基于flask搭建的3DMM人脸三维建模系统

# 3DMM-fitting This project is designed to fit a 3DMM to a frontal-face picture profile and a profile picture of the same person simultaneously. This is supposed to lead to a more reliable fitting result than the traditional way in which only one 2D picture is used, since we acquired additional depth information from the extra profile image. ## Library requirements * Python3.6 * [OpenCV](http://opencv.org/) * [Dlib](http://dlib.net/) * [Numpy](http://www.numpy.org/) * [toml](https://github.com/uiri/toml) The code has only been tested on Windows10 with Anaconda Python. ## Instructions You can find sample code at `test\fitting_test.py`. In the folder `data\`, two sets of sample images are already given to test the code. These images are from [color FERET database](https://www.nist.gov/itl/iad/image-group/color-feret-database). The facial landmarks are saved as pts files with the same name as the pictures. Please note that the frontal-face landmarks are annotated according to the iBug but the profile landmarks are annotated in a new way showed as below.  Not all the landmarks are used in the process of 3D-fitting. The frontal face image is automatically annotated with Dlib library. You can call the `frontal_face_marker` funtion at `assist\marker.py` to get a pts file contains the landmarks of the frontal face image. The profile image is presently marked manually. You can call the `manual_marker` fuction at `assist\marker.py` to do it. ## Presentation Run `test\fitting_test.py` with default imput images, you should get a picture discribes the accuracy of the fitting.  This picture will be saved in the `test\` folder, along with the generated 3D model as ply file.