使用TensorFlow进行图像识别 (Image Recognition using TensorFlow)

TensorFlow includes a special feature of image recognition and these images are stored in a specific folder. With relatively same images, it will be easy to implement this logic for security purposes.

TensorFlow包含图像识别的特殊功能，并且这些图像存储在特定的文件夹中。 对于相对相同的映像，出于安全目的很容易实现此逻辑。

The folder structure of image recognition code implementation is as shown below −

图像识别代码实现的文件夹结构如下图所示-

The dataset_image includes the related images, which need to be loaded. We will focus on image recognition with our logo defined in it. The images are loaded with “load_data.py” script, which helps in keeping a note on various image recognition modules within them.

dataset_image包括需要加载的相关图像。 我们将专注于其中定义了徽标的图像识别。 图像使用“ load_data.py”脚本加载，这有助于在其中的各个图像识别模块上保留注释。

import pickle
from sklearn.model_selection import train_test_split
from scipy import miscimport numpy as np
import oslabel = os.listdir("dataset_image")
label = label[1:]
dataset = []for image_label in label:images = os.listdir("dataset_image/"+image_label)for image in images:img = misc.imread("dataset_image/"+image_label+"/"+image)img = misc.imresize(img, (64, 64))dataset.append((img,image_label))
X = []
Y = []for input,image_label in dataset:X.append(input)Y.append(label.index(image_label))X = np.array(X)
Y = np.array(Y)X_train,y_train, = X,Ydata_set = (X_train,y_train)save_label = open("int_to_word_out.pickle","wb")
pickle.dump(label, save_label)
save_label.close()

The training of images helps in storing the recognizable patterns within specified folder.

图像训练有助于将可识别的图案存储在指定的文件夹中。

import numpy
import matplotlib.pyplot as pltfrom keras.layers import Dropout
from keras.layers import Flatten
from keras.constraints import maxnorm
from keras.optimizers import SGD
from keras.layers import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
from keras import backend as Kimport load_data
from keras.models import Sequential
from keras.layers import Denseimport keras
K.set_image_dim_ordering('tf')# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)# load data
(X_train,y_train) = load_data.data_set# normalize inputs from 0-255 to 0.0-1.0
X_train = X_train.astype('float32')#X_test = X_test.astype('float32')
X_train = X_train / 255.0#X_test = X_test / 255.0
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)#y_test = np_utils.to_categorical(y_test)
num_classes = y_train.shape[1]# Create the model
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), padding = 'same', activation = 'relu', kernel_constraint = maxnorm(3)))model.add(Dropout(0.2))
model.add(Conv2D(32, (3, 3), activation = 'relu', padding = 'same', kernel_constraint = maxnorm(3)))model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Flatten())
model.add(Dense(512, activation = 'relu', kernel_constraint = maxnorm(3)))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation = 'softmax'))# Compile model
epochs = 10
lrate = 0.01
decay = lrate/epochs
sgd = SGD(lr = lrate, momentum = 0.9, decay = decay, nesterov = False)
model.compile(loss = 'categorical_crossentropy', optimizer = sgd, metrics = ['accuracy'])
print(model.summary())#callbacks = [keras.callbacks.EarlyStopping(monitor = 'val_loss', min_delta = 0, patience = 0, verbose = 0, mode = 'auto')]
callbacks = [keras.callbacks.TensorBoard(log_dir='./logs', histogram_freq = 0, batch_size = 32, write_graph = True, write_grads = False, write_images = True, embeddings_freq = 0, embeddings_layer_names = None, embeddings_metadata = None)]# Fit the modelmodel.fit(X_train, y_train, epochs = epochs, batch_size = 32,shuffle = True,callbacks = callbacks)# Final evaluation of the model
scores = model.evaluate(X_train, y_train, verbose = 0)
print("Accuracy: %.2f%%" % (scores[1]*100))# serialize model to JSONx
model_json = model.to_json()
with open("model_face.json", "w") as json_file:json_file.write(model_json)# serialize weights to HDF5
model.save_weights("model_face.h5")
print("Saved model to disk")

The above line of code generates an output as shown below −

上面的代码行生成如下所示的输出-

翻译自: https://www.tutorialspoint.com/tensorflow/image_recognition_using_tensorflow.htm