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090b-autoencoder_colorize_V0.1_predict.py
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090b-autoencoder_colorize_V0.1_predict.py
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#!/usr/bin/env python
__author__ = "Sreenivas Bhattiprolu"
__license__ = "Feel free to copy, I appreciate if you acknowledge Python for Microscopists"
# https://www.youtube.com/watch?v=EujccFRio7o
import tensorflow as tf
from keras.preprocessing.image import img_to_array, load_img
from skimage.transform import resize
from skimage.io import imsave, imshow
import numpy as np
from skimage.color import rgb2lab, lab2rgb
###########################################################
#Load saved model and test on images.
#colorize_autoencoder300.model is trained for 300 epochs
#
model = tf.keras.models.load_model('other_files/colorize_autoencoder_VGG16_10000.model',
custom_objects=None,
compile=True)
img1_color=[]
img1=img_to_array(load_img('images/barn.png'))
img1 = resize(img1 ,(256,256))
img1_color.append(img1)
img1_color = np.array(img1_color, dtype=float)
img1_color = rgb2lab(1.0/255*img1_color)[:,:,:,0]
img1_color = img1_color.reshape(img1_color.shape+(1,))
output1 = model.predict(img1_color)
output1 = output1*128
result = np.zeros((256, 256, 3))
result[:,:,0] = img1_color[0][:,:,0]
result[:,:,1:] = output1[0]
imshow(lab2rgb(result))
imsave("result.png", lab2rgb(result))