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, include_maps=args.debug)

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)