Unverified 提交 14797370 authored 作者: Robin's avatar Robin 提交者: GitHub

Flask REST API Example (#2732)

* add files * Update README.md * Update README.md * Update restapi.py pretrained=True and model.eval() are used by default when loading a model now, so no need to call them manually. * PEP8 reformat * PEP8 reformat Co-authored-by: 's avatarGlenn Jocher <glenn.jocher@ultralytics.com>
上级 1487bc84
# 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:
```shell
[{'class': 0,
'confidence': 0.8197850585,
'name': 'person',
'xmax': 1159.1403808594,
'xmin': 750.912902832,
'ymax': 711.2583007812,
'ymin': 44.0350036621},
{'class': 0,
'confidence': 0.5667674541,
'name': 'person',
'xmax': 1065.5523681641,
'xmin': 116.0448303223,
'ymax': 713.8904418945,
'ymin': 198.4603881836},
{'class': 27,
'confidence': 0.5661227107,
'name': 'tie',
'xmax': 516.7975463867,
'xmin': 416.6880187988,
'ymax': 717.0524902344,
'ymin': 429.2020568848}]
```
An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py`
"""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)
"""
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)
data = results.pandas().xyxy[0].to_json(orient="records")
return data
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).autoshape() # force_reload to recache
app.run(host="0.0.0.0", port=args.port) # debug=True causes Restarting with stat
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