def caller(inputs, inputs_labels, output, functions, params):
make_dirs(output)
# Make cells. cells are a list of regionproperties or subclasses.
logger.info('Postprocess.\tcollecting cells...')
store = []
for frame, (path, pathl) in enumerate(zip(inputs, inputs_labels)):
img, labels = imread(path), lbread(pathl)
cells = regionprops(labels, img)
cells = [Cell(cell) for cell in cells]
for cell in cells:
cell.frame = frame
if frame > 0:
all_labels = [i.label for i in store[frame - 1]]
if cell.label in all_labels:
store[frame - 1][all_labels.index(cell.label)].nxt = cell
store.append(cells)
cells = [i for j in store for i in j]
# Each function receives cells (regionprops) and finally return labels generated by cells.label
for function, param in zip(functions, params):
logger.info('\trunning {0}'.format(function))
func = getattr(postprocess_operation, function)
cells = func(cells, **param)
logger.info('\tsaving images...')
for frame, (path, pathl) in enumerate(zip(inputs, inputs_labels)):
labels = cells2labels(cells, frame, lbread(pathl))
imsave(labels, output, path, dtype=np.int16)
def caller(inputs_list, inputs_labels_list, output, primary, secondary):
make_dirs(dirname(abspath(output)))
inputs_list = [inputs_list, ] if isinstance(inputs_list[0], str) else inputs_list
inputs_labels_list = [inputs_labels_list, ] if isinstance(inputs_labels_list[0], str) else inputs_labels_list
obj_names = [basename(dirname(i[0])) for i in inputs_labels_list] if primary is None else primary
ch_names = [basename(dirname(i[0])) for i in inputs_list] if secondary is None else secondary
store = []
for inputs, ch in zip(inputs_list, ch_names):
for inputs_labels, obj in zip(inputs_labels_list, obj_names):
logger.info("Channel {0}: {1} applied...".format(ch, obj))
for frame, (path, pathl) in enumerate(zip(inputs, inputs_labels)):
img, labels = imread(path), lbread(pathl, nonneg=False)
cells = regionprops(labels, img)
if (labels < 0).any():
cells = add_parent(cells, labels)
[setattr(cell, 'frame', frame) for cell in cells]
cells = [Cell(cell) for cell in cells]
store.append(cells)
logger.info("\tmaking dataframe...")
df = multi_index([i for ii in store for i in ii], obj, ch)
if exists(join(output, 'df.csv')):
ex_df = pd.read_csv(join(output, 'df.csv'), index_col=['object', 'ch', 'prop', 'frame'])
ex_df.columns = pd.to_numeric(ex_df.columns)
ex_df = ex_df.astype(np.float32)
df = pd.concat([df, ex_df])
df.to_csv(join(output, 'df.csv'))
larr = df2larr(df)
larr.save(join(output, 'df.npz'))
logger.info("\tdf.npz saved.")
def caller(inputs, inputs_labels, output, functions, params):
make_dirs(output)
inputs = inputs[FRAME_REVTRACK:0:-1] + inputs
inputs_labels = inputs_labels[FRAME_REVTRACK:0:-1] + inputs_labels
img0, labels0 = imread(inputs[0]), lbread(inputs_labels[0]).astype(
np.int16)
labels0 = neg2poslabels(labels0)
imsave(labels0, output, basename(inputs[0]), dtype=np.int16)
for holder.frame, (path,
pathl) in enumerate(zip(inputs[1:], inputs_labels[1:])):
img1, labels1 = imread(path), lbread(pathl)
labels1 = -labels1
for fnum, (function, param) in enumerate(zip(functions, params)):
func = getattr(track_operation, function)
if not (labels1 < 0).any():
continue
labels0, labels1 = func(img0, img1, labels0, -labels1, **param)
logger.debug('\t{0} with {1}: {2}'.format(
function, param, len(set(labels1[labels1 < 0]))))
logger.info("\tframe {0}: {1} objects linked and {2} unlinked.".format(
holder.frame, len(set(labels1[labels1 > 0])),
len(set(labels1[labels1 < 0]))))
labels0 = neg2poslabels(labels1)
img0 = img1
imsave(labels0, output, path, dtype=np.int16)
def call_operations(contents):
make_dirs(contents['OUTPUT_DIR'])
logging.basicConfig(filename=join(contents['OUTPUT_DIR'], 'log.txt'),
level=logging.DEBUG)
logging.getLogger("PIL").setLevel(logging.WARNING)
run_operations(contents['OUTPUT_DIR'], contents['operations'])
logger.info("Caller finished.")
return
def caller(inputs_list, inputs_labels_list, output, primary, secondary):
make_dirs(dirname(abspath(output)))
inputs_list = [
inputs_list,
] if isinstance(inputs_list[0], str) else inputs_list
inputs_labels_list = [
inputs_labels_list,
] if isinstance(inputs_labels_list[0], str) else inputs_labels_list
obj_names = [basename(dirname(i[0]))
for i in inputs_labels_list] if primary is None else primary
ch_names = [basename(dirname(i[0]))
for i in inputs_list] if secondary is None else secondary
for inputs, ch in zip(inputs_list, ch_names):
for inputs_labels, obj in zip(inputs_labels_list, obj_names):
logger.info("Channel {0}: {1} applied...".format(ch, obj))
arr = np.ones((MAX_NUMCELL, len(PROP_SAVE), len(inputs)),
np.float32) * np.nan
for frame, (path, pathl) in enumerate(zip(inputs, inputs_labels)):
img, labels = imread(path), lbread(pathl, nonneg=False)
cells = regionprops(labels, img)
if (labels < 0).any():
cells = add_parent(cells, labels)
[setattr(cell, 'frame', frame) for cell in cells]
cells = [Cell(cell) for cell in cells]
tarr = _cells2array(cells)
index = tarr[:, 1].astype(np.int32)
arr[index, :, frame] = tarr
logger.info("\tmaking dataframe...")
cellids = np.where(~np.isnan(arr[:, 0, :]).all(axis=1))[0]
marr = np.zeros((len(cellids), arr.shape[1], arr.shape[2]))
for pn, i in enumerate(cellids):
marr[pn] = arr[i]
sarr = np.swapaxes(marr, 0, 2)
narr = sarr.reshape((sarr.shape[0] * sarr.shape[1], sarr.shape[2]),
order='F')
index = pd.MultiIndex.from_product(
[obj, ch, PROP_SAVE, range(arr.shape[-1])],
names=['object', 'ch', 'prop', 'frame'])
df = pd.DataFrame(narr, index=index, columns=cellids)
if exists(join(output, FILE_NAME + '.csv')):
ex_df = pd.read_csv(
join(output, FILE_NAME + '.csv'),
index_col=['object', 'ch', 'prop', 'frame'])
ex_df.columns = pd.to_numeric(ex_df.columns)
ex_df = ex_df.astype(np.float32)
df = pd.concat([df, ex_df])
df.to_csv(join(output, FILE_NAME + '.csv'))
larr = df2larr(df)
larr.save(join(output, FILE_NAME + '.npz'))
logger.info("\t" + FILE_NAME + ".npz saved.")
def caller(inputs, output, functions, params):
holder.inputs = inputs
make_dirs(output)
logger.info("Functions {0} for {1} images.".format(functions, len(inputs)))
for holder.frame, holder.path in enumerate(inputs):
img = imread(holder.path)
for function, param in zip(functions, params):
func = getattr(preprocess_operation, function)
img = func(img, **param)
imsave(img, output, holder.path)
logger.info("\tframe {0} done.".format(holder.frame))
def caller(inputs, output, functions, params):
make_dirs(output)
logger.info("Functions {0} for {1} images.".format(functions, len(inputs)))
for holder.frame, path in enumerate(inputs):
img = imread(path)
for function, param in zip(functions, params):
func = getattr(segment_operation, function)
img = func(img, **param)
if isinstance(path, list) or isinstance(path, tuple):
path = path[0]
labels = clean_labels(img, radius)
imsave(labels, output, path, dtype=np.int16)
logger.info("\tframe {0}: {1} objects segmented.".format(
holder.frame, len(np.unique(labels))))
def caller(inputs, inputs_labels, output, functions, params):
make_dirs(output)
logger.info("Functions {0} for {1} images.".format(functions, len(inputs)))
img = None
for holder.frame, (path,
pathl) in enumerate(izip_longest(inputs,
inputs_labels)):
if path is not None:
img = imread(path)
labels0 = lbread(pathl)
for function, param in zip(functions, params):
func = getattr(subdetect_operation, function)
if img is not None:
labels = func(labels0, img, **param)
else:
labels = func(labels0, **param)
imsave(labels, output, pathl, dtype=np.int16)
logger.info("\tframe {0} done.".format(holder.frame))
