# Save models
self.autoencoder.save(self.autoencoder_model_pkl)
self.encoder.save(self.encoder_model_pkl)
self.decoder.save(self.decoder_model_pkl)
else:
# Save models
self.autoencoder = load_model(self.autoencoder_model_pkl)
self.encoder = load_model(self.encoder_model_pkl)
self.decoder = load_model(self.decoder_model_pkl)
def predict(self, test_profile_images):
png_profile_images = self.process_images(test_profile_images)
encoded_imgs = self.encoder.predict(png_profile_images)
decoded_imgs = self.decoder.predict(encoded_imgs)
return test_profile_images, decoded_imgs
if __name__ == "__main__":
profile_gray_objs, midcurve_gray_objs = get_training_data()
test_gray_images = random.sample(profile_gray_objs, 5)
profile_gray_objs = np.asarray(profile_gray_objs) / 255.
midcurve_gray_objs = np.asarray(midcurve_gray_objs) / 255.
test_gray_images = np.asarray(test_gray_images) / 255.
endec = dense_encoderdecoder()
endec.train(profile_gray_objs, midcurve_gray_objs)
original_profile_imgs, predicted_midcurve_imgs = endec.predict(
test_gray_images)
plot_results(original_profile_imgs, predicted_midcurve_imgs)
import sys
#sys.path.append('...')
from utils.utils import get_training_data
from utils.utils import plot_results
from core.cnnencoderdecoder.build_cnn_encoderdecoder_model import cnn_encoderdecoder
import numpy as np
import random
if __name__ == "__main__":
profile_pngs_objs, midcurve_pngs_objs = get_training_data(size=(128, 128))
test_gray_images = random.sample(profile_pngs_objs, 5)
test_gray_images = np.expand_dims(np.asarray(test_gray_images),
axis=-1) / 255.
profile_pngs_objs = np.asarray(profile_pngs_objs)
midcurve_pngs_objs = np.asarray(midcurve_pngs_objs)
profile_pngs_objs = np.expand_dims(profile_pngs_objs, axis=-1)
midcurve_pngs_objs = np.expand_dims(midcurve_pngs_objs, axis=-1)
profile_pngs_objs = profile_pngs_objs / 255. #Normalize [0,1]
midcurve_pngs_objs = midcurve_pngs_objs / 255. #Normalize [0,1]
#profile_pngs_objs = (profile_pngs_objs - 127.5)/127.5 #Normalize [-1, 1]
#midcurve_pngs_objs = (midcurve_pngs_objs - 127.5)/127.5 #Normalize [-1, 1]
x_coord = np.zeros(shape=(128, 128, 1))
y_coord = np.zeros(shape=(128, 128, 1))
