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| from typing import Optional | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from PIL import Image | |
| import io | |
| import base64, os | |
| from utils import check_ocr_box, get_yolo_model, get_caption_model_processor, get_som_labeled_img | |
| from PIL import Image | |
| from ultralytics import YOLO | |
| yolo_model = YOLO('weights/icon_detect/best.pt') # Removed .to('cuda') | |
| from transformers import AutoProcessor, AutoModelForCausalLM | |
| processor = AutoProcessor.from_pretrained("microsoft/Florence-2-base", trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained("weights/icon_caption_florence", torch_dtype=torch.float32, trust_remote_code=True) # Changed dtype to float32 and removed .to('cuda') | |
| caption_model_processor = {'processor': processor, 'model': model} | |
| print('Finished loading model.') | |
| platform = 'pc' | |
| draw_bbox_config = { | |
| 'text_scale': 0.8, | |
| 'text_thickness': 2, | |
| 'text_padding': 2, | |
| 'thickness': 2, | |
| } | |
| MARKDOWN = """ | |
| # OmniParser for Pure Vision Based General GUI Agent 🔥 | |
| <div> | |
| <a href="https://arxiv.org/pdf/2408.00203"> | |
| <img src="https://img.shields.io/badge/arXiv-2408.00203-b31b1b.svg" alt="Arxiv" style="display:inline-block;"> | |
| </a> | |
| </div> | |
| OmniParser is a screen parsing tool to convert general GUI screens to structured elements. | |
| """ | |
| def process( | |
| image_input, | |
| box_threshold, | |
| iou_threshold | |
| ) -> Optional[Image.Image]: | |
| image_save_path = 'imgs/saved_image_demo.png' | |
| image_input.save(image_save_path) | |
| ocr_bbox_rslt, is_goal_filtered = check_ocr_box( | |
| image_save_path, | |
| display_img=False, | |
| output_bb_format='xyxy', | |
| goal_filtering=None, | |
| easyocr_args={'paragraph': False, 'text_threshold': 0.9}, | |
| use_paddleocr=True | |
| ) | |
| text, ocr_bbox = ocr_bbox_rslt | |
| dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img( | |
| image_save_path, | |
| yolo_model, | |
| BOX_TRESHOLD=box_threshold, | |
| output_coord_in_ratio=True, | |
| ocr_bbox=ocr_bbox, | |
| draw_bbox_config=draw_bbox_config, | |
| caption_model_processor=caption_model_processor, | |
| ocr_text=text, | |
| iou_threshold=iou_threshold | |
| ) | |
| image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img))) | |
| print('Finished processing.') | |
| parsed_content_list = '\n'.join(parsed_content_list) | |
| return image, str(parsed_content_list) | |
| with gr.Blocks() as demo: | |
| gr.Markdown(MARKDOWN) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_input_component = gr.Image(type='pil', label='Upload Image') | |
| box_threshold_component = gr.Slider( | |
| label='Box Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.05) | |
| iou_threshold_component = gr.Slider( | |
| label='IOU Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.1) | |
| submit_button_component = gr.Button( | |
| value='Submit', variant='primary') | |
| with gr.Column(): | |
| image_output_component = gr.Image(type='pil', label='Image Output') | |
| text_output_component = gr.Textbox( | |
| label='Parsed Screen Elements', placeholder='Text Output') | |
| submit_button_component.click( | |
| fn=process, | |
| inputs=[ | |
| image_input_component, | |
| box_threshold_component, | |
| iou_threshold_component | |
| ], | |
| outputs=[image_output_component, text_output_component] | |
| ) | |
| demo.queue().launch(share=False) |