def post_processing_segmentation(test_image_path):
"""
post_processing_segmentation iterates through all the test results in
the given directory and retouches all of them using binary morphological
operators
param: test_image_path
return: void
"""
file_list = test_image_path
post_process_folder = os.path.join('postprocess')
create_dir(post_process_folder)
for iImage, file in enumerate(file_list):
tic = time.time()
path, filename = os.path.split(file)
basename = os.path.splitext(filename)[0]
savename = os.path.join(post_process_folder, basename + '.mat')
if not os.path.isfile(savename):
labels = retouch_segmentation(file)
matlab.save(savename, {'mask': labels})
duration = time.time() - tic
print('process %d / %d images (%.2f sec)' %
(iImage + 1, len(file_list), duration))
def validation_loop(sess,define_graph_output,train_val_variables,nValBatches,epoch,checkpoint_folder,flags):
"""
validation_loop contains the definition for the loop where the weights
are iteratively modified to optimize the loss function for the validation set
param: sess
param: define_graph_output
param: train_val_variables
param: nTrainBatches
param: epoch
param: checkpoint_folder
param: n_classes
return: none
"""
for step in xrange(nValBatches):
start_time = time.time()
# run session
loss_value_val, precision, recall, f1score, TP, FP, FN, TN,\
out_val, pred_val = \
sess.run([define_graph_output['loss_val']['avg_cross_entropy_log_loss'],
define_graph_output['accuracy_val_output']['precision'],
define_graph_output['accuracy_val_output']['recall'],
define_graph_output['accuracy_val_output']['f1score'],
define_graph_output['accuracy_val_output']['TP'],
define_graph_output['accuracy_val_output']['FP'],
define_graph_output['accuracy_val_output']['FN'],
define_graph_output['accuracy_val_output']['TN'],
define_graph_output['out_content_val'],
define_graph_output['sigmoid_all_val']
],
feed_dict={define_graph_output['keep_prob']: 1.0})
duration = time.time() - start_time
assert not np.isnan(loss_value_val), 'Model diverged with loss = NaN'
if step % 100 == 0:
matlab.save(os.path.join(checkpoint_folder, 'val_content.mat'),
{'out_val':out_val,'pred_val':pred_val})
# evaluate
if not np.isnan(loss_value_val):
train_val_variables['avg_val_loss'].append(loss_value_val)
for iclass in range(flags['n_classes']):
if not np.isnan(precision[iclass]):
train_val_variables['avg_val_precision'][iclass].append(precision[iclass])
if not np.isnan(recall[iclass]):
train_val_variables['avg_val_recall'][iclass].append(recall[iclass])
if not np.isnan(f1score[iclass]):
train_val_variables['avg_val_f1score'][iclass].append(f1score[iclass])
# print
format_str = ('%s: epoch %d, step %d/ %d (%.2f sec/step)')
print(format_str % (datetime.now(), epoch, step + 1, nValBatches, duration))
format_str = ('Validation Loss = %.2f, Precision = %.2f, Recall = %.2f, F1 = %.2f, ' +
'TP = %.2f, FP = %.2f, FN = %.2f, TN = %.2f')
for iclass in range(flags['n_classes']):
print(format_str % (loss_value_val, precision[iclass], recall[iclass],
f1score[iclass], TP[iclass], FP[iclass], FN[iclass], TN[iclass]))
def training_loop(sess, define_graph_output, train_val_variables,
nTrainBatches, epoch, checkpoint_folder):
for step in xrange(nTrainBatches):
start_time = time.time()
# run sessing
_, loss_value_train, precision, recall, f1score, TP, FP, FN, TN,\
out_train, pred_train = \
sess.run([define_graph_output['train_op'],
define_graph_output['loss_train']['avg_cross_entropy_log_loss'],
define_graph_output['accuracy_train_output']['precision'],
define_graph_output['accuracy_train_output']['recall'],
define_graph_output['accuracy_train_output']['f1score'],
define_graph_output['accuracy_train_output']['TP'],
define_graph_output['accuracy_train_output']['FP'],
define_graph_output['accuracy_train_output']['FN'],
define_graph_output['accuracy_train_output']['TN'],
define_graph_output['out_content_train'],
define_graph_output['sigmoid_all_train']
],
feed_dict={define_graph_output['keep_prob']: 0.5})
duration = time.time() - start_time
assert not np.isnan(loss_value_train), 'Model diverged with loss = NaN'
if step % 100 == 0:
matlab.save(os.path.join(checkpoint_folder, 'train_content.mat'), {
'out_train': out_train,
'pred_train': pred_train
})
# evaluate
if not np.isnan(loss_value_train):
train_val_variables['avg_train_loss'].append(loss_value_train)
if not np.isnan(precision):
train_val_variables['avg_train_precision'].append(precision)
if not np.isnan(recall):
train_val_variables['avg_train_recall'].append(recall)
if not np.isnan(f1score):
train_val_variables['avg_train_f1score'].append(f1score)
# print
format_str = ('%s: epoch %d, step %d/ %d (%.2f sec/step)')
print(format_str %
(datetime.now(), epoch, step + 1, nTrainBatches, duration))
format_str = (
'Training Loss = %.2f, Precision = %.2f, Recall = %.2f, F1 = %.2f, '
+ 'TP = %.2f, FP = %.2f, FN = %.2f, TN = %.2f')
print(format_str %
(loss_value_train, precision, recall, f1score, TP, FP, FN, TN))
def calculate_mean_variance_image(object_folder, flags):
key_values = list(flags['dict_path'].keys())
key_values.remove('group')
# Setup
network_stats_file_path = os.path.join(object_folder, 'checkpoint',
'network_stats.mat')
routine.create_dir(os.path.join(object_folder, 'checkpoint'))
mean_dict = {}
for key in key_values:
image_folder = os.path.join(object_folder, 'train', key)
list_images = glob.glob(
os.path.join(image_folder, '*' + flags['dict_ext'][key]))
image = KSimage.imread(list_images[0])
#if np.random.randint(2, size=1) == 1:
# image = np.flipud(image)
#if np.random.randint(2, size=1) == 1:
# image = np.fliplr(image)
image = np.float32(image)
mean_image = image
variance_image = np.zeros(shape=image.shape, dtype=np.float32)
for t, image_file in enumerate(list_images[1:]):
image = KSimage.imread(image_file)
# image = np.dstack((image[:, :, 2], image[:, :, 1], image[:, :, 0]))
#if np.random.randint(2, size=1) == 1:
# image = np.flipud(image)
#if np.random.randint(2, size=1) == 1:
# image = np.fliplr(image)
image = np.float32(image)
mean_dict[key + '_mean'] = (np.float32(t + 1) * mean_image +
image) / np.float32(t + 2)
mean_dict[key + '_var'] = np.float32(t + 1) / np.float32(t + 2) * variance_image \
+ np.float32(1) / np.float32(t + 1) * ((image - mean_image) ** 2)
print('calculate mean and variance: processing %d out of %d' %
(t + 2, len(list_images)))
matlab.save(network_stats_file_path, mean_dict)
def main(nth_fold, mode, flags, testdir):
"""
main trains, tests, or executes the model on the provided
data based on the specified preferences
param: nth_fold
param: mode
param: experiment_folder
param: image_ext
param: test_model
param: test_image_list
return: saves segmentation results to appropriate file/directory
"""
# check if cv or perm
list_dir = os.listdir(os.path.join(flags['experiment_folder']))
if ('cv' + str(nth_fold) in list_dir) and ('perm' + str(nth_fold)
in list_dir):
raise ValueError('Dangerous! You have both cv and perm on the path.')
elif 'cv' + str(nth_fold) in list_dir:
object_folder = os.path.join(flags['experiment_folder'],
'cv' + str(nth_fold))
elif 'perm' + str(nth_fold) in list_dir:
object_folder = os.path.join(flags['experiment_folder'],
'perm' + str(nth_fold))
else:
raise ValueError('No cv or perm folder!')
# Train model
if mode == 'train':
checkpoint_folder = os.path.join(object_folder, 'checkpoint')
network_stats_file_path = os.path.join(checkpoint_folder,
'network_stats.mat')
train_images_folder = os.path.join(object_folder, 'train', 'image')
if not os.path.isfile(network_stats_file_path):
list_images = glob.glob(
os.path.join(train_images_folder, '*' + flags['image_ext']))
print('calculating mean and variance image')
mean_image, variance_image = utils.calculate_mean_variance_image(
list_images)
routine.create_dir(checkpoint_folder)
matlab.save(network_stats_file_path, {
'mean_image': mean_image,
'variance_image': variance_image
})
tf_model_train.train(object_folder, flags)
# Test model on validation set
elif mode == 'test_model':
checkpointlist = glob.glob(
os.path.join(object_folder, 'checkpoint', 'model*meta'))
checkpointlist = [
file for file in checkpointlist if 'pretrain' not in file
]
temp = []
for filepath in checkpointlist:
basename = os.path.basename(filepath)
temp.append(int(float(basename.split('-')[-1].split('.')[0])))
temp = np.sort(temp)
model_path = os.path.join(
object_folder, 'checkpoint',
'model.ckpt-' + str(temp[flags['test_model']]))
print('use epoch %d : model %s' % (flags['test_model'], 'model.ckpt-' +
str(temp[flags['test_model']])))
test_images_list = flags['test_image_list']
filename_list = KScsv.read_csv(test_images_list)
tf_model_test.test(object_folder, model_path, filename_list, flags)
#Segment WSIs
elif mode == 'test_WSI':
checkpointlist = glob.glob(
os.path.join(object_folder, 'checkpoint', 'model*meta'))
checkpointlist = [
file for file in checkpointlist if 'pretrain' not in file
]
temp = []
for filepath in checkpointlist:
basename = os.path.basename(filepath)
temp.append(int(float(basename.split('-')[-1].split('.')[0])))
temp = np.sort(temp)
model_path = os.path.join(
object_folder, 'checkpoint',
'model.ckpt-' + str(temp[flags['test_model']]))
print('use epoch %d : model %s' % (flags['test_model'], 'model.ckpt-' +
str(temp[flags['test_model']])))
#should iterate over all subdirectories
paths = get_immediate_subdirectories(testdir)
list.sort(paths) #sort WSIs into ascending numerical order
#paths = paths[100:] #TODO: Enable based on which batch this code is running
print("TEST DIR: " + str(testdir))
for path in paths:
print(os.path.join(testdir, path))
if not os.path.isdir(
os.path.join(testdir, path + 'epiStromalSeg')
): #prevents this from being executed with exsiting directories
tf_model_test.testWSI(object_folder, model_path,
os.path.join(testdir, path), flags)
#TODO: uncomment to process only controls
#imageCSV = open(os.path.join('/data', 'avellal14', 'WSI_patches', 'BBD_NCC_Covariate_Outcome_KK_JH_modifiedWithPaths.csv'),'rb')
#reader = csv.reader(imageCSV)
#csvList = list(reader)
#patientId = path[:path.index('_')]
#caseControlList = next(subl for subl in csvList if patientId in subl)
#TODO: uncomment to process only cases
# if(caseControlList[1] == '1'): #only test the WSI if the image is indeed a case(1)
# tf_model_test.testWSI(object_folder, model_path, os.path.join(testdir,path), flags)
#Segment WSIs at patient level using data from CSV
elif mode == 'test_Case_Control':
checkpointlist = glob.glob(
os.path.join(object_folder, 'checkpoint', 'model*meta'))
checkpointlist = [
file for file in checkpointlist if 'pretrain' not in file
]
temp = []
for filepath in checkpointlist:
basename = os.path.basename(filepath)
temp.append(int(float(basename.split('-')[-1].split('.')[0])))
temp = np.sort(temp)
model_path = os.path.join(
object_folder, 'checkpoint',
'model.ckpt-' + str(temp[flags['test_model']]))
print('use epoch %d : model %s' % (flags['test_model'], 'model.ckpt-' +
str(temp[flags['test_model']])))
with open(
os.path.join('/home', 'avellal14', 'data', 'Adithya_BBD_NHS',
'NHS_BBD_CODE',
'casesAndMatchedControls224.csv')) as csvFile:
csvReader = csv.DictReader(csvFile)
for row in csvReader:
if (row['path'] == 'BBD_NCC_extractedat20x'
or row['path'] == 'BBD_NCC_extractedat20x_round2'):
testdir = os.path.join('/home', 'avellal14', 'data',
'Adithya_BBD_NHS', row['path'])
paths = get_subdirectories_by_patient(testdir, row['id'])
for path in paths:
print('CURRENT WSI BEING SEGMENTED',
os.path.join(testdir, path))
if not os.path.isdir(
os.path.join(testdir, path + '_cellSeg')
): #prevents this from being executed with exsiting directories
tf_model_test.testWSI(object_folder, model_path,
os.path.join(testdir, path),
flags)
def validation_loop(sess,define_graph_output,train_val_variables,nValBatches,epoch,checkpoint_folder,flags):
for step in xrange(nValBatches):
start_time = time.time()
# # random brightness field
# r0 = np.random.uniform(0, flags['size_input_patch'][0], 10)
# r1 = np.random.uniform(0, flags['size_input_patch'][1], 10)
#
# r0 = np.unique(np.rint(np.append(0, np.append(r0, flags['size_input_patch'][0])))).astype(np.int)
# r1 = np.unique(np.rint(np.append(0, np.append(r1, flags['size_input_patch'][1])))).astype(np.int)
#
# random_field_mat = np.zeros(shape=(flags['size_input_patch'][0], flags['size_input_patch'][1], 1),
# dtype=np.float32)
# for i in xrange(len(r0) - 1):
# for j in xrange(len(r1) - 1):
# random_field_mat[r0[i]:r0[i + 1], r1[j]:r1[j + 1], :] = np.random.uniform(0.5, 1.5, 1)
# run session
loss_value_val, precision, recall, f1score, TP, FP, FN, TN,\
out_val, pred_val = \
sess.run([define_graph_output['loss_val']['avg_cross_entropy_log_loss'],
define_graph_output['accuracy_val_output']['precision'],
define_graph_output['accuracy_val_output']['recall'],
define_graph_output['accuracy_val_output']['f1score'],
define_graph_output['accuracy_val_output']['TP'],
define_graph_output['accuracy_val_output']['FP'],
define_graph_output['accuracy_val_output']['FN'],
define_graph_output['accuracy_val_output']['TN'],
define_graph_output['out_content_val'],
define_graph_output['sigmoid_all_val']
],
feed_dict={define_graph_output['keep_prob']: 1.0
# define_graph_output['random_field']: random_field_mat
})
duration = time.time() - start_time
assert not np.isnan(loss_value_val), 'Model diverged with loss = NaN'
if step % 100 == 0:
matlab.save(os.path.join(checkpoint_folder, 'val_content.mat'),
{'out_val':out_val,'pred_val':pred_val})
# evaluate
if not np.isnan(loss_value_val):
train_val_variables['avg_val_loss'].append(loss_value_val)
for iclass in range(flags['n_classes']):
if not np.isnan(precision[iclass]):
train_val_variables['avg_val_precision'][iclass].append(precision[iclass])
if not np.isnan(recall[iclass]):
train_val_variables['avg_val_recall'][iclass].append(recall[iclass])
if not np.isnan(f1score[iclass]):
train_val_variables['avg_val_f1score'][iclass].append(f1score[iclass])
# print
format_str = ('%s: epoch %d, step %d/ %d (%.2f sec/step)')
print(format_str % (datetime.now(), epoch, step + 1, nValBatches, duration))
format_str = ('Validation Loss = %.2f, Precision = %.2f, Recall = %.2f, F1 = %.2f, ' +
'TP = %.2f, FP = %.2f, FN = %.2f, TN = %.2f')
for iclass in range(flags['n_classes']):
print(format_str % (loss_value_val, precision[iclass], recall[iclass],
f1score[iclass], TP[iclass], FP[iclass], FN[iclass], TN[iclass]))
def training_loop(sess, define_graph_output, train_val_variables,
nTrainBatches, epoch, checkpoint_folder, flags):
"""
training_loop contains the definition for the loop where the weights
are iteratively modified to optimize the loss function for the training set
param: sess
param: define_graph_output
param: train_val_variables
param: nTrainBatches
param: epoch
param: checkpoint_folder
param: n_classes
return: none
"""
print("into training loop")
for step in xrange(nTrainBatches):
start_time = time.time()
print("GOES INTO BLACK HOLE")
_, loss_value_train, precision, recall, f1score, TP, FP, FN, TN,\
out_train, pred_train,mean_image,variance_image = \
sess.run([define_graph_output['train_op'],
define_graph_output['loss_train']['avg_cross_entropy_log_loss'],
define_graph_output['accuracy_train_output']['precision'],
define_graph_output['accuracy_train_output']['recall'],
define_graph_output['accuracy_train_output']['f1score'],
define_graph_output['accuracy_train_output']['TP'],
define_graph_output['accuracy_train_output']['FP'],
define_graph_output['accuracy_train_output']['FN'],
define_graph_output['accuracy_train_output']['TN'],
define_graph_output['out_content_train'],
define_graph_output['sigmoid_all_train'],
define_graph_output['mean_image'],
define_graph_output['variance_image']
],
feed_dict={define_graph_output['keep_prob']: 0.5})
print("COMES OUT OF BLACK HOLE")
duration = time.time() - start_time
assert not np.isnan(loss_value_train), 'Model diverged with loss = NaN'
if step % 100 == 0:
matlab.save(os.path.join(checkpoint_folder, 'train_content.mat'), {
'out_train': out_train,
'pred_train': pred_train
})
# evaluate
if not np.isnan(loss_value_train):
train_val_variables['avg_train_loss'].append(loss_value_train)
for iclass in range(flags['n_classes']):
if not np.isnan(precision[iclass]):
train_val_variables['avg_train_precision'][iclass].append(
precision[iclass])
if not np.isnan(recall[iclass]):
train_val_variables['avg_train_recall'][iclass].append(
recall[iclass])
if not np.isnan(f1score[iclass]):
train_val_variables['avg_train_f1score'][iclass].append(
f1score[iclass])
# print
format_str = ('%s: epoch %d, step %d/ %d (%.2f sec/step)')
print(format_str %
(datetime.now(), epoch, step + 1, nTrainBatches, duration))
format_str = (
'Training Loss = %.2f, Precision = %.2f, Recall = %.2f, F1 = %.2f, '
+ 'TP = %.2f, FP = %.2f, FN = %.2f, TN = %.2f')
for iclass in range(flags['n_classes']):
#if(iclass ==3): iclass = 4
print(format_str % (loss_value_train, precision[iclass],
recall[iclass], f1score[iclass], TP[iclass],
FP[iclass], FN[iclass], TN[iclass]))
print("out of training loop")
