73 lines
3.6 KiB
Python
73 lines
3.6 KiB
Python
import os
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from PytorchBoot.runners.runner import Runner
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from PytorchBoot.config import ConfigManager
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import PytorchBoot.stereotype as stereotype
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@stereotype.runner("strategy_generator")
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class StrategyGenerator(Runner):
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def __init__(self, config):
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super().__init__(config)
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self.load_experiment("generate")
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def run(self):
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self.demo(seq=16,num=100)
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def create_experiment(self, backup_name=None):
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super().create_experiment(backup_name)
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output_dir = os.path.join(str(self.experiment_path), "output")
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os.makedirs(output_dir)
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def load_experiment(self, backup_name=None):
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super().load_experiment(backup_name)
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def demo(self, seq, num=100):
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import os
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from utils.data_load import DataLoadUtil
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from utils.reconstruction import ReconstructionUtil
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import numpy as np
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component = self.config["generate"][0]["component"] #r"C:\Document\Local Project\nbv_rec\output"
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data_dir = ConfigManager.get("datasets", "components", component, "root_dir")
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model_path = os.path.join(data_dir, f"sequence.{seq}\\world_points.txt")
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model_pts = np.loadtxt(model_path)
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output_dir = os.path.join(str(self.experiment_path), "output")
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pts_list = []
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for idx in range(0,num):
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path = DataLoadUtil.get_path(data_dir, seq, idx)
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point_cloud = DataLoadUtil.get_point_cloud_world_from_path(path)
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sampled_point_cloud = ReconstructionUtil.downsample_point_cloud(point_cloud, 0.005)
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pts_list.append(sampled_point_cloud)
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sampled_model_pts = ReconstructionUtil.downsample_point_cloud(model_pts, 0.005)
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np.savetxt(os.path.join(output_dir,"sampled_model_points.txt"), sampled_model_pts)
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thre = 0.005
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useful_view, useless_view = ReconstructionUtil.compute_next_best_view_sequence(model_pts, pts_list, threshold=thre)
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print("useful:", useful_view)
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print("useless:", useless_view)
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selected_full_views = ReconstructionUtil.combine_point_with_view_sequence(pts_list, useful_view)
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downsampled_selected_full_views = ReconstructionUtil.downsample_point_cloud(selected_full_views, thre)
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np.savetxt(os.path.join(output_dir,"selected_full_views.txt"), downsampled_selected_full_views)
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limited_useful_view, limited_useless_view = ReconstructionUtil.compute_next_best_view_sequence_with_overlap(model_pts, pts_list, threshold=thre, overlap_threshold=0.3)
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print("limited_useful:", limited_useful_view)
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print("limited_useless:", limited_useless_view)
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limited_selected_full_views = ReconstructionUtil.combine_point_with_view_sequence(pts_list, limited_useful_view)
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downsampled_limited_selected_full_views = ReconstructionUtil.downsample_point_cloud(limited_selected_full_views, thre)
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np.savetxt(os.path.join(output_dir,"selected_full_views_limited.txt"), downsampled_limited_selected_full_views)
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import json
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for idx, score in limited_useful_view:
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path = DataLoadUtil.get_path(data_dir, seq, idx)
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point_cloud = DataLoadUtil.get_point_cloud_world_from_path(path)
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print("saving useful view: ", idx, " | score: ", score)
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np.savetxt(os.path.join(output_dir,f"useful_view_{idx}.txt"), point_cloud)
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with open(os.path.join(output_dir,f"useful_view.json"), 'w') as f:
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json.dump(limited_useful_view, f)
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print("seq length: ", len(useful_view), "limited seq length: ", len(limited_useful_view))
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