finish nbv_reconstruction_dataset

core/dataset.py

@@ -1,17 +1,89 @@
+import numpy as np
 from PytorchBoot.dataset import BaseDataset
 import PytorchBoot.stereotype as stereotype
+from utils.data_load import DataLoadUtil
 
-@stereotype.dataset("nbv_reconstruction_dataset", comment="unfinished")
+
+@stereotype.dataset("nbv_reconstruction_dataset")
 class NBVReconstructionDataset(BaseDataset):
     def __init__(self, config):
         super(NBVReconstructionDataset, self).__init__(config)
         self.config = config
-        
+        self.label_dir = config["label_dir"]
+        self.root_dir = config["root_dir"]
+        self.datalist = self.get_datalist()
+
     def get_datalist(self):
-        pass
-    
-    def load_view(path):
-        pass
-    
-    def load_data_item(self, idx):
-        pass
+        datalist = []
+        scene_idx_list = DataLoadUtil.get_scene_idx_list(self.root_dir)
+        for scene_idx in scene_idx_list:
+            label_path = DataLoadUtil.get_label_path(self.label_dir, scene_idx)
+            label_data = DataLoadUtil.load_label(label_path)
+            for data_pair in label_data["data_pairs"]:
+                scanned_views = data_pair[0]
+                next_best_view = data_pair[1]
+                max_coverage_rate = label_data["max_coverage_rate"]
+                datalist.append(
+                    {
+                        "scanned_views": scanned_views,
+                        "next_best_view": next_best_view,
+                        "max_coverage_rate": max_coverage_rate,
+                        "scene_idx": scene_idx,
+                    }
+                )
+        return datalist
+
+    def __getitem__(self, index):
+        data_item_info = self.datalist[index]
+        scanned_views = data_item_info["scanned_views"]
+        nbv = data_item_info["next_best_view"]
+        max_coverage_rate = data_item_info["max_coverage_rate"]
+        scene_idx = data_item_info["scene_idx"]
+        scanned_views_pts, scanned_coverages_rate, scanned_cam_pose = [], [], []
+        for view in scanned_views:
+            frame_idx = view[0]
+            coverage_rate = view[1]
+            view_path = DataLoadUtil.get_path(self.root_dir, scene_idx, frame_idx)
+            pts = DataLoadUtil.load_depth(view_path)
+            scanned_views_pts.append(pts)
+            scanned_coverages_rate.append(coverage_rate)
+            cam_pose = DataLoadUtil.load_cam_info(view_path)["cam_to_world"]
+            scanned_cam_pose.append(cam_pose)
+
+        nbv_idx, nbv_coverage_rate = nbv[0], nbv[1]
+        nbv_path = DataLoadUtil.get_path(self.root_dir, scene_idx, nbv_idx)
+        nbv_pts = DataLoadUtil.load_depth(nbv_path)
+        cam_info = DataLoadUtil.load_cam_info(nbv_path)
+        nbv_cam_pose = cam_info["cam_to_world"]
+
+        data_item = {
+            "scanned_views_pts": np.asarray(scanned_views_pts,dtype=np.float32),
+            "scanned_coverages_rate": np.asarray(scanned_coverages_rate,dtype=np.float32),
+            "scanned_cam_pose": np.asarray(scanned_cam_pose,dtype=np.float32),
+            "nbv_pts": np.asarray(nbv_pts,dtype=np.float32),
+            "nbv_coverage_rate": nbv_coverage_rate,
+            "nbv_cam_pose": nbv_cam_pose,
+            "max_coverage_rate": max_coverage_rate,
+        }
+
+        return data_item
+
+    def __len__(self):
+        return len(self.datalist)
+
+if __name__ == "__main__":
+    import torch
+    config = {
+        "root_dir": "C:\\Document\\Local Project\\nbv_rec\\sample_dataset",
+        "label_dir": "C:\\Document\\Local Project\\nbv_rec\\sample_output",
+        "ratio": 0.1,
+        "batch_size": 1,
+        "num_workers": 0,
+    }
+    ds = NBVReconstructionDataset(config)
+    dl = ds.get_loader(shuffle=True)
+    for idx, data in enumerate(dl):
+        for key, value in data.items():
+            if isinstance(value, torch.Tensor):
+                print(key, ":" ,value.shape)
+        print()