-
Notifications
You must be signed in to change notification settings - Fork 649
/
ocr_text.py
96 lines (73 loc) · 4.22 KB
/
ocr_text.py
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
import os
import argparse
import json
import time
from collections import defaultdict
import torch
from surya.input.langs import replace_lang_with_code, get_unique_langs
from surya.input.load import load_from_folder, load_from_file, load_lang_file
from surya.model.detection.model import load_model as load_detection_model, load_processor as load_detection_processor
from surya.model.recognition.model import load_model as load_recognition_model
from surya.model.recognition.processor import load_processor as load_recognition_processor
from surya.model.recognition.tokenizer import _tokenize
from surya.ocr import run_ocr
from surya.postprocessing.text import draw_text_on_image
from surya.settings import settings
def main():
parser = argparse.ArgumentParser(description="Detect bboxes in an input file or folder (PDFs or image).")
parser.add_argument("input_path", type=str, help="Path to pdf or image file or folder to detect bboxes in.")
parser.add_argument("--results_dir", type=str, help="Path to JSON file with OCR results.", default=os.path.join(settings.RESULT_DIR, "surya"))
parser.add_argument("--max", type=int, help="Maximum number of pages to process.", default=None)
parser.add_argument("--start_page", type=int, help="Page to start processing at.", default=0)
parser.add_argument("--images", action="store_true", help="Save images of detected bboxes.", default=False)
parser.add_argument("--langs", type=str, help="Optional 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)
parser.add_argument("--lang_file", type=str, help="Optional 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)
parser.add_argument("--debug", action="store_true", help="Enable debug logging.", default=False)
args = parser.parse_args()
if os.path.isdir(args.input_path):
images, names = load_from_folder(args.input_path, args.max, args.start_page)
folder_name = os.path.basename(args.input_path)
else:
images, names = load_from_file(args.input_path, args.max, args.start_page)
folder_name = os.path.basename(args.input_path).split(".")[0]
if args.lang_file:
# We got all of our language settings from a file
langs = load_lang_file(args.lang_file, names)
for lang in langs:
replace_lang_with_code(lang)
image_langs = langs
elif args.langs:
# We got our language settings from the input
langs = args.langs.split(",")
replace_lang_with_code(langs)
image_langs = [langs] * len(images)
else:
image_langs = [None] * len(images)
det_processor = load_detection_processor()
det_model = load_detection_model()
rec_model = load_recognition_model()
rec_processor = load_recognition_processor()
result_path = os.path.join(args.results_dir, folder_name)
os.makedirs(result_path, exist_ok=True)
start = time.time()
predictions_by_image = run_ocr(images, image_langs, det_model, det_processor, rec_model, rec_processor)
if args.debug:
print(f"OCR took {time.time() - start:.2f} seconds")
max_chars = max([len(l.text) for p in predictions_by_image for l in p.text_lines])
print(f"Max chars: {max_chars}")
if args.images:
for idx, (name, image, pred, langs) in enumerate(zip(names, images, predictions_by_image, image_langs)):
bboxes = [l.bbox for l in pred.text_lines]
pred_text = [l.text for l in pred.text_lines]
page_image = draw_text_on_image(bboxes, pred_text, image.size, langs, has_math="_math" in langs)
page_image.save(os.path.join(result_path, f"{name}_{idx}_text.png"))
out_preds = defaultdict(list)
for name, pred, image in zip(names, predictions_by_image, images):
out_pred = pred.model_dump()
out_pred["page"] = len(out_preds[name]) + 1
out_preds[name].append(out_pred)
with open(os.path.join(result_path, "results.json"), "w+", encoding="utf-8") as f:
json.dump(out_preds, f, ensure_ascii=False)
print(f"Wrote results to {result_path}")
if __name__ == "__main__":
main()