self.x = profile_pngs_objs
self.y = midcurve_pngs_objs
self.autoencoder.fit(self.x, self.y,
epochs=self.epochs,
batch_size=5,
shuffle=True)
# 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()
endec = simple_encoderdecoder()
endec.train(profile_gray_objs, midcurve_gray_objs)
test_gray_images = random.sample(profile_gray_objs,5)
original_profile_imgs,predicted_midcurve_imgs = endec.predict(test_gray_images)
plot_results(original_profile_imgs,predicted_midcurve_imgs)
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 - 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))
for i in range(0, 128):
x_coord[:, i, 0] = i
y_coord[i, :, 0] = i
coords = np.append(x_coord, y_coord, axis=-1)
coords = (coords - 63.5) / 63.5 #Normalize [-1, 1]
#coords = np.expand_dims(coords, axis=0)
profile_pngs = []
for i in range(len(profile_pngs_objs)):
profile_pngs.append(np.append(profile_pngs_objs[i], coords, axis=-1))
profile_pngs = np.asarray(profile_pngs)
original_imgs, decoded_imgs = build_cnn_encoderdecoder_model(
profile_pngs, midcurve_pngs_objs)
plot_results(original_imgs, decoded_imgs)
self.denoiser_autoencoder.save(self.denoiser_autoencoder_model_pkl)
else:
# Save models
self.denoiser_autoencoder = load_model(
self.denoiser_autoencoder_model_pkl)
def predict(self, test_noisy_images):
png_profile_images = self.process_images(test_noisy_images)
encoded_imgs = self.denoiser_autoencoder.predict(png_profile_images)
decoded_imgs = self.denoiser_autoencoder.predict(encoded_imgs)
return test_noisy_images, decoded_imgs
if __name__ == "__main__":
profile_gray_objs, midcurve_gray_objs = get_training_data()
endec = simple_encoderdecoder()
endec.train(profile_gray_objs, midcurve_gray_objs)
original_profile_images, noisy_predicted_midcurve_images = endec.predict(
profile_gray_objs)
plot_results(original_profile_images[:5],
noisy_predicted_midcurve_images[:5])
denoiser = denoiser_encoderdecoder()
denoiser.train(noisy_predicted_midcurve_images, midcurve_gray_objs)
sample_noisy_midcurve_images = random.sample(
noisy_predicted_midcurve_images, 5)
original_noisy_midcurve_images, clean_predicted_midcurve_images = denoiser.predict(
sample_noisy_midcurve_images)
plot_results(original_noisy_midcurve_images,
clean_predicted_midcurve_images)