from utils.prepare_data import get_training_data
from utils.prepare_plots import plot_results
from core.denoiser.build_simple_encoderdecoder_w_denoiser_model import simple_encoderdecoder_w_denoiser
if __name__ == "__main__":
profile_pngs_objs, midcurve_pngs_objs = get_training_data()
endec = simple_encoderdecoder_w_denoiser()
original_imgs, decoded_imgs = endec.simple_encoderdecoder_w_denoiser(
profile_pngs_objs, midcurve_pngs_objs)
plot_results(original_imgs, decoded_imgs)
from utils.prepare_data import get_training_data
from utils.prepare_plots import plot_results
from simpleencoderdecoder.build_simple_encoderdecoder_model import simple_encoderdecoder
import random
import numpy as np
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.
retrain_model = True
endec = simple_encoderdecoder()
endec.train(profile_gray_objs, midcurve_gray_objs, retrain_model)
original_profile_imgs, predicted_midcurve_imgs = endec.predict(
test_gray_images)
plot_results(original_profile_imgs, predicted_midcurve_imgs)
from utils.prepare_data import get_training_data
from utils.prepare_plots import plot_results
from denoiserencoderdecoder.build_denoiser_encoderdecoder_model import denoiser_encoderdecoder
from simpleencoderdecoder.build_simple_encoderdecoder_model import simple_encoderdecoder
from random import random
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()
retrain_model = True
denoiser.train(noisy_predicted_midcurve_images, midcurve_gray_objs,
retrain_model)
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)