import tensorflow as tf
import numpy as np
from PIL import Image
import os

flower_dict = {0:'小熊猫',1:'滑稽',2:'萌妹子',3:'小坏坏',4:'小黄鸡'}
path = 'C:/Users/yuanshao/Desktop/test/'

w=100
h=100
c=3

def read_one_image(path):
    image = Image.open(path).convert('RGB')
    img = image.resize((w, h), Image.ANTIALIAS)
    return np.asarray(img)

def preprocess():
    count = 0
    for file in os.listdir(path):
        new_name = os.path.join(path, str(count))
        os.rename(os.path.join(path, file),new_name)
        count += 1
    for file in os.listdir(path):
        ori_name = path+file
        os.rename(ori_name,ori_name+'.jpg')

with tf.Session() as sess:
    PATH = cate=[path+x for x in os.listdir(path)]
    data = []
    pic = []
    preprocess()
    for i in range(len(PATH)):
        picture = path+str(i)+'.jpg';
        print(picture)
        pic.append(picture)
        data.append(read_one_image(picture))

    saver = tf.train.import_meta_graph('D:/tensorflow/saver/model.ckpt.meta')
    saver.restore(sess,tf.train.latest_checkpoint('D:/tensorflow/saver/'))

    graph = tf.get_default_graph()
    x = graph.get_tensor_by_name("x:0")

    logits = graph.get_tensor_by_name("logits_eval:0")

    classification_result = sess.run(logits,feed_dict={x:data})

    #打印出预测矩阵
    print(classification_result)
    #打印出预测矩阵每一行最大值的索引
    print(tf.argmax(classification_result,1).eval())
    output = []
    output = tf.argmax(classification_result,1).eval()
    for i in range(len(output)):
        print("第",i,"张图片预测:"+flower_dict[output[i]])