def set_feature_model(cls, model_name):
if model_name == 'vgg16':
cls.feature_model = VGG16(weights='imagenet',
include_top=False,
pooling='avg')
cls.fmf = lambda self, x: preprocess_input_vgg16(x)
elif model_name == 'vgg19':
cls.feature_model = VGG19(weights='imagenet',
include_top=False,
pooling='avg')
cls.fmf = lambda self, x: preprocess_input_vgg19(x)
elif model_name == 'xception':
cls.feature_model = Xception(weights='imagenet',
include_top=False,
pooling='avg')
cls.fmf = lambda self, x: preprocess_input_xception(x)
elif model_name == 'resnet50':
cls.feature_model = ResNet50(weights='imagenet',
include_top=False,
pooling='avg')
cls.fmf = lambda self, x: preprocess_input_resnet50(x)
else:
raise ValueError("Unknown model")
def preprocess_input(x, model_name):
"""
Preprocess input in accordance to Keras preprocessing standards
:param x: Numpy array with values between 0 and 255
:param model_name: Name of NN model
:return: Numpy array with preprocessed values
"""
if model_name[:8] == 'resnet50':
return preprocess_input_resnet50(x.copy())
elif model_name[:5] == 'vgg19':
return preprocess_input_vgg19(x.copy())
elif model_name[:8] == 'xception':
return preprocess_input_xception(x.copy())
elif model_name[:6] == 'nasnet':
return preprocess_input_nasnet(x.copy())
elif model_name[:16] == 'inception_resnet':
return preprocess_input_inception_resnet(x.copy())
else:
raise Exception('No such model preprocessing defined!')
def preprocess_input(feature_extractor, img_data):
"""
Use to preprocess img before fit to model
Args:
feature_extractor: string, name of feature extractor
Return:
img_data: keras img, preprocessed img
"""
if feature_extractor == 'vgg16':
img_data = preprocess_input_vgg16(img_data)
elif feature_extractor == 'vgg19':
img_data = preprocess_input_vgg19(img_data)
elif feature_extractor == 'inception_v3':
img_data = preprocess_input_inception_v3(img_data)
elif feature_extractor == 'resnet50':
img_data = preprocess_input_resnet50(img_data)
else:
raise Exception('Select right feature exractor')
return img_data
def get_resnet50_network_with_images(file_paths):
tensors = convert_images_to_tensors(file_paths).astype('float32')
preprocessed_input = preprocess_input_resnet50(tensors)
return ResNet50(weights='imagenet', include_top=False).predict(preprocessed_input, batch_size=32)
def extract_Resnet50(file_paths):
tensors = paths_to_tensor(file_paths).astype('float32')
preprocessed_input = preprocess_input_resnet50(tensors)
return ResNet50(weights='imagenet',
include_top=False).predict(preprocessed_input,
batch_size=32)
def ResNet50_predict_labels(self, img_path):
# returns prediction vector for image located at img_path
img = preprocess_input_resnet50(self.path_to_tensor(img_path))
return np.argmax(self.ResNet50_model.predict(img))
def extract_Resnet50_single(img):
#tensors = paths_to_tensor(file_paths).astype('float32')
preprocessed_input = preprocess_input_resnet50(np.expand_dims(img.astype('float32'),axis=0))
return ResNet50(weights='imagenet', include_top=False).predict(preprocessed_input, batch_size=128)
def paths_to_tensor(img_paths):
list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
return np.vstack(list_of_tensors)
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
from keras.applications.vgg19 import VGG19
from keras.applications.vgg19 import preprocess_input as preprocess_input_vgg19
from keras.applications.resnet50 import ResNet50
from keras.applications.resnet50 import preprocess_input as preprocess_input_resnet50
train_vgg19 = VGG19(weights='imagenet', include_top=False).predict(preprocess_input_vgg19(train_tensors), batch_size=8)
valid_vgg19 = VGG19(weights='imagenet', include_top=False).predict(preprocess_input_vgg19(valid_tensors), batch_size=8)
train_resnet50 = ResNet50(weights='imagenet', include_top=False).predict(preprocess_input_resnet50(train_tensors), batch_size=8)
valid_resnet50 = ResNet50(weights='imagenet', include_top=False).predict(preprocess_input_resnet50(valid_tensors), batch_size=8)
np.save(open('train_vgg19.npy', 'wb'), train_vgg19)
np.save(open('valid_vgg19.npy', 'wb'), valid_vgg19)
np.save(open('train_resnet50.npy', 'wb'), train_resnet50)
np.save(open('valid_resnet50.npy', 'wb'), valid_resnet50)