def test_display_vector_field(file_a=_file_a, file_b=_file_b, test_file=_test_file):
a = imread(file_a)
b = imread(file_b)
window_size = 32
overlap = 16
search_area_size = 40
u, v, s2n = extended_search_area_piv(a, b, window_size,
search_area_size=search_area_size,
overlap=overlap,
correlation_method='circular',
normalized_correlation=False)
x, y = get_coordinates(a.shape, search_area_size=search_area_size, overlap=overlap)
x, y, u, v = transform_coordinates(x, y, u, v)
mask = np.zeros_like(x)
mask[-1,1] = 1 # test of invalid vector plot
save(x, y, u, v, mask, 'tmp.txt')
fig, ax = plt.subplots(figsize=(6, 6))
display_vector_field('tmp.txt', on_img=True, image_name=file_a, ax=ax)
decorators.remove_ticks_and_titles(fig)
fig.savefig('./tmp.png')
res = compare.compare_images('./tmp.png', test_file, 0.001)
assert res is None
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
#####################
# Here goes you code
#####################
# read images into numpy arrays
frame_a = tools.imread(os.path.join(path, file_a))
frame_b = tools.imread(os.path.join(path, file_b))
frame_a = (frame_a * 1024).astype(np.int32)
frame_b = (frame_b * 1024).astype(np.int32)
# process image pair with extended search area piv algorithm.
u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a, frame_b, \
window_size=64, overlap=32, dt=0.02, search_area_size=128, sig2noise_method='peak2peak')
u, v, mask = validation.sig2noise_val(u, v, sig2noise, threshold=1.5)
u, v = filters.replace_outliers(u,
v,
method='localmean',
max_iter=10,
kernel_size=2)
# get window centers coordinates
x, y = pyprocess.get_coordinates(image_size=frame_a.shape,
search_area_size=128,
overlap=32)
# save to a file
tools.save(x, y, u, v, mask, 'test2_%03d.txt' % counter)
tools.display_vector_field('test2_%03d.txt' % counter)
def openpiv_default_run(im1, im2):
""" default settings for OpenPIV analysis using
extended_search_area_piv algorithm for two images
Inputs:
im1,im2 : str,str = path of two image
"""
frame_a = tools.imread(im1)
frame_b = tools.imread(im2)
u, v, sig2noise = process.extended_search_area_piv(
frame_a.astype(np.int32),
frame_b.astype(np.int32),
window_size=32,
overlap=8,
dt=1,
search_area_size=64,
sig2noise_method='peak2peak')
x, y = process.get_coordinates(image_size=frame_a.shape,
window_size=32,
overlap=8)
u, v, mask = validation.sig2noise_val(u, v, sig2noise, threshold=1.3)
u, v = filters.replace_outliers(u,
v,
method='localmean',
max_iter=10,
kernel_size=2)
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=1)
tools.save(x, y, u, v, mask, list_of_images[0] + '.txt')
fig, ax = tools.display_vector_field(list_of_images[0] + '.txt',
on_img=True,
image_name=list_of_images[0],
scaling_factor=1,
ax=None)
def run_single(index, scale=1, src_dir=None, save_dir=None):
frame_a = tools.imread(os.path.join(src_dir, f'{index:06}.tif'))
frame_b = tools.imread(os.path.join(src_dir, f'{index + 1:06}.tif'))
# no background removal will be performed so 'mask' is initialized to 1 everywhere
mask = np.ones(frame_a.shape, dtype=np.int32)
# main algorithm
with warnings.catch_warnings():
warnings.simplefilter("ignore")
x, y, u, v, mask = process.WiDIM(frame_a.astype(np.int32),
frame_b.astype(np.int32),
mask,
min_window_size=MIN_WINDOW_SIZE,
overlap_ratio=0.0,
coarse_factor=2,
dt=DT,
validation_method='mean_velocity',
trust_1st_iter=1,
validation_iter=1,
tolerance=0.4,
nb_iter_max=3,
sig2noise_method='peak2peak')
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=SCALING_FACTOR)
tmp_fname = '.tmp_' + ''.join(random.choices(string.ascii_uppercase + string.digits, k=32))
tools.save(x, y, u, v, mask, filename=tmp_fname)
tools.display_vector_field(tmp_fname, scale=scale, width=LINE_WIDTH) # scale: vector length ratio; width: line width of vector arrows
os.remove(tmp_fname)
# plt.quiver(x, y, u3, v3, color='blue')
if save_dir is not None:
save_path = os.path.join(save_dir, f'{index:06}.pdf')
print(save_path)
plt.savefig(save_path)
u, v = filters.replace_outliers(u,
v,
method='localmean',
max_iter=3,
kernel_size=3)
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=96.52)
# save to a file
tools.save(x,
y,
u,
v,
mask,
'../data/test4/test.txt',
fmt='%9.6f',
delimiter='\t')
tools.display_vector_field('../data/test4/test.txt', scale=50, width=0.002)
# masking using not optimal choice of the methods or parameters:
masked_a, _ = preprocess.dynamic_masking(frame_a,
method='edges',
filter_size=7,
threshold=0.005)
masked_b, _ = preprocess.dynamic_masking(frame_b,
method='intensity',
filter_size=3,
threshold=0.0)
plt.imshow(np.c_[masked_a, masked_b], cmap='gray')
# masking using optimal (manually tuned) set of parameters and the right method:
masked_a, _ = preprocess.dynamic_masking(frame_a,
method='edges',
from openpiv import tools, validation, process, filters, scaling, pyprocess
import numpy as np
frame_a = tools.imread( 'exp1_001_a.bmp' )
frame_b = tools.imread( 'exp1_001_b.bmp' )
u, v, sig2noise = process.extended_search_area_piv( frame_a.astype(np.int32),
frame_b.astype(np.int32), window_size=24, overlap=12, dt=0.02, search_area_size=64,
sig2noise_method='peak2peak' )
x, y = process.get_coordinates( image_size=frame_a.shape, window_size=24, overlap=12 )
u, v, mask = validation.sig2noise_val( u, v, sig2noise, threshold = 2.5 )
u, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2)
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 96.52 )
tools.save(x, y, u, v, mask, 'exp1_001.txt' )
tools.display_vector_field('exp1_001.txt', scale=100, width=0.0025)
u, v, s2n= pyprocess.piv(frame_a, frame_b, corr_method='fft', window_size=24, overlap=12,
dt=0.02, sig2noise_method='peak2peak' )
x, y = pyprocess.get_coordinates( image_size=frame_a.shape, window_size=24, overlap=12 )
u, v, mask = validation.sig2noise_val( u, v, s2n, threshold = 2.5 )
u, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2.5)
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 96.52 )
tools.save(x, y, u, v, mask, 'exp1_002.txt' )
tools.display_vector_field('exp1_002.txt', scale=100, width=0.0025)
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 = imread(os.path.join(settings.filepath_images, file_a))
frame_b = 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.invert is True:
frame_a = invert(frame_a)
frame_b = invert(frame_b)
if settings.show_all_plots:
fig, ax = plt.subplots(1, 1)
ax.imshow(frame_a, cmap=plt.get_cmap('Reds'))
ax.imshow(frame_b, cmap=plt.get_cmap('Blues'), alpha=.5)
plt.show()
if settings.dynamic_masking_method in ("edge", "intensity"):
frame_a, mask_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, mask_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, s2n = first_pass(frame_a, frame_b, settings)
if settings.show_all_plots:
plt.figure()
plt.quiver(x, y, u, -v, color='b')
# plt.gca().invert_yaxis()
# plt.gca().set_aspect(1.)
# plt.title('after first pass, invert')
# plt.show()
# " Image masking "
if settings.image_mask:
image_mask = np.logical_and(mask_a, mask_b)
mask_coords = preprocess.mask_coordinates(image_mask)
# mark those points on the grid of PIV inside the mask
grid_mask = preprocess.prepare_mask_on_grid(x, y, mask_coords)
# mask the velocity
u = np.ma.masked_array(u, mask=grid_mask)
v = np.ma.masked_array(v, mask=grid_mask)
else:
mask_coords = []
u = np.ma.masked_array(u, mask=np.ma.nomask)
v = np.ma.masked_array(v, mask=np.ma.nomask)
if settings.validation_first_pass:
u, v, mask = validation.typical_validation(u, v, s2n, settings)
if settings.show_all_plots:
# plt.figure()
plt.quiver(x, y, u, -v, color='r')
plt.gca().invert_yaxis()
plt.gca().set_aspect(1.)
plt.title('after first pass validation new, inverted')
plt.show()
# "filter to replace the values that where marked by the validation"
if settings.num_iterations == 1 and settings.replace_vectors:
# for multi-pass we cannot have holes in the data
# after the first pass
u, v = filters.replace_outliers(
u,
v,
method=settings.filter_method,
max_iter=settings.max_filter_iteration,
kernel_size=settings.filter_kernel_size,
)
# don't even check if it's true or false
elif settings.num_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:
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.image_mask:
grid_mask = preprocess.prepare_mask_on_grid(x, y, mask_coords)
u = np.ma.masked_array(u, mask=grid_mask)
v = np.ma.masked_array(v, mask=grid_mask)
else:
u = np.ma.masked_array(u, np.ma.nomask)
v = np.ma.masked_array(v, np.ma.nomask)
if settings.show_all_plots:
plt.figure()
plt.quiver(x, y, u, -v)
plt.gca().invert_yaxis()
plt.gca().set_aspect(1.)
plt.title('before multi pass, inverted')
plt.show()
if not isinstance(u, np.ma.MaskedArray):
raise ValueError("Expected masked array")
""" Multi pass """
for i in range(1, settings.num_iterations):
if not isinstance(u, np.ma.MaskedArray):
raise ValueError("Expected masked array")
x, y, u, v, s2n, mask = multipass_img_deform(
frame_a,
frame_b,
i,
x,
y,
u,
v,
settings,
mask_coords=mask_coords)
# If the smoothing is active, we do it at each pass
# but not the last one
if settings.smoothn is True and i < settings.num_iterations - 1:
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 not isinstance(u, np.ma.MaskedArray):
raise ValueError('not a masked array anymore')
if hasattr(settings, 'image_mask') and settings.image_mask:
grid_mask = preprocess.prepare_mask_on_grid(x, y, mask_coords)
u = np.ma.masked_array(u, mask=grid_mask)
v = np.ma.masked_array(v, mask=grid_mask)
else:
u = np.ma.masked_array(u, np.ma.nomask)
v = np.ma.masked_array(v, np.ma.nomask)
if settings.show_all_plots:
plt.figure()
plt.quiver(x, y, u, -1 * v, color='r')
plt.gca().set_aspect(1.)
plt.gca().invert_yaxis()
plt.title('end of the multipass, invert')
plt.show()
if settings.show_all_plots and settings.num_iterations > 1:
plt.figure()
plt.quiver(x, y, u, -v)
plt.gca().invert_yaxis()
plt.gca().set_aspect(1.)
plt.title('after multi pass, before saving, inverted')
plt.show()
# we now use only 0s instead of the image
# masked regions.
# we could do Nan, not sure what is best
u = u.filled(0.)
v = v.filled(0.)
# "scales the results pixel-> meter"
x, y, u, v = scaling.uniform(x,
y,
u,
v,
scaling_factor=settings.scaling_factor)
if settings.image_mask:
grid_mask = preprocess.prepare_mask_on_grid(x, y, mask_coords)
u = np.ma.masked_array(u, mask=grid_mask)
v = np.ma.masked_array(v, mask=grid_mask)
else:
u = np.ma.masked_array(u, np.ma.nomask)
v = np.ma.masked_array(v, np.ma.nomask)
# before saving we conver to the "physically relevant"
# right-hand coordinate system with 0,0 at the bottom left
# x to the right, y upwards
# and so u,v
x, y, u, v = transform_coordinates(x, y, u, v)
# import pdb; pdb.set_trace()
# "save to a file"
tools.save(x,
y,
u,
v,
mask,
os.path.join(save_path, "field_A%03d.txt" % counter),
delimiter="\t")
# "some other stuff that one might want to use"
if settings.show_plot or settings.save_plot:
Name = os.path.join(save_path, "Image_A%03d.png" % counter)
fig, _ = display_vector_field(
os.path.join(save_path, "field_A%03d.txt" % counter),
scale=settings.scale_plot,
)
if settings.save_plot is True:
fig.savefig(Name)
if settings.show_plot is True:
plt.show()
print(f"Image Pair {counter + 1}")
print(file_a.rsplit('/')[-1], file_b.rsplit('/')[-1])
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))
scaling_factor = 100
# we can run it from any folder
path = os.path.dirname(os.path.abspath(__file__))
frame_a = tools.imread( os.path.join(path,'../test2/2image_00.tif'))
frame_b = tools.imread( os.path.join(path,'../test2/2image_01.tif'))
#no background removal will be performed so 'mark' is initialized to 1 everywhere
mark = np.zeros(frame_a.shape, dtype=np.int32)
for I in range(mark.shape[0]):
for J in range(mark.shape[1]):
mark[I,J]=1
#main algorithm
with warnings.catch_warnings():
warnings.simplefilter("ignore")
x,y,u,v, mask=process.WiDIM( frame_a.astype(np.int32), frame_b.astype(np.int32), mark, min_window_size=16, overlap_ratio=0.0, coarse_factor=2, dt=0.02, validation_method='mean_velocity', trust_1st_iter=1, validation_iter=1, tolerance=0.7, nb_iter_max=3, sig2noise_method='peak2peak')
#display results
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = scaling_factor )
tools.save(x, y, u, v, mask, '2image_00.txt' )
tools.display_vector_field('2image_00.txt',on_img=True, image_name=os.path.join(path,'../test2/2image_00.tif'), window_size=16, scaling_factor=scaling_factor, scale=200, width=0.001)
#further validation can be performed to eliminate the few remaining wrong vectors
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
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,
#u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a, frame_b, window_size=32, overlap=8, dt=.1, sig2noise_method='peak2peak' )
x, y = pyprocess.get_coordinates( image_size=frame_a.shape, window_size=32, overlap=8 )
x, y = process.get_coordinates( image_size=frame_a.shape, window_size=32, overlap=8 )
# %%
u, v, mask = validation.sig2noise_val( u, v, sig2noise, threshold = 1.3 )
u, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2)
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 96.52 )
# %%
plt.figure()
plt.quiver(x,y,u,v)
# %%
tools.save(x, y, u, v, mask, 'Y4-S3_Camera000398.txt' )
tools.display_vector_field('Y4-S3_Camera000398.txt', scale=3, width=0.0125)
# frame_vectors = io.imshow(vectors)
# %%
x,y,u,v, mask = process.WiDIM(frame_a.astype(np.int32), frame_b.astype(np.int32), ones_like(frame_a).astype(np.int32), min_window_size=32, overlap_ratio=0.25, coarse_factor=0, dt=0.1, validation_method='mean_velocity', trust_1st_iter=0, validation_iter=0, tolerance=0.7, nb_iter_max=1, sig2noise_method='peak2peak')
# %%
tools.save(x, y, u, v, zeros_like(u), 'Y4-S3_Camera000398.txt' )
tools.display_vector_field('Y4-S3_Camera000398.txt', scale=300, width=0.005)
# %%
x,y,u,v, mask = process.WiDIM(frame_a.astype(np.int32), frame_b.astype(np.int32), ones_like(frame_a).astype(np.int32), min_window_size=16, overlap_ratio=0.25, coarse_factor=2, dt=0.1, validation_method='mean_velocity', trust_1st_iter=1, validation_iter=2, tolerance=0.7, nb_iter_max=4, sig2noise_method='peak2peak')
# %%
tools.save(x, y, u, v, zeros_like(u), 'Y4-S3_Camera000398.txt' )
tools.display_vector_field('Y4-S3_Camera000398.txt', scale=300, width=0.005)
settings.save_plot = False
# Choose wether you want to see the vectorfield or not :True or False
settings.show_plot = False
settings.scale_plot = 50 # select a value to scale the quiver plot of the vectorfield
# run the script with the given settings
# %%
windef.piv(settings)
# %%
for i, f in enumerate(res_list):
fig, ax = plt.subplots(figsize=(12, 12))
tools.display_vector_field(f,
on_img=True,
image_name=image_path / file_list[i],
scaling_factor=1,
scale=50,
width=0.002,
ax=ax,
widim=True)
fig.savefig(f'{str(f)[:-4]}.png', dpi=150)
# settings.frame_pattern_a = f1
# settings.frame_pattern_b = f2
# windef.piv(settings);
# for f1,f2 in zip(file_list[0:-1:2],file_list[1::2]):
# settings.frame_pattern_a = f1
# settings.frame_pattern_b = f2
# windef.piv(settings);
# fig,ax = plt.subplots(figsize=(12,6))
def run_piv(
frame_a,
frame_b,
winsize=16, # pixels, interrogation window size in frame A
searchsize=20, # pixels, search in image B
overlap=8, # pixels, 50% overlap
dt=0.0001, # sec, time interval between pulses
image_check=False,
show_vertical_profiles=False,
figure_export_name='_results.png',
text_export_name="_results.txt",
scale_factor=1,
pixel_density=36.74,
arrow_width=0.001,
show_result=True,
u_bounds=(-100, 100),
v_bounds=(-100, 100)):
u0, v0, sig2noise = pyprocess.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 = pyprocess.get_coordinates(image_size=frame_a.shape,
search_area_size=searchsize,
overlap=overlap)
x, y, u0, v0 = scaling.uniform(
x, y, u0, v0, scaling_factor=pixel_density) # no. pixel per distance
u0, v0, mask = validation.global_val(u0, v0, u_bounds, v_bounds)
u1, v1, mask = validation.sig2noise_val(u0, v0, sig2noise, threshold=1.05)
u3, v3 = filters.replace_outliers(u1,
v1,
method='localmean',
max_iter=10,
kernel_size=3)
#save in the simple ASCII table format
if np.std(u3) < 480:
tools.save(x, y, u3, v3, sig2noise, mask, text_export_name)
if image_check == True:
fig, ax = plt.subplots(2, 1, figsize=(24, 12))
ax[0].imshow(frame_a)
ax[1].imshow(frame_b)
io.imwrite(figure_export_name, frame_a)
if show_result == True:
fig, ax = plt.subplots(figsize=(24, 12))
tools.display_vector_field(
text_export_name,
ax=ax,
scaling_factor=pixel_density,
scale=scale_factor, # scale defines here the arrow length
width=arrow_width, # width is the thickness of the arrow
on_img=True, # overlay on the image
image_name=figure_export_name)
fig.savefig(figure_export_name)
if show_vertical_profiles:
field_shape = pyprocess.get_field_shape(image_size=frame_a.shape,
search_area_size=searchsize,
overlap=overlap)
vertical_profiles(text_export_name, field_shape)
print('Std of u3: %.3f' % np.std(u3))
print('Mean of u3: %.3f' % np.mean(u3))
return np.std(u3)
u2, v2 = filters.replace_outliers(u1,
v1,
method='localmean',
max_iter=10,
kernel_size=2)
# %%
x, y, u3, v3 = scaling.uniform(x, y, u2, v2, scaling_factor=1.)
# %%
tools.save(x, y, u3, v3, mask, 'exp1_001.txt')
# %%
# tools.display_vector_field('exp1_001.txt', scaling_factor=100., width=0.0025)
# %%
# If you need a larger view:
fig, ax = plt.subplots(figsize=(12, 12))
tools.display_vector_field(
'exp1_001.txt',
ax=ax,
scaling_factor=1.0,
scale=3500,
width=0.0045,
on_img=True,
image_name='~/Downloads/bridgepile_wake/frame0001.tif')
# %%
# %%
from openpiv import tools, pyprocess, scaling, validation, filters
import numpy as np
import os
# we can run it from any folder
path = os.path.dirname(os.path.abspath(__file__))
frame_a = tools.imread( os.path.join(path,'../data/test1/exp1_001_a.bmp'))
frame_b = tools.imread( os.path.join(path,'../data/test1/exp1_001_b.bmp'))
frame_a = (frame_a*1024).astype(np.int32)
frame_b = (frame_b*1024).astype(np.int32)
u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a, frame_b, \
window_size=32, overlap=16, dt=0.02, search_area_size=64, sig2noise_method='peak2peak' )
print(u,v,sig2noise)
x, y = pyprocess.get_coordinates( image_size=frame_a.shape, search_area_size=64, overlap=16 )
u, v, mask = validation.sig2noise_val( u, v, sig2noise, threshold = 1.3 )
u, v, mask = validation.global_val( u, v, (-1000, 2000), (-1000, 1000) )
u, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2)
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 96.52 )
tools.save(x, y, u, v, mask, '../data/test1/test_data.vec' )
tools.display_vector_field('../data/test1/test_data.vec', scale=75, width=0.0035)
overlap=12,
dt=0.02,
search_area_size=64,
sig2noise_method='peak2peak')
x, y = process.get_coordinates(image_size=frame_a.shape,
window_size=24,
overlap=12)
u, v, mask = validation.sig2noise_val(u, v, sig2noise, threshold=2.5)
u, v = filters.replace_outliers(u,
v,
method='localmean',
max_iter=10,
kernel_size=2)
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=96.52)
tools.save(x, y, u, v, mask, 'exp1_001_extended.txt')
tools.display_vector_field('exp1_001_extended.txt', scale=100, width=0.0025)
# %%
# %%time
u, v, sig2noise = pyprocess.extended_search_area_piv(
frame_a,
frame_b,
corr_method='fft',
window_size=24,
overlap=12,
dt=0.02,
sig2noise_method='peak2peak')
x, y = pyprocess.get_coordinates(image_size=frame_a.shape,
window_size=24,
overlap=12)
u, v, mask = validation.sig2noise_val(u, v, sig2noise, threshold=2.5)
import os
# we can run it from any folder
path = os.path.dirname(os.path.abspath(__file__))
frame_a = tools.imread(os.path.join(path, '../test1/exp1_001_a.bmp'))
frame_b = tools.imread(os.path.join(path, '../test1/exp1_001_b.bmp'))
frame_a = (frame_a * 1024).astype(np.int32)
frame_b = (frame_b * 1024).astype(np.int32)
u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a, frame_b, \
window_size=32, overlap=16, dt=0.02, search_area_size=64, sig2noise_method='peak2peak' )
print(u, v, sig2noise)
x, y = pyprocess.get_coordinates(image_size=frame_a.shape,
search_area_size=64,
overlap=16)
u, v, mask = validation.sig2noise_val(u, v, sig2noise, threshold=1.3)
u, v, mask = validation.global_val(u, v, (-1000, 2000), (-1000, 1000))
u, v = filters.replace_outliers(u,
v,
method='localmean',
max_iter=10,
kernel_size=2)
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=96.52)
tools.save(x, y, u, v, mask, 'test1.vec')
tools.display_vector_field('test1.vec', scale=75, width=0.0035)
os.chdir(inputFilePath)
data = np.loadtxt('0.txt')
m, n = data.shape
Result = data
Result[:, 2] = 0.
Result[:, 3] = 0.
Result[:, 4] = 0.
for i in range(390):
txtName = str(i) + '.txt'
data = np.loadtxt(txtName)
m, n = data.shape
for j in range(m):
if (data[j][4] == 0):
Result[j][4] += 1.
Result[j][2] += data[j][2]
Result[j][3] += data[j][3]
for i in range(m):
if (Result[i][4] != 0.):
Result[i][2] /= Result[i][4]
Result[i][3] /= Result[i][4]
#%% save the Result data as txt for tecplot
CalibrateData = 1 #micro meter per pixel
Result[:, 0:5] *= CalibrateData
#tools.save(Result[:,0], Result[:,1], Result[:,2], Result[:,3], 'Result.txt')
np.savetxt('result.txt', Result, delimiter=',', fmt='%8f')
tools.display_vector_field('389.txt', scale=1000, width=0.0025)
#%% Check data size
print(data.shape)
sig2noise_ratio,
outliers_mask,
os.path.join(save_path, "field_A%03d.txt" % counter),
delimiter="\t",
)
# "some other stuff that one might want to use"
settings.show_plot = True
settings.save_plot = True
if settings.show_plot is True or settings.save_plot is True:
plt.close("all")
plt.ioff()
filename = os.path.join(save_path, "Image_A%03d.png" % counter)
tools.display_vector_field(
os.path.join(save_path, "field_A%03d.txt" % counter),
scale=settings.scale_plot,
)
if settings.save_plot is True:
plt.savefig(filename)
if settings.show_plot is True:
plt.show()
print("Image Pair " + str(counter+1))
# In[40]:
attrs = vars(settings)
print(', '.join("%s: %s \n" % item for item in attrs.items()))
def quick_piv(self, search_dict, index_a=100, index_b=101, folder=None):
self.show_piv_param()
ns = Namespace(**self.piv_param)
if folder == None:
img_a, img_b = self.read_two_images(search_dict,
index_a=index_a,
index_b=index_b)
location_path = [
x['path'] for x in self.piv_dict_list
if search_dict.items() <= x.items()
]
results_path = os.path.join(self.results_path, *location_path)
try:
os.makedirs(results_path)
except FileExistsError:
pass
else:
try:
file_a_path = os.path.join(self.path, folder,
'frame_%06d.tiff' % index_a)
file_b_path = os.path.join(self.path, folder,
'frame_%06d.tiff' % index_b)
img_a = np.array(Image.open(file_a_path))
img_b = np.array(Image.open(file_b_path))
except:
return None
# crop
img_a = img_a[ns.crop[0]:-ns.crop[1] - 1, ns.crop[2]:-ns.crop[3] - 1]
img_b = img_b[ns.crop[0]:-ns.crop[1] - 1, ns.crop[2]:-ns.crop[3] - 1]
u0, v0, sig2noise = pyprocess.extended_search_area_piv(
img_a.astype(np.int32),
img_b.astype(np.int32),
window_size=ns.winsize,
overlap=ns.overlap,
dt=ns.dt,
search_area_size=ns.searchsize,
sig2noise_method='peak2peak')
x, y = pyprocess.get_coordinates(image_size=img_a.shape,
search_area_size=ns.searchsize,
overlap=ns.overlap)
x, y, u0, v0 = scaling.uniform(
x, y, u0, v0,
scaling_factor=ns.pixel_density) # no. pixel per distance
u0, v0, mask = validation.global_val(
u0, v0, (ns.u_lower_bound, ns.u_upper_bound),
(ns.v_lower_bound, ns.v_upper_bound))
u1, v1, mask = validation.sig2noise_val(u0,
v0,
sig2noise,
threshold=1.01)
u3, v3 = filters.replace_outliers(u1,
v1,
method='localmean',
max_iter=500,
kernel_size=3)
#save in the simple ASCII table format
tools.save(x, y, u3, v3, sig2noise, mask,
os.path.join(results_path, ns.text_export_name))
if ns.image_check == True:
fig, ax = plt.subplots(2, 1, figsize=(24, 12))
ax[0].imshow(img_a)
ax[1].imshow(img_b)
io.imwrite(os.path.join(results_path, ns.figure_export_name), img_a)
if ns.show_result == True:
fig, ax = plt.subplots(figsize=(24, 12))
tools.display_vector_field(
os.path.join(results_path, ns.text_export_name),
ax=ax,
scaling_factor=ns.pixel_density,
scale=ns.scale_factor, # scale defines here the arrow length
width=ns.arrow_width, # width is the thickness of the arrow
on_img=True, # overlay on the image
image_name=os.path.join(results_path, ns.figure_export_name))
fig.savefig(os.path.join(results_path, ns.figure_export_name))
if ns.show_vertical_profiles:
field_shape = pyprocess.get_field_shape(
image_size=img_a.shape,
search_area_size=ns.searchsize,
overlap=ns.overlap)
vertical_profiles(ns.text_export_name, field_shape)
print('Mean of u: %.3f' % np.mean(u3))
print('Std of u: %.3f' % np.std(u3))
print('Mean of v: %.3f' % np.mean(v3))
print('Std of v: %.3f' % np.std(v3))
output = np.array([np.mean(u3), np.std(u3), np.mean(v3), np.std(v3)])
# if np.absolute(np.mean(v3)) < 50:
# output = self.quick_piv(search_dict,index_a = index_a + 1, index_b = index_b + 1)
return x, y, u3, v3
# %%
u2, v2 = filters.replace_outliers(u1,
v1,
method='localmean',
max_iter=10,
kernel_size=2)
# %%
x, y, u3, v3 = scaling.uniform(x, y, u2, v2, scaling_factor=96.52)
# %%
tools.save(x, y, u3, v3, mask, 'exp1_001.txt')
# %%
tools.display_vector_field('exp1_001.txt', scale=50, width=0.0025)
# %%
# If you need a larger view:
fig, ax = plt.subplots(figsize=(12, 12))
tools.display_vector_field('exp1_001.txt',
ax=ax,
scaling_factor=96.52,
scale=50,
width=0.0025,
on_img=True,
image_name='../test1/exp1_001_a.bmp')
# %%
tools.save(x, y, u, v, mask, 'Y4-S3_Camera000398_b.txt')
# %%
# "natural" view without image
fig, ax = plt.subplots(2, 1, figsize=(6, 12))
ax[0].invert_yaxis()
ax[0].quiver(x, y, u, v)
ax[0].set_title(' Sort of natural view ')
ax[1].quiver(x, y, u, -v)
ax[1].set_title('Quiver with 0,0 origin needs `negative` v for display')
# plt.quiver(x,y,u,v)
# %%
tools.display_vector_field('Y4-S3_Camera000398_a.txt',
on_img=True,
image_name='../test3/Y4-S3_Camera000398.tif',
scaling_factor=96.52)
# %%
tools.display_vector_field('Y4-S3_Camera000398_a.txt')
# %%
tools.display_vector_field('Y4-S3_Camera000398_b.txt')
# %%
x, y, u, v, mask = process.WiDIM(frame_a.astype(np.int32),
frame_b.astype(np.int32),
ones_like(frame_a).astype(np.int32),
min_window_size=32,
overlap_ratio=0.25,
coarse_factor=0,
tools.save(
values_u, u_values, u_avg, v_avg, mask,
'C:/Users/SSchurer/Documents/TU_Delft/Thesis/LSPIV/Edited/After rain/OSGGC/amount_of_values.txt'
)
# save the average vectors in a .txt file
tools.save(
x, y, u_avg, v_avg, mask_avg,
'C:/Users/SSchurer/Documents/TU_Delft/Thesis/LSPIV/Edited/After rain/OSGGC/average.txt'
)
# display the vector field that is saved (blue field)
tools.display_vector_field(
'C:/Users/SSchurer/Documents/TU_Delft/Thesis/LSPIV/Edited/After rain/OSGGC/average.txt',
scale=6,
width=0.0035,
)
plt.savefig(
'C:/Users/SSchurer/Documents/TU_Delft/Thesis/LSPIV/Edited/After rain/OSGGC/average.jpg'
)
plt.axis('scaled')
plt.plot(u_values, v_values, 'ko', ms=1)
plt.axis('scaled')
plt.xlabel('u')
plt.ylabel('v')
plt.savefig(
'C:/Users/SSchurer/Documents/TU_Delft/Thesis/LSPIV/Edited/After rain/OSGGC/deviation_U_V_adjusted'
)
ax[0].imshow(frame_a,cmap=plt.cm.gray)
ax[1].imshow(frame_b,cmap=plt.cm.gray)
# %%
winsize = 24 # pixels
searchsize = 64 # pixels, search in image B
overlap = 12 # pixels
dt = 0.02 # sec
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=10, kernel_size=2)
# %%
x, y, u3, v3 = scaling.uniform(x, y, u2, v2, scaling_factor = 96.52 )
# %%
tools.save(x, y, u3, v3, mask, 'exp1_001.txt' )
# %%
tools.display_vector_field('exp1_001.txt', scale=100, width=0.0025)
u_avg = u_sum / N
v_avg = v_sum / N
mask_avg = np.zeros_like(mask)
# save the average vectors in a .txt file
tools.save(
x * winsize, y * winsize, u_avg, v_avg, mask_avg,
'C:/Users/User/Documents/Universiteit STUDIE/TU Delft/MSC Thesis/PythonPIV/Dommel_Images_KH/average.txt'
)
# display the vector field that is saved (blue field)
tools.display_vector_field(
'C:/Users/User/Documents/Universiteit STUDIE/TU Delft/MSC Thesis/PythonPIV/Dommel_Images_KH/average.txt',
# on_img=True,
# image_name='Brenta/'+str(images[0]),
# window_size=winsize,
# scaling_factor=scaling_factor,
scale=6,
width=0.0035,
)
plt.savefig('average.jpg')
plt.axis('scaled')
plt.plot(u, v, 'ko', ms=1)
plt.axis('scaled')
plt.xlabel('u')
plt.ylabel('v')
plt.savefig('deviation_U_V')
fig = plt.figure(figsize=(9, 4))
