```
├── .gitattributes (omitted)
├── .github/
├── ISSUE_TEMPLATE/
├── bug_report.md (100 tokens)
├── workflows/
├── ruff.yml (100 tokens)
├── .gitignore (100 tokens)
├── CONTRIBUTING.md (300 tokens)
├── LICENSE (omitted)
├── README.md (3.1k tokens)
├── benchmark_pipeline.py (1200 tokens)
├── locales/
├── de.json (500 tokens)
├── es.json (500 tokens)
├── fi.json (400 tokens)
├── id.json (400 tokens)
├── km.json (400 tokens)
├── ko.json (300 tokens)
├── pt-br.json (500 tokens)
├── ru.json (500 tokens)
├── th.json (400 tokens)
├── zh.json (300 tokens)
├── media/
├── Download.png
├── avgpcperformancedemo.gif
├── deepwarebench.gif
├── demo.gif
├── instruction.png
├── live_show.gif
├── ludwig.gif
├── meme.gif
├── movie.gif
├── streamers.gif
├── models/
├── instructions.txt
├── modules/
├── __init__.py (100 tokens)
├── capturer.py (200 tokens)
├── cluster_analysis.py (200 tokens)
├── core.py (3.3k tokens)
├── custom_types.py
├── face_analyser.py (2.6k tokens)
├── gettext.py (200 tokens)
├── globals.py (600 tokens)
├── gpu_processing.py (1900 tokens)
├── metadata.py
├── onnx_optimize.py (4k tokens)
├── paths.py
├── platform_info.py (500 tokens)
├── predicter.py (300 tokens)
├── processors/
├── __init__.py
├── frame/
├── __init__.py
├── _onnx_enhancer.py (1700 tokens)
├── core.py (3.1k tokens)
├── face_enhancer.py (3.1k tokens)
├── face_enhancer_gpen256.py (800 tokens)
├── face_enhancer_gpen512.py (800 tokens)
├── face_masking.py (4.9k tokens)
├── face_swapper.py (14.6k tokens)
├── run.py
├── tkinter_fix.py (200 tokens)
├── typing.py
├── ui.json (900 tokens)
├── ui.py (11k tokens)
├── ui_tooltip.py (400 tokens)
├── utilities.py (2.4k tokens)
├── video_capture.py (1300 tokens)
├── mypi.ini
├── pyproject.toml (100 tokens)
├── requirements.txt (100 tokens)
├── run-cuda.bat
├── run-directml.bat
├── run.py (600 tokens)
├── tests/
├── test_face_analyser_get_one_face.py (600 tokens)
├── tkinter_fix.py (200 tokens)
```
## /.github/ISSUE_TEMPLATE/bug_report.md
***[Remove this]The issue would be closed without notice and be considered spam if the template is not followed.***
**Describe the bug**
A clear and concise description of what the bug is.
**Screenshots**
If applicable, add screenshots to help explain your problem.
**Error Message**
`<The error message in terminal>`
**Desktop (please complete the following information):**
- OS: [e.g. Windows]
- Version [e.g. 22]
- GPU
- CPU
**Additional context**
Add any other context about the problem here.
**Confirmation (Mandatory)**
- [ ] I have followed the template
- [ ] This is not a query about how to increase performance
- [ ] I have checked the issues page, and this is not a duplicate
## /.github/workflows/ruff.yml
```yml path="/.github/workflows/ruff.yml"
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"
```
## /.gitignore
```gitignore path="/.gitignore"
__pycache__/
*.py[cod]
*$py.class
*.pyc
.idea
.todo
*.log
*.backup
tf_env/
*.png
*.mp4
*.mkv
.tmp/
temp/
.venv/
venv/
env/
workflow/
gfpgan/
models/inswapper_128.onnx
models/GFPGANv1.4.pth
*.onnx
models/DMDNet.pth
faceswap/
.vscode/
switch_states.json
/models
install.bat
/.claude
*.bat
```
## /CONTRIBUTING.md
# Collaboration Guidelines and Codebase Quality Standards
To ensure smooth collaboration and maintain the high quality of our codebase, please adhere to the following guidelines:
## Branching Strategy
* **`premain`**:
* Always push your changes to the `premain` branch initially.
* This safeguards the `main` branch from unintentional disruptions.
* All tests will be performed on the `premain` branch.
* Changes will only be merged into `main` after several hours or days of rigorous testing.
* **`experimental`**:
* For large or potentially disruptive changes, use the `experimental` branch.
* This allows for thorough discussion and review before considering a merge into `main`.
## Pre-Pull Request Checklist
Before creating a Pull Request (PR), ensure you have completed the following tests:
### Functionality
* **Realtime Faceswap**:
* Test with face enhancer **enabled** and **disabled**.
* **Map Faces**:
* Test with both options (**enabled** and **disabled**).
* **Camera Listing**:
* Verify that all cameras are listed accurately.
### Stability
* **Realtime FPS**:
* Confirm that there is no drop in real-time frames per second (FPS).
* **Boot Time**:
* Changes should not negatively impact the boot time of either the application or the real-time faceswap feature.
* **GPU Overloading**:
* Test for a minimum of 15 minutes to guarantee no GPU overloading, which could lead to crashes.
* **App Performance**:
* The application should remain responsive and not exhibit any lag.
## /README.md
<h1 align="center">Deep-Live-Cam 2.1.6</h1>
<p align="center">
Real-time face swap and video deepfake with a single click and only a single image.
</p>
<p align="center">
<a href="https://trendshift.io/repositories/11395" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11395" alt="hacksider%2FDeep-Live-Cam | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</p>
<p align="center">
<img src="media/demo.gif" alt="Demo GIF" width="800">
</p>
## Disclaimer
This deepfake software is designed to be a productive tool for the AI-generated media industry. It can assist artists in animating custom characters, creating engaging content, and even using models for clothing design.
We are aware of the potential for unethical applications and are committed to preventative measures. A built-in check prevents the program from processing inappropriate media (nudity, graphic content, sensitive material like war footage, etc.). We will continue to develop this project responsibly, adhering to the law and ethics. We may shut down the project or add watermarks if legally required.
- Ethical Use: Users are expected to use this software responsibly and legally. If using a real person's face, obtain their consent and clearly label any output as a deepfake when sharing online.
- Content Restrictions: The software includes built-in checks to prevent processing inappropriate media, such as nudity, graphic content, or sensitive material.
- Legal Compliance: We adhere to all relevant laws and ethical guidelines. If legally required, we may shut down the project or add watermarks to the output.
- User Responsibility: We are not responsible for end-user actions. Users must ensure their use of the software aligns with ethical standards and legal requirements.
By using this software, you agree to these terms and commit to using it in a manner that respects the rights and dignity of others.
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.
## Exclusive v2.7 RC2 Quick Start - Pre-built (Windows/Mac Silicon/CPU)
<a href="https://deeplivecam.net/index.php/quickstart"> <img src="media/Download.png" width="285" height="77" />
##### This is the fastest build you can get if you have a discrete NVIDIA or AMD GPU, CPU or Mac Silicon, And you'll receive special priority support. 2.7 beta is the best you can have with 30+ extra features than the open source version.
###### These Pre-builts are perfect for non-technical users or those who don't have time to, or can't manually install all the requirements. Just a heads-up: this is an open-source project, so you can also install it manually.
## TLDR; Live Deepfake in just 3 Clicks
1. Select a face
2. Select which camera to use
3. Press live!
## Features & Uses - Everything is in real-time
### Mouth Mask
**Retain your original mouth for accurate movement using Mouth Mask**
<p align="center">
<img src="media/ludwig.gif" alt="resizable-gif">
</p>
### Face Mapping
**Use different faces on multiple subjects simultaneously**
<p align="center">
<img src="media/streamers.gif" alt="face_mapping_source">
</p>
### Your Movie, Your Face
**Watch movies with any face in real-time**
<p align="center">
<img src="media/movie.gif" alt="movie">
</p>
### Live Show
**Run Live shows and performances**
<p align="center">
<img src="media/live_show.gif" alt="show">
</p>
### Memes
**Create Your Most Viral Meme Yet**
<p align="center">
<img src="media/meme.gif" alt="show" width="450">
<br>
<sub>Created using Many Faces feature in Deep-Live-Cam</sub>
</p>
### Omegle
**Surprise people on Omegle**
<p align="center">
<video src="https://github.com/user-attachments/assets/2e9b9b82-fa04-4b70-9f56-b1f68e7672d0" width="450" controls></video>
</p>
## Installation (Manual)
**Please be aware that the installation requires technical skills and is not for beginners. Consider downloading the quickstart version.**
<details>
<summary>Click to see the process</summary>
### Installation
This is more likely to work on your computer but will be slower as it utilizes the CPU.
**1. Set up Your Platform**
- Python (3.11 recommended)
- pip
- git
- [ffmpeg](https://www.youtube.com/watch?v=OlNWCpFdVMA) - ```iex (irm ffmpeg.tc.ht)```
- [Visual Studio 2022 Runtimes (Windows)](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
**2. Clone the Repository**
```bash
git clone https://github.com/hacksider/Deep-Live-Cam.git
cd Deep-Live-Cam
```
**3. Download the Models**
1. [GFPGANv1.4](https://huggingface.co/hacksider/deep-live-cam/resolve/main/GFPGANv1.4.onnx)
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.
**4. Install Dependencies**
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 3.11
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```
**For macOS:**
Apple Silicon (M1/M2/M3) requires specific setup:
```bash
# Install Python 3.11 (specific version is important)
brew install python@3.11
# Install tkinter package (required for the GUI)
brew install python-tk@3.11
# Create and activate virtual environment with Python 3.11
python3.11 -m venv venv
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
```
** In case something goes wrong and you need to reinstall the virtual environment **
```bash
# Deactivate the virtual environment
rm -rf venv
# Reinstall the virtual environment
python -m venv venv
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).
### GPU Acceleration
**CUDA Execution Provider (Nvidia)**
1. Install [CUDA Toolkit 12.8.0](https://developer.nvidia.com/cuda-12-8-0-download-archive)
2. Install [cuDNN v8.9.7 for CUDA 12.x](https://developer.nvidia.com/rdp/cudnn-archive) (required for onnxruntime-gpu):
- Download cuDNN v8.9.7 for CUDA 12.x
- Make sure the cuDNN bin directory is in your system PATH
3. Install dependencies:
```bash
pip install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
pip uninstall onnxruntime onnxruntime-gpu
pip install onnxruntime-gpu==1.21.0
```
3. Usage:
```bash
python run.py --execution-provider cuda
```
**CoreML Execution Provider (Apple Silicon)**
Apple Silicon (M1/M2/M3) specific installation:
1. Make sure you've completed the macOS setup above using Python 3.11.
2. Install dependencies:
```bash
pip uninstall onnxruntime onnxruntime-silicon
pip install onnxruntime-silicon==1.13.1
```
3. Usage:
```bash
python3.11 run.py --execution-provider coreml
```
**Important Notes for macOS:**
- You **must** use Python 3.11, not newer versions like 3.13
- Always run with `python3.11` 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@3.11`
- If you get model loading errors, check that your models are in the correct folder
- If you encounter conflicts with other Python versions, consider uninstalling them:
```bash
# List all installed Python versions
brew list | grep python
# Uninstall conflicting versions if needed
brew uninstall --ignore-dependencies python@3.13
# Keep only Python 3.11
brew cleanup
```
**CoreML Execution Provider (Apple Legacy)**
1. Install dependencies:
```bash
pip uninstall onnxruntime onnxruntime-coreml
pip install onnxruntime-coreml==1.21.0
```
2. Usage:
```bash
python run.py --execution-provider coreml
```
**DirectML Execution Provider (Windows)**
1. Install dependencies:
```bash
pip uninstall onnxruntime onnxruntime-directml
pip install onnxruntime-directml==1.21.0
```
2. Usage:
```bash
python run.py --execution-provider directml
```
**OpenVINO™ Execution Provider (Intel)**
1. Install dependencies:
```bash
pip uninstall onnxruntime onnxruntime-openvino
pip install onnxruntime-openvino==1.21.0
```
2. Usage:
```bash
python run.py --execution-provider openvino
```
</details>
## Usage
**1. Image/Video Mode**
- Execute `python run.py`.
- Choose a source face image and a target image/video.
- Click "Start".
- The output will be saved in a directory named after the target video.
**2. Webcam Mode**
- Execute `python run.py`.
- Select a source face image.
- Click "Live".
- Wait for the preview to appear (10-30 seconds).
- Use a screen capture tool like OBS to stream.
- To change the face, select a new source image.
## Download all models in this huggingface link
- [**Download models here**](https://huggingface.co/hacksider/deep-live-cam/tree/main)
## Command Line Arguments (Unmaintained)
```
options:
-h, --help show this help message and exit
-s SOURCE_PATH, --source SOURCE_PATH select a source image
-t TARGET_PATH, --target TARGET_PATH select a target image or video
-o OUTPUT_PATH, --output OUTPUT_PATH select output file or directory
--frame-processor FRAME_PROCESSOR [FRAME_PROCESSOR ...] frame processors (choices: face_swapper, face_enhancer, ...)
--keep-fps keep original fps
--keep-audio keep original audio
--keep-frames keep temporary frames
--many-faces process every face
--map-faces map source target faces
--mouth-mask mask the mouth region
--video-encoder {libx264,libx265,libvpx-vp9} adjust output video encoder
--video-quality [0-51] adjust output video quality
--live-mirror the live camera display as you see it in the front-facing camera frame
--live-resizable the live camera frame is resizable
--max-memory MAX_MEMORY maximum amount of RAM in GB
--execution-provider {cpu} [{cpu} ...] available execution provider (choices: cpu, ...)
--execution-threads EXECUTION_THREADS number of execution threads
-v, --version show program's version number and exit
```
Looking for a CLI mode? Using the -s/--source argument will make the run program in cli mode.
## 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"*
- [**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!"*
## 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
- [pereiraroland26](https://github.com/pereiraroland26): Multiple faces support
- [vic4key](https://github.com/vic4key): For supporting/contributing to this project
- [kier007](https://github.com/kier007): for improving the user experience
- [qitianai](https://github.com/qitianai): for multi-lingual support
- [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 ❤️
[](https://github.com/hacksider/Deep-Live-Cam/stargazers)
## Contributions
## Stars to the Moon 🚀
<a href="https://star-history.com/#hacksider/deep-live-cam&Date">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date&theme=dark" />
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date" />
<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=hacksider/deep-live-cam&type=Date" />
</picture>
</a>
## /benchmark_pipeline.py
```py path="/benchmark_pipeline.py"
"""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}")
```
## /locales/de.json
```json path="/locales/de.json"
{
"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."
}
```
## /locales/es.json
```json path="/locales/es.json"
{
"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."
}
```
## /locales/fi.json
```json path="/locales/fi.json"
{
"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."
}
```
## /locales/id.json
```json path="/locales/id.json"
{
"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."
}
```
## /locales/km.json
```json path="/locales/km.json"
{
"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។"
}
```
## /locales/ko.json
```json path="/locales/ko.json"
{
"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."
}
```
## /locales/pt-br.json
```json path="/locales/pt-br.json"
{
"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."
}
```
## /locales/ru.json
```json path="/locales/ru.json"
{
"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."
}
```
## /locales/th.json
```json path="/locales/th.json"
{
"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"
}
```
## /locales/zh.json
```json path="/locales/zh.json"
{
"Source x Target Mapper": "Source x Target Mapper",
"select a 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",
"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",
"source": "源",
"Select a target": "选择一个目标",
"Select a face": "选择一张脸",
"Keep audio": "保留音频",
"Face Enhancer": "面纹增强器",
"Many faces": "多脸",
"Show FPS": "显示帧率",
"Keep fps": "保持帧率",
"Keep frames": "保持帧数",
"Fix Blueish Cam": "修复偏蓝的摄像头",
"Mouth Mask": "口罩",
"Show Mouth Mask Box": "显示口罩盒",
"Start": "开始",
"Live": "直播",
"Destroy": "结束",
"Map faces": "识别人脸",
"Processing...": "处理中...",
"Processing succeed!": "处理成功!",
"Processing ignored!": "处理被忽略!",
"Failed to start camera": "启动相机失败",
"Please complete pop-up or close it.": "请先完成弹出窗口或者关闭它",
"Getting unique faces": "获取独特面部",
"Please select a source image first": "请先选择一个源图像",
"No faces found in target": "目标图像中没有人脸",
"Add": "添加",
"Clear": "清除",
"Submit": "确认",
"Select source image": "请选取源图像",
"Select target image": "请选取目标图像",
"Please provide mapping!": "请提供映射",
"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！",
"At least 1 source with target is required!": "至少需要一个来源图像与目标图像相关！",
"Face could not be detected in last upload!": "最近上传的图像中没有检测到人脸!",
"Select Camera:": "选择摄像头",
"All mappings cleared!": "所有映射均已清除!",
"Mappings successfully submitted!": "成功提交映射！",
"Source x Target Mapper is already open.": "源 x 目标映射器已打开。"
}
```
## /media/Download.png
Binary file available at https://raw.githubusercontent.com/hacksider/Deep-Live-Cam/refs/heads/main/media/Download.png
## /media/avgpcperformancedemo.gif
Binary file available at https://raw.githubusercontent.com/hacksider/Deep-Live-Cam/refs/heads/main/media/avgpcperformancedemo.gif
## /media/deepwarebench.gif
Binary file available at https://raw.githubusercontent.com/hacksider/Deep-Live-Cam/refs/heads/main/media/deepwarebench.gif
## /media/demo.gif
Binary file available at https://raw.githubusercontent.com/hacksider/Deep-Live-Cam/refs/heads/main/media/demo.gif
## /media/instruction.png
Binary file available at https://raw.githubusercontent.com/hacksider/Deep-Live-Cam/refs/heads/main/media/instruction.png
## /media/live_show.gif
Binary file available at https://raw.githubusercontent.com/hacksider/Deep-Live-Cam/refs/heads/main/media/live_show.gif
## /media/ludwig.gif
Binary file available at https://raw.githubusercontent.com/hacksider/Deep-Live-Cam/refs/heads/main/media/ludwig.gif
## /media/meme.gif
Binary file available at https://raw.githubusercontent.com/hacksider/Deep-Live-Cam/refs/heads/main/media/meme.gif
## /media/movie.gif
Binary file available at https://raw.githubusercontent.com/hacksider/Deep-Live-Cam/refs/heads/main/media/movie.gif
## /media/streamers.gif
Binary file available at https://raw.githubusercontent.com/hacksider/Deep-Live-Cam/refs/heads/main/media/streamers.gif
## /models/instructions.txt
just put the models in this folder -
https://huggingface.co/hacksider/deep-live-cam/resolve/main/inswapper_128_fp16.onnx?download=true
https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth
## /modules/__init__.py
```py path="/modules/__init__.py"
import os
import cv2
import numpy as np
# Utility function to support unicode characters in file paths for reading
def imread_unicode(path, flags=cv2.IMREAD_COLOR):
return cv2.imdecode(np.fromfile(path, dtype=np.uint8), flags)
# Utility function to support unicode characters in file paths for writing
def imwrite_unicode(path, img, params=None):
root, ext = os.path.splitext(path)
if not ext:
ext = ".png"
result, encoded_img = cv2.imencode(ext, img, params if params is not None else [])
if not result:
return False
encoded_img.tofile(path)
return True
```
## /modules/capturer.py
```py path="/modules/capturer.py"
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:
capture = cv2.VideoCapture(video_path)
# Set MJPEG format to ensure correct color space handling
capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*'MJPG'))
# Only force RGB conversion if color correction is enabled
if modules.globals.color_correction:
capture.set(cv2.CAP_PROP_CONVERT_RGB, 1)
frame_total = capture.get(cv2.CAP_PROP_FRAME_COUNT)
capture.set(cv2.CAP_PROP_POS_FRAMES, min(frame_total, frame_number - 1))
has_frame, frame = capture.read()
if has_frame and modules.globals.color_correction:
# Convert the frame color if necessary
frame = gpu_cvt_color(frame, cv2.COLOR_BGR2RGB)
capture.release()
return frame if has_frame else None
def get_video_frame_total(video_path: str) -> int:
capture = cv2.VideoCapture(video_path)
video_frame_total = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
capture.release()
return video_frame_total
```
## /modules/cluster_analysis.py
```py path="/modules/cluster_analysis.py"
import numpy as np
from sklearn.cluster import KMeans
from typing import Any
def find_cluster_centroids(embeddings, max_k=10) -> Any:
inertia = []
cluster_centroids = []
K = range(1, max_k+1)
for k in K:
kmeans = KMeans(n_clusters=k, random_state=0)
kmeans.fit(embeddings)
inertia.append(kmeans.inertia_)
cluster_centroids.append({"k": k, "centroids": kmeans.cluster_centers_})
diffs = [inertia[i] - inertia[i+1] for i in range(len(inertia)-1)]
optimal_centroids = cluster_centroids[diffs.index(max(diffs)) + 1]['centroids']
return optimal_centroids
def find_closest_centroid(centroids: list, normed_face_embedding) -> list:
try:
centroids = np.array(centroids)
normed_face_embedding = np.array(normed_face_embedding)
similarities = np.dot(centroids, normed_face_embedding)
closest_centroid_index = np.argmax(similarities)
return closest_centroid_index, centroids[closest_centroid_index]
except ValueError:
return None
```
## /modules/core.py
```py path="/modules/core.py"
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'
# reduce tensorflow log level
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import warnings
from typing import List
import platform
import signal
import shutil
import argparse
try:
import torch
HAS_TORCH = True
except ImportError:
HAS_TORCH = False
import onnxruntime
try:
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.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:
del torch
warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
if HAS_TORCH:
warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
def parse_args() -> None:
signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
program = argparse.ArgumentParser()
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('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=False)
program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=False)
program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)
program.add_argument('--nsfw-filter', help='filter the NSFW image or video', dest='nsfw_filter', action='store_true', default=False)
program.add_argument('--map-faces', help='map source target faces', dest='map_faces', action='store_true', default=False)
program.add_argument('--mouth-mask', help='mask the mouth region', dest='mouth_mask', action='store_true', default=False)
program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')
program.add_argument('-l', '--lang', help='Ui language', default="en")
program.add_argument('--live-mirror', help='The live camera display as you see it in the front-facing camera frame', dest='live_mirror', action='store_true', default=False)
program.add_argument('--live-resizable', help='The live camera frame is resizable', dest='live_resizable', action='store_true', default=False)
program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
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-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
program.add_argument('-f', '--face', help=argparse.SUPPRESS, dest='source_path_deprecated')
program.add_argument('--cpu-cores', help=argparse.SUPPRESS, dest='cpu_cores_deprecated', type=int)
program.add_argument('--gpu-vendor', help=argparse.SUPPRESS, dest='gpu_vendor_deprecated')
program.add_argument('--gpu-threads', help=argparse.SUPPRESS, dest='gpu_threads_deprecated', type=int)
args = program.parse_args()
modules.globals.source_path = args.source_path
modules.globals.target_path = args.target_path
modules.globals.output_path = normalize_output_path(modules.globals.source_path, modules.globals.target_path, args.output_path)
modules.globals.frame_processors = args.frame_processor
modules.globals.headless = args.source_path or args.target_path or args.output_path
modules.globals.keep_fps = args.keep_fps
modules.globals.keep_audio = args.keep_audio
modules.globals.keep_frames = args.keep_frames
modules.globals.many_faces = args.many_faces
modules.globals.mouth_mask = args.mouth_mask
modules.globals.nsfw_filter = args.nsfw_filter
modules.globals.map_faces = args.map_faces
modules.globals.video_encoder = args.video_encoder
modules.globals.video_quality = args.video_quality
modules.globals.live_mirror = args.live_mirror
modules.globals.live_resizable = args.live_resizable
modules.globals.max_memory = args.max_memory
modules.globals.execution_providers = decode_execution_providers(args.execution_provider)
modules.globals.execution_threads = args.execution_threads
modules.globals.lang = args.lang
#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:
print('\033[33mArgument -f and --face are deprecated. Use -s and --source instead.\033[0m')
modules.globals.source_path = args.source_path_deprecated
modules.globals.output_path = normalize_output_path(args.source_path_deprecated, modules.globals.target_path, args.output_path)
if args.cpu_cores_deprecated:
print('\033[33mArgument --cpu-cores is deprecated. Use --execution-threads instead.\033[0m')
modules.globals.execution_threads = args.cpu_cores_deprecated
if args.gpu_vendor_deprecated == 'apple':
print('\033[33mArgument --gpu-vendor apple is deprecated. Use --execution-provider coreml instead.\033[0m')
modules.globals.execution_providers = decode_execution_providers(['coreml'])
if args.gpu_vendor_deprecated == 'nvidia':
print('\033[33mArgument --gpu-vendor nvidia is deprecated. Use --execution-provider cuda instead.\033[0m')
modules.globals.execution_providers = decode_execution_providers(['cuda'])
if args.gpu_vendor_deprecated == 'amd':
print('\033[33mArgument --gpu-vendor amd is deprecated. Use --execution-provider cuda instead.\033[0m')
modules.globals.execution_providers = decode_execution_providers(['rocm'])
if args.gpu_threads_deprecated:
print('\033[33mArgument --gpu-threads is deprecated. Use --execution-threads instead.\033[0m')
modules.globals.execution_threads = args.gpu_threads_deprecated
def encode_execution_providers(execution_providers: List[str]) -> List[str]:
return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
def decode_execution_providers(execution_providers: List[str]) -> List[str]:
return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
def suggest_max_memory() -> int:
if platform.system().lower() == 'darwin':
return 4
return 16
def suggest_default_execution_provider() -> str:
"""Pick the best available provider: cuda > rocm > coreml > dml > cpu."""
available = encode_execution_providers(onnxruntime.get_available_providers())
for pref in ('cuda', 'rocm', 'coreml', '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 2
# 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:
memory = modules.globals.max_memory * 1024 ** 3
if platform.system().lower() == 'windows':
import ctypes
kernel32 = ctypes.windll.kernel32
kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
else:
import resource
resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
def release_resources() -> None:
if 'CUDAExecutionProvider' in modules.globals.execution_providers and HAS_TORCH:
torch.cuda.empty_cache()
def pre_check() -> bool:
if sys.version_info < (3, 9):
update_status('Python version is not supported - please upgrade to 3.9 or higher.')
return False
if not shutil.which('ffmpeg'):
update_status('ffmpeg is not installed.')
return False
return True
def update_status(message: str, scope: str = 'DLC.CORE') -> None:
print(f'[{scope}] {message}')
if not modules.globals.headless:
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):
return
try:
shutil.copy2(modules.globals.target_path, modules.globals.output_path)
except Exception as e:
print("Error copying file:", str(e))
for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
update_status('Progressing...', frame_processor.NAME)
frame_processor.process_image(modules.globals.source_path, modules.globals.output_path, modules.globals.output_path)
release_resources()
if is_image(modules.globals.target_path):
elapsed = time.time() - start_time
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...')
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()
if not modules.globals.map_faces:
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
fps_processing = total_frames / processing_time if processing_time > 0 else 0
update_status(f'Frame processing completed in {processing_time:.2f}s ({fps_processing:.2f} fps)')
encoding_start = time.time()
update_status(f'Creating video with {fps} fps...')
video_created = create_video(modules.globals.target_path, fps)
encoding_time = time.time() - encoding_start
if video_created:
update_status(f'Video encoding completed in {encoding_time:.2f}s')
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:
update_status('Restoring audio...')
else:
update_status('Restoring audio might cause issues as fps are not kept...')
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)
total_time = time.time() - start_time
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!')
def destroy(to_quit=True) -> None:
if modules.globals.target_path:
clean_temp(modules.globals.target_path)
if to_quit:
quit()
def run() -> None:
parse_args()
if not pre_check():
return
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()
else:
window = ui.init(start, destroy, modules.globals.lang)
window.mainloop()
```
## /modules/custom_types.py
```py path="/modules/custom_types.py"
from typing import Any
from insightface.app.common import Face
import numpy
Face = Face
Frame = numpy.ndarray[Any, Any]
```
## /modules/face_analyser.py
```py path="/modules/face_analyser.py"
import os
import shutil
from typing import Any
import insightface
import threading
import cv2
import modules.globals
from tqdm import tqdm
from modules.typing import Frame
from modules.cluster_analysis import find_cluster_centroids, find_closest_centroid
from modules.utilities import get_temp_directory_path, create_temp, extract_frames, clean_temp, get_temp_frame_paths
from pathlib import Path
FACE_ANALYSER = None
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:
# Double-check after acquiring lock
if FACE_ANALYSER is None:
from modules.processors.frame._onnx_enhancer import (
build_provider_config,
)
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:
"""Replace the detection model's ONNX session with a CoreML-optimized one.
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])
except ValueError:
return None
def get_many_faces(frame: Frame) -> Any:
try:
if _is_dml():
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:
return True
return False
def default_source_face() -> Any:
for map in modules.globals.source_target_map:
if "source" in map:
return map['source']['face']
return None
def simplify_maps() -> Any:
centroids = []
faces = []
for map in modules.globals.source_target_map:
if "source" in map and "target" in map:
centroids.append(map['target']['face'].normed_embedding)
faces.append(map['source']['face'])
modules.globals.simple_map = {'source_faces': faces, 'target_embeddings': centroids}
return None
def add_blank_map() -> Any:
try:
max_id = -1
if len(modules.globals.source_target_map) > 0:
max_id = max(modules.globals.source_target_map, key=lambda x: x['id'])['id']
modules.globals.source_target_map.append({
'id' : max_id + 1
})
except ValueError:
return None
def get_unique_faces_from_target_image() -> Any:
try:
modules.globals.source_target_map = []
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:
x_min, y_min, x_max, y_max = face['bbox']
modules.globals.source_target_map.append({
'id' : i,
'target' : {
'cv2' : target_frame[int(y_min):int(y_max), int(x_min):int(x_max)],
'face' : face
}
})
i = i + 1
except ValueError:
return None
def get_unique_faces_from_target_video() -> Any:
try:
modules.globals.source_target_map = []
frame_face_embeddings = []
face_embeddings = []
print('Creating temp resources...')
clean_temp(modules.globals.target_path)
create_temp(modules.globals.target_path)
print('Extracting frames...')
extract_frames(modules.globals.target_path)
temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
i = 0
for temp_frame_path in tqdm(temp_frame_paths, desc="Extracting face embeddings from frames"):
temp_frame = cv2.imread(temp_frame_path)
many_faces = get_many_faces(temp_frame)
if many_faces is None:
continue
for face in many_faces:
face_embeddings.append(face.normed_embedding)
frame_face_embeddings.append({'frame': i, 'faces': many_faces, 'location': temp_frame_path})
i += 1
centroids = find_cluster_centroids(face_embeddings)
for frame in frame_face_embeddings:
for face in frame['faces']:
closest_centroid_index, _ = find_closest_centroid(centroids, face.normed_embedding)
face['target_centroid'] = closest_centroid_index
for i in range(len(centroids)):
modules.globals.source_target_map.append({
'id' : i
})
temp = []
for frame in tqdm(frame_face_embeddings, desc=f"Mapping frame embeddings to centroids-{i}"):
temp.append({'frame': frame['frame'], 'faces': [face for face in frame['faces'] if face['target_centroid'] == i], 'location': frame['location']})
modules.globals.source_target_map[i]['target_faces_in_frame'] = temp
# dump_faces(centroids, frame_face_embeddings)
default_target_face()
except ValueError:
return None
def default_target_face():
for map in modules.globals.source_target_map:
best_face = None
best_frame = None
for frame in map['target_faces_in_frame']:
if len(frame['faces']) > 0:
best_face = frame['faces'][0]
best_frame = frame
break
for frame in map['target_faces_in_frame']:
for face in frame['faces']:
if face['det_score'] > best_face['det_score']:
best_face = face
best_frame = frame
x_min, y_min, x_max, y_max = best_face['bbox']
target_frame = cv2.imread(best_frame['location'])
map['target'] = {
'cv2' : target_frame[int(y_min):int(y_max), int(x_min):int(x_max)],
'face' : best_face
}
def dump_faces(centroids: Any, frame_face_embeddings: list):
temp_directory_path = get_temp_directory_path(modules.globals.target_path)
for i in range(len(centroids)):
if os.path.exists(temp_directory_path + f"/{i}") and os.path.isdir(temp_directory_path + f"/{i}"):
shutil.rmtree(temp_directory_path + f"/{i}")
Path(temp_directory_path + f"/{i}").mkdir(parents=True, exist_ok=True)
for frame in tqdm(frame_face_embeddings, desc=f"Copying faces to temp/./{i}"):
temp_frame = cv2.imread(frame['location'])
j = 0
for face in frame['faces']:
if face['target_centroid'] == i:
x_min, y_min, x_max, y_max = face['bbox']
if temp_frame[int(y_min):int(y_max), int(x_min):int(x_max)].size > 0:
cv2.imwrite(temp_directory_path + f"/{i}/{frame['frame']}_{j}.png", temp_frame[int(y_min):int(y_max), int(x_min):int(x_max)])
j += 1
```
## /modules/gettext.py
```py path="/modules/gettext.py"
import json
from pathlib import Path
class LanguageManager:
def __init__(self, default_language="en"):
self.current_language = default_language
self.translations = {}
self.load_language(default_language)
def load_language(self, language_code) -> bool:
"""load language file"""
if language_code == "en":
return True
try:
file_path = Path(__file__).parent.parent / f"locales/{language_code}.json"
with open(file_path, "r", encoding="utf-8") as file:
self.translations = json.load(file)
self.current_language = language_code
return True
except FileNotFoundError:
print(f"Language file not found: {language_code}")
return False
def _(self, key, default=None) -> str:
"""get translate text"""
return self.translations.get(key, default if default else key)
```
## /modules/globals.py
```py path="/modules/globals.py"
# --- 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")
file_types = [
("Image", ("*.png", "*.jpg", "*.jpeg", "*.gif", "*.bmp")),
("Video", ("*.mp4", "*.mkv")),
]
# Face Mapping Data
source_target_map: List[Dict[str, Any]] = [] # Stores detailed map for image/video processing
simple_map: Dict[str, Any] = {} # Stores simplified map (embeddings/faces) for live/simple mode
# Paths
source_path: str | None = None
target_path: str | None = None
output_path: str | None = None
# Processing Options
frame_processors: List[str] = []
keep_fps: bool = True
keep_audio: bool = True
keep_frames: bool = False
many_faces: bool = False # Process all detected faces with default source
map_faces: bool = False # Use source_target_map or simple_map for specific swaps
poisson_blend: bool = False # Enable Poisson Blending for smoother face swaps
color_correction: bool = False # Enable color correction (implementation specific)
nsfw_filter: bool = False
# Video Output Options
video_encoder: str | None = None
video_quality: int | None = None # Typically a CRF value or bitrate
# Live Mode Options
live_mirror: bool = False
live_resizable: bool = True
camera_input_combobox: Any | None = None # Placeholder for UI element if needed
webcam_preview_running: bool = False
show_fps: bool = False
# System Configuration
max_memory: int | None = None # Memory limit in GB? (Needs clarification)
execution_providers: List[str] = [] # e.g., ['CUDAExecutionProvider', 'CPUExecutionProvider']
execution_threads: int | None = None # Number of threads for CPU execution
headless: bool | None = None # Run without UI?
log_level: str = "error" # Logging level (e.g., 'debug', 'info', 'warning', 'error')
# Face Processor UI Toggles (Example)
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()
```
## /modules/gpu_processing.py
```py path="/modules/gpu_processing.py"
# --- 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 ---
```
## /modules/metadata.py
```py path="/modules/metadata.py"
name = 'Deep-Live-Cam'
version = '2.1.5'
edition = 'GitHub Edition'
```
## /modules/onnx_optimize.py
```py path="/modules/onnx_optimize.py"
"""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)
```
## /modules/paths.py
```py path="/modules/paths.py"
"""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")
```
## /modules/platform_info.py
```py path="/modules/platform_info.py"
"""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
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_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,
)
```
## /modules/predicter.py
```py path="/modules/predicter.py"
import numpy
import opennsfw2
from PIL import Image
import cv2 # Add OpenCV import
import modules.globals # Import globals to access the color correction toggle
from modules.gpu_processing import gpu_cvt_color
from modules.typing import Frame
MAX_PROBABILITY = 0.85
# Preload the model once for efficiency
model = None
def predict_frame(target_frame: Frame) -> bool:
# Convert the frame to RGB before processing if color correction is enabled
if modules.globals.color_correction:
target_frame = gpu_cvt_color(target_frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(target_frame)
image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)
global model
if model is None:
model = opennsfw2.make_open_nsfw_model()
views = numpy.expand_dims(image, axis=0)
_, probability = model.predict(views)[0]
return probability > MAX_PROBABILITY
def predict_image(target_path: str) -> bool:
return opennsfw2.predict_image(target_path) > MAX_PROBABILITY
def predict_video(target_path: str) -> bool:
_, probabilities = opennsfw2.predict_video_frames(video_path=target_path, frame_interval=100)
return any(probability > MAX_PROBABILITY for probability in probabilities)
```
## /modules/processors/__init__.py
```py path="/modules/processors/__init__.py"
```
## /modules/processors/frame/__init__.py
```py path="/modules/processors/frame/__init__.py"
```
## /modules/processors/frame/_onnx_enhancer.py
```py path="/modules/processors/frame/_onnx_enhancer.py"
"""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
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,
},
))
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)
```
## /modules/processors/frame/core.py
```py path="/modules/processors/frame/core.py"
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 = [
'pre_check',
'pre_start',
'process_frame',
'process_image',
'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")
sys.exit()
return frame_processor_module
def get_frame_processors_modules(frame_processors: List[str]) -> List[ModuleType]:
global FRAME_PROCESSORS_MODULES
if not FRAME_PROCESSORS_MODULES:
for frame_processor in frame_processors:
frame_processor_module = load_frame_processor_module(frame_processor)
FRAME_PROCESSORS_MODULES.append(frame_processor_module)
set_frame_processors_modules_from_ui(frame_processors)
return FRAME_PROCESSORS_MODULES
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:
try:
frame_processor_module = load_frame_processor_module(frame_processor)
FRAME_PROCESSORS_MODULES.append(frame_processor_module)
if frame_processor not in modules.globals.frame_processors:
modules.globals.frame_processors.append(frame_processor)
except SystemExit:
print(f"Warning: Failed to load frame processor {frame_processor} requested by UI state.")
except Exception as e:
print(f"Warning: Error loading frame processor {frame_processor} requested by UI state: {e}")
elif 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)
if module_to_remove:
FRAME_PROCESSORS_MODULES.remove(module_to_remove)
if frame_processor in modules.globals.frame_processors:
modules.globals.frame_processors.remove(frame_processor)
except Exception as e:
print(f"Warning: Error removing frame processor {frame_processor}: {e}")
def multi_process_frame(source_path: str, temp_frame_paths: List[str], process_frames: Callable[[str, List[str], Any], None], progress: Any = None) -> None:
"""Process frames in parallel with optimized batching and memory management."""
max_workers = modules.globals.execution_threads
# Determine optimal batch size based on available memory and thread count
# Process frames in batches to avoid memory overflow
batch_size = max(1, min(32, len(temp_frame_paths) // max(1, max_workers)))
with ThreadPoolExecutor(max_workers=max_workers) as executor:
# Process in batches to manage memory better
for i in range(0, len(temp_frame_paths), batch_size):
batch = temp_frame_paths[i:i + batch_size]
futures = []
for path in batch:
future = executor.submit(process_frames, source_path, [path], progress)
futures.append(future)
# Wait for batch to complete before starting next batch
for future in futures:
try:
future.result()
except Exception as e:
print(f"Error processing frame: {e}")
def process_video(source_path: str, frame_paths: list[str], process_frames: Callable[[str, List[str], Any], None]) -> None:
progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
total = len(frame_paths)
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).
"""
import cv2
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 = cv2.imread(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
```
## /modules/processors/frame/face_enhancer.py
```py path="/modules/processors/frame/face_enhancer.py"
# Uses ONNX Runtime for GFPGAN face enhancement (no torch/gfpgan dependency)
from typing import Any, List
import cv2
import threading
import numpy as np
import os
import onnxruntime
import modules.globals
import modules.processors.frame.core
from modules.core import update_status
from modules.face_analyser import get_many_faces
from modules.typing import Frame, Face
from modules.utilities import (
is_image,
is_video,
)
FACE_ENHANCER = None
THREAD_SEMAPHORE = threading.Semaphore()
THREAD_LOCK = threading.Lock()
NAME = "DLC.FACE-ENHANCER"
abs_dir = os.path.dirname(os.path.abspath(__file__))
models_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
)
# Standard FFHQ 5-point face template for 512x512 resolution
# Points: left_eye, right_eye, nose, left_mouth, right_mouth
FFHQ_TEMPLATE_512 = np.array(
[
[192.98138, 239.94708],
[318.90277, 240.19366],
[256.63416, 314.01935],
[201.26117, 371.41043],
[313.08905, 371.15118],
],
dtype=np.float32,
)
def pre_check() -> bool:
model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
if not os.path.exists(model_path):
update_status(
f"GFPGAN ONNX model not found at {model_path}. "
"Please place gfpgan-1024.onnx in the models folder.",
NAME,
)
return False
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_face_enhancer() -> onnxruntime.InferenceSession:
"""
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:
model_path = os.path.join(models_dir, "gfpgan-1024.onnx")
if not os.path.exists(model_path):
raise FileNotFoundError(
f"{NAME}: Model not found at {model_path}"
)
try:
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(
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 (
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,
detected_faces=None) -> Frame:
"""Processes a frame: enhances face if detected."""
return enhance_face(temp_frame, detected_faces=detected_faces)
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
) -> None:
"""Processes multiple frames from file paths."""
for temp_frame_path in temp_frame_paths:
if not os.path.exists(temp_frame_path):
print(
f"{NAME}: Warning: Frame path not found {temp_frame_path}, skipping."
)
if progress:
progress.update(1)
continue
temp_frame = cv2.imread(temp_frame_path)
if temp_frame is None:
print(
f"{NAME}: Warning: Failed to read frame {temp_frame_path}, skipping."
)
if progress:
progress.update(1)
continue
result_frame = process_frame(None, temp_frame)
cv2.imwrite(temp_frame_path, result_frame)
if progress:
progress.update(1)
def process_image(
source_path: str | None, target_path: str, output_path: str
) -> None:
"""Processes a single image file."""
target_frame = cv2.imread(target_path)
if target_frame is None:
print(f"{NAME}: Error: Failed to read target image {target_path}")
return
result_frame = process_frame(None, target_frame)
cv2.imwrite(output_path, result_frame)
print(f"{NAME}: Enhanced image saved to {output_path}")
def process_video(
source_path: str | None, temp_frame_paths: List[str]
) -> None:
"""Processes video frames using the frame processor core."""
modules.processors.frame.core.process_video(
source_path, temp_frame_paths, process_frames
)
```
## /modules/processors/frame/face_enhancer_gpen256.py
```py path="/modules/processors/frame/face_enhancer_gpen256.py"
"""GPEN-BFR-256 face enhancer — ONNX-based face restoration at 256x256."""
from typing import Any, List
import os
import threading
import cv2
import modules.globals
import modules.processors.frame.core
from modules.core import update_status
from modules.face_analyser import get_one_face
from modules.typing import Frame, Face
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_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 pre_check() -> bool:
model_path = os.path.join(models_dir, MODEL_FILE)
if not os.path.exists(model_path):
update_status(f"Downloading {MODEL_FILE}...", NAME)
from modules.utilities import conditional_download
conditional_download(models_dir, [MODEL_URL])
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 = os.path.join(models_dir, MODEL_FILE)
if not os.path.exists(model_path):
from modules.utilities import conditional_download
conditional_download(models_dir, [MODEL_URL])
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model file not found: {model_path}")
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 = cv2.imread(temp_frame_path)
if temp_frame is None:
if progress:
progress.update(1)
continue
result = process_frame(None, temp_frame)
cv2.imwrite(temp_frame_path, result)
if progress:
progress.update(1)
def process_image(source_path: str | None, target_path: str, output_path: str) -> None:
target_frame = cv2.imread(target_path)
if target_frame is None:
print(f"{NAME}: Error: Failed to read target image {target_path}")
return
result_frame = process_frame(None, target_frame)
cv2.imwrite(output_path, result_frame)
print(f"{NAME}: Enhanced image saved to {output_path}")
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)
```
## /modules/processors/frame/face_enhancer_gpen512.py
```py path="/modules/processors/frame/face_enhancer_gpen512.py"
"""GPEN-BFR-512 face enhancer — ONNX-based face restoration at 512x512."""
from typing import Any, List
import os
import threading
import cv2
import modules.globals
import modules.processors.frame.core
from modules.core import update_status
from modules.face_analyser import get_one_face
from modules.typing import Frame, Face
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_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 pre_check() -> bool:
model_path = os.path.join(models_dir, MODEL_FILE)
if not os.path.exists(model_path):
update_status(f"Downloading {MODEL_FILE}...", NAME)
from modules.utilities import conditional_download
conditional_download(models_dir, [MODEL_URL])
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 = os.path.join(models_dir, MODEL_FILE)
if not os.path.exists(model_path):
from modules.utilities import conditional_download
conditional_download(models_dir, [MODEL_URL])
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model file not found: {model_path}")
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 = cv2.imread(temp_frame_path)
if temp_frame is None:
if progress:
progress.update(1)
continue
result = process_frame(None, temp_frame)
cv2.imwrite(temp_frame_path, result)
if progress:
progress.update(1)
def process_image(source_path: str | None, target_path: str, output_path: str) -> None:
target_frame = cv2.imread(target_path)
if target_frame is None:
print(f"{NAME}: Error: Failed to read target image {target_path}")
return
result_frame = process_frame(None, target_frame)
cv2.imwrite(output_path, result_frame)
print(f"{NAME}: Enhanced image saved to {output_path}")
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)
```
## /modules/processors/frame/face_masking.py
```py path="/modules/processors/frame/face_masking.py"
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
```
## /modules/run.py
```py path="/modules/run.py"
#!/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()
```
## /modules/tkinter_fix.py
```py path="/modules/tkinter_fix.py"
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()
```
## /modules/typing.py
```py path="/modules/typing.py"
from typing import Any
from insightface.app.common import Face
import numpy
Face = Face
Frame = numpy.ndarray[Any, Any]
```
## /modules/ui.json
```json path="/modules/ui.json"
{
"CTk": {
"fg_color": ["gray95", "gray10"]
},
"CTkToplevel": {
"fg_color": ["gray95", "gray10"]
},
"CTkFrame": {
"corner_radius": 0,
"border_width": 0,
"fg_color": ["gray90", "gray13"],
"top_fg_color": ["gray85", "gray16"],
"border_color": ["gray65", "gray28"]
},
"CTkButton": {
"corner_radius": 0,
"border_width": 0,
"fg_color": ["#2aa666", "#1f538d"],
"hover_color": ["#3cb666", "#14375e"],
"border_color": ["#3e4a40", "#949A9F"],
"text_color": ["#f3faf6", "#f3faf6"],
"text_color_disabled": ["gray74", "gray60"]
},
"CTkLabel": {
"corner_radius": 0,
"fg_color": "transparent",
"text_color": ["gray14", "gray84"]
},
"CTkEntry": {
"corner_radius": 0,
"border_width": 2,
"fg_color": ["#F9F9FA", "#343638"],
"border_color": ["#979DA2", "#565B5E"],
"text_color": ["gray14", "gray84"],
"placeholder_text_color": ["gray52", "gray62"]
},
"CTkCheckbox": {
"corner_radius": 0,
"border_width": 3,
"fg_color": ["#2aa666", "#1f538d"],
"border_color": ["#3e4a40", "#949A9F"],
"hover_color": ["#3cb666", "#14375e"],
"checkmark_color": ["#f3faf6", "gray90"],
"text_color": ["gray14", "gray84"],
"text_color_disabled": ["gray60", "gray45"]
},
"CTkSwitch": {
"corner_radius": 1000,
"border_width": 3,
"button_length": 0,
"fg_color": ["#939BA2", "#4A4D50"],
"progress_color": ["#2aa666", "#1f538d"],
"button_color": ["gray36", "#D5D9DE"],
"button_hover_color": ["gray20", "gray100"],
"text_color": ["gray14", "gray84"],
"text_color_disabled": ["gray60", "gray45"]
},
"CTkRadiobutton": {
"corner_radius": 1000,
"border_width_checked": 6,
"border_width_unchecked": 3,
"fg_color": ["#2aa666", "#1f538d"],
"border_color": ["#3e4a40", "#949A9F"],
"hover_color": ["#3cb666", "#14375e"],
"text_color": ["gray14", "gray84"],
"text_color_disabled": ["gray60", "gray45"]
},
"CTkProgressBar": {
"corner_radius": 1000,
"border_width": 0,
"fg_color": ["#939BA2", "#4A4D50"],
"progress_color": ["#2aa666", "#1f538d"],
"border_color": ["gray", "gray"]
},
"CTkSlider": {
"corner_radius": 1000,
"button_corner_radius": 1000,
"border_width": 6,
"button_length": 0,
"fg_color": ["#939BA2", "#4A4D50"],
"progress_color": ["gray40", "#AAB0B5"],
"button_color": ["#2aa666", "#1f538d"],
"button_hover_color": ["#3cb666", "#14375e"]
},
"CTkOptionMenu": {
"corner_radius": 0,
"fg_color": ["#2aa666", "#1f538d"],
"button_color": ["#3cb666", "#14375e"],
"button_hover_color": ["#234567", "#1e2c40"],
"text_color": ["#f3faf6", "#f3faf6"],
"text_color_disabled": ["gray74", "gray60"]
},
"CTkComboBox": {
"corner_radius": 0,
"border_width": 2,
"fg_color": ["#F9F9FA", "#343638"],
"border_color": ["#979DA2", "#565B5E"],
"button_color": ["#979DA2", "#565B5E"],
"button_hover_color": ["#6E7174", "#7A848D"],
"text_color": ["gray14", "gray84"],
"text_color_disabled": ["gray50", "gray45"]
},
"CTkScrollbar": {
"corner_radius": 1000,
"border_spacing": 4,
"fg_color": "transparent",
"button_color": ["gray55", "gray41"],
"button_hover_color": ["gray40", "gray53"]
},
"CTkSegmentedButton": {
"corner_radius": 0,
"border_width": 2,
"fg_color": ["#979DA2", "gray29"],
"selected_color": ["#2aa666", "#1f538d"],
"selected_hover_color": ["#3cb666", "#14375e"],
"unselected_color": ["#979DA2", "gray29"],
"unselected_hover_color": ["gray70", "gray41"],
"text_color": ["#f3faf6", "#f3faf6"],
"text_color_disabled": ["gray74", "gray60"]
},
"CTkTextbox": {
"corner_radius": 0,
"border_width": 0,
"fg_color": ["gray100", "gray20"],
"border_color": ["#979DA2", "#565B5E"],
"text_color": ["gray14", "gray84"],
"scrollbar_button_color": ["gray55", "gray41"],
"scrollbar_button_hover_color": ["gray40", "gray53"]
},
"CTkScrollableFrame": {
"label_fg_color": ["gray80", "gray21"]
},
"DropdownMenu": {
"fg_color": ["gray90", "gray20"],
"hover_color": ["gray75", "gray28"],
"text_color": ["gray14", "gray84"]
},
"CTkFont": {
"macOS": {
"family": "Avenir",
"size": 18,
"weight": "normal"
},
"Windows": {
"family": "Corbel",
"size": 18,
"weight": "normal"
},
"Linux": {
"family": "Montserrat",
"size": 18,
"weight": "normal"
}
},
"URL": {
"text_color": ["gray74", "gray60"]
}
}
```
## /modules/ui_tooltip.py
```py path="/modules/ui_tooltip.py"
"""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
```
## /modules/utilities.py
```py path="/modules/utilities.py"
import glob
import mimetypes
import os
import platform
import shutil
import ssl
import subprocess
import urllib
from pathlib import Path
from typing import List, Any
from tqdm import tqdm
import modules.globals
TEMP_FILE = "temp.mp4"
TEMP_DIRECTORY = "temp"
def run_ffmpeg(args: List[str]) -> bool:
"""Run ffmpeg with hardware acceleration and optimized settings."""
commands = [
"ffmpeg",
"-hide_banner",
"-hwaccel", "auto", # Auto-detect hardware acceleration
"-hwaccel_output_format", "auto", # Use hardware format when possible
"-threads", str(modules.globals.execution_threads or 0), # 0 = auto-detect optimal thread count
"-loglevel", modules.globals.log_level,
]
commands.extend(args)
try:
subprocess.check_output(commands, stderr=subprocess.STDOUT)
return True
except subprocess.CalledProcessError as error:
output = error.output.decode(errors="ignore").strip()
if output:
print(output)
except Exception as error:
print(f"ffmpeg execution failed: {error}")
return False
def detect_fps(target_path: str) -> float:
command = [
"ffprobe",
"-v",
"error",
"-select_streams",
"v:0",
"-show_entries",
"stream=r_frame_rate",
"-of",
"default=noprint_wrappers=1:nokey=1",
target_path,
]
output = subprocess.check_output(command).decode().strip().split("/")
try:
numerator, denominator = map(int, output)
return numerator / denominator
except Exception:
pass
return 30.0
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,
"-vf", "format=rgb24", # Use video filter for format conversion (faster)
"-vsync", "0", # Prevent frame duplication
os.path.join(temp_directory_path, "%04d.png"),
]
)
def create_video(target_path: str, fps: float = 30.0) -> bool:
"""Create video with hardware-accelerated encoding and optimized settings."""
temp_output_path = get_temp_output_path(target_path)
temp_directory_path = get_temp_directory_path(target_path)
# Determine optimal encoder based on available hardware
encoder = modules.globals.video_encoder
encoder_options = []
# GPU-accelerated encoding options
if 'CUDAExecutionProvider' in modules.globals.execution_providers:
# NVIDIA GPU encoding
if encoder == 'libx264':
encoder = 'h264_nvenc'
encoder_options = [
"-preset", "p7", # Highest quality preset for NVENC
"-tune", "hq", # High quality tuning
"-rc", "vbr", # Variable bitrate
"-cq", str(modules.globals.video_quality), # Quality level
"-b:v", "0", # Let CQ control bitrate
"-multipass", "fullres", # Two-pass encoding for better quality
]
elif encoder == 'libx265':
encoder = 'hevc_nvenc'
encoder_options = [
"-preset", "p7",
"-tune", "hq",
"-rc", "vbr",
"-cq", str(modules.globals.video_quality),
"-b:v", "0",
]
elif 'DmlExecutionProvider' in modules.globals.execution_providers:
# AMD/Intel GPU encoding (DirectML on Windows)
if encoder == 'libx264':
# Try AMD AMF encoder
encoder = 'h264_amf'
encoder_options = [
"-quality", "quality", # Quality mode
"-rc", "vbr_latency",
"-qp_i", str(modules.globals.video_quality),
"-qp_p", str(modules.globals.video_quality),
]
elif encoder == 'libx265':
encoder = 'hevc_amf'
encoder_options = [
"-quality", "quality",
"-rc", "vbr_latency",
"-qp_i", str(modules.globals.video_quality),
"-qp_p", str(modules.globals.video_quality),
]
else:
# CPU encoding with optimized settings
if encoder == 'libx264':
encoder_options = [
"-preset", "medium", # Balance speed/quality
"-crf", str(modules.globals.video_quality),
"-tune", "film", # Optimize for film content
]
elif encoder == 'libx265':
encoder_options = [
"-preset", "medium",
"-crf", str(modules.globals.video_quality),
"-x265-params", "log-level=error",
]
elif encoder == 'libvpx-vp9':
encoder_options = [
"-crf", str(modules.globals.video_quality),
"-b:v", "0", # Constant quality mode
"-cpu-used", "2", # Speed vs quality (0-5, lower=slower/better)
]
# Build ffmpeg command
ffmpeg_args = [
"-r", str(fps),
"-i", os.path.join(temp_directory_path, "%04d.png"),
"-c:v", encoder,
]
# Add encoder-specific options
ffmpeg_args.extend(encoder_options)
# Add common options
ffmpeg_args.extend([
"-pix_fmt", "yuv420p",
"-movflags", "+faststart", # Enable fast start for web playback
"-vf", "colorspace=bt709:iall=bt601-6-625:fast=1",
"-y",
temp_output_path,
])
# Try with hardware encoder first, fallback to software if it fails
success = run_ffmpeg(ffmpeg_args)
if not success and encoder in ['h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf']:
# Fallback to software encoding
print(f"Hardware encoding with {encoder} failed, falling back to software encoding...")
fallback_encoder = 'libx264' if 'h264' in encoder else 'libx265'
ffmpeg_args_fallback = [
"-r", str(fps),
"-i", os.path.join(temp_directory_path, "%04d.png"),
"-c:v", fallback_encoder,
"-preset", "medium",
"-crf", str(modules.globals.video_quality),
"-pix_fmt", "yuv420p",
"-movflags", "+faststart",
"-vf", "colorspace=bt709:iall=bt601-6-625:fast=1",
"-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:
temp_output_path = get_temp_output_path(target_path)
done = run_ffmpeg(
[
"-i",
temp_output_path,
"-i",
target_path,
"-c:v",
"copy",
"-map",
"0:v:0",
"-map",
"1:a:0",
"-y",
output_path,
]
)
if not done:
move_temp(target_path, output_path)
def get_temp_frame_paths(target_path: str) -> List[str]:
temp_directory_path = get_temp_directory_path(target_path)
return glob.glob((os.path.join(glob.escape(temp_directory_path), "*.png")))
def get_temp_directory_path(target_path: str) -> str:
target_name, _ = os.path.splitext(os.path.basename(target_path))
target_directory_path = os.path.dirname(target_path)
return os.path.join(target_directory_path, TEMP_DIRECTORY, target_name)
def get_temp_output_path(target_path: str) -> str:
temp_directory_path = get_temp_directory_path(target_path)
return os.path.join(temp_directory_path, TEMP_FILE)
def normalize_output_path(source_path: str, target_path: str, output_path: str) -> Any:
if source_path and target_path:
source_name, _ = os.path.splitext(os.path.basename(source_path))
target_name, target_extension = os.path.splitext(os.path.basename(target_path))
if os.path.isdir(output_path):
return os.path.join(
output_path, source_name + "-" + target_name + target_extension
)
return output_path
def create_temp(target_path: str) -> None:
temp_directory_path = get_temp_directory_path(target_path)
Path(temp_directory_path).mkdir(parents=True, exist_ok=True)
def move_temp(target_path: str, output_path: str) -> None:
temp_output_path = get_temp_output_path(target_path)
if os.path.isfile(temp_output_path):
if os.path.isfile(output_path):
os.remove(output_path)
shutil.move(temp_output_path, output_path)
def clean_temp(target_path: str) -> None:
temp_directory_path = get_temp_directory_path(target_path)
parent_directory_path = os.path.dirname(temp_directory_path)
if not modules.globals.keep_frames and os.path.isdir(temp_directory_path):
shutil.rmtree(temp_directory_path)
if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
os.rmdir(parent_directory_path)
def has_image_extension(image_path: str) -> bool:
return image_path.lower().endswith(("png", "jpg", "jpeg"))
def is_image(image_path: str) -> bool:
if image_path and os.path.isfile(image_path):
mimetype, _ = mimetypes.guess_type(image_path)
return bool(mimetype and mimetype.startswith("image/"))
return False
def is_video(video_path: str) -> bool:
if video_path and os.path.isfile(video_path):
mimetype, _ = mimetypes.guess_type(video_path)
return bool(mimetype and mimetype.startswith("video/"))
return False
def conditional_download(download_directory_path: str, urls: List[str]) -> None:
if not os.path.exists(download_directory_path):
os.makedirs(download_directory_path)
for url in urls:
download_file_path = os.path.join(
download_directory_path, os.path.basename(url)
)
if not os.path.exists(download_file_path):
request = urllib.request.Request(url)
# 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",
unit="B",
unit_scale=True,
unit_divisor=1024,
) as progress:
with open(download_file_path, "wb") as f:
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
```
## /modules/video_capture.py
```py path="/modules/video_capture.py"
import cv2
import numpy as np
import time
from typing import Optional, Tuple, Callable
import platform
import threading
# Only import Windows-specific library if on Windows
if platform.system() == "Windows":
from pygrabber.dshow_graph import FilterGraph
class VideoCapturer:
def __init__(self, device_index: int):
self.device_index = device_index
self.frame_callback = None
self._current_frame = None
self._frame_ready = threading.Event()
self.is_running = False
self.cap = None
# 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":
self.graph = FilterGraph()
# Verify device exists
devices = self.graph.get_input_devices()
if self.device_index >= len(devices):
raise ValueError(
f"Invalid device index {device_index}. Available devices: {len(devices)}"
)
def start(self, width: int = 960, height: int = 540, fps: int = 60) -> bool:
"""Initialize and start video capture"""
try:
if platform.system() == "Windows":
# device_index comes from pygrabber.FilterGraph (DirectShow
# 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_MSMF),
(self.device_index, cv2.CAP_ANY),
]
for dev_id, backend in capture_methods:
try:
self.cap = cv2.VideoCapture(dev_id, backend, open_params)
if self.cap.isOpened():
break
self.cap.release()
except Exception:
continue
else:
# Unix-like systems (Linux/Mac) capture method
self.cap = cv2.VideoCapture(self.device_index)
if not self.cap or not self.cap.isOpened():
raise RuntimeError("Failed to open camera")
# Belt-and-braces: also set via cap.set() for backends that honor
# 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
except Exception as e:
print(f"Failed to start capture: {str(e)}")
if self.cap:
self.cap.release()
return False
def read(self) -> Tuple[bool, Optional[np.ndarray]]:
"""Read a frame from the camera"""
if not self.is_running or self.cap is None:
return False, None
ret, frame = self.cap.read()
if ret:
self._current_frame = frame
if self.frame_callback:
self.frame_callback(frame)
return True, frame
return False, None
def release(self) -> None:
"""Stop capture and release resources"""
if self.is_running and self.cap is not None:
self.cap.release()
self.is_running = False
self.cap = None
def _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
```
## /mypi.ini
```ini path="/mypi.ini"
[mypy]
check_untyped_defs = True
disallow_any_generics = True
disallow_untyped_calls = True
disallow_untyped_defs = True
ignore_missing_imports = True
strict_optional = False
```
## /pyproject.toml
```toml path="/pyproject.toml"
[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"]
```
## /requirements.txt
numpy>=1.23.5,<2
typing-extensions>=4.8.0
opencv-python==4.10.0.84
cv2_enumerate_cameras==1.1.15
onnx==1.18.0
insightface==0.7.3
psutil==5.9.8
PySide6>=6.7,<7
pillow==12.1.1
tqdm>=4.65.0
onnxruntime-silicon==1.16.3; sys_platform == 'darwin' and platform_machine == 'arm64'
onnxruntime-gpu==1.23.2; sys_platform != 'darwin'
tensorflow>=2.15.0; sys_platform != 'darwin'
tensorflow>=2.15.0; sys_platform == 'darwin' and python_version < '3.13'
opennsfw2==0.10.2
protobuf==4.25.1
pygrabber; sys_platform == 'win32'
## /run-cuda.bat
```bat path="/run-cuda.bat"
python run.py --execution-provider cuda
```
## /run-directml.bat
```bat path="/run-directml.bat"
python run.py --execution-provider dml
```
## /run.py
```py path="/run.py"
#!/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 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__':
core.run()
```
## /tests/test_face_analyser_get_one_face.py
```py path="/tests/test_face_analyser_get_one_face.py"
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()
```
## /tkinter_fix.py
```py path="/tkinter_fix.py"
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()
```
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