def runner(parser, options, args):
if not hasattr(parser, 'runner'):
options.psf_path = None
options.input_path = None
options.output_path = None
if args:
if len(args) in [2, 3]:
if options.psf_path:
print >> sys.stderr, "WARNING: overwriting psf path %r with %r" % (
options.psf_path, args[0])
options.psf_path = args[0]
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[1])
options.input_path = args[1]
else:
parser.error(
"Incorrect number of arguments (expected 2 or 3 but got %r)" %
((args)))
if len(args) == 3:
if options.output_path:
print >> sys.stderr, "WARNING: overwriting output path %r with %r" % (
options.output_path, args[2])
options.output_path = args[2]
options.input_path = fix_path(options.input_path)
if options.output_path is None:
deconvolve_dir = get_path_dir(options.input_path, 'iocbio.deconvolve')
else:
deconvolve_dir = get_path_dir(options.output_path, 'iocbio.deconvolve')
psf_path = get_psf_path(options)
psf = ImageStack.load(psf_path, options=options)
stack = ImageStack.load(options.input_path, options=options)
deconvolved_image = deconvolve(psf, stack, deconvolve_dir, options=options)
if options.output_path is None:
b, e = os.path.splitext(options.input_path)
suffix = deconvolved_image.pathinfo.suffix
options.output_path = b + suffix + (e or '.tif')
options.output_path = fix_path(options.output_path)
if 1:
print 'Saving result to %r' % (options.output_path)
deconvolved_image.save(options.output_path)
def runner(parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
raise NotImplementedError( ` args `)
if len(args) == 1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
elif len(args) == 2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
options.output_path = args[1]
else:
parser.error(
"incorrect number of arguments (expected upto 2 but got %s)" %
(len(args)))
options.kernel_path = fix_path(options.kernel_path)
options.input_path = fix_path(options.input_path)
if options.output_path is None:
b, e = os.path.splitext(options.input_path)
suffix = '_convolved' % ()
options.output_path = b + suffix + (e or '.tif')
options.output_path = fix_path(options.output_path)
kernel = ImageStack.load(options.kernel_path, options=options)
stack = ImageStack.load(options.input_path, options=options)
result = convolve(kernel.images, stack.images, options=options)
if 1:
print 'Saving result to', options.output_path
ImageStack(result, stack.pathinfo).save(options.output_path)
def runner(parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
raise NotImplementedError (`args`)
if len (args)==1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
elif len(args)==2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
options.output_path = args[1]
else:
parser.error("incorrect number of arguments (expected upto 2 but got %s)" % (len(args)))
options.kernel_path = fix_path(options.kernel_path)
options.input_path = fix_path(options.input_path)
if options.output_path is None:
b,e = os.path.splitext(options.input_path)
suffix = '_convolved' % ()
options.output_path = b + suffix + (e or '.tif')
options.output_path = fix_path(options.output_path)
kernel = ImageStack.load(options.kernel_path, options=options)
stack = ImageStack.load(options.input_path, options=options)
result = convolve (kernel.images, stack.images, options=options)
if 1:
print 'Saving result to',options.output_path
ImageStack(result, stack.pathinfo).save(options.output_path)
def runner(parser, options, args):
if not hasattr(parser, 'runner'):
options.input_path = None
options.output_path = None
if args:
if len(args) in [1, 2]:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
else:
parser.error(
"Incorrect number of arguments (expected 1 or 2 but got %r)" %
((args)))
if len(args) == 2:
if options.output_path:
print >> sys.stderr, "WARNING: overwriting output path %r with %r" % (
options.output_path, args[1])
options.output_path = args[1]
options.input_path = fix_path(options.input_path)
if options.output_path is None:
deconvolve_dir = get_path_dir(options.input_path,
'ioc.deconvolve_w_sphere')
else:
deconvolve_dir = get_path_dir(options.output_path,
'ioc.deconvolve_w_sphere')
stack = ImageStack.load(options.input_path, options=options)
deconvolved_image = deconvolve_sphere(stack,
options.diameter * 1e-9,
deconvolve_dir,
options=options)
if options.output_path is None:
b, e = os.path.splitext(options.input_path)
suffix = deconvolved_image.pathinfo.suffix
options.output_path = b + suffix + (e or '.tif')
options.output_path = fix_path(options.output_path)
if 1:
print 'Saving result to %r' % (options.output_path)
deconvolved_image.save(options.output_path)
def runner(parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
options.input_path = fix_path(options.input_path)
stack = ImageStack.load(options.input_path, options=options)
voxel_sizes = stack.get_voxel_sizes()
roi_center_line = [
int(n.strip()) for n in options.roi_center_line.split(',')
]
assert len(roi_center_line) == 4, ` roi_center_line `
roi_width = int(options.roi_width)
N = options.nof_points
lines = []
time_lst = []
last_lines = []
for i, image in enumerate(stack.images):
result, labels = sarcomere_length(image, voxel_sizes[1:],
roi_center_line, roi_width, N)
if not lines:
for r in result:
lines.append([])
last_lines.append([])
for j, r in enumerate(result):
last_lines[j].append(r)
if len(last_lines[j]) >= 2:
last_lines[j].pop(0)
lines[j].append(numpy.mean(last_lines[j]))
time_lst.append(i)
import matplotlib.pyplot as plt
for line in lines:
plt.plot(time_lst, line)
plt.legend(labels)
plt.show()
def runner(parser, options, args):
verbose = options.verbose if options.verbose is not None else True
if not hasattr(parser, "runner"):
options.output_path = None
if args:
if len(args) == 1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
elif len(args) == 2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
if options.output_path:
print >> sys.stderr, "WARNING: overwriting output path %r with %r" % (options.output_path, args[1])
options.output_path = args[1]
else:
parser.error("incorrect number of arguments (expected upto 2 but got %s)" % (len(args)))
options.input_path = fix_path(options.input_path)
if options.output_path is None:
b, e = os.path.splitext(options.input_path)
suffix = "_noised_%s" % (options.noise_type)
options.output_path = b + suffix + (e or ".tif")
options.output_path = fix_path(options.output_path)
stack = ImageStack.load(options.input_path, options=options)
if options.noise_type == "poisson":
new_images = stack.images.copy()
new_images[new_images <= 0] = 1
new_images = poisson.rvs(new_images)
else:
raise NotImplementedError(` options.noise_type `)
if verbose:
print "Saving result to", options.output_path
ImageStack(new_images, stack.pathinfo).save(options.output_path)
def runner(parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
options.input_path = fix_path(options.input_path)
stack = ImageStack.load(options.input_path, options=options)
voxel_sizes = stack.get_voxel_sizes()
roi_center_line = [int(n.strip()) for n in options.roi_center_line.split(',')]
assert len (roi_center_line)==4,`roi_center_line`
roi_width = int (options.roi_width)
N = options.nof_points
lines = []
time_lst = []
last_lines = []
for i,image in enumerate(stack.images):
result, labels = sarcomere_length (image, voxel_sizes[1:], roi_center_line, roi_width, N)
if not lines:
for r in result:
lines.append([])
last_lines.append([])
for j,r in enumerate (result):
last_lines[j].append(r)
if len(last_lines[j])>=2:
last_lines[j].pop(0)
lines[j].append(numpy.mean (last_lines[j]))
time_lst.append (i)
import matplotlib.pyplot as plt
for line in lines:
plt.plot (time_lst, line)
plt.legend (labels)
plt.show ()
def runner (parser, options, args):
if not hasattr(parser, 'runner'):
options.input_path = None
options.output_path = None
if args:
if len(args) in [1,2]:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
else:
parser.error("Incorrect number of arguments (expected 1 or 2 but got %r)" % ((args)))
if len(args)==2:
if options.output_path:
print >> sys.stderr, "WARNING: overwriting output path %r with %r" % (options.output_path, args[1])
options.output_path = args[1]
options.input_path = fix_path (options.input_path)
if options.output_path is None:
deconvolve_dir = get_path_dir(options.input_path, 'ioc.deconvolve_w_sphere')
else:
deconvolve_dir = get_path_dir(options.output_path, 'ioc.deconvolve_w_sphere')
stack = ImageStack.load(options.input_path, options=options)
deconvolved_image = deconvolve_sphere(stack, options.diameter*1e-9, deconvolve_dir, options=options)
if options.output_path is None:
b,e = os.path.splitext(options.input_path)
suffix = deconvolved_image.pathinfo.suffix
options.output_path = b + suffix + (e or '.tif')
options.output_path = fix_path(options.output_path)
if 1:
print 'Saving result to %r' % (options.output_path)
deconvolved_image.save(options.output_path)
import numpy
from iocbio.ops import convolve
from iocbio.microscope.deconvolution import deconvolve
from iocbio.io import ImageStack
import scipy.stats
from matplotlib import pyplot as plt
kernel = numpy.array([0, 1, 3, 1, 0])
test_data = numpy.array([0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0]) * 50
data = convolve(kernel, test_data)
degraded_data = scipy.stats.poisson.rvs(numpy.where(data <= 0, 1e-16,
data)).astype(data.dtype)
psf = ImageStack(kernel, voxel_sizes=(1, ))
stack = ImageStack(degraded_data, voxel_sizes=(1, ))
deconvolved_data = deconvolve(psf, stack).images
plt.plot(test_data, label='test')
plt.plot(data, label='convolved')
plt.plot(degraded_data, label='degraded')
plt.plot(deconvolved_data, label='deconvolved')
plt.legend()
plt.ylabel('data')
plt.xlabel('index')
plt.title('Deconvolving degraded test data.')
plt.savefig('deconvolve_poisson_1d.png')
plt.show()
def runner(parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
options.input_path = fix_path(options.input_path)
stack = ImageStack.load(options.input_path, options=options)
voxel_sizes = stack.get_voxel_sizes()
dr = stack.get_lateral_resolution()
dz = stack.get_axial_resolution()
if dr is not None:
print 'lateral resolution: %.3f um (%.1f x %.1f px^2)' % (1e6*dr, dr/voxel_sizes[1], dr/voxel_sizes[2])
print 'axial resolution: %.3f um (%.1fpx)' % (1e6*dz, dz / voxel_sizes[0])
vz,vy,vx = voxel_sizes
m = 1
scales = (m*vz/dz, m*vy/dr, m*vx/dr)
else:
raise NotImplementedError ('get_lateral_resolution')
kdims = [1+2*(int(numpy.ceil(1/s))//2) for s in scales]
k = 'x'.join (map (str, kdims))
print 'Averaging window box:', k
kernel_type = options.kernel
smoothing_method = options.method
boundary_condition = options.boundary
mn, mx = stack.images.min (), stack.images.max()
high_indices = numpy.where(stack.images >= mn + 0.9*(mx-mn))
high = stack.images[high_indices]
from iocbio.ops import regress
average, average_grad = regress (stack.images, scales,
kernel = kernel_type,
method = smoothing_method,
boundary = boundary_condition,
verbose = True, enable_fft=True)
ImageStack(average, pathinfo=stack.pathinfo).save('average.tif')
noise = stack.images - average
ImageStack(noise-noise.min(), pathinfo=stack.pathinfo).save('noise.tif')
bright_level = 0.999 *average.max() + 0.001 * average.min()
bright_indices = numpy.where (average >= bright_level)
print len(bright_indices[0])
bright_noise = stack.images[bright_indices] - average[bright_indices]
a = stack.images[bright_indices].mean()
d = stack.images[bright_indices].std()
print 'mean=',a,'std=',d
print 'peak SNR=',a/d
print 'AVERAGE min, max, mean = %s, %s, %s' % (average.min (), average.max (), average.mean ())
print numpy.histogram(stack.images)[0]
sys.exit ()
noise = stack.images - average
var, var_grad = regress (noise*noise, scales,
kernel = kernel_type,
method = smoothing_method,
boundary = boundary_condition,
verbose = True, enable_fft=True)
print 'VAR min, max, mean = %s, %s, %s' % (var.min (), var.max (), var.mean ())
indices = numpy.where (var > 0)
print len(numpy.where (var==0)[0]), var.shape, var.dtype
var[numpy.where (var<=0)] = 1
snr = average / numpy.sqrt(var)
snr1 = snr[indices]
print 'STACK min, max = %s, %s' % (mn, mx)
print 'SNR min, max, mean = %s, %s, %s' % (snr1.min (), snr1.max (), snr1.mean ())
ImageStack(average, pathinfo=stack.pathinfo).save('average.tif')
ImageStack(snr, pathinfo=stack.pathinfo).save('snr.tif')
ImageStack(noise-noise.min(), pathinfo=stack.pathinfo).save('noise.tif')
def runner(parser, options, args):
verbose = options.verbose if options.verbose is not None else True
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len(args) == 1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
elif len(args) == 2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
if options.output_path:
print >> sys.stderr, "WARNING: overwriting output path %r with %r" % (
options.output_path, args[1])
options.output_path = args[1]
else:
parser.error(
"incorrect number of arguments (expected upto 2 but got %s)" %
(len(args)))
options.input_path = fix_path(options.input_path)
kernel_width = options.kernel_width or None
kernel_type = options.kernel
smoothing_method = options.method
boundary_condition = options.boundary
stack = ImageStack.load(options.input_path, options=options)
voxel_sizes = stack.get_voxel_sizes()
if kernel_width is None:
dr = stack.get_lateral_resolution()
dz = stack.get_axial_resolution()
if dr is None or dz is None:
kernel_width = 3
else:
print 'lateral resolution: %.3f um (%.1f x %.1f px^2)' % (
1e6 * dr, dr / voxel_sizes[1], dr / voxel_sizes[2])
print 'axial resolution: %.3f um (%.1fpx)' % (1e6 * dz,
dz / voxel_sizes[0])
vz, vy, vx = voxel_sizes
m = 1
scales = (m * vz / dz, m * vy / dr, m * vx / dr)
if kernel_width is not None:
w = float(kernel_width) * min(voxel_sizes)
scales = tuple([s / w for s in voxel_sizes])
print 'Window sizes:', [1 / s for s in scales]
kdims = [1 + 2 * (int(numpy.ceil(1 / s)) // 2) for s in scales]
k = 'x'.join(map(str, kdims))
if options.output_path is None:
b, e = os.path.splitext(options.input_path)
suffix = '_%s_%s%s' % (smoothing_method, kernel_type, k)
options.output_path = b + suffix + (e or '.tif')
options.output_path = fix_path(options.output_path)
if options.link_function == 'identity':
images = stack.images
elif options.link_function == 'log':
images = stack.images
mn, mx = get_dtype_min_max(images.dtype)
images = numpy.log(images)
images[numpy.where(numpy.isnan(images))] = numpy.log(mn)
else:
raise NotImplementedError( ` options.link_function `)
new_images, new_images_grad = regress(images,
scales,
kernel=kernel_type,
method=smoothing_method,
boundary=boundary_condition,
verbose=verbose)
if options.link_function == 'identity':
pass
elif options.link_function == 'log':
new_images = numpy.exp(new_images)
new_images = numpy.nan_to_num(new_images)
else:
raise NotImplementedError( ` options.link_function `)
if verbose:
print 'Leak: %.3f%%' % (100 *
(1 - new_images.sum() / stack.images.sum()))
print 'MSE:', ((new_images - stack.images)**2).mean()
print 'Energy:', ((stack.images)**2).sum()
print 'Saving result to', options.output_path
ImageStack(new_images, stack.pathinfo).save(options.output_path)
def runner(parser, options, args):
options = Options(options)
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len(args) == 1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
if options.input_path is None:
parser.error('Expected --input-path but got nothing')
options.input_path = fix_path(options.input_path)
stack = ImageStack.load(options.input_path, options=options)
images = stack.images
roll_axis = dict(XY=0, XZ=1, YZ=2).get(options.projection, 0)
axis_labels = ('Z', 'Y', 'X')
#roll_axis = int (options.roll_axis)
voxel_sizes = stack.get_voxel_sizes()
resolution = ['', '', '']
title = '%s[%s]' % (os.path.basename(options.input_path), images.dtype)
dr = stack.get_lateral_resolution()
dz = stack.get_axial_resolution()
if voxel_sizes:
if dr is not None:
resolution[1] = tostr(dr / voxel_sizes[1]) + 'px'
resolution[2] = tostr(dr / voxel_sizes[2]) + 'px'
if dz is not None:
resolution[0] = tostr(dz / voxel_sizes[0]) + 'px'
resolution = tuple(resolution)
if roll_axis:
images = numpy.rollaxis(images, roll_axis)
if voxel_sizes:
voxel_sizes = (
voxel_sizes[roll_axis],
) + voxel_sizes[:roll_axis] + voxel_sizes[roll_axis + 1:]
axis_labels = (axis_labels[roll_axis],
) + axis_labels[:roll_axis] + axis_labels[roll_axis +
1:]
resolutions = (resolution[roll_axis],
) + resolution[:roll_axis] + resolution[roll_axis + 1:]
if voxel_sizes:
xlabel = '%s, resol=%s, px size=%sum, size=%sum' \
% (axis_labels[-1], resolution[-1], tostr(voxel_sizes[-1]*1e6), tostr(voxel_sizes[-1]*1e6*images.shape[-1]))
ylabel = '%s, resol=%s, px size=%sum, size=%sum' \
% (axis_labels[-2], resolution[-2], tostr(voxel_sizes[-2]*1e6), tostr(voxel_sizes[-2]*1e6*images.shape[-2]))
else:
xlabel = '%s' % (axis_labels[-1])
ylabel = '%s' % (axis_labels[-2])
import matplotlib.cm as cm
import matplotlib.pyplot as pyplot
from iocbio.io.tifffile import imshow
if options.invert_cmap:
cmap = getattr(cm, options.cmap + '_r', options.cmap)
else:
cmap = getattr(cm, options.cmap, 'gray')
view_3d = options.get(view_3d='none')
if view_3d and view_3d.lower() == 'none':
view_3d = None
if view_3d:
l = []
for i, d in enumerate(view_3d.split(',')):
d = d.strip()
if d.lower() == 'c':
d = images.shape[i] // 2
else:
try:
d = int(d)
except:
d = images.shape[i] // 2
d = max(min(images.shape[i] - 1, d), 0)
l.append(d)
view_3d = l
def mark_image(image, c1, c2, ln, wd, mx):
image[c1 - wd:c1 + wd + 1, :ln] = mx
image[c1 - wd:c1 + wd + 1, -ln:] = mx
image[:ln, c2 - wd:c2 + wd + 1] = mx
image[-ln:, c2 - wd:c2 + wd + 1] = mx
return image
if view_3d:
import scipy.ndimage as ndimage
pyplot.rc('font', family='sans-serif', weight='normal', size=8)
figure = pyplot.figure(dpi=options.get(dpi=96),
figsize=(8, 8),
frameon=True,
facecolor='1.0',
edgecolor='w')
yz_scale = voxel_sizes[1] / voxel_sizes[0]
zx_scale = voxel_sizes[0] / voxel_sizes[2]
yx_scale = voxel_sizes[1] / voxel_sizes[2]
yx_image = images[view_3d[0]].copy()
zx_image = images[:, view_3d[1], :].copy()
yz_image = images[:, :, view_3d[2]].T.copy()
image = numpy.zeros((yx_image.shape[0] + zx_image.shape[0] + 1,
yx_image.shape[1] + yz_image.shape[1] + 1))
wd = image.shape[0] // 300
ln = max(1, image.shape[1] // 20)
mx = yx_image.max()
def fix_image(image, scale, c1, c2, ln, wd):
mx = image.max()
if scale == 1:
mark_image(image, c1, c2, ln, wd, mx)
elif scale > 1:
image = ndimage.interpolation.zoom(image, [scale, 1.0],
order=0)
mark_image(image, c1 * scale, c2, ln, wd, mx)
else:
image = ndimage.interpolation.zoom(image, [1.0, 1.0 / scale],
order=0)
mark_image(image, c1, c2 / scale, ln, wd, mx)
return image
yx_image = fix_image(yx_image, yx_scale, view_3d[1], view_3d[2], ln,
wd)
zx_image = fix_image(zx_image, zx_scale, view_3d[0], view_3d[2], ln,
wd)
yz_image = fix_image(yz_image, yz_scale, view_3d[1], view_3d[0], ln,
wd)
image = numpy.zeros((yx_image.shape[0] + zx_image.shape[0] + 1,
yx_image.shape[1] + yz_image.shape[1] + 1))
image[:yx_image.shape[0], :yx_image.shape[1]] = yx_image
image[:yx_image.shape[0], yx_image.shape[1] + 1:] = yz_image
image[yx_image.shape[0] + 1:, :yx_image.shape[1]] = zx_image
image_plot = pyplot.imshow(
image,
interpolation=options.get(interpolation='nearest'),
cmap=cmap,
)
pyplot.title(title, size=11)
axes = pyplot.gca()
xtickdata = [
(0, '0'),
#(yx_image.shape[1]/2, 'X'),
(yx_image.shape[1],
'%.1fum' % (voxel_sizes[2] * images.shape[2] * 1e6)),
((yx_image.shape[1] + image.shape[1]) / 2, 'Z'),
(image.shape[1] - 1,
'%.1fum' % (voxel_sizes[0] * images.shape[0] * 1e6)),
(yx_image.shape[1] * view_3d[2] / images.shape[2],
'X=%spx' % (view_3d[2])),
]
ytickdata = [
(0, '0'),
#(yx_image.shape[0]/2, 'Y'),
(yx_image.shape[0],
'%.1fum' % (voxel_sizes[1] * images.shape[1] * 1e6)),
#((yx_image.shape[0]+image.shape[0])/2, 'Z'),
(image.shape[0] - 1,
'%.1fum' % (voxel_sizes[0] * images.shape[0] * 1e6)),
(yx_image.shape[0] * view_3d[1] / images.shape[1],
'Y=%spx' % (view_3d[1])),
(yx_image.shape[0] +
yz_image.shape[1] * view_3d[0] / images.shape[0],
'Z=%spx' % (view_3d[0])),
]
xtickdata.sort()
xticks, xticklabels = zip(*xtickdata)
ytickdata.sort()
yticks, yticklabels = zip(*ytickdata)
axes.set_xticks(xticks)
axes.set_xticklabels(xticklabels)
axes.set_yticks(yticks)
axes.set_yticklabels(yticklabels)
cbar = pyplot.colorbar(shrink=0.8)
def on_keypressed(event):
"""Callback for key press event."""
key = event.key
if key == 'q':
sys.exit(0)
figure.canvas.mpl_connect('key_press_event', on_keypressed)
else:
figure, subplot, image = imshow(
images,
title=title,
#miniswhite=page.photometric=='miniswhite',
interpolation=options.interpolation,
cmap=cmap,
dpi=options.dpi,
isrgb=options.rgb,
show_hist=options.histogram_bins,
auto_scale=options.auto_scale)
axes = figure.get_axes()[0]
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
output_path = options.get(output_path='')
if output_path:
pyplot.savefig(output_path)
print 'wrote', output_path
sys.exit(0)
pyplot.show()
def runner (parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len (args)==1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
elif len(args)==2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
if options.output_path:
print >> sys.stderr, "WARNING: overwriting output path %r with %r" % (options.output_path, args[1])
options.output_path = args[1]
else:
parser.error("Incorrect number of arguments (expected upto 2 but got %s)" % (len(args)))
if options.input_path is None:
parser.error('Expected --input-path but got nothing')
options.input_path = fix_path (options.input_path)
stack = ImageStack.load(options.input_path, options=options)
numpy_types = numpy.typeDict.values()
if options.output_type in ['<detect>', None]:
output_type_name = stack.images.dtype.name
else:
output_type_name = options.output_type.lower()
output_type = getattr (numpy, output_type_name, None)
nof_stacks = stack.get_nof_stacks()
old_shape = stack.images.shape
new_shape = (nof_stacks, old_shape[0]//nof_stacks) + old_shape[1:]
new_images = numpy.zeros (new_shape[1:], dtype=output_type_name)
first_stack = None
last_stack = None
for i, stacki in enumerate(stack.images.reshape(new_shape)):
if i==0:
first_stack = stacki.astype (float)
new_images[:] = stacki
else:
err_first = abs(stacki - first_stack).mean()
err_last = abs(stacki - last_stack).mean()
print ('Stack %i: mean abs difference from first and last stack: %.3f, %.3f' % (i+1, err_first, err_last))
new_images += stacki
last_stack = stacki.astype(float)
output_path = options.output_path
output_ext = options.output_ext
if output_path is None:
dn = os.path.dirname(options.input_path)
bn = os.path.basename(options.input_path)
if os.path.isfile(options.input_path):
fn, ext = os.path.splitext (bn)
fn += '_sumstacks%s' % (nof_stacks)
type_part = None
for t in numpy_types:
if fn.endswith('_' + t.__name__):
type_part = t.__name__
break
if type_part is None:
output_path = os.path.join(dn, fn + '_' + output_type_name + '.' + output_ext)
else:
output_path = os.path.join(dn, fn[:-len(type_part)] + output_type_name + '.' + output_ext)
elif os.path.isdir (options.input_path):
bn += '_sumstacks%s' % (nof_stacks)
output_path = os.path.join (dn, bn+'_'+output_type_name + '.' + output_ext)
else:
raise NotImplementedError ('%s is not file nor directory' % (options.input_path))
output_path = fix_path(output_path)
print 'Saving new stack to',output_path
if output_ext=='tif':
ImageStack(new_images, stack.pathinfo, options=options).save(output_path)
elif output_ext=='data':
from iocbio.microscope.psf import normalize_unit_volume, discretize
value_resolution = stack.pathinfo.get_value_resolution()
normal_images = normalize_unit_volume(new_images, stack.get_voxel_sizes())
discrete = discretize(new_images / value_resolution)
signal_indices = numpy.where(discrete>0)
new_value_resolution = value_resolution * normal_images.max() / new_images.max()
ImageStack(normal_images, stack.pathinfo,
value_resolution = new_value_resolution).save(output_path, zip(*signal_indices))
elif output_ext=='vtk':
from pyvtk import VtkData, StructuredPoints, PointData, Scalars
vtk = VtkData (StructuredPoints (new_images.shape), PointData(Scalars(new_images.T.ravel())))
vtk.tofile(output_path, 'binary')
else:
raise NotImplementedError (`output_ext`)
def runner(parser, options, args):
smoothness = int(options.smoothness or 1)
verbose = options.verbose if options.verbose is not None else True
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len(args) == 1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
elif len(args) == 2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
options.output_path = args[1]
else:
parser.error(
"incorrect number of arguments (expected upto 2 but got %s)" %
(len(args)))
options.input_path = fix_path(options.input_path)
stack = ImageStack.load(options.input_path, options=options)
voxel_sizes = stack.get_voxel_sizes()
new_images = stack.images.copy()
background = (stack.pathinfo.get_background() or [0, 0])[0]
print 'Image has background', background
window_width = options.window_width or None
if window_width is None:
dr = stack.get_lateral_resolution()
dz = stack.get_axial_resolution()
if dr is None or dz is None:
window_width = 3.0
scales = tuple(
[s / (window_width * min(voxel_sizes)) for s in voxel_sizes])
else:
print 'lateral resolution: %.3f um (%.1f x %.1f px^2)' % (
1e6 * dr, dr / voxel_sizes[1], dr / voxel_sizes[2])
print 'axial resolution: %.3f um (%.1fpx)' % (1e6 * dz,
dz / voxel_sizes[0])
vz, vy, vx = voxel_sizes
m = 3
scales = (m * vz / dz, m * vy / dr, m * vx / dr)
window_width = '%.1fx%.1f' % (dz / m / vz, dr / m / vy)
else:
window_width = options.window_width
scales = tuple(
[s / (window_width * min(voxel_sizes)) for s in voxel_sizes])
print 'Window size in pixels:', [1 / s for s in scales]
apply_window_inplace(new_images, scales, smoothness, background)
if options.output_path is None:
b, e = os.path.splitext(options.input_path)
suffix = '_window%s_%s' % (window_width, smoothness)
options.output_path = b + suffix + (e or '.tif')
options.output_path = fix_path(options.output_path)
if verbose:
print 'Leak: %.3f%%' % (100 *
(1 - new_images.sum() / stack.images.sum()))
print 'MSE:', ((new_images - stack.images)**2).mean()
print 'Energy:', ((stack.images)**2).sum()
print 'Saving result to', options.output_path
ImageStack(new_images, stack.pathinfo).save(options.output_path)
def runner(parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len(args) == 1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
elif len(args) == 2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
if options.output_path:
print >> sys.stderr, "WARNING: overwriting output path %r with %r" % (
options.output_path, args[1])
options.output_path = args[1]
else:
parser.error(
"Incorrect number of arguments (expected upto 2 but got %s)" %
(len(args)))
if options.input_path is None:
parser.error('Expected --input-path but got nothing')
options.input_path = fix_path(options.input_path)
stack = ImageStack.load(options.input_path, options=options)
numpy_types = numpy.typeDict.values()
if options.output_type in ['<detect>', None]:
if str(stack.images.dtype).startswith('float'):
output_type_name = 'float32'
elif str(stack.images.dtype).startswith('int'):
output_type_name = 'int32'
elif str(stack.images.dtype).startswith('uint'):
output_type_name = 'uint32'
else:
output_type_name = 'int32'
else:
output_type_name = options.output_type.lower()
output_type = getattr(numpy, output_type_name, None)
mn, mx = stack.images.min(), stack.images.max()
print 'Input minimum and maximum: %s, %s' % (mn, mx)
if options.scale and 'int' in output_type_name:
tmn, tmx = get_dtype_min_max(output_type)
new_images = (tmn + float(tmx - tmn) * (stack.images - float(mn)) /
(mx - mn)).astype(output_type)
else:
new_images = stack.images.astype(output_type)
print 'Output minimum and maximum: %s, %s' % (new_images.min(),
new_images.max())
output_path = options.output_path
output_ext = options.output_ext
if output_path is None:
dn = os.path.dirname(options.input_path)
bn = os.path.basename(options.input_path)
if os.path.isfile(options.input_path):
fn, ext = os.path.splitext(bn)
type_part = None
for t in numpy_types:
if fn.endswith('_' + t.__name__):
type_part = t.__name__
break
if type_part is None:
output_path = os.path.join(
dn, fn + '_' + output_type_name + '.' + output_ext)
else:
output_path = os.path.join(
dn,
fn[:-len(type_part)] + output_type_name + '.' + output_ext)
elif os.path.isdir(options.input_path):
output_path = os.path.join(
dn, bn + '_' + output_type_name + '.' + output_ext)
else:
raise NotImplementedError('%s is not file nor directory' %
(options.input_path))
output_path = fix_path(output_path)
print 'Saving new stack to', output_path
if output_ext == 'tif':
ImageStack(new_images, stack.pathinfo,
options=options).save(output_path)
elif output_ext == 'data':
from iocbio.microscope.psf import normalize_unit_volume, discretize
value_resolution = stack.pathinfo.get_value_resolution()
normal_images = normalize_unit_volume(new_images,
stack.get_voxel_sizes())
discrete = discretize(new_images / value_resolution)
signal_indices = numpy.where(discrete > 0)
new_value_resolution = value_resolution * normal_images.max(
) / new_images.max()
ImageStack(normal_images,
stack.pathinfo,
value_resolution=new_value_resolution).save(
output_path, zip(*signal_indices))
elif output_ext == 'vtk':
from pyvtk import VtkData, StructuredPoints, PointData, Scalars
vtk = VtkData(StructuredPoints(new_images.shape),
PointData(Scalars(new_images.T.ravel())))
vtk.tofile(output_path, 'binary')
else:
raise NotImplementedError( ` output_ext `)
def runner(parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
options.input_path = fix_path(options.input_path)
stack = ImageStack.load(options.input_path, options=options)
voxel_sizes = stack.get_voxel_sizes()
dr = stack.get_lateral_resolution()
dz = stack.get_axial_resolution()
if dr is not None:
print 'lateral resolution: %.3f um (%.1f x %.1f px^2)' % (
1e6 * dr, dr / voxel_sizes[1], dr / voxel_sizes[2])
print 'axial resolution: %.3f um (%.1fpx)' % (1e6 * dz,
dz / voxel_sizes[0])
vz, vy, vx = voxel_sizes
m = 1
scales = (m * vz / dz, m * vy / dr, m * vx / dr)
else:
raise NotImplementedError('get_lateral_resolution')
kdims = [1 + 2 * (int(numpy.ceil(1 / s)) // 2) for s in scales]
k = 'x'.join(map(str, kdims))
print 'Averaging window box:', k
kernel_type = options.kernel
smoothing_method = options.method
boundary_condition = options.boundary
mn, mx = stack.images.min(), stack.images.max()
high_indices = numpy.where(stack.images >= mn + 0.9 * (mx - mn))
high = stack.images[high_indices]
from iocbio.ops import regress
average, average_grad = regress(stack.images,
scales,
kernel=kernel_type,
method=smoothing_method,
boundary=boundary_condition,
verbose=True,
enable_fft=True)
ImageStack(average, pathinfo=stack.pathinfo).save('average.tif')
noise = stack.images - average
ImageStack(noise - noise.min(), pathinfo=stack.pathinfo).save('noise.tif')
bright_level = 0.999 * average.max() + 0.001 * average.min()
bright_indices = numpy.where(average >= bright_level)
print len(bright_indices[0])
bright_noise = stack.images[bright_indices] - average[bright_indices]
a = stack.images[bright_indices].mean()
d = stack.images[bright_indices].std()
print 'mean=', a, 'std=', d
print 'peak SNR=', a / d
print 'AVERAGE min, max, mean = %s, %s, %s' % (
average.min(), average.max(), average.mean())
print numpy.histogram(stack.images)[0]
sys.exit()
noise = stack.images - average
var, var_grad = regress(noise * noise,
scales,
kernel=kernel_type,
method=smoothing_method,
boundary=boundary_condition,
verbose=True,
enable_fft=True)
print 'VAR min, max, mean = %s, %s, %s' % (var.min(), var.max(),
var.mean())
indices = numpy.where(var > 0)
print len(numpy.where(var == 0)[0]), var.shape, var.dtype
var[numpy.where(var <= 0)] = 1
snr = average / numpy.sqrt(var)
snr1 = snr[indices]
print 'STACK min, max = %s, %s' % (mn, mx)
print 'SNR min, max, mean = %s, %s, %s' % (snr1.min(), snr1.max(),
snr1.mean())
ImageStack(average, pathinfo=stack.pathinfo).save('average.tif')
ImageStack(snr, pathinfo=stack.pathinfo).save('snr.tif')
ImageStack(noise - noise.min(), pathinfo=stack.pathinfo).save('noise.tif')
def runner(parser, options, args):
smoothness = int(options.smoothness or 1)
verbose = options.verbose if options.verbose is not None else True
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len (args)==1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
elif len(args)==2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
options.output_path = args[1]
else:
parser.error("incorrect number of arguments (expected upto 2 but got %s)" % (len(args)))
options.input_path = fix_path(options.input_path)
stack = ImageStack.load(options.input_path, options=options)
voxel_sizes = stack.get_voxel_sizes()
new_images = stack.images.copy()
background = (stack.pathinfo.get_background() or [0,0])[0]
print 'Image has background', background
window_width = options.window_width or None
if window_width is None:
dr = stack.get_lateral_resolution()
dz = stack.get_axial_resolution()
if dr is None or dz is None:
window_width = 3.0
scales = tuple([s/(window_width*min(voxel_sizes)) for s in voxel_sizes])
else:
print 'lateral resolution: %.3f um (%.1f x %.1f px^2)' % (1e6*dr, dr/voxel_sizes[1], dr/voxel_sizes[2])
print 'axial resolution: %.3f um (%.1fpx)' % (1e6*dz, dz / voxel_sizes[0])
vz,vy,vx = voxel_sizes
m = 3
scales = (m*vz/dz, m*vy/dr, m*vx/dr)
window_width = '%.1fx%.1f' % (dz/m/vz, dr/m/vy)
else:
window_width = options.window_width
scales = tuple([s/(window_width*min(voxel_sizes)) for s in voxel_sizes])
print 'Window size in pixels:', [1/s for s in scales]
apply_window_inplace (new_images, scales, smoothness, background)
if options.output_path is None:
b,e = os.path.splitext(options.input_path)
suffix = '_window%s_%s' % (window_width, smoothness)
options.output_path = b + suffix + (e or '.tif')
options.output_path = fix_path(options.output_path)
if verbose:
print 'Leak: %.3f%%' % ( 100*(1-new_images.sum ()/stack.images.sum ()))
print 'MSE:', ((new_images - stack.images)**2).mean()
print 'Energy:', ((stack.images)**2).sum()
print 'Saving result to',options.output_path
ImageStack(new_images, stack.pathinfo).save(options.output_path)
def runner (parser, options, args):
options = Options (options)
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len (args)==1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
if options.input_path is None:
parser.error('Expected --input-path but got nothing')
options.input_path = fix_path (options.input_path)
stack = ImageStack.load(options.input_path, options=options)
images = stack.images
roll_axis = dict(XY=0, XZ=1, YZ=2).get(options.projection, 0)
axis_labels = ('Z', 'Y', 'X')
#roll_axis = int (options.roll_axis)
voxel_sizes = stack.get_voxel_sizes()
resolution = ['', '', '']
title = '%s[%s]' % (os.path.basename (options.input_path), images.dtype)
dr = stack.get_lateral_resolution()
dz = stack.get_axial_resolution()
if voxel_sizes:
if dr is not None:
resolution[1] = tostr(dr/voxel_sizes[1]) + 'px'
resolution[2] = tostr(dr/voxel_sizes[2]) + 'px'
if dz is not None:
resolution[0] = tostr(dz/voxel_sizes[0]) + 'px'
resolution = tuple (resolution)
if roll_axis:
images = numpy.rollaxis(images, roll_axis)
if voxel_sizes:
voxel_sizes = (voxel_sizes[roll_axis],) + voxel_sizes[:roll_axis] + voxel_sizes[roll_axis+1:]
axis_labels = (axis_labels[roll_axis],) + axis_labels[:roll_axis] + axis_labels[roll_axis+1:]
resolutions = (resolution[roll_axis],) + resolution[:roll_axis] + resolution[roll_axis+1:]
if voxel_sizes:
xlabel = '%s, resol=%s, px size=%sum, size=%sum' \
% (axis_labels[-1], resolution[-1], tostr(voxel_sizes[-1]*1e6), tostr(voxel_sizes[-1]*1e6*images.shape[-1]))
ylabel = '%s, resol=%s, px size=%sum, size=%sum' \
% (axis_labels[-2], resolution[-2], tostr(voxel_sizes[-2]*1e6), tostr(voxel_sizes[-2]*1e6*images.shape[-2]))
else:
xlabel = '%s' % (axis_labels[-1])
ylabel = '%s' % (axis_labels[-2])
import matplotlib.cm as cm
import matplotlib.pyplot as pyplot
from iocbio.io.tifffile import imshow
if options.invert_cmap:
cmap = getattr(cm, options.cmap+'_r', options.cmap)
else:
cmap = getattr(cm, options.cmap, 'gray')
view_3d = options.get(view_3d = 'none')
if view_3d and view_3d.lower()=='none':
view_3d = None
if view_3d:
l = []
for i,d in enumerate(view_3d.split (',')):
d = d.strip()
if d.lower()=='c':
d = images.shape[i]//2
else:
try:
d = int(d)
except:
d = images.shape[i]//2
d = max(min(images.shape[i]-1, d), 0)
l.append(d)
view_3d = l
def mark_image (image, c1, c2, ln, wd, mx):
image[c1-wd:c1+wd+1,:ln] = mx
image[c1-wd:c1+wd+1,-ln:] = mx
image[:ln,c2-wd:c2+wd+1] = mx
image[-ln:,c2-wd:c2+wd+1] = mx
return image
if view_3d:
import scipy.ndimage as ndimage
pyplot.rc('font', family='sans-serif', weight='normal', size=8)
figure = pyplot.figure(dpi=options.get(dpi=96),
figsize=(8, 8), frameon=True,
facecolor='1.0', edgecolor='w')
yz_scale = voxel_sizes[1] / voxel_sizes[0]
zx_scale = voxel_sizes[0] / voxel_sizes[2]
yx_scale = voxel_sizes[1] / voxel_sizes[2]
yx_image = images[view_3d[0]].copy ()
zx_image = images[:,view_3d[1],:].copy ()
yz_image = images[:,:,view_3d[2]].T.copy()
image = numpy.zeros((yx_image.shape[0] + zx_image.shape[0]+1, yx_image.shape[1]+yz_image.shape[1]+1))
wd = image.shape[0]//300
ln = max(1, image.shape[1]//20)
mx = yx_image.max()
def fix_image (image, scale, c1, c2, ln, wd):
mx = image.max()
if scale==1:
mark_image (image, c1, c2, ln, wd, mx)
elif scale>1:
image = ndimage.interpolation.zoom(image, [scale, 1.0], order=0)
mark_image (image, c1*scale, c2, ln, wd, mx)
else:
image = ndimage.interpolation.zoom(image, [1.0, 1.0/scale], order=0)
mark_image (image, c1, c2/scale, ln, wd, mx)
return image
yx_image = fix_image(yx_image, yx_scale, view_3d[1], view_3d[2], ln, wd)
zx_image = fix_image(zx_image, zx_scale, view_3d[0], view_3d[2], ln, wd)
yz_image = fix_image(yz_image, yz_scale, view_3d[1], view_3d[0], ln, wd)
image = numpy.zeros((yx_image.shape[0] + zx_image.shape[0]+1, yx_image.shape[1]+yz_image.shape[1]+1))
image[:yx_image.shape[0], :yx_image.shape[1]] = yx_image
image[:yx_image.shape[0], yx_image.shape[1]+1:] = yz_image
image[yx_image.shape[0]+1:, :yx_image.shape[1]] = zx_image
image_plot = pyplot.imshow(image,
interpolation=options.get(interpolation='nearest'),
cmap = cmap,
)
pyplot.title(title, size=11)
axes = pyplot.gca()
xtickdata = [(0,'0'),
#(yx_image.shape[1]/2, 'X'),
(yx_image.shape[1], '%.1fum' % (voxel_sizes[2]*images.shape[2]*1e6)),
( (yx_image.shape[1]+image.shape[1])/2, 'Z'),
(image.shape[1]-1, '%.1fum' % (voxel_sizes[0]*images.shape[0]*1e6)),
(yx_image.shape[1]*view_3d[2]/images.shape[2],'X=%spx' % (view_3d[2])),
]
ytickdata = [(0,'0'),
#(yx_image.shape[0]/2, 'Y'),
(yx_image.shape[0], '%.1fum' % (voxel_sizes[1]*images.shape[1]*1e6)),
#((yx_image.shape[0]+image.shape[0])/2, 'Z'),
( image.shape[0]-1, '%.1fum' % (voxel_sizes[0]*images.shape[0]*1e6)),
(yx_image.shape[0]*view_3d[1]/images.shape[1],'Y=%spx' % (view_3d[1])),
(yx_image.shape[0]+yz_image.shape[1]*view_3d[0]/images.shape[0], 'Z=%spx' % (view_3d[0])),
]
xtickdata.sort ()
xticks, xticklabels = zip(*xtickdata)
ytickdata.sort ()
yticks, yticklabels = zip(*ytickdata)
axes.set_xticks(xticks)
axes.set_xticklabels (xticklabels)
axes.set_yticks(yticks)
axes.set_yticklabels (yticklabels)
cbar = pyplot.colorbar(shrink=0.8)
def on_keypressed(event):
"""Callback for key press event."""
key = event.key
if key == 'q':
sys.exit(0)
figure.canvas.mpl_connect('key_press_event', on_keypressed)
else:
figure, subplot, image = imshow(images, title = title,
#miniswhite=page.photometric=='miniswhite',
interpolation=options.interpolation,
cmap=cmap,
dpi=options.dpi, isrgb=options.rgb,
show_hist = options.histogram_bins,
auto_scale = options.auto_scale)
axes = figure.get_axes()[0]
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
output_path = options.get (output_path='')
if output_path:
pyplot.savefig (output_path)
print 'wrote',output_path
sys.exit(0)
pyplot.show()
def runner (parser, options, args):
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len (args)==1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
elif len(args)==2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
if options.output_path:
print >> sys.stderr, "WARNING: overwriting output path %r with %r" % (options.output_path, args[1])
options.output_path = args[1]
else:
parser.error("Incorrect number of arguments (expected upto 2 but got %s)" % (len(args)))
if options.input_path is None:
parser.error('Expected --input-path but got nothing')
options.input_path = fix_path (options.input_path)
stack = ImageStack.load(options.input_path, options=options)
numpy_types = numpy.typeDict.values()
if options.output_type in ['<detect>', None]:
if str (stack.images.dtype).startswith ('float'):
output_type_name = 'float32'
elif str (stack.images.dtype).startswith ('int'):
output_type_name = 'int32'
elif str (stack.images.dtype).startswith ('uint'):
output_type_name = 'uint32'
else:
output_type_name = 'int32'
else:
output_type_name = options.output_type.lower()
output_type = getattr (numpy, output_type_name, None)
mn, mx = stack.images.min (), stack.images.max ()
print 'Input minimum and maximum: %s, %s' % (mn, mx)
if options.scale and 'int' in output_type_name:
tmn, tmx = get_dtype_min_max(output_type)
new_images = (tmn + float(tmx - tmn) * (stack.images-float(mn)) / (mx - mn)).astype (output_type)
else:
new_images = stack.images.astype (output_type)
print 'Output minimum and maximum: %s, %s' % (new_images.min (), new_images.max ())
output_path = options.output_path
output_ext = options.output_ext
if output_path is None:
dn = os.path.dirname(options.input_path)
bn = os.path.basename(options.input_path)
if os.path.isfile(options.input_path):
fn, ext = os.path.splitext (bn)
type_part = None
for t in numpy_types:
if fn.endswith('_' + t.__name__):
type_part = t.__name__
break
if type_part is None:
output_path = os.path.join(dn, fn + '_' + output_type_name + '.' + output_ext)
else:
output_path = os.path.join(dn, fn[:-len(type_part)] + output_type_name + '.' + output_ext)
elif os.path.isdir (options.input_path):
output_path = os.path.join (dn, bn+'_'+output_type_name + '.' + output_ext)
else:
raise NotImplementedError ('%s is not file nor directory' % (options.input_path))
output_path = fix_path(output_path)
print 'Saving new stack to',output_path
if output_ext=='tif':
ImageStack(new_images, stack.pathinfo, options=options).save(output_path)
elif output_ext=='data':
from iocbio.microscope.psf import normalize_unit_volume, discretize
value_resolution = stack.pathinfo.get_value_resolution()
normal_images = normalize_unit_volume(new_images, stack.get_voxel_sizes())
discrete = discretize(new_images / value_resolution)
signal_indices = numpy.where(discrete>0)
new_value_resolution = value_resolution * normal_images.max() / new_images.max()
ImageStack(normal_images, stack.pathinfo,
value_resolution = new_value_resolution).save(output_path, zip(*signal_indices))
elif output_ext=='vtk':
from pyvtk import VtkData, StructuredPoints, PointData, Scalars
vtk = VtkData (StructuredPoints (new_images.shape), PointData(Scalars(new_images.T.ravel())))
vtk.tofile(output_path, 'binary')
else:
raise NotImplementedError (`output_ext`)
def runner(parser, options, args):
verbose = options.verbose if options.verbose is not None else True
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len (args)==1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
elif len(args)==2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
if options.output_path:
print >> sys.stderr, "WARNING: overwriting output path %r with %r" % (options.output_path, args[1])
options.output_path = args[1]
else:
parser.error("incorrect number of arguments (expected upto 2 but got %s)" % (len(args)))
options.input_path = fix_path(options.input_path)
kernel_width = options.kernel_width or None
kernel_type = options.kernel
smoothing_method = options.method
boundary_condition = options.boundary
stack = ImageStack.load(options.input_path, options=options)
voxel_sizes = stack.get_voxel_sizes()
if kernel_width is None:
dr = stack.get_lateral_resolution()
dz = stack.get_axial_resolution()
if dr is None or dz is None:
kernel_width = 3
else:
print 'lateral resolution: %.3f um (%.1f x %.1f px^2)' % (1e6*dr, dr/voxel_sizes[1], dr/voxel_sizes[2])
print 'axial resolution: %.3f um (%.1fpx)' % (1e6*dz, dz / voxel_sizes[0])
vz,vy,vx = voxel_sizes
m = 1
scales = (m*vz/dz, m*vy/dr, m*vx/dr)
if kernel_width is not None:
w = float(kernel_width) * min (voxel_sizes)
scales = tuple([s/w for s in voxel_sizes])
print 'Window sizes:', [1/s for s in scales]
kdims = [1+2*(int(numpy.ceil(1/s))//2) for s in scales]
k = 'x'.join (map (str, kdims))
if options.output_path is None:
b,e = os.path.splitext(options.input_path)
suffix = '_%s_%s%s' % (smoothing_method, kernel_type, k)
options.output_path = b + suffix + (e or '.tif')
options.output_path = fix_path(options.output_path)
if options.link_function == 'identity':
images = stack.images
elif options.link_function == 'log':
images = stack.images
mn, mx = get_dtype_min_max (images.dtype)
images = numpy.log(images)
images[numpy.where (numpy.isnan(images))] = numpy.log(mn)
else:
raise NotImplementedError (`options.link_function`)
new_images, new_images_grad = regress (images, scales,
kernel = kernel_type,
method = smoothing_method,
boundary = boundary_condition,
verbose = verbose)
if options.link_function == 'identity':
pass
elif options.link_function == 'log':
new_images = numpy.exp(new_images)
new_images = numpy.nan_to_num(new_images)
else:
raise NotImplementedError (`options.link_function`)
if verbose:
print 'Leak: %.3f%%' % ( 100*(1-new_images.sum()/stack.images.sum()) )
print 'MSE:', ((new_images - stack.images)**2).mean()
print 'Energy:', ((stack.images)**2).sum()
print 'Saving result to',options.output_path
ImageStack(new_images, stack.pathinfo).save(options.output_path)