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| """ | |
| Simple app to upload an image via a web form | |
| and view the inference results on the image in the browser. | |
| """ | |
| import argparse | |
| import io | |
| import os | |
| from PIL import Image | |
| import torch | |
| from flask import Flask, render_template, request, redirect | |
| app = Flask(__name__) | |
| def predict(): | |
| if request.method == "POST": | |
| if "file" not in request.files: | |
| return redirect(request.url) | |
| file = request.files["file"] | |
| if not file: | |
| return | |
| img_bytes = file.read() | |
| img = Image.open(io.BytesIO(img_bytes)) | |
| results = model(img, size=640) | |
| # for debugging | |
| # data = results.pandas().xyxy[0].to_json(orient="records") | |
| # return data | |
| results.render() # updates results.imgs with boxes and labels | |
| for img in results.imgs: | |
| img_base64 = Image.fromarray(img) | |
| img_base64.save("static/image0.jpg", format="JPEG") | |
| return redirect("static/image0.jpg") | |
| return render_template("index.html") | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser(description="Flask app exposing yolov5 models") | |
| parser.add_argument("--port", default=5000, type=int, help="port number") | |
| args = parser.parse_args() | |
| model = torch.hub.load('../yolov5', 'custom', path='../saved_model/s1000_best.pt', source='local') # force_reload = recache latest code | |
| model.eval() | |
| app.run(host="0.0.0.0", port=args.port) # debug=True causes Restarting with stat | |
| #python3 webapp.py --port 5000 | |
| # conda activate test | |
| # python webapp.py --port 5000 | |
| # http://localhost:5000/ |