def convert_tif_to_hdf(input_path,
output_path,
key_path="entry/data",
crop=(0, 0, 0, 0),
pattern=None,
**options):
"""
Convert a folder of tif files to a hdf/nxs file.
Parameters
----------
input_path : str
Folder path to the tif files.
output_path : str
Path to the hdf/nxs file.
key_path : str, optional
Key path to the dataset.
crop : tuple of int, optional
Crop the images from the edges, i.e.
crop = (crop_top, crop_bottom, crop_left, crop_right).
pattern : str, optional
Used to find tif files with names matching the pattern.
options : dict, optional
Add metadata. E.g. options={"entry/angles": angles, "entry/energy": 53}.
Returns
-------
str
Path to the hdf/nxs file.
"""
if pattern is None:
list_file = losa.find_file(input_path + "/*.tif*")
else:
list_file = losa.find_file(input_path + "/*" + pattern + "*.tif*")
depth = len(list_file)
if depth == 0:
raise ValueError("No tif files in the folder: {}".format(input_path))
(height, width) = np.shape(losa.load_image(list_file[0]))
file_base, file_ext = os.path.splitext(output_path)
if not (file_ext == '.hdf' or file_ext == '.h5' or file_ext == ".nxs"):
file_ext = '.hdf'
output_path = file_base + file_ext
cr_top, cr_bottom, cr_left, cr_right = crop
cr_height = height - cr_top - cr_bottom
cr_width = width - cr_left - cr_right
data_out = losa.open_hdf_stream(output_path, (depth, cr_height, cr_width),
key_path=key_path,
overwrite=True,
**options)
for i, fname in enumerate(list_file):
data_out[i] = losa.load_image(fname)[cr_top:cr_height + cr_top,
cr_left:cr_width + cr_left]
return output_path
result_tmp = []
for slice_idx in range(slice_start, slice_stop):
pos = np.squeeze(np.where(list1 == slice_idx)[0])
if pos.size == 1:
result_tmp.append(pos)
if len(result_tmp) > 0:
result_tmp = np.asarray(result_tmp)
results.append([i, result_tmp[0], result_tmp[-1]])
return results
print("!!! Start !!!")
time_start = timeit.default_timer()
# Open hdf_stream for saving data.
hdf_stream = losa.open_hdf_stream(output_base + "/" + output_file,
(dsp_slice, dsp_height, dsp_width), key_path)
list_idx_nslice = np.reshape(np.arange(total_slice_r), (dsp_slice, cube[0]))
dsp_method = np.mean # Use mean for downsampling
for idx in np.arange(dsp_slice):
slice_start = list_idx_nslice[idx, 0]
slice_stop = list_idx_nslice[idx, -1] + 1
slices = locate_slice_chunk(slice_start, slice_stop, list_slices)
if len(slices) == 1:
data_chunk = list_hdf_object[slices[0][0]][slices[0][1]:slices[0][2] +
1, :height_r, :width_r]
else:
data_chunk1 = list_hdf_object[slices[0][0]][slices[0][1]:slices[0][2] +
1, :height_r, :width_r]
data_chunk2 = list_hdf_object[slices[1][0]][slices[1][1]:slices[1][2] +
1, :height_r, :width_r]
def test_open_hdf_stream(self):
data_out = losa.open_hdf_stream("data/data.hdf", (64, 64))
self.assertTrue(isinstance(data_out, object))
def rescale_dataset(input_,
output,
nbit=16,
minmax=None,
skip=None,
key_path=None):
"""
Rescale a dataset to 8-bit or 16-bit data-type. The dataset can be a
folder of tif files, a hdf file, or a 3D array.
Parameters
----------
input_ : str, array_like
It can be a folder path to tif files, a hdf file, or 3D array.
output : str, None
It can be a folder path, a hdf file path, or None (memory consuming).
nbit : {8,16}
Rescaled data-type: 8-bit or 16-bit.
minmax : tuple of float, or None
Minimum and maximum values used for rescaling. They are calculated if
None is given.
skip : int or None
Skipping step of reading input used for getting statistical information.
key_path : str, optional
Key path to the dataset if the input is the hdf file.
Returns
-------
array_like or None
If output is None, returning an 3D array.
"""
if output is not None:
file_base, file_ext = os.path.splitext(output)
if file_ext != "":
file_base = os.path.dirname(output)
if os.path.exists(file_base):
raise ValueError("Folder exists!!! Please choose another path!!!")
if isinstance(input_, str) and (os.path.splitext(input_)[-1] == ""):
list_file = losa.find_file(input_ + "/*.tif*")
depth = len(list_file)
if depth == 0:
raise ValueError("No tif files in the folder: {}".format(input_))
if minmax is None:
if skip is None:
skip = int(np.ceil(0.15 * depth))
(gmin, gmax) = get_statical_information_dataset(input_,
skip=skip)[0:2]
else:
(gmin, gmax) = minmax
if output is not None:
file_base, file_ext = os.path.splitext(output)
if file_ext != "":
if not (file_ext == '.hdf' or file_ext == '.h5'
or file_ext == ".nxs"):
raise ValueError("File extension must be hdf, h5, or nxs")
output = file_base + file_ext
(height, width) = np.shape(losa.load_image(list_file[0]))
if nbit == 8:
data_type = "uint8"
else:
data_type = "uint16"
data_out = losa.open_hdf_stream(output, (depth, height, width),
key_path="rescale/data",
data_type=data_type,
overwrite=False)
data_res = []
for i in range(0, depth):
mat = rescale(losa.load_image(list_file[i]),
nbit=nbit,
minmax=(gmin, gmax))
if output is None:
data_res.append(mat)
else:
file_base, file_ext = os.path.splitext(output)
if file_ext == "":
out_name = "0000" + str(i)
losa.save_image(output + "/img_" + out_name[-5:] + ".tif",
mat)
else:
data_out[i] = mat
else:
if isinstance(input_, str):
file_ext = os.path.splitext(input_)[-1]
if not (file_ext == '.hdf' or file_ext == '.h5'
or file_ext == ".nxs"):
raise ValueError(
"Can't open this type of file format {}".format(file_ext))
if key_path is None:
raise ValueError(
"Please provide the key path to the dataset!!!")
input_ = losa.load_hdf(input_, key_path)
(depth, height, width) = input_.shape
if minmax is None:
if skip is None:
skip = int(np.ceil(0.15 * depth))
(gmin,
gmax) = get_statical_information_dataset(input_,
skip=skip,
key_path=key_path)[0:2]
else:
(gmin, gmax) = minmax
data_res = []
if output is not None:
file_base, file_ext = os.path.splitext(output)
if file_ext != "":
if not (file_ext == '.hdf' or file_ext == '.h5'
or file_ext == ".nxs"):
raise ValueError("File extension must be hdf, h5, or nxs")
output = file_base + file_ext
if nbit == 8:
data_type = "uint8"
else:
data_type = "uint16"
data_out = losa.open_hdf_stream(output, (depth, height, width),
key_path="rescale/data",
data_type=data_type,
overwrite=False)
for i in range(0, depth):
mat = rescale(input_[i], nbit=nbit, minmax=(gmin, gmax))
if output is None:
data_res.append(mat)
else:
file_base, file_ext = os.path.splitext(output)
if file_ext != "":
data_out[i] = mat
else:
out_name = "0000" + str(i)
losa.save_image(output + "/img_" + out_name[-5:] + ".tif",
mat)
if output is None:
return np.asarray(data_res)
def downsample_dataset(input_,
output,
cell_size,
method="mean",
key_path=None):
"""
Downsample a dataset. This can be a folder of tif files, a hdf file,
or a 3D array.
Parameters
----------
input_ : str, array_like
It can be a folder path to tif files, a hdf file, or 3D array.
output : str, None
It can be a folder path, a hdf file path, or None (memory consuming).
cell_size : int or tuple of int
Window size along axes used for grouping pixels.
method : {"mean", "median", "max", "min"}
Downsampling method.
key_path : str, optional
Key path to the dataset if the input is the hdf file.
Returns
-------
array_like or None
If output is None, returning an 3D array.
"""
if output is not None:
file_base, file_ext = os.path.splitext(output)
if file_ext != "":
file_base = os.path.dirname(output)
if os.path.exists(file_base):
raise ValueError("Folder exists!!! Please choose another path!!!")
if method == "median":
dsp_method = np.median
elif method == "max":
dsp_method = np.max
elif method == "min":
dsp_method = np.amin
else:
dsp_method = np.mean
if isinstance(cell_size, int):
cell_size = (cell_size, cell_size, cell_size)
if isinstance(input_, str) and (os.path.splitext(input_)[-1] == ""):
list_file = losa.find_file(input_ + "/*.tif*")
depth = len(list_file)
if depth == 0:
raise ValueError("No tif files in the folder: {}".format(input_))
(height, width) = np.shape(losa.load_image(list_file[0]))
depth_dsp = depth // cell_size[0]
height_dsp = height // cell_size[1]
width_dsp = width // cell_size[2]
num = 0
if (depth_dsp != 0) and (height_dsp != 0) and (width_dsp != 0):
if output is not None:
file_base, file_ext = os.path.splitext(output)
if file_ext != "":
if not (file_ext == '.hdf' or file_ext == '.h5'
or file_ext == ".nxs"):
raise ValueError(
"File extension must be hdf, h5, or nxs")
output = file_base + file_ext
data_out = losa.open_hdf_stream(
output, (depth_dsp, height_dsp, width_dsp),
key_path="downsample/data",
overwrite=False)
data_dsp = []
for i in range(0, depth, cell_size[0]):
if (i + cell_size[0]) > depth:
break
else:
mat = []
for j in range(i, i + cell_size[0]):
mat.append(losa.load_image(list_file[j]))
mat = np.asarray(mat)
mat = mat[:, :height_dsp * cell_size[1], :width_dsp *
cell_size[2]]
mat = mat.reshape(1, cell_size[0], height_dsp,
cell_size[1], width_dsp, cell_size[2])
mat_dsp = dsp_method(dsp_method(dsp_method(mat, axis=-1),
axis=1),
axis=2)
if output is None:
data_dsp.append(mat_dsp[0])
else:
if file_ext == "":
out_name = "0000" + str(num)
losa.save_image(
output + "/img_" + out_name[-5:] + ".tif",
mat_dsp[0])
else:
data_out[num] = mat_dsp[0]
num += 1
else:
raise ValueError("Incorrect cell size {}".format(cell_size))
else:
if isinstance(input_, str):
file_ext = os.path.splitext(input_)[-1]
if not (file_ext == '.hdf' or file_ext == '.h5'
or file_ext == ".nxs"):
raise ValueError(
"Can't open this type of file format {}".format(file_ext))
if key_path is None:
raise ValueError(
"Please provide the key path to the dataset!!!")
input_ = losa.load_hdf(input_, key_path)
(depth, height, width) = input_.shape
depth_dsp = depth // cell_size[0]
height_dsp = height // cell_size[1]
width_dsp = width // cell_size[2]
if (depth_dsp != 0) and (height_dsp != 0) and (width_dsp != 0):
if output is None:
input_ = input_[:depth_dsp * cell_size[0], :height_dsp *
cell_size[1], :width_dsp * cell_size[2]]
input_ = input_.reshape(depth_dsp, cell_size[0], height_dsp,
cell_size[1], width_dsp, cell_size[2])
data_dsp = dsp_method(dsp_method(dsp_method(input_, axis=-1),
axis=1),
axis=2)
else:
file_base, file_ext = os.path.splitext(output)
if file_ext != "":
if not (file_ext == '.hdf' or file_ext == '.h5'
or file_ext == ".nxs"):
raise ValueError(
"File extension must be hdf, h5, or nxs")
output = file_base + file_ext
data_out = losa.open_hdf_stream(
output, (depth_dsp, height_dsp, width_dsp),
key_path="downsample/data",
overwrite=False)
num = 0
for i in range(0, depth, cell_size[0]):
if (i + cell_size[0]) > depth:
break
else:
mat = input_[i:i + cell_size[0], :height_dsp *
cell_size[1], :width_dsp * cell_size[2]]
mat = mat.reshape(1, cell_size[0], height_dsp,
cell_size[1], width_dsp,
cell_size[2])
mat_dsp = dsp_method(dsp_method(dsp_method(mat,
axis=-1),
axis=1),
axis=2)
if file_ext != "":
data_out[num] = mat_dsp[0]
else:
out_name = "0000" + str(num)
losa.save_image(
output + "/img_" + out_name[-5:] + ".tif",
mat_dsp[0])
num += 1
else:
raise ValueError("Incorrect cell size {}".format(cell_size))
if output is None:
return np.asarray(data_dsp)
# print(sam_shifts)
# Align image stacks
(ref_stack_cr, sam_stack_cr) = f_alias4(ref_stack, sam_stack, sr_shifts,
sam_shifts)
# Check results to make sure
for i in range(num_use):
name = ("0000" + str(i))[-5:]
losa.save_image(output_base + "/aligned/ref/img_" + name + ".tif",
ref_stack_cr[i])
losa.save_image(output_base + "/aligned/sam/img_" + name + ".tif",
sam_stack_cr[i])
# Open hdf stream to save data
phase_hdf = losa.open_hdf_stream(output_base + "/phase.hdf",
(num_proj, height1, width1),
key_path="entry/data")
if get_trans_dark_signal:
trans_hdf = losa.open_hdf_stream(output_base + "/transmission.hdf",
(num_proj, height1, width1),
key_path="entry/data")
dark_hdf = losa.open_hdf_stream(output_base + "/dark_signal.hdf",
(num_proj, height1, width1),
key_path="entry/data")
# Assign aliases to functions for convenient use
f_alias5 = losa.get_reference_sample_stacks
f_alias6 = ps.retrieve_phase_based_speckle_tracking
f_alias7 = ps.get_transmission_dark_field_signal
t0 = timeit.default_timer()
for i in range(num_proj):
# Note: we don't apply MTF deconvolution to dark-noise images.
dark_field = corr.unwarp_projection(dark_field, xcenter, ycenter, list_fact)
# Calculate parameters for looping.
start_image = proj_idx[0]
stop_image = proj_idx[-1] + 1
total_image = stop_image - start_image
offset = start_image
if chunk > total_image:
chunk = total_image
num_iter = total_image // chunk
num_rest = total_image - num_iter * chunk
# Open hdf stream for saving data.
output_hdf = losa.open_hdf_stream(output_base + "/" + ofile_name ,
(total_image, height, width),
key_path="entry/data", data_type="float32")
for i in range(num_iter):
start_proj = i * chunk + offset
stop_proj = start_proj + chunk
# Load projections
proj_chunk = data[start_proj:stop_proj]
# Apply MTF deconvolution
proj_chunk = np.asarray(Parallel(n_jobs=ncore, backend="threading")(
delayed(corr.mtf_deconvolution)(proj_chunk[i], mtf_win, mtf_pad) for i in range(chunk)))
# Apply distortion correction
proj_chunk = np.asarray(Parallel(n_jobs=ncore, backend="threading")(
delayed(corr.unwarp_projection)(proj_chunk[i], xcenter, ycenter, list_fact) for i in range(chunk)))
# Apply flat-field correction
proj_corr = corr.flat_field_correction(proj_chunk[:, top:bot, left:right], flat_field[top:bot, left:right],
dark_field[top:bot, left:right])