| """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"))) |
|
|
| |
| 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() |
|
|
