UNet Model Implemented Using Python, Google Colab, Tensorflow 1.15 and Keras API
Unet model to remove artifacts from the sparseview images.
This project uses Keras API and Tensorflow 1.15 to implement the original Unet model. Planning to upgrade code to tensorflow 2.X. Some part of code might be from Google course on Tensorflow with Laurence Moroney. Total training time 5 minutes.
GitHub link: https://github.com/npovey/research2020/tree/master/unet_15
youtube link: https://youtu.be/kaE7jQqIFI8
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
To keep using Tensoftlow 1.15 need the code below. By defaut the colab will start using Tnsorflow 2.X
# %tensorflow_version 1.x
# import tensorflow as tf
print(tf.__version__)
1.15.0
import os
import zipfile
import random
import keras
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from keras.layers import Concatenate
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Input, Dense
from keras.layers import BatchNormalization, Activation, Dropout, Subtract
from keras.models import Model
from glob import glob
from keras.layers.convolutional import Conv2DTranspose
from keras.layers import concatenate
If we have weights we can downoad weight without training a model. loaded_model.load_weights(“model.h5”)
# loaded_model.load_weights("model.h5")
print(len(os.listdir('/content/drive/My Drive/Colab Notebooks/CT_data/sparseview_60/train')))
print(len(os.listdir('/content/drive/My Drive/Colab Notebooks/CT_data/ndct/train')))
# 3600
# 3600
3600
3600
print(len(os.listdir('/content/drive/My Drive/Colab Notebooks/CT_data/sparseview_60/test/')))
print(len(os.listdir('/content/drive/My Drive/Colab Notebooks/CT_data/ndct/test/')))
# # 354
# # 354
354
354
ndct = sorted(glob('/content/drive/My Drive/Colab Notebooks/CT_data/ndct/train/*'))
ldct = sorted(glob('/content/drive/My Drive/Colab Notebooks/CT_data/sparseview_60/train/*'))
ndct_test = sorted(glob('/content/drive/My Drive/Colab Notebooks/CT_data/ndct/test/*'))
ldct_test = sorted(glob('/content/drive/My Drive/Colab Notebooks/CT_data/sparseview_60/test/*'))
print(len(ndct))
print(len(ldct))
print(len(ndct_test))
print(len(ldct_test))
3600
3600
354
354
The formulas below will be used to calculate the quality of the reconstruction. Higher PSNR generally indicates high quality of reconstruction.
def cal_psnr(im1, im2):
# assert pixel value range is 0-255 and type is uint8
mse = ((im1.astype(np.float) - im2.astype(np.float)) ** 2).mean()
maxval = np.amax(im1)
psnr = 10 * np.log10(maxval ** 2 / mse)
return psnr
def tf_psnr(im1, im2):
# assert pixel value range is 0-1
#mse = tf.losses.mean_squared_error(labels=im2 * 255.0, predictions=im1 * 255.0)
mse = tf.compat.v1.losses.mean_squared_error(labels=im2 * 255.0, predictions=im1 * 255.0)
return 10.0 * (tf.log(255.0 ** 2 / mse) / tf.log(10.0))
Useing less data: #for i in range(0, 600). Have 3600 in all. Processing 3600 images takes aprox. 20 minutes to run. But once we create .npy aray we don’t have to rerun this code in the future and we will have .npy form of our data. Colab has 11GB RAM limit.
ndct_imgs_train = []
# for i in range(0, len(ndct)):
for i in range(0, 600):
f = open(ndct[i],'rb')
a = np.fromfile(f, np.float32)
ndct_imgs_train.append(a)
f.close()
print("len(ndct_imgs_train)....: ",len(ndct_imgs_train))
#len(ndct_imgs_train)....: 3600
len(ndct_imgs_train)....: 600
Using different range to use less data to train #for i in range(0, 600). In all have 3600 images. It takes aprox. 20 minutes to process all 3600. But once we create .npy aray we don’t have to rerun this code in the future and we will have .npy form of our data.
ldct_imgs_train = []
# for i in range(0, len(ldct)):
for i in range(0, 600):
f = open(ldct[i],'rb')
a = np.fromfile(f, np.float32)
ldct_imgs_train.append(a)
f.close()
print("len(ldct_imgs_train)....: ",len(ldct_imgs_train))
len(ldct_imgs_train)....: 600
only using 100 images to test. In total we have 354 testing images.
ndct_imgs_test = []
# for i in range(0, len(ndct_test)):
for i in range(0, 100):
f = open(ndct_test[i],'rb')
a = np.fromfile(f, np.float32)
ndct_imgs_test.append(a)
f.close()
print("len(ndct_imgs_test)....: ",len(ndct_imgs_test))
len(ndct_imgs_test)....: 100
only using 100 images to test. In total we have 354 images
# load the image
ldct_imgs_test = []
# for i in range(0, len(ldct_test)):
for i in range(0, 100):
f = open(ldct_test[i],'rb')
a = np.fromfile(f, np.float32)
ldct_imgs_test.append(a)
f.close()
print("len(ldct_imgs_test)....: ",len(ldct_imgs_test))
len(ldct_imgs_test)....: 100
Must reshape images to train
ldct_train = np.asarray(ldct_imgs_train)
ndct_train = np.asarray(ndct_imgs_train)
ldct_train = ldct_train.reshape(600,512,512,1)
ndct_train = ndct_train.reshape(600,512,512,1)
ldct_test = np.asarray(ldct_imgs_test)
ndct_test = np.asarray(ndct_imgs_test)
# ldct_test = ldct_test.reshape(len(ldct_imgs_test),512,512,1)
# ndct_test = ndct_test.reshape(len(ldct_imgs_test),512,512,1)
ldct_test = ldct_test.reshape(100,512,512,1)
ndct_test = ndct_test.reshape(100,512,512,1)
print(ldct_train.shape)
print(ndct_train.shape)
print(ldct_test.shape)
print(ndct_test.shape)
(600, 512, 512, 1)
(600, 512, 512, 1)
(100, 512, 512, 1)
(100, 512, 512, 1)
# np.save('sparseview_60_train_600', ldct_train) # save the file as "sparseview_60_train.npy"
# np.save('ndct_train_600', ndct_train) # save the file as "ndct_train.npy"
# np.save('sparseview_60_test_100', ldct_test) # save the file as "sparseview_60_test.npy"
# np.save('ndct_test_100', ndct_test) # save the file as "ndct_test.npy"
np.save('/content/drive/My Drive/Colab Notebooks/unet_15/sparseview_60_train_600', ldct_train) # save the file as "sparseview_60_train.npy"
np.save('/content/drive/My Drive/Colab Notebooks/unet_15/ndct_train_600', ndct_train) # save the file as "ndct_train.npy"
np.save('/content/drive/My Drive/Colab Notebooks/unet_15/sparseview_60_test_100', ldct_test) # save the file as "sparseview_60_test.npy"
np.save('/content/drive/My Drive/Colab Notebooks/unet_15/ndct_test_100', ndct_test) # save the file as "ndct_test.npy"
sparseview_60_train = np.load('/content/drive/My Drive/Colab Notebooks/unet_15/sparseview_60_train_600.npy') # loads saved array into variable sparseview_60_train.
ndct_train = np.load('/content/drive/My Drive/Colab Notebooks/unet_15/ndct_train_600.npy') # loads saved array into variable ndct_train.
sparseview_60_test = np.load('/content/drive/My Drive/Colab Notebooks/unet_15/sparseview_60_test_100.npy') # loads saved array into variable sparseview_60_test.
ndct_test = np.load('/content/drive/My Drive/Colab Notebooks/unet_15/ndct_test_100.npy') # loads saved array into variable ndct_test.
# sparseview_60_train = np.load('sparseview_60_train_600.npy') # loads saved array into variable sparseview_60_train.
# ndct_train = np.load('ndct_train_600.npy') # loads saved array into variable ndct_train.
# sparseview_60_test = np.load('sparseview_60_test_100.npy') # loads saved array into variable sparseview_60_test.
# ndct_test = np.load('ndct_test_100.npy') # loads saved array into variable ndct_test.
Basic UNet model
inputs = Input((None, None,1))
c1 = Conv2D(8, (3, 3), activation='relu', padding='same') (inputs)
c1 = Conv2D(8, (3, 3), activation='relu', padding='same') (c1)
p1 = MaxPooling2D((2, 2)) (c1)
c2 = Conv2D(16, (3, 3), activation='relu', padding='same') (p1)
c2 = Conv2D(16, (3, 3), activation='relu', padding='same') (c2)
p2 = MaxPooling2D((2, 2)) (c2)
c3 = Conv2D(32, (3, 3), activation='relu', padding='same') (p2)
c3 = Conv2D(32, (3, 3), activation='relu', padding='same') (c3)
p3 = MaxPooling2D((2, 2)) (c3)
c4 = Conv2D(64, (3, 3), activation='relu', padding='same') (p3)
c4 = Conv2D(64, (3, 3), activation='relu', padding='same') (c4)
p4 = MaxPooling2D(pool_size=(2, 2)) (c4)
c5 = Conv2D(128, (3, 3), activation='relu', padding='same') (p4)
c5 = Conv2D(128, (3, 3), activation='relu', padding='same') (c5)
u6 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (c5)
u6 = concatenate([u6, c4])
c6 = Conv2D(64, (3, 3), activation='relu', padding='same') (u6)
c6 = Conv2D(64, (3, 3), activation='relu', padding='same') (c6)
u7 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c6)
u7 = concatenate([u7, c3])
c7 = Conv2D(32, (3, 3), activation='relu', padding='same') (u7)
c7 = Conv2D(32, (3, 3), activation='relu', padding='same') (c7)
u8 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c7)
u8 = concatenate([u8, c2])
c8 = Conv2D(16, (3, 3), activation='relu', padding='same') (u8)
c8 = Conv2D(16, (3, 3), activation='relu', padding='same') (c8)
u9 = Conv2DTranspose(8, (2, 2), strides=(2, 2), padding='same') (c8)
u9 = concatenate([u9, c1])
c9 = Conv2D(8, (3, 3), activation='relu', padding='same') (u9)
c9 = Conv2D(8, (3, 3), activation='relu', padding='same') (c9)
output_img = Conv2D(1, (1, 1)) (c9)
subtracted = Subtract()([inputs, output_img])
# subtracted = keras.layers.Subtract()([net, output_img])
unet_15_model = Model(inputs=[inputs], outputs=[subtracted])
unet_15_model.compile(optimizer='adam', loss='mse', metrics=[tf_psnr])
unet_15_model.summary()
Model: "model_4"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_4 (InputLayer) (None, None, None, 1 0
__________________________________________________________________________________________________
conv2d_58 (Conv2D) (None, None, None, 8 80 input_4[0][0]
__________________________________________________________________________________________________
conv2d_59 (Conv2D) (None, None, None, 8 584 conv2d_58[0][0]
__________________________________________________________________________________________________
max_pooling2d_13 (MaxPooling2D) (None, None, None, 8 0 conv2d_59[0][0]
__________________________________________________________________________________________________
conv2d_60 (Conv2D) (None, None, None, 1 1168 max_pooling2d_13[0][0]
__________________________________________________________________________________________________
conv2d_61 (Conv2D) (None, None, None, 1 2320 conv2d_60[0][0]
__________________________________________________________________________________________________
max_pooling2d_14 (MaxPooling2D) (None, None, None, 1 0 conv2d_61[0][0]
__________________________________________________________________________________________________
conv2d_62 (Conv2D) (None, None, None, 3 4640 max_pooling2d_14[0][0]
__________________________________________________________________________________________________
conv2d_63 (Conv2D) (None, None, None, 3 9248 conv2d_62[0][0]
__________________________________________________________________________________________________
max_pooling2d_15 (MaxPooling2D) (None, None, None, 3 0 conv2d_63[0][0]
__________________________________________________________________________________________________
conv2d_64 (Conv2D) (None, None, None, 6 18496 max_pooling2d_15[0][0]
__________________________________________________________________________________________________
conv2d_65 (Conv2D) (None, None, None, 6 36928 conv2d_64[0][0]
__________________________________________________________________________________________________
max_pooling2d_16 (MaxPooling2D) (None, None, None, 6 0 conv2d_65[0][0]
__________________________________________________________________________________________________
conv2d_66 (Conv2D) (None, None, None, 1 73856 max_pooling2d_16[0][0]
__________________________________________________________________________________________________
conv2d_67 (Conv2D) (None, None, None, 1 147584 conv2d_66[0][0]
__________________________________________________________________________________________________
conv2d_transpose_13 (Conv2DTran (None, None, None, 6 32832 conv2d_67[0][0]
__________________________________________________________________________________________________
concatenate_13 (Concatenate) (None, None, None, 1 0 conv2d_transpose_13[0][0]
conv2d_65[0][0]
__________________________________________________________________________________________________
conv2d_68 (Conv2D) (None, None, None, 6 73792 concatenate_13[0][0]
__________________________________________________________________________________________________
conv2d_69 (Conv2D) (None, None, None, 6 36928 conv2d_68[0][0]
__________________________________________________________________________________________________
conv2d_transpose_14 (Conv2DTran (None, None, None, 3 8224 conv2d_69[0][0]
__________________________________________________________________________________________________
concatenate_14 (Concatenate) (None, None, None, 6 0 conv2d_transpose_14[0][0]
conv2d_63[0][0]
__________________________________________________________________________________________________
conv2d_70 (Conv2D) (None, None, None, 3 18464 concatenate_14[0][0]
__________________________________________________________________________________________________
conv2d_71 (Conv2D) (None, None, None, 3 9248 conv2d_70[0][0]
__________________________________________________________________________________________________
conv2d_transpose_15 (Conv2DTran (None, None, None, 1 2064 conv2d_71[0][0]
__________________________________________________________________________________________________
concatenate_15 (Concatenate) (None, None, None, 3 0 conv2d_transpose_15[0][0]
conv2d_61[0][0]
__________________________________________________________________________________________________
conv2d_72 (Conv2D) (None, None, None, 1 4624 concatenate_15[0][0]
__________________________________________________________________________________________________
conv2d_73 (Conv2D) (None, None, None, 1 2320 conv2d_72[0][0]
__________________________________________________________________________________________________
conv2d_transpose_16 (Conv2DTran (None, None, None, 8 520 conv2d_73[0][0]
__________________________________________________________________________________________________
concatenate_16 (Concatenate) (None, None, None, 1 0 conv2d_transpose_16[0][0]
conv2d_59[0][0]
__________________________________________________________________________________________________
conv2d_74 (Conv2D) (None, None, None, 8 1160 concatenate_16[0][0]
__________________________________________________________________________________________________
conv2d_75 (Conv2D) (None, None, None, 8 584 conv2d_74[0][0]
__________________________________________________________________________________________________
conv2d_76 (Conv2D) (None, None, None, 1 9 conv2d_75[0][0]
__________________________________________________________________________________________________
subtract_4 (Subtract) (None, None, None, 1 0 input_4[0][0]
conv2d_76[0][0]
==================================================================================================
Total params: 485,673
Trainable params: 485,673
Non-trainable params: 0
__________________________________________________________________________________________________
history=unet_15_model.fit(sparseview_60_train, ndct_train, validation_split=0.05, batch_size=10, epochs=40)
Train on 570 samples, validate on 30 samples
Epoch 1/40
570/570 [==============================] - 8s 14ms/step - loss: 0.0011 - tf_psnr: 29.8746 - val_loss: 6.0528e-04 - val_tf_psnr: 32.1868
Epoch 2/40
570/570 [==============================] - 6s 11ms/step - loss: 6.4226e-04 - tf_psnr: 31.9359 - val_loss: 4.4407e-04 - val_tf_psnr: 33.5280
Epoch 3/40
570/570 [==============================] - 6s 11ms/step - loss: 5.5474e-04 - tf_psnr: 32.5680 - val_loss: 3.7929e-04 - val_tf_psnr: 34.2114
Epoch 4/40
570/570 [==============================] - 6s 11ms/step - loss: 5.1210e-04 - tf_psnr: 32.9381 - val_loss: 3.5278e-04 - val_tf_psnr: 34.5253
Epoch 5/40
570/570 [==============================] - 6s 11ms/step - loss: 4.2431e-04 - tf_psnr: 33.7333 - val_loss: 3.5429e-04 - val_tf_psnr: 34.5068
Epoch 6/40
570/570 [==============================] - 6s 11ms/step - loss: 3.5475e-04 - tf_psnr: 34.5162 - val_loss: 3.0247e-04 - val_tf_psnr: 35.1934
Epoch 7/40
570/570 [==============================] - 6s 11ms/step - loss: 3.0184e-04 - tf_psnr: 35.2188 - val_loss: 3.0599e-04 - val_tf_psnr: 35.1436
Epoch 8/40
570/570 [==============================] - 6s 11ms/step - loss: 2.7854e-04 - tf_psnr: 35.5686 - val_loss: 2.7163e-04 - val_tf_psnr: 35.6607
Epoch 9/40
570/570 [==============================] - 6s 11ms/step - loss: 2.5537e-04 - tf_psnr: 35.9455 - val_loss: 2.5682e-04 - val_tf_psnr: 35.9038
Epoch 10/40
570/570 [==============================] - 6s 11ms/step - loss: 2.3720e-04 - tf_psnr: 36.2658 - val_loss: 2.4841e-04 - val_tf_psnr: 36.0484
Epoch 11/40
570/570 [==============================] - 6s 11ms/step - loss: 2.2914e-04 - tf_psnr: 36.4175 - val_loss: 2.3741e-04 - val_tf_psnr: 36.2453
Epoch 12/40
570/570 [==============================] - 6s 11ms/step - loss: 2.2234e-04 - tf_psnr: 36.5442 - val_loss: 2.3879e-04 - val_tf_psnr: 36.2200
Epoch 13/40
570/570 [==============================] - 6s 11ms/step - loss: 2.1329e-04 - tf_psnr: 36.7304 - val_loss: 2.4082e-04 - val_tf_psnr: 36.1833
Epoch 14/40
570/570 [==============================] - 6s 11ms/step - loss: 2.0434e-04 - tf_psnr: 36.9095 - val_loss: 2.2666e-04 - val_tf_psnr: 36.4464
Epoch 15/40
570/570 [==============================] - 6s 11ms/step - loss: 2.0087e-04 - tf_psnr: 36.9880 - val_loss: 2.2676e-04 - val_tf_psnr: 36.4444
Epoch 16/40
570/570 [==============================] - 6s 11ms/step - loss: 1.9180e-04 - tf_psnr: 37.1796 - val_loss: 2.2391e-04 - val_tf_psnr: 36.4994
Epoch 17/40
570/570 [==============================] - 6s 11ms/step - loss: 1.8997e-04 - tf_psnr: 37.2328 - val_loss: 2.1857e-04 - val_tf_psnr: 36.6044
Epoch 18/40
570/570 [==============================] - 6s 11ms/step - loss: 1.8650e-04 - tf_psnr: 37.3124 - val_loss: 2.1908e-04 - val_tf_psnr: 36.5942
Epoch 19/40
570/570 [==============================] - 6s 11ms/step - loss: 1.8018e-04 - tf_psnr: 37.4578 - val_loss: 2.1413e-04 - val_tf_psnr: 36.6933
Epoch 20/40
570/570 [==============================] - 6s 11ms/step - loss: 1.8042e-04 - tf_psnr: 37.4549 - val_loss: 2.1338e-04 - val_tf_psnr: 36.7085
Epoch 21/40
570/570 [==============================] - 6s 11ms/step - loss: 1.7169e-04 - tf_psnr: 37.6727 - val_loss: 2.1194e-04 - val_tf_psnr: 36.7379
Epoch 22/40
570/570 [==============================] - 6s 11ms/step - loss: 1.6985e-04 - tf_psnr: 37.7154 - val_loss: 2.0944e-04 - val_tf_psnr: 36.7894
Epoch 23/40
570/570 [==============================] - 6s 11ms/step - loss: 1.6716e-04 - tf_psnr: 37.7864 - val_loss: 2.0763e-04 - val_tf_psnr: 36.8274
Epoch 24/40
570/570 [==============================] - 6s 11ms/step - loss: 1.6385e-04 - tf_psnr: 37.8715 - val_loss: 2.0485e-04 - val_tf_psnr: 36.8857
Epoch 25/40
570/570 [==============================] - 6s 11ms/step - loss: 1.6253e-04 - tf_psnr: 37.9081 - val_loss: 2.0259e-04 - val_tf_psnr: 36.9339
Epoch 26/40
570/570 [==============================] - 6s 11ms/step - loss: 1.5754e-04 - tf_psnr: 38.0414 - val_loss: 2.0225e-04 - val_tf_psnr: 36.9414
Epoch 27/40
570/570 [==============================] - 6s 11ms/step - loss: 1.5816e-04 - tf_psnr: 38.0354 - val_loss: 2.1229e-04 - val_tf_psnr: 36.7308
Epoch 28/40
570/570 [==============================] - 6s 11ms/step - loss: 1.5898e-04 - tf_psnr: 38.0002 - val_loss: 1.9912e-04 - val_tf_psnr: 37.0088
Epoch 29/40
570/570 [==============================] - 6s 11ms/step - loss: 1.4925e-04 - tf_psnr: 38.2711 - val_loss: 1.9682e-04 - val_tf_psnr: 37.0593
Epoch 30/40
570/570 [==============================] - 6s 11ms/step - loss: 1.4811e-04 - tf_psnr: 38.3144 - val_loss: 1.9958e-04 - val_tf_psnr: 36.9988
Epoch 31/40
570/570 [==============================] - 6s 11ms/step - loss: 1.4744e-04 - tf_psnr: 38.3285 - val_loss: 2.0227e-04 - val_tf_psnr: 36.9408
Epoch 32/40
570/570 [==============================] - 6s 11ms/step - loss: 1.4593e-04 - tf_psnr: 38.3782 - val_loss: 1.9899e-04 - val_tf_psnr: 37.0118
Epoch 33/40
570/570 [==============================] - 6s 11ms/step - loss: 1.4471e-04 - tf_psnr: 38.4073 - val_loss: 1.9264e-04 - val_tf_psnr: 37.1526
Epoch 34/40
570/570 [==============================] - 6s 11ms/step - loss: 1.3978e-04 - tf_psnr: 38.5577 - val_loss: 1.9053e-04 - val_tf_psnr: 37.2004
Epoch 35/40
570/570 [==============================] - 6s 11ms/step - loss: 1.3916e-04 - tf_psnr: 38.5758 - val_loss: 1.9076e-04 - val_tf_psnr: 37.1951
Epoch 36/40
570/570 [==============================] - 6s 11ms/step - loss: 1.3793e-04 - tf_psnr: 38.6208 - val_loss: 1.9459e-04 - val_tf_psnr: 37.1088
Epoch 37/40
570/570 [==============================] - 6s 11ms/step - loss: 1.3965e-04 - tf_psnr: 38.5716 - val_loss: 1.9142e-04 - val_tf_psnr: 37.1801
Epoch 38/40
570/570 [==============================] - 6s 11ms/step - loss: 1.3258e-04 - tf_psnr: 38.7885 - val_loss: 1.9386e-04 - val_tf_psnr: 37.1253
Epoch 39/40
570/570 [==============================] - 6s 11ms/step - loss: 1.3325e-04 - tf_psnr: 38.7649 - val_loss: 1.8809e-04 - val_tf_psnr: 37.2565
Epoch 40/40
570/570 [==============================] - 6s 11ms/step - loss: 1.3310e-04 - tf_psnr: 38.7767 - val_loss: 1.8868e-04 - val_tf_psnr: 37.2427
Save weights for the future reuse.
unet_15_model.save_weights("/content/drive/My Drive/Colab Notebooks/unet_15/model.h5")
Plotting PSNR values to see the trend.
import matplotlib.pyplot as plt
acc = history.history['tf_psnr']
val_acc = history.history['val_tf_psnr']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend(loc=0)
plt.figure()
plt.show()
<Figure size 432x288 with 0 Axes>
reconstructed = unet_15_model.predict(sparseview_60_test)
psnr = cal_psnr(ndct_test, reconstructed)
print("psnr 40 epochs.....",psnr)
psnr 40 epochs..... 34.96571490414341
Save images to a folder
from PIL import Image
a = reconstructed[0].reshape(512, 512)
scalef = np.amax(a)
a = np.clip(255 * a/scalef, 0, 255).astype('uint8')
#result = Image.fromarray((a * 255).astype(np.uint8))
result = Image.fromarray((a).astype(np.uint8))
# result.save('unet_15_600_0.png')
result.save('/content/drive/My Drive/Colab Notebooks/unet_15/reconstructed_unet_15_0.png')
a = reconstructed[99].reshape(512, 512)
scalef = np.amax(a)
a = np.clip(255 * a/scalef, 0, 255).astype('uint8')
#result = Image.fromarray((a * 255).astype(np.uint8))
result = Image.fromarray((a).astype(np.uint8))
# result.save('unet_15_600_99.png')
result.save('/content/drive/My Drive/Colab Notebooks/unet_15/reconstructed_unet_15_99.png')
Save original sparseview images to a folder
a = sparseview_60_test[0].reshape(512, 512)
scalef = np.amax(a)
a = np.clip(255 * a/scalef, 0, 255).astype('uint8')
#result = Image.fromarray((a * 255).astype(np.uint8))
result = Image.fromarray((a).astype(np.uint8))
# result.save('unet_15_600_0.png')
result.save('/content/drive/My Drive/Colab Notebooks/unet_15/sparseview_60_test_unet_15_0.png')
a = sparseview_60_test[99].reshape(512, 512)
scalef = np.amax(a)
a = np.clip(255 * a/scalef, 0, 255).astype('uint8')
#result = Image.fromarray((a * 255).astype(np.uint8))
result = Image.fromarray((a).astype(np.uint8))
# result.save('unet_15_600_99.png')
result.save('/content/drive/My Drive/Colab Notebooks/unet_15/sparseview_60_test_unet_15_99.png')
Plot model acuracy
# Plot training & validation accuracy values
plt.plot(history.history['tf_psnr'])
plt.plot(history.history['val_tf_psnr'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
Output images
img=mpimg.imread('/content/drive/My Drive/Colab Notebooks/unet_15/sparseview_60_test_unet_15_0.png')
imgplot = plt.imshow(img)
plt.show()
img=mpimg.imread('/content/drive/My Drive/Colab Notebooks/unet_15/reconstructed_unet_15_0.png')
imgplot = plt.imshow(img)
plt.show()
img=mpimg.imread('/content/drive/My Drive/Colab Notebooks/unet_15/sparseview_60_test_unet_15_99.png')
imgplot = plt.imshow(img)
plt.show()
img=mpimg.imread('/content/drive/My Drive/Colab Notebooks/unet_15/reconstructed_unet_15_99.png')
imgplot = plt.imshow(img)
plt.show()
