def preprocess_data(infile):
images = tsa.read_data(INPUT_FOLDER + infile)
subject = infile.split('.')[0]
# transpose so that the slice is the first dimension shape(16, 620, 512)
images = images.transpose()
threat_zone_examples = []
# for each threat zone, loop through each image, mask off the zone and then crop it
for tz_num, threat_zone_x_crop_dims in enumerate(
zip(tsa.zone_slice_list, tsa.zone_crop_list)):
threat_zone = threat_zone_x_crop_dims[0]
crop_dims = threat_zone_x_crop_dims[1]
# get label
label = np.array(
tsa.get_subject_zone_label(
tz_num, tsa.get_subject_labels(STAGE1_LABELS, subject)))
for img_num, img in enumerate(images):
if threat_zone[img_num] is not None:
base_img = np.flipud(img)
rescaled_img = tsa.convert_to_grayscale(base_img)
high_contrast_img = tsa.spread_spectrum(rescaled_img)
masked_img = tsa.roi(high_contrast_img, threat_zone[img_num])
cropped_img = tsa.crop(masked_img, crop_dims[img_num])
normalized_img = tsa.normalize(cropped_img)
zero_centered_img = tsa.zero_center(normalized_img)
threat_zone_examples.append([[tz_num], zero_centered_img,
label])
for tz_num, tz in enumerate(tsa.zone_slice_list):
tz_examples_to_save = []
tz_examples = [
example for example in threat_zone_examples
if example[0] == [tz_num]
]
tz_examples_to_save.append([[features_label[1], features_label[2]]
for features_label in tz_examples])
#save batch
np.save(
PROCESSED_FOLDER + 'input-tz{}-{}-{}.npy'.format(
tz_num + 1, len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1])), tz_examples_to_save)
def preprocess_tsa_data(infile):
images = tsa.read_data(INPUT_FOLDER + infile)
subject = infile.split('.')[0]
# transpose so that the slice is the first dimension shape(16, 620, 512)
images = images.transpose()
threat_zone_examples = []
# for each threat zone, loop through each image, mask off the zone and then crop it
for tz_num, threat_zone_x_crop_dims in enumerate(zip(tsa.zone_slice_list, tsa.zone_crop_list)):
threat_zone = threat_zone_x_crop_dims[0]
crop_dims = threat_zone_x_crop_dims[1]
# get label
label = np.array(tsa.get_subject_zone_label(tz_num, tsa.get_subject_labels(STAGE1_LABELS, subject)))
for img_num, img in enumerate(images):
# print('Threat Zone:Image -> {}:{}'.format(tz_num, img_num))
# print('Threat Zone Label -> {}'.format(label))
if threat_zone[img_num] is not None:
# correct the orientation of the image
# print('-> reorienting base image')
base_img = np.flipud(img)
# print('-> shape {}|mean={}'.format(base_img.shape, base_img.mean()))
# convert to grayscale
# print('-> converting to grayscale')
rescaled_img = tsa.convert_to_grayscale(base_img)
# print('-> shape {}|mean={}'.format(rescaled_img.shape, rescaled_img.mean()))
# spread the spectrum to improve contrast
# print('-> spreading spectrum')
high_contrast_img = tsa.spread_spectrum(rescaled_img)
# print('-> shape {}|mean={}'.format(high_contrast_img.shape,high_contrast_img.mean()))
# get the masked image
# print('-> masking image')
masked_img = tsa.roi(high_contrast_img, threat_zone[img_num])
# print('-> shape {}|mean={}'.format(masked_img.shape, masked_img.mean()))
# crop the image
# print('-> cropping image')
cropped_img = tsa.crop(masked_img, crop_dims[img_num])
# print('-> shape {}|mean={}'.format(cropped_img.shape, cropped_img.mean()))
# normalize the image
# print('-> normalizing image')
normalized_img = tsa.normalize(cropped_img)
# print('-> shape {}|mean={}'.format(normalized_img.shape, normalized_img.mean()))
# zero center the image
# print('-> zero centering')
zero_centered_img = tsa.zero_center(normalized_img)
# print('-> shape {}|mean={}'.format(zero_centered_img.shape,zero_centered_img.mean()))
# append the features and labels to this threat zone's example array
# print ('-> appending example to threat zone {}'.format(tz_num))
threat_zone_examples.append([[tz_num], zero_centered_img, label])
print ('-> shape {:d}:{:d}:{:d}:{:d}:{:d}:{:d}'.format(len(threat_zone_examples),len(threat_zone_examples[0]),len(threat_zone_examples[0][0]),len(threat_zone_examples[0][1][0]),len(threat_zone_examples[0][1][1]),len(threat_zone_examples[0][2])))
else:
print('-> No view of tz:{} in img:{}. Skipping to next...'.format(tz_num, img_num))
print('------------------------------------------------')
for tz_num, tz in enumerate(tsa.zone_slice_list):
tz_examples_to_save = []
# write out the batch and reset
print(' -> writing: ' + PROCESSED_FOLDER + 'preprocessed_TSA_scans-tz{}-{}-{}.npy'.format(tz_num+1, len(threat_zone_examples[0][1][0]),len(threat_zone_examples[0][1][1])))
# get this tz's examples
tz_examples = [example for example in threat_zone_examples if example[0] == [tz_num]]
# drop unused columns
tz_examples_to_save.append([[features_label[1], features_label[2]] for features_label in tz_examples])
#save batch
np.save(PROCESSED_FOLDER + 'input-tz{}-{}-{}.npy'.format(tz_num+1, len(threat_zone_examples[0][1][0]),len(threat_zone_examples[0][1][1])), tz_examples_to_save)
def preprocess_tsa_data():
# OPTION 1: get a list of all subjects for which there are labels
#df = pd.read_csv(STAGE1_LABELS)
#df['Subject'], df['Zone'] = df['Id'].str.split('_',1).str
#SUBJECT_LIST = df['Subject'].unique()
# OPTION 2: get a list of all subjects for whom there is data
# SUBJECT_LIST = [os.path.splitext(subject)[0] for subject in os.listdir(INPUT_FOLDER)]
#print(len(SUBJECT_LIST))
#print(SUBJECT_LIST)
# OPTION 3: get a list of subjects for small bore test purposes
SUBJECT_LIST = [
'00360f79fd6e02781457eda48f85da90', '0043db5e8c819bffc15261b1f1ac5e42',
'0050492f92e22eed3474ae3a6fc907fa', '006ec59fa59dd80a64c85347eef810c7',
'0097503ee9fa0606559c56458b281a08', '011516ab0eca7cad7f5257672ddde70e',
'47e2a4a8e13ec7100f6af8cd839d1bb3', 'e087226320cc189142228b5fb93ed58f'
]
# intialize tracking and saving items
batch_num = 1
count = 0
threat_zone_examples = []
start_time = timer()
print(len(SUBJECT_LIST))
for subject in SUBJECT_LIST:
count += 1
# read in the images
print('--------------------------------------------------------------')
print('t+> {:5.3f} |Reading images for subject #: {}'.format(
timer() - start_time, subject))
print('--------------------------------------------------------------')
images = tsa.read_data(INPUT_FOLDER + '/' + subject + '.aps')
# transpose so that the slice is the first dimension shape(16, 620, 512)
images = images.transpose()
# for each threat zone, loop through each image, mask off the zone and then crop it
for tz_num, threat_zone_x_crop_dims in enumerate(
zip(tsa.zone_slice_list, tsa.zone_crop_list)):
threat_zone = threat_zone_x_crop_dims[0]
crop_dims = threat_zone_x_crop_dims[1]
# get label
label = np.array(
tsa.get_subject_zone_label(
tz_num, tsa.get_subject_labels(STAGE1_LABELS, subject)))
# print(STAGE1_LABELS, subject)
for img_num, img in enumerate(images):
print('Threat Zone:Image -> {}:{}'.format(tz_num, img_num))
print('Threat Zone Label -> {}'.format(label))
if label[0] == 0:
print('threat is present')
if threat_zone[img_num] is not None:
# correct the orientation of the image
print('-> reorienting base image')
base_img = np.flipud(img)
print('-> shape {}|mean={}'.format(
base_img.shape, base_img.mean()))
# convert to grayscale
print('-> converting to grayscale')
rescaled_img = tsa.convert_to_grayscale(base_img)
print('-> shape {}|mean={}'.format(
rescaled_img.shape, rescaled_img.mean()))
# spread the spectrum to improve contrast
print('-> spreading spectrum')
high_contrast_img = tsa.spread_spectrum(rescaled_img)
print('-> shape {}|mean={}'.format(
high_contrast_img.shape, high_contrast_img.mean()))
# get the masked image
print('-> masking image')
masked_img = tsa.roi(high_contrast_img,
threat_zone[img_num])
print('-> shape {}|mean={}'.format(
masked_img.shape, masked_img.mean()))
# crop the image
print('-> cropping image')
cropped_img = tsa.crop(masked_img, crop_dims[img_num])
print('-> shape {}|mean={}'.format(
cropped_img.shape, cropped_img.mean()))
# normalize the image
print('-> normalizing image')
normalized_img = tsa.normalize(cropped_img)
print('-> shape {}|mean={}'.format(
normalized_img.shape, normalized_img.mean()))
# zero center the image
print('-> zero centering')
zero_centered_img = tsa.zero_center(normalized_img)
print('-> shape {}|mean={}'.format(
zero_centered_img.shape, zero_centered_img.mean()))
# append the features and labels to this threat zone's example array
print('-> appending example to threat zone {}'.format(
tz_num))
threat_zone_examples.append([[tz_num],
zero_centered_img, label])
center = (125, 125)
M = cv2.getRotationMatrix2D(center, 5, 1.0)
rotated = cv2.warpAffine(zero_centered_img, M,
(250, 250))
# print('rotated image shape {} | mean= {}'.format(zero_centered_img.shape,
# zero_centered_img.mean()))
# cv2.imwrite("thumbnail.png", cropped)
# cv2.imwrite("rotated.jpg", rotated)
# cv2.imshow("original.jpg", zero_centered_img)
# cv2.waitKey(0)
# cv2.imshow("rotated.jpg", rotated)
# cv2.waitKey(0)
threat_zone_examples.append([[tz_num], rotated, label])
M = cv2.getRotationMatrix2D(center, 10, 1.0)
rotated1 = cv2.warpAffine(zero_centered_img, M,
(250, 250))
threat_zone_examples.append([[tz_num], rotated1,
label])
# cv2.imshow("rotated1.jpg", rotated1)
# cv2.waitKey(0)
M = cv2.getRotationMatrix2D(center, 15, 1.0)
rotated2 = cv2.warpAffine(zero_centered_img, M,
(250, 250))
threat_zone_examples.append([[tz_num], rotated2,
label])
# cv2.imshow("rotated2.jpg", rotated2)
# cv2.waitKey(0)
# M = cv2.getRotationMatrix2D(center, 20, 1.0)
rotated3 = cv2.warpAffine(zero_centered_img, M,
(250, 250))
threat_zone_examples.append([[tz_num], rotated3,
label])
# cv2.imshow("rotated3.jpg", rotated3)
# cv2.waitKey(0)
print('-> shape {:d}:{:d}:{:d}:{:d}:{:d}:{:d}'.format(
len(threat_zone_examples),
len(threat_zone_examples[0]),
len(threat_zone_examples[0][0]),
len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]),
len(threat_zone_examples[0][2])))
else:
print(
'-> No view of tz:{} in img:{}. Skipping to next...'
.format(tz_num, img_num))
print('------------------------------------------------')
else:
print('threat not present and label is', label[0])
if count >= 0:
# count = 0
print('IN LOOP')
if threat_zone[img_num] is not None:
# correct the orientation of the image
print('-> reorienting base image')
base_img = np.flipud(img)
print('-> shape {}|mean={}'.format(
base_img.shape, base_img.mean()))
# convert to grayscale
print('-> converting to grayscale')
rescaled_img = tsa.convert_to_grayscale(base_img)
print('-> shape {}|mean={}'.format(
rescaled_img.shape, rescaled_img.mean()))
# spread the spectrum to improve contrast
print('-> spreading spectrum')
high_contrast_img = tsa.spread_spectrum(
rescaled_img)
print('-> shape {}|mean={}'.format(
high_contrast_img.shape,
high_contrast_img.mean()))
# get the masked image
print('-> masking image')
masked_img = tsa.roi(high_contrast_img,
threat_zone[img_num])
print('-> shape {}|mean={}'.format(
masked_img.shape, masked_img.mean()))
# crop the image
print('-> cropping image')
cropped_img = tsa.crop(masked_img,
crop_dims[img_num])
print('-> shape {}|mean={}'.format(
cropped_img.shape, cropped_img.mean()))
# normalize the image
print('-> normalizing image')
normalized_img = tsa.normalize(cropped_img)
print('-> shape {}|mean={}'.format(
normalized_img.shape, normalized_img.mean()))
# zero center the image
print('-> zero centering')
zero_centered_img = tsa.zero_center(normalized_img)
print('-> shape {}|mean={}'.format(
zero_centered_img.shape,
zero_centered_img.mean()))
# append the features and labels to this threat zone's example array
print('-> appending example to threat zone {}'.
format(tz_num))
threat_zone_examples.append([[tz_num],
zero_centered_img,
label])
print('-> shape {:d}:{:d}:{:d}:{:d}:{:d}:{:d}'.
format(len(threat_zone_examples),
len(threat_zone_examples[0]),
len(threat_zone_examples[0][0]),
len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]),
len(threat_zone_examples[0][2])))
# count = 0
# each subject gets EXAMPLES_PER_SUBJECT number of examples (182 to be exact,
# so this section just writes out the the data once there is a full minibatch
# complete.
if ((len(threat_zone_examples) %
(BATCH_SIZE * EXAMPLES_PER_SUBJECT)) == 0):
for tz_num, tz in enumerate(tsa.zone_slice_list):
tz_examples_to_save = []
# write out the batch and reset
print(' -> writing: ' + PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num + 1, len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]), batch_num))
# get this tz's examples
tz_examples = [
example for example in threat_zone_examples
if example[0] == [tz_num]
]
# drop unused columns
tz_examples_to_save.append(
[[features_label[1], features_label[2]]
for features_label in tz_examples])
# save batch. Note that the trainer looks for tz{} where {} is a
# tz_num 1 based in the minibatch file to select which batches to
# use for training a given threat zone
np.save(
PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num + 1, len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]), batch_num),
tz_examples_to_save)
del tz_examples_to_save
#reset for next batch
del threat_zone_examples
threat_zone_examples = []
batch_num += 1
# we may run out of subjects before we finish a batch, so we write out
# the last batch stub
if (len(threat_zone_examples) > 0):
for tz_num, tz in enumerate(tsa.zone_slice_list):
tz_examples_to_save = []
# write out the batch and reset
print(' -> writing: ' + PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num + 1, len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]), batch_num))
# get this tz's examples
tz_examples = [
example for example in threat_zone_examples
if example[0] == [tz_num]
]
# drop unused columns
tz_examples_to_save.append([[features_label[1], features_label[2]]
for features_label in tz_examples])
#save batch
np.save(
PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num + 1, len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]), batch_num),
tz_examples_to_save)
def preprocess_tsa_data():
# OPTION 1: get a list of all subjects for which there are labels
# df = pd.read_csv(STAGE1_LABELS)
# df['Subject'], df['Zone'] = df['Id'].str.split('_',1).str
# SUBJECT_LIST = df['Subject'].unique()
# OPTION 2: get a list of all subjects for whom there is data
SUBJECT_LIST = [os.path.splitext(subject)[0] for subject in os.listdir(INPUT_FOLDER)]
# OPTION 3: get a list of subjects for small bore test purposes
# SUBJECT_LIST = ['0043db5e8c819bffc15261b1f1ac5e42',
# '00360f79fd6e02781457eda48f85da90',
# '01c08047f617de893bef104fb309203a',
# '011516ab0eca7cad7f5257672ddde70e',
# '01941f33fd090ae5df8c95992c027862',
# '0050492f92e22eed3474ae3a6fc907fa',
# '0097503ee9fa0606559c56458b281a08']
# intialize tracking and saving items
batch_num = 1
threat_zone_examples = []
start_time = timer()
for subject in SUBJECT_LIST:
# read in the images
print('--------------------------------------------------------------')
print('t+> {:5.3f} |Reading images for subject #: {}'.format(timer()-start_time,
subject))
print('--------------------------------------------------------------')
images = tsa.read_data(INPUT_FOLDER + '/' + subject + '.aps')
# transpose so that the slice is the first dimension shape(16, 620, 512)
images = images.transpose()
# for each threat zone, loop through each image, mask off the zone and then crop it
for tz_num, threat_zone_x_crop_dims in enumerate(zip(tsa.zone_slice_list, tsa.zone_crop_list)):
threat_zone = threat_zone_x_crop_dims[0]
crop_dims = threat_zone_x_crop_dims[1]
# get label
label = np.array(tsa.get_subject_zone_label(tz_num, tsa.get_subject_labels(STAGE1_LABELS, subject)))
for img_num, img in enumerate(images):
print('Threat Zone:Image -> {}:{}'.format(tz_num, img_num))
print('Threat Zone Label -> {}'.format(label))
if threat_zone[img_num] is not None:
# correct the orientation of the image
print('-> reorienting base image')
base_img = np.flipud(img)
print('-> shape {}|mean={}'.format(base_img.shape,
base_img.mean()))
# convert to grayscale
print('-> converting to grayscale')
rescaled_img = tsa.convert_to_grayscale(base_img)
print('-> shape {}|mean={}'.format(rescaled_img.shape,
rescaled_img.mean()))
# spread the spectrum to improve contrast
print('-> spreading spectrum')
high_contrast_img = tsa.spread_spectrum(rescaled_img)
print('-> shape {}|mean={}'.format(high_contrast_img.shape,high_contrast_img.mean()))
# get the masked image
print('-> masking image')
masked_img = tsa.roi(high_contrast_img, threat_zone[img_num])
print('-> shape {}|mean={}'.format(masked_img.shape, masked_img.mean()))
# crop the image
print('-> cropping image')
cropped_img = tsa.crop(masked_img, crop_dims[img_num])
print('-> shape {}|mean={}'.format(cropped_img.shape, cropped_img.mean()))
# normalize the image
print('-> normalizing image')
normalized_img = tsa.normalize(cropped_img)
print('-> shape {}|mean={}'.format(normalized_img.shape, normalized_img.mean()))
# zero center the image
print('-> zero centering')
zero_centered_img = tsa.zero_center(normalized_img)
print('-> shape {}|mean={}'.format(zero_centered_img.shape,zero_centered_img.mean()))
# append the features and labels to this threat zone's example array
print ('-> appending example to threat zone {}'.format(tz_num))
threat_zone_examples.append([[tz_num], zero_centered_img, label])
print ('-> shape {:d}:{:d}:{:d}:{:d}:{:d}:{:d}'.format(len(threat_zone_examples),len(threat_zone_examples[0]),len(threat_zone_examples[0][0]),len(threat_zone_examples[0][1][0]),len(threat_zone_examples[0][1][1]),len(threat_zone_examples[0][2])))
else:
print('-> No view of tz:{} in img:{}. Skipping to next...'.format(tz_num, img_num))
print('------------------------------------------------')
# each subject gets EXAMPLES_PER_SUBJECT number of examples (182 to be exact,
# so this section just writes out the the data once there is a full minibatch
# complete.
if ((len(threat_zone_examples) % (BATCH_SIZE * EXAMPLES_PER_SUBJECT)) == 0):
for tz_num, tz in enumerate(tsa.zone_slice_list):
tz_examples_to_save = []
# write out the batch and reset
print(' -> writing: ' + PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(
tz_num+1,
len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]),
batch_num))
# get this tz's examples
tz_examples = [example for example in threat_zone_examples if example[0] ==
[tz_num]]
# drop unused columns
tz_examples_to_save.append([[features_label[1], features_label[2]]
for features_label in tz_examples])
# save batch. Note that the trainer looks for tz{} where {} is a
# tz_num 1 based in the minibatch file to select which batches to
# use for training a given threat zone
np.save(PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(tz_num+1,
len(threat_zone_examples[0][1][0]),
len(threat_zone_examples[0][1][1]),
batch_num),
tz_examples_to_save)
del tz_examples_to_save
#reset for next batch
del threat_zone_examples
threat_zone_examples = []
batch_num += 1
# we may run out of subjects before we finish a batch, so we write out
# the last batch stub
if (len(threat_zone_examples) > 0):
for tz_num, tz in enumerate(tsa.zone_slice_list):
tz_examples_to_save = []
# write out the batch and reset
print(' -> writing: ' + PREPROCESSED_DATA_FOLDER + 'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(tz_num+1, len(threat_zone_examples[0][1][0]), len(threat_zone_examples[0][1][1]), batch_num))
# get this tz's examples
tz_examples = [example for example in threat_zone_examples if example[0] == [tz_num]]
# drop unused columns
tz_examples_to_save.append([[features_label[1], features_label[2]] for features_label in tz_examples])
#save batch
np.save(PREPROCESSED_DATA_FOLDER +
'preprocessed_TSA_scans-tz{}-{}-{}-b{}.npy'.format(tz_num+1, len(threat_zone_examples[0][1][0]), len(threat_zone_examples[0][1][1]), batch_num), tz_examples_to_save)
def preprocess_tsa_data():
df = pd.read_csv(STAGE1_LABELS)
df['Subject'], df['Zone'] = df['Id'].str.split('_', 1).str
SUBJECT_LIST = df['Subject'].unique()
# SUBJECT_LIST = ['00360f79fd6e02781457eda48f85da90','0043db5e8c819bffc15261b1f1ac5e42',
# '0050492f92e22eed3474ae3a6fc907fa','006ec59fa59dd80a64c85347eef810c7',
# '0097503ee9fa0606559c56458b281a08','011516ab0eca7cad7f5257672ddde70e']
batch_num = 1
threat_zone_examples = []
start_time = timer()
for subject in SUBJECT_LIST:
print('-------------------------------------------------------------')
print('t+>{:5.3f} |Reading subject #:{}'.format(
timer() - start_time, subject))
print('-------------------------------------------------------------')
images = tsa.read_data(INPUT_FOLDER + '/' + subject + '.aps')
images = images.transpose()
for tz_num, threat_zone_x_crop_dims in enumerate(
zip(tsa.zone_slice_list, tsa.zone_crop_list)):
threat_zone = threat_zone_x_crop_dims[0]
crop_dims = threat_zone_x_crop_dims[1]
label = np.array(
tsa.get_subject_zone_label(
tz_num, tsa.get_subject_labels(STAGE1_LABELS, subject)))
for img_num, img in enumerate(images):
print('Threat Zone:Image -> {}:{}'.format(tz_num, img_num))
print('Threat Zone Label -> {}'.format(label))
if threat_zone[img_num] is not None:
print('-> reorienting base image')
base_img = np.flipud(img)
print('-> shape {}| mean = {}'.format(
base_img.shape, base_img.mean()))
print('-> rescaling image')
rescaled_img = tsa.convert_to_grayscale((base_img))
print('-> shape {}| mean = {}'.format(
rescaled_img.shape, rescaled_img.mean()))
print('-> making high contrast')
high_contrast_img = tsa.spread_spectrum(rescaled_img)
print('-> shape {}| mean = {}'.format(
high_contrast_img.shape, high_contrast_img.mean()))
masked_img = tsa.roi(high_contrast_img,
threat_zone[img_num])
print('-> cropping image')
cropped_img = tsa.crop(masked_img, crop_dims[img_num])
print('-> shape {}| mean = {}'.format(
cropped_img.shape, cropped_img.mean()))
print('-> normalizing image')
normalized_img = tsa.normalize(cropped_img)
print('-> shape {}| mean = {}'.format(
normalized_img.shape, normalized_img.mean()))
zeroed_img = tsa.zero_center(normalized_img)
print('-> shape {}| mean = {}'.format(
zeroed_img.shape, zeroed_img.mean()))
# threat_zone_examples.append([[tz_num],zeroed_img,label])
np.save(
PREPROCESSED_DATA_FOLDER + subject +
'Zone{}.npy'.format(tz_num + 1), zeroed_img)
# com_img = np.reshape(zeroed_img,[1,250*250])
else:
print('-> No view...')
print('----------------------weeee-----------------------')