mirror of
https://github.com/VikParuchuri/surya.git
synced 2026-06-04 21:03:53 +08:00
82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
import argparse
|
|
import copy
|
|
import json
|
|
import time
|
|
from collections import defaultdict
|
|
|
|
from surya.input.load import load_from_folder, load_from_file
|
|
from surya.model.detection.model import load_model, load_processor
|
|
from surya.detection import batch_text_detection
|
|
from surya.postprocessing.affinity import draw_lines_on_image
|
|
from surya.postprocessing.heatmap import draw_polys_on_image
|
|
from surya.settings import settings
|
|
import os
|
|
from tqdm import tqdm
|
|
|
|
|
|
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("--images", action="store_true", help="Save images of detected bboxes.", default=False)
|
|
parser.add_argument("--debug", action="store_true", help="Run in debug mode.", default=False)
|
|
args = parser.parse_args()
|
|
|
|
checkpoint = settings.DETECTOR_MODEL_CHECKPOINT
|
|
model = load_model(checkpoint=checkpoint)
|
|
processor = load_processor(checkpoint=checkpoint)
|
|
|
|
if os.path.isdir(args.input_path):
|
|
images, names = load_from_folder(args.input_path, args.max)
|
|
folder_name = os.path.basename(args.input_path)
|
|
else:
|
|
images, names = load_from_file(args.input_path, args.max)
|
|
folder_name = os.path.basename(args.input_path).split(".")[0]
|
|
|
|
start = time.time()
|
|
predictions = batch_text_detection(images, model, processor)
|
|
result_path = os.path.join(args.results_dir, folder_name)
|
|
os.makedirs(result_path, exist_ok=True)
|
|
end = time.time()
|
|
if args.debug:
|
|
print(f"Detection took {end - start} seconds")
|
|
|
|
if args.images:
|
|
for idx, (image, pred, name) in enumerate(zip(images, predictions, names)):
|
|
polygons = [p.polygon for p in pred.bboxes]
|
|
bbox_image = draw_polys_on_image(polygons, copy.deepcopy(image))
|
|
bbox_image.save(os.path.join(result_path, f"{name}_{idx}_bbox.png"))
|
|
|
|
column_image = draw_lines_on_image(pred.vertical_lines, copy.deepcopy(image))
|
|
column_image.save(os.path.join(result_path, f"{name}_{idx}_column.png"))
|
|
|
|
if args.debug:
|
|
heatmap = pred.heatmap
|
|
heatmap.save(os.path.join(result_path, f"{name}_{idx}_heat.png"))
|
|
|
|
affinity_map = pred.affinity_map
|
|
affinity_map.save(os.path.join(result_path, f"{name}_{idx}_affinity.png"))
|
|
|
|
predictions_by_page = defaultdict(list)
|
|
for idx, (pred, name, image) in enumerate(zip(predictions, names, images)):
|
|
out_pred = pred.model_dump(exclude=["heatmap", "affinity_map"])
|
|
out_pred["page"] = len(predictions_by_page[name]) + 1
|
|
predictions_by_page[name].append(out_pred)
|
|
|
|
with open(os.path.join(result_path, "results.json"), "w+", encoding="utf-8") as f:
|
|
json.dump(predictions_by_page, f, ensure_ascii=False)
|
|
|
|
print(f"Wrote results to {result_path}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|