def inception_one_image(image_path):
img = image.load_img(image_path, target_size=(299, 299))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
predictions = new_inception.predict(x)
return predictions
def extract_features_keras(list_images):
## Extract the transfer values of the last Avg_pool layer of new_inception
## model. Weights are kept from new inception model
## A new model is made with Avg_pool as the last layer. Image are processed
## in the CNN and features extracted
nb_features = 2048
features = np.empty((len(list_images), nb_features))
model = Model(inputs=new_inception.input,
outputs=new_inception.get_layer('avg_pool').output)
for i, image_path in enumerate(list_images):
img = image.load_img(image_path, target_size=(299, 299))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
predictions = model.predict(x)
features[i, :] = np.squeeze(predictions)
return features
def preprocess_input(*args, **kwargs):
return inception_v3.preprocess_input(*args, **kwargs)
def preprocess_input(*args, **kwargs): return inception_v3.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
try:
test_data = pd.read_csv('../PreProcessing/test_data.csv')
submission = pd.read_csv('../DL3 Dataset/meta-data/sample_submission.csv')
test_array = np.load("../PreProcessing/test_images_array.npz")
model = load_model('../ModelCheckpoints/InceptionV3.01-0.908.hdf5')
except Exception as e:
print(e)
exit(0)
test_array = test_array['arr_0']
test_array=test_array/255
print('pre-processing input images\n')
test_array = preprocess_input(test_array)
test_img_name = list(test_data['img_name'].values)
print('Done. Prediction started')
l = []
for img_name in tqdm(submission.Image_name.values):
pred = model.predict(test_array[test_img_name.index(img_name)].reshape(1, 250,250, 3))
pred[pred >= 0.5] = 1
pred[pred < 0.5] = 0
pred = (pred[0]).tolist()
pred.insert(0, img_name)
l.append(pred)
prediction = pd.DataFrame()
prediction = prediction.append(l, ignore_index=True)
prediction.columns = submission.columns
print(prediction.head())
