File size: 2,982 Bytes
82b9cde | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | # 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
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