def main():
img_size = 128
classSize = 5000
num_epochs = 15
# Loading Data
print("\nImporting data..")
food_files = ImageTools.parseImagePaths('./img/food/')
sandwich_files = ImageTools.parseImagePaths('./img/sandwich/')
print("\t..done.\n")
print("\nAssigning Labels, Generating more images via transformation..")
print("\tParsing/Labeling foods (sandwiches exclusive)..")
food_x, food_y = ImageTools.expandClass(food_files, 0, classSize, img_size)
print("\t\t..done.")
print("\tParsing/Labeling sandwiches..")
sandwich_x, sandwich_y = ImageTools.expandClass(sandwich_files, 1,
classSize, img_size)
print("\t\t..done.\n")
# Arranging
X = np.array(food_x + sandwich_x)
y = np.array(food_y + sandwich_y)
# Greyscaling and normalizing inputs to reduce features and improve comparability
print("\nGreyscaling and Normalizing Images..")
X = ImageTools.greyscaleImgs(X)
X = ImageTools.normalizeImgs(X)
print("\t..done.\n")
y = to_categorical(y)
# Train n' test:
print("\nSplitting data into training and testing..")
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=np.random.randint(0, 100))
print("\t..done.\n")
print("\tCalling model..")
model = network(img_size) # Calling of CNN
model.compile('adam', 'categorical_crossentropy', ['accuracy'])
print("\t..done.\n")
print("\nTraining Model..")
model.fit(X_train, y_train, nb_epoch=num_epochs, validation_split=0.1)
print("\t..done.\n")
# Saving model
print("\nPickling and saving model as 'model.pkl'...")
modelsave = open('model.pkl', 'wb')
pickle.dump(model, modelsave)
modelsave.close()
print("\t..done.\n")
def main():
img_size = 128
classSize = 2000
# Loading model from .pkl
model_file = open('model.pkl', 'rb')
model = pickle.load(model_file)
# Loading data
print("\nImporting data..")
hotdog_files = ImageTools.parseImagePaths('./img/hotdog/')
print("\t..done.\n")
# Preprocess the hotdog files, just like what was done in trainModel.py
# note that the class label isn't necessary, as that is what we're trying to determine.
print("\nGreyscaling and Normalizing Images..")
x, _ = ImageTools.expandClass(hotdog_files, 0, classSize, img_size)
x = np.array(x)
x = ImageTools.greyscaleImgs(x)
x = ImageTools.normalizeImgs(x)
print("\t..done.\n")
# Generating results from the model:
results = model.predict(x)
mean = np.mean(results)
stddev = np.std(results)
print("--")
print("'Is a hotdog a sandwich?''")
print("RESULTS:")
print("\tMean: {}".format(mean))
print("\tStandard Deviation: {}".format(stddev))
print("--")