def __init__(self, image_filename, model_object, input_cube_size=None):
self.shutdown_signal = False
self.worker_threads = []
self.worker_thread_count = 1
self.requires_padding = False
self.model_cube_size = np.array([15, 15, 23])
if input_cube_size is None:
self.input_cube_size = self.model_cube_size
else:
self.input_cube_size = np.array(input_cube_size)
for i in range(3):
if self.input_cube_size[i] != self.model_cube_size[i]:
self.requires_padding = True
if self.input_cube_size[i] > self.model_cube_size[i]:
raise ValueError(
"Input Cube size cannot be larger than model cube size " +
str(self.model_cube_size))
self.q = None
self.spacing_factor, self.image = ImageHelper.read_image(
image_filename, correct_spacing=True)
self.org_ndarray = sitk.GetArrayFromImage(self.image)
ndarray = np.float32(np.swapaxes(self.org_ndarray, 0, 2))
ndarray -= np.mean(ndarray, axis=0)
ndarray /= np.std(ndarray, axis=0)
self.ndarray = ndarray
self.model = model_object
def get_image(self, id):
is_last_run = False
self.image_locks[id].acquire()
if id not in self.images:
image_path = os.path.abspath(self.input_path + id + '.img.gz')
self.images[id] = ImageHelper.read_image(image_path)
self.image_locks[id].release()
return self.images[id], is_last_run
def load_cubes_raw(self):
cube_shape = None
counter = 0
for idx in self.image_data:
sys.stdout.write("\rLoading Images {0} out of {1}...".format(
counter, len(self.image_data)))
sys.stdout.flush()
counter += 1
image_path = os.path.abspath(self.input_path + idx + '.img.gz')
image = ImageHelper.read_image(image_path, correct_spacing=False)
roi_list = self.image_data[idx]
cube_shape = self.load_image_cubes(image, roi_list,
self.roi_size_max)
return self.roi_size_max
def extract_cubes_one_image(self):
counter = 0
rois = []
self.read_rois()
idx = None
idx = '081832_20060531_1149725568.2203'
roi_list = self.image_data[idx]
spacing_factor = 1
if idx not in self.images:
image_path = os.path.abspath(self.input_path + idx + '.img.gz')
# spacing_factor, self.images[idx] = ImageHelper.read_image(image_path, correct_spacing=True)
spacing_factor = 1
self.images[idx] = ImageHelper.read_image(image_path,
correct_spacing=False)
the_one_roi = None
for roi in roi_list:
if roi.roi_start_idx[0] != 101:
continue
the_one_roi = roi
roi.rescale_depth(spacing_factor)
cube = roi.load_cube(self.images[idx], padded_size=self.cube_size)
assert (list(cube.shape) == self.cube_max_size)
counter += 1
print(counter)
counter = 0
roi_list = [the_one_roi]
for roi_1 in roi_list:
counter += 1
roi_1.lookup_table = self.class_lookup
roi_1.y = self.class_lookup[roi_1.roi_class]
for roi_2 in roi_list:
roi_1.intersects(roi_2)
print(counter)
rois.extend(self.image_data[idx])
pickle.dump([the_one_roi], open("one_image.p", "wb"))
