# YOLOv5 🚀 by Ultralytics, AGPL-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