本文整理汇总了Python中utils.get_dir_path函数的典型用法代码示例。如果您正苦于以下问题:Python get_dir_path函数的具体用法?Python get_dir_path怎么用?Python get_dir_path使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了get_dir_path函数的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_luna_patches_3d
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 '------------------------------------------'
示例2: test_luna3d_2
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)
示例3: test_luna3d
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)
示例4: build_segmentation_model
def build_segmentation_model(l_in):
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, patch_segmentation_config.__name__.split('.')[-1])
metadata = utils.load_pkl(metadata_path)
model = patch_segmentation_config.build_model(l_in=l_in, patch_size=p_transform['patch_size'])
nn.layers.set_all_param_values(model.l_out, metadata['param_values'])
return model
示例5: test3
def test3():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
id2mm_shape = utils.load_pkl(image_dir + '/pid2mm.pkl')
s = [(key, value) for (key, value) in sorted(id2mm_shape.items(), key=lambda x: x[1][0])]
for i in xrange(5):
print s[i]
print '--------------------------'
for i in xrange(1,6):
print s[-i]
示例6: test1
def test1():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
image_dir = image_dir + '/test_1/'
utils.auto_make_dir(image_dir)
sys.stdout = logger.Logger(image_dir + '/%s.log' % 'test1_log')
sys.stderr = sys.stdout
patient_data_paths = utils_lung.get_patient_data_paths(pathfinder.DATA_PATH)
print len(patient_data_paths)
for k, p in enumerate(patient_data_paths):
pid = utils_lung.extract_pid_dir(p)
try:
sid2data, sid2metadata = utils_lung.get_patient_data(p)
sids_sorted = utils_lung.sort_sids_by_position(sid2metadata)
sids_sorted_jonas = utils_lung.sort_slices_jonas(sid2metadata)
sid2position = utils_lung.slice_location_finder(sid2metadata)
try:
slice_thickness_pos = np.abs(sid2metadata[sids_sorted[0]]['ImagePositionPatient'][2] -
sid2metadata[sids_sorted[1]]['ImagePositionPatient'][2])
except:
print 'This patient has no ImagePosition!'
slice_thickness_pos = 0.
try:
slice_thickness_loc = np.abs(
sid2metadata[sids_sorted[0]]['SliceLocation'] - sid2metadata[sids_sorted[1]]['SliceLocation'])
except:
print 'This patient has no SliceLocation!'
slice_thickness_loc = 0.
jonas_slicethick = []
for i in xrange(len(sids_sorted_jonas) - 1):
s = np.abs(sid2position[sids_sorted_jonas[i + 1]] - sid2position[sids_sorted_jonas[i]])
jonas_slicethick.append(s)
full_img = np.stack([data_transforms.ct2normHU(sid2data[sid], sid2metadata[sid]) for sid in sids_sorted])
del sid2data, sid2metadata
print np.min(full_img), np.max(full_img)
# spacing = sid2metadata[sids_sorted[0]]['PixelSpacing']
# spacing = [slice_thickness, spacing[0], spacing[1]]
# resampled_image, _ = resample(full_img, spacing)
plot_2d(full_img, axis=0, pid=pid + 'ax0', img_dir=image_dir)
plot_2d(full_img, axis=1, pid=pid + 'ax1', img_dir=image_dir)
plot_2d(full_img, axis=2, pid=pid + 'ax2', img_dir=image_dir)
print k, pid, full_img.shape, slice_thickness_pos, slice_thickness_loc, set(jonas_slicethick)
del full_img
except:
print 'exception!!!', pid
示例7: test_luna3d
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]))
示例8: test_dsb
def test_dsb():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
image_dir = image_dir + '/test_1/'
utils.auto_make_dir(image_dir)
patient_data_paths = utils_lung.get_patient_data_paths(pathfinder.DATA_PATH)
print len(patient_data_paths)
patient_data_paths = [pathfinder.DATA_PATH + '/01de8323fa065a8963533c4a86f2f6c1']
for k, p in enumerate(patient_data_paths):
pid = utils_lung.extract_pid_dir(p)
# sid2data, sid2metadata = utils_lung.get_patient_data(p)
# sids_sorted = utils_lung.sort_sids_by_position(sid2metadata)
# sids_sorted_jonas = utils_lung.sort_slices_jonas(sid2metadata)
# sid2position = utils_lung.slice_location_finder(sid2metadata)
#
# jonas_slicethick = []
# for i in xrange(len(sids_sorted_jonas) - 1):
# s = np.abs(sid2position[sids_sorted_jonas[i + 1]] - sid2position[sids_sorted_jonas[i]])
# jonas_slicethick.append(s)
#
# img = np.stack([data_transforms.ct2HU(sid2data[sid], sid2metadata[sid]) for sid in sids_sorted])
# xx = (jonas_slicethick[0],
# sid2metadata[sids_sorted[0]]['PixelSpacing'][0],
# sid2metadata[sids_sorted[0]]['PixelSpacing'][1])
# pixel_spacing = np.asarray(xx)
img, pixel_spacing = utils_lung.read_dicom_scan(p)
mask = lung_segmentation.segment_HU_scan_ira(img)
print pid
print pixel_spacing
print '===================================='
img_out, transform_matrix, mask_out = data_transforms.transform_scan3d(img,
pixel_spacing=pixel_spacing,
p_transform=config().p_transform,
p_transform_augment=None,
lung_mask=mask)
for i in xrange(100, img_out.shape[0], 5):
plot_slice_3d_2(img_out, mask_out, 0, str(pid) + str(i), idx=np.array([i, 200, 200]))
plot_slice_3d_2(img_out, mask_out, 0, pid, idx=np.array(img_out.shape) / 2)
plot_slice_3d_2(mask_out, img_out, 0, pid, idx=np.array(img_out.shape) / 4)
plot_slice_3d_2(mask_out, img_out, 0, pid, idx=np.array(img_out.shape) / 8)
示例9: test2
def test2():
image_dir = utils.get_dir_path('analysis', pathfinder.METADATA_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)
pid2mm_shape = {}
for k, p in enumerate(luna_data_paths):
img, origin, spacing = utils_lung.read_mhd(p)
id = os.path.basename(p).replace('.mhd', '')
mm_shape = img.shape * spacing
pid2mm_shape[id] = mm_shape
print k, id, mm_shape
if k % 50 == 0:
print 'Saved'
utils.save_pkl(pid2mm_shape, image_dir + '/pid2mm.pkl')
utils.save_pkl(pid2mm_shape, image_dir + '/pid2mm.pkl')
示例10: build_model
def build_model():
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, patch_class_config.__name__.split('.')[-1])
metadata = utils.load_pkl(metadata_path)
print 'Build model'
model = patch_class_config.build_model()
all_layers = nn.layers.get_all_layers(model.l_out)
num_params = nn.layers.count_params(model.l_out)
print ' number of parameters: %d' % num_params
print string.ljust(' layer output shapes:', 36),
print string.ljust('#params:', 10),
print 'output shape:'
for layer in all_layers:
name = string.ljust(layer.__class__.__name__, 32)
num_param = sum([np.prod(p.get_value().shape) for p in layer.get_params()])
num_param = string.ljust(num_param.__str__(), 10)
print ' %s %s %s' % (name, num_param, layer.output_shape)
nn.layers.set_all_param_values(model.l_out, metadata['param_values'])
return model
示例11: test1
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)
示例12: test1
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)
示例13: build_model
def build_model():
l_in = nn.layers.InputLayer((None, n_candidates_per_patient, 1,) + p_transform['patch_size'])
l_in_rshp = nn.layers.ReshapeLayer(l_in, (-1, 1,) + p_transform['patch_size'])
l_target = nn.layers.InputLayer((batch_size,))
base_n_filters = 128
l = conv_prelu_layer(l_in_rshp, n_filters=base_n_filters)
l = conv_prelu_layer(l, n_filters=base_n_filters)
l = conv_prelu_layer(l, n_filters=base_n_filters)
l = max_pool3d(l)
l = conv_prelu_layer(l, n_filters=base_n_filters)
l = conv_prelu_layer(l, n_filters=base_n_filters)
l = conv_prelu_layer(l, n_filters=base_n_filters)
l_enc = conv_prelu_layer(l, n_filters=base_n_filters)
num_units_dense = 512
l_d01 = dense_prelu_layer(l, num_units=512)
l_d01 = nn.layers.ReshapeLayer(l_d01, (-1, n_candidates_per_patient, num_units_dense))
l_d02 = dense_prelu_layer(l_d01, num_units=512)
l_out = nn.layers.DenseLayer(l_d02, num_units=2,
W=nn.init.Constant(0.),
b=np.array([np.log((1397. - 362) / 1398), np.log(362. / 1397)], dtype='float32'),
nonlinearity=nn.nonlinearities.softmax)
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, 'luna_p8a1')
metadata = utils.load_pkl(metadata_path)
for p, pv in zip(nn.layers.get_all_params(l_enc), metadata['param_values']):
if p.get_value().shape != pv.shape:
raise ValueError("mismatch: parameter has shape %r but value to "
"set has shape %r" %
(p.get_value().shape, pv.shape))
p.set_value(pv)
return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
示例14: test_luna3d
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)
示例15: len
import pathfinder
theano.config.warn_float64 = 'raise'
if len(sys.argv) < 2:
sys.exit("Usage: train.py <configuration_name>")
config_name = sys.argv[1]
set_configuration('configs_luna_props_patch', config_name)
expid = utils.generate_expid(config_name)
print
print "Experiment ID: %s" % expid
print
# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = metadata_dir + '/%s.pkl' % expid
# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % expid)
sys.stderr = sys.stdout
print 'Build model'
model = config().build_model()
all_layers = nn.layers.get_all_layers(model.l_out)
all_params = nn.layers.get_all_params(model.l_out)
num_params = nn.layers.count_params(model.l_out)
print ' number of parameters: %d' % num_params
print string.ljust(' layer output shapes:', 36),
print string.ljust('#params:', 10),