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2023-07-13 12:36| 来源: 网络整理| 查看: 265

face2face-demo

This is a pix2pix demo that learns from facial landmarks and translates this into a face. A webcam-enabled application is also provided that translates your face to the trained face in real-time.

Getting Started 1. Prepare Environment # Clone this repo git clone [email protected]:datitran/face2face-demo.git # Create the conda environment from file (Mac OSX) conda env create -f environment.yml 2. Generate Training Data python generate_train_data.py --file angela_merkel_speech.mp4 --num 400 --landmark-model shape_predictor_68_face_landmarks.dat

Input:

file is the name of the video file from which you want to create the data set. num is the number of train data to be created. landmark-model is the facial landmark model that is used to detect the landmarks. A pre-trained facial landmark model is provided here.

Output:

Two folders original and landmarks will be created.

If you want to download my dataset, here is also the video file that I used and the generated training dataset (400 images already split into training and validation).

3. Train Model # Clone the repo from Christopher Hesse's pix2pix TensorFlow implementation git clone https://github.com/affinelayer/pix2pix-tensorflow.git # Move the original and landmarks folder into the pix2pix-tensorflow folder mv face2face-demo/landmarks face2face-demo/original pix2pix-tensorflow/photos # Go into the pix2pix-tensorflow folder cd pix2pix-tensorflow/ # Resize original images python tools/process.py \ --input_dir photos/original \ --operation resize \ --output_dir photos/original_resized # Resize landmark images python tools/process.py \ --input_dir photos/landmarks \ --operation resize \ --output_dir photos/landmarks_resized # Combine both resized original and landmark images python tools/process.py \ --input_dir photos/landmarks_resized \ --b_dir photos/original_resized \ --operation combine \ --output_dir photos/combined # Split into train/val set python tools/split.py \ --dir photos/combined # Train the model on the data python pix2pix.py \ --mode train \ --output_dir face2face-model \ --max_epochs 200 \ --input_dir photos/combined/train \ --which_direction AtoB

For more information around training, have a look at Christopher Hesse's pix2pix-tensorflow implementation.

4. Export Model

First, we need to reduce the trained model so that we can use an image tensor as input:

python reduce_model.py --model-input face2face-model --model-output face2face-reduced-model

Input:

model-input is the model folder to be imported. model-output is the model (reduced) folder to be exported.

Output:

It returns a reduced model with less weights file size than the original model.

Second, we freeze the reduced model to a single file.

python freeze_model.py --model-folder face2face-reduced-model

Input:

model-folder is the model folder of the reduced model.

Output:

It returns a frozen model file frozen_model.pb in the model folder.

I have uploaded a pre-trained frozen model here. This model is trained on 400 images with epoch 200.

5. Run Demo python run_webcam.py --source 0 --show 0 --landmark-model shape_predictor_68_face_landmarks.dat --tf-model face2face-reduced-model/frozen_model.pb

Input:

source is the device index of the camera (default=0). show is an option to either display the normal input (0) or the facial landmark (1) alongside the generated image (default=0). landmark-model is the facial landmark model that is used to detect the landmarks. tf-model is the frozen model file.

Example:

Requirements Anaconda / Python 3.5 TensorFlow 1.2 OpenCV 3.0 Dlib 19.4 Acknowledgments

Kudos to Christopher Hesse for his amazing pix2pix TensorFlow implementation and Gene Kogan for his inspirational workshop.

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