Spaces:
Runtime error
Runtime error
File size: 1,558 Bytes
5b2fcab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
# YOLOv5 🚀 by Ultralytics, GPL-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
|