def _func(i):
wpu.print_blue("MESSAGE: loop " + str(i) + ": " + listOfDataFiles[i])
img = dxchange.read_tiff(listOfDataFiles[i])
darkMeanValue = np.mean(wpu.crop_matrix_at_indexes(img, idx4cropDark))
#TODO xshi, need to add option of input one value
img = img - darkMeanValue # calculate and remove dark
img = wpu.crop_matrix_at_indexes(img, idx4crop)
pv = int(period_harm_Vert / (sourceDistanceV + zvec[i]) *
(sourceDistanceV + np.min(zvec)))
ph = int(period_harm_Horz / (sourceDistanceH + zvec[i]) *
(sourceDistanceH + np.min(zvec)))
if plotFourierImages:
wgi.plot_harmonic_grid(img, [pv, ph], isFFT=False)
plt.savefig('FFT_{:.0f}mm.png'.format(zvec[i] * 1e3))
plt.show(block=False)
plt.close()
wgi.plot_harmonic_peak(img, [pv, ph], isFFT=False)
plt.savefig('FFT_peaks_{:.0f}mm.png'.format(zvec[i] * 1e3))
plt.show(block=False)
plt.close()
return wgi.visib_1st_harmonics(img, [pv, ph],
searchRegion=searchRegion,
unFilterSize=unFilterSize)
def files_to_array(list_sample_files, list_ref_files, list_dark_files,
idx4crop=[0, -1, 0, -1]):
img = wpu.crop_matrix_at_indexes(dxchange.read_tiff(list_sample_files[0]),
idx4crop)
(nlines, ncolums) = img.shape
img_stack = np.zeros((len(list_sample_files), nlines, ncolums))
ref_stack = img_stack*0.0
dark_im = img_stack[0, :, :]*0.0
for i in range(len(list_dark_files)):
dark_im += wpu.crop_matrix_at_indexes(dxchange.read_tiff(list_dark_files[i]),
idx4crop)
for i in range(len(list_sample_files)):
img_stack[i, :, :] = wpu.crop_matrix_at_indexes(dxchange.read_tiff(list_sample_files[i]),
idx4crop) - dark_im
ref_stack[i, :, :] = wpu.crop_matrix_at_indexes(dxchange.read_tiff(list_ref_files[i]),
idx4crop) - dark_im
return img_stack, ref_stack
def files_to_array(list_sample_files,
list_ref_files,
list_dark_files,
idx4crop=[0, -1, 0, -1]):
img = wpu.crop_matrix_at_indexes(dxchange.read_tiff(list_sample_files[0]),
idx4crop)
(nlines, ncolums) = img.shape
img_stack = np.zeros((len(list_sample_files), nlines, ncolums))
ref_stack = img_stack * 0.0
dark_im = img_stack[0, :, :] * 0.0
for i in range(len(list_dark_files)):
dark_im += wpu.crop_matrix_at_indexes(
dxchange.read_tiff(list_dark_files[i]), idx4crop)
for i in range(len(list_sample_files)):
img_stack[i, :, :] = wpu.crop_matrix_at_indexes(
dxchange.read_tiff(list_sample_files[i]), idx4crop) - dark_im
ref_stack[i, :, :] = wpu.crop_matrix_at_indexes(
dxchange.read_tiff(list_ref_files[i]), idx4crop) - dark_im
return img_stack, ref_stack
def dpc_integration(dpc01,
dpc10,
pixelsize,
idx4crop=[0, -1, 0, -1],
plotErrorIntegration=False,
saveFileSuf=None,
shifthalfpixel=False,
method='FC'):
'''
TODO: Write Docstring
Integration of DPC to obtain phase. Currently only supports
Frankot Chellappa
'''
if idx4crop == '':
vmin = wpu.mean_plus_n_sigma(dpc01**2 + dpc10**2, -3)
vmax = wpu.mean_plus_n_sigma(dpc01**2 + dpc10**2, 3)
_, idx = wpu.crop_graphic_image(dpc01**2 + dpc10**2,
kargs4graph={
'cmap': 'viridis',
'vmin': vmin,
'vmax': vmax
})
else:
idx = idx4crop
dpc01 = wpu.crop_matrix_at_indexes(dpc01, idx)
dpc10 = wpu.crop_matrix_at_indexes(dpc10, idx)
if method == 'FC':
phase = wps.frankotchellappa(dpc01 * pixelsize[1],
dpc10 * pixelsize[0],
reflec_pad=True)
phase = np.real(phase)
else:
wpu.print_red('ERROR: Unknown integration method: ' + method)
if plotErrorIntegration:
wps.error_integration(dpc01 * pixelsize[1],
dpc10 * pixelsize[0],
phase,
pixelsize,
errors=False,
shifthalfpixel=shifthalfpixel,
plot_flag=True)
if saveFileSuf is not None:
wpu.save_figs_with_idx(saveFileSuf)
return phase
def _func(i):
wpu.print_blue("MESSAGE: loop " + str(i) + ": " + \
listOfDataFiles[i])
if idx == [0, -1, 0, -1]:
img = dxchange.read_tiff(listOfDataFiles[i]) - dark_im
else:
img = wpu.crop_matrix_at_indexes(
dxchange.read_tiff(listOfDataFiles[i]), idx) - dark_im
pv, ph = period_harm_Vert, period_harm_Horz
pv = int(period_harm_Vert / (sourceDistance + zvec[i] - zvec[0]) *
sourceDistance)
ph = int(period_harm_Horz / (sourceDistance + zvec[i] - zvec[0]) *
sourceDistance)
wgi.plot_harmonic_grid(img, [pv, ph], isFFT=False)
plt.savefig('FFT_{:04.0f}_ms_'.format(tvec[i] * 1e3) +
'{:04.0f}mm.png'.format(zvec[i] * 1e3))
plt.show(block=False)
plt.close()
# wgi.plot_harmonic_peak(img,
# [pv, ph],
# isFFT=False,
# fname='FFT_peaks_{:04.0f}_ms_'.format(tvec[i]*1e3) +
# '{:04.0f}mm.png'.format(zvec[i]*1e3))
#
#
# plt.close()
return wgi.visib_1st_harmonics(img, [pv, ph], searchRegion=10)
def dpc_integration(dpc01, dpc10, pixelsize, idx4crop=[0, -1, 0, -1],
plotErrorIntegration=False,
saveFileSuf=None,
shifthalfpixel=False, method='FC'):
'''
TODO: Write Docstring
Integration of DPC to obtain phase. Currently only supports
Frankot Chellappa
'''
if idx4crop == '':
vmin = wpu.mean_plus_n_sigma(dpc01**2+dpc10**2, -3)
vmax = wpu.mean_plus_n_sigma(dpc01**2+dpc10**2, 3)
_, idx = wpu.crop_graphic_image(dpc01**2+dpc10**2,
kargs4graph={'cmap': 'viridis',
'vmin': vmin,
'vmax': vmax})
else:
idx = idx4crop
dpc01 = wpu.crop_matrix_at_indexes(dpc01, idx)
dpc10 = wpu.crop_matrix_at_indexes(dpc10, idx)
if method == 'FC':
phase = wps.frankotchellappa(dpc01*pixelsize[1],
dpc10*pixelsize[0],
reflec_pad=True)
phase = np.real(phase)
else:
wpu.print_red('ERROR: Unknown integration method: ' + method)
if plotErrorIntegration:
wps.error_integration(dpc01*pixelsize[1],
dpc10*pixelsize[0],
phase, pixelsize, errors=False,
shifthalfpixel=shifthalfpixel, plot_flag=True)
if saveFileSuf is not None:
wpu.save_figs_with_idx(saveFileSuf)
return phase
def _func(i):
wpu.print_blue("MESSAGE: loop " + str(i) + ": " + \
listOfDataFiles[i])
if idx == [0, -1, 0, -1]:
img = dxchange.read_tiff(listOfDataFiles[i]) - dark_im
else:
img = wpu.crop_matrix_at_indexes(dxchange.read_tiff(listOfDataFiles[i]), idx) - dark_im
pv, ph = period_harm_Vert, period_harm_Horz
pv = int(period_harm_Vert/(sourceDistance + zvec[i]-zvec[0])*sourceDistance)
ph = int(period_harm_Horz/(sourceDistance + zvec[i]-zvec[0])*sourceDistance)
return wgi.visib_1st_harmonics(img, [pv, ph], searchRegion=10)
zvec = np.zeros(nfiles)
tvec = np.zeros(nfiles)
for i in range(nfiles):
zvec[i] = float(listOfDataFiles[i].rsplit('mm')[0].split('_')[-1]) * 1e-3
tvec[i] = tvec_labels[int(listOfDataFiles[i].rsplit('.')[0].split('_')[-1])
- 1]
# =============================================================================
# %% Crop
# =============================================================================
idx = [200, -200, 0, 2160]
img = wpu.crop_matrix_at_indexes(img, idx)
wpu.print_blue("MESSAGE: idx for cropping")
wpu.print_blue(idx)
if idx != [0, -1, 0, -1]:
dark_im = wpu.crop_matrix_at_indexes(dark_im, idx)
idx[1] = idx[0] + np.max([idx[1] - idx[0], idx[3] - idx[2]])
idx[3] = idx[2] + np.max([idx[1] - idx[0], idx[3] - idx[2]])
# =============================================================================
# %% Plot Real Image
# =============================================================================
#
gratingPeriod = 4.8e-6 # in meters
# uncomment proper pattern period:
patternPeriod = gratingPeriod/np.sqrt(2.0) # if half Pi grating
# patternPeriod = gratingPeriod/2.0 # if Pi grating
img = dxchange.read_tiff('../../data4wavepy_examples/Dialens/cb_halfpi_4p8um_distace110mm_sample.tif')
imgRef = dxchange.read_tiff('../../data4wavepy_examples/Dialens/cb_halfpi_4p8um_distace110mm_ref.tif')
darkImg = dxchange.read_tiff('../../data4wavepy_examples/Dialens/dark.tif')
img = img - darkImg
imgRef = imgRef - darkImg
# %% crop
img, idx4crop = wpu.crop_graphic_image(img)
imgRef = wpu.crop_matrix_at_indexes(imgRef, idx4crop)
# %% Find harmonic in the Fourier images
# calculate the theoretical position of the hamonics
period_harm_Vert_o = np.int(pixelsize[0]/patternPeriod*img.shape[0] /
(sourceDistance + distDet2sample)*sourceDistance)
period_harm_Hor_o = np.int(pixelsize[1]/patternPeriod*img.shape[1] /
(sourceDistance + distDet2sample)*sourceDistance)
# Obtain harmonic periods from images
wpu.print_blue('MESSAGE: Obtain harmonic 01 exprimentally')
(_,
xlabel=r'x [$\mu m$ ]',
ylabel=r'y [$\mu m$ ]',
extent=wpu.extent_func(img, pixelSize) * 1e6)
idx4crop = wpu.graphical_roi_idx(img,
verbose=True,
kargs4graph={
'cmap': cmap,
'vmin': colorlimit[0],
'vmax': colorlimit[1]
})
wpu.print_blue("MESSAGE: idx for cropping")
wpu.print_blue(idx4crop)
img = wpu.crop_matrix_at_indexes(img, idx4crop)
# =============================================================================
# %% Dark indexes
# =============================================================================
darkRegionSelctionFlag = easyqt.get_yes_or_no(
'Do you want to select ' + 'region for dark calculation?\n' +
'Press ESC to use [0, 100, 0, 100]')
if darkRegionSelctionFlag:
idx4cropDark = wpu.graphical_roi_idx(img,
verbose=True,
kargs4graph={
'cmap': cmap,
def main_terminal(data_dir,
zvec_from,
startDist,
step_z_scan,
image_per_point,
strideFile,
pixelSize=0.65e-6,
gratingPeriod=4.8e-6,
pattern='Diagonal',
sourceDistanceV=-1,
sourceDistanceH=32,
unFilterSize=1,
searchRegion=20,
idx4crop=[0, -1, 0, -1],
darkRegionSelctionFlag=True):
'''
*** all unit in [m]
data_dir: data folder path
zvec_from: distance type:
'Calculated'
'Tabled'
startDist: started distance postion
step_z_scan: step size
image_per_point: images number for every distance
strideFile: Stride (Use only every XX files)
pixelSize: Pixel Size
gratingPeriod: CB Grating Period
pattern: grating pattern
'Diagonal' or 'Edge']
sourceDistanceV: Distance to Source
in the VERTICAL [m]
sourceDistanceH: Distance to Source
in the Horizontal [m]
unFilterSize: Size for Uniform Filter [Pixels]
default_value 1
searchRegion: Size of Region for Searching
the Peak [in Pixels]
default_value=20
idx4crop: crop area
[low_y, high_y, low_x, high_x ]
darkRegionSelctionFlag: use dark region [0, 20, 0, 20]?
'''
wpu._mpl_settings_4_nice_graphs()
# =============================================================================
# %% Load Image
# =============================================================================
originalDir = os.getcwd()
# samplefileName = easyqt.get_file_names("Choose one of the scan files")[0]
# data_dir = samplefileName.rsplit('/', 1)[0]
os.chdir(data_dir)
try:
os.mkdir(data_dir + '/output/')
except:
pass
fname2save = data_dir + '/output/' + 'zscan'
# wpu.print_blue('MESSAGE: Loading files ' +
# samplefileName.rsplit('_', 1)[0] + '*.tif')
wpu.print_blue('MESSAGE: Loading files ' + data_dir + '/*.tif')
# listOfDataFiles = glob.glob(samplefileName.rsplit('_', 2)[0] + '*.tif')
listOfDataFiles = glob.glob(os.path.join(data_dir, '*.tif'))
listOfDataFiles.sort()
nfiles = len(listOfDataFiles)
# zvec_from = easyqt.get_choice(message='z distances is calculated or from table?',
# title='Title',
# choices=['Calculated', 'Tabled'])
# %%
if zvec_from == 'Calculated':
# startDist = easyqt.get_float('Starting distance scan [mm]',
# title='Title',
# default_value=20)*1e-3
# step_z_scan = easyqt.get_float('Step size scan [mm]',
# title='Title',
# default_value=5)*1e-3
# image_per_point = easyqt.get_int('Number of images by step',
# title='Title',
# default_value=1)
zvec = np.linspace(
startDist,
startDist + step_z_scan * (nfiles / image_per_point - 1),
int(nfiles / image_per_point))
zvec = zvec.repeat(image_per_point)
# strideFile = easyqt.get_int('Stride (Use only every XX files)',
# title='Title',
# default_value=1)
listOfDataFiles = listOfDataFiles[0::strideFile]
zvec = zvec[0::strideFile]
print(zvec)
elif zvec_from == 'Tabled':
zvec = np.loadtxt(
easyqt.get_file_names("Table with the z distance values in mm")
[0]) * 1e-3
step_z_scan = np.mean(np.diff(zvec))
if step_z_scan > 0:
pass
else:
listOfDataFiles = listOfDataFiles[::-1]
zvec = zvec[::-1]
img = dxchange.read_tiff(listOfDataFiles[0])
# =============================================================================
# %% Experimental parameters
# =============================================================================
# pixelSize = easyqt.get_float("Enter Pixel Size [um]",
# title='Experimental Values',
# default_value=.6500, decimals=5)*1e-6
# gratingPeriod = easyqt.get_float("Enter CB Grating Period [um]",
# title='Experimental Values',
# default_value=4.8)*1e-6
# pattern = easyqt.get_choice(message='Select CB Grating Pattern',
# title='Title',
# choices=['Diagonal', 'Edge'])
# # choices=['Edge', 'Diagonal'])
# sourceDistanceV = easyqt.get_float("Enter Distance to Source\n in the VERTICAL [m]",
# title='Experimental Values',
# default_value=-0.73)
# sourceDistanceH = easyqt.get_float("Enter Distance to Source\n in the Horizontal [m]",
# title='Experimental Values',
# default_value=34.0)
# unFilterSize = easyqt.get_int("Enter Size for Uniform Filter [Pixels]\n" +
# " (Enter 1 to NOT use the filter)",
# title='Experimental Values',
# default_value=1)
# searchRegion = easyqt.get_int("Enter Size of Region for Searching\n the Peak [in Pixels]",
# title='Experimental Values',
# default_value=20)
os.chdir(originalDir)
# =============================================================================
# %% Crop
# =============================================================================
idx4crop = [0, -1, 0, -1]
# [colorlimit,
# cmap] = wpu.plot_slide_colorbar(img,
# title='SELECT COLOR SCALE,\n' +
# 'Raw Image, No Crop',
# xlabel=r'x [$\mu m$ ]',
# ylabel=r'y [$\mu m$ ]',
# extent=wpu.extent_func(img,
# pixelSize)*1e6)
# idx4crop = wpu.graphical_roi_idx(img, verbose=True,
# kargs4graph={'cmap': cmap,
# 'vmin': colorlimit[0],
# 'vmax': colorlimit[1]})
wpu.print_blue("MESSAGE: idx for cropping")
wpu.print_blue(idx4crop)
# =============================================================================
# %% Dark indexes
# =============================================================================
# darkRegionSelctionFlag = easyqt.get_yes_or_no('Do you want to select ' +
# 'region for dark calculation?\n' +
# 'Press ESC to use [0, 20, 0, 20]')
print(darkRegionSelctionFlag)
if darkRegionSelctionFlag:
idx4cropDark = wpu.graphical_roi_idx(img,
verbose=True,
kargs4graph={
'cmap': cmap,
'vmin': colorlimit[0],
'vmax': colorlimit[1]
})
else:
idx4cropDark = [0, 20, 0, 20]
# dark_im = dxchange.read_tiff(listOfDataFiles[0])*0.0 + avgDark
img = wpu.crop_matrix_at_indexes(img, idx4crop)
# ==============================================================================
# %% Harmonic Periods
# ==============================================================================
if pattern == 'Diagonal':
period_harm_Vert = np.int(
np.sqrt(2) * pixelSize / gratingPeriod * img.shape[0])
period_harm_Horz = np.int(
np.sqrt(2) * pixelSize / gratingPeriod * img.shape[1])
elif pattern == 'Edge':
period_harm_Vert = np.int(2 * pixelSize / gratingPeriod * img.shape[0])
period_harm_Horz = np.int(2 * pixelSize / gratingPeriod * img.shape[1])
# Obtain harmonic periods from images
(period_harm_Vert,
_) = wgi.exp_harm_period(img, [period_harm_Vert, period_harm_Horz],
harmonic_ij=['1', '0'],
searchRegion=40,
isFFT=False,
verbose=True)
(_, period_harm_Horz) = wgi.exp_harm_period(
img, [period_harm_Vert, period_harm_Horz],
harmonic_ij=['0', '1'],
searchRegion=40,
isFFT=False,
verbose=True)
wpu.log_this('Input folder: ' + data_dir, preffname=fname2save)
wpu.log_this('\nNumber of files : ' + str(nfiles))
wpu.log_this('Stride : ' + str(strideFile))
print(zvec_from)
wpu.log_this('Z distances is ' + zvec_from)
if zvec_from == 'Calculated':
wpu.log_this('Step zscan [mm] : {:.4g}'.format(step_z_scan * 1e3))
wpu.log_this('Start point zscan [mm] : {:.4g}'.format(startDist * 1e3))
wpu.log_this('Pixel Size [um] : {:.4g}'.format(pixelSize * 1e6))
wpu.log_this('Grating Period [um] : {:.4g}'.format(gratingPeriod * 1e6))
wpu.log_this('Grating Pattern : ' + pattern)
wpu.log_this('Crop idxs : ' + str(idx4crop))
wpu.log_this('Dark idxs : ' + str(idx4cropDark))
wpu.log_this('Vertical Source Distance: ' + str(sourceDistanceV))
wpu.log_this('Horizontal Source Distance: ' + str(sourceDistanceH))
wpu.log_this('Uniform Filter Size : {:d}'.format(unFilterSize))
wpu.log_this('Search Region : {:d}'.format(searchRegion))
# =============================================================================
# %% Calculate everything
# =============================================================================
# =============================================================================
# %% multiprocessing
# =============================================================================
ncpus = cpu_count()
wpu.print_blue("MESSAGE: %d cpu's available" % ncpus)
tzero = time.time()
p = Pool(ncpus - 2)
indexes = range(len(listOfDataFiles))
parameters = []
for i in indexes:
parameters.append([
i, listOfDataFiles, zvec, idx4cropDark, idx4crop, period_harm_Vert,
sourceDistanceV, period_harm_Horz, sourceDistanceH, searchRegion,
unFilterSize
])
res = p.map(_func, parameters)
p.close()
wpu.print_blue('MESSAGE: Time spent: {0:.3f} s'.format(time.time() -
tzero))
'''
res = []
for i in range(len(listOfDataFiles)):
res.append(_func(i))
print(res)
'''
# =============================================================================
# %% Sorting the data
# =============================================================================
contrastV = np.asarray([x[0] for x in res])
contrastH = np.asarray([x[1] for x in res])
p0 = np.asarray([x[2] for x in res])
pv = np.asarray([x[3] for x in res])
ph = np.asarray([x[4] for x in res])
pattern_period_Vert_z = pixelSize / (pv[:, 0] - p0[:, 0]) * img.shape[0]
pattern_period_Horz_z = pixelSize / (ph[:, 1] - p0[:, 1]) * img.shape[1]
# =============================================================================
# %% Save csv file
# =============================================================================
outputfname = wpu.get_unique_filename(fname2save, 'csv')
wpu.save_csv_file(np.c_[zvec.T, contrastV.T, contrastH.T,
pattern_period_Vert_z.T, pattern_period_Horz_z.T],
outputfname,
headerList=[
'z [m]', 'Vert Contrast', 'Horz Contrast',
'Vert Period [m]', 'Horz Period [m]'
])
wpu.log_this('\nOutput file: ' + outputfname)
# =============================================================================
# %% Plot
# =============================================================================
# contrast vs z
fig = plt.figure(figsize=(10, 7))
plt.plot(zvec * 1e3, contrastV * 100, '-ko', label='Vert')
plt.plot(zvec * 1e3, contrastH * 100, '-ro', label='Hor')
plt.xlabel(r'Distance $z$ [mm]', fontsize=14)
plt.ylabel(r'Visibility $\times$ 100 [%]', fontsize=14)
plt.title('Visibility vs detector distance', fontsize=14, weight='bold')
plt.legend(fontsize=14, loc=0)
wpu.save_figs_with_idx(fname2save)
plt.show(block=False)
# =============================================================================
# %% Plot Harmonic position and calculate source distance
# =============================================================================
from wavepytools.diag.coherence.fit_singleGratingCoherence_z_scan import fit_period_vs_z
#xshi 20190719
#from fit_singleGratingCoherence_z_scan import fit_period_vs_z
(sourceDistance_from_fit_V,
patternPeriodFromData_V) = fit_period_vs_z(zvec,
pattern_period_Vert_z,
contrastV,
direction='Vertical',
threshold=.002,
fname4graphs=fname2save)
(sourceDistance_from_fit_H,
patternPeriodFromData_H) = fit_period_vs_z(zvec,
pattern_period_Horz_z,
contrastH,
direction='Horizontal',
threshold=0.0005,
fname4graphs=fname2save)
pixelsizeImg = stride * pixelsizeImg sx_raw = np.array(f['displacement/displacement_x'])[::stride, ::stride] sy_raw = np.array(f['displacement/displacement_y'])[::stride, ::stride] error_raw = np.array(f['displacement/error'])[::stride, ::stride] xVec_raw = np.array(f['displacement/xvec'])[::stride] yVec_raw = np.array(f['displacement/yvec'])[::stride] #============================================================================== # %% Crop #============================================================================== idx4crop = wpu.graphical_roi_idx(np.sqrt(sx_raw**2 + sy_raw**2), verbose=True) sx = wpu.crop_matrix_at_indexes(sx_raw, idx4crop) sy = wpu.crop_matrix_at_indexes(sy_raw, idx4crop) error = wpu.crop_matrix_at_indexes(error_raw, idx4crop) xVec = wpu.realcoordvec(sx.shape[1], pixelsizeImg) yVec = wpu.realcoordvec(sx.shape[0], pixelsizeImg) xmatrix, ymatrix = np.meshgrid(xVec, yVec) #============================================================================== # %% Calculations of physical quantities #============================================================================== totalS = np.sqrt(sx**2 + sy**2) # Differenctial Phase
patternPeriod = gratingPeriod / np.sqrt(2.0) # if half Pi grating
#patternPeriod = gratingPeriod/2.0 # if Pi grating
img = dxchange.read_tiff(
'data_example_for_single_grating/cb4p8um_halfPi_8KeV_10s_img.tif')
imgRef = dxchange.read_tiff(
'data_example_for_single_grating/cb4p8um_halfPi_8KeV_10s_ref.tif')
darkImg = dxchange.read_tiff('data_example_for_single_grating/10s_dark.tif')
img = img - darkImg
imgRef = imgRef - darkImg
# %% crop
img, idx4crop = wpu.crop_graphic_image(img)
imgRef = wpu.crop_matrix_at_indexes(imgRef, idx4crop)
# %% Find harmonic in the Fourier images
# calculate the theoretical position of the hamonics
period_harm_Vert_o = np.int(pixelsize[0] / patternPeriod * img.shape[0] /
(sourceDistance + distDet2sample) * sourceDistance)
period_harm_Hor_o = np.int(pixelsize[1] / patternPeriod * img.shape[1] /
(sourceDistance + distDet2sample) * sourceDistance)
# Obtain harmonic periods from images
wpu.print_blue('MESSAGE: Obtain harmonic 01 exprimentally')
(_, period_harm_Hor) = wgi.exp_harm_period(
imgRef, [period_harm_Vert_o, period_harm_Hor_o], harmonic_ij=['0', '1'])
def main_single_gr_Talbot(img, imgRef,
phenergy, pixelsize, distDet2sample,
period_harm, saveFileSuf,
unwrapFlag=True,
plotFlag=True,
saveFigFlag=False):
global inifname # name of .ini file
[period_harm_Vert, period_harm_Hor] = period_harm
# img, imgRef = wpu.align_two_images(img, imgRef)
# Crop
img_size_o = np.shape(img)
# take index from ini file
idx4crop = list(map(int, (wpu.get_from_ini_file(inifname, 'Parameters',
'Crop').split(','))))
# Plot Real Image wiht default crop
tmpImage = wpu.crop_matrix_at_indexes(img, idx4crop)
plt.figure()
plt.imshow(tmpImage,
cmap='viridis',
extent=wpu.extent_func(tmpImage, pixelsize)*1e6)
plt.xlabel(r'$[\mu m]$')
plt.ylabel(r'$[\mu m]$')
plt.colorbar()
plt.title('Raw Image with initial Crop', fontsize=18, weight='bold')
plt.pause(.1)
# ask if the crop need to be changed
newCrop = easyqt.get_yes_or_no('New Crop?')
if saveFigFlag and not newCrop:
wpu.save_figs_with_idx(saveFileSuf + '_Talbot_image')
plt.close(plt.gcf())
if newCrop:
[colorlimit,
cmap] = wpu.plot_slide_colorbar(img,
title='SELECT COLOR SCALE,\n' +
'Raw Image, No Crop',
xlabel=r'x [$\mu m$ ]',
ylabel=r'y [$\mu m$ ]',
extent=wpu.extent_func(img,
pixelsize)*1e6)
idx4crop = wpu.graphical_roi_idx(img, verbose=True,
kargs4graph={'cmap': cmap,
'vmin': colorlimit[0],
'vmax': colorlimit[1]})
wpu.set_at_ini_file(inifname, 'Parameters', 'Crop',
'{}, {}, {}, {}'.format(idx4crop[0], idx4crop[1],
idx4crop[2], idx4crop[3]))
img = wpu.crop_matrix_at_indexes(img, idx4crop)
# Plot Real Image AFTER crop
plt.imshow(img, cmap='viridis',
extent=wpu.extent_func(img, pixelsize)*1e6)
plt.xlabel(r'$[\mu m]$')
plt.ylabel(r'$[\mu m]$')
plt.colorbar()
plt.title('Raw Image with New Crop', fontsize=18, weight='bold')
if saveFigFlag:
wpu.save_figs_with_idx(saveFileSuf + '_Talbot_image')
plt.show(block=True)
else:
img = tmpImage
imgRef = wpu.crop_matrix_at_indexes(imgRef, idx4crop)
# calculate harmonic position after crop
period_harm_Vert = int(period_harm_Vert*(idx4crop[1] - idx4crop[0]) /
img_size_o[0])
period_harm_Hor = int(period_harm_Hor*(idx4crop[3] - idx4crop[2]) /
img_size_o[1])
# Obtain harmonic periods from images
(period_harm_Vert,
_) = wgi.exp_harm_period(img, [period_harm_Vert,
period_harm_Hor],
harmonic_ij=['1', '0'],
searchRegion=20,
isFFT=False, verbose=True)
(_,
period_harm_Horz) = wgi.exp_harm_period(img, [period_harm_Vert,
period_harm_Hor],
harmonic_ij=['0', '1'],
searchRegion=20,
isFFT=False, verbose=True)
# Calculate everything
harmPeriod = [period_harm_Vert, period_harm_Hor]
[int00, int01, int10,
darkField01, darkField10,
phaseFFT_01,
phaseFFT_10] = wgi.single_2Dgrating_analyses(img, imgRef,
harmonicPeriod=harmPeriod,
plotFlag=plotFlag,
unwrapFlag=unwrapFlag,
verbose=True)
virtual_pixelsize = [0, 0]
virtual_pixelsize[0] = pixelsize[0]*img.shape[0]/int00.shape[0]
virtual_pixelsize[1] = pixelsize[1]*img.shape[1]/int00.shape[1]
diffPhase01 = phaseFFT_01*virtual_pixelsize[1]/distDet2sample/hc*phenergy
diffPhase10 = phaseFFT_10*virtual_pixelsize[0]/distDet2sample/hc*phenergy
return [int00, int01, int10,
darkField01, darkField10,
diffPhase01, diffPhase10,
virtual_pixelsize]
sx_raw = np.array(f['displacement/displacement_x']) sy_raw = np.array(f['displacement/displacement_y']) error_raw = np.array(f['displacement/error']) xVec_raw = np.array(f['displacement/xvec']) yVec_raw = np.array(f['displacement/yvec']) #============================================================================== # %% Crop #============================================================================== idx4crop = wpu.graphical_roi_idx(np.sqrt(sx_raw**2 + sy_raw**2), verbose=True) sx = wpu.crop_matrix_at_indexes(sx_raw, idx4crop) sy = wpu.crop_matrix_at_indexes(sy_raw, idx4crop) error = wpu.crop_matrix_at_indexes(error_raw, idx4crop) xVec = wpu.realcoordvec(sx.shape[1], pixelsizeImg) yVec = wpu.realcoordvec(sx.shape[0], pixelsizeImg) xmatrix, ymatrix = np.meshgrid(xVec, yVec) #============================================================================== # %% Calculations of physical quantities #==============================================================================
if kb_input.lower() == 'y':
# Graphical Crop
idx = wpu.graphical_roi_idx(image, verbose=True)
print('New idx:')
print(idx)
ini_pars['crop'] = str('{0}, {1}, {2}, {3}'.format(idx[0], idx[1], idx[2], idx[3]))
with open(inifname, 'w') as configfile: # update values in the ini file
config.write(configfile)
image = wpu.crop_matrix_at_indexes(image, idx)
image_ref = wpu.crop_matrix_at_indexes(image_ref, idx)
# %%
# =============================================================================
# Displacement
# =============================================================================
sx, sy, \
error, step = wps.speckleDisplacement(image, image_ref,
halfsubwidth=halfsubwidth,
halfTemplateSize=halfTemplateSize,
subpixelResolution=subpixelResolution,
npointsmax=npointsmax,
ncores=ncores, taskPerCore=15,
#image_ref = np.rot90(image_ref)
kb_input = input('\nGraphic Crop? [N/y] : ')
if kb_input.lower() == 'y':
# Graphical Crop
idx = wpu.graphical_roi_idx(image, verbose=True)
print('New idx:')
print(idx)
ini_pars['crop'] = str('{0}, {1}, {2}, {3}'.format(idx[0], idx[1], idx[2], idx[3]))
with open(inifname, 'w') as configfile: # update values in the ini file
config.write(configfile)
image = wpu.crop_matrix_at_indexes(image, idx)
image_ref = wpu.crop_matrix_at_indexes(image_ref, idx)
# %%
# =============================================================================
# Displacement
# =============================================================================
sx, sy, \
error, step = wps.speckleDisplacement(image, image_ref,
halfsubwidth=halfsubwidth,
halfTemplateSize=halfTemplateSize,
subpixelResolution=subpixelResolution,
npointsmax=npointsmax,
ncores=ncores, taskPerCore=15,
exit(-1)
thickness -= np.nanmin(thickness)
saveFigFlag = True
# %% Crop
metrology_flag = False
if metrology_flag:
thickness_temp = np.copy(thickness)
thickness_temp[np.isnan(thickness)] = 0.0
idx4crop = wpu.graphical_roi_idx(thickness_temp * 1e6, verbose=True)
thickness = wpu.crop_matrix_at_indexes(thickness, idx4crop)
xx = wpu.crop_matrix_at_indexes(xx, idx4crop)
yy = wpu.crop_matrix_at_indexes(yy, idx4crop)
stride = thickness.shape[0] // 125
if gui_mode:
wpu.plot_profile(xx[::stride, ::stride] * 1e6,
yy[::stride, ::stride] * 1e6,
thickness[::stride, ::stride] * 1e6,
xlabel=r'$x$ [$\mu m$ ]',
ylabel=r'$y$ [$\mu m$ ]',
zlabel=r'$z$ [$\mu m$ ]',
arg4main={'cmap': 'Spectral_r'})