nbv_rec_control/runners/cad_strategy.py

import os
import trimesh
import tempfile
import subprocess
import numpy as np
from PytorchBoot.runners.runner import Runner
from PytorchBoot.config import ConfigManager
import PytorchBoot.stereotype as stereotype
from PytorchBoot.utils.log_util import Log
from PytorchBoot.status import status_manager

from utils.control_util import ControlUtil
from utils.communicate_util import CommunicateUtil
from utils.pts_util import PtsUtil
from utils.reconstruction_util import ReconstructionUtil
from utils.preprocess_util import save_scene_data
from utils.data_load import DataLoadUtil

@stereotype.runner("CAD_strategy_runner")
class CADStrategyRunner(Runner):

    def __init__(self, config_path: str):
        super().__init__(config_path)
        self.load_experiment("cad_strategy")
        self.status_info = {
            "status_manager": status_manager,
            "app_name": "cad",
            "runner_name": "cad_strategy"
        }
        self.generate_config = ConfigManager.get("runner", "generate")
        self.reconstruct_config = ConfigManager.get("runner", "reconstruct")
        self.model_dir = self.generate_config["model_dir"]
        self.voxel_size = self.generate_config["voxel_size"]
        self.max_view = self.generate_config["max_view"]
        self.min_view = self.generate_config["min_view"]
        self.max_diag = self.generate_config["max_diag"]
        self.min_diag = self.generate_config["min_diag"]
        self.min_cam_table_included_degree = self.generate_config["min_cam_table_included_degree"]
        self.random_view_ratio = self.generate_config["random_view_ratio"]
        
        self.soft_overlap_threshold = self.reconstruct_config["soft_overlap_threshold"]
        self.hard_overlap_threshold = self.reconstruct_config["hard_overlap_threshold"]
        self.scan_points_threshold = self.reconstruct_config["scan_points_threshold"]
        
    def create_experiment(self, backup_name=None):
        super().create_experiment(backup_name)
    
    def load_experiment(self, backup_name=None):
        super().load_experiment(backup_name)
        
    def get_pts_from_view_data(self, view_data):
        depth = view_data["depth_image"]
        depth_intrinsics = view_data["depth_intrinsics"]
        depth_extrinsics = view_data["depth_extrinsics"]
        cam_pts = PtsUtil.get_pts_from_depth(depth, depth_intrinsics, depth_extrinsics)
        return cam_pts
    
    def split_scan_pts_and_obj_pts(self, world_pts, scan_pts_z, z_threshold = 0.003):
        scan_pts = world_pts[scan_pts_z < z_threshold]
        obj_pts = world_pts[scan_pts_z >= z_threshold]
        return scan_pts, obj_pts

    def run_one_model(self, model_name):
        
        ''' init robot '''
        #ControlUtil.init()
        ''' load CAD model '''
        model_path = os.path.join(self.model_dir, model_name,"mesh.obj")
        cad_model = trimesh.load(model_path)
        ''' take first view '''
        #view_data = CommunicateUtil.get_view_data(init=True)
        #first_cam_pts = self.get_pts_from_view_data(view_data)
        ''' register '''
        #cad_to_cam = PtsUtil.register(first_cam_pts, cad_model)
        #cam_to_world = ControlUtil.get_pose()
        cad_to_world = np.eye(4) #cam_to_world @ cad_to_cam
        
        
        world_to_blender_world = np.eye(4)
        world_to_blender_world[:3, 3] = np.asarray([0, 0, 0.9215])
        cad_to_blender_world = np.dot(world_to_blender_world, cad_to_world)
        cad_model:trimesh.Trimesh = cad_model.apply_transform(cad_to_blender_world)
        
        with tempfile.TemporaryDirectory() as temp_dir:
            name = "cad_model_world"
            
            cad_model.export(os.path.join(temp_dir, f"{name}.obj"))
            temp_dir = "/home/user/nbv_rec/nbv_rec_control/test_output"
            scene_dir = os.path.join(temp_dir, name)
            script_path = "/home/user/nbv_rec/blender_app/data_generator.py"
            ''' sample view '''
            # import ipdb; ipdb.set_trace()
            # print("start running renderer")
            # result = subprocess.run([
            #     'blender', '-b', '-P', script_path, '--', temp_dir
            # ], capture_output=True, text=True)
            # print("finish running renderer")
            # 
        
            world_model_points = np.loadtxt(os.path.join(scene_dir, "points_and_normals.txt"))[:,:3]
            ''' preprocess '''
            # save_scene_data(temp_dir, name)
            
            pts_dir = os.path.join(temp_dir,name,"pts")
            sample_view_pts_list = []
            scan_points_idx_list = []
            frame_num = len(os.listdir(pts_dir))
            
            for frame_idx in range(frame_num):
                pts_path = os.path.join(temp_dir,name, "pts", f"{frame_idx}.txt")
                idx_path = os.path.join(temp_dir,name, "scan_points_indices", f"{frame_idx}.txt")
                point_cloud = np.loadtxt(pts_path)
                sampled_point_cloud = PtsUtil.voxel_downsample_point_cloud(point_cloud, self.voxel_size)
                indices = np.loadtxt(idx_path, dtype=np.int32) 
                try:
                    len(indices)
                except:
                    indices = np.array([indices])
                sample_view_pts_list.append(sampled_point_cloud)
                scan_points_idx_list.append(indices)

            ''' generate strategy '''
            
            limited_useful_view, _, _ = ReconstructionUtil.compute_next_best_view_sequence_with_overlap(
                world_model_points, sample_view_pts_list, 
                scan_points_indices_list = scan_points_idx_list,
                init_view=0,
                threshold=self.voxel_size, 
                soft_overlap_threshold = self.soft_overlap_threshold,
                hard_overlap_threshold = self.hard_overlap_threshold,
                scan_points_threshold = self.scan_points_threshold, 
                status_info=self.status_info
            )
           
            ''' extract cam_to_world sequence  '''
            cam_to_world_seq = []
            coveraget_rate_seq = []
            
            from ipdb import set_trace; set_trace()
            for idx, coverage_rate in limited_useful_view:
                path = DataLoadUtil.get_path(temp_dir, name, idx)
                cam_info = DataLoadUtil.load_cam_info(path, binocular=True)
                cam_to_world_seq.append(cam_info["cam_to_world"])
                coveraget_rate_seq.append(coverage_rate)
            
        # ''' take best seq view '''
        # for cam_to_world in cam_to_world_seq:
        #     ControlUtil.move_to(cam_to_world)
        #     ''' get world pts '''
        #     view_data = CommunicateUtil.get_view_data()
        #     cam_pts = self.get_pts_from_view_data(view_data)
        #     scan_points_idx = None
        #     world_pts = PtsUtil.transform_point_cloud(cam_pts, cam_to_world)
        #     sample_view_pts_list.append(world_pts)
        #     scan_points_idx_list.append(scan_points_idx)
        
    
    def run(self):
        total = len(os.listdir(self.model_dir))
        model_start_idx = self.generate_config["model_start_idx"]
        count_object = model_start_idx
        for model_name in os.listdir(self.model_dir[model_start_idx:]):
            Log.info(f"[{count_object}/{total}]Processing {model_name}")
            self.run_one_model(model_name)
            Log.success(f"[{count_object}/{total}]Finished processing {model_name}")
        
            
# ---------------------------- test ---------------------------- #
if __name__ == "__main__":
    
    model_path = r"C:\Users\hofee\Downloads\mesh.obj"
    model = trimesh.load(model_path)
    
    ''' test register '''
    # test_pts_L = np.load(r"C:\Users\hofee\Downloads\0.npy")
    
    # import open3d as o3d
    # def add_noise(points, translation, rotation):
    #     R = o3d.geometry.get_rotation_matrix_from_axis_angle(rotation)
    #     noisy_points = points @ R.T + translation
    #     return noisy_points
    
    # translation_noise = np.random.uniform(-0.5, 0.5, size=3)
    # rotation_noise = np.random.uniform(-np.pi/4, np.pi/4, size=3) 
    # noisy_pts_L = add_noise(test_pts_L, translation_noise, rotation_noise)
    
    # cad_to_cam_L = PtsUtil.register(noisy_pts_L, model)
    
    # cad_pts_L = PtsUtil.transform_point_cloud(noisy_pts_L, cad_to_cam_L)
    # np.savetxt(r"test.txt", cad_pts_L)
    # np.savetxt(r"src.txt", noisy_pts_L)