3extract_features.py

# filter warnings
import warnings
warnings.simplefilter(action="ignore", category=FutureWarning)

# keras imports
from keras.applications.vgg16 import VGG16, preprocess_input
from keras.applications.vgg19 import VGG19, preprocess_input
from keras.applications.xception import Xception, preprocess_input
from keras.applications.resnet50 import ResNet50, preprocess_input
from keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input
from keras.applications.mobilenet import MobileNet, preprocess_input
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.preprocessing import image
from keras.models import Model
from keras.models import model_from_json
from keras.layers import Input

# other imports
from sklearn.preprocessing import LabelEncoder
import numpy as np
import glob
import cv2
import h5py
import os
import json
import datetime
import time

# load the user configs
with open('conf.json') as f:    
  config = json.load(f)

# config variables
model_name    = config["model"]
weights     = config["weights"]
include_top   = config["include_top"]
train_path    = config["train_path"]
features_path   = config["features_path"]
labels_path   = config["labels_path"]
test_size     = config["test_size"]
results     = config["results"]
model_path    = config["model_path"]

# start time
print("[STATUS] start time - {}".format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M")))
start = time.time()

# create the pretrained models
# check for pretrained weight usage or not
# check for top layers to be included or not
if model_name == "vgg16":
  base_model = VGG16(weights=weights)
  model = Model(input=base_model.input, output=base_model.get_layer('fc1').output)
  image_size = (224, 224)
elif model_name == "vgg19":
  base_model = VGG19(weights=weights)
  model = Model(input=base_model.input, output=base_model.get_layer('fc1').output)
  image_size = (224, 224)
elif model_name == "resnet50":
  base_model = ResNet50(weights=weights)
  model = Model(input=base_model.input, output=base_model.get_layer('flatten').output)
  image_size = (224, 224)
elif model_name == "inceptionv3":
  base_model = InceptionV3(include_top=include_top, weights=weights, input_tensor=Input(shape=(299,299,3)))
  model = Model(input=base_model.input, output=base_model.layers[-1].output)
  image_size = (299, 299)
elif model_name == "inceptionresnetv2":
  base_model = InceptionResNetV2(include_top=include_top, weights=weights, input_tensor=Input(shape=(299,299,3)))
  model = Model(input=base_model.input, output=base_model.get_layer('custom').output)
  image_size = (299, 299)
elif model_name == "mobilenet":
  base_model = MobileNet(include_top=include_top, weights=weights, input_tensor=Input(shape=(224,224,3)), input_shape=(224,224,3))
  model = Model(input=base_model.input, output=base_model.get_layer('custom').output)
  image_size = (224, 224)
elif model_name == "xception":
  base_model = Xception(weights=weights)
  model = Model(input=base_model.input, output=base_model.get_layer('avg_pool').output)
  image_size = (299, 299)
else:
  base_model = None

print("[INFO] successfully loaded base model and model...")

# path to training dataset
train_labels = os.listdir(train_path)

# encode the labels
print("[INFO] encoding labels...")
le = LabelEncoder()
le.fit([tl for tl in train_labels])

# variables to hold features and labels
features = []
labels   = []

# loop over all the labels in the folder
count = 1
for i, label in enumerate(train_labels):
  cur_path = train_path + "/" + label
  count = 1
  for image_path in glob.glob(cur_path + "/*.jpg"):
    img = image.load_img(image_path, target_size=image_size)
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    feature = model.predict(x)
    flat = feature.flatten()
    features.append(flat)
    labels.append(label)
    print("[INFO] processed - " + str(count))
    count += 1
  print("[INFO] completed label - " + label)

# encode the labels using LabelEncoder
le = LabelEncoder()
le_labels = le.fit_transform(labels)

# get the shape of training labels
print("[STATUS] training labels: {}".format(le_labels))
print("[STATUS] training labels shape: {}".format(le_labels.shape))

# save features and labels
h5f_data = h5py.File(features_path, 'w')
h5f_data.create_dataset('dataset_1', data=np.array(features))

h5f_label = h5py.File(labels_path, 'w')
h5f_label.create_dataset('dataset_1', data=np.array(le_labels))

h5f_data.close()
h5f_label.close()

# save weights
model.save_weights(model_path + str(test_size) + ".h5")
print("[STATUS] saved model and weights to disk..")

print("[STATUS] features and labels saved..")

# end time
end = time.time()
print("[STATUS] end time - {}".format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M")))