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| # -*- coding: utf-8 -*- | |
| # Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is | |
| # holder of all proprietary rights on this computer program. | |
| # You can only use this computer program if you have closed | |
| # a license agreement with MPG or you get the right to use the computer | |
| # program from someone who is authorized to grant you that right. | |
| # Any use of the computer program without a valid license is prohibited and | |
| # liable to prosecution. | |
| # | |
| # Copyright©2019 Max-Planck-Gesellschaft zur Förderung | |
| # der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute | |
| # for Intelligent Systems. All rights reserved. | |
| # | |
| # Contact: ps-license@tuebingen.mpg.de | |
| from lib.renderer.gl.normal_render import NormalRender | |
| from lib.dataset.mesh_util import projection | |
| from lib.common.render import Render | |
| from PIL import Image | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| import trimesh | |
| import os.path as osp | |
| from PIL import Image | |
| class Evaluator: | |
| _normal_render = None | |
| def init_gl(): | |
| Evaluator._normal_render = NormalRender(width=512, height=512) | |
| def __init__(self, device): | |
| self.device = device | |
| self.render = Render(size=512, device=self.device) | |
| self.error_term = nn.MSELoss() | |
| self.offset = 0.0 | |
| self.scale_factor = None | |
| def set_mesh(self, result_dict, scale_factor=1.0, offset=0.0): | |
| for key in result_dict.keys(): | |
| if torch.is_tensor(result_dict[key]): | |
| result_dict[key] = result_dict[key].detach().cpu().numpy() | |
| for k, v in result_dict.items(): | |
| setattr(self, k, v) | |
| self.scale_factor = scale_factor | |
| self.offset = offset | |
| def _render_normal(self, mesh, deg, norms=None): | |
| view_mat = np.identity(4) | |
| rz = deg / 180.0 * np.pi | |
| model_mat = np.identity(4) | |
| model_mat[:3, :3] = self._normal_render.euler_to_rot_mat(0, rz, 0) | |
| model_mat[1, 3] = self.offset | |
| view_mat[2, 2] *= -1 | |
| self._normal_render.set_matrices(view_mat, model_mat) | |
| if norms is None: | |
| norms = mesh.vertex_normals | |
| self._normal_render.set_normal_mesh(self.scale_factor * mesh.vertices, | |
| mesh.faces, norms, mesh.faces) | |
| self._normal_render.draw() | |
| normal_img = self._normal_render.get_color() | |
| return normal_img | |
| def render_mesh_list(self, mesh_lst): | |
| self.offset = 0.0 | |
| self.scale_factor = 1.0 | |
| full_list = [] | |
| for mesh in mesh_lst: | |
| row_lst = [] | |
| for deg in np.arange(0, 360, 90): | |
| normal = self._render_normal(mesh, deg) | |
| row_lst.append(normal) | |
| full_list.append(np.concatenate(row_lst, axis=1)) | |
| res_array = np.concatenate(full_list, axis=0) | |
| return res_array | |
| def _get_reproj_normal_error(self, deg): | |
| tgt_normal = self._render_normal(self.tgt_mesh, deg) | |
| src_normal = self._render_normal(self.src_mesh, deg) | |
| error = (((src_normal[:, :, :3] - | |
| tgt_normal[:, :, :3])**2).sum(axis=2).mean(axis=(0, 1))) | |
| return error, [src_normal, tgt_normal] | |
| def render_normal(self, verts, faces): | |
| verts = verts[0].detach().cpu().numpy() | |
| faces = faces[0].detach().cpu().numpy() | |
| mesh_F = trimesh.Trimesh(verts * np.array([1.0, -1.0, 1.0]), faces) | |
| mesh_B = trimesh.Trimesh(verts * np.array([1.0, -1.0, -1.0]), faces) | |
| self.scale_factor = 1.0 | |
| normal_F = self._render_normal(mesh_F, 0) | |
| normal_B = self._render_normal(mesh_B, | |
| 0, | |
| norms=mesh_B.vertex_normals * | |
| np.array([-1.0, -1.0, 1.0])) | |
| mask = normal_F[:, :, 3:4] | |
| normal_F = (torch.as_tensor(2.0 * (normal_F - 0.5) * mask).permute( | |
| 2, 0, 1)[:3, :, :].float().unsqueeze(0).to(self.device)) | |
| normal_B = (torch.as_tensor(2.0 * (normal_B - 0.5) * mask).permute( | |
| 2, 0, 1)[:3, :, :].float().unsqueeze(0).to(self.device)) | |
| return {"T_normal_F": normal_F, "T_normal_B": normal_B} | |
| def calculate_normal_consist( | |
| self, | |
| frontal=True, | |
| back=True, | |
| left=True, | |
| right=True, | |
| save_demo_img=None, | |
| return_demo=False, | |
| ): | |
| # reproj error | |
| # if save_demo_img is not None, save a visualization at the given path (etc, "./test.png") | |
| if self._normal_render is None: | |
| print( | |
| "In order to use normal render, " | |
| "you have to call init_gl() before initialing any evaluator objects." | |
| ) | |
| return -1 | |
| side_cnt = 0 | |
| total_error = 0 | |
| demo_list = [] | |
| if frontal: | |
| side_cnt += 1 | |
| error, normal_lst = self._get_reproj_normal_error(0) | |
| total_error += error | |
| demo_list.append(np.concatenate(normal_lst, axis=0)) | |
| if back: | |
| side_cnt += 1 | |
| error, normal_lst = self._get_reproj_normal_error(180) | |
| total_error += error | |
| demo_list.append(np.concatenate(normal_lst, axis=0)) | |
| if left: | |
| side_cnt += 1 | |
| error, normal_lst = self._get_reproj_normal_error(90) | |
| total_error += error | |
| demo_list.append(np.concatenate(normal_lst, axis=0)) | |
| if right: | |
| side_cnt += 1 | |
| error, normal_lst = self._get_reproj_normal_error(270) | |
| total_error += error | |
| demo_list.append(np.concatenate(normal_lst, axis=0)) | |
| if save_demo_img is not None: | |
| res_array = np.concatenate(demo_list, axis=1) | |
| res_img = Image.fromarray((res_array * 255).astype(np.uint8)) | |
| res_img.save(save_demo_img) | |
| if return_demo: | |
| res_array = np.concatenate(demo_list, axis=1) | |
| return res_array | |
| else: | |
| return total_error | |
| def space_transfer(self): | |
| # convert from GT to SDF | |
| self.verts_pr -= self.recon_size / 2.0 | |
| self.verts_pr /= self.recon_size / 2.0 | |
| self.verts_gt = projection(self.verts_gt, self.calib) | |
| self.verts_gt[:, 1] *= -1 | |
| self.tgt_mesh = trimesh.Trimesh(self.verts_gt, self.faces_gt) | |
| self.src_mesh = trimesh.Trimesh(self.verts_pr, self.faces_pr) | |
| # (self.tgt_mesh+self.src_mesh).show() | |
| def export_mesh(self, dir, name): | |
| self.tgt_mesh.visual.vertex_colors = np.array([255, 0, 0]) | |
| self.src_mesh.visual.vertex_colors = np.array([0, 255, 0]) | |
| (self.tgt_mesh + self.src_mesh).export( | |
| osp.join(dir, f"{name}_gt_pr.obj")) | |
| def calculate_chamfer_p2s(self, sampled_points=1000): | |
| """calculate the geometry metrics [chamfer, p2s, chamfer_H, p2s_H] | |
| Args: | |
| verts_gt (torch.cuda.tensor): [N, 3] | |
| faces_gt (torch.cuda.tensor): [M, 3] | |
| verts_pr (torch.cuda.tensor): [N', 3] | |
| faces_pr (torch.cuda.tensor): [M', 3] | |
| sampled_points (int, optional): use smaller number for faster testing. Defaults to 1000. | |
| Returns: | |
| tuple: chamfer, p2s, chamfer_H, p2s_H | |
| """ | |
| gt_surface_pts, _ = trimesh.sample.sample_surface_even( | |
| self.tgt_mesh, sampled_points) | |
| pred_surface_pts, _ = trimesh.sample.sample_surface_even( | |
| self.src_mesh, sampled_points) | |
| _, dist_pred_gt, _ = trimesh.proximity.closest_point( | |
| self.src_mesh, gt_surface_pts) | |
| _, dist_gt_pred, _ = trimesh.proximity.closest_point( | |
| self.tgt_mesh, pred_surface_pts) | |
| dist_pred_gt[np.isnan(dist_pred_gt)] = 0 | |
| dist_gt_pred[np.isnan(dist_gt_pred)] = 0 | |
| chamfer_dist = 0.5 * (dist_pred_gt.mean() + | |
| dist_gt_pred.mean()).item() * 100 | |
| p2s_dist = dist_pred_gt.mean().item() * 100 | |
| return chamfer_dist, p2s_dist | |
| def calc_acc(self, output, target, thres=0.5, use_sdf=False): | |
| # # remove the surface points with thres | |
| # non_surf_ids = (target != thres) | |
| # output = output[non_surf_ids] | |
| # target = target[non_surf_ids] | |
| with torch.no_grad(): | |
| output = output.masked_fill(output < thres, 0.0) | |
| output = output.masked_fill(output > thres, 1.0) | |
| if use_sdf: | |
| target = target.masked_fill(target < thres, 0.0) | |
| target = target.masked_fill(target > thres, 1.0) | |
| acc = output.eq(target).float().mean() | |
| # iou, precison, recall | |
| output = output > thres | |
| target = target > thres | |
| union = output | target | |
| inter = output & target | |
| _max = torch.tensor(1.0).to(output.device) | |
| union = max(union.sum().float(), _max) | |
| true_pos = max(inter.sum().float(), _max) | |
| vol_pred = max(output.sum().float(), _max) | |
| vol_gt = max(target.sum().float(), _max) | |
| return acc, true_pos / union, true_pos / vol_pred, true_pos / vol_gt | |