77 lines
2.9 KiB
Python
77 lines
2.9 KiB
Python
# 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__))))
|
|
import time
|
|
from 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
|
|
from fastapi import FastAPI
|
|
from pydantic import BaseModel
|
|
|
|
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):
|
|
# Convert base64 to image directly without saving to disk
|
|
image_bytes = base64.b64decode(image_base64)
|
|
image = Image.open(io.BytesIO(image_bytes))
|
|
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 = config['BOX_TRESHOLD']
|
|
|
|
(text, ocr_bbox), _ = check_ocr_box(image, display_img=False, output_bb_format='xyxy', easyocr_args={'text_threshold': 0.8}, use_paddleocr=False)
|
|
dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image, 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
|
|
|
|
|
|
|
|
|
|
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...')
|
|
|
|
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}
|
|
|
|
@app.get("/")
|
|
async def root():
|
|
return {"message": "Omniparser API ready"} |