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imaging_method.py
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from PySide2 import QtCore, QtWidgets, QtGui
from widgets import MplCanvas, PlotWidget
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg, NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure
import numpy as np
import hex_grid
import lmi_pattern
from hexSimProcessor import HexSimProcessor
import scipy
import tifffile
import os
import json
class ImagingMethod:
def __init__(self, method_name):
self.method_name = method_name
self.parameters_widget = None
self.reconstruction_parameters_widget = None
self.patterns_widget = None
self.debug_tabs = [] # list of tuples ("name", widget)
self.frames = None
self.reconstruction = None
self.params = None
def parse_parameters(self):
pass
def load_parameters(self, params):
pass
def update_patterns(self, global_params):
pass
def take_images(self, system):
pass
def calibrate(self):
pass
def reconstruct(self):
pass
def save_images(self, folder):
pass
def load_images(self, folder):
pass
def save_images(self, folder, save_raw = True, save_reconstruction = True, save_metadata = True, prefix = ""):
if save_raw:
tifffile.imwrite(os.path.join(folder, f"{prefix}{self.method_name}.tiff"), self.frames)
if self.reconstruction is not None and save_reconstruction:
tifffile.imwrite(os.path.join(folder, f"{prefix}{self.method_name}_reconstruction.tiff"), self.reconstruction)
if save_metadata:
with open(os.path.join(folder, f"{prefix}{self.method_name}_metadata.json"), 'w') as f:
json.dump(self.params, f)
def load_images(self, folder):
self.frames = tifffile.imread(os.path.join(folder, f"{self.method_name}.tiff"))
self.reconstruction = None
self.plot_images()
class SIMImaging(ImagingMethod):
def __init__(self):
super().__init__("sim")
sim_group = QtWidgets.QGroupBox("SIM")
layout = QtWidgets.QFormLayout()
self.sim_enabled_chb = QtWidgets.QCheckBox("Do SIM")
self.sim_enabled_chb.setChecked(True)
layout.addRow("Do SIM", self.sim_enabled_chb)
self.desired_distance_txt = QtWidgets.QLineEdit("0.0345")
layout.addRow("Desired dot distance [deg]", self.desired_distance_txt)
self.second_order_chb = QtWidgets.QCheckBox("Second order")
layout.addRow("Second order", self.second_order_chb)
self.steps_x_lbl = QtWidgets.QLabel()
layout.addRow("Steps X", self.steps_x_lbl)
self.steps_y_lbl = QtWidgets.QLabel()
layout.addRow("Steps Y", self.steps_y_lbl)
self.dot_distance_x_lbl = QtWidgets.QLabel()
layout.addRow("Dot distance x", self.dot_distance_x_lbl)
self.aspect_lbl = QtWidgets.QLabel()
layout.addRow("Aspect ratio", self.aspect_lbl)
self.pattern_hz_txt = QtWidgets.QLineEdit("40000")
layout.addRow("Projection rate [Hz]", self.pattern_hz_txt)
self.exposure_lbl = QtWidgets.QLabel()
layout.addRow("Exposure time", self.exposure_lbl)
sim_group.setLayout(layout)
self.parameters_widget = sim_group
reconstruction_group = QtWidgets.QGroupBox("Reconstruction")
layout = QtWidgets.QFormLayout()
self.reconstruction_size_txt = QtWidgets.QLineEdit("256")
layout.addRow("Reconstruction size N", self.reconstruction_size_txt)
self.reconstruction_offset_x = QtWidgets.QLineEdit("0")
layout.addRow("Reconstruction offset x", self.reconstruction_offset_x)
self.reconstruction_offset_y = QtWidgets.QLineEdit("0")
layout.addRow("Reconstruction offset y", self.reconstruction_offset_y)
self.reconstruction_eta_txt = QtWidgets.QLineEdit("0.5")
layout.addRow("Reconstruction eta", self.reconstruction_eta_txt)
self.reconstruction_w_txt = QtWidgets.QLineEdit("0.3")
layout.addRow("Wiener parameter", self.reconstruction_w_txt)
self.reconstruction_alpha_txt = QtWidgets.QLineEdit("0.3")
layout.addRow("Zero order attenuation width", self.reconstruction_alpha_txt)
self.reconstruction_beta_txt = QtWidgets.QLineEdit("0.999")
layout.addRow("Zero order attenuation", self.reconstruction_beta_txt)
self.use_filter_chb = QtWidgets.QCheckBox("Use frequency space filtering")
layout.addRow("Filter", self.use_filter_chb)
reconstruction_group.setLayout(layout)
self.reconstruction_parameters_widget = reconstruction_group
w = QtWidgets.QWidget()
lay = QtWidgets.QVBoxLayout()
self.pattern_plot = MplCanvas(self, width=5, height=4, dpi=100)
toolbar = NavigationToolbar(self.pattern_plot, w)
lay.addWidget(toolbar)
lay.addWidget(self.pattern_plot)
w.setLayout(lay)
self.patterns_widget = w
self.image_plot = PlotWidget()
self.fft_plot = PlotWidget()
self.carrier_plot = PlotWidget()
self.bands_plot = PlotWidget()
self.psf_plot = PlotWidget()
self.recon_plot = PlotWidget(None, num_clim = 2)
self.debug_tabs = [
('Recorded Images', self.image_plot),
('FFT', self.fft_plot),
('Carrier', self.carrier_plot),
('Bands', self.bands_plot),
('PSF shaping', self.psf_plot),
('Reconstructed Image', self.recon_plot),
]
self.p = HexSimProcessor()
self.p.debug = False
self.frames = None
self.reconstruction = None
def parse_parameters(self):
return {
"enabled": self.sim_enabled_chb.isChecked(),
"desired_distance": float(self.desired_distance_txt.text()),
"pattern_rate_Hz": float(self.pattern_hz_txt.text()),
"second_order": self.second_order_chb.isChecked(),
"reconstruction_size": int(self.reconstruction_size_txt.text()),
"reconstruction_offset_x": int(self.reconstruction_offset_x.text()),
"reconstruction_offset_y": int(self.reconstruction_offset_y.text()),
"reconstruction_eta": float(self.reconstruction_eta_txt.text()),
"reconstruction_w": float(self.reconstruction_w_txt.text()),
"reconstruction_alpha": float(self.reconstruction_alpha_txt.text()),
"reconstruction_beta": float(self.reconstruction_beta_txt.text()),
"reconstruction_use_filter": self.use_filter_chb.isChecked(),
}
def load_parameters(self, params):
self.sim_enabled_chb.setChecked(bool(params.get("enabled", "True")))
self.desired_distance_txt.setText(str(params.get("desired_distance", "0.013")))
self.pattern_hz_txt.setText(str(params.get("pattern_rate_Hz", "10000")))
self.second_order_chb.setChecked(params.get("second_order", False))
self.reconstruction_size_txt.setText(str(params.get("reconstruction_size", "1000")))
self.reconstruction_offset_x.setText(str(params.get("reconstruction_offset_x", "0")))
self.reconstruction_offset_y.setText(str(params.get("reconstruction_offset_y", "0")))
self.reconstruction_eta_txt.setText(str(params.get("reconstruction_eta", "0.5")))
self.reconstruction_w_txt.setText(str(params.get("reconstruction_w", "0.3")))
self.reconstruction_alpha_txt.setText(str(params.get("reconstruction_alpha", "0.3")))
self.reconstruction_beta_txt.setText(str(params.get("reconstruction_beta", "0.999")))
self.use_filter_chb.setChecked(params.get("reconstruction_use_filter", False))
def update_patterns(self, global_params):
if not self.sim_enabled_chb.isChecked():
return
params = {**self.parse_parameters(), **global_params}
desired_distance = params['desired_distance']
grating_distance_x = params['grating_distance_x']
steps_x = round(grating_distance_x / desired_distance)
distance_x = grating_distance_x / steps_x
grating_distance_y = params['grating_distance_y']
desired_distance_y = np.sin(np.deg2rad(60)) * distance_x * 2
steps_y = round(grating_distance_y / desired_distance_y)
distance_y = grating_distance_y / steps_y
aspect = grating_distance_y / (desired_distance_y * steps_y)
self.aspect_lbl.setText(str(aspect))
self.steps_x_lbl.setText(str(steps_x))
self.steps_y_lbl.setText(str(steps_y))
self.dot_distance_x_lbl.setText(str(distance_x))
orientation_deg = params['orientation_deg']
self.pattern_rate_Hz = params['pattern_rate_Hz']
second_order = params['second_order']
pattern_deg = hex_grid.projection_hex_pattern_deg(distance_x, steps_x, steps_y, orientation_rad = np.deg2rad(orientation_deg), aspect_ratio=aspect, second_order=second_order)
num_shifts = 19 if second_order else 7
self.exposure_time_sec = pattern_deg.shape[1] / self.pattern_rate_Hz
self.exposure_lbl.setText(f"{self.exposure_time_sec * 1e3:.1f} ms * {num_shifts} = {self.exposure_time_sec * 1e3 * num_shifts:.1f} ms")
self.pattern_plot.fig.clear()
ax1, ax2 = self.pattern_plot.fig.subplots(1, 2, sharex=True, sharey=True)
for i in range(num_shifts):
ax1.scatter(pattern_deg[i, :, 0], pattern_deg[i, :, 1])
ax2.scatter(pattern_deg[0, :, 0], pattern_deg[0, :, 1])
ax1.set_aspect(1)
ax2.set_aspect(1)
self.pattern_plot.draw()
self.pattern_deg = pattern_deg
self.params = params
self.update_psf_plots()
def update_psf_plots(self):
desired_distance = self.params['desired_distance']
aod_deg_to_um_in_sample_plane = self.params['aod_deg_to_um_in_sample_plane']
pixelsize = self.params['pixelsize']
magnification = self.params['magnification']
ex_NA = self.params['ex_NA']
ex_wavelength = self.params['ex_wavelength']
em_NA = self.params['em_NA']
em_wavelength = self.params['em_wavelength']
mtf_data = self.params['mtf_data']
# TODO could do all this in pattern calculation also, would make more sense
#Expected PSF
def gaussian(x, mu, sig):
return np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.)))
def plot_psf_modulation(plt, wavelength, NA, k):
N = 9
dx = pixelsize / magnification
res = wavelength / (2 * NA)
x = np.linspace(-dx*N/2, dx*N/2, N * 10) # [um]
sigma = res / 2.355 # FWHM to sigma
# TODO res is not FWHM, or is it?. And PSF is not really gaussian but a sinc
psf_orig = gaussian(x, 0, sigma)
plt.plot(x, psf_orig, label="Emission PSF")
mod = (1 + np.cos(k * x)) / 2
plt.plot(x, mod, '--', label="Modulation Function")
plt.plot(x, psf_orig * mod, label="SIM PSF")
plt.vlines(np.arange(-dx*N/2, dx*N/2, dx), 0, 0.1, 'r', label="Pixel in Sample Plane")
plt.set_xlabel("x [um]")
plt.set_title("PSF Shaping")
plt.legend(loc="upper right")
def plot_projected_pattern(plt, wavelength, NA, dot_distance_um):
N = 9
dx = pixelsize / magnification
res = wavelength / (2 * NA)
x = np.linspace(-dx*N/2, dx*N/2, N * 10) # [um]
sigma = res / 2.355 # FWHM to sigma
# TODO res is not FWHM, or is it?. And PSF is not really gaussian but a sinc
psf_orig = gaussian(x, 0, sigma)
plt.plot(x, psf_orig, label="Excitation PSF")
dot_positions = np.arange(-dx*N, dx*N, dot_distance_um)
dot_positions -= np.mean(dot_positions) # Center at zero
projected_pattern = np.sum(np.stack([gaussian(x, mean, sigma) for mean in dot_positions]), axis=0)
plt.plot(x, projected_pattern, label="Projected Pattern")
plt.set_xlabel("x [um]")
plt.set_title("Projected Pattern")
plt.legend(loc="upper right")
def plot_otf(plt, wavelength, NA, k, mtf_data = "", name = ""):
N = 100
dx = pixelsize / magnification
res = wavelength / (2 * NA)
oversampling = res / dx
dk = oversampling / (N / 2) # Sampling in frequency plane. This is unitless, not sure what is means exactly
k_to_cycles_per_um = NA / wavelength
kx = np.arange(-dk * N / 2, dk * N / 2, dk)
plt.plot(kx * k_to_cycles_per_um, self.p._tf(abs(kx)) * 2, label=f"{name} MTF");
nyq = 0.5 / dx
plt.vlines([-nyq, nyq], 0, 0.2, 'r', label="Nyquist Frequency")
plt.vlines([-k / (2 * np.pi), k / (2 * np.pi)], 0, 0.2, 'g', label="Illumination Frequency")
if mtf_data != "":
mtf = np.fromstring("\n".join(mtf_data.split("\n")[1:]), sep='\t').reshape(-1, 2)
plt.plot(mtf[:, 0] / 1000, mtf[:, 1], label="Measured MTF")
plt.set_ylim(0, None)
plt.set_xlabel("Frequency [cycles / um]")
plt.legend(loc="upper right")
plt.set_title(f"{name} MTF")
dot_distance_um = desired_distance * aod_deg_to_um_in_sample_plane
illumination_freq = (1 / dot_distance_um) * 2 * np.pi
self.psf_plot.plot.fig.clear()
axs = self.psf_plot.plot.fig.subplots(2, 2)
plot_projected_pattern(axs[0, 0], wavelength = ex_wavelength, NA = ex_NA, dot_distance_um = dot_distance_um)
plot_otf(axs[0, 1], wavelength = ex_wavelength, NA = ex_NA, k = illumination_freq, name="Excitation")
plot_psf_modulation(axs[1, 0], wavelength = em_wavelength, NA = em_NA, k = illumination_freq)
plot_otf(axs[1, 1], wavelength = em_wavelength, NA = em_NA, k = illumination_freq, mtf_data = mtf_data, name="Emission")
self.psf_plot.plot.draw()
def take_images(self, system):
if not self.sim_enabled_chb.isChecked():
return
self.frames = system.project_patterns_and_take_images(self.pattern_deg, self.pattern_rate_Hz, self.params['pattern_delay_sec'])
self.reconstruction = None
self.plot_images()
def plot_images(self):
self.image_plot.plot.fig.clear()
axs = self.image_plot.plot.fig.subplots(3, 3, sharex=True, sharey=True)
ims = []
for i in range(3):
ims.append(axs[0, i].imshow(self.frames[i]))
for i in range(3):
ims.append(axs[1, i].imshow(self.frames[i + 3]))
ims.append(axs[2, i].imshow(self.frames[6]))
self.image_plot.connect_clim(ims)
self.image_plot.plot.draw()
self.fft_plot.plot.clear()
im = self.fft_plot.plot.axes.imshow(np.abs(np.fft.fftshift(np.fft.fft2(self.frames[0]))))
self.fft_plot.connect_clim(im)
self.fft_plot.plot.draw()
def calibrate(self):
if not self.sim_enabled_chb.isChecked():
return
N = int(self.reconstruction_size_txt.text())
offset_x = int(self.reconstruction_offset_x.text())
offset_y = int(self.reconstruction_offset_y.text())
alpha = float(self.reconstruction_alpha_txt.text())
beta = float(self.reconstruction_beta_txt.text())
w = float(self.reconstruction_w_txt.text())
eta = float(self.reconstruction_eta_txt.text())
pixelsize = self.params['pixelsize']
magnification = self.params['magnification']
em_NA = self.params['em_NA']
em_wavelength = self.params['em_wavelength']
mtf_data = self.params['mtf_data']
assert self.frames.shape[0] == 7
frames = self.frames[:, offset_y:offset_y + N, offset_x:offset_x + N]
self.p.N = N
self.p.pixelsize = pixelsize
self.p.magnification = magnification
self.p.NA = em_NA
self.p.wavelength = em_wavelength
self.p.alpha = alpha
self.p.beta = beta
self.p.w = w
self.p.eta = eta
self.p.calibrate(frames)
self.carrier_plot.plot.fig.clear()
axs = self.carrier_plot.plot.fig.subplots(2, 3)
ims = []
for i in range(3):
ims.append(axs[0, i].imshow(self.p.carrier_debug_img[i]))
for i in range(3):
ims.append(axs[1, i].imshow(self.p.carrier_debug_zoom_img[i]))
self.carrier_plot.connect_clim(ims)
self.carrier_plot.plot.draw()
self.bands_plot.plot.fig.clear()
axs = self.bands_plot.plot.fig.subplots(2, 4)
ims = []
for i in range(4):
ims.append(axs[0, i].imshow(self.p.bands_debug_img[i].real))
for i in range(4):
ims.append(axs[1, i].imshow(self.p.bands_debug_img[i].imag))
self.bands_plot.connect_clim(ims)
self.bands_plot.plot.draw()
def reconstruct(self):
if not self.sim_enabled_chb.isChecked():
return None, None
# TODO this breaks if params changed inbetween
N = int(self.reconstruction_size_txt.text())
offset_x = int(self.reconstruction_offset_x.text())
offset_y = int(self.reconstruction_offset_y.text())
assert self.frames.shape[0] == 7
frames = self.frames[:, offset_y:offset_y + N, offset_x:offset_x + N]
self.p.use_filter = self.use_filter_chb.isChecked()
reconstruct = self.p.reconstruct_fftw(frames)
sumall = scipy.ndimage.zoom(np.sum(frames, axis=0), (2, 2), order=1)
self.recon_plot.plot.fig.clear()
ax1, ax2 = self.recon_plot.plot.fig.subplots(1, 2, sharex=True, sharey=True)
im1 = ax1.imshow(reconstruct)
im2 = ax2.imshow(sumall)
self.recon_plot.connect_clim(im1, 0)
self.recon_plot.connect_clim(im2, 1)
self.recon_plot.plot.draw()
self.reconstruction = reconstruct
return reconstruct, sumall
def save_images(self, *args, **kwargs):
if self.sim_enabled_chb.isChecked():
super().save_images(*args, **kwargs)
def load_images(self, folder):
if self.sim_enabled_chb.isChecked():
super().load_images(folder)
class LMIImaging(ImagingMethod):
def __init__(self):
super().__init__("lmi")
lmi_group = QtWidgets.QGroupBox("LMI")
layout = QtWidgets.QFormLayout()
self.lmi_enabled_chb = QtWidgets.QCheckBox("Do LMI")
self.lmi_enabled_chb.setChecked(True)
layout.addRow("Do LMI", self.lmi_enabled_chb)
self.steps_x_txt = QtWidgets.QLineEdit("5")
layout.addRow("Steps X", self.steps_x_txt)
self.steps_y_txt = QtWidgets.QLineEdit("5")
layout.addRow("Steps Y", self.steps_y_txt)
self.multiscan_x_txt = QtWidgets.QLineEdit("10")
layout.addRow("Multiscan X", self.multiscan_x_txt)
self.multiscan_y_txt = QtWidgets.QLineEdit("10")
layout.addRow("Multiscan Y", self.multiscan_y_txt)
self.dot_distance_x_lbl = QtWidgets.QLabel()
layout.addRow("Dot distance X", self.dot_distance_x_lbl)
self.dot_distance_y_lbl = QtWidgets.QLabel()
layout.addRow("Dot distance Y", self.dot_distance_y_lbl)
self.multiscan_distance_x_lbl = QtWidgets.QLabel()
layout.addRow("Multiscan distance X", self.multiscan_distance_x_lbl)
self.multiscan_distance_y_lbl = QtWidgets.QLabel()
layout.addRow("Multiscan distance Y", self.multiscan_distance_y_lbl)
self.pattern_hz_txt = QtWidgets.QLineEdit("40000")
layout.addRow("Projection rate [Hz]", self.pattern_hz_txt)
self.exposure_lbl = QtWidgets.QLabel()
layout.addRow("Exposure time", self.exposure_lbl)
lmi_group.setLayout(layout)
self.parameters_widget = lmi_group
w = QtWidgets.QWidget()
lay = QtWidgets.QVBoxLayout()
self.pattern_plot = MplCanvas(self, width=5, height=4, dpi=100)
toolbar = NavigationToolbar(self.pattern_plot, w)
lay.addWidget(toolbar)
lay.addWidget(self.pattern_plot)
w.setLayout(lay)
self.patterns_widget = w
self.image_plot = PlotWidget()
self.recon_plot = PlotWidget(None, num_clim = 2)
self.debug_tabs = [
('LMI Recorded Images', self.image_plot),
('LMI Reconstructed Image', self.recon_plot),
]
def parse_parameters(self):
return {
"enabled": self.lmi_enabled_chb.isChecked(),
"steps_x": int(self.steps_x_txt.text()),
"steps_y": int(self.steps_y_txt.text()),
"multiscan_x": int(self.multiscan_x_txt.text()),
"multiscan_y": int(self.multiscan_y_txt.text()),
"pattern_rate_Hz": float(self.pattern_hz_txt.text()),
}
def load_parameters(self, params):
self.lmi_enabled_chb.setChecked(bool(params.get("enabled", "True")))
self.steps_x_txt.setText(str(params.get("steps_x", "10")))
self.steps_y_txt.setText(str(params.get("steps_y", "10")))
self.multiscan_x_txt.setText(str(params.get("multiscan_x", "10")))
self.multiscan_y_txt.setText(str(params.get("multiscan_y", "10")))
self.pattern_hz_txt.setText(str(params.get("pattern_rate_Hz", "10000")))
def update_patterns(self, global_params):
if not self.lmi_enabled_chb.isChecked():
return
params = {**self.parse_parameters(), **global_params}
grating_distance_x = params['grating_distance_x']
grating_distance_y = params['grating_distance_y']
orientation_deg = params['orientation_deg']
steps_x = params['steps_x']
steps_y = params['steps_y']
multiscan_x = params['multiscan_x']
multiscan_y = params['multiscan_y']
self.pattern_rate_Hz = params['pattern_rate_Hz']
pattern_deg = lmi_pattern.lmi_pattern_deg(steps_x, steps_y, multiscan_x, multiscan_y, grating_distance_x, grating_distance_y, orientation_rad = np.deg2rad(orientation_deg))
self.exposure_time_sec = pattern_deg.shape[1] / self.pattern_rate_Hz
num_patterns = pattern_deg.shape[0]
self.exposure_lbl.setText(f"{self.exposure_time_sec * 1e3:.1f} ms * {num_patterns} = {self.exposure_time_sec * 1e3 * num_patterns:.1f} ms")
self.dot_distance_x_lbl.setText(str(grating_distance_x / multiscan_x / steps_x))
self.dot_distance_y_lbl.setText(str(grating_distance_y / multiscan_y / steps_y))
self.multiscan_distance_x_lbl.setText(str(grating_distance_x / multiscan_x))
self.multiscan_distance_y_lbl.setText(str(grating_distance_y / multiscan_y))
self.pattern_plot.fig.clear()
ax1, ax2 = self.pattern_plot.fig.subplots(1, 2, sharex=True, sharey=True)
for i in range(num_patterns):
ax1.scatter(pattern_deg[i, :, 0], pattern_deg[i, :, 1])
ax2.scatter(pattern_deg[0, :, 0], pattern_deg[0, :, 1])
ax1.set_aspect(1)
ax2.set_aspect(1)
self.pattern_plot.draw()
self.pattern_deg = pattern_deg
self.params = params
def take_images(self, system):
if not self.lmi_enabled_chb.isChecked():
return
self.frames = system.project_patterns_and_take_images(self.pattern_deg, self.pattern_rate_Hz, self.params['pattern_delay_sec'])
self.reconstruction = None
self.plot_images()
def plot_images(self):
self.image_plot.plot.fig.clear()
axs = self.image_plot.plot.fig.subplots(3, 3, sharex=True, sharey=True)
ims = []
for i in range(9):
ims.append(axs[i // 3, i % 3].imshow(self.frames[i]))
self.image_plot.connect_clim(ims)
self.image_plot.plot.draw()
def reconstruct(self):
if not self.lmi_enabled_chb.isChecked():
return None, None
reconstruct = np.max(self.frames, axis=0)
#reconstruct = np.max(self.frames, axis=0)
sumall = np.sum(self.frames, axis=0)
self.recon_plot.plot.fig.clear()
ax1, ax2 = self.recon_plot.plot.fig.subplots(1, 2, sharex=True, sharey=True)
im1 = ax1.imshow(reconstruct)
im2 = ax2.imshow(sumall)
self.recon_plot.connect_clim(im1, 0)
self.recon_plot.connect_clim(im2, 1)
self.recon_plot.plot.draw()
self.reconstruction = reconstruct
return reconstruct, sumall
def save_images(self, *args, **kwargs):
if self.lmi_enabled_chb.isChecked():
super().save_images(*args, **kwargs)
def load_images(self, folder):
if self.lmi_enabled_chb.isChecked():
super().load_images(folder)
class LineLMIImaging(ImagingMethod):
def __init__(self):
super().__init__("lmi")
lmi_group = QtWidgets.QGroupBox("Line LMI")
layout = QtWidgets.QFormLayout()
self.lmi_enabled_chb = QtWidgets.QCheckBox("Do Line LMI")
self.lmi_enabled_chb.setChecked(True)
layout.addRow("Do LMI", self.lmi_enabled_chb)
self.grating_nr_cbb = QtWidgets.QComboBox()
self.grating_nr_cbb.addItems(["1", "2", "3"])
layout.addRow("Nr of gratings", self.grating_nr_cbb)
self.steps_txt = QtWidgets.QLineEdit("5")
layout.addRow("Steps", self.steps_txt)
self.multiscan_txt = QtWidgets.QLineEdit("10")
layout.addRow("Multiscan", self.multiscan_txt)
self.pattern_repeat_txt = QtWidgets.QLineEdit("1")
layout.addRow("Pattern repeat", self.pattern_repeat_txt)
self.dot_distance_lbl = QtWidgets.QLabel()
layout.addRow("Dot distance", self.dot_distance_lbl)
self.multiscan_distance_lbl = QtWidgets.QLabel()
layout.addRow("Multiscan distance", self.multiscan_distance_lbl)
self.pattern_hz_txt = QtWidgets.QLineEdit("40000")
layout.addRow("Projection rate [Hz]", self.pattern_hz_txt)
self.exposure_lbl = QtWidgets.QLabel()
layout.addRow("Exposure time", self.exposure_lbl)
lmi_group.setLayout(layout)
self.parameters_widget = lmi_group
w = QtWidgets.QWidget()
lay = QtWidgets.QVBoxLayout()
self.pattern_plot = MplCanvas(self, width=5, height=4, dpi=100)
toolbar = NavigationToolbar(self.pattern_plot, w)
lay.addWidget(toolbar)
lay.addWidget(self.pattern_plot)
w.setLayout(lay)
self.patterns_widget = w
self.image_plot = PlotWidget()
self.recon_plot = PlotWidget(None, num_clim = 2)
self.debug_tabs = [
('LMI Recorded Images', self.image_plot),
('LMI Reconstructed Image', self.recon_plot),
]
def parse_parameters(self):
return {
"grating_nr": int(self.grating_nr_cbb.currentText()),
"steps": int(self.steps_txt.text()),
"multiscan": int(self.multiscan_txt.text()),
"pattern_repeat": int(self.pattern_repeat_txt.text()),
"pattern_rate_Hz": float(self.pattern_hz_txt.text()),
}
def load_parameters(self, params):
grating_nr = params.get("grating_nr", 2 if params.get("two_gratings", False) else 3)
self.grating_nr_cbb.setCurrentIndex(grating_nr - 1)
self.steps_txt.setText(str(params.get("steps", "10")))
self.multiscan_txt.setText(str(params.get("multiscan", "10")))
self.pattern_repeat_txt.setText(str(params.get("pattern_repeat", "1")))
self.pattern_hz_txt.setText(str(params.get("pattern_rate_Hz", "10000")))
def update_patterns(self, global_params):
if not self.lmi_enabled_chb.isChecked():
return
params = {**self.parse_parameters(), **global_params}
grating_dot_distances = params['grating_dot_distances']
orientation_deg = params['orientation_deg']
distance_between_gratings = params["distance_between_gratings"]
grating_nr = params['grating_nr']
steps = params['steps']
multiscan = params['multiscan']
pattern_repeat = params['pattern_repeat']
self.pattern_rate_Hz = params['pattern_rate_Hz']
if grating_nr == 2:
pattern_deg = lmi_pattern.line_lmi_pattern_two_grating(steps, multiscan, grating_dot_distances, distance_between_gratings, orientation_deg=orientation_deg)
elif grating_nr == 3:
pattern_deg = lmi_pattern.line_lmi_pattern_deg(steps, multiscan, grating_dot_distances, distance_between_gratings, orientation_rad=np.deg2rad(orientation_deg))
elif grating_nr == 1:
pattern_deg = lmi_pattern.line_lmi_pattern_one_grating(steps, multiscan, grating_dot_distances, orientation_deg=orientation_deg)
else:
raise RuntimeError("not implemented")
pattern_deg = np.tile(pattern_deg, [1, pattern_repeat, 1])
self.exposure_time_sec = pattern_deg.shape[1] / self.pattern_rate_Hz
num_patterns = pattern_deg.shape[0]
self.exposure_lbl.setText(f"{self.exposure_time_sec * 1e3:.1f} ms * {num_patterns} = {self.exposure_time_sec * 1e3 * num_patterns:.1f} ms")
self.dot_distance_lbl.setText(str([grating_dot_distance / multiscan / steps for grating_dot_distance in grating_dot_distances]))
self.multiscan_distance_lbl.setText(str([grating_dot_distance / multiscan for grating_dot_distance in grating_dot_distances]))
self.pattern_plot.fig.clear()
ax1, ax2 = self.pattern_plot.fig.subplots(1, 2, sharex=True, sharey=True)
for i in range(num_patterns):
ax1.scatter(pattern_deg[i, :, 0], pattern_deg[i, :, 1])
ax2.scatter(pattern_deg[0, :, 0], pattern_deg[0, :, 1])
ax1.set_aspect(1)
ax2.set_aspect(1)
self.pattern_plot.draw()
self.pattern_deg = pattern_deg
self.num_gratings = grating_nr
self.params = params
def take_images(self, system):
if not self.lmi_enabled_chb.isChecked():
return
self.frames = system.project_patterns_and_take_images(self.pattern_deg, self.pattern_rate_Hz, self.params['pattern_delay_sec'], single_aod = self.params['single_aod'])
self.reconstruction = None
self.plot_images()
def plot_images(self):
self.image_plot.plot.fig.clear()
axs = self.image_plot.plot.fig.subplots(self.num_gratings, 3, sharex=True, sharey=True)
if self.num_gratings == 1:
axs = axs[np.newaxis, :]
ims = []
frames_per_grating = int(len(self.frames) / 3)
for i in range(self.num_gratings):
for j in range(3):
ims.append(axs[i, j].imshow(self.frames[i * frames_per_grating + j]))
self.image_plot.connect_clim(ims)
self.image_plot.plot.draw()
def reconstruct(self):
if not self.lmi_enabled_chb.isChecked():
return None, None
# TODO reconstruct multi direction
reconstruct = np.max(self.frames, axis=0)
#reconstruct = np.max(self.frames, axis=0)
sumall = np.sum(self.frames, axis=0)
self.recon_plot.plot.fig.clear()
ax1, ax2 = self.recon_plot.plot.fig.subplots(1, 2, sharex=True, sharey=True)
im1 = ax1.imshow(reconstruct)
im2 = ax2.imshow(sumall)
self.recon_plot.connect_clim(im1, 0)
self.recon_plot.connect_clim(im2, 1)
self.recon_plot.plot.draw()
self.reconstruction = reconstruct
return reconstruct, sumall
def save_images(self, *args, **kwargs):
if self.lmi_enabled_chb.isChecked():
super().save_images(*args, **kwargs)
def load_images(self, folder):
if self.lmi_enabled_chb.isChecked():
super().load_images(folder)