import torch
import torch.nn as nn
import numpy as np
import os


class Face_3DMM(nn.Module):
    def __init__(self, modelpath, id_dim, exp_dim, tex_dim, point_num):
        super(Face_3DMM, self).__init__()
        # id_dim = 100
        # exp_dim = 79
        # tex_dim = 100
        self.point_num = point_num
        DMM_info = np.load(
            os.path.join(modelpath, "3DMM_info.npy"), allow_pickle=True
        ).item()
        base_id = DMM_info["b_shape"][:id_dim, :]
        mu_id = DMM_info["mu_shape"]
        base_exp = DMM_info["b_exp"][:exp_dim, :]
        mu_exp = DMM_info["mu_exp"]
        mu = mu_id + mu_exp
        mu = mu.reshape(-1, 3)
        for i in range(3):
            mu[:, i] -= np.mean(mu[:, i])
        mu = mu.reshape(-1)
        self.base_id = torch.as_tensor(base_id).cuda() /1000.0
        self.base_exp = torch.as_tensor(base_exp).cuda() /1000.0
        self.mu = torch.as_tensor(mu).cuda() /1000.0
        base_tex = DMM_info["b_tex"][:tex_dim, :]
        mu_tex = DMM_info["mu_tex"]
        self.base_tex = torch.as_tensor(base_tex).cuda()
        self.mu_tex = torch.as_tensor(mu_tex).cuda()
        sig_id = DMM_info["sig_shape"][:id_dim]
        sig_tex = DMM_info["sig_tex"][:tex_dim]
        sig_exp = DMM_info["sig_exp"][:exp_dim]
        self.sig_id = torch.as_tensor(sig_id).cuda()
        self.sig_tex = torch.as_tensor(sig_tex).cuda()
        self.sig_exp = torch.as_tensor(sig_exp).cuda()

    def forward_geo_sub(self, id_para, exp_para, sub_index):
        id_para = id_para*self.sig_id
        exp_para = exp_para*self.sig_exp
        sel_index = torch.cat((3*sub_index.unsqueeze(1), 3*sub_index.unsqueeze(1)+1,
                               3*sub_index.unsqueeze(1)+2), dim=1).reshape(-1)
        geometry = torch.mm(id_para, self.base_id[:, sel_index]) + \
            torch.mm(exp_para, self.base_exp[:,
                                             sel_index]) + self.mu[sel_index]
        return geometry.reshape(-1, sub_index.shape[0], 3)

    def forward_geo(self, id_para, exp_para):
        id_para = id_para*self.sig_id
        exp_para = exp_para*self.sig_exp
        geometry = torch.mm(id_para, self.base_id) + \
            torch.mm(exp_para, self.base_exp) + self.mu
        return geometry.reshape(-1, self.point_num, 3)

    def forward_tex(self, tex_para):
        tex_para = tex_para*self.sig_tex
        texture = torch.mm(tex_para, self.base_tex) + self.mu_tex
        return texture.reshape(-1, self.point_num, 3)