def main(img_dir):
img = get_img(img_dir).astype('float32')
img /= 255.
# Getting model:
model_file = open('Data/Model/model.json', 'r')
model = model_file.read()
model_file.close()
model = model_from_json(model)
# Getting weights
model.load_weights("Data/Model/weights.h5")
Y = predict(model, img)
name = 'segmentated.jpg'
save_img(Y, name)
print('Segmentated image saved as ' + name)
def image_preprocess(self, img_dir):
img = get_img(img_dir)
X = np.zeros((1, 64, 64, 3), dtype='float64')
X[0] = img
return X
# Arda Mavi
import sys
import numpy as np
from get_dataset import get_img
from scipy.misc import imresize
from database_process import get_data
from keras.models import model_from_json
image_size = 64
channel_size = 1
num_class = 10 # Default value
def predict(model, X):
Y = model.predict(X)
Y = np.argmax(Y, axis=1)
Y = get_data('SELECT char FROM "id_char" WHERE id={0}'.format(Y[0]))
return Y
if __name__ == '__main__':
img_dir = sys.argv[1]
img = 1-np.array(get_img(img_dir)).astype('float32')/255.
img = img.reshape(1, image_size, image_size, channel_size)
# Getting model:
model_file = open('Data/Model/model.json', 'r')
model = model_file.read()
model_file.close()
model = model_from_json(model)
# Getting weights
model.load_weights("Data/Model/weights.h5")
print('Class:', predict(model, img)[0][0])
from keras.models import Sequential
from keras.models import model_from_json
def predict(model, X):
return model.predict(X)
if __name__ == '__main__':
import sys
img_dir = sys.argv[1]
from get_dataset import get_img
img = get_img(img_dir)
import numpy as np
X = np.zeros((1, 64, 64, 3), dtype='float64')
X[0] = img
# Getting model:
model_file = open('Data/Model/model.json', 'r')
model = model_file.read()
model_file.close()
model = model_from_json(model)
# Getting weights
model.load_weights("Data/Model/weights.h5")
print(
"\n\n<<--------------------------------------------------------------------->>\n\n"
)
print(
" Let me think ... "
)
print(
"\n\n<<--------------------------------------------------------------------->>\n\n"