Generate face mesh dataset using Google's FaceMesh model from annotated face datasets.

Watch this 30s video demo:

.

There are built in features to help generating the dataset more efficiently.

• Automatically centralize the marked face.
• Rotate the image to align the face horizontally.
• Crop the face with custom scale range.
• Generate mark heatmaps.
• Write TensorFlow Record files, or export the processed image and marks.
• Support multiple public datasets. Check the full list here

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

First clone this repo.

# From your favorite development directory
git clone https://github.com/yinguobing/face-mesh-generator.git

Then download Google's FaceMesh tflite model and put it in the assets directory.

Model link: https://github.com/google/mediapipe/blob/master/mediapipe/modules/face_landmark/face_landmark.tflite

Take WFLW as an example. Download the dataset files from the official website. Extract all files to one directory.

First, Construct the dataset.

ds_wflw = fmd.wflw.WFLW("wflw")
ds_wflw.populate_dataset(wflw_dir)

wflw_dir is the directory for the extracted files.

Then, process the dataset.

process(ds_wflw)

There is a demo file generate_mesh_dataset.py demonstrating how to generate face mesh data and save them in a TFRecord file. Please refer to it for more details.

Yin Guobing (尹国冰) - yinguobing

All the authors who made their datasets and model public.