255).astype(np.uint8)
@@ -1,17 +1,12 @@
 import os
 import subprocess
 import sys
 import importlib
 from concurrent.futures import ThreadPoolExecutor
 from types import ModuleType
 from typing import Any, List, Callable
 import numpy as np
 from tqdm import tqdm
 import modules
 import modules.globals                   
 from modules.face_analyser import get_one_face
 FRAME_PROCESSORS_MODULES: List[ModuleType] = []
 FRAME_PROCESSORS_INTERFACE = [
@@ -22,22 +17,12 @@ FRAME_PROCESSORS_INTERFACE = [
    'process_video'
 ]
 ALLOWED_PROCESSORS = {
    'face_swapper',
    'face_enhancer',
    'face_enhancer_gpen256',
    'face_enhancer_gpen512'
 }
 def load_frame_processor_module(frame_processor: str) -> Any:
    if frame_processor not in ALLOWED_PROCESSORS:
        print(f"Frame processor {frame_processor} is not allowed")
        sys.exit()
    try:
        frame_processor_module = importlib.import_module(f'modules.processors.frame.{frame_processor}')
        for method_name in FRAME_PROCESSORS_INTERFACE:
            if not hasattr(frame_processor_module, method_name):
                print(f"Frame processor {frame_processor} is missing required method {method_name}")
                sys.exit()
    except ImportError:
        print(f"Frame processor {frame_processor} not found")
@@ -57,54 +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 and frame_processor not in current_processor_names:
+        if state == True and frame_processor not in frame_processors:
            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:
+        if state == False:
                 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 not state 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)
+                frame_processor_module = load_frame_processor_module(frame_processor)
-                if module_to_remove:
+                FRAME_PROCESSORS_MODULES.remove(frame_processor_module)
                    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:
+            except:
-                 print(f"Warning: Error removing frame processor {frame_processor}: {e}")
+                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."""
+    with ThreadPoolExecutor(max_workers=modules.globals.execution_threads) as executor:
    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 temp_frame_paths:
            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}")
 def process_video(source_path: str, frame_paths: list[str], process_frames: Callable[[str, List[str], Any], None]) -> None:
@@ -113,295 +71,3 @@ def process_video(source_path: str, frame_paths: list[str], process_frames: Call
    with tqdm(total=total, desc='Processing', unit='frame', dynamic_ncols=True, bar_format=progress_bar_format) as progress:
        progress.set_postfix({'execution_providers': modules.globals.execution_providers, 'execution_threads': modules.globals.execution_threads, 'max_memory': modules.globals.max_memory})
        multi_process_frame(source_path, frame_paths, process_frames, progress)
 def process_video_in_memory(source_path: str, target_path: str, fps: float) -> bool:
    """Process video frames in-memory using FFmpeg pipes, eliminating disk I/O.
    Reads raw frames from the source video via an FFmpeg decoder pipe, runs each
    frame through all active frame processors sequentially, and writes the
    result directly to an FFmpeg encoder pipe.  This avoids extracting frames to
    PNG on disk, which is the biggest I/O bottleneck in the disk-based pipeline.
    Returns True on success, False on failure (caller should fall back to the
    disk-based pipeline).
    """
    from modules import imread_unicode
    from modules.face_analyser import get_one_face
    from modules.utilities import (
        get_video_dimensions,
        estimate_frame_count,
        get_temp_output_path,
    )
    temp_output_path = get_temp_output_path(target_path)
    # --- Pre-load source face (needed by face_swapper in simple mode) ---
    source_face = None
    if source_path and os.path.exists(source_path):
        source_img = imread_unicode(source_path)
        if source_img is not None:
            source_face = get_one_face(source_img)
            del source_img
        if source_face is None:
            print("[DLC.CORE] Warning: No face detected in source image. "
                  "Face swapping will be skipped.")
    # --- Collect frame processors & reset per-video state ---
    frame_processors = get_frame_processors_modules(modules.globals.frame_processors)
    for fp in frame_processors:
        if hasattr(fp, 'PREVIOUS_FRAME_RESULT'):
            fp.PREVIOUS_FRAME_RESULT = None
    # --- Video metadata ---
    try:
        width, height = get_video_dimensions(target_path)
    except Exception as e:
        print(f"[DLC.CORE] Failed to get video dimensions: {e}")
        return False
    total_frames = estimate_frame_count(target_path, fps)
    frame_size = width * height * 3
    # --- Build encoder arguments ---
    encoder = modules.globals.video_encoder
    encoder_options: List[str] = []
    is_hw_encoder = False
    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
        if encoder == 'libx264':
            encoder = 'h264_nvenc'
            is_hw_encoder = True
            encoder_options = [
                '-preset', 'p4', '-tune', 'hq', '-rc', 'vbr',
                '-cq', str(modules.globals.video_quality), '-b:v', '0',
            ]
        elif encoder == 'libx265':
            encoder = 'hevc_nvenc'
            is_hw_encoder = True
            encoder_options = [
                '-preset', 'p4', '-tune', 'hq', '-rc', 'vbr',
                '-cq', str(modules.globals.video_quality), '-b:v', '0',
            ]
    elif 'DmlExecutionProvider' in modules.globals.execution_providers:
        if encoder == 'libx264':
            encoder = 'h264_amf'
            is_hw_encoder = True
            encoder_options = [
                '-quality', 'quality', '-rc', 'vbr_latency',
                '-qp_i', str(modules.globals.video_quality),
                '-qp_p', str(modules.globals.video_quality),
            ]
        elif encoder == 'libx265':
            encoder = 'hevc_amf'
            is_hw_encoder = True
            encoder_options = [
                '-quality', 'quality', '-rc', 'vbr_latency',
                '-qp_i', str(modules.globals.video_quality),
                '-qp_p', str(modules.globals.video_quality),
            ]
    if not is_hw_encoder:
        if encoder == 'libx264':
            encoder_options = [
                '-preset', 'medium',
                '-crf', str(modules.globals.video_quality),
                '-tune', 'film',
            ]
        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', '-cpu-used', '2',
            ]
    # --- Attempt pipeline (hw encoder first, then sw fallback) ---
    encoders_to_try = [(encoder, encoder_options)]
    if is_hw_encoder:
        # Software fallback
        sw_encoder = 'libx264'
        sw_options = [
            '-preset', 'medium',
            '-crf', str(modules.globals.video_quality),
            '-tune', 'film',
        ]
        encoders_to_try.append((sw_encoder, sw_options))
    for attempt, (enc, enc_opts) in enumerate(encoders_to_try):
        # Reset interpolation state on retry
        if attempt > 0:
            for fp in frame_processors:
                if hasattr(fp, 'PREVIOUS_FRAME_RESULT'):
                    fp.PREVIOUS_FRAME_RESULT = None
        success = _run_pipe_pipeline(
            target_path, temp_output_path, fps,
            source_face, frame_processors,
            width, height, frame_size, total_frames,
            enc, enc_opts,
        )
        if success:
            return True
        if attempt == 0 and is_hw_encoder:
            print(f"[DLC.CORE] Hardware encoder '{enc}' failed, "
                  f"retrying with software encoder...")
    return False
 def _run_pipe_pipeline(
    target_path: str,
    temp_output_path: str,
    fps: float,
    source_face: Any,
    frame_processors: List[Any],
    width: int,
    height: int,
    frame_size: int,
    total_frames: int,
    encoder: str,
    encoder_options: List[str],
 ) -> bool:
    """Run the FFmpeg-pipe read → process → encode pipeline once."""
    # --- Reader: decode source video to raw BGR24 on stdout ---
    reader_cmd = [
        'ffmpeg', '-hide_banner',
        '-hwaccel', 'auto',
        '-i', target_path,
        '-f', 'rawvideo',
        '-pix_fmt', 'bgr24',
        '-v', 'error',
        '-',
    ]
    # --- Writer: encode raw BGR24 from stdin ---
    writer_cmd = [
        'ffmpeg', '-hide_banner',
        '-f', 'rawvideo',
        '-pix_fmt', 'bgr24',
        '-s', f'{width}x{height}',
        '-r', str(fps),
        '-i', '-',
        '-c:v', encoder,
    ]
    writer_cmd.extend(encoder_options)
    writer_cmd.extend([
        '-pix_fmt', 'yuv420p',
        '-movflags', '+faststart',
        '-vf', 'colorspace=bt709:iall=bt601-6-625:fast=1',
        '-v', 'error',
        '-y', temp_output_path,
    ])
    reader = None
    writer = None
    try:
        reader = subprocess.Popen(
            reader_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
        )
        writer = subprocess.Popen(
            writer_cmd, stdin=subprocess.PIPE, stderr=subprocess.PIPE,
        )
    except Exception as e:
        print(f"[DLC.CORE] Failed to start FFmpeg pipes: {e}")
        for proc in (reader, writer):
            if proc:
                try:
                    proc.kill()
                except Exception:
                    pass
        return False
    processed_count = 0
    bar_fmt = ('{l_bar}{bar}| {n_fmt}/{total_fmt} '
               '[{elapsed}<{remaining}, {rate_fmt}{postfix}]')
    try:
        with tqdm(total=total_frames, desc='Processing', unit='frame',
                  dynamic_ncols=True, bar_format=bar_fmt) as progress:
            progress.set_postfix({
                'execution_providers': modules.globals.execution_providers,
                'threads': modules.globals.execution_threads,
                'mode': 'in-memory',
            })
            # Pipelined detection: while processing frame N (swap on
            # ANE), start detecting the face in the next frame
            # (detection on GPU).  They use different hardware units
            # so the work overlaps.
            detect_executor = ThreadPoolExecutor(max_workers=1)
            pending_detect = None
            use_pipeline = not modules.globals.many_faces
            while True:
                raw = reader.stdout.read(frame_size)
                if len(raw) != frame_size:
                    break
                frame = np.frombuffer(raw, dtype=np.uint8).reshape(
                    (height, width, 3)
                ).copy()
                # Get the detection result for THIS frame
                if use_pipeline:
                    if pending_detect is not None:
                        target_face = pending_detect.result()
                    else:
                        target_face = get_one_face(frame)
                    # Start detecting on THIS frame eagerly — the result
                    # will be used for the next iteration.  At video
                    # frame rates the face barely moves between frames.
                    # Hand the detector its own copy: the frame processors
                    # below mutate `frame` in place (paste-back), which
                    # would otherwise race with detection.
                    pending_detect = detect_executor.submit(
                        get_one_face, frame.copy())
                else:
                    target_face = None
                # Run frame through every active processor
                for fp in frame_processors:
                    try:
                        frame = fp.process_frame(source_face, frame, target_face=target_face)
                    except TypeError:
                        frame = fp.process_frame(source_face, frame)
                writer.stdin.write(frame.tobytes())
                processed_count += 1
                progress.update(1)
            detect_executor.shutdown(wait=True)
        # Graceful shutdown
        writer.stdin.close()
        writer.wait()
        reader.wait()
        if writer.returncode != 0:
            stderr_out = writer.stderr.read().decode(errors='ignore').strip()
            if stderr_out:
                print(f"[DLC.CORE] FFmpeg encoder error: {stderr_out}")
            return False
        return processed_count > 0 and os.path.isfile(temp_output_path)
    except BrokenPipeError:
        print("[DLC.CORE] FFmpeg pipe broken (encoder may not be available).")
        return False
    except Exception as e:
        print(f"[DLC.CORE] In-memory processing error: {e}")
        return False
    finally:
        for proc in (reader, writer):
            if proc:
                try:
                    proc.kill()
                except Exception:
                    pass
@@ -1,20 +1,18 @@
 # 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 import imread_unicode, imwrite_unicode
 from modules.core import update_status
-from modules.face_analyser import 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,
 )
@@ -23,37 +21,21 @@ FACE_ENHANCER = None
 THREAD_SEMAPHORE = threading.Semaphore()
 THREAD_LOCK = threading.Lock()
 NAME = "DLC.FACE-ENHANCER"
 MODEL_FILE = "gfpgan-1024.onnx"
 abs_dir = os.path.dirname(os.path.abspath(__file__))
 models_dir = os.path.join(
    os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
 )
 # 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:
-    from modules.model_downloader import ensure_model
+    download_directory_path = models_dir
-
+    conditional_download(
-    if ensure_model(MODEL_FILE) is None:
+        download_directory_path,
-        update_status(
+        [
-            f"Could not obtain {MODEL_FILE}. Place it in the models folder "
+            "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth"
-            "manually or check your internet connection.",
+        ],
            NAME,
    )
        return False
    return True
@@ -66,386 +48,62 @@ def pre_start() -> bool:
    return True
-def get_face_enhancer() -> onnxruntime.InferenceSession:
+def get_face_enhancer() -> Any:
    """
    Initializes and returns the GFPGAN ONNX Runtime inference session,
    using the execution providers configured in modules.globals.
    """
    global FACE_ENHANCER
    with THREAD_LOCK:
        if FACE_ENHANCER is None:
-            from modules.model_downloader import ensure_model
+            model_path = os.path.join(models_dir, "GFPGANv1.4.pth")
-            model_path = ensure_model(MODEL_FILE)
+            match platform.system():
-
+                case "Darwin":  # Mac OS
-            if model_path is None:
+                    if torch.backends.mps.is_available():
-                raise FileNotFoundError(
+                        mps_device = torch.device("mps")
-                    f"{NAME}: Model not found at "
+                        FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=mps_device)  # type: ignore[attr-defined]
-                    f"{os.path.join(models_dir, MODEL_FILE)} and could not be "
+                    else:
-                    "downloaded"
+                        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]
            try:
                from modules.processors.frame._onnx_enhancer import (
                    create_onnx_session,
                )
                FACE_ENHANCER = create_onnx_session(model_path)
                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."
        )
    return FACE_ENHANCER
-def _align_face(
+def enhance_face(temp_frame: Frame) -> Frame:
    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
 _HAS_TORCH_CUDA = False
 try:
    import torch
    if torch.cuda.is_available():
        _HAS_TORCH_CUDA = True
 except ImportError:
    pass
 # Cache the feathered mask — it's the same for every call at a given size
 _enhancer_cache: dict = {'mask': None, 'mask_size': 0}
 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.
    Optimized: operates on a tight crop around the face bbox instead of the
    full frame, and uses GPU for blending when available.
    """
    h, w = frame.shape[:2]
    inv_matrix = cv2.invertAffineTransform(affine_matrix)
    # Build or reuse cached feathered mask (uint8 — blended via cv2 SIMD ops)
    if _enhancer_cache['mask_size'] != output_size:
        face_mask_f = np.ones((output_size, output_size), dtype=np.float32)
        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)
        face_mask_f[:border, :] *= ramp_up[:, None]
        face_mask_f[-border:, :] *= ramp_down[:, None]
        face_mask_f[:, :border] *= ramp_up[None, :]
        face_mask_f[:, -border:] *= ramp_down[None, :]
        _enhancer_cache['mask'] = (face_mask_f * 255.0).astype(np.uint8)
        _enhancer_cache['mask_size'] = output_size
    # Compute tight bbox from affine corners (avoids full-frame warpAffine scan)
    corners = np.array([[0, 0], [output_size, 0],
                        [output_size, output_size], [0, output_size]],
                       dtype=np.float32)
    transformed = (inv_matrix[:, :2] @ corners.T).T + inv_matrix[:, 2]
    x1 = max(0, int(np.floor(transformed[:, 0].min())))
    x2 = min(w, int(np.ceil(transformed[:, 0].max())))
    y1 = max(0, int(np.floor(transformed[:, 1].min())))
    y2 = min(h, int(np.ceil(transformed[:, 1].max())))
    if x1 >= x2 or y1 >= y2:
        return frame
    # Pad a few pixels for feathering
    pad = max(1, int(output_size * 0.05)) + 2
    y1p, y2p = max(0, y1 - pad), min(h, y2 + pad)
    x1p, x2p = max(0, x1 - pad), min(w, x2 + pad)
    crop_w, crop_h = x2p - x1p, y2p - y1p
    # Warp enhanced face and mask into crop space only
    inv_crop = inv_matrix.copy()
    inv_crop[0, 2] -= x1p
    inv_crop[1, 2] -= y1p
    inv_restored_crop = cv2.warpAffine(
        enhanced_face, inv_crop, (crop_w, crop_h),
        borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0),
    )
    inv_mask_crop = cv2.warpAffine(
        _enhancer_cache['mask'], inv_crop, (crop_w, crop_h),
        borderMode=cv2.BORDER_CONSTANT, borderValue=0,
    )
    target_crop = frame[y1p:y2p, x1p:x2p]
    if _HAS_TORCH_CUDA:
        # Upload uint8 alpha — smaller transfer, scale on device.
        mask_t = torch.from_numpy(inv_mask_crop).cuda().float().mul_(1.0 / 255.0).unsqueeze(2)
        enhanced_t = torch.from_numpy(inv_restored_crop).float().cuda()
        target_t = torch.from_numpy(target_crop).float().cuda()
        blended = (mask_t * enhanced_t + (1.0 - mask_t) * target_t
                   ).to(torch.uint8).cpu().numpy()
        frame[y1p:y2p, x1p:x2p] = blended
    else:
        # Fused uint8 blend via cv2 SIMD — ~7× faster than the float32 round-trip.
        alpha_3c = cv2.merge([inv_mask_crop, inv_mask_crop, inv_mask_crop])
        inv_alpha = 255 - alpha_3c
        a_enh = cv2.multiply(inv_restored_crop, alpha_3c, scale=1.0 / 255.0)
        a_tgt = cv2.multiply(target_crop, inv_alpha, scale=1.0 / 255.0)
        frame[y1p:y2p, x1p:x2p] = cv2.add(a_enh, a_tgt)
    return frame
 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, normalize, and transpose in one pass
    # Fused: (x / 255.0 - 0.5) / 0.5 = x / 127.5 - 1.0
    rgb = aligned_face[:, :, ::-1]  # BGR->RGB zero-copy view
    chw = np.transpose(rgb, (2, 0, 1)).astype(np.float32)
    chw *= (1.0 / 127.5)
    chw -= 1.0
    return chw[np.newaxis, ...]  # 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].
    """
    # Fused: ((x + 1.0) / 2.0) * 255 = (x + 1.0) * 127.5
    face = output[0]  # remove batch dim -> (3, H, W)
    face = (face + 1.0) * 127.5
    np.clip(face, 0, 255, out=face)
    face = face.astype(np.uint8).transpose(1, 2, 0)  # CHW -> HWC
    return face[:, :, ::-1].copy()  # RGB -> BGR
 # Cache for temporal enhancement skipping in live mode.
 # GFPGAN output barely changes between consecutive frames (same face,
 # same position), so we run inference every _ENH_INTERVAL frames and
 # reuse the cached enhanced face + affine matrix in between.
 _enh_live_cache: dict = {
    'enhanced_bgr': None,
    'affine_matrix': None,
    'align_size': 0,
    'frame_count': 0,
 }
 _ENH_INTERVAL = 2  # run inference every N frames, paste cached result otherwise
 def enhance_face(temp_frame: Frame, detected_faces=None) -> Frame:
    """Enhances all faces in a frame using the GFPGAN ONNX model.
    Args:
        detected_faces: Pre-detected face list. When provided, skips
            the internal detection call (saves ~15-20ms per frame).
            Also enables temporal caching — inference runs every
            _ENH_INTERVAL frames, reusing the cached result otherwise.
    """
    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]
    try:
        align_size = int(input_shape[2])
        if align_size <= 0:
            align_size = 512
    except (ValueError, TypeError, IndexError):
        align_size = 512
    # Use pre-detected faces if available, otherwise detect
    faces = detected_faces if detected_faces is not None else get_many_faces(temp_frame)
    if not faces:
        return temp_frame
    # Temporal caching: only available when faces are pre-detected (live mode)
    # AND we're in single-face mode — the cache holds exactly one enhancement,
    # so reusing it in many_faces mode would paste the same face onto every
    # detected target.
    many_faces_mode = getattr(modules.globals, "many_faces", False)
    use_cache = detected_faces is not None and not many_faces_mode
    if use_cache:
        _enh_live_cache['frame_count'] += 1
        run_inference_this_frame = (_enh_live_cache['frame_count'] % _ENH_INTERVAL == 0
                                   or _enh_live_cache['enhanced_bgr'] is None)
    else:
        run_inference_this_frame = True
    for face in faces:
        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
        if run_inference_this_frame:
            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:
-                    from modules.processors.frame._onnx_enhancer import (
+        _, _, temp_frame = get_face_enhancer().enhance(temp_frame, paste_back=True)
                        run_inference,
                    )
                    input_tensor = _preprocess_face(aligned_face)
                    output_tensor = run_inference(session, input_name, input_tensor)
                    enhanced_bgr = _postprocess_face(output_tensor)
                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,
                    )
                # Cache for reuse on next frame
                if use_cache:
                    _enh_live_cache['enhanced_bgr'] = enhanced_bgr
                    _enh_live_cache['affine_matrix'] = affine_matrix
                    _enh_live_cache['align_size'] = align_size
                _paste_back(
                    temp_frame, enhanced_bgr, affine_matrix, output_size=align_size
                )
            except Exception as e:
                print(f"{NAME}: Error enhancing a face: {e}")
                continue
        else:
            # Reuse cached enhanced face — just paste back onto current frame
            cached = _enh_live_cache
            if cached['enhanced_bgr'] is not None:
                _paste_back(
                    temp_frame, cached['enhanced_bgr'],
                    cached['affine_matrix'],
                    output_size=cached['align_size'],
                )
        if not many_faces_mode:
            break  # single-face live mode — only process first face
    return temp_frame
-def process_frame(source_face: Face | None, temp_frame: Frame,
+def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
-                   detected_faces=None) -> Frame:
+    target_face = get_one_face(temp_frame)
-    """Processes a frame: enhances face if detected."""
+    if target_face:
-    return enhance_face(temp_frame, detected_faces=detected_faces)
+        temp_frame = enhance_face(temp_frame)
-
+    return temp_frame
 def process_frame_v2(temp_frame: Frame, detected_faces=None) -> Frame:
    """Processes a frame without source face (used by live webcam preview)."""
    return enhance_face(temp_frame, detected_faces=detected_faces)
 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):
+        temp_frame = cv2.imread(temp_frame_path)
-            print(
+        result = process_frame(None, temp_frame)
-                f"{NAME}: Warning: Frame path not found {temp_frame_path}, skipping."
+        cv2.imwrite(temp_frame_path, result)
            )
            if progress:
                progress.update(1)
            continue
        temp_frame = imread_unicode(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)
        imwrite_unicode(temp_frame_path, result_frame)
        if progress:
            progress.update(1)
-def process_image(
+def process_image(source_path: str, target_path: str, output_path: str) -> None:
-    source_path: str | None, target_path: str, output_path: str
+    target_frame = cv2.imread(target_path)
-) -> None:
+    result = process_frame(None, target_frame)
-    """Processes a single image file."""
+    cv2.imwrite(output_path, result)
    target_frame = imread_unicode(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)
    imwrite_unicode(output_path, result_frame)
    print(f"{NAME}: Enhanced image saved to {output_path}")
-def process_video(
+def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
-    source_path: str | None, temp_frame_paths: List[str]
+    modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
-) -> None:
+
-    """Processes video frames using the frame processor core."""
+
-    modules.processors.frame.core.process_video(
+def process_frame_v2(temp_frame: Frame) -> Frame:
-        source_path, temp_frame_paths, process_frames
+    target_face = get_one_face(temp_frame)
-    )
+    if target_face:
        temp_frame = enhance_face(temp_frame)
    return temp_frame
@@ -1,146 +0,0 @@
 """GPEN-BFR-256 face enhancer — ONNX-based face restoration at 256x256."""
 from typing import Any, List
 import os
 import threading
 import modules.globals
 import modules.processors.frame.core
 from modules import imread_unicode, imwrite_unicode
 from modules.core import update_status
 from modules.face_analyser import get_one_face
 from modules.typing import Frame, Face
 from modules.utilities import (
    is_image,
    is_video,
 )
 from modules.processors.frame._onnx_enhancer import (
    create_onnx_session,
    warmup_session,
    enhance_face_onnx,
 )
 NAME = "DLC.FACE-ENHANCER-GPEN256"
 INPUT_SIZE = 256
 MODEL_MIRROR_URL = "https://github.com/harisreedhar/Face-Upscalers-ONNX/releases/download/GPEN-BFR/GPEN-BFR-256.onnx"
 MODEL_FILE = "GPEN-BFR-256.onnx"
 ENHANCER = None
 THREAD_LOCK = threading.Lock()
 abs_dir = os.path.dirname(os.path.abspath(__file__))
 models_dir = os.path.join(
    os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
 )
 def _obtain_model():
    from modules.model_downloader import ensure_model
    model_path = ensure_model(MODEL_FILE)
    if model_path is not None:
        return model_path
    update_status(f"Retrying {MODEL_FILE} from the mirror...", NAME)
    from modules.utilities import conditional_download
    try:
        conditional_download(models_dir, [MODEL_MIRROR_URL])
    except Exception as error:
        update_status(f"Mirror download failed: {error}", NAME)
        return None
    fallback = os.path.join(models_dir, MODEL_FILE)
    return fallback if os.path.exists(fallback) else None
 def pre_check() -> bool:
    if _obtain_model() is None:
        update_status(
            f"Could not obtain {MODEL_FILE}. Place it in the models folder "
            "manually or check your internet connection.",
            NAME,
        )
        return False
    return True
 def pre_start() -> bool:
    if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
        update_status("Select an image or video for target path.", NAME)
        return False
    return True
 def get_enhancer() -> Any:
    global ENHANCER
    with THREAD_LOCK:
        if ENHANCER is None:
            model_path = _obtain_model()
            if model_path is None:
                raise FileNotFoundError(
                    f"Model file not found: {os.path.join(models_dir, MODEL_FILE)}"
                )
            print(f"{NAME}: Loading ONNX model from {model_path}")
            ENHANCER = create_onnx_session(model_path)
            warmup_session(ENHANCER)
            print(f"{NAME}: Model loaded successfully.")
    return ENHANCER
 def enhance_face(temp_frame: Frame, face: Face) -> Frame:
    try:
        session = get_enhancer()
    except Exception as e:
        print(f"{NAME}: {e}")
        return temp_frame
    try:
        return enhance_face_onnx(temp_frame, face, session, INPUT_SIZE)
    except Exception as e:
        print(f"{NAME}: Error during face enhancement: {e}")
        return temp_frame
 def process_frame(source_face: Face | None, temp_frame: Frame, detected_faces=None) -> Frame:
    if detected_faces:
        target_face = detected_faces[0]
    else:
        target_face = get_one_face(temp_frame)
    if target_face is None:
        return temp_frame
    return enhance_face(temp_frame, target_face)
 def process_frame_v2(temp_frame: Frame) -> Frame:
    target_face = get_one_face(temp_frame)
    if target_face:
        temp_frame = enhance_face(temp_frame, target_face)
    return temp_frame
 def process_frames(
    source_path: str | None, temp_frame_paths: List[str], progress: Any = None
 ) -> None:
    for temp_frame_path in temp_frame_paths:
        temp_frame = imread_unicode(temp_frame_path)
        if temp_frame is None:
            if progress:
                progress.update(1)
            continue
        result = process_frame(None, temp_frame)
        imwrite_unicode(temp_frame_path, result)
        if progress:
            progress.update(1)
 def process_image(source_path: str | None, target_path: str, output_path: str) -> None:
    target_frame = imread_unicode(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)
    imwrite_unicode(output_path, result_frame)
    print(f"{NAME}: Enhanced image saved to {output_path}")
 def process_video(source_path: str | None, temp_frame_paths: List[str]) -> None:
    modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)
@@ -1,146 +0,0 @@
 """GPEN-BFR-512 face enhancer — ONNX-based face restoration at 512x512."""
 from typing import Any, List
 import os
 import threading
 import modules.globals
 import modules.processors.frame.core
 from modules import imread_unicode, imwrite_unicode
 from modules.core import update_status
 from modules.face_analyser import get_one_face
 from modules.typing import Frame, Face
 from modules.utilities import (
    is_image,
    is_video,
 )
 from modules.processors.frame._onnx_enhancer import (
    create_onnx_session,
    warmup_session,
    enhance_face_onnx,
 )
 NAME = "DLC.FACE-ENHANCER-GPEN512"
 INPUT_SIZE = 512
 MODEL_MIRROR_URL = "https://github.com/harisreedhar/Face-Upscalers-ONNX/releases/download/GPEN-BFR/GPEN-BFR-512.onnx"
 MODEL_FILE = "GPEN-BFR-512.onnx"
 ENHANCER = None
 THREAD_LOCK = threading.Lock()
 abs_dir = os.path.dirname(os.path.abspath(__file__))
 models_dir = os.path.join(
    os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
 )
 def _obtain_model():
    from modules.model_downloader import ensure_model
    model_path = ensure_model(MODEL_FILE)
    if model_path is not None:
        return model_path
    update_status(f"Retrying {MODEL_FILE} from the mirror...", NAME)
    from modules.utilities import conditional_download
    try:
        conditional_download(models_dir, [MODEL_MIRROR_URL])
    except Exception as error:
        update_status(f"Mirror download failed: {error}", NAME)
        return None
    fallback = os.path.join(models_dir, MODEL_FILE)
    return fallback if os.path.exists(fallback) else None
 def pre_check() -> bool:
    if _obtain_model() is None:
        update_status(
            f"Could not obtain {MODEL_FILE}. Place it in the models folder "
            "manually or check your internet connection.",
            NAME,
        )
        return False
    return True
 def pre_start() -> bool:
    if not is_image(modules.globals.target_path) and not is_video(modules.globals.target_path):
        update_status("Select an image or video for target path.", NAME)
        return False
    return True
 def get_enhancer() -> Any:
    global ENHANCER
    with THREAD_LOCK:
        if ENHANCER is None:
            model_path = _obtain_model()
            if model_path is None:
                raise FileNotFoundError(
                    f"Model file not found: {os.path.join(models_dir, MODEL_FILE)}"
                )
            print(f"{NAME}: Loading ONNX model from {model_path}")
            ENHANCER = create_onnx_session(model_path)
            warmup_session(ENHANCER)
            print(f"{NAME}: Model loaded successfully.")
    return ENHANCER
 def enhance_face(temp_frame: Frame, face: Face) -> Frame:
    try:
        session = get_enhancer()
    except Exception as e:
        print(f"{NAME}: {e}")
        return temp_frame
    try:
        return enhance_face_onnx(temp_frame, face, session, INPUT_SIZE)
    except Exception as e:
        print(f"{NAME}: Error during face enhancement: {e}")
        return temp_frame
 def process_frame(source_face: Face | None, temp_frame: Frame, detected_faces=None) -> Frame:
    if detected_faces:
        target_face = detected_faces[0]
    else:
        target_face = get_one_face(temp_frame)
    if target_face is None:
        return temp_frame
    return enhance_face(temp_frame, target_face)
 def process_frame_v2(temp_frame: Frame) -> Frame:
    target_face = get_one_face(temp_frame)
    if target_face:
        temp_frame = enhance_face(temp_frame, target_face)
    return temp_frame
 def process_frames(
    source_path: str | None, temp_frame_paths: List[str], progress: Any = None
 ) -> None:
    for temp_frame_path in temp_frame_paths:
        temp_frame = imread_unicode(temp_frame_path)
        if temp_frame is None:
            if progress:
                progress.update(1)
            continue
        result = process_frame(None, temp_frame)
        imwrite_unicode(temp_frame_path, result)
        if progress:
            progress.update(1)
 def process_image(source_path: str | None, target_path: str, output_path: str) -> None:
    target_frame = imread_unicode(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)
    imwrite_unicode(output_path, result_frame)
    print(f"{NAME}: Enhanced image saved to {output_path}")
 def process_video(source_path: str | None, temp_frame_paths: List[str]) -> None:
    modules.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)
@@ -1,577 +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
 def apply_color_transfer(source, target):
    """
    Apply color transfer from target to source image using LAB color space.
    Uses float32 throughout for performance (sufficient precision for 8-bit images).
    """
    # Convert to float32 [0,1] range for proper LAB conversion
    source_f32 = source.astype(np.float32) / 255.0
    target_f32 = target.astype(np.float32) / 255.0
    source_lab = cv2.cvtColor(source_f32, cv2.COLOR_BGR2LAB)
    target_lab = cv2.cvtColor(target_f32, cv2.COLOR_BGR2LAB)
    source_mean, source_std = cv2.meanStdDev(source_lab)
    target_mean, target_std = cv2.meanStdDev(target_lab)
    # Reshape mean and std to be broadcastable (already float64 from meanStdDev, cast to f32)
    source_mean = source_mean.reshape(1, 1, 3).astype(np.float32)
    source_std = np.maximum(source_std.reshape(1, 1, 3), 1e-6).astype(np.float32)
    target_mean = target_mean.reshape(1, 1, 3).astype(np.float32)
    target_std = target_std.reshape(1, 1, 3).astype(np.float32)
    # Perform the color transfer in LAB space
    result_lab = (source_lab - source_mean) * (target_std / source_std) + target_mean
    # Convert back to BGR and uint8
    result_bgr = cv2.cvtColor(result_lab, cv2.COLOR_LAB2BGR)
    return np.clip(result_bgr * 255.0, 0, 255).astype(np.uint8)
 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)
        # Vectorized hull padding — expand each point outward from center
        center = np.mean(face_outline, axis=0, dtype=np.float32)
        hull_pts = hull.reshape(-1, 2).astype(np.float32)
        directions = hull_pts - center
        norms = np.linalg.norm(directions, axis=1, keepdims=True)
        norms = np.maximum(norms, 1e-6)  # avoid division by zero
        directions /= norms
        hull_padded = (hull_pts + directions * padding).astype(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 (52-71) to capture the full mouth area
        lower_lip_order = list(range(52, 72))
        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
        mouth_mask_size = getattr(modules.globals, "mouth_mask_size", 0.0) # 0-100 slider
        expansion_factor = 1 + (mouth_mask_size / 100.0) * 2.5
        # Expand with extra downward bias toward chin
        offsets = lower_lip_landmarks - center
        chin_bias = 1 + (mouth_mask_size / 100.0) * 1.5
        scale_y = np.where(offsets[:, 1] > 0, expansion_factor * chin_bias, expansion_factor)
        expanded_landmarks = lower_lip_landmarks.copy()
        expanded_landmarks[:, 0] = center[0] + offsets[:, 0] * expansion_factor
        expanded_landmarks[:, 1] = center[1] + offsets[:, 1] * scale_y
        # 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(np.float32), (0, 0), feather_amount
        )
        max_val = feathered_mask.max()
        if max_val > 1e-6:
            feathered_mask *= np.float32(1.0 / max_val)
        # 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.astype(np.float32) * np.float32(1.0 / 255.0))
        combined_mask_3ch = combined_mask[:, :, np.newaxis]
        inv_mask = np.float32(1.0) - combined_mask_3ch
        blended = (
            color_corrected_area * combined_mask_3ch + roi * inv_mask
        ).astype(np.uint8)
        # Apply face mask to blended result
        face_mask_f32 = face_mask_roi[:, :, np.newaxis].astype(np.float32) * np.float32(1.0 / 255.0)
        face_mask_3channel = np.broadcast_to(face_mask_f32, blended.shape)
        final_blend = blended * face_mask_3channel + roi * (np.float32(1.0) - 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
@@ -1,9 +0,0 @@
 #!/usr/bin/env python3
 # Import the tkinter fix to patch the ScreenChanged error (module patches Tk on import)
 import tkinter_fix  # noqa: F401
 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() 
@@ -1,74 +0,0 @@
 """Lightweight hover tooltip for CustomTkinter widgets."""
 import customtkinter as ctk
 class ToolTip:
    """Show a floating tooltip popup when the user hovers over a widget.
    Usage:
        ToolTip(my_button, "Helpful description text")
    """
    def __init__(self, widget: ctk.CTkBaseClass, text: str, delay: int = 500):
        self._widget = widget
        self._text = text
        self._delay = delay
        self._tooltip_window = None
        self._after_id = None
        widget.bind("<Enter>", self._schedule_show, add="+")
        widget.bind("<Leave>", self._hide, add="+")
    def _schedule_show(self, event=None):
        self._cancel()
        self._after_id = self._widget.after(self._delay, self._show)
    def _show(self):
        if self._tooltip_window is not None:
            return
        x = self._widget.winfo_rootx() + 20
        y = self._widget.winfo_rooty() + self._widget.winfo_height() + 5
        self._tooltip_window = tw = ctk.CTkToplevel(self._widget)
        tw.withdraw()
        tw.overrideredirect(True)
        label = ctk.CTkLabel(
            tw,
            text=self._text,
            fg_color="#333333",
            text_color="#EEEEEE",
            corner_radius=6,
            padx=8,
            pady=4,
        )
        label.pack()
        tw.update_idletasks()
        # Clamp to screen bounds
        screen_w = tw.winfo_screenwidth()
        screen_h = tw.winfo_screenheight()
        tip_w = tw.winfo_reqwidth()
        tip_h = tw.winfo_reqheight()
        if x + tip_w > screen_w:
            x = screen_w - tip_w - 5
        if y + tip_h > screen_h:
            y = self._widget.winfo_rooty() - tip_h - 5
        tw.geometry(f"+{x}+{y}")
        tw.deiconify()
    def _hide(self, event=None):
        self._cancel()
        if self._tooltip_window is not None:
            self._tooltip_window.destroy()
            self._tooltip_window = None
    def _cancel(self):
        if self._after_id is not None:
            self._widget.after_cancel(self._after_id)
            self._after_id = None
@@ -15,27 +15,26 @@ import modules.globals
 TEMP_FILE = "temp.mp4"
 TEMP_DIRECTORY = "temp"
 # monkey patch ssl for mac
 if platform.system().lower() == "darwin":
    ssl._create_default_https_context = ssl._create_unverified_context
 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",
-        "-hwaccel_output_format", "auto",  # Use hardware format when possible
+        "auto",
-        "-threads", str(modules.globals.execution_threads or 0),  # 0 = auto-detect optimal thread count
+        "-loglevel",
-        "-loglevel", modules.globals.log_level,
+        modules.globals.log_level,
    ]
    commands.extend(args)
    try:
        subprocess.check_output(commands, stderr=subprocess.STDOUT)
        return True
-    except subprocess.CalledProcessError as error:
+    except Exception:
-        output = error.output.decode(errors="ignore").strip()
+        pass
        if output:
            print(output)
    except Exception as error:
        print(f"ffmpeg execution failed: {error}")
    return False
@@ -62,132 +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)
    # Write a contiguous image sequence so the later "%04d.png" input pattern
    # used during encoding can consume every frame reliably.
    run_ffmpeg(
        [
-            "-i", target_path,
+            "-i",
-            "-vf", "format=rgb24",  # Use video filter for format conversion (faster)
+            target_path,
-            "-vsync", "0",  # Prevent frame duplication
+            "-pix_fmt",
            "rgb24",
            os.path.join(temp_directory_path, "%04d.png"),
        ]
    )
-def create_video(target_path: str, fps: float = 30.0) -> bool:
+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)
-    
+    run_ffmpeg(
-    # Determine optimal encoder based on available hardware
+        [
-    encoder = modules.globals.video_encoder
+            "-r",
-    encoder_options = []
+            str(fps),
-    
+            "-i",
-    # GPU-accelerated encoding options
+            os.path.join(temp_directory_path, "%04d.png"),
-    if 'CUDAExecutionProvider' in modules.globals.execution_providers:
+            "-c:v",
-        # NVIDIA GPU encoding
+            modules.globals.video_encoder,
-        if encoder == 'libx264':
+            "-crf",
-            encoder = 'h264_nvenc'
+            str(modules.globals.video_quality),
-            encoder_options = [
+            "-pix_fmt",
-                "-preset", "p7",  # Highest quality preset for NVENC
+            "yuv420p",
-                "-tune", "hq",  # High quality tuning
+            "-vf",
-                "-rc", "vbr",  # Variable bitrate
+            "colorspace=bt709:iall=bt601-6-625:fast=1",
                "-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",
            "-y",
            temp_output_path,
        ]
-        success = run_ffmpeg(ffmpeg_args_fallback)
+    )
    return success and os.path.isfile(temp_output_path)
 def restore_audio(target_path: str, output_path: str) -> None:
@@ -262,16 +168,11 @@ def clean_temp(target_path: str) -> None:
 def has_image_extension(image_path: str) -> bool:
-    # splitext so only the real extension counts (e.g. "photo.png.bak" is not
+    return image_path.lower().endswith(("png", "jpg", "jpeg"))
    # an image); the set is centralized in globals to stay in sync with dialogs.
    return os.path.splitext(image_path)[1].lower() in modules.globals.IMAGE_EXTENSIONS
 def is_image(image_path: str) -> bool:
    if image_path and os.path.isfile(image_path):
        # Extension check first — Windows mimetypes doesn't always register webp
        if has_image_extension(image_path):
            return True
        mimetype, _ = mimetypes.guess_type(image_path)
        return bool(mimetype and mimetype.startswith("image/"))
    return False
@@ -292,15 +193,8 @@ def conditional_download(download_directory_path: str, urls: List[str]) -> None:
            download_directory_path, os.path.basename(url)
        )
        if not os.path.exists(download_file_path):
-            request = urllib.request.Request(url)
+            request = urllib.request.urlopen(url)  # type: ignore[attr-defined]
-            
+            total = int(request.headers.get("Content-Length", 0))
            # Create a specific SSL context for macOS to avoid globally disabling verification
            ctx = None
            if platform.system().lower() == "darwin":
                ctx = ssl._create_unverified_context()
            response = urllib.request.urlopen(request, context=ctx)
            total = int(response.headers.get("Content-Length", 0))
            with tqdm(
                total=total,
                desc="Downloading",
@@ -308,46 +202,8 @@ def conditional_download(download_directory_path: str, urls: List[str]) -> None:
                unit_scale=True,
                unit_divisor=1024,
            ) as progress:
-                with open(download_file_path, "wb") as f:
+                urllib.request.urlretrieve(url, download_file_path, reporthook=lambda count, block_size, total_size: progress.update(block_size))  # type: ignore[attr-defined]
                    while True:
                        buffer = response.read(8192)
                        if not buffer:
                            break
                        f.write(buffer)
                        progress.update(len(buffer))
 def resolve_relative_path(path: str) -> str:
    return os.path.abspath(os.path.join(os.path.dirname(__file__), path))
 def get_video_dimensions(target_path: str) -> tuple:
    """Get video width and height using ffprobe."""
    command = [
        "ffprobe", "-v", "error",
        "-select_streams", "v:0",
        "-show_entries", "stream=width,height",
        "-of", "csv=p=0:s=x",
        target_path,
    ]
    output = subprocess.check_output(command).decode().strip()
    width, height = map(int, output.split("x"))
    return width, height
 def estimate_frame_count(target_path: str, fps: float = None) -> int:
    """Estimate total frame count from video duration and fps."""
    if fps is None:
        fps = detect_fps(target_path)
    command = [
        "ffprobe", "-v", "error",
        "-show_entries", "format=duration",
        "-of", "csv=p=0",
        target_path,
    ]
    try:
        output = subprocess.check_output(command).decode().strip()
        duration = float(output)
        return int(duration * fps)
    except Exception:
        return 0
@@ -1,6 +1,5 @@
 import cv2
 import numpy as np
 import time
 from typing import Optional, Tuple, Callable
 import platform
 import threading
@@ -18,10 +17,6 @@ class VideoCapturer:
        self._frame_ready = threading.Event()
        self.is_running = False
        self.cap = None
        # Actual values reported by the camera after configuration
        self.actual_width: int = 0
        self.actual_height: int = 0
        self.actual_fps: float = 0.0
        # Initialize Windows-specific components if on Windows
        if platform.system() == "Windows":
@@ -37,72 +32,34 @@ class VideoCapturer:
        """Initialize and start video capture"""
        try:
            if platform.system() == "Windows":
-                # device_index comes from pygrabber.FilterGraph (DirectShow
+                # Windows-specific capture methods
                # enumeration), so open with DSHOW first to preserve mapping.
                # MSMF and DirectShow enumerate cameras in different orders, so
                # opening MSMF with a DSHOW index silently selects the wrong
                # camera. MSMF/ANY remain as fallbacks for cameras DSHOW can't
                # open.
                #
                # Pass codec + resolution + fps as construction params (OpenCV
                # 4.6+). DSHOW locks the pixel format at open time and ignores
                # later cap.set(CAP_PROP_FOURCC, ...) — without this, DSHOW
                # falls back to uncompressed YUYV at 1080p, which is USB-
                # bandwidth-limited to ~5 fps. Setting MJPG at construction
                # negotiates compressed frames from the first read.
                mjpg = cv2.VideoWriter_fourcc(*'MJPG')
                open_params = [
                    cv2.CAP_PROP_FOURCC, mjpg,
                    cv2.CAP_PROP_FRAME_WIDTH, width,
                    cv2.CAP_PROP_FRAME_HEIGHT, height,
                    cv2.CAP_PROP_FPS, fps,
                ]
                capture_methods = [
-                    (self.device_index, cv2.CAP_DSHOW),
+                    (self.device_index, cv2.CAP_DSHOW),  # Try DirectShow first
-                    (self.device_index, cv2.CAP_MSMF),
+                    (self.device_index, cv2.CAP_ANY),  # Then try default backend
-                    (self.device_index, cv2.CAP_ANY),
+                    (-1, cv2.CAP_ANY),  # Try -1 as fallback
                    (0, cv2.CAP_ANY),  # Finally try 0 without specific backend
                ]
                for dev_id, backend in capture_methods:
                    try:
-                        self.cap = cv2.VideoCapture(dev_id, backend, open_params)
+                        self.cap = cv2.VideoCapture(dev_id, backend)
                        if self.cap.isOpened():
                            break
                        self.cap.release()
                    except Exception:
                        continue
            elif platform.system() == "Linux":
                self.cap = cv2.VideoCapture(f"/dev/video{self.device_index}")
            else:
                # Unix-like systems (Linux/Mac) capture method
                self.cap = cv2.VideoCapture(self.device_index)
            if not self.cap or not self.cap.isOpened():
                raise RuntimeError("Failed to open camera")
-            # Belt-and-braces: also set via cap.set() for backends that honor
+            # Configure format
            # post-open changes (MSMF, V4L2). DSHOW ignores these, but the
            # construction params above already handled it.
            if platform.system() != "Windows":
                self.cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
            self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
            self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
            self.cap.set(cv2.CAP_PROP_FPS, fps)
            # Read back resolution (usually reliable)
            self.actual_width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
            self.actual_height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
            # CAP_PROP_FPS is unreliable on DirectShow — often reports 30
            # even when the camera delivers 60.  Measure empirically by
            # timing a burst of frames.
            reported_fps = self.cap.get(cv2.CAP_PROP_FPS)
            self.actual_fps = self._measure_fps(warmup=10, sample=30,
                                                fallback=reported_fps or fps)
            print(f"[VideoCapturer] {self.actual_width}x{self.actual_height} "
                  f"@ {self.actual_fps:.1f}fps (reported={reported_fps:.0f})",
                  flush=True)
            self.is_running = True
            return True
@@ -132,29 +89,6 @@ class VideoCapturer:
            self.is_running = False
            self.cap = None
    def _measure_fps(self, warmup: int = 10, sample: int = 30,
                     fallback: float = 30.0) -> float:
        """Read warmup+sample frames and return measured FPS.
        This is more reliable than CAP_PROP_FPS which often lies on
        DirectShow.  Takes ~0.5-1s at startup but gives a ground-truth
        number for adaptive polling/detection intervals.
        """
        try:
            for _ in range(warmup):
                self.cap.read()
            t0 = time.perf_counter()
            for _ in range(sample):
                ret, _ = self.cap.read()
                if not ret:
                    return fallback
            elapsed = time.perf_counter() - t0
            if elapsed <= 0:
                return fallback
            return sample / elapsed
        except Exception:
            return fallback
    def set_frame_callback(self, callback: Callable[[np.ndarray], None]) -> None:
        """Set callback for frame processing"""
        self.frame_callback = callback
@@ -1,9 +0,0 @@
 [tool.ruff]
 target-version = "py310"
 [tool.ruff.lint]
 # Deterministic, low-risk rules enforced in CI. Other rules (F841, E402, F821)
 # surface real findings but require human judgement to fix safely, so they are
 # left out of the gate for now. Intentional side-effect imports should be
 # annotated with `# noqa: F401`.
 select = ["E701", "E711", "E712", "F401", "F541"]
@@ -1,18 +1,25 @@
-numpy>=2.0,<3
+--extra-index-url https://download.pytorch.org/whl/cu118
-typing-extensions>=4.15.0
+
-opencv-python==4.14.0.94
+numpy>=1.23.5,<2
-opencv-python-headless==4.14.0.94
+typing-extensions>=4.8.0
-cv2_enumerate_cameras==1.3.3
+opencv-python==4.10.0.84
-onnx==1.22.0
+cv2_enumerate_cameras==1.1.15
 onnx==1.16.0
 insightface==0.7.3
-psutil==7.2.2
+psutil==5.9.8
-PySide6>=6.7,<7
+tk==0.1.0
-pillow==12.3.0
+customtkinter==5.2.2
-tqdm>=4.66.3
+pillow==11.1.0
-onnxruntime==1.28.0; sys_platform == 'darwin' and platform_machine == 'arm64'
+torch==2.5.1+cu118; sys_platform != 'darwin'
-onnxruntime==1.23.0; sys_platform == 'darwin' and platform_machine != 'arm64'
+torch==2.5.1; sys_platform == 'darwin'
-onnxruntime-gpu==1.26.0; sys_platform != 'darwin'
+torchvision==0.20.1; sys_platform != 'darwin'
-opennsfw2==0.18.0
+torchvision==0.20.1; sys_platform == 'darwin'
-keras>=3.0.0
+onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
-protobuf>=6.33.5,<8
+onnxruntime-gpu==1.16.3; sys_platform != 'darwin'
-pygrabber; sys_platform == 'win32'
+tensorflow; sys_platform != 'darwin'
 opennsfw2==0.10.2
 protobuf==4.23.2
 tqdm==4.66.4
 gfpgan==1.3.8
 tkinterdnd2==0.4.2
 pygrabber==0.2
@@ -1,98 +1,5 @@
 #!/usr/bin/env python3
 import os
 import sys
 # Add the project root to PATH so bundled ffmpeg/ffprobe are found
 project_root = os.path.dirname(os.path.abspath(__file__))
 os.environ["PATH"] = project_root + os.pathsep + os.environ.get("PATH", "")
 # On Windows, register NVIDIA CUDA DLL directories so onnxruntime-gpu can
 # find cuDNN/cublas. Python 3.8+ ignores PATH for extension-module native deps —
 # os.add_dll_directory() is required. Also keep PATH for child processes/ffmpeg.
 if sys.platform == "win32":
    _site_packages = os.path.join(sys.prefix, "Lib", "site-packages")
    _venv_site_packages = os.path.join(project_root, "venv", "Lib", "site-packages")
    for _sp in (_site_packages, _venv_site_packages):
        _candidate_dirs = []
        _torch_lib = os.path.join(_sp, "torch", "lib")
        if os.path.isdir(_torch_lib):
            _candidate_dirs.append(_torch_lib)
        _nvidia_dir = os.path.join(_sp, "nvidia")
        if os.path.isdir(_nvidia_dir):
            for _pkg in os.listdir(_nvidia_dir):
                _bin_dir = os.path.join(_nvidia_dir, _pkg, "bin")
                if os.path.isdir(_bin_dir):
                    _candidate_dirs.append(_bin_dir)
        for _d in _candidate_dirs:
            os.environ["PATH"] = _d + os.pathsep + os.environ["PATH"]
            try:
                os.add_dll_directory(_d)
            except (OSError, AttributeError):
                pass
    # On Windows, register OpenVINO DLL directories so onnxruntime's
    # OpenVINOExecutionProvider can find openvino.dll.  This must happen
    # before any ONNX InferenceSession is created.  Failure is non-fatal:
    # OpenVINO simply isn't installed, and onnxruntime will fall back to CPU.
    try:
        from onnxruntime.tools.add_openvino_win_libs import (  # type: ignore[import-untyped]  # noqa: E501
            add_openvino_libs_to_path,
        )
        add_openvino_libs_to_path()
    except ImportError:
        # onnxruntime build without the OpenVINO tooling module — no-op.
        pass
    except FileNotFoundError:
        # OpenVINO site-packages dir absent — no-op.
        pass
    except SystemExit as exc:
        # add_openvino_libs_to_path() calls sys.exit() when OpenVINO libs
        # can't be located (e.g. OPENVINO_LIB_PATHS unset).  Log the message
        # it raised with so the failure is visible, but keep startup alive.
        print(
            f"[startup] OpenVINO DLL registration skipped: {exc}",
            flush=True,
        )
 # On Linux, pre-load NVIDIA shared libraries (cuDNN, cuBLAS, nvrtc...) shipped
 # inside the venv via pip wheels (nvidia-cudnn-cu12, etc.). LD_LIBRARY_PATH
 # cannot be set after Python starts, so we use ctypes.CDLL with RTLD_GLOBAL
 # instead. This makes symbols available to onnxruntime when it dlopens its
 # CUDA provider.
 if sys.platform.startswith("linux"):
    import ctypes
    import glob
    _py_lib = f"python{sys.version_info.major}.{sys.version_info.minor}"
    _site_packages_candidates = [
        os.path.join(project_root, "venv", "lib", _py_lib, "site-packages"),
        os.path.join(sys.prefix, "lib", _py_lib, "site-packages"),
    ]
    for _sp in _site_packages_candidates:
        _nvidia_dir = os.path.join(_sp, "nvidia")
        if not os.path.isdir(_nvidia_dir):
            continue
        for _pkg in os.listdir(_nvidia_dir):
            _lib_dir = os.path.join(_nvidia_dir, _pkg, "lib")
            if not os.path.isdir(_lib_dir):
                continue
            # Also expose the directory to child processes, without
            # duplicating an entry that is already present.
            _ldp = os.environ.get("LD_LIBRARY_PATH", "")
            if _lib_dir not in _ldp.split(os.pathsep):
                os.environ["LD_LIBRARY_PATH"] = (
                    _lib_dir + (os.pathsep + _ldp if _ldp else "")
                )
            for _so in sorted(glob.glob(os.path.join(_lib_dir, "lib*.so*"))):
                try:
                    ctypes.CDLL(_so, mode=ctypes.RTLD_GLOBAL)
                except OSError:
                    pass
        break
 from modules import platform_info
 platform_info.print_banner()
 from modules import core
 if __name__ == '__main__':
@@ -1,137 +0,0 @@
 import importlib
 import sys
 import types
 import unittest
 from contextlib import contextmanager
 from unittest.mock import patch
@contextmanager
 def _patched_core_import_stubs(calls, pipe_result=False):
    class Processor:
        NAME = "test_processor"
        def pre_start(self):
            return True
        def pre_check(self):
            return True
        def process_image(self, *_args, **_kwargs):
            raise AssertionError("image path should not be used")
        def process_video(self, source_path, frame_paths):
            calls.append(("process_video", source_path, tuple(frame_paths)))
    stubs = {
        "cv2": types.SimpleNamespace(
            IMREAD_COLOR=1,
            imdecode=lambda *_args, **_kwargs: None,
            imencode=lambda *_args, **_kwargs: (
                True,
                types.SimpleNamespace(tofile=lambda *_a, **_k: None),
            ),
        ),
        "numpy": types.SimpleNamespace(uint8=object, fromfile=lambda *_args, **_kwargs: b""),
        "torch": types.SimpleNamespace(
            cuda=types.SimpleNamespace(empty_cache=lambda: None)
        ),
        "onnxruntime": types.SimpleNamespace(
            get_available_providers=lambda: ["CPUExecutionProvider"]
        ),
        "tensorflow": types.SimpleNamespace(),
        "modules.metadata": types.SimpleNamespace(name="Deep-Live-Cam", version="test"),
        "modules.ui": types.SimpleNamespace(
            check_and_ignore_nsfw=lambda *_args, **_kwargs: False,
            update_status=lambda *_args, **_kwargs: None,
            init=lambda *_args, **_kwargs: types.SimpleNamespace(mainloop=lambda: None),
        ),
        "modules.processors.frame.core": types.SimpleNamespace(
            get_frame_processors_modules=lambda _names: [Processor()],
            process_video_in_memory=lambda *_args, **_kwargs: calls.append(("pipe",))
            or pipe_result,
        ),
        "modules.utilities": types.SimpleNamespace(
            has_image_extension=lambda _path: False,
            is_image=lambda _path: False,
            is_video=lambda _path: True,
            detect_fps=lambda _path: 24.0,
            create_video=lambda target_path, fps: calls.append(
                ("create_video", target_path, fps)
            )
            or True,
            extract_frames=lambda target_path: calls.append(
                ("extract_frames", target_path)
            ),
            get_temp_frame_paths=lambda target_path: [f"{target_path}/0001.png"],
            restore_audio=lambda *_args, **_kwargs: calls.append(("restore_audio",)),
            create_temp=lambda target_path: calls.append(("create_temp", target_path)),
            move_temp=lambda target_path, output_path: calls.append(
                ("move_temp", target_path, output_path)
            ),
            clean_temp=lambda target_path: calls.append(("clean_temp", target_path)),
            normalize_output_path=lambda _source, _target, output: output,
        ),
    }
    with patch.dict(sys.modules, stubs, clear=False):
        sys.modules.pop("modules.core", None)
        yield importlib.import_module("modules.core")
        sys.modules.pop("modules.core", None)
 def _configure_video_run(core, *, map_faces):
    core.modules.globals.source_path = "source.jpg"
    core.modules.globals.target_path = "target.mp4"
    core.modules.globals.output_path = "output.mp4"
    core.modules.globals.frame_processors = ["face_swapper"]
    core.modules.globals.headless = True
    core.modules.globals.keep_fps = False
    core.modules.globals.keep_audio = False
    core.modules.globals.keep_frames = False
    core.modules.globals.map_faces = map_faces
    core.modules.globals.nsfw_filter = False
    core.modules.globals.execution_threads = 1
    core.modules.globals.execution_providers = ["CPUExecutionProvider"]
    core.modules.globals.max_memory = None
 class MapFacesFallbackTests(unittest.TestCase):
    def test_map_faces_disk_fallback_extracts_frames_before_processing(self):
        calls = []
        with _patched_core_import_stubs(calls, pipe_result=False) as core:
            _configure_video_run(core, map_faces=True)
            with patch.object(core.os.path, "isfile", return_value=True):
                core.start()
        self.assertNotIn(("pipe",), calls)
        self.assertIn(("create_temp", "target.mp4"), calls)
        self.assertIn(("extract_frames", "target.mp4"), calls)
        self.assertIn(("process_video", "source.jpg", ("target.mp4/0001.png",)), calls)
        self.assertIn(("create_video", "target.mp4", 30.0), calls)
        self.assertIn(("move_temp", "target.mp4", "output.mp4"), calls)
        step_indices = {}
        for index, call in enumerate(calls):
            step_indices.setdefault(call[0], index)
        self.assertLess(step_indices["create_temp"], step_indices["extract_frames"])
        self.assertLess(step_indices["extract_frames"], step_indices["process_video"])
        self.assertLess(step_indices["process_video"], step_indices["create_video"])
        self.assertLess(step_indices["create_video"], step_indices["move_temp"])
    def test_non_map_faces_pipe_success_does_not_extract_frames(self):
        calls = []
        with _patched_core_import_stubs(calls, pipe_result=True) as core:
            _configure_video_run(core, map_faces=False)
            with patch.object(core.os.path, "isfile", return_value=True):
                core.start()
        self.assertIn(("pipe",), calls)
        self.assertNotIn(("extract_frames", "target.mp4"), calls)
        self.assertNotIn(("process_video", "source.jpg", ("target.mp4/0001.png",)), calls)
 if __name__ == "__main__":
    unittest.main()
@@ -1,97 +0,0 @@
 import importlib
 import sys
 import types
 import unittest
 from unittest.mock import patch
 def _install_import_stubs():
    sys.modules.setdefault(
        "insightface",
        types.SimpleNamespace(app=types.SimpleNamespace(FaceAnalysis=object)),
    )
    sys.modules.setdefault(
        "cv2",
        types.SimpleNamespace(
            IMREAD_COLOR=1,
            imread=lambda *_args, **_kwargs: None,
            imdecode=lambda *_args, **_kwargs: None,
            imencode=lambda *_args, **_kwargs: (
                True,
                types.SimpleNamespace(tofile=lambda *_a, **_k: None),
            ),
        ),
    )
    sys.modules.setdefault(
        "numpy",
        types.SimpleNamespace(uint8=object, fromfile=lambda *_args, **_kwargs: b""),
    )
    sys.modules.setdefault(
        "tqdm",
        types.SimpleNamespace(tqdm=lambda iterable, **_kwargs: iterable),
    )
    sys.modules["modules.typing"] = types.SimpleNamespace(Frame=object)
    sys.modules["modules.cluster_analysis"] = types.SimpleNamespace(
        find_cluster_centroids=lambda *args, **kwargs: [],
        find_closest_centroid=lambda *args, **kwargs: (0, None),
    )
    sys.modules["modules.utilities"] = types.SimpleNamespace(
        get_temp_directory_path=lambda path: path,
        create_temp=lambda path: None,
        extract_frames=lambda path: None,
        clean_temp=lambda path: None,
        get_temp_frame_paths=lambda path: [],
    )
 def _load_face_analyser():
    _install_import_stubs()
    sys.modules.pop("modules.face_analyser", None)
    return importlib.import_module("modules.face_analyser")
 class Face:
    def __init__(self, left):
        self.bbox = [left, 0, 10, 10]
 class GetOneFaceTests(unittest.TestCase):
    def test_uses_supplied_detected_faces_without_reanalysing_frame(self):
        face_analyser = _load_face_analyser()
        right = Face(20)
        left = Face(5)
        with patch.object(
            face_analyser,
            "_analyse_faces",
            side_effect=AssertionError("should not analyse"),
        ):
            self.assertIs(face_analyser.get_one_face("frame", [right, left]), left)
    def test_supplied_empty_detected_faces_returns_none(self):
        face_analyser = _load_face_analyser()
        with patch.object(
            face_analyser,
            "_analyse_faces",
            side_effect=AssertionError("should not analyse"),
        ):
            self.assertIsNone(face_analyser.get_one_face("frame", []))
    def test_without_supplied_faces_preserves_existing_detection_path(self):
        face_analyser = _load_face_analyser()
        right = Face(30)
        left = Face(3)
        with patch.object(face_analyser, "_is_dml", return_value=False), patch.object(
            face_analyser,
            "_analyse_faces",
            return_value=[right, left],
        ) as analyse:
            self.assertIs(face_analyser.get_one_face("frame"), left)
        analyse.assert_called_once_with("frame")
 if __name__ == "__main__":
    unittest.main()
@@ -1,29 +0,0 @@
 import os
 os.environ.setdefault('TK_SILENCE_DEPRECATION', '1')
 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()
Author	SHA1	Message	Date
KRSHH	30ffbc1042	add Drag and drop functionality	2025-03-26 13:08:18 +05:30
KRSHH	2b2caf6aba	Update ui.py	2025-03-26 12:14:29 +05:30