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test.py

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+from PIL import Image
+import numpy as np
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import forward
+from batchdealing import get_files
+
+
+# 获取一张图片
+def get_one_image(train):
+    # 输入参数：train,训练图片的路径
+    # 返回参数：image，从训练图片中随机抽取一张图片
+    n = len(train)
+    ind = np.random.randint(0, n)
+    img_dir = train[ind]  # 随机选择测试的图片
+
+    img = Image.open(img_dir)
+
+    # 显示图片，在jupyter notebook下当然也可以不用plt.show()
+    plt.imshow(img)
+    plt.show(img)
+    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
+    image = np.array(imag)
+    return image
+
+
+# 测试图片
+def evaluate_one_image(image_array):
+    with tf.Graph().as_default():
+        BATCH_SIZE = 1
+        N_CLASSES = 4
+
+        image = tf.cast(image_array, tf.float32)
+
+        # 线性缩放图像以具有零均值和单位范数。
+        image = tf.image.per_image_standardization(image)
+        image = tf.reshape(image, [1, 64, 64, 3])
+
+        # 构建卷积神经网络
+        logit = forward.inference(image, BATCH_SIZE, N_CLASSES)
+
+        # softmax函数的作用就是归一化
+        # 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。
+        # 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。
+        logit = tf.nn.softmax(logit)
+
+        x = tf.compat.v1.placeholder(tf.float32, shape=[64, 64, 3])
+
+        # you need to change the directories to yours. /Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/
+        logs_train_dir = '/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/'
+
+        # tf.train.Saver() 保存和加载模型
+        saver = tf.compat.v1.train.Saver()
+
+        with tf.compat.v1.Session() as sess:
+
+            print("Reading checkpoints...")
+            ckpt = tf.train.get_checkpoint_state(logs_train_dir)
+            if ckpt and ckpt.model_checkpoint_path:
+                global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
+                saver.restore(sess, ckpt.model_checkpoint_path)
+                print('Loading success, global_step is %s' % global_step)
+            else:
+                print('No checkpoint file found')
+
+            # feed_dict的作用是给使用placeholder创建出来的tensor赋值。
+            # 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。
+            # 你可以提供feed数据作为run()调用的参数。
+            # feed只在调用它的方法内有效,方法结束,feed就会消失。
+            # 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。
+            # 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，
+            # 传入一些tensor，这些tensor会帮助你取回结果。
+            prediction = sess.run(logit, feed_dict={x: image_array})
+
+            #  取出prediction中元素最大值所对应的索引，也就是最大的可能
+            max_index = np.argmax(prediction)
+
+            if max_index == 0:
+                print('This could be the first category %.6f' % prediction[:, 0])
+            elif max_index == 1:
+                print('This could be the second category %.6f' % prediction[:, 1])
+            elif max_index == 2:
+                print('This could be the third category %.6f' % prediction[:, 2])
+            elif max_index == 3:
+                print('This could be the fourth category %.6f' % prediction[:, 3])
+            elif max_index == 4:
+                print('This could be the fifth category %.6f' % prediction[:, 4])
+            elif max_index == 5:
+                print('This could be the sixth category %.6f' % prediction[:, 5])
+            elif max_index == 6:
+                print('This could be the seventh category %.6f' % prediction[:, 6])
+            elif max_index == 7:
+                print('This could be the eighth category %.6f' % prediction[:, 7])
+            else:
+                print('This could be the ninth category %.6f' % prediction[:, 9])
+
+
+if __name__ == '__main__':
+    train_dir = '/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/'
+    train, train_label, val, val_label = get_files(train_dir, 0.3)
+    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
+    evaluate_one_image(img)
+