import tensorflow as tf
import numpy as np
from matplotlib import pyplot as plt

img_path = r'C:\Users\yishui\Desktop\1111.jpg'
# 读入图片
img = tf.io.read_file(img_path)

print(" 读取的原始图片的类型为 ", type(img), "形状为 ", img.shape, "数据类型 dtype:", img.dtype)

# 解码为tensor格式
decode_img = tf.image.decode_jpeg(img)
print(" 解码后的图片的类型为 ", type(decode_img), "形状为 ", decode_img.shape, "数据类型 dtype:", decode_img.dtype)

# 将图片转换问灰度图片，即最后一维只有1
convert_img = tf.image.rgb_to_grayscale(decode_img)

print(" 图片转换问灰度图片的类型为 ", type(convert_img), "形状为 ", convert_img.shape, "数据类型 dtype:", convert_img.dtype)

# 剪裁图片， 高度和宽度
resize_img = tf.image.resize_with_crop_or_pad(convert_img, 250, 151)

print(" 剪裁图片后图片的类型为 ", type(resize_img), "形状为 ", resize_img.shape, "数据类型 dtype:", resize_img.dtype)

# 编码tensor
encode_jpeg = tf.image.encode_jpeg(resize_img, quality=100)

print(" 编码tensor后图片的类型为 ", type(encode_jpeg), "形状为 ", encode_jpeg.shape, "数据类型 dtype:", encode_jpeg.dtype)

# 写入./221.jpg这个文件里面
with tf.io.gfile.GFile('./221.jpg', 'wb') as file:
    file.write(encode_jpeg.numpy())

读取的原始图片的类型为  <class 'tensorflow.python.framework.ops.EagerTensor'> 形状为  () 数据类型 dtype: <dtype: 'string'>
           
解码后的图片的类型为  <class 'tensorflow.python.framework.ops.EagerTensor'> 形状为  (600, 600, 3) 数据类型 dtype: <dtype: 'uint8'>
       
图片转换问灰度图片的类型为  <class 'tensorflow.python.framework.ops.EagerTensor'> 形状为  (600, 600, 1) 数据类型 dtype: <dtype: 'uint8'>
       
剪裁图片后图片的类型为  <class 'tensorflow.python.framework.ops.EagerTensor'> 形状为  (250, 151, 1) 数据类型 dtype: <dtype: 'uint8'>
       
编码tensor后图片的类型为  <class 'tensorflow.python.framework.ops.EagerTensor'> 形状为  () 数据类型 dtype: <dtype: 'string'>

import tensorflow as tf
import os
import numpy as np

# 训练集图片所在的文件夹，以训练集为例，文件的命名为“类别_顺序.jpg”
train_path = "C:\\Users\\yishui\\Desktop\\images\\train\\"

# 这个函数用于返回符合,可以使用正则路径，*表示任意字符
path_list = tf.data.Dataset.list_files(train_path + "*.jpg")


# 定义一个读取图片的函数
def read_image(path_list):
    '''
    :param path_list:文件的路径list
    :return: 图片张量列表,图片标签列表
    '''
    images = []  # 图片列表
    image_labels = []  # 图片标签列表

    # 根据文件路径列表依次读取
    for i in path_list:
        # tesnsorflow的io读取文件
        image_temp = tf.io.read_file(i)  
        
        # 根据图片的格式进行编码转化为张量，这里图片是jpg格式
        image_temp = tf.image.decode_jpeg(image_temp)  
        
         # 图片加入到数据集
        images.append(image_temp) 
        
        # 获取文件名加入到标签，这里要张量i转化为字符串
        image_labels.append(os.path.basename(str(i)).split('_')[0])  

    return np.array(images), np.array(image_labels)


# 读取训练图片
train_images, train_labels = read_image(path_list=path_list)
print(" 读取的图片的类型为 ", type(train_images), "形状为 ", train_images.shape, "数据类型 dtype:", train_images.dtype)

读取的图片的类型为  <class 'numpy.ndarray'> 形状为  (2, 600, 600, 3) 数据类型 dtype: uint8

image_size = (180, 180)
img = keras.preprocessing.image.load_img(
    "PetImages/Cat/6779.jpg", target_size=image_size
)
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)  # Create batch axis

predictions = model.predict(img_array)
score = predictions[0]
print(
    "This image is %.2f percent cat and %.2f percent dog."
    % (100 * (1 - score), 100 * score)
)

　　
　　tf.keras.preprocessing.image.ImageDataGenerator()

　
ImageDataGenerator.fit()

import tensorflow as tf
import os
import numpy as np

# 训练集图片所在的文件夹，以训练集为例，文件的命名为“类别_顺序.jpg”
train_path = "C:\\Users\\yishui\\Desktop\\images\\train\\"

# 这个函数用于返回符合,可以使用正则路径，*表示任意字符
path_list = tf.data.Dataset.list_files(train_path + "*.jpg")


# 定义一个读取图片的函数
def read_image(path_list):
    '''
    :param path_list:文件的路径list
    :return: 图片张量列表,图片标签列表
    '''
    images = []  # 图片聊表
    image_labels = []  # 图片标签列表

    # 根据文件路径列表依次读取
    for i in path_list:
        image_temp = tf.io.read_file(i)  # tesnsorflow的io读取文件
        image_temp = tf.image.decode_jpeg(image_temp)  # 根据图片的格式进行编码转化为张量，这里图片是jpg格式
        images.append(image_temp)  # 图片加入到数据集
        image_labels.append(os.path.basename(str(i)).split('_')[0])  # 获取文件名加入到标签，这里要张量i转化为字符串

    return np.array(images), np.array(image_labels)


# 读取训练图片
train_images, train_labels = read_image(path_list=path_list)

# 增强数据，中心化并随机旋转45度
image_gen = tf.keras.preprocessing.image.ImageDataGenerator(featurewise_center=True,
                                                            rotation_range=45)
# featurewise_center等设计均值方差的参数为True才需要使用fit方法
image_gen.fit(x=train_images)

# 生成强化数据集迭代器
data_gen = image_gen.flow(train_images, train_labels)

# 模型训练
# model = tf.keras.Sequential().fit_generator(data_gen)

import tensorflow as tf
import numpy as np

print("\n 当前的tensorflow版本为 ", tf.__version__, " \n")
keras = tf.keras


def load_model():
    _new_model = keras.models.load_model('saved_model/my_model')

    # 检查其架构
    # _new_model.summary()

    return _new_model


def prepare(img_path):
    # 读入图片
    _img = tf.io.read_file(img_path)
    # 解码为tensor格式
    _img = tf.image.decode_jpeg(_img)

    # 将图片转换问灰度图片，即最后一维只有1
    _img = tf.image.rgb_to_grayscale(_img)

    # 剪裁图片， 高度和宽度
    # _img = tf.image.resize_with_crop_or_pad(_img, 28, 28)

    # 缩放图片
    _img = tf.image.resize(_img, [28, 28])

    # 转换张量数据类型
    _img = tf.cast(_img, dtype=tf.uint8)

    # 使用expand_dims来将维度加1。比如我自己代码中遇到的情况，
    # 在对图像维度降到二维做特定操作后，
    # 要还原成四维[batch, height, width, channels]，前后各增加一维

    # 写入./221.jpg这个文件里面
    encode_jpeg = tf.image.encode_jpeg(_img, quality=100)
    with tf.io.gfile.GFile('./2222.jpg', 'wb') as file:
        file.write(encode_jpeg.numpy())

    # -1表示最后一维,0表示在前面一维
    # _img = (np.expand_dims(_img, 0))

    _img = tf.expand_dims(_img, axis=0)
    return _img


if __name__ == "__main__":
    model = load_model()

    img = prepare(img_path=r'C:\Users\yishui\Desktop\2222.jpg')

    print("img.dtype =", img.dtype, " img.shape = ", img.shape)

    predictions = model.predict(img)

    print(" predictions  = ", len(predictions))

    print(" predictions[0]  = ", predictions[0])

    print(" predictions[0] index  = ", np.argmax(predictions[0]), " predictions[0] val  = ", np.max(predictions[0]))

from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_data_dir = r"C:\\Users\\yishui\\Desktop\\images\\train\\"

val_data_dir = r"C:\\Users\\yishui\\Desktop\\images\\train\\"

img_width, img_height = 48, 48

batch_size = 16


train_datagen = ImageDataGenerator(

    rescale=1. / 255,

    shear_range=0.2,

    horizontal_flip=True)


val_datagen = ImageDataGenerator(rescale=1. / 255)


train_generator = train_datagen.flow_from_directory(

    train_data_dir,

    target_size=(img_width, img_height),

    batch_size=batch_size)


val_generator = val_datagen.flow_from_directory(

    val_data_dir,

    target_size=(img_width, img_height),

    batch_size=batch_size)

tf.keras.preprocessing.image.ImageDataGenerator(

featurewise_center=False,

samplewise_center=False,

featurewise_std_normalization=False,

samplewise_std_normalization=False,

zca_whitening=False,

zca_epsilon=1e-6,

rotation_range=0.,

width_shift_range=0.,

height_shift_range=0.,

brightness_range,

shear_range=0.,

zoom_range=0.,

channel_shift_range=0.,

fill_mode=‘nearest’,

cval=0.,

horizontal_flip=False,

vertical_flip=False,

rescale=None,

preprocessing_function=None,

data_format=K.image_data_format())

train_datagen.flow_from_directory(
	train_data_dir,

		target_size=(img_width, img_height),

		batch_size=batch_size)

flower_photo/
  daisy/
  dandelion/
  roses/
  sunflowers/
  tulips/

import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
import tensorflow as tf

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import pathlib

dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)

image_count = len(list(data_dir.glob('*/*.jpg')))
print(image_count)

batch_size = 32
img_height = 180
img_width = 180

Found 1853 files belonging to 3 classes.
Using 370 files for validation.
['roses', 'sunflowers', 'tulips']

for image_batch, labels_batch in train_ds:
    print("image_batch.shape ", image_batch.shape)
    print(" labels_batch.shape ", labels_batch.shape)
    break

AUTOTUNE = tf.data.AUTOTUNE

train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)

normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(normalized_ds))
first_image = image_batch[0]

num_classes = 5

model = Sequential([
  layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
  layers.Conv2D(16, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(32, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(64, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Flatten(),
  layers.Dense(128, activation='relu'),
  layers.Dense(num_classes)
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.summary()

Found 1853 files belonging to 3 classes.
Using 370 files for validation.
['roses', 'sunflowers', 'tulips']
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
rescaling_1 (Rescaling)      (None, 180, 180, 3)       0         
_________________________________________________________________
conv2d (Conv2D)              (None, 180, 180, 16)      448       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 90, 90, 16)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 90, 90, 32)        4640      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 45, 45, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 45, 45, 64)        18496     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 22, 22, 64)        0         
_________________________________________________________________
flatten (Flatten)            (None, 30976)             0         
_________________________________________________________________
dense (Dense)                (None, 128)               3965056   
_________________________________________________________________
dense_1 (Dense)              (None, 5)                 645       
=================================================================
Total params: 3,989,285
Trainable params: 3,989,285
Non-trainable params: 0
_________________________________________________________________

epochs=10
history = model.fit(
  train_ds,
  validation_data=val_ds,
  epochs=epochs
)

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs_range = range(epochs)

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

import tensorflow as tf
import numpy as np

print("\n 当前的tensorflow版本为 ", tf.__version__, " \n")

keras = tf.keras


def exec(images, labels):
    """
        训练模型
    :param images:
    :param labels:
    :return:
    """

    model = keras.Sequential([
        keras.layers.Flatten(input_shape=(28, 28)),
        keras.layers.Dense(128, activation='relu'),
        keras.layers.Dense(10)
    ])
    model.compile(optimizer='adam',
                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                  metrics=['accuracy'])
    model.fit(images, labels, epochs=10)
    return model


def prepare():
    """
    准备数据
    :return:
    """
    fashion_mnist = keras.datasets.fashion_mnist

    (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

    return (train_images, train_labels), (test_images, test_labels)


if __name__ == "__main__":
    # 准备数据
    (train_images, train_labels), (test_images, test_labels) = prepare()

    # 获取模型
    model = exec(train_images, train_labels)
    # 将整个模型另存为 SavedModel。
    model.save('saved_model/my_model')

import tensorflow as tf
import numpy as np

print("\n 当前的tensorflow版本为 ", tf.__version__, " \n")
keras = tf.keras


def load_model():
    _new_model = keras.models.load_model('saved_model/my_model')

    # 检查其架构
    # _new_model.summary()

    return _new_model


def prepare(img_path):
    # 读入图片
    _img = tf.io.read_file(img_path)
    # 解码为tensor格式
    _img = tf.image.decode_jpeg(_img)

    # 将图片转换问灰度图片，即最后一维只有1
    _img = tf.image.rgb_to_grayscale(_img)

    # 剪裁图片， 高度和宽度
    _img = tf.image.resize_with_crop_or_pad(_img, 28, 28)

    _img = (np.expand_dims(_img, 0))

    # 也可以使用tf.expand_dims修改
    #_img = tf.expand_dims(_img, axis=0)
    return _img


if __name__ == "__main__":
    model = load_model()

    img = prepare(img_path=r'C:\Users\yishui\Desktop\1111.jpg')

    print("img.dtype =", img.dtype, " img.shape = ", img.shape)

    predictions = model.predict(img)

    print(" predictions  = ", len(predictions))

    print(" predictions[0]  = ", predictions[0])

    print(" predictions[0] index  = ", np.argmax(predictions[0]), " predictions[0] val  = ", np.max(predictions[0]))

import numpy as np

x = np.array([1, 2])

print(" x 的属性为 shape = ", x.shape, "   x.dtype = ", x.dtype, " x.ndim =", x.ndim, " x = \n", x)

print("\n ")

y = np.expand_dims(x, axis=0)

print(" y 的属性为 shape = ", y.shape, "   y.dtype = ", y.dtype, " y.ndim =", y.ndim, " y =\n ", y)

print("\n ")

z = np.expand_dims(x, axis=-1)

print(" z 的属性为 shape = ", z.shape, "   z.dtype = ", z.dtype, " z.ndim =", z.ndim, " z = \n", z)

print("\n ")

a = np.expand_dims(x, axis=1)

print(" a 的属性为 shape = ", a.shape, "   a.dtype = ", a.dtype, " a.ndim =", a.ndim, " a =\n ", a)

# 这里会报错
# b = np.expand_dims(x, axis=2)
# print(" b = ", b)
# print(" b 的属性为 shape = ", b.shape, "   b.dtype = ", b.dtype, " b.ndim =", b.ndim)

print("\n ")
c = np.expand_dims(x, axis=(0, 1))

print(" c 的属性为 shape = ", c.shape, "   c.dtype = ", c.dtype, " c.ndim =", c.ndim, " c =\n ", c)

print("\n ")
d = np.expand_dims(x, axis=(0, 2))

print(" d 的属性为 shape = ", d.shape, "   d.dtype = ", d.dtype, " d.ndim =", d.ndim, " d =\n ", d)

print("\n")
e = np.expand_dims(y, axis=(0, 1))

print(" e 的属性为 shape = ", e.shape, "   e.dtype = ", e.dtype, " e.ndim =", e.ndim, " e = \n", e)

print("\n ")
f = np.expand_dims(y, axis=(0, 2))

print(" f 的属性为 shape = ", f.shape, "   f.dtype = ", f.dtype, " f.ndim =", f.ndim, " f = \n", f)

x 的属性为 shape =  (2,)    x.dtype =  int32  x.ndim = 1  x = 
[1 2]

y 的属性为 shape =  (1, 2)    y.dtype =  int32  y.ndim = 2  y =
 [[1 2]]

z 的属性为 shape =  (2, 1)    z.dtype =  int32  z.ndim = 2  z = 
[[1]
[2]]

a 的属性为 shape =  (2, 1)    a.dtype =  int32  a.ndim = 2  a =
 [[1]
[2]]

c 的属性为 shape =  (1, 1, 2)    c.dtype =  int32  c.ndim = 3  c =
 [[[1 2]]]

d 的属性为 shape =  (1, 2, 1)    d.dtype =  int32  d.ndim = 3  d =
 [[[1]
 [2]]]
e 的属性为 shape =  (1, 1, 1, 2)    e.dtype =  int32  e.ndim = 4  e = 
[[[[1 2]]]]

f 的属性为 shape =  (1, 1, 1, 2)    f.dtype =  int32  f.ndim = 4  f = 
[[[[1 2]]]]

import tensorflow as tf

image = tf.zeros([2, 2, 2])

print(" image 的属性为 shape = ", image.shape, "type=", type(image), "   image.dtype = ", image.dtype, " image.ndim =",
      image.ndim, " image = \n",
      image, "\n")

a = tf.expand_dims(image, axis=0)
print(" image 的属性为 shape = ", a.shape, "type=", type(a), "   a.dtype = ", a.dtype, " a.ndim =", a.ndim, " a = \n",
      a, "\n")

av = a.shape.as_list()
print(" image 的属性为 ", "type=", type(av),
      " av = ", av, "\n")

b = tf.expand_dims(image, axis=-1)
print(" image 的属性为 shape = ", b.shape, "type=", type(b), "   b.dtype = ", b.dtype, " b.ndim =", b.ndim, " b = \n",
      b, "\n")

bv = b.shape.as_list()
print(" image 的属性为 ", "type=", type(bv),
      " bv = ", bv, "\n")

numpy.squeeze(a,axis = None)

 1）a表示输入的数组；
 2）axis用于指定需要删除的维度，但是指定的维度必须为单维度，否则将会报错；
 3）axis的取值可为None 或 int 或 tuple of ints, 可选。若axis为空，则删除所有单维度的条目；
 4）返回值：数组
 5) 不会修改原数组；

import matplotlib.pyplot as plt
from PIL import Image
import numpy as np

img = Image.open(r'C:\Users\yishui\Desktop\train_data\1.jpg')

print("原始图片数据为 ", img)

# 将读入的图片转化为float32类型的numpy.ndarray
img_array = np.array(img).astype('float32')

print("转换数组后图片数据为 ", img_array)

# 图片读入成ndarry时，形状是[H, W, 3]，
# 将通道这一维度调整到最前面
hw3_img = np.transpose(img_array, (2, 0, 1))

print("形状是[H, W, 3]格式的图片数据为 ", hw3_img)

# 将数据形状调整为[N, C, H, W]格式
x = hw3_img.reshape(1, 3, img.height, img.width)

print("形状是[N, C, H, W]格式的图片数据为 ", hw3_img)

# 解码为tensor格式
_img = tf.image.decode_jpeg(_img)

# 将图片转换问灰度图片，即最后一维只有1
_img = tf.image.rgb_to_grayscale(_img)

_img = tf.image.resize_with_crop_or_pad(_img, 28, 28)