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jblitzar
/
github-python-corpus

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def get_single_image_x_RGB(self, image_path):
image_x = np.zeros((224, 224, 3))
binary_mask = np.zeros((28, 28))
# RGB
image_x_temp = cv2.imread(image_path)
#cv2.imwrite('temp.jpg', image_x_temp)
image_x = cv2.resize(image_x_temp, (224, 224))
# data augment from 'imgaug' --> Add (value=(-40,40), per_channel=True), GammaContrast (gamma=(0.5,1.5))
image_x_aug = seq.augment_image(image_x)
image_x_temp_gray = cv2.imread(image_path, 0)
image_x_temp_gray = cv2.resize(image_x_temp_gray, (28, 28))
for i in range(28):
for j in range(28):
if image_x_temp_gray[i,j]>0:
binary_mask[i,j]=1
else:
binary_mask[i,j]=0
return image_x_aug, binary_mask
def get_single_image_x(self, image_path):
image_x = np.zeros((224, 224, 3))
# RGB
image_x_temp = cv2.imread(image_path)
#cv2.imwrite('temp.jpg', image_x_temp)
image_x = cv2.resize(image_x_temp, (224, 224))
# data augment from 'imgaug' --> Add (value=(-40,40), per_channel=True), GammaContrast (gamma=(0.5,1.5))
image_x_aug = seq.augment_image(image_x)
return image_x_aug
# <FILESEP>
# Arda Mavi
import os
import sys
import tensorflow as tf
from keras import backend as K
from keras.models import model_from_json
def get_keras_model(model_path, weights_path):
# Reading model file:
with open(model_path, 'r') as model_file:
model = model_file.read()
# Readed model file to model:
model = model_from_json(model)
# Loading weights:
model.load_weights(weights_path)
print('Model Summary:')
print(model.summary())
return model
def keras_to_tf(tf_model_path):
saver = tf.train.Saver()
with K.get_session() as sess:
K.set_learning_phase(0)
saver.save(sess, tf_model_path)
return True
def tf_to_graph(tf_model_path, model_in, model_out, graph_path):
os.system('mvNCCompile {0}.meta -in {1} -on {2} -o {3}'.format(tf_model_path, model_in, model_out, graph_path))
return True
def keras_to_graph(model_path, model_in, model_out, weights_path, graph_path, take_tf_files = False):
# Getting Keras Model:
keras_model = get_keras_model(model_path, weights_path)
# Saving TensorFlow Model from Keras Model:
tf_model_path = './TF_Model/tf_model'
keras_to_tf(tf_model_path)
tf_to_graph(tf_model_path, model_in, model_out, graph_path)
if take_tf_files == False:
os.system('rm -rf ./TF_Model')
if __name__ == '__main__':
try:
model_path = sys.argv[1]
model_in = sys.argv[2]