grounding-dino/main.py

from pathlib import Path

import cv2
import supervision as sv
import torch
import yaml
from tqdm import tqdm

from groundingdino.util.inference import Model, preprocess_caption
from pdf_converter import PdfConverter

GROUNDING_DINO_CONFIG = "groundingdino/config/GroundingDINO_SwinT_OGC.py"
GROUNDING_DINO_CHECKPOINT = "gdino_checkpoints/groundingdino_swint_ogc.pth"
BOX_THRESHOLD = 0.4
TEXT_THRESHOLD = 0.25


def main(
    data_dir: str | Path,
    text: str = "ID card. Carte Vitale. Bank details. Human face.",
    concept_list_yaml: str | None = None,
    device: str = "cuda:0" if torch.cuda.is_available() else "cpu",
):
    output_dir = Path("outputs") / "extract"
    output_dir.mkdir(parents=True, exist_ok=True)
    if concept_list_yaml:
        print(f"Overriding concepts !")
        with open(concept_list_yaml, "r") as f:
            concepts = yaml.load(f)
        text = "".join([f" {x}." for x in concepts])

    print(f"List of concepts to detect: {text}")

    if isinstance(data_dir, str):
        data_dir = Path(data_dir)

    for img_path in tqdm(
        data_dir.glob("*.pdf"), total=len(list(data_dir.glob("*.pdf")))
    ):
        pdf_convertor = PdfConverter(120)
        if img_path.suffix == ".pdf":
            imgs = pdf_convertor.convert_pdf_to_jpg(str(img_path))
            img = imgs[0]
            pdf_convertor.save_image_as_png(img, img_path.parent / "test.png")
            img = pdf_convertor.to_cv2_image(img)
        else:
            img = cv2.imread(str(img_path))

        # image_source, image = load_image(str(img_path.parent / "test.png"))

        grounding_model = Model(
            model_config_path=GROUNDING_DINO_CONFIG,
            model_checkpoint_path=GROUNDING_DINO_CHECKPOINT,
            device=device,
        )
        caption = preprocess_caption(text)
        detections, labels = grounding_model.predict_with_caption(
            image=img,
            caption=caption,
            box_threshold=BOX_THRESHOLD,
            text_threshold=TEXT_THRESHOLD,
        )
        confidences = detections.confidence.tolist()
        class_names = labels

        labels = [
            f"{class_name} {confidence:.2f}"
            for class_name, confidence in zip(class_names, confidences)
        ]

        for i, bbox in enumerate(detections.xyxy):
            x_min, y_min, x_max, y_max = tuple(bbox)
            patch = img[int(y_min) : int(y_max), int(x_min) : int(x_max)]
            patch_img_path = str(
                Path("outputs") / "extract" / f"{img_path.stem}_{i:d}.png"
            )
            cv2.imwrite(patch_img_path, patch)

        box_annotator = sv.BoxAnnotator(color_lookup=sv.ColorLookup.INDEX)
        annotated_frame = box_annotator.annotate(
            scene=img.copy(), detections=detections
        )
        label_annotator = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX)
        annotated_frame = label_annotator.annotate(
            scene=annotated_frame, detections=detections, labels=labels
        )

        cv2.imwrite(str(Path("outputs") / f"{img_path.stem}.png"), annotated_frame)


if __name__ == "__main__":
    main("data")