inference on YCB

configs/local/inference_config.yaml

@@ -15,7 +15,7 @@ runner:
       - OmniObject3d_test
 
   blender_script_path: "/media/hofee/data/project/python/nbv_reconstruction/blender/data_renderer.py"
-  output_dir: "/media/hofee/data/data/new_inference_test_output"
+  output_dir: "/media/hofee/data/results/nbv_rec_inference/global_only_ycb_241204"
   pipeline: nbv_reconstruction_pipeline
   voxel_size: 0.003
   min_new_area: 1.0
@@ -34,8 +34,8 @@ dataset:
   #   load_from_preprocess: True
 
   OmniObject3d_test:
-    root_dir: "/media/hofee/data/data/new_testset_output"
-    model_dir: "/media/hofee/data/data/scaled_object_meshes"
+    root_dir: "/media/hofee/data/results/ycb_preprocessed_dataset"
+    model_dir: "/media/hofee/data/data/ycb_obj"
     source: seq_reconstruction_dataset_preprocessed
     # split_file: "C:\\Document\\Datasets\\data_list\\OmniObject3d_test.txt"
     type: test

configs/local/strategy_generate_config.yaml

@@ -22,6 +22,6 @@ runner:
   
 datasets:
     OmniObject3d:
-      root_dir: /data/hofee/nbv_rec_part2_preprocessed
-      from: 155
-      to: 165 # ..-1 means end
+      root_dir: /media/hofee/data/results/ycb_view_data
+      from: 0
+      to: -1 # ..-1 means end

configs/local/view_generate_config.yaml

@@ -10,9 +10,9 @@ runner:
     port: 5002
     from: 1
     to: 50 # -1 means all
-    object_dir: C:\\Document\\Datasets\\scaled_object_meshes
-    table_model_path: C:\\Document\\Datasets\\table.obj
-    output_dir: C:\\Document\\Datasets\\debug_generate_view
+    object_dir: /media/hofee/data/data/ycb_obj
+    table_model_path: /media/hofee/data/data/others/table.obj
+    output_dir: /media/hofee/data/results/ycb_view_data
     binocular_vision: true
     plane_size: 10
     max_views: 512

core/seq_dataset.py

@@ -179,9 +179,9 @@ if __name__ == "__main__":
     np.random.seed(seed)
     
     config = {
-        "root_dir": "/media/hofee/data/data/new_testset",
+        "root_dir": "/media/hofee/data/results/ycb_view_data",
         "source": "seq_reconstruction_dataset",
-        "split_file": "/media/hofee/data/data/OmniObject3d_test.txt",
+        "split_file": "/media/hofee/data/results/ycb_test.txt",
         "load_from_preprocess": True,
         "filter_degree": 75,
         "num_workers": 0,
@@ -189,7 +189,7 @@ if __name__ == "__main__":
         "type": namespace.Mode.TEST,
     }
     
-    output_dir = "/media/hofee/data/data/new_testset_output"
+    output_dir = "/media/hofee/data/results/ycb_preprocessed_dataset"
     os.makedirs(output_dir, exist_ok=True)
 
     ds = SeqReconstructionDataset(config)

core/seq_dataset_preprocessed.py

@@ -21,7 +21,7 @@ class SeqReconstructionDatasetPreprocessed(BaseDataset):
         super(SeqReconstructionDatasetPreprocessed, self).__init__(config)
         self.config = config
         self.root_dir = config["root_dir"]
-        self.real_root_dir = r"/media/hofee/data/data/new_testset"
+        self.real_root_dir = r"/media/hofee/data/results/ycb_view_data"
         self.item_list = os.listdir(self.root_dir)
 
     def __getitem__(self, index):

preprocess/preprocessor.py

@@ -164,10 +164,10 @@ def save_scene_data(root, scene, scene_idx=0, scene_total=1,file_type="txt"):
 
 if __name__ == "__main__":
     #root = "/media/hofee/repository/new_data_with_normal"
-    root = r"H:\AI\Datasets\nbv_rec_part2"
+    root = r"/media/hofee/data/results/ycb_view_data"
     scene_list = os.listdir(root)
     from_idx = 0 # 1000
-    to_idx = 600 # 1500
+    to_idx = len(scene_list) # 1500
 
 
     cnt = 0

runners/inferencer.py

@@ -138,6 +138,8 @@ class Inferencer(Runner):
         import time
         while len(pred_cr_seq) < max_iter and retry < max_retry and success < max_success:
             Log.green(f"iter: {len(pred_cr_seq)}, retry: {retry}/{max_retry}, success: {success}/{max_success}")
+            combined_scanned_pts = np.vstack(scanned_view_pts)
+            voxel_downsampled_combined_scanned_pts_np, inverse =  self.voxel_downsample_with_mapping(combined_scanned_pts, voxel_threshold)
             output = self.pipeline(input_data)
             pred_pose_9d = output["pred_pose_9d"]
             pred_pose = torch.eye(4, device=pred_pose_9d.device)
@@ -154,7 +156,7 @@ class Inferencer(Runner):
                     curr_overlap_area_threshold = overlap_area_threshold * 0.5  
 
                 downsampled_new_target_pts = PtsUtil.voxel_downsample_point_cloud(new_target_pts, voxel_threshold)
-                overlap, _ = ReconstructionUtil.check_overlap(downsampled_new_target_pts, down_sampled_model_pts, overlap_area_threshold = curr_overlap_area_threshold, voxel_size=voxel_threshold, require_new_added_pts_num = True)
+                overlap, _ = ReconstructionUtil.check_overlap(downsampled_new_target_pts, voxel_downsampled_combined_scanned_pts_np, overlap_area_threshold = curr_overlap_area_threshold, voxel_size=voxel_threshold, require_new_added_pts_num = True)
                 if not overlap:
                     Log.yellow("no overlap!")
                     retry += 1
@@ -241,6 +243,13 @@ class Inferencer(Runner):
         down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud,threshold)
         return ReconstructionUtil.compute_coverage_rate(model_pts, down_sampled_combined_point_cloud, threshold)
     
+    def voxel_downsample_with_mapping(self, point_cloud, voxel_size=0.003):
+        voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
+        unique_voxels, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True)
+        idx_sort = np.argsort(inverse)
+        idx_unique = idx_sort[np.cumsum(counts)-counts]
+        downsampled_points = point_cloud[idx_unique]
+        return downsampled_points, inverse
 
     def save_inference_result(self, dataset_name, scene_name, output):
         dataset_dir = os.path.join(self.output_dir, dataset_name)

runners/view_generator.py

@@ -9,7 +9,7 @@ class ViewGenerator(Runner):
         self.config_path = config_path
         
     def run(self):
-        result = subprocess.run(['blender', '-b', '-P', '../blender/run_blender.py', '--', self.config_path])
+        result = subprocess.run(['/home/hofee/blender-4.0.2-linux-x64/blender', '-b', '-P', '../blender/run_blender.py', '--', self.config_path])
         print()
 
     def create_experiment(self, backup_name=None):