update

Causal3D_Dataset.py

@@ -86,12 +86,12 @@ class Causal3dDataset(datasets.GeneratorBasedBuilder):
         ]
     
     def _generate_examples(self, data_dir):
-        print(f"Generating examples from: {data_dir}")
         image_files = {}
         for ext in ("*.png", "*.jpg", "*.jpeg"):
             for img_path in Path(data_dir).rglob(ext):
                 relative = str(img_path.relative_to(data_dir))
                 image_files[relative] = str(img_path)
+        print(f"Found {len(image_files)} images in {data_dir}")
 
         csv_files = [f for f in Path(data_dir).rglob("*.csv") if not f.name.startswith("._")]
         df = pd.read_csv(csv_files[0]) if csv_files else None
@@ -118,86 +118,3 @@ class Causal3dDataset(datasets.GeneratorBasedBuilder):
                     "file_name": fname,
                     "metadata": None,
                 }
-
-
-    # def _generate_examples(self, data_dir):
-    #     def color(text, code):
-    #         return f"\033[{code}m{text}\033[0m"
-    #     print("load data from {}".format(data_dir))  
-    #     try:
-    #         image_files = {}
-    #         for ext in ("*.png", "*.jpg", "*.jpeg"):
-    #             for img_path in Path(data_dir).rglob(ext):
-    #                 relative_path = str(img_path.relative_to(data_dir))
-    #                 image_files[relative_path] = str(img_path)
-    #         parts = [i.split('/')[0] for i in list(image_files.keys())]
-    #         parts = set(parts)
-    #         if "part_000" not in parts:
-    #            parts= ['']
-
-
-    #     except Exception as e:
-    #         print(color(f"Error loading images: {e}", "31"))  # Red
-    #         return
-    
-    #     # Find the .csv file
-    #     csv_files = [f for f in Path(data_dir).rglob("*.csv") if not f.name.startswith("._")]
-    #     if not csv_files:
-    #         # print(f"\033[33m[SKIP] No CSV found in {data_dir}, skipping this config.\033[0m")
-    #         pass
-    #     # print(f"\033[33m[INFO] Found CSV: {csv_files}\033[0m")
-    #     csv_path = csv_files[0] if csv_files else None
-    #     df = pd.read_csv(csv_path) if csv_path else None
-    #     image_col_exists = True
-    #     if df is not None and "imgs" not in df.columns:
-    #         image_col_exists = False
-
-    #     images = df["imgs"].tolist() if image_col_exists and df is not None else []
-    #     images = [i.split('/')[-1].split('.')[0] for i in images if i.endswith(('.png', '.jpg', '.jpeg'))]
-
-    #     try:
-    #         # Match CSV rows with image paths
-    #         if df is None:
-    #             for i, j in tqdm(image_files.items(), desc="Processing images", unit="image"):
-    #                 yield i, {
-    #                     "image": j,
-    #                     "file_name": i,
-    #                     "metadata": None,
-    #                 }
-
-    #         else:
-    #             for idx, row in tqdm(df.iterrows(), total=len(df), desc="Processing rows", unit="row"):
-    #                 fname = row["imgs"]
-    #                 raw_record_img_path = row["imgs"] #images[idx] if images else "" #row["image"] 
-    #                 record_img_name = raw_record_img_path.split('/')[-1]
-    #                 render_img_path = record_img_name
-
-
-    #                 # for part in parts:
-    #                 #     if part == '':
-    #                 #         record_img_path = record_img_name
-    #                 #     else:
-    #                 #         record_img_path = "/".join([part, record_img_name.strip()])
-    #                 #         if "Water_flow_scene_render" in data_dir:
-    #                 #             record_img_path = "/".join([part, str(int(record_img_name.strip().split('.')[0]))+".png"])
-    #                 #     if record_img_path in image_files:
-    #                 #         # print(color(f"record_img_path: { image_files[record_img_path]}", "34"))  # Blue
-    #                 #         yield idx, {
-    #                 #             "image": image_files[record_img_path],
-    #                 #             "file_name": fname,
-    #                 #             "metadata": row.to_json(),
-    #                 #         }
-    #                 #         break
-                            
-    #                 #     else:
-    #                 #         yield idx, {
-    #                 #             # "image": "",
-    #                 #             "file_name": fname,
-    #                 #             "metadata": row.to_json(),
-    #                 #         }
-    #                 #         break
-
-
-    #     except Exception as e:
-    #         print(color(f"Error processing CSV rows: {e}", "31"))
-