0010-flask调用keras模型

深藏阁楼爱情的钟 2022-11-19 11:28 201阅读 0赞

USAGE
    # Start the server:
    #     python run_keras_server.py
    # Submit a request via cURL:
    #     curl -X POST -F image=@dog.jpg 'http://localhost:5000/predict'
    # Submita a request via Python:
    #    python simple_request.py
    
    # import the necessary packages
    from keras.applications import ResNet50
    from keras.preprocessing.image import img_to_array
    from keras.applications import imagenet_utils
    from PIL import Image
    import numpy as np
    import flask
    import io
    import tensorflow as tf
    
    # initialize our Flask application and the Keras model
    app = flask.Flask(__name__)
    global graph
    graph = tf.get_default_graph()
    model = None
    
    def load_model():
        # load the pre-trained Keras model (here we are using a model
        # pre-trained on ImageNet and provided by Keras, but you can
        # substitute in your own networks just as easily)
        global model
        model = ResNet50(weights="imagenet")
    
    def prepare_image(image, target):
        # if the image mode is not RGB, convert it
        if image.mode != "RGB":
            image = image.convert("RGB")
    
        # resize the input image and preprocess it
        image = image.resize(target)
        image = img_to_array(image)
        image = np.expand_dims(image, axis=0)
        image = imagenet_utils.preprocess_input(image)
    
        # return the processed image
        return image
    
    @app.route("/predict", methods=["POST"])
    def predict():
        # initialize the data dictionary that will be returned from the
        # view
        data = {"success": False}
    
        # ensure an image was properly uploaded to our endpoint
        if flask.request.method == "POST":
            if flask.request.files.get("image"):
                # read the image in PIL format
                image = flask.request.files["image"].read()
                image = Image.open(io.BytesIO(image))
    
                # preprocess the image and prepare it for classification
                image = prepare_image(image, target=(224, 224))
    
                # classify the input image and then initialize the list
                # of predictions to return to the client
                with graph.as_default():
                    preds = model.predict(image)
                results = imagenet_utils.decode_predictions(preds)
                data["predictions"] = []
    
                # loop over the results and add them to the list of
                # returned predictions
                for (imagenetID, label, prob) in results[0]:
                    r = {"label": label, "probability": float(prob)}
                    data["predictions"].append(r)
    
                # indicate that the request was a success
                data["success"] = True
    
        # return the data dictionary as a JSON response
        return flask.jsonify(data)
    
    # if this is the main thread of execution first load the model and
    # then start the server
    if __name__ == "__main__":
        print(("* Loading Keras model and Flask starting server..."
            "please wait until server has fully started"))
        load_model()
        app.run()

### 客户端 ###

# USAGE
    # python simple_request.py
    
    # import the necessary packages
    import requests
    
    # initialize the Keras REST API endpoint URL along with the input
    # image path
    KERAS_REST_API_URL = "http://localhost:5000/predict"
    IMAGE_PATH = "dog.jpg"
    
    # load the input image and construct the payload for the request
    image = open(IMAGE_PATH, "rb").read()
    payload = {"image": image}
    
    # submit the request
    r = requests.post(KERAS_REST_API_URL, files=payload).json()
    
    # ensure the request was sucessful
    if r["success"]:
        # loop over the predictions and display them
        for (i, result) in enumerate(r["predictions"]):
            print("{}. {}: {:.4f}".format(i + 1, result["label"],
                result["probability"]))
    
    # otherwise, the request failed
    else:
        print("Request failed")