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| from util.utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, check_ocr_box | |
| import torch | |
| from PIL import Image | |
| import io | |
| import base64 | |
| from typing import Dict | |
| class Omniparser(object): | |
| def __init__(self, config: Dict): | |
| self.config = config | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| self.som_model = get_yolo_model(model_path=config['som_model_path']) | |
| self.caption_model_processor = get_caption_model_processor(model_name=config['caption_model_name'], model_name_or_path=config['caption_model_path'], device=device) | |
| print('Omniparser initialized!!!') | |
| def parse(self, image_base64: str): | |
| image_bytes = base64.b64decode(image_base64) | |
| image = Image.open(io.BytesIO(image_bytes)) | |
| print('image size:', image.size) | |
| box_overlay_ratio = max(image.size) / 3200 | |
| draw_bbox_config = { | |
| 'text_scale': 0.8 * box_overlay_ratio, | |
| 'text_thickness': max(int(2 * box_overlay_ratio), 1), | |
| 'text_padding': max(int(3 * box_overlay_ratio), 1), | |
| 'thickness': max(int(3 * box_overlay_ratio), 1), | |
| } | |
| (text, ocr_bbox), _ = check_ocr_box(image, display_img=False, output_bb_format='xyxy', easyocr_args={'text_threshold': 0.8}, use_paddleocr=False) | |
| dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image, self.som_model, BOX_TRESHOLD = self.config['BOX_TRESHOLD'], output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128) | |
| return dino_labled_img, parsed_content_list |