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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 | |
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') |