TensorFlow - 图像识别

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  简述

  TensorFlow 包含图像识别的特殊功能，这些图像存储在特定文件夹中。使用相对相同的图像，出于安全目的，很容易实现此逻辑。

  图像识别代码实现的文件夹结构如下图所示 -

  

  dataset_image 包含需要加载的相关图片。我们将专注于图像识别，其中定义了我们的徽标。这些图像使用“load_data.py”脚本加载，这有助于记录其中的各种图像识别模块。

  
  import pickle
  from sklearn.model_selection import train_test_split
  from scipy import misc
  import numpy as np
  import os
  label = 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,Y
  data_set = (X_train,y_train)
  save_label = open("int_to_word_out.pickle","wb")
  pickle.dump(label, save_label)
  save_label.close()
  

  图像的训练有助于在指定文件夹中存储可识别的模式。

  
  import numpy
  import matplotlib.pyplot as plt
  from 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 K
  import load_data
  from keras.models import Sequential
  from keras.layers import Dense
  import 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 model
  model.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")
  

  上面的代码行生成如下所示的输出 -