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Fix bug with chooseframe
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@jadball
jadball committed Jan 16, 2025
1 parent f075eba commit aeeaa0d
Showing 1 changed file with 73 additions and 69 deletions.
142 changes: 73 additions & 69 deletions ImageD11/nbGui/segmenter_gui.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,3 @@



import os
import numpy as np
import matplotlib.pyplot as plt
Expand All @@ -10,35 +7,37 @@
import ImageD11.sinograms.lima_segmenter
import ImageD11.sparseframe


def guess_ESRF_paths(): # This should be in silx somewhere?
""" Locates:
dataroot holds raw data in folders dataroot + {sample}/{sample}_{dataset}
analysisroot holds output results in folders analysisroot + {sample}/{sample}_{dataset}
"""
path_items = os.getcwd().split('/')
if 'visitor' in path_items:
if 'visitor' in path_items:
idx = path_items.index('visitor')
experiment, session = path_items[ idx + 1 ], path_items[ idx + 3 ]
path = os.path.join( "/data", "visitor", experiment, "id11", session )
return [os.path.join( path, folder ) for folder in
("RAW_DATA", "PROCESSED_DATA") ]
experiment, session = path_items[idx + 1], path_items[idx + 3]
path = os.path.join("/data", "visitor", experiment, "id11", session)
return [os.path.join(path, folder) for folder in
("RAW_DATA", "PROCESSED_DATA")]
return "", ""

def printsamples( dataroot ):
samples = sorted( [ name for name in os.listdir( dataroot )
if os.path.isdir( os.path.join( dataroot, name ) ) ] )
print("Samples:\n\t"+"\n\t".join(sorted( samples ) ))

def printdatasets( dataroot, sample):
sroot = os.path.join(dataroot, sample)
print("Datsets:\n\t"+"\n\t".join(sorted(
[ name[len(sample)+1:] for name in os.listdir( sroot )
if os.path.isdir( os.path.join( sroot, name ) )
and name.startswith( sample ) ] ) ) )

def printsamples(dataroot):
samples = sorted([name for name in os.listdir(dataroot)
if os.path.isdir(os.path.join(dataroot, name))])
print("Samples:\n\t " + "\n\t".join(sorted(samples)))


def chooseframe(self, dset, scan=None, idx=None, counter="_roi1", fetch_raw_image=False):
def printdatasets(dataroot, sample):
sroot = os.path.join(dataroot, sample)
print("Datsets:\n\t " + "\n\t".join(sorted(
[name[len(sample) + 1:] for name in os.listdir(sroot)
if os.path.isdir(os.path.join(sroot, name))
and name.startswith(sample)])))


def chooseframe(dset, scan=None, idx=None, counter="_roi1", fetch_raw_image=False):
"""
Locate a busy frame from the dataset and optionally fetch the raw image.
Expand Down Expand Up @@ -78,28 +77,28 @@ def chooseframe(self, dset, scan=None, idx=None, counter="_roi1", fetch_raw_imag
return scan, idx, raw_image



class SegmenterGui:

""" UI for a jupyter notebook to set the segmentation parameters
From @jadball notebook, @jonwright refactored to put in a python file
"""
def __init__(self, dset, counter="_roi1", scan=None, frame=None, cut=1, pixels_in_spot=3, howmany=100000,):

def __init__(self, dset, counter="_roi1", scan=None, frame=None, cut=1, pixels_in_spot=3, howmany=100000, ):
self.dset = dset
self.fig = None
self.options = { "maskfile" : dset.maskfile ,
"cut" : cut,
"pixels_in_spot" : pixels_in_spot,
"howmany": howmany }
self.options = {"maskfile": dset.maskfile,
"cut": cut,
"pixels_in_spot": pixels_in_spot,
"howmany": howmany}
self.scan = scan
self.idx = frame
self.chooseframe(counter)
cut_slider = widgets.IntSlider(value=cut, min=0, max=200, step=1, description='Cut:')
pixels_in_spot_slider = widgets.IntSlider(value=pixels_in_spot, min=0, max=20, step=1, description='Pixels in Spot:')
pixels_in_spot_slider = widgets.IntSlider(value=pixels_in_spot, min=0, max=20, step=1,
description='Pixels in Spot:')
howmany_slider = widgets.IntSlider(value=np.log10(howmany), min=1, max=15, step=1, description='log(howmany):')
self.widget = widgets.interactive(self.update_image, cut=cut_slider, pixels_in_spot=pixels_in_spot_slider, howmany=howmany_slider)
display( self.widget )
self.widget = widgets.interactive(self.update_image, cut=cut_slider, pixels_in_spot=pixels_in_spot_slider,
howmany=howmany_slider)
display(self.widget)

def chooseframe(self, counter):
# Use the base class method without fetching the raw image
Expand All @@ -113,40 +112,43 @@ def segment_frame(self):
frame_num = which frame in the file
"""
ds = self.dset
opts = ImageD11.sinograms.lima_segmenter.OPTIONS = ImageD11.sinograms.lima_segmenter.SegmenterOptions(**self.options)
opts = ImageD11.sinograms.lima_segmenter.OPTIONS = ImageD11.sinograms.lima_segmenter.SegmenterOptions \
(**self.options)
opts.setup()
with h5py.File( ds.masterfile, 'r' ) as hin:
with h5py.File(ds.masterfile, 'r') as hin:
frms = hin[self.scan]['measurement'][ds.detector]
for i, spf in enumerate( ImageD11.sinograms.lima_segmenter.reader( frms, opts.mask, opts.cut, start = self.idx ) ):
for i, spf in enumerate(
ImageD11.sinograms.lima_segmenter.reader(frms, opts.mask, opts.cut, start=self.idx)):
if spf is None:
print("Warning: no pixels found",self.scan,i,self.idx)
ref = frms[i+self.idx]
break
print("Warning: no pixels found", self.scan, i, self.idx)
ref = frms[i + self.idx]
break
if spf is None:
spi = np.zeros_like(ref)
npeaks = 0
else:
spi = spf.to_dense('intensity')
npeaks = ImageD11.sparseframe.sparse_localmax( spf )
npeaks = ImageD11.sparseframe.sparse_localmax(spf)
if opts.mask is not None:
ref = ref * opts.mask
spi = spi * opts.mask
spi = spi * opts.mask
return ref, spi, npeaks

def display(self):
# Display the image initially
self.fig, self.axs = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(12, 8), constrained_layout=True)
self.im1 = self.axs[0].imshow(self.raw_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000, interpolation="nearest")
self.im2 = self.axs[1].imshow(self.segmented_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000, interpolation="nearest")
self.im1 = self.axs[0].imshow(self.raw_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000,
interpolation="nearest")
self.im2 = self.axs[1].imshow(self.segmented_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000,
interpolation="nearest")
self.axs[0].set_title("Raw image")
self.axs[1].set_title("Segmented image")
self.fig.suptitle("%d peaks found with cut=%d, pixels_in_spot=%d, howmany=%d"%
(self.nblobs,self.options['cut'],self.options['pixels_in_spot'],self.options['howmany']))
self.fig.suptitle("%d peaks found with cut=%d, pixels_in_spot=%d, howmany=%d " %
(self.nblobs, self.options['cut'], self.options['pixels_in_spot'], self.options['howmany']))
# self.fig.show()


def update_image(self, cut, pixels_in_spot, howmany):
howmany_exp = 10**howmany
howmany_exp = 10 ** howmany
self.options["cut"] = cut
self.options["pixels_in_spot"] = pixels_in_spot
self.options["howmany"] = howmany_exp
Expand All @@ -155,23 +157,21 @@ def update_image(self, cut, pixels_in_spot, howmany):
self.display()
self.im1.set_data(self.raw_image)
self.im2.set_data(self.segmented_image)
self.fig.suptitle("%d peaks found with cut=%d, pixels_in_spot=%d, howmany=%d"%
(self.nblobs,cut,pixels_in_spot,howmany))
self.fig.suptitle("%d peaks found with cut=%d, pixels_in_spot=%d, howmany=%d " %
(self.nblobs, cut, pixels_in_spot, howmany))
self.fig.canvas.draw()


def getopts(self):
opts = { name: self.options[name] for name in ('cut','pixels_in_spot','howmany') }
print("options = ",repr(opts))
opts = {name: self.options[name] for name in ('cut', 'pixels_in_spot', 'howmany')}
print("options = ", repr(opts))
return opts


class FrelonSegmenterGui:

""" UI for a jupyter notebook to set the segmentation parameters
From @jadball notebook, @jadball refactored to put in a python file
"""

def __init__(self, dset, worker_func, process_func, counter="_roi1", scan=None, frame=None, **options):
self.dset = dset
self.worker_func = worker_func
Expand All @@ -181,20 +181,24 @@ def __init__(self, dset, worker_func, process_func, counter="_roi1", scan=None,
self.scan = scan
self.idx = frame
self.chooseframe(counter)
thresh_slider = widgets.IntSlider(value=options["threshold"], min=0, max=10000, step=1, description='Threshold:')
smsig_slider = widgets.FloatSlider(value=options["smoothsigma"], min=0.0, max=3.0, step=0.05, description='Smoothsigma:')
thresh_slider = widgets.IntSlider(value=options["threshold"], min=0, max=10000, step=1,
description='Threshold:')
smsig_slider = widgets.FloatSlider(value=options["smoothsigma"], min=0.0, max=3.0, step=0.05,
description='Smoothsigma:')
bgc_slider = widgets.FloatSlider(value=options["bgc"], min=0.0, max=1.0, step=0.05, description='bgc:')
minpx_slider = widgets.IntSlider(value=options["minpx"], min=0, max=50, step=1, description='minpx:')
mofft_slider = widgets.IntSlider(value=options["m_offset_thresh"], min=0, max=200, step=1, description='m_offset_thresh:')
mratt_slider = widgets.IntSlider(value=options["m_ratio_thresh"], min=0, max=2000, step=1, description='m_ratio_thresh:')
mofft_slider = widgets.IntSlider(value=options["m_offset_thresh"], min=0, max=200, step=1,
description='m_offset_thresh:')
mratt_slider = widgets.IntSlider(value=options["m_ratio_thresh"], min=0, max=2000, step=1,
description='m_ratio_thresh:')
self.widget = widgets.interactive(self.update_image,
threshold=thresh_slider,
smoothsigma=smsig_slider,
bgc=bgc_slider,
minpx=minpx_slider,
m_offset_thresh=mofft_slider,
m_ratio_thresh=mratt_slider)
display( self.widget )
display(self.widget)

def chooseframe(self, counter):
self.scan, self.idx, self.raw_image = chooseframe(self.dset, self.scan, self.idx, counter, fetch_raw_image=True)
Expand All @@ -205,41 +209,41 @@ def segment_frame(self):
goodpeaks = image_worker.peaksearch(img=self.raw_image, omega=0)
fc, sc = goodpeaks[:, 23:25].T # 23 and 24 are the columns for fc and sc from blob properties
return image_worker, fc, sc, len(fc)

def display(self):
self.fig, self.axs = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(12, 8), layout="constrained")
self.im1 = self.axs[0].imshow(self.raw_image, norm='log', vmin=100, vmax=1000)
self.axs[0].set_title("Original image")
self.im2 = self.axs[1].imshow(self.smoothed_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000, interpolation="nearest")
self.im2 = self.axs[1].imshow(self.smoothed_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000,
interpolation="nearest")
self.axs[1].set_title("Background corrected")
self.im3 = self.axs[2].imshow(self.smoothed_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000, interpolation="nearest")
self.im3 = self.axs[2].imshow(self.smoothed_image, cmap="viridis", norm='log', vmin=0.5, vmax=1000,
interpolation="nearest")
self.axs[2].set_title(str(self.npeaks) + " peaks")
self.sc1, = self.axs[2].plot(self.fc, self.sc, marker='+', c="r", ls="")
self.axs[2].set_aspect(1)

def update_image(self, threshold, smoothsigma, bgc, minpx, m_offset_thresh, m_ratio_thresh):

self.options["threshold"] = threshold
self.options["smoothsigma"] = smoothsigma
self.options["bgc"] = bgc
self.options["minpx"] = minpx
self.options["m_offset_thresh"] = m_offset_thresh
self.options["m_ratio_thresh"] = m_ratio_thresh

image_worker, self.fc, self.sc, self.npeaks = self.segment_frame()
self.smoothed_image = image_worker.smoothed
if self.fig is None:
self.display()

self.im1.set_data(self.raw_image)
self.im2.set_data(self.smoothed_image)
self.im3.set_data(self.smoothed_image)
self.sc1.set_data(self.fc, self.sc)
self.fig.canvas.draw()



def getopts(self):
opts = { name: self.options[name] for name in ('bgfile','maskfile','threshold','smoothsigma','bgc','minpx','m_offset_thresh','m_ratio_thresh') }
print("options = ",repr(opts))
opts = {name: self.options[name] for name in
('bgfile', 'maskfile', 'threshold', 'smoothsigma', 'bgc', 'minpx', 'm_offset_thresh', 'm_ratio_thresh')}
print("options = ", repr(opts))
return opts

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