def main(argv=None):
keep_probability = tf.placeholder(
tf.float32, name="keep_probabilty") #Dropout probability
Sparse_Sampled_Image = tf.placeholder(
tf.float32, shape=[None, None, None, 3],
name="input_Sparse_image") #Input image sparsly sampled image
Full_Image = tf.placeholder(
tf.float32, shape=[None, None, None, 3],
name="Full_image") # Full image all pixels filled
ReconstructImage = BuildNet.inference(
Sparse_Sampled_Image, keep_probability, 3,
Vgg_Model_Dir) # Here the graph(net) is builded
loss = tf.reduce_mean(
tf.abs(ReconstructImage - Full_Image, name="L1_Loss")
) # Define loss function for training as the difference between reconstruct image and ground truth image
# tf.summary.scalar("L1_Loss", loss)
trainable_var = tf.trainable_variables()
train_op = train(loss, trainable_var)
#print("Setting up summary op...")
#summary_op = tf.summary.merge_all()
print("Reading images list")
TrainImages = [] #Train Image List
TrainImages += [
each for each in os.listdir(Train_Image_Dir)
if each.endswith('.PNG') or each.endswith('.JPG') or each.endswith(
'.TIF') or each.endswith('.GIF') or each.endswith('.png') or
each.endswith('.jpg') or each.endswith('.tif') or each.endswith('.gif')
] # Get list of training images
print('Number of Train images=' + str(len(TrainImages)))
#-------------------------Training Region-----------------------------------------------------------------------------------------------------------------------------
sess = tf.Session() #Start Tensorflow session
print("Setting up Saver...")
saver = tf.train.Saver()
# summary_writer = tf.summary.FileWriter(logs_dir, sess.graph)
sess.run(tf.global_variables_initializer())
#sess.run(tf.initialize_all_variables())
ckpt = tf.train.get_checkpoint_state(logs_dir)
if ckpt and ckpt.model_checkpoint_path: # if trained model exist restore it
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored...")
#---------------------------Start Training: Create loss files----------------------------------------------------------------------------------------------------------
Nimg = 0
f = open(
TrainLossTxtFile,
"w") # Create text file for writing the loss trough out the training
f.write("Iteration\tTrain_Loss\t Learning Rate=" + str(learning_rate))
f.close()
#-----------------------------------------------------------------------------------------------------------------
Epoch = 0
#..............Start Training loop: Main Training....................................................................
for itr in range(1, MAX_ITERATION + 1):
if Nimg >= len(TrainImages) - 1: # End of an epoch
Nimg = 0
random.shuffle(TrainImages) #Suffle images every epoch
Epoch += 1
print("Epoch " + str(Epoch) + " Completed")
#.....................Load images for training
batch_size = np.min([Batch_Size, len(TrainImages) - Nimg])
FullImages = np.zeros([batch_size, Im_Hight, Im_Width, 3],
dtype=np.int)
SparseSampledImages = np.zeros([batch_size, Im_Hight, Im_Width, 3],
dtype=np.int)
for fi in range(batch_size):
FullImages[fi], SparseSampledImages[fi] = ImageReader.LoadImages(
Train_Image_Dir + TrainImages[Nimg], Im_Hight, Im_Width,
SamplingRate)
Nimg += 1
#.......................Run one batch of training...............................................................................
feed_dict = {
Sparse_Sampled_Image: SparseSampledImages,
Full_Image: FullImages,
keep_probability: 0.4 + np.random.rand() * 0.6
} # Run one cycle of traning
sess.run(train_op, feed_dict=feed_dict)
#......................Write training set loss..........................................................................
if itr % 10 == 0:
feed_dict = {
Sparse_Sampled_Image: SparseSampledImages,
Full_Image: FullImages,
keep_probability: 1
}
train_loss = sess.run(loss, feed_dict=feed_dict)
print("Step: %d, Train_loss:%g " % (itr, train_loss))
# summary_writer.add_summary(summary_str, itr)
with open(TrainLossTxtFile,
"a") as f: #Write training loss for file
f.write("\n" + str(itr) + "\t" + str(train_loss))
f.close()
#....................Save Trained net (ones every 1000 training cycles...............................................
if itr % 200 == 0:
print("Saving Model")
saver.save(sess, logs_dir + "model.ckpt",
itr) # save trained model
def main(argv=None):
keep_probability = tf.placeholder(
tf.float32, name="keep_probabilty") #Dropout probability
Sparse_Sampled_Image = tf.placeholder(
tf.float32, shape=[None, None, None, 3],
name="input_Sparse_image") #Input image sparsly sampled image
ReconstructImage = BuildNet.inference(
Sparse_Sampled_Image, keep_probability, 3,
Vgg_Model_Dir) # Here the graph(net) is builded
print("Reading images list")
#---------------------Read list of image for recostruction------------------------------------------------------------
Images = [] #Train Image List
Images += [
each for each in os.listdir(Image_Dir)
if each.endswith('.PNG') or each.endswith('.JPG') or each.endswith(
'.TIF') or each.endswith('.GIF') or each.endswith('.png') or
each.endswith('.jpg') or each.endswith('.tif') or each.endswith('.gif')
] # Get list of training images
print('Number of images=' + str(len(Images)))
#-------------------------Load trained mode----------------------------------------------------------------------------------------------------------------------------
sess = tf.Session() #Start Tensorflow session
print("Setting up Saver...")
saver = tf.train.Saver()
# summary_writer = tf.summary.FileWriter(logs_dir, sess.graph)
sess.run(tf.global_variables_initializer())
#sess.run(tf.initialize_all_variables())
ckpt = tf.train.get_checkpoint_state(logs_dir)
if ckpt and ckpt.model_checkpoint_path: # if trained model exist restore it
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored...")
else:
print("Error no trained model found in log dir " + logs_dir +
"For creating trained model see: Train.py")
return
#..............Start image reconstruction....................................................................
for itr in range(len(Images)):
#.....................Load images for prediction-------------------------------------
print(str(itr) + ") Reconstructing: " + Image_Dir + Images[itr])
FullImage, SparseSampledImage = ImageReader.LoadImages(
Image_Dir + Images[itr], 0, 0, SamplingRate)
#.......................Run one prediction...............................................................................
feed_dict = {
Sparse_Sampled_Image: SparseSampledImage,
keep_probability: 1
} # Run one cycle of traning
ReconImage = sess.run(
ReconstructImage,
feed_dict=feed_dict) # run image reconstruction using network
#......................Save image..........................................................................
#ReconImage[ReconImage>255]=255
#ReconImage[ReconImage<0]=0
misc.imsave(
OUTPUT_Dir + "/" + Images[itr][0:-4] + "_Reconstructed" +
Images[itr][-4:], ReconImage[0])
misc.imsave(
OUTPUT_Dir + "/" + Images[itr][0:-4] + "_Original" +
Images[itr][-4:], FullImage[0])
misc.imsave(
OUTPUT_Dir + "/" + Images[itr][0:-4] + "_Sampled" +
Images[itr][-4:], SparseSampledImage[0])