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extract_features.py
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extract_features.py
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from keras.applications.resnet import ResNet50
from keras.applications.densenet import DenseNet121,DenseNet169,DenseNet201
from keras.applications.resnet import ResNet101, ResNet152
from keras.preprocessing import image
from keras.applications.resnet import preprocess_input
import numpy as np
import os
def extract_features(nn_model, fine_tune=False):
if fine_tune is True:
if nn_model == 'resnet50':
model = ResNet50(include_top=False, weights=None, pooling='avg')
elif nn_model == 'resnet101':
model = ResNet101(include_top=False, weights=None, pooling='avg')
elif nn_model == 'resnet152':
model = ResNet152(include_top=False, weights=None, pooling='avg')
elif nn_model == 'densenet121':
model = DenseNet121(include_top=False, weights=None, pooling='avg')
elif nn_model == 'densenet169':
model = DenseNet169(include_top=False, weights=None, pooling='avg')
elif nn_model == 'densenet201':
model = DenseNet201(include_top=False, weights=None, pooling='avg')
else:
raise NotImplementedError("The NN model is not implemented!")
model.load_weights('./finetune/' + nn_model + '/finetune_weights_50_epoch.h5', by_name=True)
else:
if nn_model == 'resnet50':
model = ResNet50(include_top=False, weights='imagenet', pooling='avg')
elif nn_model == 'resnet101':
model = ResNet101(include_top=False, weights='imagenet', pooling='avg')
elif nn_model == 'resnet152':
model = ResNet152(include_top=False, weights='imagenet', pooling='avg')
elif nn_model == 'densenet121':
model = DenseNet121(include_top=False, weights='imagenet', pooling='avg')
elif nn_model == 'densenet169':
model = DenseNet169(include_top=False, weights='imagenet', pooling='avg')
elif nn_model == 'densenet201':
model = DenseNet201(include_top=False, weights='imagenet', pooling='avg')
else:
raise NotImplementedError("The NN model is not implemented!")
ImgPath = './dataset/img'
ProcessedPath = './dataset/feature_'+nn_model
Lastlist = os.listdir(ImgPath)
sum = 0
for lastfolder in Lastlist:
LastPath = os.path.join(ImgPath,lastfolder)
savepath =os.path.join(ProcessedPath,lastfolder)
imagelist = os.listdir(LastPath)
for image1 in imagelist:
sum += 1
print(image1)
print('sum is ', sum)
image_pre, ext = os.path.splitext(image1)
imgfile = LastPath + '/'+ image1
img = image.load_img(imgfile, target_size=(64, 64))
x = image.img_to_array(img) # shape:(64,64,3)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x) # shape:(1,64,64,3)
print(x.shape)
want = model.predict(x)
print(np.shape(want)) # shape:(1,2048)
# normalize
a = np.min(want)
b = np.max(want)
want = (want-a)/(b-a)
if(not os.path.exists(savepath)):
os.makedirs(savepath)
np.save(os.path.join(savepath,image_pre+'.npz'),want)
if __name__ == '__main__':
nn_model = 'densenet169'
extract_features(nn_model)