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from PIL import Image
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import numpy as np
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import tensorflow as tf
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import matplotlib.pyplot as plt
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import forward
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from batchdealing import get_files
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# 获取一张图片
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def get_one_image(train):
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# 输入参数：train,训练图片的路径
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# 返回参数：image，从训练图片中随机抽取一张图片
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n = len(train)
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ind = np.random.randint(0, n)
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img_dir = train[ind] # 随机选择测试的图片
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img = Image.open(img_dir)
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# 显示图片，在jupyter notebook下当然也可以不用plt.show()
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plt.imshow(img)
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plt.show(img)
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imag = img.resize([64, 64]) # 由于图片在预处理阶段以及resize，因此该命令可略
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image = np.array(imag)
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return image
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# 测试图片
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def evaluate_one_image(image_array):
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with tf.Graph().as_default():
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BATCH_SIZE = 1
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N_CLASSES = 4
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image = tf.cast(image_array, tf.float32)
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# 线性缩放图像以具有零均值和单位范数。
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image = tf.image.per_image_standardization(image)
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image = tf.reshape(image, [1, 64, 64, 3])
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# 构建卷积神经网络
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logit = forward.inference(image, BATCH_SIZE, N_CLASSES)
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# softmax函数的作用就是归一化
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# 输入: 全连接层（往往是模型的最后一层）的值，一般代码中叫做logits。
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# 输出: 归一化的值，含义是属于该位置的概率，一般代码叫做probs。
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logit = tf.nn.softmax(logit)
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x = tf.compat.v1.placeholder(tf.float32, shape=[64, 64, 3])
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# you need to change the directories to yours. /Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/
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logs_train_dir = '/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/'
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# tf.train.Saver() 保存和加载模型
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saver = tf.compat.v1.train.Saver()
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with tf.compat.v1.Session() as sess:
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print("Reading checkpoints...")
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ckpt = tf.train.get_checkpoint_state(logs_train_dir)
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if ckpt and ckpt.model_checkpoint_path:
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global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
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saver.restore(sess, ckpt.model_checkpoint_path)
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print('Loading success, global_step is %s' % global_step)
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else:
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print('No checkpoint file found')
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# feed_dict的作用是给使用placeholder创建出来的tensor赋值。
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# 其实，他的作用更加广泛：feed使用一个值临时替换一个op的输出结果。
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# 你可以提供feed数据作为run()调用的参数。
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# feed只在调用它的方法内有效,方法结束,feed就会消失。
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# 当我们构建完图后，需要在一个会话中启动图，启动的第一步是创建一个Session对象。
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# 为了取回（Fetch）操作的输出内容，可以在使用Session对象的run()调用执行图时，
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# 传入一些tensor，这些tensor会帮助你取回结果。
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prediction = sess.run(logit, feed_dict={x: image_array})
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# 取出prediction中元素最大值所对应的索引，也就是最大的可能
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max_index = np.argmax(prediction)
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if max_index == 0:
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print('This could be the first category %.6f' % prediction[:, 0])
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elif max_index == 1:
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print('This could be the second category %.6f' % prediction[:, 1])
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elif max_index == 2:
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print('This could be the third category %.6f' % prediction[:, 2])
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elif max_index == 3:
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print('This could be the fourth category %.6f' % prediction[:, 3])
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elif max_index == 4:
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print('This could be the fifth category %.6f' % prediction[:, 4])
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elif max_index == 5:
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print('This could be the sixth category %.6f' % prediction[:, 5])
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elif max_index == 6:
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print('This could be the seventh category %.6f' % prediction[:, 6])
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elif max_index == 7:
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print('This could be the eighth category %.6f' % prediction[:, 7])
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else:
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print('This could be the ninth category %.6f' % prediction[:, 9])
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if __name__ == '__main__':
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train_dir = '/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/'
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train, train_label, val, val_label = get_files(train_dir, 0.3)
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img = get_one_image(val) # 通过改变参数train or val，进而验证训练集或测试集
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evaluate_one_image(img)
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