Datasets:
File size: 4,227 Bytes
f79f2ee bd36b5c f79f2ee bd36b5c f79f2ee bd36b5c f79f2ee bd36b5c f79f2ee | 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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | """Lay out the released images the way the experiment scripts expect.
Run once from this folder after downloading the dataset:
python prepare_data.py
Creates, in data/ next to this script:
combined_output/ <id>.<ext>, <id>_occluded.png, annotations.json
combined_output_blue/ <id>_occluded.png, annotations.json (App. C box style)
annotated/ <id>.<ext> (highlighted cue region)
Image categories (3).xlsx image id / direct or inferential / subcategory
and, for the zoomed-in recognition and source-mapping probes,
sec4_2_why_vlms_fail/stage2_zoomed_recognition/ crops/, crop_meta.json
sec4_2_why_vlms_fail/stage3_source_mapping/ annotated_reverse/, regions_reverse.json
Files are hard-linked where possible, copied otherwise.
"""
import json
import os
import shutil
import openpyxl
HERE = os.path.dirname(os.path.abspath(__file__))
DATASET = os.path.dirname(HERE)
def link(src_rel, dst):
src = os.path.join(DATASET, src_rel)
os.makedirs(os.path.dirname(dst), exist_ok=True)
if os.path.exists(dst):
os.remove(dst)
try:
os.link(src, dst)
except OSError:
shutil.copyfile(src, dst)
def main():
records = json.load(open(os.path.join(DATASET, "annotations.json")))
# Annotation format read by the experiment scripts
legacy = [{
"id": r["id"],
"original_file": os.path.basename(r["images"]["original"]),
"url": r["source_url"],
"answer": r["answer"],
"reasoning": r["annotator_note"],
"physical": r["type"] == "direct",
"annotations": r["occluders"],
} for r in records]
out = os.path.join(HERE, "data")
dirs = {
"main": os.path.join(out, "combined_output"),
"blue": os.path.join(out, "combined_output_blue"),
"highlighted": os.path.join(out, "annotated"),
}
for r in records:
rid, im = r["id"], r["images"]
link(im["original"], os.path.join(dirs["main"], os.path.basename(im["original"])))
link(im["occluded"], os.path.join(dirs["main"], f"{rid}_occluded.png"))
link(im["blue_box"], os.path.join(dirs["blue"], f"{rid}_occluded.png"))
if im["highlighted_cue"]:
link(im["highlighted_cue"], os.path.join(dirs["highlighted"], os.path.basename(im["highlighted_cue"])))
for key in ("main", "blue"):
with open(os.path.join(dirs[key], "annotations.json"), "w") as f:
json.dump(legacy, f, indent=2, ensure_ascii=False)
wb = openpyxl.Workbook()
ws = wb.active
ws.append(["image id", "direct or inferential", "subcategory"])
for r in records:
ws.append([r["id"], r["type"], r["fine_category"]])
wb.save(os.path.join(out, "Image categories (3).xlsx"))
stage2 = os.path.join(HERE, "sec4_2_why_vlms_fail", "stage2_zoomed_recognition")
crop_meta = []
for r in records:
for c in r["cue_crops"]:
crop_file = f"crops/{os.path.basename(c['image'])}"
link(c["image"], os.path.join(stage2, crop_file))
crop_meta.append({"id": r["id"], "ground_truth": r["answer"], "label": "", "crop_box": c["box"],
"crop_file": crop_file, "original_file": os.path.basename(r["images"]["original"])})
with open(os.path.join(stage2, "crop_meta.json"), "w") as f:
json.dump(crop_meta, f, indent=2, ensure_ascii=False)
stage3 = os.path.join(HERE, "sec4_2_why_vlms_fail", "stage3_source_mapping")
regions = []
for r in records:
if r["reverse_mapping"]:
link(r["reverse_mapping"]["image"], os.path.join(stage3, "annotated_reverse", f"{r['id']}.png"))
regions.append({"id": r["id"], "original_file": os.path.basename(r["images"]["original"]),
"ground_truth": r["answer"], "status": "auto", "correct_letter": r["reverse_mapping"]["answer"]})
with open(os.path.join(stage3, "regions_reverse.json"), "w") as f:
json.dump(regions, f, indent=2, ensure_ascii=False)
print(f"Prepared {len(records)} items under {out}, {len(crop_meta)} crops, {len(regions)} source-mapping images")
if __name__ == "__main__":
main()
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