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# 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/<model>'
@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
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