import os
import json
from PytorchBoot.runners.runner import Runner
from PytorchBoot.config import ConfigManager
import PytorchBoot.stereotype as stereotype

from utils.data_load import DataLoadUtil
from utils.reconstruction import ReconstructionUtil

@stereotype.runner("strategy_generator", comment="unfinished")
class StrategyGenerator(Runner):
    def __init__(self, config):
        super().__init__(config)
        self.load_experiment("generate")
        
    def run(self):
        dataset_name_list =  ConfigManager.get("runner", "dataset_list")
        voxel_threshold, overlap_threshold = ConfigManager.get("runner","generate","voxel_threshold"), ConfigManager.get("runner","generate","overlap_threshold")
        for dataset_name in dataset_name_list:
            root_dir = ConfigManager.get("datasets", dataset_name, "root_dir")
            output_dir = ConfigManager.get("datasets", dataset_name, "output_dir")
            if not os.path.exists(output_dir):
                os.makedirs(output_dir)
                
            scene_idx_list = DataLoadUtil.get_scene_idx_list(root_dir)
            for scene_idx in scene_idx_list:
                self.generate_sequence(root_dir, output_dir, scene_idx,voxel_threshold, overlap_threshold)
    
    def create_experiment(self, backup_name=None):
        super().create_experiment(backup_name)
        output_dir = os.path.join(str(self.experiment_path), "output")
        os.makedirs(output_dir)
    
    def load_experiment(self, backup_name=None):
        super().load_experiment(backup_name)
    
    def generate_sequence(self,root, output_dir, seq, voxel_threshold, overlap_threshold):
        frame_idx_list = DataLoadUtil.get_frame_idx_list(root, seq)
        model_pts = DataLoadUtil.load_model_points(root, seq)
        pts_list = []
        for frame_idx in frame_idx_list:
            path = DataLoadUtil.get_path(root, seq, frame_idx)
            point_cloud = DataLoadUtil.get_point_cloud_world_from_path(path)
            sampled_point_cloud = ReconstructionUtil.downsample_point_cloud(point_cloud, voxel_threshold)
            pts_list.append(sampled_point_cloud)
        limited_useful_view, _ = ReconstructionUtil.compute_next_best_view_sequence_with_overlap(model_pts, pts_list, threshold=voxel_threshold, overlap_threshold=overlap_threshold)
        data_pairs = self.generate_data_pairs(limited_useful_view)
        seq_save_data = {
            "data_pairs": data_pairs,
            "best_sequence": limited_useful_view,
            "max_coverage_rate": limited_useful_view[-1][1]
        }
        output_label_path = DataLoadUtil.get_label_path(output_dir, seq)
        with open(output_label_path, 'w') as f:
            json.dump(seq_save_data, f)
            
    def generate_data_pairs(self, useful_view):
        data_pairs = []
        for next_view_idx in range(len(useful_view)):
            scanned_views = useful_view[:next_view_idx]
            next_view = useful_view[next_view_idx]
            data_pairs.append((scanned_views, next_view))
        return data_pairs