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.
The folder structure of image recognition code implementation is as shown below −
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.
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()
The training of images helps in storing the recognizable patterns within specified folder.
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 # 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*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 −