文件列表:

assist (0, 2018-08-02)
assist\__init__.py (0, 2018-08-02)
assist\content_search.py (1519, 2018-08-02)
assist\get_adj_dict.py (810, 2018-08-02)
assist\marker.py (10645, 2018-08-02)
assist\model_transfer.py (2331, 2018-08-02)
assist\search.ipynb (1224, 2018-08-02)
core (0, 2018-08-02)
core\Blendshape.py (2196, 2018-08-02)
core\EdgeTopology.py (2130, 2018-08-02)
core\Landmark.py (1854, 2018-08-02)
core\LandmarkMapper.py (2437, 2018-08-02)
core\Mesh.py (2172, 2018-08-02)
core\MorphableModel.py (3572, 2018-08-02)
core\PcaModel.py (5194, 2018-08-02)
core\RenderingParameters.py (7100, 2018-08-02)
core\__init__.py (0, 2018-08-02)
core\blendshape_fitting.py (7138, 2018-08-02)
core\closest_edge_fitting.py (14966, 2018-08-02)
core\contour_correspondence.py (12696, 2018-08-02)
core\fitting.py (56756, 2018-08-02)
core\glm.py (5418, 2018-08-02)
core\linear_shape_fitting.py (13594, 2018-08-02)
core\orthographic_camera_estimation_linear.py (3998, 2018-08-02)
core\render.py (37681, 2018-08-02)
core\utils.py (2720, 2018-08-02)
data (0, 2018-08-02)
data\00019fa010_940128.pts (1103, 2018-08-02)
data\00019fa010_940128.tif (98598, 2018-08-02)
data\00019pr010_940128.pts (476, 2018-08-02)
data\00019pr010_940128.tif (98667, 2018-08-02)
data\00029ba010_960521.pts (1108, 2018-08-02)
data\00029ba010_960521.tif (98586, 2018-08-02)
data\00029pr010_940128.pts (476, 2018-08-02)
data\00029pr010_940128.tif (98667, 2018-08-02)
py_share (0, 2018-08-02)
py_share\adj_dict_3448.pkl (329554, 2018-08-02)
... ...

# 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. 

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