| import argparse |
| import json |
| import os |
| from collections import Counter |
|
|
|
|
| def canonical_answer(answers): |
| if not answers: |
| return "" |
| counts = Counter(answers) |
| best = max(counts.values()) |
| for answer in answers: |
| if counts[answer] == best: |
| return answer |
| return answers[0] |
|
|
|
|
| def ensure_symlink(src, dst): |
| if os.path.islink(dst) or os.path.exists(dst): |
| return |
| os.symlink(src, dst) |
|
|
|
|
| def convert_split(src_json, image_dir, out_jsonl): |
| with open(src_json, "r") as f: |
| data = json.load(f)["data"] |
|
|
| with open(out_jsonl, "w") as writer: |
| for item in data: |
| answers = item.get("answers", []) |
| record = { |
| "image": os.path.join(image_dir, f"{item['image_id']}.jpg"), |
| "question": item["question"], |
| "question_id": item["question_id"], |
| "answer": canonical_answer(answers), |
| } |
| writer.write(json.dumps(record) + "\n") |
| return data |
|
|
|
|
| def build_val_questions(val_data, out_path): |
| payload = { |
| "questions": [ |
| { |
| "image_id": item["image_id"], |
| "question": item["question"], |
| "question_id": item["question_id"], |
| } |
| for item in val_data |
| ] |
| } |
| with open(out_path, "w") as f: |
| json.dump(payload, f) |
|
|
|
|
| def build_val_annotations(val_data, out_path): |
| payload = { |
| "annotations": [ |
| { |
| "image_id": item["image_id"], |
| "question_id": item["question_id"], |
| "answers": [{"answer": ans} for ans in item.get("answers", [])], |
| } |
| for item in val_data |
| ] |
| } |
| with open(out_path, "w") as f: |
| json.dump(payload, f) |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--official-root", type=str, default="data/textvqa_official") |
| parser.add_argument("--output-root", type=str, default="data/textvqa") |
| args = parser.parse_args() |
|
|
| official_root = args.official_root |
| output_root = args.output_root |
| os.makedirs(output_root, exist_ok=True) |
|
|
| train_images_src = os.path.abspath(os.path.join(official_root, "train_images")) |
| test_images_src = os.path.abspath(os.path.join(official_root, "test_images")) |
| train_images_dst = os.path.join(output_root, "train_images") |
| test_images_dst = os.path.join(output_root, "test_images") |
| ensure_symlink(train_images_src, train_images_dst) |
| ensure_symlink(test_images_src, test_images_dst) |
|
|
| train_json = os.path.join(official_root, "TextVQA_0.5.1_train.json") |
| val_json = os.path.join(official_root, "TextVQA_0.5.1_val.json") |
| test_json = os.path.join(official_root, "TextVQA_0.5.1_test.json") |
|
|
| train_data = convert_split( |
| train_json, |
| os.path.join(output_root, "train_images"), |
| os.path.join(output_root, "textvqa_train.jsonl"), |
| ) |
| val_data = convert_split( |
| val_json, |
| os.path.join(output_root, "train_images"), |
| os.path.join(output_root, "textvqa_val.jsonl"), |
| ) |
| _ = convert_split( |
| test_json, |
| os.path.join(output_root, "test_images"), |
| os.path.join(output_root, "textvqa_test.jsonl"), |
| ) |
|
|
| build_val_questions(val_data, os.path.join(output_root, "textvqa_val_questions.json")) |
| build_val_annotations(val_data, os.path.join(output_root, "textvqa_val_annotations.json")) |
|
|
| print(f"wrote {len(train_data)} train samples") |
| print(f"wrote {len(val_data)} val samples") |
| print(f"prepared TextVQA data under {output_root}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
