【机器学习部署】Machine Learning Operations(MLOps) --1(利用fastapi部署yolov3模型)

本文主要是介绍【机器学习部署】Machine Learning Operations(MLOps) --1(利用fastapi部署yolov3模型)，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

建立一个文件夹用来返回图片预测的结果

import os
dir_name = "images_uploaded"
if not os.path.exists(dir_name):
    os.mkdir(dir_name)

接下来实现部署模型代码

import io
import uvicorn
import numpy as np
import nest_asyncio
from enum import Enum
from fastapi import FastAPI, UploadFile, File, HTTPException
from fastapi.responses import StreamingResponse
import cv2
import cvlib as cv
from cvlib.object_detection import draw_bbox


# Assign an instance of the FastAPI class to the variable "app".
# You will interact with your api using this instance.
app = FastAPI(title=Deploying a ML Model with FastAPI: 终于成功了！！！)


# List available models using Enum for convenience. This is useful when the options are pre-defined.
class Model(str, Enum):
    yolov3tiny = "yolov3-tiny"
    yolov3 = "yolov3"


# By using @app.get("/") you are allowing the GET method to work for the / endpoint.
@app.get("/")
def home():
    return "Congratulations! Your API is working as expected. Now head over to http://localhost:8000/docs."


# This endpoint handles all the logic necessary for the object detection to work.
# It requires the desired model and the image in which to perform object detection.
@app.post("/predict")
def prediction(model: Model, file: UploadFile = File(...)):
    # 1. VALIDATE INPUT FILE
    filename = file.filename
    fileExtension = filename.split(".")[-1] in ("jpg", "jpeg", "png")
    if not fileExtension:
        raise HTTPException(status_code=415, detail="Unsupported file provided.")

    # 2. TRANSFORM RAW IMAGE INTO CV2 image

    # Read image as a stream of bytes
    image_stream = io.BytesIO(file.file.read())

    # Start the stream from the beginning (position zero)
    image_stream.seek(0)

    # Write the stream of bytes into a numpy array
    file_bytes = np.asarray(bytearray(image_stream.read()), dtype=np.uint8)

    # Decode the numpy array as an image
    image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)

    # 3. RUN OBJECT DETECTION MODEL

    # Run object detection
    bbox, label, conf = cv.detect_common_objects(image, model=model)

    # Create image that includes bounding boxes and labels
    output_image = draw_bbox(image, bbox, label, conf)

    # Save it in a folder within the server
    cv2.imwrite(fimages_uploaded/{filename}, output_image)

    # 4. STREAM THE RESPONSE BACK TO THE CLIENT

    # Open the saved image for reading in binary mode
    file_image = open(fimages_uploaded/{filename}, mode="rb")

    # Return the image as a stream specifying media type
    return StreamingResponse(file_image, media_type="image/jpeg")


# Allows the server to be run in this interactive environment
nest_asyncio.apply()

# Host depends on the setup you selected (docker or virtual env)
host = "0.0.0.0" if os.getenv("DOCKER-SETUP") else "127.0.0.1"

# Spin up the server!
uvicorn.run(app, host=host, port=8000)

运行后可以看到：

点击链接得到：

再访问： http://localhost:8000/docs 如下图，并点击prediction

再点击Try it out

并且选择上传的图片，点击execute。 成功后在 images_uploaded文件夹可以得到结果。

这篇关于【机器学习部署】Machine Learning Operations(MLOps) --1(利用fastapi部署yolov3模型)的文章就介绍到这儿，希望我们推荐的文章对大家有所帮助，也希望大家多多支持为之网！