SHOW/modules/MICA/micalib/flame.py

# -*- coding: utf-8 -*-

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# Copyright©2022 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
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# For commercial licensing contact, please contact ps-license@tuebingen.mpg.de


import pickle

import loguru
import numpy as np
import torch
import torch.nn as nn

from .lbs import lbs, batch_rodrigues, vertices2landmarks, rot_mat_to_euler


def to_tensor(array, dtype=torch.float32):
    if 'torch.tensor' not in str(type(array)):
        return torch.tensor(array, dtype=dtype)


def to_np(array, dtype=np.float32):
    if 'scipy.sparse' in str(type(array)):
        array = array.todense()
    return np.array(array, dtype=dtype)


class Struct(object):
    def __init__(self, **kwargs):
        for key, val in kwargs.items():
            setattr(self, key, val)


class FLAME(nn.Module):
    """
    borrowed from https://github.com/soubhiksanyal/FLAME_PyTorch/blob/master/FLAME.py
    Given flame parameters this class generates a differentiable FLAME function
    which outputs the a mesh and 2D/3D facial landmarks
    """

    def __init__(self, config, optimize_basis=False):
        super(FLAME, self).__init__()
        loguru.logger.info("[FLAME] creating the FLAME Decoder")
        with open(config.flame_model_path, 'rb') as f:
            ss = pickle.load(f, encoding='latin1')
            flame_model = Struct(**ss)

        self.optimize_basis = optimize_basis
        self.cfg = config
        self.dtype = torch.float32
        self.register_buffer('faces_tensor', to_tensor(to_np(flame_model.f, dtype=np.int64), dtype=torch.long))
        # The vertices of the template model
        self.register_buffer('v_template', to_tensor(to_np(flame_model.v_template), dtype=self.dtype))
        self.n_vertices = self.v_template.shape[0]
        # The shape components and expression
        shapedirs = to_tensor(to_np(flame_model.shapedirs), dtype=self.dtype)
        shapedirs = torch.cat([shapedirs[:, :, :config.n_shape], shapedirs[:, :, 300:]], 2)

        if optimize_basis:
            self.register_parameter('shapedirs', torch.nn.Parameter(shapedirs))
        else:
            self.register_buffer('shapedirs', shapedirs)

        self.n_shape = config.n_shape
        # The pose components
        num_pose_basis = flame_model.posedirs.shape[-1]
        posedirs = np.reshape(flame_model.posedirs, [-1, num_pose_basis]).T
        self.register_buffer('posedirs', to_tensor(to_np(posedirs), dtype=self.dtype))
        # 
        self.register_buffer('J_regressor', to_tensor(to_np(flame_model.J_regressor), dtype=self.dtype))
        parents = to_tensor(to_np(flame_model.kintree_table[0])).long();
        parents[0] = -1
        self.register_buffer('parents', parents)
        self.register_buffer('lbs_weights', to_tensor(to_np(flame_model.weights), dtype=self.dtype))

        # Fixing Eyeball and neck rotation
        default_eyball_pose = torch.zeros([1, 6], dtype=self.dtype, requires_grad=False)
        self.register_parameter('eye_pose', nn.Parameter(default_eyball_pose, requires_grad=False))
        default_neck_pose = torch.zeros([1, 3], dtype=self.dtype, requires_grad=False)
        self.register_parameter('neck_pose', nn.Parameter(default_neck_pose, requires_grad=False))

        # Static and Dynamic Landmark embeddings for FLAME
        lmk_embeddings = np.load(config.flame_lmk_embedding_path, allow_pickle=True, encoding='latin1')
        lmk_embeddings = lmk_embeddings[()]
        self.register_buffer('lmk_faces_idx', torch.from_numpy(lmk_embeddings['static_lmk_faces_idx']).long())
        self.register_buffer('lmk_bary_coords', torch.from_numpy(lmk_embeddings['static_lmk_bary_coords']).to(self.dtype))
        self.register_buffer('dynamic_lmk_faces_idx', lmk_embeddings['dynamic_lmk_faces_idx'].long())
        self.register_buffer('dynamic_lmk_bary_coords', lmk_embeddings['dynamic_lmk_bary_coords'].to(self.dtype))
        self.register_buffer('full_lmk_faces_idx', torch.from_numpy(lmk_embeddings['full_lmk_faces_idx']).long())
        self.register_buffer('full_lmk_bary_coords', torch.from_numpy(lmk_embeddings['full_lmk_bary_coords']).to(self.dtype))

        neck_kin_chain = [];
        NECK_IDX = 1
        curr_idx = torch.tensor(NECK_IDX, dtype=torch.long)
        while curr_idx != -1:
            neck_kin_chain.append(curr_idx)
            curr_idx = self.parents[curr_idx]
        self.register_buffer('neck_kin_chain', torch.stack(neck_kin_chain))

    def _find_dynamic_lmk_idx_and_bcoords(self, pose, dynamic_lmk_faces_idx,
                                          dynamic_lmk_b_coords,
                                          neck_kin_chain, dtype=torch.float32):
        """
            Selects the face contour depending on the reletive position of the head
            Input:
                vertices: N X num_of_vertices X 3
                pose: N X full pose
                dynamic_lmk_faces_idx: The list of contour face indexes
                dynamic_lmk_b_coords: The list of contour barycentric weights
                neck_kin_chain: The tree to consider for the relative rotation
                dtype: Data type
            return:
                The contour face indexes and the corresponding barycentric weights
        """

        batch_size = pose.shape[0]

        aa_pose = torch.index_select(pose.view(batch_size, -1, 3), 1,
                                     neck_kin_chain)
        rot_mats = batch_rodrigues(
            aa_pose.view(-1, 3), dtype=dtype).view(batch_size, -1, 3, 3)

        rel_rot_mat = torch.eye(3, device=pose.device,
                                dtype=dtype).unsqueeze_(dim=0).expand(batch_size, -1, -1)
        for idx in range(len(neck_kin_chain)):
            rel_rot_mat = torch.bmm(rot_mats[:, idx], rel_rot_mat)

        y_rot_angle = torch.round(
            torch.clamp(rot_mat_to_euler(rel_rot_mat) * 180.0 / np.pi,
                        max=39)).to(dtype=torch.long)

        neg_mask = y_rot_angle.lt(0).to(dtype=torch.long)
        mask = y_rot_angle.lt(-39).to(dtype=torch.long)
        neg_vals = mask * 78 + (1 - mask) * (39 - y_rot_angle)
        y_rot_angle = (neg_mask * neg_vals +
                       (1 - neg_mask) * y_rot_angle)

        dyn_lmk_faces_idx = torch.index_select(dynamic_lmk_faces_idx,
                                               0, y_rot_angle)
        dyn_lmk_b_coords = torch.index_select(dynamic_lmk_b_coords,
                                              0, y_rot_angle)
        return dyn_lmk_faces_idx, dyn_lmk_b_coords

    def _vertices2landmarks(self, vertices, faces, lmk_faces_idx, lmk_bary_coords):
        """
            Calculates landmarks by barycentric interpolation
            Input:
                vertices: torch.tensor NxVx3, dtype = torch.float32
                    The tensor of input vertices
                faces: torch.tensor (N*F)x3, dtype = torch.long
                    The faces of the mesh
                lmk_faces_idx: torch.tensor N X L, dtype = torch.long
                    The tensor with the indices of the faces used to calculate the
                    landmarks.
                lmk_bary_coords: torch.tensor N X L X 3, dtype = torch.float32
                    The tensor of barycentric coordinates that are used to interpolate
                    the landmarks

            Returns:
                landmarks: torch.tensor NxLx3, dtype = torch.float32
                    The coordinates of the landmarks for each mesh in the batch
        """
        # Extract the indices of the vertices for each face
        # NxLx3
        batch_size, num_verts = vertices.shape[:dd2]
        lmk_faces = torch.index_select(faces, 0, lmk_faces_idx.view(-1)).view(
            1, -1, 3).view(batch_size, lmk_faces_idx.shape[1], -1)

        lmk_faces += torch.arange(batch_size, dtype=torch.long).view(-1, 1, 1).to(
            device=vertices.device) * num_verts

        lmk_vertices = vertices.view(-1, 3)[lmk_faces]
        landmarks = torch.einsum('blfi,blf->bli', [lmk_vertices, lmk_bary_coords])
        return landmarks

    # def seletec_3d68(self, vertices):
    def compute_landmarks(self, vertices):
        landmarks3d = vertices2landmarks(vertices, self.faces_tensor,
                                         self.full_lmk_faces_idx.repeat(vertices.shape[0], 1),
                                         self.full_lmk_bary_coords.repeat(vertices.shape[0], 1, 1))
        return landmarks3d

    def seletec_3d68(self, vertices):
        landmarks3d = vertices2landmarks(vertices, self.faces_tensor,
                                         self.full_lmk_faces_idx.repeat(vertices.shape[0], 1),
                                         self.full_lmk_bary_coords.repeat(vertices.shape[0], 1, 1))
        return landmarks3d

    def project_to_shape_basis(self, shape_vector, shape_as_offset=False):
        batch_size = shape_vector.shape[0]
        n_vertices = self.v_template.shape[0]
        n_eigenvectors = self.n_shape
        # shape_params = basis dot (shape_vector - average)  # uses properties of the PCA
        if shape_as_offset:
            diff = shape_vector
        else:
            diff = shape_vector - self.v_template
        return torch.matmul(diff.reshape(batch_size, -1), self.shapedirs[:, :, :n_eigenvectors].reshape(3 * n_vertices, n_eigenvectors))

    def compute_distance_to_basis(self, shape_vector, shape_as_offset=False):
        batch_size = shape_vector.shape[0]
        n_vertices = self.v_template.shape[0]
        n_eigenvectors = self.n_shape

        # shape_vector torch.Size([3, 5023, 3])
        # self.v_template torch.Size([5023, 3])
        # self.shapedirs torch.Size([5023, 3, 150])
        # diff torch.Size([3, 5023, 3])
        # shape_params torch.Size([5023, 15069])

        # shape_params = basis dot (shape_vector - average)  # uses properties of the PCA
        if shape_as_offset:
            diff = shape_vector
        else:
            diff = shape_vector - self.v_template
        shape_params = torch.matmul(diff.reshape(batch_size, -1), self.shapedirs[:, :, :n_eigenvectors].reshape(3 * n_vertices, n_eigenvectors))
        distance = diff - torch.matmul(shape_params, self.shapedirs[:, :, :n_eigenvectors].reshape(n_vertices * 3, n_eigenvectors).t()).reshape(batch_size, n_vertices, 3)
        return distance

    def get_std(self):
        n_eigenvectors = self.cfg.n_shape
        basis = self.shapedirs[:, :, :n_eigenvectors]
        std = torch.norm(basis.reshape(-1, n_eigenvectors), dim=0)

        return std

    def compute_closest_shape(self, shape_vector):
        B = shape_vector.shape[0]
        N = self.v_template.shape[0]
        n_eigenvectors = self.cfg.n_shape

        basis = self.shapedirs[:, :, :n_eigenvectors]
        diff = (shape_vector - self.v_template).reshape(B, -1)
        std = torch.norm(basis.reshape(-1, n_eigenvectors), dim=0)
        inv = 1.0 / std.square()
        params = inv * torch.matmul(diff, basis.reshape(3 * N, n_eigenvectors))
        # params = torch.max(torch.min(params, std*-3.0), std*3.0)

        return self.v_template + torch.matmul(params, basis.reshape(N * 3, n_eigenvectors).T).reshape(B, N, 3), params

    def forward(self, shape_params=None, expression_params=None, pose_params=None, eye_pose_params=None, neck_pose_params=None, shape_basis_delta=None):
        """
            Input:
                shape_params: N X number of shape parameters
                expression_params: N X number of expression parameters
                pose_params: N X number of pose parameters (6)
            return:d
                vertices: N X V X 3
                landmarks: N X number of landmarks X 3
        """
        batch_size = shape_params.shape[0]
        if pose_params is None:
            pose_params = self.eye_pose.expand(batch_size, -1)
        if eye_pose_params is None:
            eye_pose_params = self.eye_pose.expand(batch_size, -1)
        if neck_pose_params is None:
            neck_pose_params = self.neck_pose.expand(batch_size, -1)
        if expression_params is None:
            expression_params = torch.zeros([1, 100], dtype=self.dtype, requires_grad=False, device=self.neck_pose.device).expand(batch_size, -1)

        betas = torch.cat([shape_params, expression_params], dim=1)
        full_pose = torch.cat([pose_params[:, :3], neck_pose_params, pose_params[:, 3:], eye_pose_params], dim=1)
        template_vertices = self.v_template.unsqueeze(0).expand(batch_size, -1, -1)

        vertices, _ = lbs(betas, full_pose, template_vertices,
                          self.shapedirs, self.posedirs,
                          self.J_regressor, self.parents,
                          self.lbs_weights, dtype=self.dtype)

        lmk_faces_idx = self.lmk_faces_idx.unsqueeze(dim=0).expand(batch_size, -1)
        lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).expand(batch_size, -1, -1)

        dyn_lmk_faces_idx, dyn_lmk_bary_coords = self._find_dynamic_lmk_idx_and_bcoords(
            full_pose, self.dynamic_lmk_faces_idx,
            self.dynamic_lmk_bary_coords,
            self.neck_kin_chain, dtype=self.dtype)
        lmk_faces_idx = torch.cat([dyn_lmk_faces_idx, lmk_faces_idx], 1)
        lmk_bary_coords = torch.cat([dyn_lmk_bary_coords, lmk_bary_coords], 1)

        landmarks2d = vertices2landmarks(vertices, self.faces_tensor,
                                         lmk_faces_idx,
                                         lmk_bary_coords)
        bz = vertices.shape[0]
        landmarks3d = vertices2landmarks(vertices, self.faces_tensor,
                                         self.full_lmk_faces_idx.repeat(bz, 1),
                                         self.full_lmk_bary_coords.repeat(bz, 1, 1))
        return vertices, landmarks2d, landmarks3d