yolov5 flask rest api调用

服务端代码：

# YOLOv5  by Ultralytics, GPL-3.0 license
"""
Run a Flask REST API exposing one or more YOLOv5s models
"""

import argparse
import io

import torch
from flask import Flask, request
from PIL import Image

app = Flask(__name__)
models = {}

DETECTION_URL = "/v1/object-detection/"


@app.route(DETECTION_URL, methods=["POST"])
def predict(model):
    if request.method != "POST":
        return

    if request.files.get("image"):
        # Method 1
        # with request.files["image"] as f:
        #     im = Image.open(io.BytesIO(f.read()))

        # Method 2
        im_file = request.files["image"]
        im_bytes = im_file.read()
        im = Image.open(io.BytesIO(im_bytes))

        if model in models:
            results = models[model](im, size=640)  # reduce size=320 for faster inference
            return results.pandas().xyxy[0].to_json(orient="records")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model")
    parser.add_argument("--port", default=5000, type=int, help="port number")
    parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s')
    opt = parser.parse_args()

    for m in opt.model:
        models[m] = torch.hub.load("ultralytics/yolov5", m, force_reload=True, skip_validation=True)

    app.run(host="0.0.0.0", port=opt.port)  # debug=True causes Restarting with stat

客户端代码：

# YOLOv5  by Ultralytics, GPL-3.0 license
"""
Perform test request
"""

import pprint

import requests

DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
IMAGE = "zidane.jpg"

# Read image
with open(IMAGE, "rb") as f:
    image_data = f.read()

response = requests.post(DETECTION_URL, files={"image": image_data}).json()

pprint.pprint(response)

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