Hugging Face's logo

Spaces:
microsoft
/
OmniParser
Running on Zero

App Files Community
3
OmniParser
File size: 3,821 Bytes 
0fc5095
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
 
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
import torch
from PIL import Image

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
# @torch.inference_mode()
# @torch.autocast(device_type="cuda", dtype=torch.bfloat16)
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)
    # 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)



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')

    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')