def copy_image(filename, new_filename):
_, original_extension = os.path.splitext(filename)
_, new_extension = os.path.splitext(new_filename)
# Read image
if original_extension.lower() == '.mhd':
metaimage = MetaImage(filename=filename)
if new_extension.lower() == '.mhd':
metaimage.write(new_filename)
elif new_extension.lower() == '.png':
pil_image = metaimage.get_image()
pil_image.save(new_filename)
else:
raise Exception('Unknown output image extension ' + new_extension)
elif original_extension.lower() == '.png':
if new_extension.lower() == '.mhd':
pil_image = PIL.Image.open(filename)
metaimage = MetaImage(data=np.asarray(pil_image))
metaimage.write(new_filename)
elif new_extension.lower() == '.png':
copyfile(filename, new_filename)
else:
raise Exception('Unknown output image extension ' + new_extension)
else:
raise Exception('Unknown input image extension ' + original_extension)
def get_image_as_http_response(filename, post_processing_method=''):
_, extension = os.path.splitext(filename)
buffer = BytesIO()
start = time.time()
if extension.lower() == '.mhd':
reader = MetaImage(filename=filename)
source = reader
# Convert raw data to image, and then to a http response
pil_image = reader.get_image()
spacing = reader.get_spacing()
if spacing[0] != spacing[1]:
# Compensate for anistropic pixel spacing
real_aspect = pil_image.width * spacing[0] / (pil_image.height *
spacing[1])
current_aspect = float(pil_image.width) / pil_image.height
new_width = int(pil_image.width * (real_aspect / current_aspect))
new_height = pil_image.height
pil_image = pil_image.resize((new_width, new_height))
elif extension.lower() == '.png':
pil_image = PIL.Image.open(filename)
source = pil_image
else:
raise Exception('Unknown output image extension ' + extension)
if post_processing_method is not '':
post_processing = post_processing_register.get(post_processing_method)
new_image = post_processing.post_process(np.asarray(pil_image), source,
filename)
if len(new_image.shape) > 2 and new_image.shape[2] == 3:
pil_image = PIL.Image.fromarray(new_image, 'RGB')
else:
pil_image = PIL.Image.fromarray(new_image, 'L')
#print('Image loading time', time.time() - start, 'seconds')
pil_image.save(buffer, "PNG", compress_level=1
) # TODO This function is very slow due to PNG compression
#print('Image loading time with save to buffer', time.time() - start, 'seconds')
return HttpResponse(buffer.getvalue(), content_type="image/png")
def add_subjects_to_path(self, path, form):
# Create label file
label_file = open(join(path, 'labels.txt'), 'w')
# Create a .txt file with the labels matching the sequence name
sequence_label_file = open(join(path, 'sequence_to_label.txt'), 'w')
# Find labels with parents
labels = form.cleaned_data['labels']
label_dict = {}
has_parent_dict = {}
label_file.write('All labels involved: \n\n')
for label_id in labels:
label = Label.objects.get(pk=label_id)
label_name = get_complete_label_name(label)
label_file.write(label_name + '\n')
has_parent_dict[label_name] = False
label_dict[label_name] = None
for start_label in has_parent_dict:
for full_label in has_parent_dict:
if full_label.startswith(start_label) & (start_label !=
full_label):
has_parent_dict[full_label] = True
# Assign children to the parent class
label_file.write(
'\nClassification based on the following parent labels: \n\n')
counter = 0
for label in has_parent_dict:
if has_parent_dict[label] == False:
label_file.write(label + '\n')
for label_name in label_dict:
if label_name.startswith(label):
label_dict[label_name] = counter
counter += 1
nb_parent_classes = counter
label_file.close()
# For each subject
subjects = Subject.objects.filter(dataset__task=self.task)
for subject in subjects:
# Get labeled images
labeled_images = ImageAnnotation.objects.filter(
task=self.task, rejected=False, image__subject=subject)
if labeled_images.count() == 0:
continue
width = form.cleaned_data['width']
height = form.cleaned_data['height']
sequence_frames = []
labels = []
for labeled_image in labeled_images:
label = ImageLabel.objects.get(
image__image_annotation=labeled_image)
if get_complete_label_name(label.label) in label_dict.keys():
# Get sequence
key_frame = KeyFrameAnnotation.objects.get(
image_annotation=labeled_image)
image_sequence = labeled_image.image
nr_of_frames = image_sequence.nr_of_frames
start_frame = 0
end_frame = nr_of_frames
if form.cleaned_data[
'displayed_frames_only'] and not self.task.show_entire_sequence:
start_frame = max(
0, key_frame.frame_nr - self.task.frames_before)
end_frame = min(
nr_of_frames,
key_frame.frame_nr + self.task.frames_after + 1)
for i in range(start_frame, end_frame):
# Get image
filename = image_sequence.format.replace('#', str(i))
if filename[-4:] == '.mhd':
metaimage = MetaImage(filename=filename)
image = metaimage.get_image()
else:
image = PIL.Image.open(filename)
# Setup assigned colormode
if form.cleaned_data['colormode'] != image.mode:
image = image.convert(
form.cleaned_data['colormode'])
# Resize
image = image.resize((width, height),
PIL.Image.BILINEAR)
# Convert to numpy array and normalize
image_array = np.array(image).astype(np.float32)
image_array /= 255
if len(image_array.shape) != 3:
image_array = image_array[..., None]
sequence_frames.append(image_array)
labels.append(label_dict[get_complete_label_name(
label.label)])
if form.cleaned_data['sequence_wise'] and len(
sequence_frames) > 0:
input = np.array(sequence_frames, dtype=np.float32)
output = np.array(labels, dtype=np.uint8)
sequence_label_file.write(
join(
subject.name,
os.path.basename(
os.path.dirname(image_sequence.format))) +
'\t' + str(output[0]) + '\n')
if form.cleaned_data['image_dim_ordering'] == 'theano':
input = np.transpose(input, [0, 3, 1, 2])
if form.cleaned_data['categorical']:
output = to_categorical(
output, nb_classes=nb_parent_classes)
subj_path = join(path, subject.name)
create_folder(subj_path)
try:
os.stat(subj_path)
except:
return False, 'Failed to create directory at ' + subj_path
f = h5py.File(
join(
subj_path,
os.path.basename(
os.path.dirname(image_sequence.format) +
'.hd5')), 'w')
f.create_dataset("data",
data=input,
compression="gzip",
compression_opts=4,
dtype='float32')
f.create_dataset("label",
data=output,
compression="gzip",
compression_opts=4,
dtype='uint8')
f.close()
sequence_frames = []
labels = []
if not form.cleaned_data['sequence_wise'] and len(
sequence_frames) > 0:
input = np.array(sequence_frames, dtype=np.float32)
output = np.array(labels, dtype=np.uint8)
if form.cleaned_data['image_dim_ordering'] == 'theano':
input = np.transpose(input, [0, 3, 1, 2])
if form.cleaned_data['categorical']:
output = to_categorical(output, nb_classes=len(label_dict))
f = h5py.File(join(path, subject.name + '.hd5'), 'w')
f.create_dataset("data",
data=input,
compression="gzip",
compression_opts=4,
dtype='float32')
f.create_dataset("label",
data=output,
compression="gzip",
compression_opts=4,
dtype='uint8')
f.close()
sequence_label_file.close()