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import torch |
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import torch.nn as nn |
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import numpy as np |
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from einops import rearrange |
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from deep_3drecon.util.mesh_renderer import MeshRenderer |
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from deep_3drecon.deep_3drecon_models.bfm import ParametricFaceModel |
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class SECC_Renderer(nn.Module): |
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def __init__(self, rasterize_size=None, device="cuda"): |
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super().__init__() |
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self.face_model = ParametricFaceModel('deep_3drecon/BFM') |
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self.fov = 2 * np.arctan(self.face_model.center / self.face_model.focal) * 180 / np.pi |
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self.znear = 5. |
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self.zfar = 15. |
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if rasterize_size is None: |
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rasterize_size = 2*self.face_model.center |
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self.face_renderer = MeshRenderer(rasterize_fov=self.fov, znear=self.znear, zfar=self.zfar, rasterize_size=rasterize_size, use_opengl=False).cuda() |
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face_feat = np.load("deep_3drecon/ncc_code.npy", allow_pickle=True) |
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self.face_feat = torch.tensor(face_feat.T).unsqueeze(0).to(device=device) |
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del_index_re = np.load('deep_3drecon/bfm_right_eye_faces.npy') |
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del_index_re = del_index_re - 1 |
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del_index_le = np.load('deep_3drecon/bfm_left_eye_faces.npy') |
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del_index_le = del_index_le - 1 |
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face_buf_list = [] |
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for i in range(self.face_model.face_buf.shape[0]): |
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if i not in del_index_re and i not in del_index_le: |
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face_buf_list.append(self.face_model.face_buf[i]) |
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face_buf_arr = np.array(face_buf_list) |
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self.face_buf = torch.tensor(face_buf_arr).to(device=device) |
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def forward(self, id, exp, euler, trans): |
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""" |
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id, exp, euler, euler: [B, C] or [B, T, C] |
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return: |
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MASK: [B, 1, 512, 512], value[0. or 1.0], 1.0 denotes is face |
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SECC MAP: [B, 3, 512, 512], value[0~1] |
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if input is BTC format, return [B, C, T, H, W] |
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""" |
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bs = id.shape[0] |
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is_btc_flag = id.ndim == 3 |
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if is_btc_flag: |
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t = id.shape[1] |
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bs = bs * t |
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id, exp, euler, trans = id.reshape([bs,-1]), exp.reshape([bs,-1]), euler.reshape([bs,-1]), trans.reshape([bs,-1]) |
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face_vertex = self.face_model.compute_face_vertex(id, exp, euler, trans) |
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face_mask, _, secc_face = self.face_renderer( |
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face_vertex, self.face_buf.unsqueeze(0).repeat([bs, 1, 1]), feat=self.face_feat.repeat([bs,1,1])) |
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secc_face = (secc_face - 0.5) / 0.5 |
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if is_btc_flag: |
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bs = bs // t |
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face_mask = rearrange(face_mask, "(n t) c h w -> n c t h w", n=bs, t=t) |
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secc_face = rearrange(secc_face, "(n t) c h w -> n c t h w", n=bs, t=t) |
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return face_mask, secc_face |
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if __name__ == '__main__': |
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import imageio |
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renderer = SECC_Renderer(rasterize_size=512) |
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ret = np.load("data/processed/videos/May/vid_coeff_fit.npy", allow_pickle=True).tolist() |
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idx = 6 |
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id = torch.tensor(ret['id']).cuda()[idx:idx+1] |
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exp = torch.tensor(ret['exp']).cuda()[idx:idx+1] |
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angle = torch.tensor(ret['euler']).cuda()[idx:idx+1] |
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trans = torch.tensor(ret['trans']).cuda()[idx:idx+1] |
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mask, secc = renderer(id, exp, angle*0, trans*0) |
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out_mask = mask[0].permute(1,2,0) |
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out_mask = (out_mask * 127.5 + 127.5).int().cpu().numpy() |
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imageio.imwrite("out_mask.png", out_mask) |
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out_img = secc[0].permute(1,2,0) |
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out_img = (out_img * 127.5 + 127.5).int().cpu().numpy() |
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imageio.imwrite("out_secc.png", out_img) |
