def count_proportion():
id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
luna_data_paths = utils_lung.get_patient_data_paths(pathfinder.LUNA_DATA_PATH)
luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]
n_white = 0
n_black = 0
for k, p in enumerate(luna_data_paths):
img, origin, pixel_spacing = utils_lung.read_mhd(p)
img = data_transforms.hu2normHU(img)
id = os.path.basename(p).replace('.mhd', '')
print id
annotations = id2zyxd[id]
img_out, annotations_out = data_transforms.transform_scan3d(img,
pixel_spacing=pixel_spacing,
p_transform=config().p_transform,
p_transform_augment=None,
# config().p_transform_augment,
luna_annotations=annotations,
luna_origin=origin)
mask = data_transforms.make_3d_mask_from_annotations(img_out.shape, annotations_out, shape='sphere')
n_white += np.sum(mask)
n_black += mask.shape[0] * mask.shape[1] * mask.shape[2] - np.sum(mask)
print 'white', n_white
print 'black', n_black
def count_proportion():
id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
luna_data_paths = utils_lung.get_patient_data_paths(
pathfinder.LUNA_DATA_PATH)
luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]
n_white = 0
n_black = 0
for k, p in enumerate(luna_data_paths):
img, origin, pixel_spacing = utils_lung.read_mhd(p)
img = data_transforms.hu2normHU(img)
id = os.path.basename(p).replace('.mhd', '')
print id
annotations = id2zyxd[id]
img_out, annotations_out = data_transforms.transform_scan3d(
img,
pixel_spacing=pixel_spacing,
p_transform=config().p_transform,
p_transform_augment=None,
# config().p_transform_augment,
luna_annotations=annotations,
luna_origin=origin)
mask = data_transforms.make_3d_mask_from_annotations(img_out.shape,
annotations_out,
shape='sphere')
n_white += np.sum(mask)
n_black += mask.shape[0] * mask.shape[1] * mask.shape[2] - np.sum(mask)
print 'white', n_white
print 'black', n_black
def data_prep_function(data, patch_centers, pixel_spacing, p_transform,
p_transform_augment, **kwargs):
x = data_transforms.transform_dsb_candidates(data=data,
patch_centers=patch_centers,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing)
x = data_transforms.hu2normHU(x)
return x
def data_prep_function(data, patch_centers, pixel_spacing, p_transform,
p_transform_augment, **kwargs):
x = data_transforms.transform_dsb_candidates(data=data,
patch_centers=patch_centers,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing)
x = data_transforms.hu2normHU(x)
return x
def data_prep_function(data, patch_center, luna_annotations, pixel_spacing, luna_origin, p_transform,
p_transform_augment, **kwargs):
x = data_transforms.hu2normHU(data)
x, patch_annotation_tf, annotations_tf = data_transforms.transform_patch3d(data=x,
luna_annotations=luna_annotations,
patch_center=patch_center,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing,
luna_origin=luna_origin)
return x, patch_annotation_tf
def data_prep_function(data, patch_center, luna_annotations, pixel_spacing, luna_origin, p_transform,
p_transform_augment, **kwargs):
x, patch_annotation_tf, annotations_tf = data_transforms.transform_patch3d(data=data,
luna_annotations=luna_annotations,
patch_center=patch_center,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing,
luna_origin=luna_origin)
x = data_transforms.hu2normHU(x)
y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
return x, y
def data_prep_function(data, patch_center, pixel_spacing, luna_origin, p_transform,
p_transform_augment, **kwargs):
x, patch_annotation_tf = data_transforms.transform_patch3d(data=data,
luna_annotations=None,
patch_center=patch_center,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing,
luna_origin=luna_origin)
x = data_transforms.hu2normHU(x)
return x
def data_prep_function(data, patch_center, luna_annotations, pixel_spacing, luna_origin, p_transform,
p_transform_augment, **kwargs):
x, patch_annotation_tf, annotations_tf = data_transforms.transform_patch3d(data=data,
luna_annotations=luna_annotations,
patch_center=patch_center,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing,
luna_origin=luna_origin)
x = data_transforms.hu2normHU(x)
y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
return x, y
def data_prep_function(data, patch_center, pixel_spacing, luna_origin, p_transform,
p_transform_augment, world_coord_system, **kwargs):
x, patch_annotation_tf = data_transforms.transform_patch3d(data=data,
luna_annotations=None,
patch_center=patch_center,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing,
luna_origin=luna_origin,
world_coord_system=world_coord_system)
x = data_transforms.hu2normHU(x)
return x
def data_prep_function(data, luna_annotations, pixel_spacing, luna_origin,
p_transform=p_transform,
p_transform_augment=None):
# make sure the data is processed the same way
x, annotations_tf, tf_matrix = data_transforms.transform_scan3d(data=data,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=luna_annotations,
p_transform_augment=None,
luna_origin=luna_origin)
x = data_transforms.hu2normHU(x)
y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
return x, y, annotations_tf, tf_matrix
def data_prep_function(data, luna_annotations, pixel_spacing, luna_origin,
p_transform=p_transform,
p_transform_augment=None):
# MAKE SURE THAT DATA IS PREPROCESSED THE SAME WAY
x, annotations_tf, tf_matrix = data_transforms.transform_scan3d(data=data,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=luna_annotations,
p_transform_augment=None,
luna_origin=luna_origin)
x = data_transforms.hu2normHU(x)
y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
return x, y, annotations_tf, tf_matrix
def data_prep_function(data, luna_annotations, pixel_spacing, luna_origin,
p_transform=p_transform,
p_transform_augment=None):
# make sure the data is processed the same way
x, annotations_tf, tf_matrix = data_transforms.transform_scan3d(data=data,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=luna_annotations,
p_transform_augment=None,
luna_origin=luna_origin)
x = data_transforms.hu2normHU(x)
y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
return x, y, annotations_tf, tf_matrix
def data_prep_function(data, pid, patch_centers, pixel_spacing, p_transform,
p_transform_augment, **kwargs):
x = data_transforms.transform_dsb_candidates(data=data,
patch_centers=patch_centers,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing)
bins, original_borders = rescale_params_hist_eq[pid]
x = data_transforms.apply_hist_eq_patch(x, bins, original_borders)
x = data_transforms.hu2normHU(x)
return x
def data_prep_function(data, pid, patch_centers, pixel_spacing, p_transform,
p_transform_augment, **kwargs):
x = data_transforms.transform_dsb_candidates(
data=data,
patch_centers=patch_centers,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing)
bins, original_borders = rescale_params_hist_eq[pid]
x = data_transforms.apply_hist_eq_patch(x, bins, original_borders)
x = data_transforms.hu2normHU(x)
return x
def data_prep_function(data, patch_center, pixel_spacing, luna_origin,
p_transform, p_transform_augment, world_coord_system,
**kwargs):
data_eq = utils_lung.histogram_equalization(data)
x, patch_annotation_tf = data_transforms.transform_patch3d(
data=data_eq,
luna_annotations=None,
patch_center=patch_center,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing,
luna_origin=luna_origin,
world_coord_system=world_coord_system)
x = data_transforms.hu2normHU(x)
return x
def data_prep_function(data, pid, patch_center, pixel_spacing, luna_origin, p_transform,
p_transform_augment, world_coord_system, **kwargs):
x, patch_annotation_tf = data_transforms.transform_patch3d(data=data,
luna_annotations=None,
patch_center=patch_center,
p_transform=p_transform,
p_transform_augment=p_transform_augment,
pixel_spacing=pixel_spacing,
luna_origin=luna_origin,
world_coord_system=world_coord_system)
bins, original_borders = rescale_params_hist_eq[pid]
x = data_transforms.apply_hist_eq_patch(x, bins, original_borders)
x = data_transforms.hu2normHU(x)
return x
def test1():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
image_dir = image_dir + '/test_luna/'
utils.auto_make_dir(image_dir)
id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
luna_data_paths = utils_lung.get_patient_data_paths(
pathfinder.LUNA_DATA_PATH)
luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]
print len(luna_data_paths)
print id2zyxd.keys()
for k, p in enumerate(luna_data_paths):
img, origin, pixel_spacing = utils_lung.read_mhd(p)
img = data_transforms.hu2normHU(img)
id = os.path.basename(p).replace('.mhd', '')
for nodule_zyxd in id2zyxd.itervalues():
zyx = np.array(nodule_zyxd[:3])
voxel_coords = utils_lung.world2voxel(zyx, origin, pixel_spacing)
diameter_mm = nodule_zyxd[-1]
radius_px = diameter_mm / pixel_spacing[1] / 2.
roi_radius = (radius_px, radius_px)
slice = img[voxel_coords[0], :, :]
slice_prev = img[voxel_coords[0] - 1, :, :]
slice_next = img[voxel_coords[0] + 1, :, :]
roi_center_yx = (voxel_coords[1], voxel_coords[2])
mask = data_transforms.make_2d_mask(slice.shape,
roi_center_yx,
roi_radius,
masked_value=0.1)
plot_2d(slice, mask, id, image_dir)
plot_2d_4(slice, slice_prev, slice_next, mask, id, image_dir)
a = [{'center': roi_center_yx, 'diameter_mm': diameter_mm}]
p_transform = {
'patch_size': (256, 256),
'mm_patch_size': (360, 360)
}
slice_patch, mask_patch = data_transforms.luna_transform_slice(
slice, a, pixel_spacing[1:], p_transform, None)
plot_2d(slice_patch, mask_patch, id, image_dir)
def test1():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
image_dir = image_dir + '/test_luna/'
utils.auto_make_dir(image_dir)
# sys.stdout = logger.Logger(image_dir + '/test_luna.log')
# sys.stderr = sys.stdout
id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
luna_data_paths = utils_lung.get_patient_data_paths(
pathfinder.LUNA_DATA_PATH)
luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]
print len(luna_data_paths)
print id2zyxd.keys()
for k, p in enumerate(luna_data_paths):
img, origin, spacing = utils_lung.read_mhd(p)
img = data_transforms.hu2normHU(img)
id = os.path.basename(p).replace('.mhd', '')
for roi in id2zyxd[id]:
zyx = np.array(roi[:3])
voxel_coords = utils_lung.world2voxel(zyx, origin, spacing)
print spacing
radius_mm = roi[-1] / 2.
radius_px = radius_mm / spacing[1]
print 'r in pixels =', radius_px
# roi_radius = (32.5, 32.5)
roi_radius = (radius_px, radius_px)
slice = img[voxel_coords[0], :, :]
roi_center_yx = (voxel_coords[1], voxel_coords[2])
# print slice.shape, slice_resample.shape
mask = make_circular_mask(slice.shape, roi_center_yx, roi_radius)
plot_2d(slice, mask, id, image_dir)
slice_mm, _ = resample(slice, spacing[1:])
roi_center_mm = tuple(
int(r * ps) for r, ps in zip(roi_center_yx, spacing[1:]))
mask_mm = make_circular_mask(slice_mm.shape, roi_center_mm,
(radius_mm, radius_mm))
plot_2d(slice_mm, mask_mm, id, image_dir)
def test1():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
image_dir = image_dir + '/test_luna/'
utils.auto_make_dir(image_dir)
id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
luna_data_paths = utils_lung.get_patient_data_paths(pathfinder.LUNA_DATA_PATH)
luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]
print len(luna_data_paths)
print id2zyxd.keys()
for k, p in enumerate(luna_data_paths):
img, origin, pixel_spacing = utils_lung.read_mhd(p)
img = data_transforms.hu2normHU(img)
id = os.path.basename(p).replace('.mhd', '')
for nodule_zyxd in id2zyxd.itervalues():
zyx = np.array(nodule_zyxd[:3])
voxel_coords = utils_lung.world2voxel(zyx, origin, pixel_spacing)
diameter_mm = nodule_zyxd[-1]
radius_px = diameter_mm / pixel_spacing[1] / 2.
roi_radius = (radius_px, radius_px)
slice = img[voxel_coords[0], :, :]
slice_prev = img[voxel_coords[0] - 1, :, :]
slice_next = img[voxel_coords[0] + 1, :, :]
roi_center_yx = (voxel_coords[1], voxel_coords[2])
mask = data_transforms.make_2d_mask(slice.shape, roi_center_yx, roi_radius, masked_value=0.1)
plot_2d(slice, mask, id, image_dir)
plot_2d_4(slice, slice_prev, slice_next, mask, id, image_dir)
a = [{'center': roi_center_yx, 'diameter_mm': diameter_mm}]
p_transform = {'patch_size': (256, 256),
'mm_patch_size': (360, 360)}
slice_patch, mask_patch = data_transforms.luna_transform_slice(slice, a, pixel_spacing[1:],
p_transform, None)
plot_2d(slice_patch, mask_patch, id, image_dir)
def test1():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
image_dir = image_dir + '/test_luna/'
utils.auto_make_dir(image_dir)
# sys.stdout = logger.Logger(image_dir + '/test_luna.log')
# sys.stderr = sys.stdout
id2zyxd = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
luna_data_paths = utils_lung.get_patient_data_paths(pathfinder.LUNA_DATA_PATH)
luna_data_paths = [p for p in luna_data_paths if '.mhd' in p]
print len(luna_data_paths)
print id2zyxd.keys()
for k, p in enumerate(luna_data_paths):
img, origin, spacing = utils_lung.read_mhd(p)
img = data_transforms.hu2normHU(img)
id = os.path.basename(p).replace('.mhd', '')
for roi in id2zyxd[id]:
zyx = np.array(roi[:3])
voxel_coords = utils_lung.world2voxel(zyx, origin, spacing)
print spacing
radius_mm = roi[-1] / 2.
radius_px = radius_mm / spacing[1]
print 'r in pixels =', radius_px
# roi_radius = (32.5, 32.5)
roi_radius = (radius_px, radius_px)
slice = img[voxel_coords[0], :, :]
roi_center_yx = (voxel_coords[1], voxel_coords[2])
# print slice.shape, slice_resample.shape
mask = make_circular_mask(slice.shape, roi_center_yx, roi_radius)
plot_2d(slice, mask, id, image_dir)
slice_mm, _ = resample(slice, spacing[1:])
roi_center_mm = tuple(int(r * ps) for r, ps in zip(roi_center_yx, spacing[1:]))
mask_mm = make_circular_mask(slice_mm.shape, roi_center_mm, (radius_mm, radius_mm))
plot_2d(slice_mm, mask_mm, id, image_dir)
