app.py

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import vgg16
import utils
from Nclasses import labels
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'


def main():
    sess = tf.compat.v1.Session()
    images = tf.placeholder(tf.float32, [1, 224, 224, 3])
    vgg = vgg16.Vgg16() 
    vgg.forward(images)
    while True:
        img_path = input('\nInput the path and image name: ')

        if img_path == '0':
            print('\nexiting-------------------------\n')
            exit(0)
        else:
            print('\nrunning-------------------------\n')
        img_ready = utils.load_image(img_path) 

        fig=plt.figure(u"Top-5 预测结果") 

        probability = sess.run(vgg.prob, feed_dict={images:img_ready})
        top5 = np.argsort(probability[0])[-1:-6:-1]
        print("top5:",top5)
        values = []
        bar_label = []
        for n, i in enumerate(top5): 
            print("n:",n)
            print("i:",i)
            values.append(probability[0][i]) 
            bar_label.append(labels[i]) 
            print(i, ":", labels[i], "----", utils.percent(probability[0][i])) 
            
        ax = fig.add_subplot(111) 
        ax.bar(range(len(values)), values, tick_label=bar_label, width=0.5, fc='g')
        ax.set_ylabel(u'probabilityit') 
        ax.set_title(u'Top-5') 
        for a,b in zip(range(len(values)), values):
            ax.text(a, b+0.0005, utils.percent(b), ha='center', va = 'bottom', fontsize=7)   
        plt.show()


if __name__ == '__main__':
        main()