49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Run a Flask REST API exposing one or more YOLOv5s models
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"""
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import argparse
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import io
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import torch
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from flask import Flask, request
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from PIL import Image
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app = Flask(__name__)
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models = {}
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DETECTION_URL = "/v1/object-detection/<model>"
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@app.route(DETECTION_URL, methods=["POST"])
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def predict(model):
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if request.method != "POST":
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return
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if request.files.get("image"):
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# Method 1
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# with request.files["image"] as f:
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# im = Image.open(io.BytesIO(f.read()))
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# Method 2
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im_file = request.files["image"]
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im_bytes = im_file.read()
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im = Image.open(io.BytesIO(im_bytes))
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if model in models:
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results = models[model](im, size=640) # reduce size=320 for faster inference
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return results.pandas().xyxy[0].to_json(orient="records")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model")
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parser.add_argument("--port", default=5000, type=int, help="port number")
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parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s')
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opt = parser.parse_args()
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for m in opt.model:
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models[m] = torch.hub.load("ultralytics/yolov5", m, force_reload=True, skip_validation=True)
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app.run(host="0.0.0.0", port=opt.port) # debug=True causes Restarting with stat
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