mirror of
https://github.com/VikParuchuri/surya.git
synced 2026-06-04 21:03:53 +08:00
91 lines
4.0 KiB
Python
91 lines
4.0 KiB
Python
import os
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import argparse
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import json
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from collections import defaultdict
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import torch
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from surya.input.langs import replace_lang_with_code, get_unique_langs
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from surya.input.load import load_from_folder, load_from_file, load_lang_file
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from surya.model.detection.model import load_model as load_detection_model, load_processor as load_detection_processor
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from surya.model.recognition.model import load_model as load_recognition_model
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from surya.model.recognition.processor import load_processor as load_recognition_processor
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from surya.model.recognition.tokenizer import _tokenize
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from surya.ocr import run_ocr
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from surya.postprocessing.text import draw_text_on_image
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from surya.settings import settings
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def main():
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parser = argparse.ArgumentParser(description="Detect bboxes in an input file or folder (PDFs or image).")
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parser.add_argument("input_path", type=str, help="Path to pdf or image file or folder to detect bboxes in.")
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parser.add_argument("--results_dir", type=str, help="Path to JSON file with OCR results.", default=os.path.join(settings.RESULT_DIR, "surya"))
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parser.add_argument("--max", type=int, help="Maximum number of pages to process.", default=None)
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parser.add_argument("--start_page", type=int, help="Page to start processing at.", default=0)
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parser.add_argument("--images", action="store_true", help="Save images of detected bboxes.", default=False)
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parser.add_argument("--langs", type=str, help="Language(s) to use for OCR. Comma separate for multiple. Can be a capitalized language name, or a 2-letter ISO 639 code.", default=None)
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parser.add_argument("--lang_file", type=str, help="Path to file with languages to use for OCR. Should be a JSON dict with file names as keys, and the value being a list of language codes/names.", default=None)
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args = parser.parse_args()
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assert args.langs or args.lang_file, "Must provide either --langs or --lang_file"
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if os.path.isdir(args.input_path):
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images, names = load_from_folder(args.input_path, args.max, args.start_page)
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folder_name = os.path.basename(args.input_path)
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else:
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images, names = load_from_file(args.input_path, args.max, args.start_page)
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folder_name = os.path.basename(args.input_path).split(".")[0]
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if args.lang_file:
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# We got all of our language settings from a file
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langs = load_lang_file(args.lang_file, names)
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for lang in langs:
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replace_lang_with_code(lang)
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image_langs = langs
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else:
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# We got our language settings from the input
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langs = args.langs.split(",")
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replace_lang_with_code(langs)
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image_langs = [langs] * len(images)
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det_processor = load_detection_processor()
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det_model = load_detection_model()
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_, lang_tokens = _tokenize("", get_unique_langs(image_langs))
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rec_model = load_recognition_model(langs=lang_tokens) # Prune model moe layer to only include languages we need
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rec_processor = load_recognition_processor()
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result_path = os.path.join(args.results_dir, folder_name)
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os.makedirs(result_path, exist_ok=True)
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predictions_by_image = run_ocr(images, image_langs, det_model, det_processor, rec_model, rec_processor)
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if args.images:
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for idx, (name, image, pred, langs) in enumerate(zip(names, images, predictions_by_image, image_langs)):
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bboxes = [l.bbox for l in pred.text_lines]
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pred_text = [l.text for l in pred.text_lines]
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page_image = draw_text_on_image(bboxes, pred_text, image.size, langs, has_math="_math" in langs)
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page_image.save(os.path.join(result_path, f"{name}_{idx}_text.png"))
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out_preds = defaultdict(list)
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for name, pred, image in zip(names, predictions_by_image, images):
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out_pred = pred.model_dump()
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out_pred["page"] = len(out_preds[name]) + 1
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out_preds[name].append(out_pred)
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with open(os.path.join(result_path, "results.json"), "w+", encoding="utf-8") as f:
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json.dump(out_preds, f, ensure_ascii=False)
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print(f"Wrote results to {result_path}")
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if __name__ == "__main__":
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main()
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