Thimira · April 13, 2021 05:52 · mehki · Apr 14, 2020
diff --git a/keras_bottleneck_multiclass.py b/keras_bottleneck_multiclass.py
 '''
 Using Bottleneck Features for Multi-Class Classification in Keras

 We use this technique to build powerful (high accuracy without overfitting) Image Classification systems with small
 amount of training data.

 The full tutorial to get this code working can be found at the "Codes of Interest" Blog at the following link,
 https://www.codesofinterest.com/2017/08/bottleneck-features-multi-class-classification-keras.html

 Please go through the tutorial before attempting to run this code, as it explains how to setup your training data.

 The code was tested on Python 3.5, with the following library versions,
 Keras 2.0.6
 TensorFlow 1.2.1
 OpenCV 3.2.0

 This should work with Theano as well, but untested.
 '''
 import numpy as np
 from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
 from keras.models import Sequential
 from keras.layers import Dropout, Flatten, Dense
 from keras import applications
 from keras.utils.np_utils import to_categorical
 import matplotlib.pyplot as plt
 import math
 import cv2

 # dimensions of our images.
 img_width, img_height = 224, 224

 top_model_weights_path = 'bottleneck_fc_model.h5'
 train_data_dir = 'data/train'
 validation_data_dir = 'data/validation'

 # number of epochs to train top model
 epochs = 50
 # batch size used by flow_from_directory and predict_generator
 batch_size = 16


 def save_bottlebeck_features():
    # build the VGG16 network
    model = applications.VGG16(include_top=False, weights='imagenet')

    datagen = ImageDataGenerator(rescale=1. / 255)

    generator = datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_width, img_height),
        batch_size=batch_size,
        class_mode=None,
        shuffle=False)

    print(len(generator.filenames))
    print(generator.class_indices)
    print(len(generator.class_indices))

    nb_train_samples = len(generator.filenames)
    num_classes = len(generator.class_indices)

    predict_size_train = int(math.ceil(nb_train_samples / batch_size))

    bottleneck_features_train = model.predict_generator(
        generator, predict_size_train)

    np.save('bottleneck_features_train.npy', bottleneck_features_train)

    generator = datagen.flow_from_directory(
        validation_data_dir,
        target_size=(img_width, img_height),
        batch_size=batch_size,
        class_mode=None,
        shuffle=False)

    nb_validation_samples = len(generator.filenames)

    predict_size_validation = int(
        math.ceil(nb_validation_samples / batch_size))

    bottleneck_features_validation = model.predict_generator(
        generator, predict_size_validation)

    np.save('bottleneck_features_validation.npy',
            bottleneck_features_validation)


 def train_top_model():
    datagen_top = ImageDataGenerator(rescale=1. / 255)
    generator_top = datagen_top.flow_from_directory(
        train_data_dir,
        target_size=(img_width, img_height),
        batch_size=batch_size,
        class_mode='categorical',
        shuffle=False)

    nb_train_samples = len(generator_top.filenames)
    num_classes = len(generator_top.class_indices)

    # save the class indices to use use later in predictions
    np.save('class_indices.npy', generator_top.class_indices)

    # load the bottleneck features saved earlier
    train_data = np.load('bottleneck_features_train.npy')

    # get the class lebels for the training data, in the original order
    train_labels = generator_top.classes

    # https://github.com/fchollet/keras/issues/3467
    # convert the training labels to categorical vectors
    train_labels = to_categorical(train_labels, num_classes=num_classes)

    generator_top = datagen_top.flow_from_directory(
        validation_data_dir,
        target_size=(img_width, img_height),
        batch_size=batch_size,
        class_mode=None,
        shuffle=False)

    nb_validation_samples = len(generator_top.filenames)

    validation_data = np.load('bottleneck_features_validation.npy')

    validation_labels = generator_top.classes
    validation_labels = to_categorical(
        validation_labels, num_classes=num_classes)

    model = Sequential()
    model.add(Flatten(input_shape=train_data.shape[1:]))
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='sigmoid'))

    model.compile(optimizer='rmsprop',
                  loss='categorical_crossentropy', metrics=['accuracy'])

    history = model.fit(train_data, train_labels,
                        epochs=epochs,
                        batch_size=batch_size,
                        validation_data=(validation_data, validation_labels))

    model.save_weights(top_model_weights_path)

    (eval_loss, eval_accuracy) = model.evaluate(
        validation_data, validation_labels, batch_size=batch_size, verbose=1)

    print("[INFO] accuracy: {:.2f}%".format(eval_accuracy * 100))
    print("[INFO] Loss: {}".format(eval_loss))

    plt.figure(1)

    # summarize history for accuracy

    plt.subplot(211)
    plt.plot(history.history['acc'])
    plt.plot(history.history['val_acc'])
    plt.title('model accuracy')
    plt.ylabel('accuracy')
    plt.xlabel('epoch')
    plt.legend(['train', 'test'], loc='upper left')

    # summarize history for loss

    plt.subplot(212)
    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])
    plt.title('model loss')
    plt.ylabel('loss')
    plt.xlabel('epoch')
    plt.legend(['train', 'test'], loc='upper left')
    plt.show()


 def predict():
    # load the class_indices saved in the earlier step
    class_dictionary = np.load('class_indices.npy').item()

    num_classes = len(class_dictionary)

    # add the path to your test image below
    image_path = 'path/to/your/test_image'

    orig = cv2.imread(image_path)

    print("[INFO] loading and preprocessing image...")
    image = load_img(image_path, target_size=(224, 224))
    image = img_to_array(image)

    # important! otherwise the predictions will be '0'
    image = image / 255

    image = np.expand_dims(image, axis=0)

    # build the VGG16 network
    model = applications.VGG16(include_top=False, weights='imagenet')

    # get the bottleneck prediction from the pre-trained VGG16 model
    bottleneck_prediction = model.predict(image)

    # build top model
    model = Sequential()
    model.add(Flatten(input_shape=bottleneck_prediction.shape[1:]))
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes, activation='sigmoid'))

    model.load_weights(top_model_weights_path)

    # use the bottleneck prediction on the top model to get the final
    # classification
    class_predicted = model.predict_classes(bottleneck_prediction)

    probabilities = model.predict_proba(bottleneck_prediction)

    inID = class_predicted[0]

    inv_map = {v: k for k, v in class_dictionary.items()}

    label = inv_map[inID]

    # get the prediction label
    print("Image ID: {}, Label: {}".format(inID, label))

    # display the predictions with the image
    cv2.putText(orig, "Predicted: {}".format(label), (10, 30),
                cv2.FONT_HERSHEY_PLAIN, 1.5, (43, 99, 255), 2)

    cv2.imshow("Classification", orig)
    cv2.waitKey(0)
    cv2.destroyAllWindows()


 save_bottlebeck_features()
 train_top_model()
 predict()

 cv2.destroyAllWindows()
	'''
	Using Bottleneck Features for Multi-Class Classification in Keras

	We use this technique to build powerful (high accuracy without overfitting) Image Classification systems with small
	amount of training data.

	The full tutorial to get this code working can be found at the "Codes of Interest" Blog at the following link,
	https://www.codesofinterest.com/2017/08/bottleneck-features-multi-class-classification-keras.html

	Please go through the tutorial before attempting to run this code, as it explains how to setup your training data.

	The code was tested on Python 3.5, with the following library versions,
	Keras 2.0.6
	TensorFlow 1.2.1
	OpenCV 3.2.0

	This should work with Theano as well, but untested.
	'''
	import numpy as np
	from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
	from keras.models import Sequential
	from keras.layers import Dropout, Flatten, Dense
	from keras import applications
	from keras.utils.np_utils import to_categorical
	import matplotlib.pyplot as plt
	import math
	import cv2

	# dimensions of our images.
	img_width, img_height = 224, 224

	top_model_weights_path = 'bottleneck_fc_model.h5'
	train_data_dir = 'data/train'
	validation_data_dir = 'data/validation'

	# number of epochs to train top model
	epochs = 50
	# batch size used by flow_from_directory and predict_generator
	batch_size = 16


	def save_bottlebeck_features():
	# build the VGG16 network
	model = applications.VGG16(include_top=False, weights='imagenet')

	datagen = ImageDataGenerator(rescale=1. / 255)

	generator = datagen.flow_from_directory(
	train_data_dir,
	target_size=(img_width, img_height),
	batch_size=batch_size,
	class_mode=None,
	shuffle=False)

	print(len(generator.filenames))
	print(generator.class_indices)
	print(len(generator.class_indices))

	nb_train_samples = len(generator.filenames)
	num_classes = len(generator.class_indices)

	predict_size_train = int(math.ceil(nb_train_samples / batch_size))

	bottleneck_features_train = model.predict_generator(
	generator, predict_size_train)

	np.save('bottleneck_features_train.npy', bottleneck_features_train)

	generator = datagen.flow_from_directory(
	validation_data_dir,
	target_size=(img_width, img_height),
	batch_size=batch_size,
	class_mode=None,
	shuffle=False)

	nb_validation_samples = len(generator.filenames)

	predict_size_validation = int(
	math.ceil(nb_validation_samples / batch_size))

	bottleneck_features_validation = model.predict_generator(
	generator, predict_size_validation)

	np.save('bottleneck_features_validation.npy',
	bottleneck_features_validation)


	def train_top_model():
	datagen_top = ImageDataGenerator(rescale=1. / 255)
	generator_top = datagen_top.flow_from_directory(
	train_data_dir,
	target_size=(img_width, img_height),
	batch_size=batch_size,
	class_mode='categorical',
	shuffle=False)

	nb_train_samples = len(generator_top.filenames)
	num_classes = len(generator_top.class_indices)

	# save the class indices to use use later in predictions
	np.save('class_indices.npy', generator_top.class_indices)

	# load the bottleneck features saved earlier
	train_data = np.load('bottleneck_features_train.npy')

	# get the class lebels for the training data, in the original order
	train_labels = generator_top.classes

	# https://github.com/fchollet/keras/issues/3467
	# convert the training labels to categorical vectors
	train_labels = to_categorical(train_labels, num_classes=num_classes)

	generator_top = datagen_top.flow_from_directory(
	validation_data_dir,
	target_size=(img_width, img_height),
	batch_size=batch_size,
	class_mode=None,
	shuffle=False)

	nb_validation_samples = len(generator_top.filenames)

	validation_data = np.load('bottleneck_features_validation.npy')

	validation_labels = generator_top.classes
	validation_labels = to_categorical(
	validation_labels, num_classes=num_classes)

	model = Sequential()
	model.add(Flatten(input_shape=train_data.shape[1:]))
	model.add(Dense(256, activation='relu'))
	model.add(Dropout(0.5))
	model.add(Dense(num_classes, activation='sigmoid'))

	model.compile(optimizer='rmsprop',
	loss='categorical_crossentropy', metrics=['accuracy'])

	history = model.fit(train_data, train_labels,
	epochs=epochs,
	batch_size=batch_size,
	validation_data=(validation_data, validation_labels))

	model.save_weights(top_model_weights_path)

	(eval_loss, eval_accuracy) = model.evaluate(
	validation_data, validation_labels, batch_size=batch_size, verbose=1)

	print("[INFO] accuracy: {:.2f}%".format(eval_accuracy * 100))
	print("[INFO] Loss: {}".format(eval_loss))

	plt.figure(1)

	# summarize history for accuracy

	plt.subplot(211)
	plt.plot(history.history['acc'])
	plt.plot(history.history['val_acc'])
	plt.title('model accuracy')
	plt.ylabel('accuracy')
	plt.xlabel('epoch')
	plt.legend(['train', 'test'], loc='upper left')

	# summarize history for loss

	plt.subplot(212)
	plt.plot(history.history['loss'])
	plt.plot(history.history['val_loss'])
	plt.title('model loss')
	plt.ylabel('loss')
	plt.xlabel('epoch')
	plt.legend(['train', 'test'], loc='upper left')
	plt.show()


	def predict():
	# load the class_indices saved in the earlier step
	class_dictionary = np.load('class_indices.npy').item()

	num_classes = len(class_dictionary)

	# add the path to your test image below
	image_path = 'path/to/your/test_image'

	orig = cv2.imread(image_path)

	print("[INFO] loading and preprocessing image...")
	image = load_img(image_path, target_size=(224, 224))
	image = img_to_array(image)

	# important! otherwise the predictions will be '0'
	image = image / 255

	image = np.expand_dims(image, axis=0)

	# build the VGG16 network
	model = applications.VGG16(include_top=False, weights='imagenet')

	# get the bottleneck prediction from the pre-trained VGG16 model
	bottleneck_prediction = model.predict(image)

	# build top model
	model = Sequential()
	model.add(Flatten(input_shape=bottleneck_prediction.shape[1:]))
	model.add(Dense(256, activation='relu'))
	model.add(Dropout(0.5))
	model.add(Dense(num_classes, activation='sigmoid'))

	model.load_weights(top_model_weights_path)

	# use the bottleneck prediction on the top model to get the final
	# classification
	class_predicted = model.predict_classes(bottleneck_prediction)

	probabilities = model.predict_proba(bottleneck_prediction)

	inID = class_predicted[0]

	inv_map = {v: k for k, v in class_dictionary.items()}

	label = inv_map[inID]

	# get the prediction label
	print("Image ID: {}, Label: {}".format(inID, label))

	# display the predictions with the image
	cv2.putText(orig, "Predicted: {}".format(label), (10, 30),
	cv2.FONT_HERSHEY_PLAIN, 1.5, (43, 99, 255), 2)

	cv2.imshow("Classification", orig)
	cv2.waitKey(0)
	cv2.destroyAllWindows()


	save_bottlebeck_features()
	train_top_model()
	predict()

	cv2.destroyAllWindows()