def add_subjects_to_path(self, path, data):
# Create label file
label_file = open(join(path, 'labels.txt'), 'w')
labels = Label.objects.filter(task=self.task)
label_dict = {}
counter = 0
for label in labels:
label_file.write(label.name + '\n')
label_dict[label.name] = counter
counter += 1
label_file.close()
# Create file_list.txt file and copy images
file_list = open(os.path.join(path, 'file_list.txt'), 'w')
labeled_images = ImageAnnotation.objects.filter(task=self.task,
rejected=False)
for labeled_image in labeled_images:
label = ImageLabel.objects.get(image=labeled_image)
name = labeled_image.image.filename
dataset_path = join(path, label.label.name)
create_folder(dataset_path)
image_id = labeled_image.image.id
new_extension = 'png'
new_filename = os.path.join(dataset_path,
str(image_id) + '.' + new_extension)
copy_image(name, new_filename)
file_list.write(new_filename + ' ' +
str(label_dict[label.label.name]) + '\n')
file_list.close()
def add_subjects_to_path(self, path, data):
# For each subject
for subject in data:
subject_path = join(path, subject.dataset.name, subject.name)
frames = KeyFrameAnnotation.objects.filter(
image_annotation__task=self.task,
image_annotation__image__subject=subject)
for frame in frames:
# Check if image was rejected
if frame.image_annotation.rejected:
continue
# Get image sequence
image_sequence = frame.image_annotation.image
# Copy image frames
sequence_id = os.path.basename(
os.path.dirname(image_sequence.format))
subject_subfolder = join(subject_path, str(sequence_id))
create_folder(subject_subfolder)
target_name = os.path.basename(image_sequence.format).replace(
'#', str(frame.frame_nr))
target_gt_name = os.path.splitext(target_name)[0] + "_gt.mhd"
filename = image_sequence.format.replace(
'#', str(frame.frame_nr))
new_filename = join(subject_subfolder, target_name)
copy_image(filename, new_filename)
# Get control points to create segmentation
x_scaling = 1
if new_filename.endswith('.mhd'):
image_mhd = MetaImage(filename=new_filename)
image_size = image_mhd.get_size()
spacing = image_mhd.get_spacing()
if spacing[0] != spacing[1]:
# In this case we have to compensate for a change in with
real_aspect = image_size[0] * spacing[0] / (
image_size[1] * spacing[1])
current_aspect = float(image_size[0]) / image_size[1]
new_width = int(image_size[0] *
(real_aspect / current_aspect))
new_height = image_size[1]
x_scaling = float(image_size[0]) / new_width
print(image_size[0], new_width, image_size[1],
new_height)
image = np.asarray(image_mhd.get_pixel_data())
else:
image_pil = PIL.Image.open(new_filename)
image_size = image_pil.size
spacing = [1, 1]
image = np.asarray(image_pil)
self.save_segmentation(frame, image_size,
join(subject_subfolder, target_gt_name),
spacing, x_scaling, image)
return True, path
def add_subjects_to_path(self, path, data):
# Get labels for this task and write it to labels.txt
label_file = open(join(path, 'labels.txt'), 'w')
labels = Label.objects.filter(task=self.task)
label_dict = {}
counter = 0
for label in labels:
label_file.write(label.name + '\n')
label_dict[label.id] = counter
counter += 1
label_file.close()
# For each subject
for subject in data:
subject_path = join(path, subject.name)
create_folder(subject_path)
frames = KeyFrameAnnotation.objects.filter(
image_annotation__task=self.task,
image_annotation__image__subject=subject,
image_annotation__rejected=False)
for frame in frames:
image_sequence = frame.image_annotation.image
# Copy image
filename = image_sequence.format.replace(
'#', str(frame.frame_nr))
target_name = os.path.basename(image_sequence.format).replace(
'#', str(frame.frame_nr))
new_filename = join(subject_path, target_name)
copy_image(filename, new_filename)
# Write bounding boxes txt file
boxes = BoundingBox.objects.filter(image=frame)
with open(join(subject_path,
str(frame.frame_nr) + '.txt'), 'w') as f:
for box in boxes:
center_x = round(box.x + box.width * 0.5)
center_y = round(box.y + box.height * 0.5)
label = label_dict[box.label.id]
f.write('{} {} {} {} {}\n'.format(
label, center_x, center_y, box.width, box.height))
return True, path
def add_subjects_to_path(self, path, data):
# For each subject
for subject in data:
subject_path = join(path, subject.dataset.name, subject.name)
frames = KeyFrameAnnotation.objects.filter(
image_annotation__task=self.task,
image_annotation__image__subject=subject)
for frame in frames:
# Check if image was rejected
if frame.image_annotation.rejected:
continue
# Get image sequence
image_sequence = frame.image_annotation.image
# Copy image frames
sequence_id = os.path.basename(
os.path.dirname(image_sequence.format))
subject_subfolder = join(subject_path, str(sequence_id))
create_folder(subject_subfolder)
target_name = os.path.basename(image_sequence.format).replace(
'#', str(frame.frame_nr))
target_gt_name = os.path.splitext(target_name)[0] + "_gt.mhd"
filename = image_sequence.format.replace(
'#', str(frame.frame_nr))
new_filename = join(subject_subfolder, target_name)
copy_image(filename, new_filename)
# Get control points to create segmentation
if new_filename.endswith('.mhd'):
image_mhd = MetaImage(filename=new_filename)
image_size = image_mhd.get_size()
spacing = image_mhd.get_spacing()
else:
image_pil = PIL.Image.open(new_filename)
image_size = image_pil.size
spacing = [1, 1]
self.save_segmentation(frame, image_size,
join(subject_subfolder, target_gt_name),
spacing)
return True, path
def export(self, form):
datasets = form.cleaned_data['dataset']
delete_existing_data = form.cleaned_data['delete_existing_data']
# Create dir, delete old if it exists
path = form.cleaned_data['path']
if delete_existing_data:
# Delete path
try:
os.stat(path)
rmtree(path)
except:
# Folder does not exist
pass
# Create folder again
try:
os.mkdir(path)
except:
return False, 'Failed to create directory at ' + path
else:
# Check that folder exist
try:
os.stat(path)
except:
return False, 'Path does not exist: ' + path
# Create label file
label_file = open(os.path.join(path, 'labels.txt'), 'w')
labels = Label.objects.filter(task=self.task)
labelDict = {}
counter = 0
for label in labels:
label_file.write(label.name + '\n')
labelDict[label.name] = counter
counter += 1
label_file.close()
# Create file_list.txt file
file_list = open(os.path.join(path, 'file_list.txt'), 'w')
labeled_images = ProcessedImage.objects.filter(
task=self.task, image__dataset__in=datasets, rejected=False)
for labeled_image in labeled_images:
name = labeled_image.image.filename
dataset_path = os.path.join(path, labeled_image.image.dataset.name)
try:
os.mkdir(dataset_path) # Make dataset path if doesn't exist
except:
pass
image_id = labeled_image.image.id
new_extension = form.cleaned_data['output_image_format']
new_filename = os.path.join(dataset_path,
str(image_id) + '.' + new_extension)
copy_image(name, new_filename)
# Get image label
label = ImageLabel.objects.get(image=labeled_image)
file_list.write(new_filename + ' ' +
str(labelDict[label.label.name]) + '\n')
file_list.close()
return True, path