111 lines
3.7 KiB
Python
111 lines
3.7 KiB
Python
import io
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import os
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import urllib.request # 必须这样写，不能只 import urllib
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import uuid
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from typing import Optional, List
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import cv2
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import litserve as ls
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import numpy as np
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import torch
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from PIL import Image, ImageDraw
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from minio import Minio
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from pydantic import BaseModel, Field
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from fastapi import Response # 导入 FastAPI 的 Response
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from config import settings
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from segment_anything import SamPredictor, sam_model_registry
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from utils.minio_client import oss_get_image, oss_upload_image
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minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
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class SAMRequest(BaseModel):
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user_id: int
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image_path: str = Field(..., description="图片路径，必填字段")
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type: str = Field(..., description="推理类型，必填字段")
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points: Optional[List[List[float]]] = None
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labels: Optional[List[int]] = None
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box: Optional[List[int]] = None
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class SimpleLitAPI(ls.LitAPI):
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# class SimpleLitAPI():
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def setup(self, device):
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# def __init__(self, device, sam_checkpoint, model_type="vit_h"):
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# 初始化SAM模型
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model_url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
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sam_checkpoint = "checkpoint/sam_vit_h_4b8939.pth"
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model_type = "vit_h"
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# 自动化下载检查
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if not os.path.exists(sam_checkpoint):
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os.makedirs(os.path.dirname(sam_checkpoint))
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if not os.path.isfile(sam_checkpoint):
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print("正在下载权重文件，请稍候...")
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urllib.request.urlretrieve(model_url, sam_checkpoint)
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print("下载完成。")
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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print(self.device)
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self.sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
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self.sam.to(device=self.device)
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self.predictor = SamPredictor(self.sam)
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def decode_request(self, request: SAMRequest):
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return request
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def predict(self, request):
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# 加载图像
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image = oss_get_image(
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oss_client=minio_client,
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path=request.image_path,
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data_type="cv2")
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image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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self.predictor.set_image(image_rgb)
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input_points = np.array(request.points)
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input_labels = np.array(request.labels)
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if request.type == "point":
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masks, scores, logits = self.predictor.predict(
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point_coords=input_points,
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point_labels=input_labels,
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multimask_output=False
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)
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elif request.type == "box":
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box = np.array(request.box)
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masks, scores, logits = self.predictor.predict(
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box=box,
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multimask_output=False
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)
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else:
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raise ValueError(
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f"request.type 参数错误！合法值为 'point' 或 'box'，实际传入：{request.type}"
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)
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mask = masks[0] # 获取第一个掩码
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image = Image.fromarray(image)
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rgba_image = image.convert("RGBA")
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rgba_np = np.array(rgba_image)
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rgba_np[:, :, 3] = mask.astype(np.uint8) * 255
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object_name = f"{request.user_id}/seg_anything/{uuid.uuid4()}"
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req = oss_upload_image(
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oss_client=minio_client,
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bucket="aida-users",
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object_name=f"{object_name}.png",
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image_bytes=cv2.imencode('.png', rgba_np)[1]
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)
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return {"output": f"{req.bucket_name}/{req.object_name}"}
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if __name__ == "__main__":
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api = SimpleLitAPI()
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server = ls.LitServer(api, accelerator="cuda")
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server.run(port=8777)
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