support remote access using fastapi; add double click/scroll; enable local run omniparser

demo/gradio/computer_use_demo/omniparser_agent/omniparser.py

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+# uvicorn remote_request:app --host 0.0.0.0 --port 8000 --reload
+
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from computer_use_demo.omniparser_agent.utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, check_ocr_box
+import torch
+from PIL import Image
+from typing import Dict, Tuple, List
+import base64
+import io
+
+
+# config = {
+#     'som_model_path': '../weights/icon_detect_v1_5/model_v1_5.pt',
+#     'device': 'cpu',
+#     'caption_model_name': 'florence2',
+#     'caption_model_path': '../weights/icon_caption_florence',
+#     'BOX_TRESHOLD': 0.05
+# }
+
+
+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_path = './demo_image.jpg'
+        with open(image_path, "wb") as fh:
+            fh.write(base64.b64decode(image_base64))
+        print('Parsing image:', image_path)
+
+        image = Image.open(image_path)
+        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),
+        }
+        BOX_TRESHOLD = self.config['BOX_TRESHOLD']
+
+        ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.8}, use_paddleocr=True)
+        text, ocr_bbox = ocr_bbox_rslt
+        dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, self.som_model, BOX_TRESHOLD = 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
+    
+
+# from fastapi import FastAPI
+# from pydantic import BaseModel
+
+# app = FastAPI()
+
+# class Item(BaseModel):
+#     base64_image: str
+#     prompt: str
+
+# Omniparser = Omniparser(config)
+
+# @app.post("/send_text/")
+# async def send_text(item: Item):
+#     print('start parsing...')
+#     import time
+#     start = time.time()
+#     dino_labled_img, parsed_content_list = Omniparser.parse(item.base64_image)
+#     latency = time.time() - start
+#     print('time:', latency)
+#     return {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, 'latency': latency}