tensorflow图像预处理_keras和tensorflow

目录

• 1 使用复杂图像
• 2 获取数据
• 3 使用ImageGenerator标记和准备数据¶
• 4 探索数据
• 5 定义模型
• 6 编译模型
• 7 ImageGenerator生成数据
• 8 训练
• 9 测试模型
• 10 可视化中间过程

1 使用复杂图像：马或人

在之前的Fashion MNIST训练图像分类器中。图像时28×28，并且图像居中。本节我们将提升一个新的水平，训练识别图像中的特征，其中主体可以在图像中的任何位置。

2 获取数据

我们将构建马匹和人类的分类来实现上述目的，该分类器将告诉图像是否包含马或人。

https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip

import os
import zipfile

local_zip = "./horse-or-human.zip"
zip_ref = zipfile.ZipFile(local_zip,"r")
zip_ref.extractall("./horse-or-human")
zip_ref.close()

.zip的内容被提取到基本目录horse-or-human中

3 使用ImageGenerator标记和准备数据

在这个样本中我们要注意到一件事是：我们没有明确地将图像标记为马或人。

下面我们将用ImageGenerator来从子目录中读取图像，并从该子目录的名称自动标记它们。

# directory with out trainging horse pictures
train_horse_dir = os.path.join("./horse-or-human/horses")

# dirctory with out training human pictures
train_human_dir = os.path.join("./horse-or-human/humans")

现在，让我们看一下马和人训练目录中的文件名是怎么样的

train_horse_names = os.listdir(train_horse_dir)
print(train_horse_names[:10])
train_human_names = os.listdir(train_human_dir)
print(train_human_names[:10])

['horse01-0.png', 'horse01-1.png', 'horse01-2.png', 'horse01-3.png', 'horse01-4.png', 'horse01-5.png', 'horse01-6.png', 'horse01-7.png', 'horse01-8.png', 'horse01-9.png']
['human01-00.png', 'human01-01.png', 'human01-02.png', 'human01-03.png', 'human01-04.png', 'human01-05.png', 'human01-06.png', 'human01-07.png', 'human01-08.png', 'human01-09.png']

让我们找出目录中马和人类图像的总数：

print("the total training horse images:",len(os.listdir(train_horse_dir)))
print("the total training human images:",len(os.listdir(train_human_dir)))

the total training horse images: 500
the total training human images: 527

4 探索数据

现在我们看看几张照片，以便更好地了解它们的外观。

import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.image as mpimg

nrows = 4
ncols = 4

pic_index = 0

现在展示一批8张马和8张人的图片

# Set up matplotlib fig, and size it to fit 4x4 pics
fig = plt.gcf()
fig.set_size_inches(ncols * 4, nrows * 4)
 
pic_index += 8
next_horse_pix = [os.path.join(train_horse_dir, fname) 
                for fname in train_horse_names[pic_index-8:pic_index]]
next_human_pix = [os.path.join(train_human_dir, fname) 
                for fname in train_human_names[pic_index-8:pic_index]]
 
for i, img_path in enumerate(next_horse_pix+next_human_pix):
  # Set up subplot; subplot indices start at 1
    sp = plt.subplot(nrows, ncols, i + 1 )
    sp.axis('Off') # Don't show axes (or gridlines)
    
    img = mpimg.imread(img_path)
    plt.imshow(img)

5 定义模型

第1步导入TensorFlow

import tensorflow as tf

这是一个二分类问题

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(16,(3,3),activation="relu",input_shape=(300,300,3)),
    tf.keras.layers.MaxPooling2D(2,2),
    
    tf.keras.layers.Conv2D(32,(3,3),activation="relu"),
    tf.keras.layers.MaxPooling2D(2,2),
    
    tf.keras.layers.Conv2D(64,(3,3),activation="relu"),
    tf.keras.layers.MaxPooling2D(2,2),
    
    tf.keras.layers.Conv2D(64,(3,3),activation="relu"),
    tf.keras.layers.MaxPooling2D(2,2),
    
    tf.keras.layers.Conv2D(64,(3,3),activation="relu"),
    tf.keras.layers.MaxPooling2D(2,2),
    
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512,activation="relu"),
    tf.keras.layers.Dense(1,activation="sigmoid")
])

WARNING:tensorflow:From D:\software\Anaconda\anaconda\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.

model.summary打印模型信息

model.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 298, 298, 16)      448       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 149, 149, 16)      0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 147, 147, 32)      4640      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 73, 73, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 71, 71, 64)        18496     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 35, 35, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 33, 33, 64)        36928     
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 16, 16, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 14, 14, 64)        36928     
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64)          0         
_________________________________________________________________
flatten (Flatten)            (None, 3136)              0         
_________________________________________________________________
dense (Dense)                (None, 512)               1606144   
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 513       
=================================================================
Total params: 1,704,097
Trainable params: 1,704,097
Non-trainable params: 0
_________________________________________________________________

6 编译模型

from tensorflow.keras.optimizers import RMSprop

model.compile(loss="binary_crossentropy",
              optimizer=RMSprop(lr=0.001),
              metrics=["acc"])

7 ImageGenerator生成数据

from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    "./horse-or-human/",
    target_size=(300,300),
    batch_size=128,
    class_mode="binary"
)

Found 1027 images belonging to 2 classes.

8 训练

history = model.fit_generator(
    train_generator,
    steps_per_epoch=8,
    epochs=15,
    verbose=1
)

WARNING:tensorflow:From D:\software\Anaconda\anaconda\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Epoch 1/15
9/9 [==============================] - 85s 9s/step - loss: 0.9717 - acc: 0.5511
Epoch 2/15
9/9 [==============================] - 78s 9s/step - loss: 0.5993 - acc: 0.7527
Epoch 3/15
9/9 [==============================] - 81s 9s/step - loss: 0.7818 - acc: 0.7858
Epoch 4/15
9/9 [==============================] - 82s 9s/step - loss: 0.4991 - acc: 0.8043
Epoch 5/15
9/9 [==============================] - 88s 10s/step - loss: 0.2824 - acc: 0.8939
Epoch 6/15
9/9 [==============================] - 74s 8s/step - loss: 0.1238 - acc: 0.9659
Epoch 7/15
9/9 [==============================] - 83s 9s/step - loss: 0.0556 - acc: 0.9747
Epoch 8/15
9/9 [==============================] - 74s 8s/step - loss: 0.7515 - acc: 0.8822
Epoch 9/15
9/9 [==============================] - 73s 8s/step - loss: 0.0387 - acc: 0.9903
Epoch 10/15
9/9 [==============================] - 74s 8s/step - loss: 0.0553 - acc: 0.9718
Epoch 11/15
9/9 [==============================] - 74s 8s/step - loss: 0.0151 - acc: 0.9961
Epoch 12/15
9/9 [==============================] - 72s 8s/step - loss: 0.0043 - acc: 1.0000
Epoch 13/15
9/9 [==============================] - 75s 8s/step - loss: 0.4870 - acc: 0.8812
Epoch 14/15
9/9 [==============================] - 72s 8s/step - loss: 0.0156 - acc: 0.9961
Epoch 15/15
9/9 [==============================] - 75s 8s/step - loss: 0.0468 - acc: 0.9805

9 测试模型

现在让我们使用该模型实际运行预测。此代码允许你从文件系统中选择一个或多个文件，然后上传它们，并通过模型运行它们，指示对象是马或人

import cv2
import numpy as np

human_img = cv2.imread("human.jpg")
plt.imshow(human_img)
classes = model.predict(human_img.reshape(1,300,300,3))
print(classes[0])
if classes[0] > 0.5:
    print("this is a human")
else:
    print("this is a horse")

[1.]
this is a human

import cv2
import numpy as np

horse_img = cv2.imread("horse.jpg")
plt.imshow(horse_img)
classes = model.predict(horse_img.reshape(1,300,300,3))
print(classes[0])
if classes[0] > 0.5:
    print("this is a human")
else:
    print("this is a horse")

[1.]
this is a human

可以看到分辨的话还是不准确的

10 可视化中间过程

从训练集中随机选择一个图像，然后生成一个图像，其中每一行是图层的输出，并且该行输出特征图中的特定 滤镜。

import numpy as np
import random
from tensorflow.keras.preprocessing.image import img_to_array, load_img
 
# Let's define a new Model that will take an image as input, and will output
# intermediate representations for all layers in the previous model after
# the first.
successive_outputs = [layer.output for layer in model.layers[1:]]
#visualization_model = Model(img_input, successive_outputs)
visualization_model = tf.keras.models.Model(inputs = model.input, outputs = successive_outputs)
# Let's prepare a random input image from the training set.
horse_img_files = [os.path.join(train_horse_dir, f) for f in train_horse_names]
human_img_files = [os.path.join(train_human_dir, f) for f in train_human_names]
img_path = random.choice(horse_img_files + human_img_files)
 
img = load_img(img_path, target_size=(300, 300))  # this is a PIL image
x = img_to_array(img)  # Numpy array with shape (150, 150, 3)
x = x.reshape((1,) + x.shape)  # Numpy array with shape (1, 150, 150, 3)
 
# Rescale by 1/255
x /= 255
 
# Let's run our image through our network, thus obtaining all
# intermediate representations for this image.
successive_feature_maps = visualization_model.predict(x)
 
# These are the names of the layers, so can have them as part of our plot
layer_names = [layer.name for layer in model.layers]
 
# Now let's display our representations
for layer_name, feature_map in zip(layer_names, successive_feature_maps):
    if len(feature_map.shape) == 4:
        # Just do this for the conv / maxpool layers, not the fully-connected layers
        n_features = feature_map.shape[-1]  # number of features in feature map
        # The feature map has shape (1, size, size, n_features)
        size = feature_map.shape[1]
        # We will tile our images in this matrix
        display_grid = np.zeros((size, size * n_features))
        for i in range(n_features):
            # Postprocess the feature to make it visually palatable
            x = feature_map[0, :, :, i]
            x -= x.mean()
            x /= x.std()
            x *= 64
            x += 128
            x = np.clip(x, 0, 255).astype('uint8')
            # We'll tile each filter into this big horizontal grid
            display_grid[:, i * size : (i + 1) * size] = x
        # Display the grid
        scale = 20. / n_features
        plt.figure(figsize=(scale * n_features, scale))
        plt.title(layer_name)
        plt.grid(False)
        plt.imshow(display_grid, aspect='auto', cmap='viridis')

D:\software\Anaconda\anaconda\lib\site-packages\ipykernel_launcher.py:43: RuntimeWarning: invalid value encountered in true_divide

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