feat(synthesizer): add CSV export and progress bar for dataset generation

mini-nav/main.py

@@ -4,15 +4,14 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "action",
-        choices=["train", "benchmark", "visualize"],
-        help="Action to perform: train, benchmark, or visualize",
+        choices=["train", "benchmark", "visualize", "generate"],
+        help="Action to perform: train, benchmark, visualize, or generate",
     )
     args = parser.parse_args()
 
     if args.action == "train":
         from compressors import train
 
-        # 启动训练
         train(
             epoch_size=10, batch_size=64, lr=1e-4, checkpoint_path="hash_checkpoint.pt"
         )
@@ -20,7 +19,27 @@ if __name__ == "__main__":
         from benchmarks import evaluate
 
         evaluate("Dinov2", "CIFAR-10", "Recall@10")
-    else:  # visualize
+    elif args.action == "visualize":
         from visualizer import app
 
         app.run(debug=True)
+    else:  # generate
+        from configs import cfg_manager
+        from data_loading.synthesizer import ImageSynthesizer
+
+        config = cfg_manager.get()
+        dataset_cfg = config.dataset
+
+        synthesizer = ImageSynthesizer(
+            dataset_root=dataset_cfg.dataset_root,
+            output_dir=dataset_cfg.output_dir,
+            num_objects_range=dataset_cfg.num_objects_range,
+            num_scenes=dataset_cfg.num_scenes,
+            object_scale_range=dataset_cfg.object_scale_range,
+            rotation_range=dataset_cfg.rotation_range,
+            overlap_threshold=dataset_cfg.overlap_threshold,
+            seed=dataset_cfg.seed,
+        )
+
+        generated_files = synthesizer.generate()
+        print(f"Generated {len(generated_files)} synthesized images in {dataset_cfg.output_dir}")