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 test_luna3d_2():
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 = [
'/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.943403138251347598519939390311.mhd']
for k, p in enumerate(luna_data_paths):
id = os.path.basename(p).replace('.mhd', '')
print id
img, origin, pixel_spacing = utils_lung.read_mhd(p)
lung_mask = lung_segmentation.segment_HU_scan(img)
annotations = id2zyxd[id]
x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=img,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=annotations,
p_transform_augment=None,
luna_origin=origin,
lung_mask=lung_mask,
world_coord_system=True)
y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
for zyxd in annotations_tf:
plot_slice_3d_3(x, lung_mask_out, y, 0, id, idx=zyxd)
plot_slice_3d_3(x, lung_mask_out, y, 1, id, idx=zyxd)
plot_slice_3d_3(x, lung_mask_out, y, 2, id, idx=zyxd)
def test_luna3d():
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]
# luna_data_paths = [
# pathfinder.LUNA_DATA_PATH + '/1.3.6.1.4.1.14519.5.2.1.6279.6001.287966244644280690737019247886.mhd']
luna_data_paths = [
'/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.943403138251347598519939390311.mhd']
for k, p in enumerate(luna_data_paths):
img, origin, pixel_spacing = utils_lung.read_mhd(p)
id = os.path.basename(p).replace('.mhd', '')
print id
annotations = id2zyxd[id]
img_out, mask, annotations_out = config().data_prep_function(img,
pixel_spacing=pixel_spacing,
luna_annotations=annotations,
luna_origin=origin)
mask[mask == 0.] = 0.1
print annotations_out
for zyxd in annotations_out:
plot_slice_3d_2(img_out, mask, 0, id, idx=zyxd)
plot_slice_3d_2(img_out, mask, 1, id, idx=zyxd)
plot_slice_3d_2(img_out, mask, 2, id, idx=zyxd)
def test_luna3d():
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]
# luna_data_paths = [
# pathfinder.LUNA_DATA_PATH + '/1.3.6.1.4.1.14519.5.2.1.6279.6001.287966244644280690737019247886.mhd']
luna_data_paths = [
'/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.943403138251347598519939390311.mhd']
for k, p in enumerate(luna_data_paths):
img, origin, pixel_spacing = utils_lung.read_mhd(p)
id = os.path.basename(p).replace('.mhd', '')
print(id)
annotations = id2zyxd[id]
img_out, mask, annotations_out = config().data_prep_function(img,
pixel_spacing=pixel_spacing,
luna_annotations=annotations,
luna_origin=origin)
mask[mask == 0.] = 0.1
print(annotations_out)
for zyxd in annotations_out:
plot_slice_3d_2(img_out, mask, 0, id, idx=zyxd)
plot_slice_3d_2(img_out, mask, 1, id, idx=zyxd)
plot_slice_3d_2(img_out, mask, 2, id, idx=zyxd)
def test_luna_patches_3d():
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]
# pid = '1.3.6.1.4.1.14519.5.2.1.6279.6001.138080888843357047811238713686'
# luna_data_paths = [pathfinder.LUNA_DATA_PATH + '/%s.mhd' % pid]
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]
print annotations
for zyxd in annotations:
img_out, mask = config().data_prep_function_train(img,
pixel_spacing=pixel_spacing,
p_transform=config().p_transform,
p_transform_augment=config().p_transform_augment,
patch_center=zyxd,
luna_annotations=annotations,
luna_origin=origin)
try:
plot_slice_3d_2(img_out, mask, 0, id)
plot_slice_3d_2(img_out, mask, 1, id)
plot_slice_3d_2(img_out, mask, 2, id)
except:
pass
print '------------------------------------------'
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 test_luna3d():
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 = [
'/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.mhd'
]
candidates = utils.load_pkl(
'/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.pkl'
)
candidates = candidates[:4]
print(candidates)
print('--------------')
print(id2zyxd[
'1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474'])
for k, p in enumerate(luna_data_paths):
id = os.path.basename(p).replace('.mhd', '')
print(id)
img, origin, pixel_spacing = utils_lung.read_mhd(p)
lung_mask = lung_segmentation.segment_HU_scan_ira(img)
print(np.min(lung_mask), np.max(lung_mask))
x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(
data=img,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=candidates,
p_transform_augment=None,
luna_origin=origin,
lung_mask=lung_mask,
world_coord_system=False)
print(np.min(lung_mask_out), np.max(lung_mask_out))
plot_slice_3d_2(x, lung_mask_out, 0, id)
plot_slice_3d_2(x, lung_mask_out, 1, id)
plot_slice_3d_2(x, lung_mask_out, 2, id)
# for zyxd in annotations_tf:
# plot_slice_3d_2(x, lung_mask_out, 0, id, idx=zyxd)
# plot_slice_3d_2(x, lung_mask_out, 1, id, idx=zyxd)
# plot_slice_3d_2(x, lung_mask_out, 2, id, idx=zyxd)
for i in range(136, x.shape[1]):
plot_slice_3d_2(x,
lung_mask_out,
1,
str(id) + str(i),
idx=np.array([200, i, 200]))
def test_luna3d():
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 = [
'/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.mhd']
candidates = utils.load_pkl(
'/mnt/sda3/data/kaggle-lung/luna_test_patient/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.pkl')
candidates = candidates[:4]
print candidates
print '--------------'
print id2zyxd['1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474']
for k, p in enumerate(luna_data_paths):
id = os.path.basename(p).replace('.mhd', '')
print id
img, origin, pixel_spacing = utils_lung.read_mhd(p)
lung_mask = lung_segmentation.segment_HU_scan_ira(img)
print np.min(lung_mask), np.max(lung_mask)
x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=img,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=candidates,
p_transform_augment=None,
luna_origin=origin,
lung_mask=lung_mask,
world_coord_system=False)
print np.min(lung_mask_out), np.max(lung_mask_out)
plot_slice_3d_2(x, lung_mask_out, 0, id)
plot_slice_3d_2(x, lung_mask_out, 1, id)
plot_slice_3d_2(x, lung_mask_out, 2, id)
# for zyxd in annotations_tf:
# plot_slice_3d_2(x, lung_mask_out, 0, id, idx=zyxd)
# plot_slice_3d_2(x, lung_mask_out, 1, id, idx=zyxd)
# plot_slice_3d_2(x, lung_mask_out, 2, id, idx=zyxd)
for i in xrange(136, x.shape[1]):
plot_slice_3d_2(x, lung_mask_out, 1, str(id) + str(i), idx=np.array([200, i, 200]))
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 test_luna3d():
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 = [
'problem_patients/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.mhd'
]
candidates = utils.load_pkl(
'problem_patients/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.pkl'
)
candidates = candidates[:4]
print candidates
print '--------------'
print id2zyxd[
'1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474']
for k, p in enumerate(luna_data_paths):
id = os.path.basename(p).replace('.mhd', '')
print id
img, origin, pixel_spacing = utils_lung.read_mhd(p)
lung_mask = lung_segmentation.segment_HU_scan_elias(img)
x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(
data=img,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=candidates,
p_transform_augment=None,
luna_origin=origin,
lung_mask=lung_mask,
world_coord_system=False)
for zyxd in annotations_tf:
plot_slice_3d_2(x, lung_mask_out, 0, id, img_dir='./', idx=zyxd)
plot_slice_3d_2(x, lung_mask_out, 1, id, img_dir='./', idx=zyxd)
plot_slice_3d_2(x, lung_mask_out, 2, id, img_dir='./', idx=zyxd)
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 test_luna_patches_3d():
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]
# pid = '1.3.6.1.4.1.14519.5.2.1.6279.6001.138080888843357047811238713686'
# luna_data_paths = [pathfinder.LUNA_DATA_PATH + '/%s.mhd' % pid]
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]
print(annotations)
for zyxd in annotations:
img_out, mask = config().data_prep_function_train(
img,
pixel_spacing=pixel_spacing,
p_transform=config().p_transform,
p_transform_augment=config().p_transform_augment,
patch_center=zyxd,
luna_annotations=annotations,
luna_origin=origin)
try:
plot_slice_3d_2(img_out, mask, 0, id)
plot_slice_3d_2(img_out, mask, 1, id)
plot_slice_3d_2(img_out, mask, 2, id)
except:
pass
print('------------------------------------------')
def test_luna3d():
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 = [
'problem_patients/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.mhd']
candidates = utils.load_pkl(
'problem_patients/1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474.pkl')
candidates = candidates[:4]
print candidates
print '--------------'
print id2zyxd['1.3.6.1.4.1.14519.5.2.1.6279.6001.877026508860018521147620598474']
for k, p in enumerate(luna_data_paths):
id = os.path.basename(p).replace('.mhd', '')
print id
img, origin, pixel_spacing = utils_lung.read_mhd(p)
lung_mask = lung_segmentation.segment_HU_scan_elias(img)
x, annotations_tf, tf_matrix, lung_mask_out = data_transforms.transform_scan3d(data=img,
pixel_spacing=pixel_spacing,
p_transform=p_transform,
luna_annotations=candidates,
p_transform_augment=None,
luna_origin=origin,
lung_mask=lung_mask,
world_coord_system=False)
for zyxd in annotations_tf:
plot_slice_3d_2(x, lung_mask_out, 0, id, img_dir='./', idx=zyxd)
plot_slice_3d_2(x, lung_mask_out, 1, id, img_dir='./', idx=zyxd)
plot_slice_3d_2(x, lung_mask_out, 2, id, img_dir='./', idx=zyxd)
if len(sys.argv) < 2:
sys.exit("Usage: test_luna_scan.py <configuration_name>")
config_name = sys.argv[1]
set_configuration('configs_fpred_scan', config_name)
# predictions path
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
pid2candidates_path = utils_lung.get_candidates_paths(outputs_path)
pid2candidates = {}
for k, v in pid2candidates_path.iteritems():
pid2candidates[k] = utils.load_pkl(v)
pid2annotations = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
n_top = 1
tp_top_n = 0
fp_top_n = 0
tp = 0
n_pos = 0
idx1 = []
for k in pid2candidates.iterkeys():
print '----------------------------------------'
print k
n_true = len(pid2annotations[k])
n_det_top = int(np.sum(pid2candidates[k][:n_top, 3]))
n_fp_top = int(n_top - np.sum(pid2candidates[k][:n_top, 3]))
n_det = int(np.sum(pid2candidates[k][:, 3]))
i1 = np.where(pid2candidates[k][:n_top, 3] == 1)[0]
from configuration import set_configuration
import data_transforms
if len(sys.argv) < 2:
sys.exit("Usage: evaluate_luna_scan.py <configuration_name>")
config_name = sys.argv[1]
set_configuration('configs_seg_scan', config_name)
predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
outputs_path = predictions_dir + '/%s' % config_name
blob_files = sorted(glob.glob(outputs_path + '/*.pkl'))
# print(blob_files)
pid2annotations = utils_lung.read_luna_annotations(pathfinder.LUNA_LABELS_PATH)
tp = 0
n_pos = 0
n_blobs = 0
for p in blob_files:
pid = utils_lung.extract_pid_filename(p, '.pkl')
blobs = utils.load_pkl(p)
n_blobs += len(blobs)
print(pid)
print('n_blobs', len(blobs))
print('tp / n pos ', int(np.sum(blobs[:, -1])), len(pid2annotations[pid]))
if int(np.sum(blobs[:, -1])) < len(pid2annotations[pid]):
print('-------- HERE!!!!!! ------------')
tp += np.sum(blobs[:, -1])
print('=====================================')