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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 spaces | |
| import base64, os | |
| from utils import check_ocr_box, get_yolo_model, get_caption_model_processor, get_som_labeled_img | |
| import torch | |
| from PIL import Image | |
| # Model source: https://huggingface.co/microsoft/OmniParser | |
| # gr.load("models/microsoft/OmniParser").launch() | |
| yolo_model = get_yolo_model(model_path='weights/icon_detect/best.pt') | |
| caption_model_processor = get_caption_model_processor(model_name="florence2", model_name_or_path="weights/icon_caption_florence") | |
| platform = 'pc' | |
| if platform == 'pc': | |
| draw_bbox_config = { | |
| 'text_scale': 0.8, | |
| 'text_thickness': 2, | |
| 'text_padding': 2, | |
| 'thickness': 2, | |
| } | |
| elif platform == 'web': | |
| draw_bbox_config = { | |
| 'text_scale': 0.8, | |
| 'text_thickness': 2, | |
| 'text_padding': 3, | |
| 'thickness': 3, | |
| } | |
| elif platform == 'mobile': | |
| draw_bbox_config = { | |
| 'text_scale': 0.8, | |
| 'text_thickness': 2, | |
| 'text_padding': 3, | |
| 'thickness': 3, | |
| } | |
| 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 screen to structured elements. | |
| """ | |
| DEVICE = torch.device('cuda') | |
| # @spaces.GPU | |
| def process( | |
| image_input, | |
| box_threshold=0.01, | |
| iou_threshold=0.01 | |
| ) -> Optional[Image.Image]: | |
| image_save_path = 'imgs/saved_image_demo.png' | |
| image_input.save(image_save_path) | |
| # import pdb; pdb.set_trace() | |
| 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}) | |
| text, ocr_bbox = ocr_bbox_rslt | |
| # print('prompt:', prompt) | |
| 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('finish processing') | |
| parsed_content_list = '\n'.join(parsed_content_list) | |
| return image, str(parsed_content_list) | |
| examples = [ | |
| ["./imgs/google_page.png", 0.05, 0.1], | |
| ["./imgs/logo.png", 0.2, 0.15], | |
| ["./imgs/windows_home.png", 0.1, 0.05], | |
| ["./imgs/windows_multitab.png", 0.1, 0.05] | |
| ] | |
| with gr.Blocks() as demo: | |
| gr.Markdown(MARKDOWN) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_input_component = gr.Image( | |
| type='pil', label='Upload image') | |
| # set the threshold for removing the bounding boxes with low confidence, default is 0.05 | |
| box_threshold_component = gr.Slider( | |
| label='Box Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.05) | |
| # set the threshold for removing the bounding boxes with large overlap, default is 0.1 | |
| 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') | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[image_input_component], | |
| outputs=[image_output_component, text_output_component], | |
| fn=process, # Function to execute | |
| cache_examples="lazy" # Enables lazy caching for examples | |
| ) | |
| submit_button_component.click( | |
| fn=process, | |
| inputs=[ | |
| image_input_component, | |
| box_threshold_component, | |
| iou_threshold_component | |
| ], | |
| outputs=[image_output_component, text_output_component] | |
| ) | |
| demo.launch(debug=False, show_error=True, share=True) | |
| # demo.launch(share=True, server_port=7861, server_name='0.0.0.0') |