def check_windows(img_a,img_b,no):
win_a = pyprocess.moving_window_array(img_a,20,8)
win_b = pyprocess.moving_window_array(img_b,20,8)
fig,ax = plt.subplots(2,figsize=(10,10))
ax[0].imshow(win_a[no,:,:])
ax[1].imshow(win_b[no,:,:])
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 first_pass(frame_a,
frame_b,
window_size,
overlap,
iterations,
correlation_method='circular',
subpixel_method='gaussian',
do_sig2noise=False,
sig2noise_method='peak2peak',
sig2noise_mask=2):
"""
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 : int
the size of the interrogation window
overlap : int
the overlap of the interrogation window normal for example window_size/2
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'
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
"""
cor_win_1 = pyprocess.moving_window_array(frame_a, window_size, overlap)
cor_win_2 = pyprocess.moving_window_array(frame_b, 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)) #create a dummy for the loop to fill
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)
shapes = np.array(
pyprocess.get_field_shape(frame_a.shape, window_size, overlap))
u = disp[:, 1].reshape(shapes)
v = -disp[:, 0].reshape(shapes)
'reshaping the interrogation window to vector field shape'
x, y = get_coordinates(frame_a.shape, window_size, overlap)
'get coordinates for to map the displacement'
if do_sig2noise == True 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
# In[5]: # let's make two images of 32 x 32 pixels a = np.random.rand(64, 64) b = np.roll(a, (-3, 2)) # In[6]: # parameters for the test window_size = 8 overlap = 4 # In[7]: # for the regular square windows case: aa = moving_window_array(normalize_intensity(a), window_size, overlap) bb = moving_window_array(normalize_intensity(b), window_size, overlap) # In[8]: c = fft_correlate_strided_images(aa, bb) # In[9]: # let's assume we want the extended search type of PIV analysis # with search_area_size in image B > window_size in image A window_size = 4 overlap = 2 search_size = 8 # In[10]:
img_a = img_a[:,415:]
img_b = img_b[:,415:]
piv.run_piv(img_a,img_b,
winsize = 16, # pixels, interrogation window size in frame A
searchsize = 20, # pixels, search in image B
overlap = 8,
u_bounds = [-100,100],
v_bounds = [-2000,0],
scale_factor=1e4),
# %%
from openpiv import pyprocess
from matplotlib import pyplot as plt
win_a = pyprocess.moving_window_array(img_a,20,8)
win_b = pyprocess.moving_window_array(img_b,20,8)
fig,ax = plt.subplots(2,figsize=(10,10))
ax[0].imshow(win_a[50,:,:])
ax[1].imshow(win_b[50,:,:])
# %%
def check_windows(img_a,img_b,no):
win_a = pyprocess.moving_window_array(img_a,20,8)
win_b = pyprocess.moving_window_array(img_b,20,8)
fig,ax = plt.subplots(2,figsize=(10,10))
ax[0].imshow(win_a[no,:,:])
ax[1].imshow(win_b[no,:,:])
