def process_node(i):
DeltaFrame = 300
winsize = 12 # pixels
searchsize = 12 #pixels
overlap = 6 # piexels
dt = DeltaFrame * 1. / fps # piexels
frame_a = tools.imread(fileNameList[i])
frame_b = tools.imread(fileNameList[i + DeltaFrame])
u0, v0, sig2noise = process.extended_search_area_piv(
frame_a.astype(np.int32),
frame_b.astype(np.int32),
window_size=winsize,
overlap=overlap,
dt=dt,
search_area_size=searchsize,
sig2noise_method='peak2peak')
x, y = process.get_coordinates(image_size=frame_a.shape,
window_size=winsize,
overlap=overlap)
u1, v1, mask = validation.sig2noise_val(u0, v0, sig2noise, threshold=1.3)
u2, v2 = filters.replace_outliers(u1,
v1,
method='localmean',
max_iter=5,
kernel_size=5)
u3, v3, mask1 = validation.local_median_val(u2, v2, 3, 3, 1)
u4, v4 = filters.replace_outliers(u3,
v3,
method='localmean',
max_iter=5,
kernel_size=5)
tools.save(x, y, u4, v4, mask1, '../testResult/' + str(i) + '.txt')
def local_median(self):
'''Filter vectors based on a local median threshold.
See Also
--------
openpiv.validation.local_median_val()
'''
result_fnames = []
for i, f in enumerate(self.p['fnames']):
data = np.loadtxt(f)
u, v, mask = piv_vld.local_median_val(
data[:, 2],
data[:, 3],
u_threshold=self.p['local_median_threshold'],
v_threshold=self.p['local_median_threshold'],
size=self.p['local_median_size'])
save_fname = create_save_vec_fname(path=f, postfix='_med_thrhld')
save(data[:, 0],
data[:, 1],
u,
v,
data[:, 4] + mask,
data[:, 5],
save_fname,
delimiter=delimiter)
result_fnames.append(save_fname)
return (result_fnames)
def local_median(self):
"""
Filter vectors based on a local median threshold.
See Also
--------
openpiv.validation.local_median_val()
"""
result_fnames = []
for i, f in enumerate(self.p['fnames']):
data = np.loadtxt(f)
shape = get_dim(data)
u, v, mask = piv_vld.local_median_val(
np.reshape(data[:, 2], shape),
np.reshape(data[:, 3], shape),
self.p['local_median_threshold'],
self.p['local_median_threshold'],
size=self.p['local_median_size'])
save_fname = create_save_vec_fname(
path=f,
postfix='_med_thrhld')
n = len(data)
save(data[:, 0],
data[:, 1],
np.reshape(u, (n,)),
np.reshape(v, (n,)),
data[:, 4] + np.reshape(mask, (n,)),
data[:, 5],
save_fname,
delimiter=self.delimiter)
result_fnames.append(save_fname)
return result_fnames
def process(args, bga, bgb, reflection):
file_a, file_b, counter = args
# read images into numpy arrays
frame_a = tools.imread(file_a)
frame_b = tools.imread(file_b)
# removing background and reflections
frame_a = frame_a - bga
frame_b = frame_b - bgb
frame_a[reflection == 255] = 0
frame_b[reflection == 255] = 0
#applying a static mask (taking out the regions where we have walls)
yp = [580, 435, 0, 0, 580, 580, 0, 0, 435, 580]
xp = [570, 570, 680, 780, 780, 0, 0, 105, 230, 230]
pnts = draw.polygon(yp, xp, frame_a.shape)
frame_a[pnts] = 0
frame_b[pnts] = 0
# checking the resulting frame
#fig, ax = plt.subplots(2,2)
#ax[0,0].imshow(frame_a_org, cmap='gray')
#ax[0,1].imshow(frame_a, cmap='gray')
#ax[1,0].imshow(frame_b_org, cmap='gray')
#ax[1,1].imshow(frame_b, cmap='gray')
#plt.tight_layout()
#plt.show()
# main piv processing
u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a, frame_b, \
window_size=48, overlap=16, dt=0.001094, search_area_size=64, sig2noise_method='peak2peak')
x, y = pyprocess.get_coordinates(image_size=frame_a.shape,
window_size=48,
overlap=16)
u, v, mask = validation.local_median_val(u, v, 2000, 2000, size=2)
u, v = filters.replace_outliers(u,
v,
method='localmean',
max_iter=10,
kernel_size=2)
u, *_ = smoothn(u, s=1.0)
v, *_ = smoothn(v, s=1.0)
# saving the results
save_file = tools.create_path(file_a, 'Analysis')
tools.save(x, y, u, v, mask, save_file + '.dat')
def ProcessPIV(args, bga, bgb, reflection, stg):
# read images into numpy arrays
file_a, file_b, counter = args
frame_a = tools.imread(file_a)
frame_b = tools.imread(file_b)
# removing background and reflections
if bgb is not None:
frame_a = frame_a - bga
frame_b = frame_b - bgb
frame_a[reflection == 255] = 0
frame_b[reflection == 255] = 0
#plt.imshow(frame_a, cmap='gray')
#plt.show()
# main piv processing
u, v, s2n = pyprocess.extended_search_area_piv( frame_a, frame_b, \
window_size=stg['WS'], overlap=stg['OL'], dt=stg['DT'], search_area_size=stg['SA'], sig2noise_method='peak2peak')
x, y = pyprocess.get_coordinates(image_size=frame_a.shape,
window_size=stg['WS'],
overlap=stg['OL'])
if stg['BVR'] == 'on':
u, v, mask = validation.local_median_val(u,
v,
stg['MF'][0],
stg['MF'][1],
size=2)
u, v, mask = validation.global_val(u,
v,
u_thresholds=stg['GF'][0],
v_thresholds=stg['GF'][1])
u, v = filters.replace_outliers(u,
v,
method='localmean',
max_iter=10,
kernel_size=2)
u, *_ = smoothn(u, s=0.5)
v, *_ = smoothn(v, s=0.5)
x, y, u, v = scaling.uniform(x, y, u, v, stg['SC'])
# saving the results
save_file = tools.create_path(file_a, 'Analysis')
tools.save(x, y, u, v, s2n, save_file + '.dat')
def process(args):
file_a, file_b, counter = args
# read images into numpy arrays
frame_a = tools.imread(file_a)
frame_b = tools.imread(file_b)
print(counter + 1)
# process image pair with piv algorithm.
u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a, frame_b, \
window_size=32, overlap=16, dt=0.0015, search_area_size=32, sig2noise_method='peak2peak')
x, y = pyprocess.get_coordinates(image_size=frame_a.shape,
window_size=32,
overlap=16)
u, v, mask1 = validation.sig2noise_val(u, v, sig2noise, threshold=1.0)
u, v, mask2 = validation.global_val(u, v, (-2000, 2000), (-2000, 4000))
u, v, mask3 = validation.local_median_val(u, v, 400, 400, size=2)
#u, v, mask4 = validation.global_std(u, v, std_threshold=3)
mask = mask1 | mask2 | mask3
#u, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2)
save_file = tools.create_path(file_a, 'Analysis')
tools.save(x, y, u, v, mask, save_file + '.dat')
def multipass_img_deform(frame_a,
frame_b,
window_size,
overlap,
iterations,
current_iteration,
x_old,
y_old,
u_old,
v_old,
correlation_method='circular',
subpixel_method='gaussian',
do_sig2noise=False,
sig2noise_method='peak2peak',
sig2noise_mask=2,
MinMaxU=(-100, 50),
MinMaxV=(-50, 50),
std_threshold=5,
median_threshold=2,
median_size=1,
filter_method='localmean',
max_filter_iteration=10,
filter_kernel_size=2,
interpolation_order=3):
"""
First pass of the PIV evaluation.
This function does the PIV evaluation of the first pass. It returns
the coordinates of the interrogation window centres, the displacment
u and v for each interrogation window as well as the mask which indicates
wether the displacement vector was interpolated or not.
Parameters
----------
frame_a : 2d np.ndarray
the first image
frame_b : 2d np.ndarray
the second image
window_size : tuple of ints
the size of the interrogation window
overlap : tuple of ints
the overlap of the interrogation window normal for example window_size/2
x_old : 2d np.ndarray
the x coordinates of the vector field of the previous pass
y_old : 2d np.ndarray
the y coordinates of the vector field of the previous pass
u_old : 2d np.ndarray
the u displacement of the vector field of the previous pass
v_old : 2d np.ndarray
the v displacement of the vector field of the previous pass
subpixel_method: string
the method used for the subpixel interpolation.
one of the following methods to estimate subpixel location of the peak:
'centroid' [replaces default if correlation map is negative],
'gaussian' [default if correlation map is positive],
'parabolic'
MinMaxU : two elements tuple
sets the limits of the u displacment component
Used for validation.
MinMaxV : two elements tuple
sets the limits of the v displacment component
Used for validation.
std_threshold : float
sets the threshold for the std validation
median_threshold : float
sets the threshold for the median validation
filter_method : string
the method used to replace the non-valid vectors
Methods:
'localmean',
'disk',
'distance',
max_filter_iteration : int
maximum of filter iterations to replace nans
filter_kernel_size : int
size of the kernel used for the filtering
interpolation_order : int
the order of the spline interpolation used for the image deformation
Returns
-------
x : 2d np.array
array containg the x coordinates of the interrogation window centres
y : 2d np.array
array containg the y coordinates of the interrogation window centres
u : 2d np.array
array containing the u displacement for every interrogation window
u : 2d np.array
array containing the u displacement for every interrogation window
mask : 2d np.array
array containg the mask values (bool) which contains information if
the vector was filtered
"""
x, y = get_coordinates(np.shape(frame_a), window_size, overlap)
'calculate the y and y coordinates of the interrogation window centres'
y_old = y_old[:, 0]
# y_old = y_old[::-1]
x_old = x_old[0, :]
y_int = y[:, 0]
# y_int = y_int[::-1]
x_int = x[0, :]
'''The interpolation function dont like meshgrids as input. Hence, the the edges
must be extracted to provide the sufficient input. x_old and y_old are the
are the coordinates of the old grid. x_int and y_int are the coordiantes
of the new grid'''
ip = RectBivariateSpline(y_old, x_old, u_old)
u_pre = ip(y_int, x_int)
ip2 = RectBivariateSpline(y_old, x_old, v_old)
v_pre = ip2(y_int, x_int)
''' interpolating the displacements from the old grid onto the new grid
y befor x because of numpy works row major
'''
frame_b_deform = frame_interpolation(
frame_b, x, y, u_pre, -v_pre, interpolation_order=interpolation_order)
'''this one is doing the image deformation (see above)'''
cor_win_1 = pyprocess.moving_window_array(frame_a, window_size, overlap)
cor_win_2 = pyprocess.moving_window_array(frame_b_deform, window_size,
overlap)
'''Filling the interrogation window. They windows are arranged
in a 3d array with number of interrogation window *window_size*window_size
this way is much faster then using a loop'''
correlation = correlation_func(cor_win_1,
cor_win_2,
correlation_method=correlation_method,
normalized_correlation=False)
'do the correlation'
disp = np.zeros((np.size(correlation, 0), 2))
for i in range(0, np.size(correlation, 0)):
''' determine the displacment on subpixel level '''
disp[i, :] = find_subpixel_peak_position(
correlation[i, :, :], subpixel_method=subpixel_method)
'this loop is doing the displacment evaluation for each window '
disp = np.array(disp) - np.floor(np.array(correlation[0, :, :].shape) / 2)
'reshaping the interrogation window to vector field shape'
shapes = np.array(
pyprocess.get_field_shape(np.shape(frame_a), window_size, overlap))
u = disp[:, 1].reshape(shapes)
v = -disp[:, 0].reshape(shapes)
'adding the recent displacment on to the displacment of the previous pass'
u = u + u_pre
v = v + v_pre
'validation using gloabl limits and local median'
u, v, mask_g = validation.global_val(u, v, MinMaxU, MinMaxV)
u, v, mask_s = validation.global_std(u, v, std_threshold=std_threshold)
u, v, mask_m = validation.local_median_val(u,
v,
u_threshold=median_threshold,
v_threshold=median_threshold,
size=median_size)
mask = mask_g + mask_m + mask_s
'adding masks to add the effect of alle the validations'
#mask=np.zeros_like(u)
'filter to replace the values that where marked by the validation'
if current_iteration != iterations:
'filter to replace the values that where marked by the validation'
u, v = filters.replace_outliers(u,
v,
method=filter_method,
max_iter=max_filter_iteration,
kernel_size=filter_kernel_size)
if do_sig2noise == True and current_iteration == iterations and iterations != 1:
sig2noise_ratio = sig2noise_ratio_function(
correlation,
sig2noise_method=sig2noise_method,
width=sig2noise_mask)
sig2noise_ratio = sig2noise_ratio.reshape(shapes)
else:
sig2noise_ratio = np.full_like(u, np.nan)
return x, y, u, v, sig2noise_ratio, mask
def func(args):
"""A function to process each image pair."""
# this line is REQUIRED for multiprocessing to work
# always use it in your custom function
file_a, file_b, counter = args
# counter2=str(counter2)
#####################
# Here goes you code
#####################
' read images into numpy arrays'
frame_a = tools.imread(os.path.join(settings.filepath_images, file_a))
frame_b = tools.imread(os.path.join(settings.filepath_images, file_b))
## Miguel: I just had a quick look, and I do not understand the reason for this step.
# I propose to remove it.
#frame_a = (frame_a*1024).astype(np.int32)
#frame_b = (frame_b*1024).astype(np.int32)
' crop to ROI'
if settings.ROI == 'full':
frame_a = frame_a
frame_b = frame_b
else:
frame_a = frame_a[settings.ROI[0]:settings.ROI[1],
settings.ROI[2]:settings.ROI[3]]
frame_b = frame_b[settings.ROI[0]:settings.ROI[1],
settings.ROI[2]:settings.ROI[3]]
if settings.dynamic_masking_method == 'edge' or 'intensity':
frame_a = preprocess.dynamic_masking(
frame_a,
method=settings.dynamic_masking_method,
filter_size=settings.dynamic_masking_filter_size,
threshold=settings.dynamic_masking_threshold)
frame_b = preprocess.dynamic_masking(
frame_b,
method=settings.dynamic_masking_method,
filter_size=settings.dynamic_masking_filter_size,
threshold=settings.dynamic_masking_threshold)
'''%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'''
'first pass'
x, y, u, v, sig2noise_ratio = first_pass(
frame_a,
frame_b,
settings.windowsizes[0],
settings.overlap[0],
settings.iterations,
correlation_method=settings.correlation_method,
subpixel_method=settings.subpixel_method,
do_sig2noise=settings.extract_sig2noise,
sig2noise_method=settings.sig2noise_method,
sig2noise_mask=settings.sig2noise_mask,
)
'validation using gloabl limits and std and local median'
'''MinMaxU : two elements tuple
sets the limits of the u displacment component
Used for validation.
MinMaxV : two elements tuple
sets the limits of the v displacment component
Used for validation.
std_threshold : float
sets the threshold for the std validation
median_threshold : float
sets the threshold for the median validation
filter_method : string
the method used to replace the non-valid vectors
Methods:
'localmean',
'disk',
'distance',
max_filter_iteration : int
maximum of filter iterations to replace nans
filter_kernel_size : int
size of the kernel used for the filtering'''
mask = np.full_like(x, False)
if settings.validation_first_pass == True:
u, v, mask_g = validation.global_val(u, v, settings.MinMax_U_disp,
settings.MinMax_V_disp)
u, v, mask_s = validation.global_std(
u, v, std_threshold=settings.std_threshold)
u, v, mask_m = validation.local_median_val(
u,
v,
u_threshold=settings.median_threshold,
v_threshold=settings.median_threshold,
size=settings.median_size)
if settings.extract_sig2noise == True and settings.iterations == 1 and settings.do_sig2noise_validation == True:
u, v, mask_s2n = validation.sig2noise_val(
u,
v,
sig2noise_ratio,
threshold=settings.sig2noise_threshold)
mask = mask + mask_g + mask_m + mask_s + mask_s2n
else:
mask = mask + mask_g + mask_m + mask_s
'filter to replace the values that where marked by the validation'
if settings.iterations > 1:
u, v = filters.replace_outliers(
u,
v,
method=settings.filter_method,
max_iter=settings.max_filter_iteration,
kernel_size=settings.filter_kernel_size)
'adding masks to add the effect of all the validations'
if settings.smoothn == True:
u, dummy_u1, dummy_u2, dummy_u3 = smoothn.smoothn(
u, s=settings.smoothn_p)
v, dummy_v1, dummy_v2, dummy_v3 = smoothn.smoothn(
v, s=settings.smoothn_p)
elif settings.iterations == 1 and settings.replace_vectors == True:
u, v = filters.replace_outliers(
u,
v,
method=settings.filter_method,
max_iter=settings.max_filter_iteration,
kernel_size=settings.filter_kernel_size)
'adding masks to add the effect of all the validations'
if settings.smoothn == True:
u, v = filters.replace_outliers(
u,
v,
method=settings.filter_method,
max_iter=settings.max_filter_iteration,
kernel_size=settings.filter_kernel_size)
u, dummy_u1, dummy_u2, dummy_u3 = smoothn.smoothn(
u, s=settings.smoothn_p)
v, dummy_v1, dummy_v2, dummy_v3 = smoothn.smoothn(
v, s=settings.smoothn_p)
i = 1
'all the following passes'
for i in range(2, settings.iterations + 1):
x, y, u, v, sig2noise_ratio, mask = multipass_img_deform(
frame_a,
frame_b,
settings.windowsizes[i - 1],
settings.overlap[i - 1],
settings.iterations,
i,
x,
y,
u,
v,
correlation_method=settings.correlation_method,
subpixel_method=settings.subpixel_method,
do_sig2noise=settings.extract_sig2noise,
sig2noise_method=settings.sig2noise_method,
sig2noise_mask=settings.sig2noise_mask,
MinMaxU=settings.MinMax_U_disp,
MinMaxV=settings.MinMax_V_disp,
std_threshold=settings.std_threshold,
median_threshold=settings.median_threshold,
median_size=settings.median_size,
filter_method=settings.filter_method,
max_filter_iteration=settings.max_filter_iteration,
filter_kernel_size=settings.filter_kernel_size,
interpolation_order=settings.interpolation_order)
# If the smoothing is active, we do it at each pass
if settings.smoothn == True:
u, dummy_u1, dummy_u2, dummy_u3 = smoothn.smoothn(
u, s=settings.smoothn_p)
v, dummy_v1, dummy_v2, dummy_v3 = smoothn.smoothn(
v, s=settings.smoothn_p)
'''%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'''
if settings.extract_sig2noise == True and i == settings.iterations and settings.iterations != 1 and settings.do_sig2noise_validation == True:
u, v, mask_s2n = validation.sig2noise_val(
u, v, sig2noise_ratio, threshold=settings.sig2noise_threshold)
mask = mask + mask_s2n
if settings.replace_vectors == True:
u, v = filters.replace_outliers(
u,
v,
method=settings.filter_method,
max_iter=settings.max_filter_iteration,
kernel_size=settings.filter_kernel_size)
'pixel/frame->pixel/sec'
u = u / settings.dt
v = v / settings.dt
'scales the results pixel-> meter'
x, y, u, v = scaling.uniform(x,
y,
u,
v,
scaling_factor=settings.scaling_factor)
'save to a file'
save(x,
y,
u,
v,
sig2noise_ratio,
mask,
os.path.join(save_path, 'field_A%03d.txt' % counter),
delimiter='\t')
'some messages to check if it is still alive'
'some other stuff that one might want to use'
if settings.show_plot == True or settings.save_plot == True:
plt.close('all')
plt.ioff()
Name = os.path.join(save_path, 'Image_A%03d.png' % counter)
display_vector_field(os.path.join(save_path,
'field_A%03d.txt' % counter),
scale=settings.scale_plot)
if settings.save_plot == True:
plt.savefig(Name)
if settings.show_plot == True:
plt.show()
print('Image Pair ' + str(counter + 1))
frame_b.astype(np.int32),
window_size=winsize,
overlap=overlap,
dt=dt,
search_area_size=searchsize,
sig2noise_method='peak2peak')
x, y = process.get_coordinates(image_size=frame_a.shape,
window_size=winsize,
overlap=overlap)
u1, v1, mask = validation.sig2noise_val(u0, v0, sig2noise, threshold=1.3)
u2, v2 = filters.replace_outliers(u1,
v1,
method='localmean',
max_iter=5,
kernel_size=5)
u3, v3, mask1 = validation.local_median_val(u2, v2, 3, 3, 1)
u4, v4 = filters.replace_outliers(u3,
v3,
method='localmean',
max_iter=5,
kernel_size=5)
tools.save(x, y, u4, v4, mask1, '../testResult/test.txt')
tools.display_vector_field('../testResult/test.txt', scale=500, width=0.0025)
#%% define node
def process_node(i):
DeltaFrame = 300
winsize = 12 # pixels
searchsize = 12 #pixels
def func(args):
file_a, file_b, counter = args
# read the iamges
frame_a = tools.imread(os.path.join(settings.filepath_images, file_a))
frame_b = tools.imread(os.path.join(settings.filepath_images, file_b))
if counter == settings.fall_start:
settings.ROI[1] = frame_a.shape[0]
"""Here we check if the interface has reached the top of the roi yet
by comparing it to the index in the observation_periods file. If it has
not reached the roi yet we skip this part, if it did then we shift the
roi for each pair after the initial one """
if counter >= settings.roi_shift_start:
# set the roi to the image height for the first frame
# if counter == settings.roi_shift_start :
# settings.current_pos = 0
# shift the roi for each pair (this is not done for the first one)
settings.ROI[0] = int(settings.current_pos)
# crop to roi
if settings.ROI == 'full':
frame_a = frame_a
frame_b = frame_b
else:
frame_a = frame_a[settings.ROI[0]:settings.ROI[1],
settings.ROI[2]:settings.ROI[3]]
frame_b = frame_b[settings.ROI[0]:settings.ROI[1],
settings.ROI[2]:settings.ROI[3]]
if settings.dynamic_masking_method == 'edge' or settings.dynamic_masking_method == 'intensity':
frame_a = preprocess.dynamic_masking(
frame_a,
method=settings.dynamic_masking_method,
filter_size=settings.dynamic_masking_filter_size,
threshold=settings.dynamic_masking_threshold)
frame_b = preprocess.dynamic_masking(
frame_b,
method=settings.dynamic_masking_method,
filter_size=settings.dynamic_masking_filter_size,
threshold=settings.dynamic_masking_threshold)
#%%
""" Here we do the first pass of the piv interrogation """
x, y, u, v, sig2noise_ratio = first_pass(
frame_a,
frame_b,
settings.window_width[0],
settings.window_height[0],
settings.overlap_width[0],
settings.overlap_height[0],
settings.iterations,
correlation_method=settings.correlation_method,
subpixel_method=settings.subpixel_method,
do_sig2noise=settings.extract_sig2noise,
sig2noise_method=settings.sig2noise_method,
sig2noise_mask=settings.sig2noise_mask,
)
mask = np.full_like(x, False)
if settings.validation_first_pass == True:
u, v, mask_g = validation.global_val(u, v, settings.MinMax_U_disp,
settings.MinMax_V_disp)
u, v, mask_s = validation.global_std(
u, v, std_threshold=settings.std_threshold)
u, v, mask_m = validation.local_median_val(
u,
v,
u_threshold=settings.median_threshold,
v_threshold=settings.median_threshold,
size=settings.median_size)
if settings.extract_sig2noise == True and settings.iterations == 1 and settings.do_sig2noise_validation == True:
u, v, mask_s2n = validation.sig2noise_val(
u,
v,
sig2noise_ratio,
threshold=settings.sig2noise_threshold)
mask = mask + mask_g + mask_m + mask_s + mask_s2n
else:
mask = mask + mask_g + mask_m + mask_s
'filter to replace the values that where marked by the validation'
if settings.iterations > 1:
u, v = filters.replace_outliers(
u,
v,
method=settings.filter_method,
max_iter=settings.max_filter_iteration,
kernel_size=settings.filter_kernel_size)
'adding masks to add the effect of all the validations'
if settings.smoothn == True:
u, dummy_u1, dummy_u2, dummy_u3 = smoothn.smoothn(
u, s=settings.smoothn_p)
v, dummy_v1, dummy_v2, dummy_v3 = smoothn.smoothn(
v, s=settings.smoothn_p)
elif settings.iterations == 1 and settings.replace_vectors == True:
u, v = filters.replace_outliers(
u,
v,
method=settings.filter_method,
max_iter=settings.max_filter_iteration,
kernel_size=settings.filter_kernel_size)
'adding masks to add the effect of all the validations'
if settings.smoothn == True:
u, v = filters.replace_outliers(
u,
v,
method=settings.filter_method,
max_iter=settings.max_filter_iteration,
kernel_size=settings.filter_kernel_size)
u, dummy_u1, dummy_u2, dummy_u3 = smoothn.smoothn(
u, s=settings.smoothn_p)
v, dummy_v1, dummy_v2, dummy_v3 = smoothn.smoothn(
v, s=settings.smoothn_p)
#%%
i = 1
""" Do the multipass until the maximum iterations are reached """
for i in range(2, settings.iterations + 1):
x, y, u, v, sig2noise_ratio, mask = multipass_img_deform(
frame_a,
frame_b,
settings.window_width[i - 1],
settings.window_height[i - 1],
settings.overlap_width[i - 1],
settings.overlap_height[i - 1],
settings.iterations,
i,
x,
y,
u,
v,
correlation_method=settings.correlation_method,
subpixel_method=settings.subpixel_method,
do_sig2noise=settings.extract_sig2noise,
sig2noise_method=settings.sig2noise_method,
sig2noise_mask=settings.sig2noise_mask,
MinMaxU=settings.MinMax_U_disp,
MinMaxV=settings.MinMax_V_disp,
std_threshold=settings.std_threshold,
median_threshold=settings.median_threshold,
median_size=settings.median_size,
filter_method=settings.filter_method,
max_filter_iteration=settings.max_filter_iteration,
filter_kernel_size=settings.filter_kernel_size,
interpolation_order=settings.interpolation_order)
# smooth on each pass in case this is wanted
if settings.smoothn == True:
u, dummy_u1, dummy_u2, dummy_u3 = smoothn.smoothn(
u, s=settings.smoothn_p)
v, dummy_v1, dummy_v2, dummy_v3 = smoothn.smoothn(
v, s=settings.smoothn_p)
# extract the sig2noise ratio in case it is desired and replace the vectors
if settings.extract_sig2noise == True and i == settings.iterations and settings.iterations != 1 and settings.do_sig2noise_validation == True:
u, v, mask_s2n = validation_patch.sig2noise_val(
u,
v,
sig2noise_ratio,
threshold_low=settings.sig2noise_threshold)
mask = mask + mask_s2n
if settings.replace_vectors == True:
u, v = filters.replace_outliers(
u,
v,
method=settings.filter_method,
max_iter=settings.max_filter_iteration,
kernel_size=settings.filter_kernel_size)
if counter >= settings.roi_shift_start:
settings.current_pos = settings.current_pos - calc_disp(
x, v, frame_b.shape[1])
if ((settings.ROI[1] - settings.current_pos) < 300):
return settings.current_pos, True
# scale the result timewise and lengthwise
u = u / settings.dt
v = v / settings.dt
x, y, u, v = scaling.uniform(x,
y,
u,
v,
scaling_factor=settings.scaling_factor)
# save the result
save(x,
y,
u,
v,
sig2noise_ratio,
mask,
os.path.join(save_path_txts, 'field_%06d.txt' % (counter)),
delimiter='\t')
# disable the grid in the rcParams file
plt.rcParams['axes.grid'] = False
# show and save the plot if it is desired
if settings.show_plot == True or settings.save_plot == True:
plt.ioff()
Name = os.path.join(save_path_images, 'Image_%06d.png' % (counter))
display_vector_field(os.path.join(save_path_txts,
'field_%06d.txt' % (counter)),
scale=settings.scale_plot)
if settings.save_plot == True:
plt.savefig(Name, dpi=100)
if settings.show_plot == True:
plt.show()
plt.close('all')
print('Image Pair ' + str(counter) + ' of ' +
settings.save_folder_suffix)
if settings.current_pos == np.nan:
return settings.current_pos, True
return settings.current_pos, False
## validation.local_median_val should also take masked array
# In[28]:
# The third filter is the median test (not normalized at the moment)
settings.median_threshold = 3 # threshold of the median validation
# On the last iteration, an additional validation can be done based on the S/N.
settings.median_size=1 #defines the size of the local median
u, v, mask_m = validation.local_median_val(
u,
v,
u_threshold=settings.median_threshold,
v_threshold=settings.median_threshold,
size=settings.median_size,
)
status_message(u)
# In[29]:
plt.quiver(x,y,u,v,sig2noise_ratio)
plt.quiver(x[mask_m],y[mask_m],u0[mask_m],v0[mask_m],color='r')
plt.gca().invert_yaxis()
plt.colorbar()
def test_local_median_validation(u_threshold=3, N=3, size=1):
u = np.random.rand(2 * N + 1, 2 * N + 1)
u[N, N] = np.median(u) * 10
# print('mockup data')
# print(u)
# prepare two copies for comparison
tmp = u.copy()
# and masked array copy
masked_u = np.ma.masked_array(u.copy(), np.ma.nomask)
masked_u[N + 1:, N + 1:-1] = np.ma.masked
# print('masked version, see inf')
# print(masked_u.filled(np.inf))
f = np.ones((2 * size + 1, 2 * size + 1))
f[size, size] = 0
# print('Kernel or footprint')
# print(f)
# # out = convolve2d(u, f, boundary='wrap', mode='same')/f.sum()
# out = median_filter(u,footprint=f)
# print('median filter does no work with nan')
# print(out)
um = generic_filter(u,
np.nanmedian,
mode='constant',
cval=np.nan,
footprint=f)
# print('generic filter output with nan')
# print(um)
ind = np.abs((u - um)) > u_threshold
# print('found outliers in places:')
# print(ind)
# mark those places
u[ind] = np.nan
# print('marked data and the mask')
# print(u)
mask = np.zeros(u.shape, dtype=bool)
mask[ind] = True
# print(mask)
# now we test our function which is just a decoration
# of the above steps
u1, u1, mask1 = validation.local_median_val(tmp, tmp, 3, 3)
# print('data and its mask')
# print(u1)
# print(mask1)
# Now we shall test a masked array (new in 0.23.3)
# for the image masked data
# image mask is a masked array property
# while nan in the matrix is the previous validation step marker
u2, u2, mask2 = validation.local_median_val(masked_u.copy(),
masked_u.copy(), 3, 3)
# print('data')
# print(u2.data)
# print('image mask')
# print(u2.mask)
# print('invalid vector mask')
# print(mask2)
# print('Assert expected results')
assert np.isnan(u[N, N])
assert mask[N, N]
assert np.isnan(u1[N, N])
assert mask1[N, N]
assert np.isnan(u2.data[N, N])
assert mask2[N, N]
assert u2.mask[N + 1, N + 1]
def process(self, args):
"""
Process chain as configured in the GUI.
Parameters
----------
args : tuple
Tuple as expected by the inherited run method:
file_a (str) -- image file a
file_b (str) -- image file b
counter (int) -- index pointing to an element of the filename
list
"""
file_a, file_b, counter = args
frame_a = piv_tls.imread(file_a)
frame_b = piv_tls.imread(file_b)
# Smoothning script borrowed from openpiv.windef
s = self.p['smoothn_val']
def smoothn(u, s):
s = s
u, _, _, _ = piv_smt.smoothn(u, s=s, isrobust=self.p['robust'])
return (u)
# delimiters placed here for safety
delimiter = self.p['separator']
if delimiter == 'tab':
delimiter = '\t'
if delimiter == 'space':
delimiter = ' '
# preprocessing
print('\nPre-pocessing image pair: {}'.format(counter + 1))
if self.p['background_subtract'] \
and self.p['background_type'] == 'minA - minB':
self.background = gen_background(self.p, frame_a, frame_b)
frame_a = frame_a.astype(np.int32)
frame_a = process_images(self,
frame_a,
self.GUI.preprocessing_methods,
background=self.background)
frame_b = frame_b.astype(np.int32)
frame_b = process_images(self,
frame_b,
self.GUI.preprocessing_methods,
background=self.background)
print('Evaluating image pair: {}'.format(counter + 1))
# evaluation first pass
start = time.time()
passes = 1
# setup custom windowing if selected
if self.parameter['custom_windowing']:
corr_window_0 = self.parameter['corr_window_1']
overlap_0 = self.parameter['overlap_1']
for i in range(2, 8):
if self.parameter['pass_%1d' % i]:
passes += 1
else:
break
else:
passes = self.parameter['coarse_factor']
if self.parameter['grid_refinement'] == 'all passes' \
and self.parameter['coarse_factor'] != 1:
corr_window_0 = self.parameter['corr_window'] * \
2**(self.parameter['coarse_factor'] - 1)
overlap_0 = self.parameter['overlap'] * \
2**(self.parameter['coarse_factor'] - 1)
# Refine all passes after first when there are more than 1 pass.
elif self.parameter['grid_refinement'] == '2nd pass on' \
and self.parameter['coarse_factor'] != 1:
corr_window_0 = self.parameter['corr_window'] * \
2**(self.parameter['coarse_factor'] - 2)
overlap_0 = self.parameter['overlap'] * \
2**(self.parameter['coarse_factor'] - 2)
# If >>none<< is selected or something goes wrong, the window
# size would remain the same.
else:
corr_window_0 = self.parameter['corr_window']
overlap_0 = self.parameter['overlap']
overlap_percent = overlap_0 / corr_window_0
sizeX = corr_window_0
u, v, sig2noise = piv_wdf.extended_search_area_piv(
frame_a.astype(np.int32),
frame_b.astype(np.int32),
window_size=corr_window_0,
overlap=overlap_0,
search_area_size=corr_window_0,
width=self.parameter['s2n_mask'],
subpixel_method=self.parameter['subpixel_method'],
sig2noise_method=self.parameter['sig2noise_method'],
correlation_method=self.parameter['corr_method'],
normalized_correlation=self.parameter['normalize_correlation'])
x, y = piv_prc.get_coordinates(frame_a.shape, corr_window_0, overlap_0)
# validating first pass
mask = np.full_like(x, 0)
if self.parameter['fp_vld_global_threshold']:
u, v, Mask = piv_vld.global_val(
u,
v,
u_thresholds=(self.parameter['fp_MinU'],
self.parameter['fp_MaxU']),
v_thresholds=(self.parameter['fp_MinV'],
self.parameter['fp_MaxV']))
# consolidate effects of mask
mask += Mask
if self.parameter['fp_local_med']:
u, v, Mask = piv_vld.local_median_val(
u,
v,
u_threshold=self.parameter['fp_local_med'],
v_threshold=self.parameter['fp_local_med'],
size=self.parameter['fp_local_med_size'])
mask += Mask
if self.parameter['adv_repl']:
u, v = piv_flt.replace_outliers(
u,
v,
method=self.parameter['adv_repl_method'],
max_iter=self.parameter['adv_repl_iter'],
kernel_size=self.parameter['adv_repl_kernel'])
print('Validated first pass result of image pair: {}.'.format(counter +
1))
# smoothning before deformation if 'each pass' is selected
if self.parameter['smoothn_each_pass']:
if self.parameter['smoothn_first_more']:
s *= 2
u = smoothn(u, s)
v = smoothn(v, s)
print('Smoothned pass 1 for image pair: {}.'.format(counter + 1))
s = self.parameter['smoothn_val1']
print('Finished pass 1 for image pair: {}.'.format(counter + 1))
print("window size: " + str(corr_window_0))
print('overlap: ' + str(overlap_0), '\n')
# evaluation of all other passes
if passes != 1:
iterations = passes - 1
for i in range(2, passes + 1):
# setting up the windowing of each pass
if self.parameter['custom_windowing']:
corr_window = self.parameter['corr_window_%1d' % i]
overlap = int(corr_window * overlap_percent)
else:
if self.parameter['grid_refinement'] == 'all passes' or \
self.parameter['grid_refinement'] == '2nd pass on':
corr_window = self.parameter['corr_window'] * \
2**(iterations - 1)
overlap = self.parameter['overlap'] * \
2**(iterations - 1)
else:
corr_window = self.parameter['corr_window']
overlap = self.parameter['overlap']
sizeX = corr_window
# translate settings to windef settings object
piv_wdf_settings = piv_wdf.Settings()
piv_wdf_settings.correlation_method = \
self.parameter['corr_method']
piv_wdf_settings.normalized_correlation = \
self.parameter['normalize_correlation']
piv_wdf_settings.windowsizes = (corr_window, ) * (passes + 1)
piv_wdf_settings.overlap = (overlap, ) * (passes + 1)
piv_wdf_settings.num_iterations = passes
piv_wdf_settings.subpixel_method = \
self.parameter['subpixel_method']
piv_wdf_settings.deformation_method = \
self.parameter['deformation_method']
piv_wdf_settings.interpolation_order = \
self.parameter['interpolation_order']
piv_wdf_settings.sig2noise_validate = True,
piv_wdf_settings.sig2noise_method = \
self.parameter['sig2noise_method']
piv_wdf_settings.sig2noise_mask = self.parameter['s2n_mask']
# do the correlation
x, y, u, v, sig2noise, mask = piv_wdf.multipass_img_deform(
frame_a.astype(np.int32),
frame_b.astype(np.int32),
i, # current iteration
x,
y,
u,
v,
piv_wdf_settings)
# validate other passes
if self.parameter['sp_vld_global_threshold']:
u, v, Mask = piv_vld.global_val(
u,
v,
u_thresholds=(self.parameter['sp_MinU'],
self.parameter['sp_MaxU']),
v_thresholds=(self.parameter['sp_MinV'],
self.parameter['sp_MaxV']))
mask += Mask # consolidate effects of mask
if self.parameter['sp_vld_global_threshold']:
u, v, Mask = piv_vld.global_std(
u, v, std_threshold=self.parameter['sp_std_threshold'])
mask += Mask
if self.parameter['sp_local_med_validation']:
u, v, Mask = piv_vld.local_median_val(
u,
v,
u_threshold=self.parameter['sp_local_med'],
v_threshold=self.parameter['sp_local_med'],
size=self.parameter['sp_local_med_size'])
mask += Mask
if self.parameter['adv_repl']:
u, v = piv_flt.replace_outliers(
u,
v,
method=self.parameter['adv_repl_method'],
max_iter=self.parameter['adv_repl_iter'],
kernel_size=self.parameter['adv_repl_kernel'])
print('Validated pass {} of image pair: {}.'.format(
i, counter + 1))
# smoothning each individual pass if 'each pass' is selected
if self.parameter['smoothn_each_pass']:
u = smoothn(u, s)
v = smoothn(v, s)
print('Smoothned pass {} for image pair: {}.'.format(
i, counter + 1))
print('Finished pass {} for image pair: {}.'.format(
i, counter + 1))
print("window size: " + str(corr_window))
print('overlap: ' + str(overlap), '\n')
iterations -= 1
if self.p['flip_u']:
u = np.flipud(u)
if self.p['flip_v']:
v = np.flipud(v)
if self.p['invert_u']:
u *= -1
if self.p['invert_v']:
v *= -1
# scaling
u = u / self.parameter['dt']
v = v / self.parameter['dt']
x, y, u, v = piv_scl.uniform(x,
y,
u,
v,
scaling_factor=self.parameter['scale'])
end = time.time()
# save data to file.
out = np.vstack([m.ravel() for m in [x, y, u, v, mask, sig2noise]])
np.savetxt(self.save_fnames[counter],
out.T,
fmt='%8.4f',
delimiter=delimiter)
print('Processed image pair: {}'.format(counter + 1))
sizeY = sizeX
sizeX = ((int(frame_a.shape[0] - sizeX) //
(sizeX - (sizeX * overlap_percent))) + 1)
sizeY = ((int(frame_a.shape[1] - sizeY) //
(sizeY - (sizeY * overlap_percent))) + 1)
time_per_vec = _round((((end - start) * 1000) / ((sizeX * sizeY) - 1)),
3)
print('Process time: {} second(s)'.format((_round((end - start), 3))))
print('Number of vectors: {}'.format(int((sizeX * sizeY) - 1)))
print('Time per vector: {} millisecond(s)'.format(time_per_vec))
