import os
import json

import numpy as np
from PytorchBoot.runners.runner import Runner
from PytorchBoot.config import ConfigManager
from PytorchBoot.utils import Log
import PytorchBoot.stereotype as stereotype
from PytorchBoot.status import status_manager

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

@stereotype.runner("strategy_generator")
class StrategyGenerator(Runner):
    def __init__(self, config):
        super().__init__(config)
        self.load_experiment("generate_strategy")
        self.status_info = {
            "status_manager": status_manager,
            "app_name": "generate_strategy",
            "runner_name": "strategy_generator"
        }
        self.overwrite = ConfigManager.get("runner", "generate", "overwrite")
        self.seq_num = ConfigManager.get("runner","generate","seq_num")
        self.overlap_area_threshold = ConfigManager.get("runner","generate","overlap_area_threshold")
        self.compute_with_normal = ConfigManager.get("runner","generate","compute_with_normal")
        self.scan_points_threshold = ConfigManager.get("runner","generate","scan_points_threshold")
        

        
    def run(self):
        dataset_name_list =  ConfigManager.get("runner", "generate", "dataset_list")
        voxel_threshold = ConfigManager.get("runner","generate","voxel_threshold")
        for dataset_idx in range(len(dataset_name_list)):
            dataset_name = dataset_name_list[dataset_idx]
            status_manager.set_progress("generate_strategy", "strategy_generator", "dataset", dataset_idx, len(dataset_name_list))
            root_dir = ConfigManager.get("datasets", dataset_name, "root_dir")
            from_idx = ConfigManager.get("datasets",dataset_name,"from")
            to_idx = ConfigManager.get("datasets",dataset_name,"to")
            scene_name_list = os.listdir(root_dir)
            if to_idx == -1:
                to_idx = len(scene_name_list)
            cnt = 0
            total = len(scene_name_list[from_idx:to_idx])
            Log.info(f"Processing Dataset: {dataset_name}, From: {from_idx}, To: {to_idx}")
            for scene_name in scene_name_list[from_idx:to_idx]:
                Log.info(f"({dataset_name})Processing [{cnt}/{total}]: {scene_name}")
                status_manager.set_progress("generate_strategy", "strategy_generator", "scene", cnt, total)
                output_label_path = DataLoadUtil.get_label_path(root_dir, scene_name,0)
                if os.path.exists(output_label_path) and not self.overwrite:
                    Log.info(f"Scene <{scene_name}> Already Exists, Skip")
                    cnt += 1
                    continue
                
                self.generate_sequence(root_dir, scene_name,voxel_threshold)
                cnt += 1
            status_manager.set_progress("generate_strategy", "strategy_generator", "scene", total, total)
        status_manager.set_progress("generate_strategy", "strategy_generator", "dataset", len(dataset_name_list), len(dataset_name_list))
    
    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, scene_name, voxel_threshold):
        status_manager.set_status("generate_strategy", "strategy_generator", "scene", scene_name)
        frame_num = DataLoadUtil.get_scene_seq_length(root, scene_name)
        
        model_points_normals = DataLoadUtil.load_points_normals(root, scene_name)
        model_pts = model_points_normals[:,:3]
        down_sampled_model_pts, idx = PtsUtil.voxel_downsample_point_cloud(model_pts, voxel_threshold, require_idx=True)
        down_sampled_model_nrm = model_points_normals[idx, 3:]
        pts_list = []
        nrm_list = []
        scan_points_indices_list = []
        non_zero_cnt = 0

        for frame_idx in range(frame_num):
            status_manager.set_progress("generate_strategy", "strategy_generator", "loading frame", frame_idx, frame_num)
            pts_path = os.path.join(root,scene_name, "pts", f"{frame_idx}.npy")
            nrm_path = os.path.join(root,scene_name, "nrm", f"{frame_idx}.npy")
            idx_path = os.path.join(root,scene_name, "scan_points_indices", f"{frame_idx}.npy")
            
            pts = np.load(pts_path)
            if self.compute_with_normal:
                if pts.shape[0] == 0:
                    nrm = np.zeros((0,3))
                else:
                    nrm = np.load(nrm_path)
                nrm_list.append(nrm)
            pts_list.append(pts)
            indices = np.load(idx_path) 
            scan_points_indices_list.append(indices)
            if pts.shape[0] > 0:
                non_zero_cnt += 1
        status_manager.set_progress("generate_strategy", "strategy_generator", "loading frame", frame_num, frame_num)
        
        seq_num = min(self.seq_num, non_zero_cnt)
        init_view_list = []
        idx = 0
        while len(init_view_list) < seq_num and idx < len(pts_list):
            if pts_list[idx].shape[0] > 50:
                init_view_list.append(idx)
            idx += 1

        seq_idx = 0
        import time
        for init_view in init_view_list:
            status_manager.set_progress("generate_strategy", "strategy_generator", "computing sequence", seq_idx, len(init_view_list))
            start = time.time()

            if not self.compute_with_normal:    
                limited_useful_view, _, _ = ReconstructionUtil.compute_next_best_view_sequence(down_sampled_model_pts, pts_list, scan_points_indices_list = scan_points_indices_list,init_view=init_view, 
                                                                                                            threshold=voxel_threshold, scan_points_threshold=self.scan_points_threshold, overlap_area_threshold=self.overlap_area_threshold, status_info=self.status_info)
            else:
                limited_useful_view, _, _ = ReconstructionUtil.compute_next_best_view_sequence_with_normal(down_sampled_model_pts, down_sampled_model_nrm, pts_list, nrm_list, scan_points_indices_list = scan_points_indices_list,init_view=init_view, 
                                                                                                            threshold=voxel_threshold, scan_points_threshold=self.scan_points_threshold, overlap_area_threshold=self.overlap_area_threshold, status_info=self.status_info)
            end = time.time()
            print(f"Time: {end-start}")
            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]
            }
        
            status_manager.set_status("generate_strategy", "strategy_generator", "max_coverage_rate", limited_useful_view[-1][1])
            Log.success(f"Scene <{scene_name}> Finished, Max Coverage Rate: {limited_useful_view[-1][1]}, Best Sequence length: {len(limited_useful_view)}")

            output_label_path = DataLoadUtil.get_label_path(root, scene_name, seq_idx)
            
            
            with open(output_label_path, 'w') as f:
                json.dump(seq_save_data, f)
            seq_idx += 1
        status_manager.set_progress("generate_strategy", "strategy_generator", "computing sequence", len(init_view_list), len(init_view_list))


    def generate_data_pairs(self, useful_view):
        data_pairs = []
        for next_view_idx in range(1, 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