update dataset

Cuasal3D.py

@@ -0,0 +1,175 @@
+import os
+import glob
+from pathlib import Path
+from typing import List
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+import datasets
+
+
+_CITATION = """\
+@article{liu2025causal3d,
+  title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data},
+  author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing},
+  journal={arXiv preprint arXiv:2503.04852},
+  year={2025}
+}
+"""
+
+_DESCRIPTION = """\
+Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. 
+It includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/LLDDSS/Causal3D"
+_LICENSE = "CC-BY-4.0"
+
+class Causal3D(datasets.GeneratorBasedBuilder):
+    DEFAULT_CONFIG_NAME = "real_scenes_Real_magnet_v3"
+    BUILDER_CONFIGS = [
+        # hypothetical_scenes
+        datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v2_linear", 
+                               version=datasets.Version("1.0.0"), 
+                               description="Hypothetic_v2_linear scene"),
+        datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v2_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetic_v2_nonlinear scene"),
+        datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v3_fully_connected_linear", version=datasets.Version("1.0.0"), description="Hypothetic_v3_fully_connected_linear scene"),
+        datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v4_linear_full_connected", version=datasets.Version("1.0.0"), description="Hypothetic_v4_linear_full_connected scene"),
+        datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v4_linear_v", version=datasets.Version("1.0.0"), description="Hypothetic_v4_linear_v scene"),
+        datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v4_nonlinear_v", version=datasets.Version("1.0.0"), description="Hypothetic_v4_nonlinear_v scene"),
+        datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v5_linear", version=datasets.Version("1.0.0"), description="Hypothetic_v5_linear scene"),
+        datasets.BuilderConfig(name="hypothetical_scenes_Hypothetic_v5_linear_full_connected", version=datasets.Version("1.0.0"), description="Hypothetic_v5_linear_full_connected scene"),
+        datasets.BuilderConfig(name="hypothetical_scenes_rendered_h3_linear_128P", version=datasets.Version("1.0.0"), description="rendered_h3_linear_128P scene"),
+        datasets.BuilderConfig(name="hypothetical_scenes_rendered_h3_nonlinear_128P", version=datasets.Version("1.0.0"), description="rendered_h3_nonlinear_128P scene"),
+        datasets.BuilderConfig(name="hypothetical_scenes_rendered_h5_nonlinear", version=datasets.Version("1.0.0"), description="rendered_h5_nonlinear scene"),
+
+        # real_scenes
+        datasets.BuilderConfig(name="real_scenes_Real_Parabola", version=datasets.Version("1.0.0"), description="Real_Parabola scene"),
+        datasets.BuilderConfig(name="real_scenes_Real_magnet_v3", version=datasets.Version("1.0.0"), description="Real_magnet_v3 scene"),
+        datasets.BuilderConfig(name="real_scenes_Real_magnet_v3_5", version=datasets.Version("1.0.0"), description="Real_magnet_v3_5 scene"),
+        datasets.BuilderConfig(name="real_scenes_Real_parabola_multi_view", version=datasets.Version("1.0.0"), description="Real_parabola_multi_view scene"),
+        datasets.BuilderConfig(name="real_scenes_Real_spring_v3_256P", version=datasets.Version("1.0.0"), description="Real_spring_v3_256P scene"),
+        datasets.BuilderConfig(name="real_scenes_Water_flow_scene_render", version=datasets.Version("1.0.0"), description="Water_flow_scene_render scene"),
+        datasets.BuilderConfig(name="real_scenes_convex_len_render_images", version=datasets.Version("1.0.0"), description="convex_len_render_images scene"),
+        datasets.BuilderConfig(name="real_scenes_real_pendulum", version=datasets.Version("1.0.0"), description="real_pendulum scene"),
+        datasets.BuilderConfig(name="real_scenes_rendered_magnetic_128", version=datasets.Version("1.0.0"), description="rendered_magnetic_128 scene"),
+        datasets.BuilderConfig(name="real_scenes_rendered_reflection_128P", version=datasets.Version("1.0.0"), description="rendered_reflection_128P scene"),
+        datasets.BuilderConfig(name="real_scenes_seesaw_scene_128P", version=datasets.Version("1.0.0"), description="seesaw_scene_128P scene"),
+        datasets.BuilderConfig(name="real_scenes_spring_scene_128P", version=datasets.Version("1.0.0"), description="spring_scene_128P scene"),
+    ]
+
+    def _info(self):
+        print(">>> Loaded config:", self.config.name)  # 🟡 加这个调试输出
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "image": datasets.Image(),
+                "file_name": datasets.Value("string"),
+                "metadata": datasets.Value("string"),  # optionally replace with structured fields
+            }),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+    
+    def _split_generators(self, dl_manager):
+        parts = self.config.name.split("_", 2)
+        category = parts[0] + "_" + parts[1]  # real_scenes or hypothetical_scenes
+
+        if category not in ["real_scenes", "hypothetical_scenes"]:
+            raise ValueError(f"Invalid category '{category}'. Must be one of ['real_scenes', 'hypothetical_scenes']")
+
+        scene = parts[2]
+        data_dir = os.path.join(category, scene)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir},
+            )
+        ]
+
+    def _generate_examples(self, data_dir):
+        # Find the .csv file
+        csv_files = list(Path(data_dir).rglob("*.csv"))
+        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")
+            return  # ✅ 跳过该 config，不报错
+        csv_path = csv_files[0]
+        df = pd.read_csv(csv_path)
+        if "image" not in df.columns:
+            print(f"\033[31m[SKIP] 'image' column not found in {csv_path}, skipping this config.\033[0m")
+            return
+        
+        # sub_folders = [os.path.join(data_dir, i) for i in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, i))]
+        
+        def color(text, code):
+            return f"\033[{code}m{text}\033[0m"
+        # print()
+        # print(color(f"data_dir: {data_dir}", "36"))  # Cyan
+        # print(color(f"csv_path: {csv_path}", "33"))  # Yellow
+        # print(color(f"csv_path.name: {csv_path.name}", "35"))  # Magenta
+        # print(color(f"CSV columns: {list(df.columns)}", "32"))  # Green
+
+        images = df["image"].tolist()
+        # images only contain image names
+
+        images = [i.split('/')[-1].split('.')[0] for i in images if i.endswith(('.png', '.jpg', '.jpeg'))]
+
+
+        # Load image paths
+        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
+        try:
+            # Match CSV rows with image paths
+            for idx, row in tqdm(df.iterrows(), total=len(df), desc="Processing rows", unit="row"):
+                fname = row["ID"]
+                raw_record_img_path = row["image"]
+                record_img_name = raw_record_img_path.split('/')[-1]
+                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"])
+
+                    # print(f"raw_record_img_path: {raw_record_img_path}")
+                    # print(f"record_img_name: {record_img_name}")
+                    # print("part: ", part)
+                    # print(f"part: {part}, record_img_name: {record_img_name}, record_img_path: {record_img_path}")
+                    # print(f"record_img_path in image_files: {record_img_path in image_files}")
+                    # print(image_files.keys())
+                    # print(f"part: {part}, record_img_name: {record_img_name}, record_img_path: {record_img_path}, "
+                    #       f"record_image_path in image_files: {record_img_path in image_files}, image_files,key[0]: {list(image_files.keys())[0]}")
+                    # print(image_files)
+                    # exit(0)
+                    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
+
+
+        except Exception as e:
+            print(color(f"Error processing CSV rows: {e}", "31"))
+            
+
+