Add dataset loading script for proper image display

dataset_info.py

@@ -0,0 +1,75 @@
+# Dataset loading script for HuggingFace
+import datasets
+import os
+from pathlib import Path
+
+_DESCRIPTION = """
+CameraBench Binary Evaluation Dataset with video frames and optical flow visualizations.
+"""
+
+class CameraBenchConfig(datasets.BuilderConfig):
+    """BuilderConfig for CameraBench."""
+    def __init__(self, **kwargs):
+        super(CameraBenchConfig, self).__init__(**kwargs)
+
+class CameraBench(datasets.GeneratorBasedBuilder):
+    """CameraBench dataset with frames and optical flows."""
+    
+    VERSION = datasets.Version("1.0.0")
+    
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "video_name": datasets.Value("string"),
+                "video_path": datasets.Value("string"),
+                "frames_path": datasets.Value("string"),
+                "optical_flows_path": datasets.Value("string"),
+                "frames": datasets.Sequence(datasets.Image()),  # All frames as sequence
+                "optical_flows": datasets.Sequence(datasets.Image()),  # All flows as sequence
+                "num_frames": datasets.Value("int32"),
+                "num_flows": datasets.Value("int32"),
+                "question": datasets.Value("string"),
+                "label": datasets.Value("string"),
+                "task": datasets.Value("string"),
+                "label_name": datasets.Value("string"),
+            })
+        )
+    
+    def _split_generators(self, dl_manager):
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"metadata_path": "data.jsonl"},
+            ),
+        ]
+    
+    def _generate_examples(self, metadata_path):
+        import json
+        idx = 0
+        with open(metadata_path, "r") as f:
+            for line in f:
+                record = json.loads(line)
+                
+                # Get paths to all frames and flows
+                video_base_name = record['video_name'].replace('.mp4', '')
+                frames_dir = f"frames/{video_base_name}"
+                flows_dir = f"optical_flows/{video_base_name}"
+                
+                # Collect all frame paths
+                frame_paths = []
+                if os.path.exists(frames_dir):
+                    frame_files = sorted([f for f in os.listdir(frames_dir) if f.endswith('.png')])
+                    frame_paths = [os.path.join(frames_dir, f) for f in frame_files]
+                
+                # Collect all flow paths
+                flow_paths = []
+                if os.path.exists(flows_dir):
+                    flow_files = sorted([f for f in os.listdir(flows_dir) if f.endswith('.png')])
+                    flow_paths = [os.path.join(flows_dir, f) for f in flow_files]
+                
+                record['frames'] = frame_paths
+                record['optical_flows'] = flow_paths
+                
+                yield idx, record
+                idx += 1