| | """ |
| | Run a rest API exposing the yolov5s object detection model |
| | """ |
| | import argparse |
| | import io |
| |
|
| | import torch |
| | from PIL import Image |
| | from flask import Flask, request |
| |
|
| | app = Flask(__name__) |
| |
|
| | DETECTION_URL = "/v1/object-detection/yolov5s" |
| |
|
| |
|
| | @app.route(DETECTION_URL, methods=["POST"]) |
| | def predict(): |
| | if not request.method == "POST": |
| | return |
| |
|
| | if request.files.get("image"): |
| | image_file = request.files["image"] |
| | image_bytes = image_file.read() |
| |
|
| | img = Image.open(io.BytesIO(image_bytes)) |
| |
|
| | results = model(img, 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") |
| | args = parser.parse_args() |
| |
|
| | model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True) |
| | app.run(host="0.0.0.0", port=args.port) |
| |
|