def add_channel_regitration(czi_base_fname,
nomenclature_file,
ch_name,
trsf_type,
temp_reg,
image_dirname,
colormap='invert_grey'):
"""
Add the registered image to tissuelab world.
"""
for n, (t_ref, t_float) in enumerate(temp_reg):
# -- Get the reference CZI file names:
ref_czi_fname = czi_base_fname.format(t_ref)
# -- Get the reference INR file names:
ref_path_suffix, ref_img_fname = get_nomenclature_channel_fname(
ref_czi_fname, nomenclature_file, ch_name)
if n == 0:
print "Reading last time-point (t{}) image filename: {}".format(
t_ref, ref_img_fname)
ref_im = imread(image_dirname + ref_path_suffix + ref_img_fname)
if ch_name.find('raw') != -1:
ref_im = z_slice_equalize_adapthist(ref_im)
world.add(ref_im, ref_img_fname, colormap='Reds')
# -- Get the float CZI file names:
float_czi_fname = czi_base_fname.format(t_float)
# -- Get the float INR file names:
float_path_suffix, float_img_fname = get_nomenclature_channel_fname(
float_czi_fname, nomenclature_file, ch_name)
# --- Get RESULT rigid transformation image filename:
res_path_suffix = float_path_suffix + trsf_type + "_registrations/"
res_img_fname = get_res_img_fname(float_img_fname, t_ref, t_float,
trsf_type)
# -- Read RESULT image file:
res_im = imread(image_dirname + res_path_suffix + res_img_fname)
print "Reading result floating (t{}) image filename: {}".format(
t_float, res_img_fname)
world.add(res_im, res_img_fname, colormap='Greens')
return "Done."
std_dev=1.0,
method='gaussian_smoothing')
# -- Remove the NUCLEI signal from the MEMBRANE signal image:
print " --> Substracting NUCLEI signal from the MEMBRANE signal image..."
zero_mask = np.greater(nuc_im.get_array(),
img.get_array())
img = img - nuc_im
img[zero_mask] = 0
# world.add(nuc_im, 'nuc_im', colormap='invert_grey')
# world.add(img, 'img'+suffix, colormap='invert_grey')
print "Done!"
del nuc_im, zero_mask
# -- Performs intensity rescaling for membrane image:
if rescaling == 'AdaptHistEq':
print " --> Adaptative Histogram Equalization..."
img = z_slice_equalize_adapthist(img)
if rescaling == 'ContrastStretch':
print " --> Contrast Stretching..."
img = z_slice_contrast_stretch(img)
if std_dev != 0:
print " --> 'gaussian_smoothing' with std_dev={}...".format(
std_dev)
img = linear_filtering(img,
'gaussian_smoothing',
std_dev=std_dev)
# - Performs seed detection:
print " --> Closing-Opening Alternate Sequencial Filter..."
asf_img = morphology(
img,
max_radius=morpho_radius,
fname = '/qDII-CLV3-PIN1-PI-E35-LD-SAM4-T0_CH2_iso.inr'
img = imread(dirname + fname)
x_sh, y_sh, z_sh = img.shape
z_slice = 107
# Display slice and histograms:
slice_n_hist(img[:,:,z_slice], 'Original image', 'z-slice {}/{}'.format(z_slice, z_sh))
# Display orthogonal view of ORIGINAL image:
slice_view(img, x_sh/2, y_sh/2, z_sh/2, 'original_image', dirname + fname[:-4] + ".png")
# Display and save orthogonal view of EQUALIZE ADAPTHIST image for various clip_limit:
for clip_limit in [0.005,0.007,0.01,0.02,0.05,0.1]:
im_eq = z_slice_equalize_adapthist(img,clip_limit=float(clip_limit))
x_sh, y_sh, z_sh = im_eq.shape
title = "Equalize adapthist (z-slice): clip limit = {}".format(clip_limit)
filename = fname[:-4]+"_z_equalize_adapthist-clip_limit_{}.png".format(clip_limit)
slice_view(im_eq, x_sh/2, y_sh/2, z_sh/2, title, dirname + filename)
# Display and save orthogonal view of EQUALIZE ADAPTHIST image for nbins=2**16:
n_bins=2**16
clip_limit=0.005
im_eq = z_slice_equalize_adapthist(img,clip_limit=clip_limit,n_bins=n_bins)
x_sh, y_sh, z_sh = im_eq.shape
title = "Equalize adapthist (z-slice):clip limit = {}".format(clip_limit)
filename = fname[:-4]+"_z_equalize_adapthist_clip_limit_{}.png".format(clip_limit)
slice_view(im_eq, x_sh/2, y_sh/2, z_sh/2, title, dirname + filename)
slice_n_hist(im_eq[:,:,z_slice], "Equalize adapthist (z-slice):clip limit = {}.format(clip_limit), 'z-slice {}/{}'.format(z_slice, z_sh))
if clearing_ch_name:
path_suffix, substract_img_fname = get_nomenclature_channel_fname(raw_czi_fname, nom_file, clearing_ch_name)
print "\n - Loading image to substract: {}".format(substract_img_fname)
substract_img = imread(image_dirname + path_suffix + substract_img_fname)
# substract the 'CLV3' signal from the 'PI' since it might have leaked:
print "\n - Performing images substraction..."
tmp_im = img2seg - substract_img
tmp_im[img2seg <= substract_img] = 0
img2seg = SpatialImage(tmp_im, voxelsize=vxs, origin=ori)
del tmp_im
# -- Display the image to segment:
# world.add(img2seg,'{}_channel'.format(ref_ch_name), colormap='invert_grey', voxelsize=microscope_orientation*vxs)
# world['{}_channel'.format(ref_ch_name)]['intensity_range'] = (-1, 2**16)
# -- Adaptative histogram equalization of the image to segment:
print "\n - Performing adaptative histogram equalization of the image to segment..."
img2seg = z_slice_equalize_adapthist(img2seg)
# -- Performs isometric resampling of the image to segment:
print "\n - Performing isometric resampling of the image to segment..."
img2seg = isometric_resampling(img2seg)
iso_vxs = np.array(img2seg.voxelsize)
iso_shape = img2seg.shape
# -- Display the isometric version of the "equalized" image to segment:
# world.add(img2seg,'{}_channel_equalized_isometric'.format(ref_ch_name), colormap='invert_grey', voxelsize=microscope_orientation*iso_vxs)
# world['{}_channel_equalized_isometric'.format(ref_ch_name)]['intensity_range'] = (-1, 2**16)
# - Create a seed image from the list of NUCLEI barycenters:
print "\n - Creating a seed image from the list of NUCLEI barycenters..."
# -- Read CSV file containing barycenter positions:
nom_names = get_nomenclature_name(nom_file)
signal_file = image_dirname + nom_names[raw_czi_fname] + '/' + nom_names[raw_czi_fname] + "_signal_data.csv"
signal_data = pd.read_csv(signal_file, sep=',')