def preprocess(image, size=(224, 224)):
"""Preprocess image.
:param image: PIL image
:param size: resize size
:return: numpy.ndarray
"""
# Resize image.
# image = image.resize(size, resample=Image.BICUBIC)
image = image.resize(size)
# Convert to numpy.
numpy_image = img_to_array(image)
# Reshape for MobileNet
numpy_image = np.expand_dims(numpy_image, axis=0)
numpy_image = preprocess_input(numpy_image, backend=tf.keras.backend)
return numpy_image
def preprocess_input(*args, **kwargs): return mobilenet.preprocess_input(*args, **kwargs)
def batch_preprocess_input(x_batch, network):
if network == 'Xception':
x_batch = xception.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'VGG16':
x_batch = vgg16.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'VGG19':
x_batch = vgg19.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'ResNet50' or network == 'ResNet101' or network == 'ResNet152':
x_batch = resnet.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'ResNet50V2' or network == 'ResNet101V2' or network == 'ResNet152V2':
x_batch = resnet_v2.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'ResNeXt50' or network == 'ResNeXt101':
x_batch = resnext.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'InceptionV3':
x_batch = inception_v3.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'InceptionResNetV2':
x_batch = inception_resnet_v2.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'MobileNet':
x_batch = mobilenet.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'MobileNetV2':
x_batch = mobilenet_v2.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'DenseNet121' or network == 'DenseNet169' or network == 'DenseNet201':
x_batch = densenet.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif network == 'NASNetMobile' or network == 'NASNetLarge':
x_batch = nasnet.preprocess_input(x_batch,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
elif 'EfficientNet' in network:
x_batch = efficientnet.preprocess_input(x_batch)
else:
return None
return x_batch