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 PIV(I0, I1, winsize, overlap, dt):
""" Normal PIV """
u0, v0, sig2noise = pyprocess.extended_search_area_piv(
I0.astype(np.int32),
I1.astype(np.int32),
window_size=winsize,
overlap=overlap,
dt=dt,
search_area_size=winsize,
sig2noise_method='peak2peak',
)
# get x, y
x, y = pyprocess.get_coordinates(image_size=I0.shape,
search_area_size=winsize,
overlap=overlap,
window_size=winsize)
u1, v1, mask_s2n = validation.sig2noise_val(
u0,
v0,
sig2noise,
threshold=1.05,
)
# replace_outliers
u2, v2 = filters.replace_outliers(
u1,
v1,
method='localmean',
max_iter=3,
kernel_size=3,
)
# median filter smoothing
u3 = medfilt2d(u2, 3)
v3 = medfilt2d(v2, 3)
return x, y, u3, v3
def sig2noise(self):
'''Filter vectors based on the signal to noise threshold.
See:
openpiv.validation.sig2noise_val()
'''
result_fnames = []
for i, f in enumerate(self.p['fnames']):
data = np.loadtxt(f)
u, v, mask = piv_vld.sig2noise_val(
data[:, 2],
data[:, 3],
data[:, 5],
threshold=self.p['sig2noise_threshold'])
save_fname = create_save_vec_fname(path=f, postfix='_sig2noise')
save(data[:, 0],
data[:, 1],
u,
v,
data[:, 4] + mask,
sig2noise=data[:, 5],
filename=save_fname,
delimiter=delimiter)
result_fnames.append(save_fname)
return (result_fnames)
def process_node(i):
DeltaFrame = 1
winsize = 50 # pixels
searchsize = 50 #pixels
overlap = 25 # 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)
tools.save(x, y, u2, v2, mask,
'../muscle10fpsbotleft_results/' + str(i) + '.txt')
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 run_piv(
frame_a,
frame_b,
):
winsize = 64 # pixels, interrogation window size in frame A
searchsize = 68 # pixels, search in image B
overlap = 32 # pixels, 50% overlap
dt = 0.0005 # sec, time interval between pulses
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,
window_size=winsize,
overlap=overlap)
u1, v1, mask = validation.sig2noise_val(u0, v0, sig2noise, threshold=1.05)
u2, v2 = filters.replace_outliers(u1,
v1,
method='localmean',
max_iter=10,
kernel_size=3)
x, y, u3, v3 = scaling.uniform(x, y, u2, v2,
scaling_factor=41.22) # 41.22 microns/pixel
mean_u = np.mean(u3)
mean_v = np.mean(v3)
deficit_u = u3 - mean_u
deficit_v = v3 - mean_v
u_prime = np.mean(np.sqrt(0.5 * (deficit_u**2 + deficit_v**2)))
u_avg = np.mean(np.sqrt(0.5 * (mean_u**2 + mean_v**2)))
turbulence_intensity = u_prime / u_avg
#save in the simple ASCII table format
fname = "./Tables/" + exp_string + ".txt"
# tools.save(x, y, u3, v3, mask, fname)
out = np.vstack([m.ravel() for m in [x, y, u3, v3]])
# print(out)
# np.savetxt(fname,out.T)
with open(fname, "ab") as f:
f.write(b"\n")
np.savetxt(f, out.T)
return turbulence_intensity
def two_images(image_1, image_2, search_area_size=64, window_size=32, overlap=16, dt=0.02):
with open("image_1.bmp", "wb") as fh1:
fh1.write(base64.b64decode(image_1))
with open("image_2.bmp", "wb") as fh2:
fh2.write(base64.b64decode(image_2))
frame_a = tools.imread( 'image_1.bmp' )
frame_b = tools.imread( 'image_2.bmp' )
frame_a = (frame_a*1024).astype(np.int32)
frame_b = (frame_b*1024).astype(np.int32)
if not search_area_size:
search_area_size = 64
if not window_size:
window_size = 32
if not overlap:
overlap = 16
if not dt:
dt = 0.02
u, v, sig2noise = process.extended_search_area_piv( frame_a, frame_b, window_size=window_size,
overlap=overlap, dt=dt, search_area_size=search_area_size, sig2noise_method='peak2peak' )
x, y = process.get_coordinates( image_size=frame_a.shape, window_size=window_size, overlap=overlap )
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 )
file_name_text = 'result.txt'
file_name_png = 'result.png'
if os.path.isfile(file_name_text):
os.remove(file_name_text)
if os.path.isfile(file_name_png):
os.remove(file_name_png)
tools.save(x, y, u, v, mask, file_name_text)
a = np.loadtxt(file_name_text)
fig = plt.figure()
invalid = a[:,4].astype('bool')
fig.canvas.set_window_title('Vector field, '+str(np.count_nonzero(invalid))+' wrong vectors')
valid = ~invalid
plt.quiver(a[invalid,0],a[invalid,1],a[invalid,2],a[invalid,3],color='r',scale=100, width=0.0025)
plt.quiver(a[valid,0],a[valid,1],a[valid,2],a[valid,3],color='b',scale=100, width=0.0025)
plt.draw()
plt.savefig(file_name_png, format="png")
with open(file_name_text, "rb") as resultFileText:
file_reader_text = resultFileText.read()
text_encode = base64.encodestring(file_reader_text)
base64_string_text = str(text_encode, 'utf-8')
with open(file_name_png, "rb") as resultFilePng:
file_reader_image = resultFilePng.read()
image_encode = base64.encodestring(file_reader_image)
base64_string_image = str(image_encode, 'utf-8')
return base64_string_text, base64_string_image
def _piv_frame(self, img1, img2, show=False, **kwargs):
"""
calculate velocity using piv method on two frames
"""
from openpiv.process import extended_search_area_piv, get_coordinates
# from openpiv.scaling import uniform
if self._debug:
print('... [PIV] window size: {}'.format(self._windowSize))
print('... [PIV] overlap: {}'.format(self._overlap))
print('... [PIV] search area size: {}'.format(self._searchArea))
print('... [PIV] threshold: {}'.format(self._piv_threshold))
u, v, sig2noise = extended_search_area_piv(
img1,
img2,
window_size=self._windowSize,
overlap=self._overlap,
dt=self._exposuretime,
search_area_size=self._searchArea,
sig2noise_method='peak2peak')
self._pivx, self._pivy = get_coordinates(image_size=img1.shape,
window_size=self._windowSize,
overlap=self._overlap)
#self._pivy = np.flipud(self._pivy)
#self._pivx, self._pivy, u, v = uniform(self._pivx, self._pivy, u, v, scaling_factor=self._mpp)
if show:
from openpiv.validation import sig2noise_val
from openpiv.filters import replace_outliers
u, v, mask = sig2noise_val(u,
v,
sig2noise,
threshold=self._piv_threshold)
u, v = replace_outliers(u,
v,
method='localmean',
max_iter=10,
kernel_size=2)
# show quiver plot
plt.figure(figsize=(12, 6))
plt.imshow(img1)
plt.quiver(self._pivx, self._pivy, u, v, color='w', pivot='mid')
plt.savefig(self._fname[:-4] + '_piv.png', dpi=100)
if self._debug:
print(
"... [PIV] mean velocity [um/sec]: ({:4.2f}, {:4.2f})".format(
np.mean(u) * self._mpp,
np.mean(v) * self._mpp))
print("... [PIV] mean velocity [pixel/frame]: ({:4.2f}, {:4.2f})".
format(
np.mean(u) * self._exposuretime,
np.mean(v) * self._exposuretime))
return (u, v, sig2noise)
def test_sig2noise_val():
u = np.ones((5, 5))
v = np.ones((5, 5))
threshold = 1.05
s2n = np.ones((5, 5)) * threshold
s2n[2, 2] -= 0.1
u, v, mask = validation.sig2noise_val(u, v, s2n, w=None, threshold=1.05)
assert np.isnan(u[2, 2])
assert np.sum(~np.isnan(u)) == 24
assert mask[0, 0] == False
assert mask[2, 2] == True
def analyzer(frame_a, frame_b, text, plot, num_scene, pathout, scal, zre, xre,
dt):
winsize = 16 # pixels
searchsize = 32 # pixels, search in image b
overlap = 8 # pixels
frame_a = cv2.adaptiveThreshold(frame_a, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 5, 5)
frame_b = cv2.adaptiveThreshold(frame_b, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 5, 5)
#frame_a = cv2.adaptiveThreshold(frame_a,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
#frame_b = cv2.adaptiveThreshold(frame_b,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
plt.imshow(np.c_[frame_a, frame_b], cmap='gray')
plt.savefig(pathout + '/filtered' + str(num_scene) + '.png', dpi=800)
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=scal) # scaling_factor (pixel per meter)
u3 = np.flip(u3, axis=0)
v3 = np.flip(v3, axis=0)
xre = np.linspace(0, xre / 100, len(x[0, :]))
zre = np.linspace(0, zre / 100, len(x[:, 0]))
if plot == 1:
piv_plotting(xre, zre, u3, v3, num_scene, pathout)
if text == 0:
tools.save(x, y, u3, v3, mask,
pathout + '/piv' + str(num_scene) + '.txt')
def two_images(image_1, image_2):
with open("image_1.bmp", "wb") as fh1:
fh1.write(base64.b64decode(image_1))
with open("image_2.bmp", "wb") as fh2:
fh2.write(base64.b64decode(image_2))
frame_a = tools.imread('image_1.bmp')
frame_b = tools.imread('image_2.bmp')
winsize = 32 # pixels
searchsize = 64 # pixels, search in image B
overlap = 12 # pixels
dt = 0.02 # sec
u, v, sig2noise = pyprocess.piv(frame_a.astype(np.int32),
frame_b.astype(np.int32),
window_size=winsize,
overlap=overlap,
dt=dt,
search_size=searchsize,
sig2noise_method='peak2peak')
x, y = pyprocess.get_coordinates(image_size=frame_a.shape,
window_size=searchsize,
overlap=overlap)
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)
file_name = 'result.txt'
if os.path.isfile(file_name):
os.remove(file_name)
tools.save(x, y, u, v, np.zeros_like(u),
file_name) # no masking, all values are valid
with open(file_name, "rb") as resultFile:
file_reader = resultFile.read()
image_encode = base64.encodestring(file_reader)
base64_string = str(image_encode, 'utf-8')
return base64_string
def PIV(image_0, image_1, winsize, searchsize, overlap, frame_rate,
scaling_factor):
frame_0 = image_0
# [0:600, :]
frame_1 = image_1
# [0:600, :]
# Processing the images with interrogation area and search area / cross correlation algortihm
u, v, sig2noise = pyprocess.extended_search_area_piv(
frame_0,
frame_1,
window_size=winsize,
overlap=overlap,
dt=dt,
search_area_size=searchsize,
sig2noise_method='peak2peak')
# Compute the coordinates of the centers of the interrogation windows
x, y = pyprocess.get_coordinates(image_size=frame_0.shape,
window_size=winsize,
overlap=overlap)
# This function actually sets to NaN all those vector for
# which the signal to noise ratio is below 1.3.
# mask is a True/False array, where elements corresponding to invalid vectors have been replace by Nan.
u, v, mask = validation.sig2noise_val(u, v, sig2noise, threshold=1.5)
# Function as described above, removing outliers deviating with more
# than twice the standard deviation
u, v, mask = remove_outliers(u, v, mask)
# Replacing the outliers with interpolation
# u, v = filters.replace_outliers(u,
# v,
# method='nan',
# max_iter=50,
# kernel_size=3)
# Apply an uniform scaling to the flow field to get dimensional units
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=scaling_factor)
return x, y, u, v, mask
def update(winsize, overlap, s2n, s2n_method):
u, v, sig2noise = process.extended_search_area_piv(
frame_a.astype(np.int32),
frame_b.astype(np.int32),
window_size=winsize,
overlap=overlap,
dt=1.0,
search_area_size=64,
sig2noise_method=s2n_method)
x, y = process.get_coordinates(image_size=frame_a.shape,
window_size=winsize,
overlap=overlap)
u, v, mask = validation.sig2noise_val(u, v, sig2noise, threshold=s2n)
# 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 = 1.0 )
# tools.save(x, y, u, v, mask, 'tutorial-part3.txt' )
fig, ax = plt.subplots()
ax.imshow(tools.imread('20110919 exp3 x10 stream 488570.TIF'),
cmap=plt.cm.gray,
origin='upper')
ax.quiver(x, np.flipud(y), u / 10., v / 10., scale=10, color='r', lw=3)
plt.show()
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 piv_analysis(contr,
relax,
outfolder,
scale,
winsize_um=10,
overlap_um=None,
searchsize_um=None,
drift_correction=True,
threshold=1.2,
scale_quiver=None):
"""
Computes deformations between 2 images by crosscorrelation using openpiv (must be installed - see Readme).
Saves several quiver plots and the maximal found deformation for later analysis.
contr: Path to active/contracted image file to calculate deformations between contracted and relaxed state
relax: Path to relaxed image file to calculate deformations between contracted and relaxed state
scale: resolution of image in µm per pixel
winsize_um: size of the search window to be applied in µm
drift_correction: Applies a drift correction before piv analysis
threshold: filters displacement
scale_quiver: can be used to scale the arrows in quiver plot (only for visualization)
Default is None meaning automatically scaling , scale is inverse
"""
from openpiv import tools, process, validation, filters, scaling
winsize = int(winsize_um / scale) # for pixel
# if not specified use defaults
if not overlap_um:
overlap = winsize / 2
else:
overlap = int(overlap_um / scale)
if not searchsize_um:
searchsize = winsize
else:
searchsize = int(searchsize_um / scale)
print(winsize, overlap, searchsize)
# odd winsize raise problems due to the overlap, thefore decrease by one pixel if odd and give warning
if not (winsize % 2) == 0:
print(
'Odd pixelnumbers raise problems due to the overlap: Winsize changed to '
+ str((winsize - 1) * scale) + ' um')
winsize -= 1
# creates folder if it doesn't exist
if not os.path.exists(outfolder):
os.makedirs(outfolder)
#read in images
relax = tools.imread(relax)
contr = tools.imread(contr)
# convert for openpiv
relax = relax.astype(np.int32)
contr = contr.astype(np.int32)
dt = 1 # sec
u0, v0, sig2noise = process.extended_search_area_piv(
relax,
contr,
window_size=winsize,
overlap=overlap,
dt=dt,
search_area_size=searchsize,
sig2noise_method='peak2peak')
x, y = process.get_coordinates(image_size=relax.shape,
window_size=winsize,
overlap=overlap)
# drift correction
if drift_correction:
u0 -= np.nanmean(u0)
v0 -= np.nanmean(v0)
# filtered deformations
u1, v1, mask = validation.sig2noise_val(u0,
v0,
sig2noise,
threshold=threshold)
# save deformations + coords
np.save(outfolder + "/u.npy", u1)
np.save(outfolder + "/v.npy", v1)
np.save(outfolder + "/x.npy", x)
np.save(outfolder + "/y.npy", y)
# show filtered+unfiltered data
plt.figure(figsize=(6, 3))
plt.subplot(121)
plt.quiver(x,
y[::-1],
u0,
v0,
alpha=1,
facecolor='orange',
scale_units='xy',
scale=scale_quiver)
plt.title('unfiltered')
plt.subplot(122)
plt.title('filtered')
plt.quiver(x,
y[::-1],
u1,
v1,
alpha=1,
facecolor='b',
scale_units='xy',
scale=scale_quiver)
plt.savefig(outfolder + '/filtered+unfilterd.png',
bbox_inches='tight',
pad_inches=0)
plt.close()
# save overlay
# different color channels
plt.figure()
overlay = np.zeros((contr.shape[0], contr.shape[1], 3), 'uint8')
overlay[..., 1] = contr #1
overlay[..., 2] = relax #2
plt.imshow(overlay, origin='upper'
) #extent=[0, contr.shape[0], contr.shape[1], 0]) # turn Y
plt.quiver(x,
y,
u1,
v1,
facecolor='orange',
alpha=1,
scale_units='xy',
scale=scale_quiver)
plt.axis('off')
plt.savefig(outfolder + '/overlay-filtered.png',
bbox_inches='tight',
pad_inches=0)
# difference image
plt.figure()
plt.imshow(np.abs(contr - relax), cmap='viridis', origin='upper')
plt.quiver(x,
y,
u1,
v1,
facecolor='orange',
alpha=1,
scale_units='xy',
scale=scale_quiver)
plt.axis('off')
plt.savefig(outfolder + '/difference-img-filtered.png',
bbox_inches='tight',
pad_inches=0)
plt.close()
# save deformation and image data
deformation_unfiltered = np.sqrt(u0**2 + v0**2)
deformation_filtered = np.sqrt(u1**2 + v1**2)
maxdefo_um_unfiltered = np.nanmax(deformation_unfiltered) * scale
maxdefo_um_filtered = np.nanmax(deformation_filtered) * scale
np.savetxt(outfolder + '/maxdefo_um_unfiltered.txt',
[maxdefo_um_unfiltered])
np.savetxt(outfolder + '/maxdefo_um_filtered.txt', [maxdefo_um_filtered])
print('Maximal deformation unfiltered: ', maxdefo_um_unfiltered,
'Maximal deformation filtered: ', maxdefo_um_filtered)
plt.imsave(outfolder + '/raw_contr.png', contr, cmap='gray')
plt.imsave(outfolder + '/raw_relax.png', relax, cmap='gray')
plt.close()
return
dt=(1 / du, 1 / dv, 1 / dw),
subpixel_method='gaussian',
sig2noise_method='peak2peak',
width=2)
# %%
# correcting stage drift between the field of views
u -= np.nanmean(u)
v -= np.nanmean(v)
w -= np.nanmean(w)
# %%
# filtering
uf, vf, wf, mask = sig2noise_val(u,
v,
w=w,
sig2noise=sig2noise,
threshold=signoise_filter)
uf, vf, wf = replace_outliers(uf,
vf,
wf,
max_iter=1,
tol=100,
kernel_size=2,
method='disk')
# %%
# plotting
# representation of the image stacks by maximums projections. The red circle marks the position of the cell
def update_plot(i, ims, ax):
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))
# In[16]:
# let's consider 5% of signoise ratio problems.
sig2noise_threshold = np.percentile(sig2noise_ratio[sig2noise_ratio>0],(5))
print(f"S2N threshold is estimated as {sig2noise_threshold:.3f}")
settings.sig2noise_threshold = sig2noise_threshold
# In[17]:
u, v, mask_s2n = validation.sig2noise_val(
u, v, sig2noise_ratio,
threshold=settings.sig2noise_threshold
)
status_message(u)
# In[18]:
plt.quiver(x,y,u,v,sig2noise_ratio)
plt.quiver(x[mask_s2n],y[mask_s2n],u0[mask_s2n],v0[mask_s2n],color='r')
plt.gca().invert_yaxis()
plt.colorbar()
# In[19]:
def piv_frames(self, topn=-1, show=True):
from openpiv.validation import sig2noise_val
from openpiv.filters import replace_outliers
frames = self._frames[::2]
topn = np.min([len(frames), topn])
frames = frames[:topn]
(ut, vt, s2nt) = self.piv_frame(frame=0, show=False)
for i in tqdm.tqdm(frames[1:]):
(u, v, s2n) = self.piv_frame(frame=i, show=False)
ut += u
vt += v
s2nt += s2n
#print(np.max(u), np.min(u), u.size)
#print(np.max(v), np.max(v), v.size)
ut /= len(frames)
vt /= len(frames)
s2nt /= len(frames)
ut, vt, mask = sig2noise_val(ut,
vt,
s2nt,
threshold=self._piv_threshold)
ut, vt = replace_outliers(ut,
vt,
method='localmean',
max_iter=10,
kernel_size=2)
self._pivu = ut
self._pivv = vt
self._pivs2n = s2nt
self.save_piv()
if show:
fig = plt.figure(figsize=(12, 6))
ax1 = fig.add_subplot(121)
ax1.imshow(self.mean())
ax1.quiver(self._pivx, self._pivy, ut, vt, pivot='mid', color='w')
ax2 = fig.add_subplot(122)
n_u, bins, patches = ax2.hist(ut.flatten() * self._mpp,
bins=20,
normed=1,
facecolor='blue',
alpha=0.75,
label='u')
n_v, bins, patches = ax2.hist(vt.flatten() * self._mpp,
bins=20,
normed=1,
facecolor='green',
alpha=0.75,
label='v')
ax2.annotate(np.mean(ut) * self._mpp,
xy=(np.mean(ut) * self._mpp, np.max(n_u)))
ax2.annotate(np.mean(vt) * self._mpp,
xy=(np.mean(vt) * self._mpp, np.max(n_v)))
plt.legend(loc='best')
plt.tight_layout()
plt.savefig(self._fname[:-4] + '_piv_t.png', dpi=150)
print("... frames: {}, {}".format(frames[0], frames[-1]))
print("... mean velocity [um/sec]: ({:4.2f}, {:4.2f})".format(
np.mean(ut) * self._umtopixel,
np.mean(vt) * self._umtopixel))
print("... mean velocity [pixel/frame]: ({:4.2f}, {:4.2f})".format(
np.mean(ut) * self._dt,
np.mean(vt) * self._dt))
def run(self):
self.is_to_stop = False
self.piv.piv_results_list = []
for i in range(0, len(self.frames_list) - 1, abs(self.jump)):
if self.piv.xy_zoom[0][0] and self.piv.xy_zoom[1][0]:
"""try:"""
frame_a = self.frames_list[i][2][
int(self.piv.xy_zoom[1][0]):int(self.piv.xy_zoom[1][1]),
int(self.piv.xy_zoom[0][0]):int(self.piv.xy_zoom[0][1])]
frame_b = self.frames_list[i + 1][2][
int(self.piv.xy_zoom[1][0]):int(self.piv.xy_zoom[1][1]),
int(self.piv.xy_zoom[0][0]):int(self.piv.xy_zoom[0][1])]
"""
except ValueError:
frame_a = self.frames_list[i][2][int(self.frames_list[i][2].shape[1] - self.piv.xy_zoom[1][0]): int(
self.frames_list[i][2].shape[1] - self.piv.xy_zoom[1][1]),
int(self.piv.xy_zoom[0][0]): int(
self.piv.xy_zoom[0][1])]
frame_b = self.frames_list[i + 1][2][
int(self.frames_list[i + 1][2].shape[1] - self.piv.xy_zoom[1][0]): int(
self.frames_list[i + 1][2].shape[1] - self.piv.xy_zoom[1][1]),
int(self.piv.xy_zoom[0][0]): int(
self.piv.xy_zoom[0][1])]
"""
else:
frame_a = self.frames_list[i][2]
frame_b = self.frames_list[i + 1][2]
try:
self.u, self.v, self.sig2noise = extended_search_area_piv(
frame_a.astype(np.int32),
frame_b.astype(np.int32),
window_size=self.winsize,
overlap=self.overlap,
dt=self.dt,
search_area_size=self.searchsize,
sig2noise_method='peak2peak')
self.x, self.y = get_coordinates(image_size=frame_a.shape,
window_size=self.winsize,
overlap=self.overlap)
self.u, self.v, self.mask = sig2noise_val(self.u,
self.v,
self.sig2noise,
threshold=1.0)
self.u, self.v = replace_outliers(self.u,
self.v,
method='localmean',
max_iter=10,
kernel_size=2)
# self.x, self.y, self.u, self.v = uniform(self.x, self.y, self.u, self.v, scaling_factor=5)
if self.piv.xy_zoom[0][0] and self.piv.xy_zoom[1][0]:
self.x += int(self.piv.xy_zoom[0][0])
self.y += int(self.piv.xy_zoom[1][0])
self.y = np.flip(self.y, 0)
self.x = np.flip(self.x, 0)
except ValueError:
if self.searchsize < self.winsize:
print("0")
self.error_message.setText(
"the search size cannot be smaller than the window size"
)
elif self.overlap > self.winsize:
print("1")
self.error_message.setText(
"Overlap has to be smaller than the window_size")
else:
print("2")
self.error_message.setText("ROI window to small")
self.error_message.exec()
break
self.piv.piv_results_list.append(
[self.x, self.y, self.u, self.v, self.mask])
self.piv.piv_images_list[i][3] = self.piv.piv_results_list[
i // abs(self.jump)]
# data = np.zeros((len(np.ravel(self.u)), 5))
# res_list = [np.ravel(self.x), np.ravel(self.y), np.ravel(self.u), np.ravel(self.v), np.ravel(self.mask)]
# for j in range(0, 4):
# for k in range(len(res_list[j])):
# data[k][j] = res_list[j][k]
# self.piv.piv_images_list[i][4] = data
# save_openpiv_vec(self.piv.piv_images_list[i][1].split('.')[0], data, 'pix', 'dt',
# len(data[0]), len(data))
self.piv.show_plot(i, self.piv.bit, True)
if i == len(self.frames_list) - 2 and self.jump == 1:
self.piv.piv_results_list.append(
[self.x, self.y, self.u, self.v, self.mask])
self.piv.piv_images_list[i +
1][3] = self.piv.piv_results_list[i +
1]
# self.piv.piv_images_list[i + 1][4] = data
self.piv.show_plot(i + 1, self.piv.bit, True)
if self.is_to_stop:
break
self.piv_finished_signal.emit()
import sys
if 'OpenPIV' not in sys.path:
sys.path.append('/Users/alex/Documents/OpenPIV/alexlib/openpiv-python')
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)
import sys
if 'OpenPIV' not in sys.path:
sys.path.append('/Users/alex/Documents/OpenPIV/alexlib/openpiv-python')
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 = 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 )
tools.save(x, y, u, v, mask, 'exp1_001.txt' )
tools.display_vector_field('exp1_001.txt', scale=100, width=0.0025)
u1, v1, sig2noise = pyprocess.piv( frame_a.astype(np.int32),
frame_b.astype(np.int32),
window_size=24, overlap=12, dt=0.02, search_size=64, sig2noise_method='peak2peak' )
# %%
# Use Cython version: process.pyx
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,
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=96.52)
tools.save(x, y, u, v, mask, 'Y4-S3_Camera000398_a.txt')
# %%
# Use Python version, pyprocess:
u, v, sig2noise = pyprocess.extended_search_area_piv(
frame_a.astype(np.int32),
frame_b.astype(np.int32),
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)
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
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
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,
def calc_piv_2_images(frame_a, frame_b, idx, dir_name):
'''
Performs Particle Image Velocimetry (PIV) of two images, and saves an image with PIV on it.
:param frame_a: first image
:param frame_b: consecutive image
:param idx: index of the first frame, for saving and ordering the images
:param dir_name: directory to save the image to
:return: -
'''
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)
u1, v1, mask = validation.sig2noise_val(u0, v0, sig2noise, threshold=1.05)
# to see where is a reasonable limit filter out
# outliers that are very different from the neighbours
u2, v2 = filters.replace_outliers(u1,
v1,
method='localmean',
max_iter=3,
kernel_size=3)
# convert x,y to mm; convert u,v to mm/sec
x, y, u3, v3 = scaling.uniform(
x, y, u2, v2, scaling_factor=scaling_factor) # 96.52 microns/pixel
# 0,0 shall be bottom left, positive rotation rate is counterclockwise
x, y, u3, v3 = tools.transform_coordinates(x, y, u3, v3)
fig, ax = plt.subplots()
im = np.negative(frame_a) # plot negative of the image for more clarity
xmax = np.amax(x) + winsize / (2 * scaling_factor)
ymax = np.amax(y) + winsize / (2 * scaling_factor)
ax.imshow(im, cmap="Greys_r", extent=[0.0, xmax, 0.0, ymax])
invalid = mask.astype("bool")
valid = ~invalid
plt.quiver(x[invalid],
y[invalid],
u3[invalid],
v3[invalid],
color="r",
width=width)
plt.quiver(x[valid],
y[valid],
u3[valid],
v3[valid],
color="b",
width=width)
ax.set_aspect(1.)
plt.title(r'Velocity Vectors Field (Frame #%d) $(\frac{\mu m}{hour})$' %
idx)
plt.savefig(dir_name + "/" + "vec_page%d.png" % idx, dpi=200)
plt.show()
plt.close()
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)
def PIV(frame_0,
frame_1,
winsize,
searchsize,
overlap,
frame_rate,
scaling_factor,
threshold=1.3,
output='fil'):
"""
Particle Image Velocimetry processing for two sequential images.
Input:
------
frame_0 - first frame to indicate potential seeds.
frame_1 - second frame to trace seed displacements.
winsize - size of the individual (square) grid cells in pixels.
searchsize - size of the search area in pixels in which the location with the highest similarity is found.
overlap - overlap over the grid cells in pixels.
frame_rate - frame rate of the video in frames per second (fps).
scaling_factor - amount of pixels per meter.
output - after which step the PIV processing is stopped ('raw', 'fil', or 'int'; default: 'fil')
"""
# determine the timestep between the two sequential frames (1/fps)
dt = 1. / frame_rate
# estimation of seed displacements in x and y direction
# and the corresponding signal-to-noise ratio
u, v, sig2noise = pyprocess.extended_search_area_piv(
frame_0,
frame_1,
window_size=winsize,
overlap=overlap,
dt=dt,
search_area_size=searchsize,
sig2noise_method='peak2peak')
# xy-coordinates of the centre of each grid cell
x, y = pyprocess.get_coordinates(image_size=frame_0.shape,
window_size=winsize,
overlap=overlap)
# if ouput is 'fil' or 'int':
# filter out grid cells with a low signal-to-noise ratio
if output == 'fil' or output == 'int':
u, v, mask = validation.sig2noise_val(u,
v,
sig2noise,
threshold=threshold)
# if output is 'int'
# fill in missing values through interpolation
if output == 'int':
u, v = filters.replace_outliers(u,
v,
method='localmean',
max_iter=50,
kernel_size=3)
# scale results based on the pixels per metres
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor=scaling_factor)
return x, y, u, v, sig2noise
