yolov5-code-main/restapi.py

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

import argparse
import io
import numpy as np
import cv2

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

app = Flask(__name__)
models = {}

DETECTION_URL = '/v1/object-detection/<model>'


@app.route(DETECTION_URL, methods=['POST'])
def predict(model):
    if request.method != 'POST':
        return
    
    if request.data:
        img = cv2.imdecode(np.frombuffer(request.data, dtype=np.uint8), cv2.IMREAD_COLOR)
        if model in models:
            results = models[model](img)  # reduce size=320 for faster inference
            results = results.render()[0]
            return cv2.imencode(".jpg", results)[1].tobytes()

    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('./', m, source="local")

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