update dataset.py

dataset.py

@@ -2,9 +2,12 @@ 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},
@@ -22,51 +25,12 @@ It includes both real-world recordings and rendered synthetic scenes demonstrati
 _HOMEPAGE = "https://huggingface.co/datasets/LLDDSS/Causal3D"
 _LICENSE = "CC-BY-4.0"
 
-# # Automatically discover configs from folder names
-# def _discover_configs(base_dir: str) -> List[datasets.BuilderConfig]:
-#     configs = []
-#     for root_folder in ["real_scenes", "hypothetical_scenes"]:
-#         full_path = os.path.join(base_dir, root_folder)
-#         if not os.path.exists(full_path):
-#             continue
-#         for scene in sorted(os.listdir(full_path)):
-#             scene_path = os.path.join(full_path, scene)
-#             if os.path.isdir(scene_path):
-#                 config_name = f"{root_folder}_{scene}"
-#                 configs.append(
-#                     datasets.BuilderConfig(
-#                         name=config_name,
-#                         version=datasets.Version("1.0.0"),
-#                         description=f"{scene} in {root_folder}",
-#                     )
-#                 )
-#     return configs
-
-
-# def _discover_configs(base_dir: str) -> List[datasets.BuilderConfig]:
-#     configs = []
-#     for root_folder in ["real_scenes", "hypothetical_scenes"]:
-#         full_path = os.path.join(base_dir, root_folder)
-#         if not os.path.exists(full_path):
-#             continue
-#         for scene in sorted(os.listdir(full_path)):
-#             scene_path = os.path.join(full_path, scene)
-#             if os.path.isdir(scene_path):
-#                 config_name = f"{root_folder}_{scene}"
-#                 configs.append(
-#                     datasets.BuilderConfig(
-#                         name=config_name,
-#                         version=datasets.Version("1.0.0"),
-#                         description=f"{scene} in {root_folder}",
-#                     )
-#                 )
-#     return configs
-
-
 class Causal3D(datasets.GeneratorBasedBuilder):
     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_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"),
@@ -105,10 +69,14 @@ class Causal3D(datasets.GeneratorBasedBuilder):
             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)
 
@@ -122,29 +90,82 @@ class Causal3D(datasets.GeneratorBasedBuilder):
     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:
-            raise FileNotFoundError(f"No CSV metadata found in {data_dir}")
+            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'))]
 
-        # Make sure a 'file_name' column exists
-        if "file_name" not in df.columns:
-            raise ValueError(f"'file_name' column not found in {csv_path.name}")
 
         # Load image paths
-        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)
-
-        # Match CSV rows with image paths
-        for idx, row in df.iterrows():
-            fname = row["file_name"]
-            if fname not in image_files:
-                continue  # skip missing images
-            yield idx, {
-                "image": image_files[fname],
-                "file_name": fname,
-                "metadata": row.to_json(),
-            }
+        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"))
+        

hypothetical_scenes/Hypothetic_v2_linear/part_001/tabular.csv

@@ -1,4 +1,4 @@
-iter,volume_ball,r_ball,volume_cube,edge_cube,img_path
+ID,volume_ball,r_ball,volume_cube,edge_cube,image
 201,1.2276555332721908,0.6642465469574618,1.8414832999082862,1.225714324440478,00201.png
 202,2.319641181897304,0.8211907845322407,3.4794617728459563,1.515318841038914,00202.png
 203,8.494210597169078,1.2657408745314087,12.741315895753615,2.335633851691395,00203.png

hypothetical_scenes/Hypothetic_v2_nonlinear/part_001/tabular.csv

@@ -1,4 +1,4 @@
-iter,volume_ball,r_ball,scaled_volume_ball,volume_cube,edge_cube,img_path
+ID,volume_ball,r_ball,scaled_volume_ball,volume_cube,edge_cube,image
 1,8.369588994,1.259520307,-0.26068306,5.797284907,1.796421378,00001.png
 2,8.748098298,1.278228064,-0.201077945,5.879111123,1.804833831,00002.png
 3,11.03841816,1.381251441,0.159586318,5.923758636,1.809391108,00003.png

hypothetical_scenes/Hypothetic_v3_fully_connected_linear/part_001/tabular.csv

@@ -1,4 +1,4 @@
-iter,volume_ball,height_of_cuboid,base_area_cone,img_path
+ID,volume_ball,height_of_cuboid,base_area_cone,image
 1,4.461709044674453,2.2308545223372267,4.461709044674453,00001.png
 2,4.641951570349036,2.320975785174518,4.641951570349036,00002.png
 3,5.732580074458467,2.8662900372292337,5.7325800744584665,00003.png

hypothetical_scenes/Hypothetic_v4_linear_full_connected/part_001/tabular.csv

@@ -1,4 +1,4 @@
-ID,volume_ball,height_cuboid,base_area_cuboid,base_area_cone,images_path
+ID,volume_ball,height_cuboid,base_area_cuboid,base_area_cone,image
 21,3.3875191596472374,0.6775038319294475,0.8468797899118095,1.6937595798236187,21.png
 11,9.833214754961691,1.9666429509923384,2.458303688740423,4.916607377480846,11.png
 1,21.434078230426127,4.286815646085226,5.358519557606533,10.717039115213064,1.png

hypothetical_scenes/Hypothetic_v4_linear_v/part_001/tabular.csv

@@ -1,4 +1,4 @@
-iter,volumn_ball,height_cuboid,base_area_cuboid,base_area_cone,images
+ID,volumn_ball,height_cuboid,base_area_cuboid,base_area_cone,image
 1,6.357351219715659,3.6156062425386826,4.986478731127171,9.972957462254342,./database/Hypothetic_v4_linear_v/1.png
 2,6.638624424255205,0.17833484754806211,3.408479635901634,6.816959271803268,./database/Hypothetic_v4_linear_v/2.png
 3,8.340578905668082,3.555331721959619,5.94795531381385,11.8959106276277,./database/Hypothetic_v4_linear_v/3.png

hypothetical_scenes/Hypothetic_v4_nonlinear_v/part_001/tabular.csv

@@ -1,4 +1,4 @@
-iter,volumn_ball,scaled_volumn_ball,height_cuboid,base_area_cuboid,scaled_base_area_cuboid,base_area_cone,images
+ID,volumn_ball,scaled_volumn_ball,height_cuboid,base_area_cuboid,scaled_base_area_cuboid,base_area_cone,image
 1,6.357351219715659,1.3101132663588948,3.6156062425386826,12.062283240823739,-0.31391936527208175,23.77826555326548,./database/Hypothetic_v4_nonlinear_v/1.png
 2,6.638624424255205,1.3697183815719198,0.17833484754806211,5.255928964219695,-1.029062930258996,12.890549194075682,./database/Hypothetic_v4_nonlinear_v/2.png
 3,8.340578905668082,1.730382644340953,3.555331721959619,12.047128974152987,-0.31551162389560594,23.76594362428198,./database/Hypothetic_v4_nonlinear_v/3.png

hypothetical_scenes/Hypothetic_v5_linear/part_001/tabular.csv

@@ -1,4 +1,4 @@
-iter,volumn_ball,height_cuboid,base_area_cuboid,base_area_cone,height_cone,images
+ID,volumn_ball,height_cuboid,base_area_cuboid,base_area_cone,height_cone,image
 1,8.44246124322853,2.910973480326474,3.406030417066501,19.40648986884316,7.814080579215056,1.png
 2,8.818598934965225,0.8277786954836739,2.8939132891346695,5.518524636557826,2.7996576095356396,2.png
 3,11.094568418540959,2.8744434678543143,4.190703565918581,19.162956452362096,7.964245828102308,3.png

hypothetical_scenes/Hypothetic_v5_linear_full_connected/part_001/tabular.csv

@@ -1,4 +1,4 @@
-iter,volumn_ball,height_cuboid,base_area_cuboid,base_area_cone,height_cone,images
+ID,volumn_ball,height_cuboid,base_area_cuboid,base_area_cone,height_cone,image
 1,9.693527257336251,2.9080581772008753,4.362087265801313,6.543130898701969,5.525310536681664,1.png
 2,10.126583641391235,3.0379750924173705,4.5569626386260556,6.835443957939083,5.772152675593004,2.png
 3,12.746962126264686,3.8240886378794055,5.736132956819108,8.604199435228661,7.26576841197087,3.png

hypothetical_scenes/rendered_h3_linear_128P/part_001/ref_scene_128P.csv

@@ -1,4 +1,4 @@
-iteration,volume_ball,volume_cylinder,tile_angle,epsilon,image_path,radius_ball,radius_cylinder,height_cylinder,PS:  c = 4 * a + 7 * b + 5 * epsilon
+ID,volume_ball,volume_cylinder,tile_angle,epsilon,image,radius_ball,radius_cylinder,height_cylinder,PS:  c = 4 * a + 7 * b + 5 * epsilon
 1,1.2142280370909548,0.9069697912825146,11.149147559318502,-0.011310625604584398,./database/rendered_h3_nonlinear_2_128P/4ef2cf50573c48c6887da53d897e3176.png,0.6618159289741131,0.3419035160331501,2.469652310381151,nan
 2,0.5405328999756235,0.2376380412554322,3.8821700278563798,0.011314427833172038,./database/rendered_h3_nonlinear_2_128P/f32a2e6f8cfe499cb1a404cb1f965de2.png,0.5053332106733465,0.1341708362624793,4.201939915174118,nan
 3,0.5756299964763172,1.071416152994136,9.744547428336316,-0.01157712570558065,./database/rendered_h3_nonlinear_2_128P/1a5c3ab6c9bd41a49b645e7c5aa431c3.png,0.5160418648262919,0.4207621332341213,1.9263492154441724,nan

hypothetical_scenes/rendered_h3_nonlinear_128P/part_001/ref_scene_128P.csv

@@ -1,4 +1,4 @@
-iteration ,volume_ball         ,radius_ball         ,volume_cylinder     ,radius_cylinder     ,height_cylinder     ,tilt_angle          ,noise_1                 ,image_path
+ID,volume_ball         ,radius_ball         ,volume_cylinder     ,radius_cylinder     ,height_cylinder     ,tilt_angle          ,noise_1                 ,image
         1 ,0.23459849915257136 ,0.3826005059721821  ,0.569191972758752   ,0.22375469195963163 ,3.6188000876934936  , 4.249022030659164  ,0.005135798644490502    ,./database/rendered_h3_nonlinear_1_128P/7fd6de8df4b447eab5101c8caed471c2.png
         2 ,0.10052331922663041 ,0.2884426013758498  ,0.230069060073269   ,0.12946087446329488 ,4.369495265661141   , 1.8458664302495802 ,0.026903944468274925    ,./database/rendered_h3_nonlinear_1_128P/9c81500774db4c36b2c5afca09a24d37.png
         3 ,0.47282233081419367 ,0.48328478580742495 ,0.5360137319981847  ,0.23750822575199732 ,3.0246054645500755  , 4.261372098892546  ,-0.00044912085671136267 ,./database/rendered_h3_nonlinear_1_128P/47d4b543bcfa46d299ec01dbba69880d.png

hypothetical_scenes/rendered_h5_nonlinear/part_001/h5_scene_150x150.csv

@@ -1,4 +1,4 @@
-iter ,volume_ball         ,height_cylinder     ,distance_ball_cylinder ,height_cylinder_above_ground ,tilt_angle           ,epsilon_1              ,epsilon_2            ,epsilon_3           ,image_path                                                                    ,"PS: b = np.sin(d) + 3epsilon_2, c =2*cos(a) + 7b + 2epsilon_2, e = tan(c) + 2d + 5epsilon_3"
+ID,volume_ball         ,height_cylinder     ,distance_ball_cylinder ,height_cylinder_above_ground ,tilt_angle           ,epsilon_1              ,epsilon_2            ,epsilon_3           ,image,"PS: b = np.sin(d) + 3epsilon_2, c =2*cos(a) + 7b + 2epsilon_2, e = tan(c) + 2d + 5epsilon_3"
   12 ,2.769763227949224   ,0.9828833585801344  ,    5.1120121129050204 ,         1.7107783653351136  ,1.4500034633335988   ,-0.0024450479569384778 ,0.04757853869852967  ,0.07923130772087676 ,./database/rendered_h5_150x150_nonlinear/a456ea0b9abb42179ade73935aab1013.png ,nan
   24 ,2.977600919455541   ,0.969234274593656   ,    4.998482994593695  ,         1.7886038699275741  ,0.9660926348649062   ,-0.0023797626285576136 ,0.09350500015961156  ,0.1576700523800217  ,./database/rendered_h5_150x150_nonlinear/11a180b791fb4b8e9739bdc2308aee3c.png ,nan
   26 ,2.781025710902497   ,0.9035599237471122  ,    4.716550452489086  ,         1.1260907961972155  ,-237.4688007837483   ,0.0002741802705891588  ,0.13151244710795473  ,0.11544214737613223 ,./database/rendered_h5_150x150_nonlinear/2f4d96f09a2649ec9923bbedb78f3640.png ,nan

real_scenes/Real_Parabola/part_001/tabular.csv

@@ -1,4 +1,4 @@
-id,deformation,angle,H,L,imgs
+ID,deformation,angle,H,L,image
 0,0.5672800675052121,48.416945274912734,1.0003766961651042,3.550588207334381,0.png
 1,0.1969696969696969,39.191540329180185,0.0860771343845725,0.4222912629861321,1.png
 2,0.6818181818181818,40.519643149940265,1.0903008166178716,5.102769458150399,2.png

real_scenes/Real_spring_v3_256P/Real_spring_v3_256P.csv

@@ -1,4 +1,4 @@
-iter,weight,spring original height,deformation,max_deformation,spring_constant,matrial,cube size,camera_location,img_path
+ID,weight,spring original height,deformation,max_deformation,spring_constant,matrial,cube size,camera_location,image
 1,44.61709044674453,13,3.845763001928744,10.79,11.60162246721079,Wood,"(0.9059759378433228, 0.9059759378433228, 0.9059759378433228)","(0, 15, 1)",1_0.png
 1,44.61709044674453,13,3.845763001928744,10.79,11.60162246721079,Wood,"(0.9059759378433228, 0.9059759378433228, 0.9059759378433228)","(0, 15, 4)",1_1.png
 1,44.61709044674453,13,3.845763001928744,10.79,11.60162246721079,Wood,"(0.9059759378433228, 0.9059759378433228, 0.9059759378433228)","(0, 15, 8)",1_2.png

real_scenes/Water_flow_scene_render/part_001/tabular.csv

@@ -1,4 +1,4 @@
-iteration,Ball Volume,Diameter of the bottom,Water height,Hole height,Water length,imgs,initial_water_height
+ID,Ball Volume,Diameter of the bottom,Water height,Hole height,Water length,image,initial_water_height
 0,0.0259003246339238,1.3628374703523634,0.8177552664785064,0.5775474906001206,0.7449332807203554, 00000.png,0.8
 1,0.026386212243451,1.1592314652850535,0.8250003937375635,0.2400640498977131,0.7494589718845341, 00001.png,0.8
 2,0.0001181429972279,1.188542884268203,0.8001064849890434,0.547810987612077,0.743532771539106, 00002.png,0.8

real_scenes/real_pendulum/tabular.csv

@@ -1,4 +1,4 @@
-iteration,x_l,y_l,x_p,y_p,l,theta,shadow length,shadow point,left,right,image_path
+ID,x_l,y_l,x_p,y_p,l,theta,shadow length,shadow point,left,right,image
 1,2.1325087955107356,0.38,2.2,0.2,0.02450193594008005,18.125483799524886,0.008371467909579981,2.3673708941573675,2.272641914630887,2.274990227210294,./database/real_pendulum/b44c0f028e394885bf5824bf65878344.png
 2,2.2530098462268735,0.38,2.2,0.2,0.15256481854952225,-35.982604040653044,0.05707758166630814,2.223346874353643,2.0734166651565316,2.14110017085903,./database/real_pendulum/38ab460682d047c193b526b988d8633f.png
 3,2.176576545905581,0.38,2.2,0.2,0.035648989873294915,50.9833438016299,0.05657311534705801,2.2986758998321575,2.2260260601049104,2.2768351123587327,./database/real_pendulum/8102a7694dde4a84a31f9a948daedc45.png

real_scenes/rendered_magnetic_128/magnet_scene_128.csv

@@ -1,4 +1,4 @@
-iter, 3D_over, magnet_direction(degree), needle_location,needle_direction(vector)\n
+ID,image,magnet_direction(degree),needle_location,needle_direction(vector)\n
 1,./database/rendered_magnetic_128/20241119_204421_Over_3D_128p.png,-1.0106183900252486,"(0.8655149431119491, 4.526390958018492, -0.03)",[-0.90155071  0.43267345]
 2,./database/rendered_magnetic_128/20241119_204424_Over_3D_128p.png,-225.54527609233517,"(-1.4280470087048818, -4.684853166121223, -0.03)",[0.92801895 0.37253298]
 3,./database/rendered_magnetic_128/20241119_204427_Over_3D_128p.png,-232.8094009599803,"(-1.90926800531539, -3.5206511042360753, -0.03)",[0.99911795 0.04199188]

real_scenes/rendered_reflection_128P/refleciton_scene_circle_128P.csv

@@ -1,4 +1,4 @@
-iter,incident_vector,reflection_vector_with_noise,noise,images
+ID,incident_vector,reflection_vector_with_noise,noise,image
 2,"[-0.03937180987581754, -0.9992246296939955]","[-0.05730616572776617, 0.9812902738420469]","[-0.017934355851948632, -0.017934355851948632]",7ff2504b-5941-429e-840a-044d2cf6d16c.png
 31,"[-0.978627420279129, -0.20564136810918382]","[-0.9827749924216449, 0.20149379596666792]","[-0.004147572142515903, -0.004147572142515903]",e79dd116-95e4-4e1f-ab98-67397d068ffb.png
 8,"[-0.9819293512693629, -0.18924785102010588]","[-1.0013990543595883, 0.1697781479298804]","[-0.019469703090225464, -0.019469703090225464]",b6a66ec9-38ea-4fe4-983e-81d46b86879e.png

real_scenes/spring_scene_128P/part_002/spring_scene_128P.csv

@@ -1,4 +1,4 @@
-iter,weight,spring high,deformation,noise,max_deformation,spring_constant,matrial,cube size
+ID,weight,spring high,deformation,noise,max_deformation,spring_constant,matrial,cube size
 1,0.8134327205078087,13,8.296761741444412,0.16243453636632418,10.79,10,Wood,"(1.1067684888839722, 1.1067684888839722, 1.1067684888839722)"
 2,0.4549692314742087,13,4.50801653000154,-0.04167578474054706,10.79,10,Wood,"(0.9118924140930176, 0.9118924140930176, 0.9118924140930176)"
 3,0.5152199911468511,13,5.331062758811543,0.17886284734303187,10.79,10,Wood,"(0.9504891037940979, 0.9504891037940979, 0.9504891037940979)"