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58 lines
1.7 KiB
58 lines
1.7 KiB
6 years ago
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import tensorflow as tf
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import numpy as np
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from PIL import Image
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import os
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flower_dict = {0:'小熊猫',1:'滑稽',2:'萌妹子',3:'小坏坏',4:'小黄鸡'}
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path = 'C:/Users/yuanshao/Desktop/test/'
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w=100
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h=100
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c=3
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def read_one_image(path):
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image = Image.open(path).convert('RGB')
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img = image.resize((w, h), Image.ANTIALIAS)
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return np.asarray(img)
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def preprocess():
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count = 0
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for file in os.listdir(path):
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new_name = os.path.join(path, str(count))
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os.rename(os.path.join(path, file),new_name)
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count += 1
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for file in os.listdir(path):
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ori_name = path+file
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os.rename(ori_name,ori_name+'.jpg')
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with tf.Session() as sess:
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PATH = cate=[path+x for x in os.listdir(path)]
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data = []
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pic = []
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preprocess()
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for i in range(len(PATH)):
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picture = path+str(i)+'.jpg';
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print(picture)
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pic.append(picture)
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data.append(read_one_image(picture))
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saver = tf.train.import_meta_graph('D:/tensorflow/saver/model.ckpt.meta')
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saver.restore(sess,tf.train.latest_checkpoint('D:/tensorflow/saver/'))
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graph = tf.get_default_graph()
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x = graph.get_tensor_by_name("x:0")
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logits = graph.get_tensor_by_name("logits_eval:0")
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classification_result = sess.run(logits,feed_dict={x:data})
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#打印出预测矩阵
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print(classification_result)
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#打印出预测矩阵每一行最大值的索引
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print(tf.argmax(classification_result,1).eval())
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output = []
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output = tf.argmax(classification_result,1).eval()
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for i in range(len(output)):
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print("第",i,"张图片预测:"+flower_dict[output[i]])
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