def eval_iou(predicted_v,predicted_f,voxels):
faces_ = srf.face_vertices(predicted_v, predicted_f).data
faces_norm = faces_ * 1. * (32. - 1) / 32. + 0.5
voxels_predict = srf.voxelization(faces_norm, 32, False).cpu().numpy()
voxels_predict = voxels_predict.transpose(0, 2, 1, 3)[:, :, :, ::-1]
iou = (voxels * voxels_predict).sum((1, 2, 3)) / (0 < (voxels + voxels_predict)).sum((1, 2, 3))
return iou
def evaluate_iou(self, images, voxels):
vertices, faces, textures = self.reconstruct(images)
faces_ = srf.face_vertices(vertices, faces).data
faces_norm = faces_ * 1. * (32. - 1) / 32. + 0.5
voxels_predict = srf.voxelization(faces_norm, 32, False).cpu().numpy()
voxels_predict = voxels_predict.transpose(0, 2, 1, 3)[:, :, :, ::-1]
iou = (voxels * voxels_predict).sum((1, 2, 3)) / (0 < (voxels + voxels_predict)).sum((1, 2, 3))
return iou, vertices, faces, textures
vertices_real = [vertices_real for _ in range(num_rendered_images)]
faces_real = [faces_real for _ in range(num_rendered_images)]
vertices_real = torch.cat(vertices_real, dim=0)
faces_real = torch.cat(faces_real, dim=0)
vertices_generate, faces_generate = model.reconstruct(
rendered_images_i)
# srf.save_obj('/mnt/zhengwen/model_synthesis/shapeNetCore/test_generate.obj', vertices_generate[0], faces_generate[0])
# srf.save_obj('/mnt/zhengwen/model_synthesis/shapeNetCore/test_real.obj', vertices_real[0], faces_real[0])
faces_real_ = srf.face_vertices(vertices_real, faces_real).data
faces_real_norm = faces_real_ * 1. * (32. - 1) / 32. + 0.5
voxels_real = srf.voxelization(faces_real_norm, 32,
False).cpu().numpy()
faces_generate_ = srf.face_vertices(vertices_generate,
faces_generate).data
faces_generate_norm = faces_generate_ * 1. * (32. - 1) / 32. + 0.5
voxels_generate = srf.voxelization(faces_generate_norm, 32,
False).cpu().numpy()
voxels_generate = voxels_generate.transpose(0, 2, 1,
3)[:, :, :, ::-1]
iou = (voxels_real * voxels_generate).sum(
(1, 2, 3)) / (0 < (voxels_real + voxels_generate)).sum(
(1, 2, 3))
category_to_IoU[sub_root_dir].append(np.mean(iou))
obj_count += 1
sub_root_count += 1
def voxelize(self, voxel_size=32):
face_vertices_norm = self.face_vertices * voxel_size / (voxel_size -
1) + 0.5
return srf.voxelization(face_vertices_norm, voxel_size, False)
