图像分类Keras预测

Question

我按照目前Keras博客上的教程，我有以下问题：一旦模型被训练，我该如何挑选图像并对其进行分类？

我知道train_generator.class_indices在模型中有类。

目标是引入图像的路径并返回相应的类。

下面是代码：

#libraries used 

from keras import backend as K 
from keras import applications 
from keras import optimizers 
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img 
from keras.models import Sequential 
from keras.layers import Conv2D, MaxPooling2D 
from keras.layers import Activation, Dropout, Flatten, Dense 
from keras.optimizers import SGD 

import cv2 

from PIL import Image 

import matplotlib.pyplot as plt 

from scipy.misc import imread 

import numpy as np 

%matplotlib inline 

# dimensions of our images. 
img_width, img_height = 150, 150 

train_data_dir = 'data/train' 
validation_data_dir = 'data/validation' 
nb_train_samples = 3000 
nb_validation_samples = 1200 
epochs = 10 #50 
batch_size = 16 

n_classes = 3 

# Get data 

# this is the augmentation configuration we will use for training 
train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) 

# this is the augmentation configuration we will use for testing: 
# only rescaling 
test_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,class_mode = 'categorical') # class_mode='binary' 

validation_generator = test_datagen.flow_from_directory(validation_data_dir, target_size=(img_width, img_height),\ 
                batch_size=batch_size, class_mode='categorical') #class_mode = 'categorical 


if K.image_data_format() == 'channels_first': 
    input_shape = (3, img_width, img_height) 
else: 
    input_shape = (img_width, img_height, 3) 

model = Sequential() 

model.add(Conv2D(32, (3, 3), input_shape=input_shape)) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size=(2, 2))) 

model.add(Conv2D(32, (3, 3))) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size=(2, 2))) 

model.add(Conv2D(64, (3, 3))) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size=(2, 2))) 

model.add(Flatten()) 
model.add(Dense(64)) 
model.add(Activation('relu')) 
model.add(Dropout(0.5)) 
model.add(Dense(n_classes)) 
model.add(Activation('softmax')) #sigmoid 

model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) #loss binary_crossentropy 
# on the other model 
#model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy']) 

model.fit_generator(train_generator, steps_per_epoch=nb_train_samples // batch_size, 
epochs=epochs,validation_data=validation_generator, validation_steps = nb_validation_samples // batch_size) 

编辑1：

我写了下面的函数，它不工作：

def predict(model, img, target_size): 
    if img.size != target_size: 
     img = img.resize(target_size) 

    x = image.img_to_array(img) 
    x = np.expand_dims(x, axis=0) 
    x = preprocess_input(x) 
    preds = model.predict(x) 
    return preds[0] 


target_size = (150, 150) 
model = load_model(model_name) 

img_path = 'image_test/test1.jpg' 
img = Image.open(img_path) 
fig = plt.figure() 
plt.imshow(img) 
plt.show() 
preds = predict(model, img, target_size) 
preds 

编辑2 ：

错误提出：

--------------------------------------------------------------------------- 
AttributeError       Traceback (most recent call last) 
<ipython-input-55-516f01bf49e9> in <module>() 
    17 plt.imshow(img) 
    18 plt.show() 
---> 19 preds = predict(model, img, target_size) 
    20 preds 

<ipython-input-55-516f01bf49e9> in predict(model, img, target_size) 
     3   img = img.resize(target_size) 
     4 
----> 5  x = image.img_to_array(img) 
     6  x = np.expand_dims(x, axis=0) 
     7  x = preprocess_input(x) 

AttributeError: 'numpy.ndarray' object has no attribute 'img_to_array' 

编辑3：该解决方案如下（我们必须重塑图）：

def predict(model, img, target_size): 
    if img.size != target_size: 
     img = img.resize(target_size) 

    x = img.getdata() #.img_to_array(img) 
    x = np.expand_dims(x, axis=0) 
    x = (x/255.) 
    print("shape = ", x.shape) 
    x = x.reshape(1,150,150,3) 
    preds = model.predict(x) 
    return preds[0] 


target_size = (150, 150) 

img_path = 'image_test/bird.jpg' 
img = Image.open(img_path) 
fig = plt.figure() 
plt.imshow(img) 
plt.show() 
preds = predict(model, img, target_size) 
preds 

Answer 1

从发电机的确切形状预期模型获取：

X, Y = train_generator.next() #or next(train_generator) 

现在看到这些：

print(X.shape) 
print(X.max()) 

获取图像库，例如Pillow（from PIL import Image），并将需要的文件加载到数组中（方法取决于库）。

确保在此加载数组中的最大值是在X最大兼容（您可能必须通过255阵列划分，例如）

您可以创建一个数组与几个图像，或只是一个图像，但要确保该批次的大小在第一维认为：

oneImageBatch = oneImageArray.reshape((1,)+oneImageArray.shape) 

然后用model.predict(oneImageBatch)。

Answer 2

这将工作肯定。

from keras.preprocessing import image 

targ_size = (150, 150) 

def predict(model, img_path): 
    x = image.load_image(img_path, target_size=targ_size) 
    x = image.img_to_array(img) 
    x = np.expand_dims(x, axis=0) 
    x = preprocess_input(x) 
    preds = model.predict(x) 
    return preds 


model = load_model(model_name) 
img_path = 'image_test/test1.jpg' 
preds = predict(model, img_path) 

这将返回每个类的概率数组。如果你想知道它是哪一个类，只是这样做：

pred_class = np.argmax(pred, axis=-1)