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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run a Flask REST API exposing a YOLOv5s model
"""
import argparse
import io
import torch
from flask import Flask, request
from PIL import Image
app = Flask(__name__)
DETECTION_URL = "/v1/object-detection/yolov5s"
@app.route(DETECTION_URL, methods=["POST"])
def predict():
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))
results = 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")
opt = parser.parse_args()
# Fix known issue urllib.error.HTTPError 403: rate limit exceeded https://github.com/ultralytics/yolov5/pull/7210
torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True) # force_reload to recache
app.run(host="0.0.0.0", port=opt.port) # debug=True causes Restarting with stat
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