mukh 0.1.14

A comprehensive face analysis library that provides unified APIs for various face-related tasks

Mukh

Mukh (मुख, meaning "face" in Sanskrit) is a comprehensive face analysis library that provides unified APIs for various face-related tasks. It simplifies the process of working with multiple face analysis models through a consistent interface.

Features

• 🎯 Unified API: Single, consistent interface for multiple face analysis tasks
• 🔄 Model Flexibility: Support for multiple models per task
• 🛠️ Custom Pipelines: Optimized preprocessing and model combinations
• 🚀 Easy to Use: Simple, intuitive APIs for quick integration

Documentation

The library is documented in detail, click here to view the documentation.

Currently Supported Tasks

• Face Detection
• Face Reenactment
• Deepfake Detection using a Single Model
• Deepfake Detection Pipeline - Ensemble of multiple models

Installation

conda create -n mukh-dev python=3.10 -y
pip install mukh==0.1.14

Usage

Face Detection

from mukh.face_detection import FaceDetector

# Initialize detector
detection_model = "mediapipe"                                  # Other models: "blazeface", "ultralight"
detector = FaceDetector.create(detection_model)

# Detect faces
detections = detector.detect(
    image_path="assets/images/img1.jpg",                       # Path to the image to detect faces in
    save_csv=True,                                             # Save the detections to a CSV file
    csv_path=f"output/{detection_model}/detections.csv",       # Path to save the CSV file
    save_annotated=True,                                       # Save the annotated image
    output_folder=f"output/detection_model",                   # Path to save the annotated image
)

Example

python examples/face_detection/basic_detection.py --detection_model mediapipe

Face Reenactment

from mukh.face_reenactment import FaceReenactor

# Initialize reenactor
reenactor_model = "tps"                           # Available models: "tps"
reenactor = FaceReenactor.create(reenactor_model)

# Reenact face
result_path = reenactor.reenact_from_video(
    source_path="assets/images/img1.jpg",         # Path to the source image
    driving_video_path="assets/videos/video.mp4", # Path to the driving video
    output_path=f"output/{reenactor_model}",      # Path to save the reenacted video
    save_comparison=True,                         # Save the comparison video
    resize_to_image_resolution=False,             # Resize the reenacted video to the image resolution
)

Example

python examples/reenactment/basic_reenactment.py \
  --reenactor_model tps \
  --source_path assets/images/img1.jpg \
  --driving_video_path assets/videos/video_1sec.mp4 \
  --output_folder output

Deepfake Detection

Images

import torch
from mukh.deepfake_detection import DeepfakeDetector

# Initialize detector
detection_model = "efficientnet"                  # Other models "resnet_inception"

detector = DeepfakeDetector(
    model_name=detection_model,
    confidence_threshold=0.5,
    device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
)

# Detect deepfakes in Images

media_path = "assets/images/img1.jpg"             

detections, final_result = detector.detect(
    media_path=media_path,                                         # Path to the media file (image/video)
    save_csv=True,                                                 # Save the detections to a CSV file
    csv_path=f"output/{detection_model}/deepfake_detections.csv",  # Path to save the CSV file
    save_annotated=True,                                           # Save the annotated media
    output_folder=f"output/{args.detection_model}",                # Path to save the annotated media
)

Example

python examples/deepfake_detection/detection.py \
  --detection_model resnet_inception \
  --media_path assets/images/img1.jpg \
  --confidence_threshold 0.5 \

Videos

import torch
from mukh.deepfake_detection import DeepfakeDetector

# Initialize detector
detection_model = "efficientnet"                  # Other models "resnet_inception"

detector = DeepfakeDetector(
    model_name=detection_model,
    confidence_threshold=0.5,
    device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
)

# Detect deepfakes in Videos

media_path = "assets/videos/deepfake_elon_musk.mp4"             

detections, final_result = detector.detect(
    media_path=media_path,                                         # Path to the media file (image/video)
    save_csv=True,                                                 # Save the detections to a CSV file
    csv_path=f"output/{detection_model}/deepfake_detections.csv",  # Path to save the CSV file
    save_annotated=True,                                           # Save the annotated media
    output_folder=f"output/{args.detection_model}",                # Path to save the annotated media
    num_frames=11,                                                 # Number of equally spaced frames for video analysis
)

Example

python examples/deepfake_detection/detection.py \
  --detection_model resnet_inception \
  --media_path assets/videos/deepfake_elon_musk.mp4 \
  --confidence_threshold 0.5 \
  --num_frames 11

Deepfake Detection Pipeline

from mukh.pipelines.deepfake_detection import PipelineDeepfakeDetection

# Define model configurations with weights
model_configs = {
    "resnet_inception": 0.5,
    "efficientnet": 0.5
}

# Create ensemble detector
detector = PipelineDeepfakeDetection(model_configs)

media_path = "assets/videos/deepfake_elon_musk.mp4" # Or pass an image path

# Detect deepfakes
result = pipeline.detect(
    media_path=media_path,
    output_folder="output/deepfake_detection_pipeline,
    save_csv=True,
    num_frames=11,        # Number of equally spaced video frames for analysis
)

Example

python examples/pipelines/deepfake_detection.py \
  --media_path assets/videos/deepfake_elon_musk.mp4 \
  --output_folder output/deepfake_detection_pipeline

Contact

For questions and feedback, please open an issue on GitHub.