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from __future__ import absolute_import |
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from __future__ import division |
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from __future__ import print_function |
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import numpy as np |
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import scipy.io as sio |
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def load_BFM(model_path): |
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''' load BFM 3DMM model |
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Args: |
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model_path: path to BFM model. |
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Returns: |
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model: (nver = 53215, ntri = 105840). nver: number of vertices. ntri: number of triangles. |
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'shapeMU': [3*nver, 1] |
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'shapePC': [3*nver, 199] |
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'shapeEV': [199, 1] |
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'expMU': [3*nver, 1] |
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'expPC': [3*nver, 29] |
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'expEV': [29, 1] |
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'texMU': [3*nver, 1] |
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'texPC': [3*nver, 199] |
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'texEV': [199, 1] |
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'tri': [ntri, 3] (start from 1, should sub 1 in python and c++) |
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'tri_mouth': [114, 3] (start from 1, as a supplement to mouth triangles) |
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'kpt_ind': [68,] (start from 1) |
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PS: |
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You can change codes according to your own saved data. |
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Just make sure the model has corresponding attributes. |
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''' |
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C = sio.loadmat(model_path) |
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model = C['model'] |
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model = model[0,0] |
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model['shapeMU'] = (model['shapeMU'] + model['expMU']).astype(np.float32) |
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model['shapePC'] = model['shapePC'].astype(np.float32) |
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model['shapeEV'] = model['shapeEV'].astype(np.float32) |
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model['expEV'] = model['expEV'].astype(np.float32) |
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model['expPC'] = model['expPC'].astype(np.float32) |
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model['tri'] = model['tri'].T.copy(order = 'C').astype(np.int32) - 1 |
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model['tri_mouth'] = model['tri_mouth'].T.copy(order = 'C').astype(np.int32) - 1 |
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model['kpt_ind'] = (np.squeeze(model['kpt_ind']) - 1).astype(np.int32) |
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return model |
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def load_BFM_info(path = 'BFM_info.mat'): |
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''' load 3DMM model extra information |
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Args: |
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path: path to BFM info. |
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Returns: |
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model_info: |
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'symlist': 2 x 26720 |
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'symlist_tri': 2 x 52937 |
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'segbin': 4 x n (0: nose, 1: eye, 2: mouth, 3: cheek) |
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'segbin_tri': 4 x ntri |
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'face_contour': 1 x 28 |
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'face_contour_line': 1 x 512 |
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'face_contour_front': 1 x 28 |
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'face_contour_front_line': 1 x 512 |
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'nose_hole': 1 x 142 |
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'nose_hole_right': 1 x 71 |
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'nose_hole_left': 1 x 71 |
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'parallel': 17 x 1 cell |
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'parallel_face_contour': 28 x 1 cell |
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'uv_coords': n x 2 |
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''' |
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C = sio.loadmat(path) |
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model_info = C['model_info'] |
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model_info = model_info[0,0] |
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return model_info |
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def load_uv_coords(path = 'BFM_UV.mat'): |
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''' load uv coords of BFM |
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Args: |
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path: path to data. |
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Returns: |
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uv_coords: [nver, 2]. range: 0-1 |
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''' |
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C = sio.loadmat(path) |
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uv_coords = C['UV'].copy(order = 'C') |
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return uv_coords |
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def load_pncc_code(path = 'pncc_code.mat'): |
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''' load pncc code of BFM |
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PNCC code: Defined in 'Face Alignment Across Large Poses: A 3D Solution Xiangyu' |
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download at http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm. |
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Args: |
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path: path to data. |
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Returns: |
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pncc_code: [nver, 3] |
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''' |
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C = sio.loadmat(path) |
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pncc_code = C['vertex_code'].T |
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return pncc_code |
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def get_organ_ind(model_info): |
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''' get nose, eye, mouth index |
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''' |
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valid_bin = model_info['segbin'].astype(bool) |
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organ_ind = np.nonzero(valid_bin[0,:])[0] |
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for i in range(1, valid_bin.shape[0] - 1): |
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organ_ind = np.union1d(organ_ind, np.nonzero(valid_bin[i,:])[0]) |
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return organ_ind.astype(np.int32) |
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