YOLOv5 v5.0 release compatibility update for YOLOv3

utils/flask_rest_api/README.md

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+# Flask REST API
+[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are commonly used to expose Machine Learning (ML)  models to other services. This folder contains an example REST API created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
+
+## Requirements
+
+[Flask](https://palletsprojects.com/p/flask/) is required. Install with:
+```shell
+$ pip install Flask
+```
+
+## Run
+
+After Flask installation run:
+
+```shell
+$ python3 restapi.py --port 5000
+```
+
+Then use [curl](https://curl.se/) to perform a request:
+
+```shell
+$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'`
+```
+
+The model inference results are returned as a JSON response:
+
+```json
+[
+  {
+    "class": 0,
+    "confidence": 0.8900438547,
+    "height": 0.9318675399,
+    "name": "person",
+    "width": 0.3264600933,
+    "xcenter": 0.7438579798,
+    "ycenter": 0.5207948685
+  },
+  {
+    "class": 0,
+    "confidence": 0.8440024257,
+    "height": 0.7155083418,
+    "name": "person",
+    "width": 0.6546785235,
+    "xcenter": 0.427829951,
+    "ycenter": 0.6334488392
+  },
+  {
+    "class": 27,
+    "confidence": 0.3771208823,
+    "height": 0.3902671337,
+    "name": "tie",
+    "width": 0.0696444362,
+    "xcenter": 0.3675483763,
+    "ycenter": 0.7991207838
+  },
+  {
+    "class": 27,
+    "confidence": 0.3527112305,
+    "height": 0.1540903747,
+    "name": "tie",
+    "width": 0.0336618312,
+    "xcenter": 0.7814827561,
+    "ycenter": 0.5065554976
+  }
+]
+```
+
+An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py`

utils/flask_rest_api/example_request.py

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+"""Perform test request"""
+import pprint
+
+import requests
+
+DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
+TEST_IMAGE = "zidane.jpg"
+
+image_data = open(TEST_IMAGE, "rb").read()
+
+response = requests.post(DETECTION_URL, files={"image": image_data}).json()
+
+pprint.pprint(response)

utils/flask_rest_api/restapi.py

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+"""
+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)  # 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 YOLOv3 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)  # force_reload to recache
+    app.run(host="0.0.0.0", port=args.port)  # debug=True causes Restarting with stat