def opencv_th_k_means_th(img, K=2, max_iter=10, epsilon=1.0, n_ripet=10):
img = normalize(img)
feat_ldg = img.reshape((-1, 1))
label, center = k_means_clustering(feat_ldg, K, max_iter, epsilon, n_ripet)
# Now convert back into uint8, and make original image
center = np.uint8(center)
res = center[label.flatten()]
return np.array([res.reshape(img.shape), np.array(center), label])
def old_opencv_threshold(img, thresh=None, max_value=255):
if not img.dtype == np.uint8:
img = normalize(img)
if thresh is not None:
return (cv2.threshold(img, thresh, max_value,
cv2.THRESH_BINARY)[1]).astype(np.uint8)
else:
return (cv2.threshold(img, 0, 255, cv2.THRESH_BINARY +
cv2.THRESH_OTSU)[1]).astype(np.uint8)
def opencv_th_otsu_after_gauss_blur(img,
max_value=255,
block_size=(5, 5),
sigmaX=0):
if not img.dtype == np.uint8:
img = normalize(img)
_blur = cv2.GaussianBlur(img, block_size, sigmaX)
return (cv2.threshold(_blur, 0, max_value, cv2.THRESH_BINARY +
cv2.THRESH_OTSU)[1]).astype(np.uint8)
else:
return None
def opencv_th_adaptive_gaussian(img, max_value=255, block_size=11):
if not img.dtype == np.uint8:
img = normalize(img)
return (cv2.adaptiveThreshold(img,
max_value,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY,
blockSize=block_size,
C=2)).astype(np.uint8)
else:
return None
def clahe(img, parameters, img_name, to_folder, _save=False):
ksize = parameters['clahe_ksize']
clip = parameters['clip_clahe']
img = normalize(
exposure.equalize_adapthist(img, clip_limit=clip, kernel_size=ksize))
if _save:
save_tiff(img=img,
img_name=img_name,
comment='eq',
folder_path=to_folder)
return img
def segment_by_masks(parsers):
print('START')
args = parser.parse_args()
if args.source_file_list == None or args.mask_file_list == None:
print('You should select the Path of Source Images and masks Images.')
parser.print_help()
source_data = make_dataset(args.source_file_list)
print('source_file_list ', args.source_file_list)
print('source_data.len() = ', len(source_data))
mask_data = make_dataset(args.mask_file_list)
print('mask_file_list ', args.mask_file_list)
print('mask_data.len() = ', len(mask_data))
dest_folder = args.output
print('dest_folder ', dest_folder)
imgs = create_stack(source_data)
print('imgs.shape[2] = ', imgs.shape[2])
masks = create_stack(mask_data)
print('imgs.shape[2] = ', imgs.shape[2])
if imgs is not None and masks is not None:
if imgs.shape[2] != masks.shape[2]:
print('Warning! Dimension of Images and masks mismatch.')
else:
# todo script
print('write in {} \n'.format(dest_folder))
segmented = np.zeros(imgs.shape)
print('\n segmented shape: ', segmented.shape)
print('imgs shape: ', imgs.shape)
print('masks shape: ', masks.shape)
for z in range(imgs.shape[2]):
img = normalize(imgs[:, :, z])
img_eq = exposure.equalize_adapthist(img, clip_limit=0.03)
segmented[:, :, z] = img_eq * masks[:, :, z]
save_tiff(img=segmented[:, :, z],
img_name=str(z),
prefix='s',
comment='dec_seg',
folder_path=dest_folder)
print('img n° {}'.format(z))
print(' \n ** Proces Finished \n')
def skimage_threshold(img, method='isodata'):
if not img.dtype == np.uint8:
print('skimage_threshold NORM because received: ', img.dtype)
img = normalize(img)
thresh = {
'otsu': filters.threshold_otsu(img), # delivers very high threshold
'yen': filters.threshold_yen(img), # delivers even higher threshold
'isodata': filters.threshold_isodata(img), # works extremely well
}.get(method, 0) # None is default value
if thresh == 0:
print(' *** ERROR - skimage_threshold select THRESHOLD = 0 ***')
return thresh, cv2.threshold(img, thresh, 255, cv2.THRESH_BINARY)[1]
def create_surrounded_images(BW_mask,
img_eq,
img_name,
to_countourned_folder,
_save=False):
# plot contour mak over original image
contours = (cv2.findContours(BW_mask.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE))[1]
# prepare image
img_eq_with_contour = normalize(img_eq.copy())
# draw contour
cv2.drawContours(img_eq_with_contour, contours, -1, 255, 1)
if _save:
save_tiff(img=img_eq_with_contour,
img_name=img_name,
comment='cont',
folder_path=to_countourned_folder)
return contours
def main(parser):
''' Docstring (TODO) parameters '''
# ====== 0 ====== Initial operations
# TODO estrare dai parametri da terminale la bool 'verbose'
verbose = False
# read args from console
args = parser.parse_args()
source_path = manage_path_argument(args.source_folder)
base_path = os.path.dirname(os.path.dirname(source_path))
stack_name = os.path.basename(source_path)
# Def .txt filepath
txt_parameters_path = os.path.join(base_path, 'parameters.txt')
txt_results_path = os.path.join(base_path, 'GAMMA_orientation_results.txt')
Results_filename = 'orientation_Results.npy'
Results_filepath = os.path.join(base_path, Results_filename)
# print to video and write in results.txt init message
init_message = [
' ***************** GAMMA - Orientation analysis of 3D stack *****************\n'
' Source from path : {}'.format(source_path),
' Base path : {}'.format(base_path),
' Stack : {}'.format(stack_name),
]
with open(txt_results_path, 'w') as f:
for line in init_message:
print(line)
f.write(line + '\n')
# reads parameters
parameters = extract_parameters(txt_parameters_path)
# analysis block dimension in z-axis
num_of_slices_P = parameters['num_of_slices_P']
# Parameters of Acquisition System:
res_z = parameters['res_z']
res_xy = parameters['res_xy']
resolution_factor = res_z / res_xy
block_side = row_P = col_P = int(num_of_slices_P * resolution_factor)
shape_P = np.array((row_P, col_P, num_of_slices_P)).astype(np.int32)
""" =====================================================================================
__________________________ -1- OPEN STACK _______________________________________________"""
loading_mess = list()
loading_mess.append(
' ***** Start load the Stack, this may take a few minutes... ')
# extract data (OLD METHOD)
volume = load_tif_data(source_path)
if len(volume.shape) == 2:
volume = np.expand_dims(volume, axis=2) # add the zeta axis
# extract stack (NEW METHOD)-------------------
# infile = InputFile(source_path) # read virtual file (shape, dtype, ecc..)
# volume = infile.whole() # load real images in RAM
# # move axes from (z, y, x) -> to (r, c, z)=(y, x, z) [S.R. ZetaStitcher -> Strip Analysis]
# volume = np.moveaxis(volume, 0, -1)
# ---------------------------------------------
loading_mess.append(' - Volume shape : {}'.format(volume.shape))
with open(txt_results_path, 'a') as f:
for m in loading_mess:
print(m)
f.write(m + '\n')
# calculate dimension
shape_V = np.array(volume.shape)
""" =====================================================================================
________________________ -2- CYCLE FOR BLOCKS EXTRACTION and ANALYSIS __________________"""
t_start = time.time()
# create empty Result matrix
R, shape_R = create_R(shape_V, shape_P)
# create 3D filter
mask = create_3D_filter(block_side, res_xy, parameters['sarc_length'])
count = 1 # count iteration
total_iter = np.prod(shape_R)
print(
'\n \n ***** Start iteration of analysis, expectd iterations : {} \n'.
format(total_iter))
with open(txt_results_path, 'a') as f:
for z in range(shape_R[2]):
for r in range(shape_R[0]):
for c in range(shape_R[1]):
# initialize list of string lines
lines = []
start_coord = create_coord_by_iter(r, c, z, shape_P)
slice_coord = create_slice_coordinate(start_coord, shape_P)
if verbose: lines.append('\n \n')
lines.append(
'- iter: {} - init_coord : {} - on total: {}'.format(
count, start_coord, total_iter))
# save init info in R
R[r, c, z]['id_block'] = count
R[r, c, z]['init_coord'] = start_coord
# extract parallelepiped
parall = volume[slice_coord]
# check dimension (if iteration is on border of volume, add zero_pad)
parall = pad_dimension(parall, shape_P)
if np.max(parall) != 0:
parall = (normalize(parall)).astype(
np.float32) # fft analysis work with float
# analysis of parallelepiped extracted
there_is_cell, there_is_freq, results = block_analysis(
parall, shape_P, parameters, block_side, mask,
verbose, lines)
# save info in R[r, c, z]
if there_is_cell: R[r, c, z]['cell_info'] = True
if there_is_freq: R[r, c, z]['freq_info'] = True
# save results in R
for key in results.keys():
R[r, c, z][key] = results[key]
else:
if verbose: lines.append(' block rejected')
for l in lines:
print(l)
count += 1
# execution time
(h, m, s) = seconds_to_min_sec(time.time() - t_start)
print('\n Iterations ended successfully \n')
""" =====================================================================================
________________________ -3- RESULTS ANALYSIS __________________________________"""
post_proc_mess = list()
# count results, rejected and accepted blocks
block_with_cell = np.count_nonzero(R['cell_info'])
block_with_peak = np.count_nonzero(R['freq_info'])
p_rejec_cell = 100 * (1 - block_with_cell / count)
p_rejec_freq_tot = 100 * (1 - block_with_peak / count)
p_rejec_freq = 100 * (1 - block_with_peak / block_with_cell)
post_proc_mess.append(
'\n ***** End of iterations, time of execution: {0:2d}h {1:2d}m {2:2d}s \n'
.format(int(h), int(m), int(s)))
post_proc_mess.append('\n - Expected iterations : {}'.format(total_iter))
post_proc_mess.append(' - Total iterations : {}'.format(count - 1))
post_proc_mess.append(
'\n - block with cell : {}, rejected from total: {} ({}%)'.format(
block_with_cell, count - block_with_cell, p_rejec_cell))
post_proc_mess.append(
' - block with freq. info : {}'
'\n rejected from total: {} ({}%)'
'\n rejected from block with cell: {} ({}%)'.format(
block_with_peak, count - block_with_peak, p_rejec_freq_tot,
block_with_cell - block_with_peak, p_rejec_freq))
with open(txt_results_path, 'a') as f:
for m in post_proc_mess:
print(m)
f.write(m + '\n')
post_proc_mess = list()
# threshold results on frequency validation parameter and save matrix
mess = '\n \n *** Analysis of Results : remove block with low frequency affidability \n'
post_proc_mess.append(mess)
print(mess)
# - 1 normalization of psd_ratio values
R = parameter_normalizer(R, 'psd_ratio')
mess = '- 1 - Normalization on \'psd_ratio\': complete'
post_proc_mess.append(mess)
print(mess)
# - 2 thresholding on psd_ratio values
R, before, after = threshold_par(R, parameters, 'psd_ratio')
mess = '- 2 - First thresholding based on PSD Information: selected {} blocks from {}'.format(
after, before)
post_proc_mess.append(mess)
print(mess)
# - 3 outlier remotion based of orientation and psd_ratio values - NOT EXECUTED
mess = '-*** NO Outlier Remotion based on PSD Information.'
post_proc_mess.append(mess)
print(mess)
# save Result matrix
np.save(Results_filepath, R)
# - 4 Estimate and write local disorder inside Result Matrix
R, shape_LD, isolated_value = estimate_local_disorder(
R, parameters, resolution_factor)
mess = '- 4 - Local Disorder estimated inside result Matrix, with grane (r, c, z): ({}, {}, {}) ' \
'and isolated points setted with local_disorder = {}'\
.format(shape_LD[0], shape_LD[1], shape_LD[2], isolated_value)
post_proc_mess.append(mess)
print(mess)
# se funziona tutto, salvare solo una versione di R
Results_filename = 'orientation_Results_after_disorder.npy'
Results_filepath = os.path.join(base_path, Results_filename)
np.save(Results_filepath, R)
with open(txt_results_path, 'a') as f:
for m in post_proc_mess:
f.write(m + '\n')
del post_proc_mess
""" =====================================================================================
________________________ -4- STATISTICS __________________________________"""
stat = statistics(R, parameters)
result_mess = list()
result_mess.append(
'\n \n *** Results of statistical analysis on accepted points: \n')
result_mess.append(' - {0} : {1:.3f} um^(-1)'.format(
'Mean module', stat['Mean Module']))
result_mess.append(' - {0} : {1:.3f} um'.format('Mean Period',
stat['Mean Period']))
result_mess.append(' - {0} : {1:.3f} % '.format('Alignment',
100 * stat['Alignment']))
result_mess.append(' - {0} : {1:.3f} % '.format(
'XZ Dispersion (area_ratio)', 100 * stat['area_ratio']))
result_mess.append(' - {0} : {1:.3f} % '.format(
'XZ Dispersion (sum dev.std)', 100 * stat['sum_std']))
result_mess.append(
' \n \n ***************************** END GAMMA - orientation_analysis.py ********************'
'\n \n \n \n ')
with open(txt_results_path, 'a') as f:
for l in result_mess:
print(l)
f.write(l + '\n')
def opencv_th_otsu(img, max_value=255):
if not img.dtype == np.uint8:
img = normalize(img)
return (cv2.threshold(img, 0, max_value, cv2.THRESH_BINARY +
cv2.THRESH_OTSU)[1]).astype(np.uint8)
def main(parser):
# --- PRELIMINARY OPERATIONS --------------------------------------------------------
# read args from console
args = parser.parse_args()
source_path = manage_path_argument(args.source_folder)
# take base path and stack name
base_path = os.path.dirname(os.path.dirname(source_path))
stack_name = os.path.basename(source_path)
# Def .txt filepath
txt_parameters_path = os.path.join(base_path, 'parameters.txt')
txt_results_path = os.path.join(base_path, 'ALFA_volume_results.txt')
txt_metadata_path = os.path.join(base_path, 'metadata.txt')
process_names = [
'clahe', 'mask_bin', 'segmented', 'contourned', 'contours'
]
# create destination paths where save images
destination_paths = []
for process in process_names:
destination_paths.append(os.path.join(base_path, process, stack_name))
# if those folders do not exist, this creates them
for path in destination_paths:
if not os.path.exists(path):
os.makedirs(path)
# --- SCRIPT ---------------------------------------------------------------
# reads parameters
parameters = extract_parameters(txt_parameters_path)
# read user preference (if save images or not)
_save_clahe = bool(parameters['save_clahe'])
_save_binary_mask = bool(parameters['save_binary_mask'])
_save_segmented = bool(parameters['save_segmented'])
_save_countourned = bool(parameters['save_countourned'])
_save_contours = bool(parameters['save_contours'])
# print to video and write in results.txt init message
init_message = [
' Source from path : {} \n'.format(base_path),
' Stack : {} \n'.format(stack_name),
'\n\n - Start load the Stack, this may take a few minutes... '
]
error_message = '\n *** ERROR *** : stack in this path is None'
with open(txt_results_path, 'w') as f:
for line in init_message:
print(line, end='')
f.write(line + '\n')
# measure units
x_step = parameters['res_xy'] # micron
y_step = parameters['res_xy'] # micron
z_step = parameters['res_z'] # micron
voxel_in_micron3 = x_step * y_step * z_step # micron^3
# extract stack (OLD METHOD)
# volume, mess = load_stack_into_numpy_ndarray([source_path])
# img_shape = volume[:, :, 0].shape
# extract stack (NEW METHOD)---------------
volume = load_tif_data(source_path)
if len(volume.shape) == 2:
volume = np.expand_dims(volume, axis=2) # add the zeta axis
img_shape = (volume.shape[0], volume.shape[1])
# ---------------------------------------------
print(' Images shape : ', img_shape, '\n')
# measure of imaging volume
area_of_slice = img_shape[0] * img_shape[1]
number_of_slices = volume.shape[2]
# Estimated Volume
total_imaging_volume = area_of_slice * number_of_slices # total volume, from z=0 to z maximum
# Initializing to zero the Volume counter
effective_myocites_volume = 0 # real estimated volume of myocites (sum of area of real cells)
# Boolean vector with length = number_of_slices.
# Element i-th is:
# True - if i-th slice have info
# False - if i-th lice i empty
slices_info = np.zeros(number_of_slices).astype(
bool) # created with all False values.
# save elaboration time for each iteration (for estimate mean time of elaboration)
slice_elab_time_list = list()
t_start = time.time()
print(' EQUALIZATION and SEGMENTATION of every frame:')
with open(txt_results_path, 'a') as f:
if volume is not None:
for z in range(number_of_slices):
# extract current slice
img = volume[:, :, z]
img_name = create_img_name_from_index(z) # img_name.tif
# check if img is empty (too black)
if image_have_info(img, parameters['t_rate_info']):
elab_start = time.time()
slices_info[z] = True
img = normalize(img)
equalized_img = clahe(img,
parameters,
img_name,
destination_paths[0],
_save=_save_clahe)
bw_mask, pixels_of_real_cells = create_byn_mask(
equalized_img,
parameters,
img_name,
destination_paths[1],
_save=_save_binary_mask)
effective_myocites_volume += pixels_of_real_cells
create_and_save_segmented_images(bw_mask,
equalized_img,
img_name,
destination_paths[2],
_save=_save_segmented)
contours = create_surrounded_images(
bw_mask,
equalized_img,
img_name,
destination_paths[3],
_save=_save_countourned)
if _save_contours:
# save image with only contours for fiji visualization:
save_contours(img_name, contours, bw_mask.shape,
destination_paths[4])
save_contours(img_name, contours, bw_mask.shape,
destination_paths[4])
slice_elab_time_list.append(time.time() - elab_start)
elapsed_time = (number_of_slices - z -
1) * np.mean(slice_elab_time_list)
(h, m, s) = seconds_to_min_sec(elapsed_time)
measure = ' - {0} --> {1:.1f} um^3 - ET: {2:2d}h {3:2d}m {4:2d}s'.format(
img_name, pixels_of_real_cells * voxel_in_micron3,
int(h), int(m), int(s))
else:
# black = np.zeros(img.shape)
# for path in destination_paths:
# save_tiff(img=black, img_name=img_name, comment='empty', folder_path=path)
measure = ' - {} is black, rejected'.format(img_name)
print(measure)
f.write(measure + '\n')
# execution time
(h, m, s) = seconds_to_min_sec(time.time() - t_start)
# Num of saved slices
saved = np.count_nonzero(slices_info)
# Num of empty slice on top = Index of first slice with info
empty_on_top = np.where(slices_info == True)[0][0]
# Num of empty slice on bottom = Index of first slice with info, searching from from z=z_max to z=0
empty_on_bottom = np.where(np.flipud(slices_info) == True)[0][0]
# volumes in micron^3
total_imaging_volume_in_micron = total_imaging_volume * voxel_in_micron3
effective_volume_in_micron3 = effective_myocites_volume * voxel_in_micron3
# in percentage
myocites_perc = 100 * effective_myocites_volume / total_imaging_volume
result_message = list()
result_message.append(
'\n *** Process successfully completed, time of execution: {0:2d}h {1:2d}m {2:2d}s \n'
.format(int(h), int(m), int(s)))
result_message.append(' Number of saved slices: {}'.format(saved))
result_message.append(
' Number of rejected slices (because empty) on the top of Volume: {}'
.format(empty_on_top))
result_message.append(
' Number of rejected slices (because empty) on the bottom of Volume: {}'
.format(empty_on_bottom))
result_message.append(
' Total Imaging volume : {0:.6f} mm^3'.format(
total_imaging_volume_in_micron / 10**9))
result_message.append(
' Effective miocytes tissue volume : {0:.6f} mm^3, {1:.3f}% of Imaging volume'
.format(effective_volume_in_micron3 / 10**9, myocites_perc))
result_message.append('\n')
result_message.append(' OUTPUT SAVED IN: \n')
for path in destination_paths:
result_message.append(path)
# write and print results
for l in result_message:
print(l)
f.write(l + '\n')
# write metadata
with open(txt_metadata_path, 'w') as m:
m.write('empty_on_top = {}\n'.format(empty_on_top))
m.write('empty_on_bottom = {}\n'.format(empty_on_bottom))
m.write('saved = {}'.format(saved))
else:
print(error_message)
f.write(error_message)
print(' \n \n \n ')
