def Run(self, img_path, model_name):
# config variables
weights = 'imagenet'
include_top = 0
train_path = 'jpg'
classfier_file = 'output/flowers_17/' + model_name + '/classifier.cpickle'
# create the pretrained models
# check for pretrained weight usage or not
# check for top layers to be included or not
if model_name == "vgg16":
from vgg16 import VGG16, preprocess_input
base_model = VGG16(weights=weights)
model = Model(inputs=base_model.input,
outputs=base_model.get_layer('fc1').output)
image_size = (224, 224)
elif model_name == "vgg19":
from vgg19 import VGG19, preprocess_input
base_model = VGG19(weights=weights)
model = Model(inputs=base_model.input,
outputs=base_model.get_layer('fc1').output)
image_size = (224, 224)
elif model_name == "resnet50":
from resnet50 import ResNet50, preprocess_input
base_model = ResNet50(weights=weights)
model = Model(inputs=base_model.input,
outputs=base_model.get_layer('avg_pool').output)
image_size = (224, 224)
elif model_name == "inceptionv3":
from inception_v3 import InceptionV3, preprocess_input
base_model = InceptionV3(weights=weights)
model = Model(inputs=base_model.input,
outputs=base_model.get_layer('mixed9').output)
image_size = (299, 299)
elif model_name == "xception":
from xception import Xception, preprocess_input
base_model = Xception(weights=weights)
model = Model(inputs=base_model.input,
outputs=base_model.get_layer('avg_pool').output)
image_size = (299, 299)
else:
base_model = None
img = image.load_img(img_path, target_size=image_size)
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = preprocess_input(img_array)
feature = model.predict(img_array)
feature = feature.flatten()
with open(classfier_file, 'rb') as f:
model2 = pickle.load(f)
pred = model2.predict(feature)
prob = model2.predict_proba(np.atleast_2d(feature))[0]
return pred, prob[0]
def load_model (args):
if args.output_layer == '0':
if args.model == 'inception':
model = InceptionV3(include_top=False, weights='imagenet', pooling=args.pooling)
preprocess_mode='tf'
elif args.model == 'xception':
model = Xception(include_top=False, weights='imagenet', pooling=args.pooling)
preprocess_mode='tf'
elif args.model == 'inceptionresnet':
model = InceptionResNetV2(include_top=False, weights='imagenet', pooling=args.pooling)
preprocess_mode='tf'
elif args.model == 'mobilenet':
model = MobileNet(include_top=False, weights='imagenet', pooling=args.pooling)
preprocess_mode='tf'
elif args.model == 'mobilenet2':
model = MobileNetV2(include_top=False, weights='imagenet', pooling=args.pooling)
preprocess_mode='tf'
elif args.model == 'nasnet':
model = NASNetLarge(include_top=False, weights='imagenet', pooling=args.pooling)
preprocess_mode='tf'
elif args.model == 'resnet':
model = ResNet50(include_top=False, weights='imagenet', pooling=args.pooling)
preprocess_mode='caffe'
elif args.model == 'vgg16':
model = VGG16(include_top=False, weights='imagenet', pooling=args.pooling)
preprocess_mode='caffe'
elif args.model == 'vgg19':
model = VGG19(include_top=False, weights='imagenet', pooling=args.pooling)
preprocess_mode='caffe'
else:
print ("Model not found")
return 0
else:
if args.model == 'inception':
base_model = InceptionV3(include_top=False, weights='imagenet', pooling=args.pooling)
model = Model(input=base_model.input, output=base_model.get_layer(args.output_layer).output)
preprocess_mode='tf'
elif args.model == 'xception':
base_model = Xception(include_top=False, weights='imagenet', pooling=args.pooling)
model = Model(input=base_model.input, output=base_model.get_layer(args.output_layer).output)
preprocess_mode='tf'
elif args.model == 'inceptionresnet':
base_model = InceptionResNetV2(include_top=False, weights='imagenet', pooling=args.pooling)
model = Model(input=base_model.input, output=base_model.get_layer(args.output_layer).output)
preprocess_mode='tf'
elif args.model == 'mobilenet':
base_model = MobileNet(include_top=False, weights='imagenet', pooling=args.pooling)
model = Model(input=base_model.input, output=base_model.get_layer(args.output_layer).output)
preprocess_mode='tf'
elif args.model == 'mobilenet2':
base_model = MobileNetV2(include_top=False, weights='imagenet', pooling=args.pooling)
model = Model(input=base_model.input, output=base_model.get_layer(args.output_layer).output)
preprocess_mode='tf'
elif args.model == 'nasnet':
base_model = NASNetLarge(include_top=False, weights='imagenet', pooling=args.pooling)
model = Model(input=base_model.input, output=base_model.get_layer(args.output_layer).output)
preprocess_mode='tf'
elif args.model == 'resnet':
base_model = ResNet50(include_top=False, weights='imagenet', pooling=args.pooling)
model = Model(input=base_model.input, output=base_model.get_layer(args.output_layer).output)
preprocess_mode='caffe'
elif args.model == 'vgg16':
base_model = VGG16(include_top=False, weights='imagenet', pooling=args.pooling)
model = Model(input=base_model.input, output=base_model.get_layer(args.output_layer).output)
preprocess_mode='caffe'
elif args.model == 'vgg19':
base_model = VGG19(include_top=False, weights='imagenet', pooling=args.pooling)
model = Model(input=base_model.input, output=base_model.get_layer(args.output_layer).output)
preprocess_mode='caffe'
else:
print ("Model not found")
return 0
return model,preprocess_mode
outputs=base_model.get_layer('fc1').output)
image_size = (224, 224)
elif model_name == "resnet50":
base_model = ResNet50(weights=weights)
model = Model(inputs=base_model.input,
outputs=base_model.get_layer('flatten').output)
image_size = (224, 224)
elif model_name == "inceptionv3":
base_model = InceptionV3(weights=weights)
model = Model(inputs=base_model.input,
outputs=base_model.get_layer('mixed9').output)
image_size = (299, 299)
elif model_name == "xception":
base_model = Xception(weights=weights)
model = Model(inputs=base_model.input,
outputs=base_model.get_layer('avg_pool').output)
image_size = (299, 299)
else:
base_model = None
# In[ ]:
# path to training dataset
train_labels = sorted(os.listdir(train_path))
# variables to hold features and labels
features = []
labels = []
label = 0
i = 0
image_size = (224, 224)
elif model_name == "vgg19":
base_model = VGG19(weights=weights)
model = Model(inputs=base_model.input, outputs=base_model.get_layer('fc1').output)
image_size = (224, 224)
elif model_name == "resnet50":
base_model = ResNet50(weights=weights)
model = Model(inputs=base_model.input, outputs=base_model.get_layer('flatten').output)
image_size = (224, 224)
elif model_name == "inceptionv3":
base_model = InceptionV3(weights=weights)
model = Model(inputs=base_model.input, outputs=base_model.get_layer('mixed9').output)
image_size = (299, 299)
elif model_name == "xception":
base_model = Xception(weights=weights)
model = Model(inputs=base_model.input, outputs=base_model.get_layer('avg_pool').output)
image_size = (299, 299)
else:
base_model = None
# In[10]:
# loop over all the labels in the folder
for label in train_labels:
cur_path = train_path + "/" + label
for image_path in glob.glob(cur_path):
img = image.load_img(image_path, target_size=image_size)
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
