predict.py

import os
import json
import glob
import numpy as np

from PIL import Image
import matplotlib.pyplot as plt
import tensorflow as tf

from model_v2 import MobileNetV2


def main():
    im_height = 224
    im_width = 224
    num_classes = 5

    # load image
    img_path = "../tulip.jpg"
    assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
    img = Image.open(img_path)
    # resize image to 224x224
    img = img.resize((im_width, im_height))
    plt.imshow(img)

    # scaling pixel value to (-1,1)
    img = np.array(img).astype(np.float32)
    img = ((img / 255.) - 0.5) * 2.0

    # Add the image to a batch where it's the only member.
    img = (np.expand_dims(img, 0))

    # read class_indict
    json_path = './class_indices.json'
    assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)

    json_file = open(json_path, "r")
    class_indict = json.load(json_file)

    # create model
    feature = MobileNetV2(include_top=False)
    model = tf.keras.Sequential([feature,
                                 tf.keras.layers.GlobalAvgPool2D(),
                                 tf.keras.layers.Dropout(rate=0.5),
                                 tf.keras.layers.Dense(num_classes),
                                 tf.keras.layers.Softmax()])
    weights_path = './save_weights/resMobileNetV2.ckpt'
    assert len(glob.glob(weights_path+"*")), "cannot find {}".format(weights_path)
    model.load_weights(weights_path)

    result = np.squeeze(model.predict(img))
    predict_class = np.argmax(result)

    print_res = "class: {}   prob: {:.3}".format(class_indict[str(predict_class)],
                                                 result[predict_class])
    plt.title(print_res)
    for i in range(len(result)):
        print("class: {:10}   prob: {:.3}".format(class_indict[str(i)],
                                                  result[i].numpy()))
    plt.show()


if __name__ == '__main__':
    main()