| |
| """ |
| 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'): |
| |
| |
| |
|
|
| |
| 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) |
| 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) |
|
|