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EtSTEDController.py
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EtSTEDController.py
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import os
import glob
import sys
import importlib
import enum
import warnings
from collections import deque
from datetime import datetime
from inspect import signature
from tkinter import Tk, filedialog
import h5py
import scipy.ndimage as ndi
import numpy as np
from scipy.optimize import least_squares
from PyQt5.QtCore import QObject, QThread, QTimer, pyqtSignal
warnings.filterwarnings("ignore")
# folder path for saved log files
_logsDir = os.path.join('C:\\etSTED', 'recordings', 'logs_etsted')
class EtSTEDController():
""" Linked to EtSTEDWidget."""
def __init__(self, camera, setupInfo, widget, *args, **kwargs):
self._setupInfo = setupInfo
self._widget = widget
print('Initializing etSTED controller')
self.camera = camera
# folders for analysis pipelines and transformations
self.analysisDir = os.path.join('analysis_pipelines')
if not os.path.exists(self.analysisDir):
os.makedirs(self.analysisDir)
sys.path.append(self.analysisDir)
self.transformDir = os.path.join('transform_pipelines')
if not os.path.exists(self.transformDir):
os.makedirs(self.transformDir)
sys.path.append(self.transformDir)
# set lists of analysis pipelines and transformations in the widget
self._widget.setAnalysisPipelines(self.analysisDir)
self._widget.setTransformations(self.transformDir)
self.detectorList = ['MockCamera'] # mock, get detector list from elsewhere in software
self._widget.setFastDetectorList(self.detectorList)
self.laserList = ['WFLaser','ExcLaser','STEDLaser'] # mock, get laser list from elsewhere in software
self._widget.setFastLaserList(self.laserList)
# create a helper controller for the coordinate transform pop-out widget
self.__coordTransformHelper = EtSTEDCoordTransformHelper(self, self._widget.coordTransformWidget, _logsDir)
# add camera image to napariviewer
self.camImageLayer = self._widget.imageViewer.add_image(self.camera.getImage())
# update camera image automatically (mock)
# real use-case: use detector manager and image updated signals of software where implemented
self.camImgThread = QThread()
self.camImgWorker = CameraImageWorker(self, camera)
self.camImgWorker.moveToThread(self.camImgThread)
self.camImgThread.started.connect(self.camImgWorker.run)
self.camImgThread.start()
# Connect EtSTEDWidget and communication channel signals
self._widget.initiateButton.clicked.connect(self.initiate)
self._widget.loadPipelineButton.clicked.connect(self.loadPipeline)
self._widget.recordBinaryMaskButton.clicked.connect(self.initiateBinaryMask)
self._widget.loadScanParametersButton.clicked.connect(self.getScanParameters)
self._widget.setBusyFalseButton.clicked.connect(self.setBusyFalse)
# initiate log for each detected event
self.resetDetLog()
self.resetRunParams()
self.getScanParameters() # mock: to avoid having to manually press the button
# initiate other parameters and flags used during experiments
self.initiateFlagsParams()
def initiateFlagsParams(self):
# initiate flags and params
self.__running = False # run flag
self.__runMode = RunMode.Experiment # run mode currently used
self.__validating = False # validation flag
self.__busy = False # running pipeline busy flag
self.__prevFrames = deque(maxlen=10) # deque for previous fast frames
self.__prevAnaFrames = deque(maxlen=10) # deque for previous preprocessed analysis frames
self.__binary_mask = None # binary mask of regions of interest, used by certain pipelines, leave None to consider the whole image
self.__binary_frames = 10 # number of frames to use for calculating binary mask
self.__init_frames = 5 # number of frames after initiating etSTED before a trigger can occur, to allow laser power settling etc
self.__validation_frames = 5 # number of fast frames to record after detecting an event in validation mode
self.t_call = 0
self.__params_exclude = ['img', 'prev_frames', 'binary_mask', 'exinfo', 'testmode'] # excluded pipeline parameters when loading param fields
def initiate(self):
""" Initiate or stop an etSTED experiment. """
if not self.__running:
# detector and laser for fast imaging
detectorFastIdx = self._widget.fastImgDetectorsPar.currentIndex()
self.detectorFast = self._widget.fastImgDetectors[detectorFastIdx]
laserFastIdx = self._widget.fastImgLasersPar.currentIndex()
self.laserFast = self._widget.fastImgLasers[laserFastIdx]
# Read GUI params for analysis pipeline
self.__param_vals = self.readParams()
# reset general run parameters
self.resetRunParams()
# Reset parameter for extra information that pipelines can input and output
self.__exinfo = None
# launch help widget, if visualization mode or validation mode
# Check if visualization mode, in case launch help widget
experimentModeIdx = self._widget.experimentModesPar.currentIndex()
self.experimentMode = self._widget.experimentModes[experimentModeIdx]
if self.experimentMode == 'TestVisualize':
self.__runMode = RunMode.TestVisualize
elif self.experimentMode == 'TestValidate':
self.__runMode = RunMode.TestValidate
else:
self.__runMode = RunMode.Experiment
# check if visualization or validation mode
if self.__runMode == RunMode.TestValidate or self.__runMode == RunMode.TestVisualize:
self.launchHelpWidget()
# load selected coordinate transform
self.loadTransform()
self.__transformCoeffs = self.__coordTransformHelper.getTransformCoeffs()
# connect signals and turn on wf laser
# connect signal from update of image to running pipeline #xxx.sigUpdateImage.connect(self.runPipeline)
self.camImgWorker.newFrame.connect(self.runPipeline) # mock: directly from mock camera worker
# connect signal from end of scan to scanEnded() #xxx.sigScanEnded.connect(self.scanEnded)
# turn on laserFast #xxx.lasersManager.laserFast.setEnabled(True)
self._widget.eventScatterPlot.show()
self._widget.initiateButton.setText('Stop')
self.__running = True
else:
# disconnect signals and turn off wf laser
# disconnect signal from update of image to running pipeline #xxx.sigUpdateImage.disconnect(self.runPipeline)
self.camImgWorker.newFrame.disconnect(self.runPipeline) # mock: directly from mock camera worker
# disconnect signal from end of scan to scanEnded() #xxx.sigScanEnded.disconnect(self.scanEnded)
# turn off laserFast #xxx.lasersManager.laserFast.setEnabled(False)
self._widget.eventScatterPlot.hide()
self._widget.initiateButton.setText('Initiate')
self.resetParamVals()
self.resetRunParams()
def scanEnded(self):
""" End an etSTED slow method scan. """
self.setDetLogLine("scan_end",datetime.now().strftime('%Ss%fus'))
# emit signal to save the last scanned image #xxx.sigSnapImg.emit()
self.endRecording()
self.continueFastModality()
self.__fast_frame = 0
def setDetLogLine(self, key, val, *args):
if args:
self.__detLog[f"{key}{args[0]}"] = val
else:
self.__detLog[key] = val
def runSlowScan(self):
""" Run event-triggered scan in small ROI. """
print(self._scanParameterDict)
# emit signal to run scan #xxx.sigRunScan.emit(self.signalDict)
self.scanEnded() # mock: force scanEnded without scanning
def endRecording(self):
""" Save an etSTED slow method scan. """
self.setDetLogLine("pipeline", self.getPipelineName())
self.logPipelineParamVals()
# save log file with temporal info of trigger event
filename = datetime.utcnow().strftime('%Hh%Mm%Ss%fus')
name = os.path.join(_logsDir, filename) + '_log'
savename = getUniqueName(name)
log = [f'{key}: {self.__detLog[key]}' for key in self.__detLog]
with open(f'{savename}.txt', 'w') as f:
[f.write(f'{st}\n') for st in log]
self.resetDetLog()
def getTransformName(self):
""" Get the name of the pipeline currently used. """
transformidx = self._widget.transformPipelinePar.currentIndex()
transformname = self._widget.transformPipelines[transformidx]
return transformname
def getPipelineName(self):
""" Get the name of the pipeline currently used. """
pipelineidx = self._widget.analysisPipelinePar.currentIndex()
pipelinename = self._widget.analysisPipelines[pipelineidx]
return pipelinename
def logPipelineParamVals(self):
""" Put analysis pipeline parameter values in the log file. """
params_ignore = ['img','bkg','binary_mask','testmode','exinfo']
param_names = list()
for pipeline_param_name, _ in self.__pipeline_params.items():
if pipeline_param_name not in params_ignore:
param_names.append(pipeline_param_name)
for key, val in zip(param_names, self.__param_vals):
self.setDetLogLine(key, val)
def continueFastModality(self):
""" Continue the fast method, after an event scan has been performed. """
if self._widget.endlessScanCheck.isChecked() and not self.__running:
# connect communication channel signals
# connect signal from update of image to running pipeline #xxx.sigUpdateImage.connect(self.runPipeline)
self.camImgWorker.newFrame.connect(self.runPipeline) # mock: directly from mock camera worker
# turn on laserFast #xxx.lasersManager.laserFast.setEnabled(True)
self._widget.eventScatterPlot.show()
self._widget.initiateButton.setText('Stop')
self.__running = True
elif not self._widget.endlessScanCheck.isChecked():
# disconnect signal from end of scan to scanEnded() #xxx.sigScanEnded.disconnect(self.scanEnded)
self._widget.initiateButton.setText('Initiate')
self.__running = False
self.resetParamVals()
def loadTransform(self):
""" Load a previously saved coordinate transform. """
transformname = self.getTransformName()
self.transform = getattr(importlib.import_module(f'{transformname}'), f'{transformname}')
def loadPipeline(self):
""" Load the selected analysis pipeline, and its parameters into the GUI. """
pipelinename = self.getPipelineName()
self.pipeline = getattr(importlib.import_module(f'{pipelinename}'), f'{pipelinename}')
self.__pipeline_params = signature(self.pipeline).parameters
self._widget.initParamFields(self.__pipeline_params, self.__params_exclude)
def initiateBinaryMask(self):
""" Initiate the process of calculating a binary mask of the region of interest. """
self.__binary_stack = None
# turn on laserFast #xxx.lasersManager.laserFast.setEnabled(True)
# connect signal from update of image to saving the image in the stack of images for binary mask calculation
self.camImgWorker.newFrame.connect(self.addImgBinStack)
self._widget.recordBinaryMaskButton.setText('Recording...')
def addImgBinStack(self, img):
""" Add image to the stack of images used to calculate a binary mask of the region of interest. """
if self.__binary_stack is None:
self.__binary_stack = img
elif len(self.__binary_stack) == self.__binary_frames:
# disconnect signal from update of image to saving the image in the stack of images for binary mask calculation
self.camImgWorker.newFrame.disconnect(self.addImgBinStack)
# turn off laserFast #xxx.lasersManager.laserFast.setEnabled(False)
self.calculateBinaryMask(self.__binary_stack)
else:
if np.ndim(self.__binary_stack) == 2:
self.__binary_stack = np.stack((self.__binary_stack, img))
else:
self.__binary_stack = np.concatenate((self.__binary_stack, [img]), axis=0)
def calculateBinaryMask(self, img_stack):
""" Calculate the binary mask of the region of interest. """
img_mean = np.mean(img_stack, 0)
img_bin = ndi.filters.gaussian_filter(img_mean, np.float(self._widget.bin_smooth_edit.text()))
self.__binary_mask = np.array(img_bin > np.float(self._widget.bin_thresh_edit.text()))
self._widget.recordBinaryMaskButton.setText('Record binary mask')
self.setAnalysisHelpImg(self.__binary_mask)
self.launchHelpWidget()
def setAnalysisHelpImg(self, img):
""" Set the preprocessed image in the analysis help widget. """
if self.__fast_frame < self.__init_frames + 3:
autolevels = True
else:
autolevels = False
self._widget.analysisHelpWidget.img.setImage(img, autoLevels=autolevels)
infotext = f'Min: {np.min(img)}, max: {np.max(img/10000)} (rel. change)'
self._widget.analysisHelpWidget.info_label.setText(infotext)
def getScanParameters(self):
""" Get scan parameters (size (per axis), pixel size (per axis), dwell time etc) from a scanning widget/scan part of software. """
self._scanParameterDict = {
'target_device': ['X-galvo', 'Y-galvo'],
'axis_size': [5,5],
'axis_centerpos': [0,0],
'axis_pixel_size': [0.03, 0.03],
'dwell_time': 0.03
}
def setBusyFalse(self):
""" Set busy flag to false. """
self.__busy = False
def updateScatter(self, coords):
""" Update the scatter plot of detected event coordinates. """
if np.size(coords) > 0:
self._widget.setEventScatterData(x=coords[:,1],y=coords[:,0])
# possibly not the below more than one time. Maybe it is enough to then update it, if the reference to the same object is kept throughout all function calls
self._widget.eventScatterPlot.hide()
self._widget.imageViewer.addItem(self._widget.eventScatterPlot)
def readParams(self):
""" Read user-provided analysis pipeline parameter values. """
param_vals = list()
for item in self._widget.param_edits:
param_vals.append(np.float(item.text()))
return param_vals
def launchHelpWidget(self):
""" Launch help widget that shows the preprocessed images in real-time. """
self._widget.launchHelpWidget(self._widget.analysisHelpWidget, init=True)
def resetDetLog(self):
""" Reset the event log dictionary. """
self.__detLog = dict()
def resetParamVals(self):
""" Reset the pipeline parameters. """
self.__param_vals = list()
def resetRunParams(self):
""" Reset general pipeline run parameters. """
self.__running = False
self.__validating = False
self.__fast_frame = 0
self.__post_event_frames = 0
def runPipeline(self, img):
""" Run the analyis pipeline, called after every fast method frame. """
if not self.__busy:
# if not still running pipeline on last frame
self.__busy = True
# get time since last pipeline run (ms) and log
dt = datetime.now()
self.t_latestcall = round(dt.microsecond/1000)
t_sincelastcall = self.t_latestcall - self.t_call
self.t_call = self.t_latestcall
self.setDetLogLine("pipeline_rep_period", str(t_sincelastcall))
self.setDetLogLine("pipeline_start", datetime.now().strftime('%Ss%fus'))
# run pipeline
if self.__runMode == RunMode.TestVisualize or self.__runMode == RunMode.TestValidate:
# if chosen a test mode: run pipeline with analysis image return
coords_detected, self.__exinfo, img_ana = self.pipeline(img, self.__prevFrames, self.__binary_mask,
(self.__runMode==RunMode.TestVisualize or
self.__runMode==RunMode.TestValidate),
self.__exinfo, *self.__param_vals)
else:
# if chosen experiment mode: run pipeline without analysis image return
coords_detected, self.__exinfo = self.pipeline(img, self.__prevFrames, self.__binary_mask,
self.__runMode==RunMode.TestVisualize,
self.__exinfo, *self.__param_vals)
self.setDetLogLine("pipeline_end", datetime.now().strftime('%Ss%fus'))
if self.__fast_frame > self.__init_frames:
# if initial settling frames have passed
if self.__runMode == RunMode.TestVisualize:
# if visualization mode: only update scatter and set analysis image in help widget
self.updateScatter(coords_detected)
self.setAnalysisHelpImg(img_ana)
elif self.__runMode == RunMode.TestValidate:
# if validation mode: update scatter, set analysis image in help widget,
# and start to record validation frames after event
self.updateScatter(coords_detected)
self.setAnalysisHelpImg(img_ana)
if self.__validating:
# if currently validating
if self.__post_event_frames > self.__validation_frames:
# if all validation frames have been recorded, pause fast imaging,
# end recording, and then continue fast imaging
self.saveValidationImages(prev=True, prev_ana=True)
self.pauseFastModality()
self.endRecording()
self.continueFastModality()
self.__fast_frame = 0
self.__validating = False
self.__post_event_frames += 1
elif coords_detected.size != 0:
# if some events where detected and not validating
# take first detected coords as event
if np.size(coords_detected) > 2:
coords_scan = coords_detected[0,:]
else:
coords_scan = coords_detected[0]
# log detected center coordinate
self.setDetLogLine("fastscan_x_center", coords_scan[0])
self.setDetLogLine("fastscan_y_center", coords_scan[1])
# log all detected coordinates
if np.size(coords_detected) > 2:
for i in range(np.size(coords_detected,0)):
self.setDetLogLine("det_coord_x_", coords_detected[i,0], i)
self.setDetLogLine("det_coord_y_", coords_detected[i,1], i)
# flag for start of validation
self.__validating = True
self.__post_event_frames = 0
elif coords_detected.size != 0:
# if experiment mode, and some events were detected
# take first detected coords as event
if np.size(coords_detected) > 2:
coords_scan = coords_detected[0,:]
else:
coords_scan = coords_detected[0]
self.setDetLogLine("prepause", datetime.now().strftime('%Ss%fus'))
# pause fast imaging
self.pauseFastModality()
self.setDetLogLine("coord_transf_start", datetime.now().strftime('%Ss%fus'))
# transform detected coordinate between fast and scanning imaging spaces
coords_center_scan = self.transform(coords_scan, self.__transformCoeffs)
# log detected and scanning center coordinate
self.setDetLogLine("fastscan_x_center", coords_scan[0])
self.setDetLogLine("fastscan_y_center", coords_scan[1])
self.setDetLogLine("slowscan_x_center", coords_center_scan[0])
self.setDetLogLine("slowscan_y_center", coords_center_scan[1])
self.setDetLogLine("scan_initiate", datetime.now().strftime('%Ss%fus'))
# log all detected coordinates
if np.size(coords_detected) > 2:
for i in range(np.size(coords_detected,0)):
self.setDetLogLine("det_coord_x_", coords_scan[0], i)
self.setDetLogLine("det_coord_y_", coords_scan[1], i)
# initiate and run scanning with transformed center coordinate
self.initiateSlowScan(position=coords_center_scan)
self.runSlowScan()
# update scatter plot of event coordinates in the shown fast method image
self.updateScatter(coords_detected)
# buffer latest fast frame and save validation images
self.__prevFrames.append(img)
self.saveValidationImages(prev=True, prev_ana=False)
self.setBusyFalse()
return
# buffer latest fast frame and save validation images
self.__prevFrames.append(img)
if self.__runMode == RunMode.TestValidate:
# if validation mode: buffer previous preprocessed analysis frame
self.__prevAnaFrames.append(img_ana)
self.__fast_frame += 1
# unset busy flag
self.setBusyFalse()
def initiateSlowScan(self, position=[0.0,0.0]):
""" Initiate a STED scan. """
# change the center coordinate of the scan parameters to the detected positions
self.setCenterScanParameter(position)
# generate scanning curves through scanning part of software and save to self.signalDic
#self.signalDict = xxx.genereateScanCurves(self._scanParameterDict)
self.signalDict = {} # mock: empty signal dictionaries
def setCenterScanParameter(self, position):
""" Set the scanning center from the detected event coordinates. """
if self._scanParameterDict != {}:
# if scan parameters have been loaded
self._scanParameterDict['axis_centerpos'] = []
# null center positions
for index,_ in enumerate(self._scanParameterDict['target_device']):
# for each scanning device (assuming X fast and Y slow)
center = position[index]
if index==0:
# if fast axis: add shift to detected position due to scanning lag etc
center = self.addFastAxisShift(center)
# save event coordinate as center for scanning device
self._scanParameterDict['axis_centerpos'].append(center)
def addFastAxisShift(self, center):
""" Add a scanning-method and microscope-specific shift to the fast axis scanning.
For Alvelid et al 2022: based on second-degree curved surface fit to 2D-sampling
of dwell time and pixel size induced shifts. """
dwell_time = float(self._scanParameterDict['dwell_time'])
px_size = float(self._scanParameterDict['axis_pixel_size'][0])
C = np.array([0, 0, 0, 0, 0, 0]) # second order plane fit, here mock null shift
params = np.array([px_size**2, dwell_time**2, px_size*dwell_time, px_size, dwell_time, 1]) # for use with second order plane fit
shift_compensation = np.sum(params*C)
center -= shift_compensation
return(center)
def saveValidationImages(self, prev=True, prev_ana=True):
""" Save the validation fast images of an event detection, fast images and/or preprocessed analysis images. """
if prev:
# save detectorFast frames leading up to event #xxx.sigSaveImage.emit(self.detectorFast, np.array(list(self.__prevFrames)), 'raw') # (detector, imagestack, name_suffix)
self.__prevFrames.clear()
if prev_ana:
# save preprocessed images leading up to event #xxx.sigSaveImage.emit(self.detectorFast, np.array(list(self.__prevAnaFrames)), 'ana') # (detector, imagestack, name_suffix)
self.__prevAnaFrames.clear()
def pauseFastModality(self):
""" Pause the fast method, when an event has been detected. """
if self.__running:
# disconnect signal from update of image to running pipeline #xxx.sigUpdateImage.disconnect(self.runPipeline)
self.camImgWorker.newFrame.disconnect(self.runPipeline) # mock: directly from mock camera worker
# turn off fast laser xxx.lasersManager.laserFast.setEnabled(False)
self.__running = False
def closeEvent(self, *args):
print('what')
#self.camImgWorker.
self.camImgThread.quit()
class EtSTEDCoordTransformHelper():
""" Coordinate transform help widget controller. """
def __init__(self, etSTEDController, coordTransformWidget, saveFolder, *args, **kwargs):
self._etSTEDController = etSTEDController
self._widget = coordTransformWidget
self.__saveFolder = saveFolder
# initiate coordinate transform parameters
self.__transformCoeffs = [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0] # "unit" transformation
self.__loResCoords = list()
self.__hiResCoords = list()
self.__loResCoordsPx = list()
self.__hiResCoordsPx = list()
self.__hiResPxSize = 1
self.__loResPxSize = 1
self.__hiResSize = 1
# connect signals from widget
self._etSTEDController._widget.coordTransfCalibButton.clicked.connect(self.calibrationLaunch)
self._widget.saveCalibButton.clicked.connect(self.calibrationFinish)
self._widget.resetCoordsButton.clicked.connect(self.resetCalibrationCoords)
self._widget.loadLoResButton.clicked.connect(lambda: self.loadCalibImage('lo'))
self._widget.loadHiResButton.clicked.connect(lambda: self.loadCalibImage('hi'))
def getTransformCoeffs(self):
""" Get transformation coefficients. """
return self.__transformCoeffs
def calibrationLaunch(self):
""" Launch calibration. """
self._etSTEDController._widget.launchHelpWidget(self._etSTEDController._widget.coordTransformWidget, init=True)
def calibrationFinish(self):
""" Finish calibration. """
# get annotated coordinates in both images and translate to real space coordinates
self.__loResCoordsPx = self._widget.pointsLayerLo.data
for pos_px in self.__loResCoordsPx:
pos = (np.around(pos_px[0]*self.__loResPxSize, 3), np.around(pos_px[1]*self.__loResPxSize, 3))
self.__loResCoords.append(pos)
self.__hiResCoordsPx = self._widget.pointsLayerHi.data
for pos_px in self.__hiResCoordsPx:
pos = (np.around(pos_px[0]*self.__hiResPxSize - self.__hiResSize/2, 3), -1 * np.around(pos_px[1]*self.__hiResPxSize - self.__hiResSize/2, 3))
self.__hiResCoords.append(pos)
# calibrate coordinate transform
self.coordinateTransformCalibrate()
print(f'Transformation coeffs: {self.__transformCoeffs}')
name = datetime.utcnow().strftime('%Hh%Mm%Ss%fus')
filename = os.path.join(self.__saveFolder, name) + '_transformCoeffs.txt'
np.savetxt(fname=filename, X=self.__transformCoeffs)
# plot the resulting transformed low-res coordinates on the hi-res image
coords_transf = []
for i in range(0,len(self.__loResCoords)):
pos = self.poly_thirdorder_transform(self.__transformCoeffs, self.__loResCoords[i])
pos_px = (np.around((pos[0] + self.__hiResSize/2)/self.__hiResPxSize, 0), np.around((-1 * pos[1] + self.__hiResSize/2)/self.__hiResPxSize, 0))
coords_transf.append(pos_px)
coords_transf = np.array(coords_transf)
self._widget.pointsLayerTransf.data = coords_transf
def resetCalibrationCoords(self):
""" Reset all selected coordinates. """
self.__loResCoords = list()
self.__loResCoordsPx = list()
self.__hiResCoords = list()
self.__hiResCoordsPx = list()
self._widget.pointsLayerLo.data = []
self._widget.pointsLayerHi.data = []
self._widget.pointsLayerTransf.data = []
def loadCalibImage(self, modality):
""" Load fast or scan calibration image. """
# open gui to choose file
img_filename = self.findFile()
# load img data from file
with h5py.File(img_filename, "r") as f:
img_key = list(f.keys())[0]
pixelsize = f.attrs['element_size_um'][1]
img_data = np.array(f[img_key])
imgsize = pixelsize*np.size(img_data,0)
# view data in corresponding viewbox
self.updateCalibImage(img_data, modality)
if modality == 'hi':
self.__hiResCoords = list()
self.__hiResPxSize = pixelsize
self.__hiResSize = imgsize
elif modality == 'lo':
self.__loResCoords = list()
self.__loResPxSize = pixelsize
def findFile(self):
""" Opens current folder in the file explorer and returns chosen filename. """
Tk().withdraw()
filename = filedialog.askopenfilename()
return filename
def updateCalibImage(self, img_data, modality):
""" Update new image in the viewbox. """
if modality == 'hi':
viewer = self._widget.napariViewerHi
elif modality == 'lo':
viewer = self._widget.napariViewerLo
viewer.add_image(img_data)
viewer.layers.unselect_all()
viewer.layers.move_selected(len(viewer.layers)-1,0)
def coordinateTransformCalibrate(self):
""" Third-order (cubic) polynomial fitting with least-squares Levenberg-Marquart algorithm. """
# prepare data and init guess
xdata = np.array([*self.__loResCoords]).astype(np.float32)
ydata = np.array([*self.__hiResCoords]).astype(np.float32)
c_init = np.hstack([np.zeros(10), np.zeros(10)])
initguess = c_init.astype(np.float32)
# fit
res_lsq = least_squares(self.poly_thirdorder, initguess, args=(xdata, ydata), method='lm')
transformCoeffs = res_lsq.x
self.__transformCoeffs = transformCoeffs
def poly_thirdorder(self, a, x, y):
""" Polynomial function that will be fit in the least-squares fit. """
res = []
for i in range(0, len(x)):
c1 = x[i,0]
c2 = x[i,1]
x_i1 = a[0]*c1**3 + a[1]*c2**3 + a[2]*c2*c1**2 + a[3]*c1*c2**2 + a[4]*c1**2 + a[5]*c2**2 + a[6]*c1*c2 + a[7]*c1 + a[8]*c2 + a[9]
x_i2 = a[10]*c1**3 + a[11]*c2**3 + a[12]*c2*c1**2 + a[13]*c1*c2**2 + a[14]*c1**2 + a[15]*c2**2 + a[16]*c1*c2 + a[17]*c1 + a[18]*c2 + a[19]
res.append(x_i1 - y[i,0])
res.append(x_i2 - y[i,1])
return res
def poly_thirdorder_transform(self, a, x):
""" Use for plotting the least-squares fit results. """
c1 = x[0]
c2 = x[1]
x_i1 = a[0]*c1**3 + a[1]*c2**3 + a[2]*c2*c1**2 + a[3]*c1*c2**2 + a[4]*c1**2 + a[5]*c2**2 + a[6]*c1*c2 + a[7]*c1 + a[8]*c2 + a[9]
x_i2 = a[10]*c1**3 + a[11]*c2**3 + a[12]*c2*c1**2 + a[13]*c1*c2**2 + a[14]*c1**2 + a[15]*c2**2 + a[16]*c1*c2 + a[17]*c1 + a[18]*c2 + a[19]
return (x_i1, x_i2)
class CameraImageWorker(QObject):
""" Worker for handling pulling of camera images. """
started = pyqtSignal()
finished = pyqtSignal()
newFrame = pyqtSignal(object)
def __init__(self, controller, camera):
QThread.__init__(self)
self.timer = QTimer(self)
self.controller = controller
self.camera = camera
def updateImg(self):
""" Check if the latest grabbed frame from the camera is new. """
newimg = self.camera.getImage()
if not np.array_equal(newimg, self.controller.camImageLayer.data):
self.controller.camImageLayer.data = newimg
self.newFrame.emit(newimg)
def run(self):
self.timer.timeout.connect(self.updateImg)
self.timer.start(self.camera.properties['update_time']/2)
class RunMode(enum.Enum):
Experiment = 1
TestVisualize = 2
TestValidate = 3
def insertSuffix(filename, suffix, newExt=None):
names = os.path.splitext(filename)
if newExt is None:
return names[0] + suffix + names[1]
else:
return names[0] + suffix + newExt
def getUniqueName(name):
name, ext = os.path.splitext(name)
n = 1
while glob.glob(name + ".*"):
if n > 1:
name = name.replace('_{}'.format(n - 1), '_{}'.format(n))
else:
name = insertSuffix(name, '_{}'.format(n))
n += 1
return ''.join((name, ext))
# Copyright (C) 2020-2022 ImSwitch developers
#
# ImSwitch is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ImSwitch is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.