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 PIV1(I0, I1, winsize, overlap, dt, smooth=True):
u0, v0 = pyprocess.extended_search_area_piv(I0.astype(np.int32),
I1.astype(np.int32),
window_size=winsize,
overlap=overlap,
dt=dt,
search_area_size=winsize)
x, y = pyprocess.get_coordinates(image_size=I0.shape,
search_area_size=winsize,
window_size=winsize,
overlap=overlap)
if smooth == True:
u1 = smoothn(u0)[0]
v1 = smoothn(v0)[0]
frame_data = pd.DataFrame(data=np.array(
[x.flatten(), y.flatten(),
u1.flatten(),
v1.flatten()]).T,
columns=['x', 'y', 'u', 'v'])
else:
frame_data = pd.DataFrame(data=np.array(
[x.flatten(), y.flatten(),
u0.flatten(),
v0.flatten()]).T,
columns=['x', 'y', 'u', 'v'])
return frame_data
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 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 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 piv_example():
"""
PIV example uses examples/test5 vortex PIV data to show the main principles
piv(im1,im2) will create a tmp.vec file with the vector filed in pix/dt
(dt=1) from two images, im1,im2 provided as full path filenames
(TIF is preferable)
"""
# if im1 is None and im2 is None:
im1 = pkg.resource_filename("openpiv", "examples/test5/frame_a.tif")
im2 = pkg.resource_filename("openpiv", "examples/test5/frame_b.tif")
frame_a = tools.imread(im1)
frame_b = tools.imread(im2)
frame_a[0:32, 512 - 32:] = 255
images = []
images.extend([frame_a, frame_b])
fig, ax = plt.subplots()
# ims is a list of lists, each row is a list of artists to draw in the
# current frame; here we are just animating one artist, the image, in
# each frame
ims = []
for i in range(2):
im = ax.imshow(images[i % 2], animated=True, cmap=plt.cm.gray)
ims.append([im])
_ = animation.ArtistAnimation(fig,
ims,
interval=500,
blit=False,
repeat_delay=0)
plt.show()
# import os
vel = pyprocess.extended_search_area_piv(frame_a.astype(np.int32),
frame_b.astype(np.int32),
window_size=32,
search_area_size=64,
overlap=8)
x, y = pyprocess.get_coordinates(image_size=frame_a.shape,
search_area_size=64,
overlap=8)
fig, ax = plt.subplots(1, 2, figsize=(11, 8))
ax[0].imshow(frame_a, cmap=plt.get_cmap("gray"), alpha=0.8)
ax[0].quiver(x, y, vel[0], -vel[1], scale=50, color="r")
ax[1].quiver(x, y[::-1, :], vel[0], -vel[1], scale=50, color="b")
ax[1].set_aspect(1)
# ax[1].invert_yaxis()
plt.show()
return x, y, vel[0], vel[1]
def process(args, bga, bgb, reflection):
file_a, file_b, counter = args
# read images into numpy arrays
frame_a = tools.imread(file_a)
frame_b = tools.imread(file_b)
# removing background and reflections
frame_a = frame_a - bga
frame_b = frame_b - bgb
frame_a[reflection == 255] = 0
frame_b[reflection == 255] = 0
#applying a static mask (taking out the regions where we have walls)
yp = [580, 435, 0, 0, 580, 580, 0, 0, 435, 580]
xp = [570, 570, 680, 780, 780, 0, 0, 105, 230, 230]
pnts = draw.polygon(yp, xp, frame_a.shape)
frame_a[pnts] = 0
frame_b[pnts] = 0
# checking the resulting frame
#fig, ax = plt.subplots(2,2)
#ax[0,0].imshow(frame_a_org, cmap='gray')
#ax[0,1].imshow(frame_a, cmap='gray')
#ax[1,0].imshow(frame_b_org, cmap='gray')
#ax[1,1].imshow(frame_b, cmap='gray')
#plt.tight_layout()
#plt.show()
# main piv processing
u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a, frame_b, \
window_size=48, overlap=16, dt=0.001094, search_area_size=64, sig2noise_method='peak2peak')
x, y = pyprocess.get_coordinates(image_size=frame_a.shape,
window_size=48,
overlap=16)
u, v, mask = validation.local_median_val(u, v, 2000, 2000, size=2)
u, v = filters.replace_outliers(u,
v,
method='localmean',
max_iter=10,
kernel_size=2)
u, *_ = smoothn(u, s=1.0)
v, *_ = smoothn(v, s=1.0)
# saving the results
save_file = tools.create_path(file_a, 'Analysis')
tools.save(x, y, u, v, mask, save_file + '.dat')
def 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 ProcessPIV(args, bga, bgb, reflection, stg):
# read images into numpy arrays
file_a, file_b, counter = args
frame_a = tools.imread(file_a)
frame_b = tools.imread(file_b)
# removing background and reflections
if bgb is not None:
frame_a = frame_a - bga
frame_b = frame_b - bgb
frame_a[reflection == 255] = 0
frame_b[reflection == 255] = 0
#plt.imshow(frame_a, cmap='gray')
#plt.show()
# main piv processing
u, v, s2n = pyprocess.extended_search_area_piv( frame_a, frame_b, \
window_size=stg['WS'], overlap=stg['OL'], dt=stg['DT'], search_area_size=stg['SA'], sig2noise_method='peak2peak')
x, y = pyprocess.get_coordinates(image_size=frame_a.shape,
window_size=stg['WS'],
overlap=stg['OL'])
if stg['BVR'] == 'on':
u, v, mask = validation.local_median_val(u,
v,
stg['MF'][0],
stg['MF'][1],
size=2)
u, v, mask = validation.global_val(u,
v,
u_thresholds=stg['GF'][0],
v_thresholds=stg['GF'][1])
u, v = filters.replace_outliers(u,
v,
method='localmean',
max_iter=10,
kernel_size=2)
u, *_ = smoothn(u, s=0.5)
v, *_ = smoothn(v, s=0.5)
x, y, u, v = scaling.uniform(x, y, u, v, stg['SC'])
# saving the results
save_file = tools.create_path(file_a, 'Analysis')
tools.save(x, y, u, v, s2n, save_file + '.dat')
def two_images(image_1, image_2):
local_dir = os.path.dirname(os.path.realpath(__file__))
newFile_1 = open('teting1.bmp', 'w+b')
newFileByteArray = bytes(image_1)
newFile_1.write(newFileByteArray)
newFile_1.close()
frame_a = tools.imread(local_dir + '/exp1_001_a.bmp')
frame_b = tools.imread(local_dir + '/exp1_001_b.bmp')
fig, ax = plt.subplots(1, 2, figsize=(10, 8))
ax[0].imshow(frame_a, cmap=plt.cm.gray)
ax[1].imshow(frame_b, cmap=plt.cm.gray)
winsize = 32 # pixels
searchsize = 64 # pixels, search in image B
overlap = 12 # pixels
dt = 0.02 # sec
u, v, 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,
window_size=searchsize,
overlap=overlap)
file_name = 'result.txt'
# tools.save(x, y, u, v, np.zeros_like(u), 'exp1_001.txt' ) # no masking, all values are valid
tools.save(x, y, u, v, np.zeros_like(u),
file_name) # no masking, all values are valid
with open(file_name, 'r') as result_file:
data = result_file.read().replace('\n', '').replace('\t', ' ')
return data
def doPIV(frame_a, frame_b, dT = 1.0, win_size = 64, overlap = 32, searchArea = 64, apply_clahe = False):
# Check if image is color or grayscale and convert to grayscale if it is color
try:
imH, imW, channels = np.shape(frame_a)
if(channels > 1):
frame_a = cv2.cvtColor(frame_a , cv2.COLOR_BGR2GRAY)
frame_b = cv2.cvtColor(frame_b , cv2.COLOR_BGR2GRAY)
except:
pass
if(apply_clahe is True):
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(12,12))
frame_a = clahe.apply(frame_a)
frame_b = clahe.apply(frame_b)
u, v, sig2noise = pyprocess.extended_search_area_piv(frame_a.astype(np.int32), frame_b.astype(np.int32), window_size = win_size, overlap = overlap, dt = dT, search_area_size = searchArea, sig2noise_method='peak2mean', normalized_correlation=True)
x, y = pyprocess.get_coordinates(frame_a.shape, win_size, overlap)
return x,y,u,v, sig2noise
def simple_piv(im1, im2, plot=True):
"""
Simplest PIV run on the pair of images using default settings
piv(im1,im2) will create a tmp.vec file with the vector filed in pix/dt
(dt=1) from two images, im1,im2 provided as full path filenames
(TIF is preferable, whatever imageio can read)
"""
if isinstance(im1, str):
im1 = tools.imread(im1)
im2 = tools.imread(im2)
u, v, s2n = pyprocess.extended_search_area_piv(im1.astype(np.int32),
im2.astype(np.int32),
window_size=32,
overlap=16,
search_area_size=32)
x, y = pyprocess.get_coordinates(image_size=im1.shape,
search_area_size=32,
overlap=16)
valid = s2n > np.percentile(s2n, 5)
if plot:
_, ax = plt.subplots(figsize=(6, 6))
ax.imshow(im1, cmap=plt.get_cmap("gray"), alpha=0.5, origin="upper")
ax.quiver(x[valid],
y[valid],
u[valid],
-v[valid],
scale=70,
color='r',
width=.005)
plt.show()
return x, y, u, v
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 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 process(self, args):
"""
Process chain as configured in the GUI.
Parameters
----------
args : tuple
Tuple as expected by the inherited run method:
file_a (str) -- image file a
file_b (str) -- image file b
counter (int) -- index pointing to an element of the filename
list
"""
file_a, file_b, counter = args
frame_a = piv_tls.imread(file_a)
frame_b = piv_tls.imread(file_b)
# Smoothning script borrowed from openpiv.windef
s = self.p['smoothn_val']
def smoothn(u, s):
s = s
u, _, _, _ = piv_smt.smoothn(u, s=s, isrobust=self.p['robust'])
return (u)
# delimiters placed here for safety
delimiter = self.p['separator']
if delimiter == 'tab':
delimiter = '\t'
if delimiter == 'space':
delimiter = ' '
# preprocessing
print('\nPre-pocessing image pair: {}'.format(counter + 1))
if self.p['background_subtract'] \
and self.p['background_type'] == 'minA - minB':
self.background = gen_background(self.p, frame_a, frame_b)
frame_a = frame_a.astype(np.int32)
frame_a = process_images(self,
frame_a,
self.GUI.preprocessing_methods,
background=self.background)
frame_b = frame_b.astype(np.int32)
frame_b = process_images(self,
frame_b,
self.GUI.preprocessing_methods,
background=self.background)
print('Evaluating image pair: {}'.format(counter + 1))
# evaluation first pass
start = time.time()
passes = 1
# setup custom windowing if selected
if self.parameter['custom_windowing']:
corr_window_0 = self.parameter['corr_window_1']
overlap_0 = self.parameter['overlap_1']
for i in range(2, 8):
if self.parameter['pass_%1d' % i]:
passes += 1
else:
break
else:
passes = self.parameter['coarse_factor']
if self.parameter['grid_refinement'] == 'all passes' \
and self.parameter['coarse_factor'] != 1:
corr_window_0 = self.parameter['corr_window'] * \
2**(self.parameter['coarse_factor'] - 1)
overlap_0 = self.parameter['overlap'] * \
2**(self.parameter['coarse_factor'] - 1)
# Refine all passes after first when there are more than 1 pass.
elif self.parameter['grid_refinement'] == '2nd pass on' \
and self.parameter['coarse_factor'] != 1:
corr_window_0 = self.parameter['corr_window'] * \
2**(self.parameter['coarse_factor'] - 2)
overlap_0 = self.parameter['overlap'] * \
2**(self.parameter['coarse_factor'] - 2)
# If >>none<< is selected or something goes wrong, the window
# size would remain the same.
else:
corr_window_0 = self.parameter['corr_window']
overlap_0 = self.parameter['overlap']
overlap_percent = overlap_0 / corr_window_0
sizeX = corr_window_0
u, v, sig2noise = piv_wdf.extended_search_area_piv(
frame_a.astype(np.int32),
frame_b.astype(np.int32),
window_size=corr_window_0,
overlap=overlap_0,
search_area_size=corr_window_0,
width=self.parameter['s2n_mask'],
subpixel_method=self.parameter['subpixel_method'],
sig2noise_method=self.parameter['sig2noise_method'],
correlation_method=self.parameter['corr_method'],
normalized_correlation=self.parameter['normalize_correlation'])
x, y = piv_prc.get_coordinates(frame_a.shape, corr_window_0, overlap_0)
# validating first pass
mask = np.full_like(x, 0)
if self.parameter['fp_vld_global_threshold']:
u, v, Mask = piv_vld.global_val(
u,
v,
u_thresholds=(self.parameter['fp_MinU'],
self.parameter['fp_MaxU']),
v_thresholds=(self.parameter['fp_MinV'],
self.parameter['fp_MaxV']))
# consolidate effects of mask
mask += Mask
if self.parameter['fp_local_med']:
u, v, Mask = piv_vld.local_median_val(
u,
v,
u_threshold=self.parameter['fp_local_med'],
v_threshold=self.parameter['fp_local_med'],
size=self.parameter['fp_local_med_size'])
mask += Mask
if self.parameter['adv_repl']:
u, v = piv_flt.replace_outliers(
u,
v,
method=self.parameter['adv_repl_method'],
max_iter=self.parameter['adv_repl_iter'],
kernel_size=self.parameter['adv_repl_kernel'])
print('Validated first pass result of image pair: {}.'.format(counter +
1))
# smoothning before deformation if 'each pass' is selected
if self.parameter['smoothn_each_pass']:
if self.parameter['smoothn_first_more']:
s *= 2
u = smoothn(u, s)
v = smoothn(v, s)
print('Smoothned pass 1 for image pair: {}.'.format(counter + 1))
s = self.parameter['smoothn_val1']
print('Finished pass 1 for image pair: {}.'.format(counter + 1))
print("window size: " + str(corr_window_0))
print('overlap: ' + str(overlap_0), '\n')
# evaluation of all other passes
if passes != 1:
iterations = passes - 1
for i in range(2, passes + 1):
# setting up the windowing of each pass
if self.parameter['custom_windowing']:
corr_window = self.parameter['corr_window_%1d' % i]
overlap = int(corr_window * overlap_percent)
else:
if self.parameter['grid_refinement'] == 'all passes' or \
self.parameter['grid_refinement'] == '2nd pass on':
corr_window = self.parameter['corr_window'] * \
2**(iterations - 1)
overlap = self.parameter['overlap'] * \
2**(iterations - 1)
else:
corr_window = self.parameter['corr_window']
overlap = self.parameter['overlap']
sizeX = corr_window
# translate settings to windef settings object
piv_wdf_settings = piv_wdf.Settings()
piv_wdf_settings.correlation_method = \
self.parameter['corr_method']
piv_wdf_settings.normalized_correlation = \
self.parameter['normalize_correlation']
piv_wdf_settings.windowsizes = (corr_window, ) * (passes + 1)
piv_wdf_settings.overlap = (overlap, ) * (passes + 1)
piv_wdf_settings.num_iterations = passes
piv_wdf_settings.subpixel_method = \
self.parameter['subpixel_method']
piv_wdf_settings.deformation_method = \
self.parameter['deformation_method']
piv_wdf_settings.interpolation_order = \
self.parameter['interpolation_order']
piv_wdf_settings.sig2noise_validate = True,
piv_wdf_settings.sig2noise_method = \
self.parameter['sig2noise_method']
piv_wdf_settings.sig2noise_mask = self.parameter['s2n_mask']
# do the correlation
x, y, u, v, sig2noise, mask = piv_wdf.multipass_img_deform(
frame_a.astype(np.int32),
frame_b.astype(np.int32),
i, # current iteration
x,
y,
u,
v,
piv_wdf_settings)
# validate other passes
if self.parameter['sp_vld_global_threshold']:
u, v, Mask = piv_vld.global_val(
u,
v,
u_thresholds=(self.parameter['sp_MinU'],
self.parameter['sp_MaxU']),
v_thresholds=(self.parameter['sp_MinV'],
self.parameter['sp_MaxV']))
mask += Mask # consolidate effects of mask
if self.parameter['sp_vld_global_threshold']:
u, v, Mask = piv_vld.global_std(
u, v, std_threshold=self.parameter['sp_std_threshold'])
mask += Mask
if self.parameter['sp_local_med_validation']:
u, v, Mask = piv_vld.local_median_val(
u,
v,
u_threshold=self.parameter['sp_local_med'],
v_threshold=self.parameter['sp_local_med'],
size=self.parameter['sp_local_med_size'])
mask += Mask
if self.parameter['adv_repl']:
u, v = piv_flt.replace_outliers(
u,
v,
method=self.parameter['adv_repl_method'],
max_iter=self.parameter['adv_repl_iter'],
kernel_size=self.parameter['adv_repl_kernel'])
print('Validated pass {} of image pair: {}.'.format(
i, counter + 1))
# smoothning each individual pass if 'each pass' is selected
if self.parameter['smoothn_each_pass']:
u = smoothn(u, s)
v = smoothn(v, s)
print('Smoothned pass {} for image pair: {}.'.format(
i, counter + 1))
print('Finished pass {} for image pair: {}.'.format(
i, counter + 1))
print("window size: " + str(corr_window))
print('overlap: ' + str(overlap), '\n')
iterations -= 1
if self.p['flip_u']:
u = np.flipud(u)
if self.p['flip_v']:
v = np.flipud(v)
if self.p['invert_u']:
u *= -1
if self.p['invert_v']:
v *= -1
# scaling
u = u / self.parameter['dt']
v = v / self.parameter['dt']
x, y, u, v = piv_scl.uniform(x,
y,
u,
v,
scaling_factor=self.parameter['scale'])
end = time.time()
# save data to file.
out = np.vstack([m.ravel() for m in [x, y, u, v, mask, sig2noise]])
np.savetxt(self.save_fnames[counter],
out.T,
fmt='%8.4f',
delimiter=delimiter)
print('Processed image pair: {}'.format(counter + 1))
sizeY = sizeX
sizeX = ((int(frame_a.shape[0] - sizeX) //
(sizeX - (sizeX * overlap_percent))) + 1)
sizeY = ((int(frame_a.shape[1] - sizeY) //
(sizeY - (sizeY * overlap_percent))) + 1)
time_per_vec = _round((((end - start) * 1000) / ((sizeX * sizeY) - 1)),
3)
print('Process time: {} second(s)'.format((_round((end - start), 3))))
print('Number of vectors: {}'.format(int((sizeX * sizeY) - 1)))
print('Time per vector: {} millisecond(s)'.format(time_per_vec))
window_size = 32 #pixels
overlap = 16 # pixels
search_area_size = 64 # pixels
frame_rate = 40 # fps
# process again with the masked images, for comparison# process once with the original images
u, v, sig2noise = pyprocess.extended_search_area_piv(
frame_a.astype(np.int32),
frame_b.astype(np.int32),
window_size=window_size,
overlap=overlap,
dt=1. / frame_rate,
search_area_size=search_area_size,
sig2noise_method='peak2peak')
x, y = pyprocess.get_coordinates(image_size=frame_a.shape,
search_area_size=search_area_size,
overlap=overlap)
u, v, mask = validation.global_val(u, v, (-300., 300.), (-300., 300.))
u, v, mask = validation.sig2noise_val(u, v, sig2noise, threshold=1.1)
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,
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)
u,v,s2n = pyprocess.piv( frame_a, frame_b, corr_method='direct', 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 )
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 )
def multipass_img_deform(
frame_a,
frame_b,
current_iteration,
x_old,
y_old,
u_old,
v_old,
settings,
mask_coords=[],
):
# window_size,
# overlap,
# iterations,
# current_iteration,
# x_old,
# y_old,
# u_old,
# v_old,
# correlation_method="circular",
# normalized_correlation=False,
# subpixel_method="gaussian",
# deformation_method="symmetric",
# sig2noise_method="peak2peak",
# sig2noise_threshold=1.0,
# sig2noise_mask=2,
# interpolation_order=1,
"""
Multi pass of the PIV evaluation.
This function does the PIV evaluation of the second and other passes.
It returns the coordinates of the interrogation window centres,
the displacement u, v for each interrogation window as well as
the signal to noise ratio array (which is full of NaNs if opted out)
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, e.g. 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
in case of the image mask - u_old and v_old are MaskedArrays
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'
interpolation_order : int
the order of the spline interpolation used for the image deformation
mask_coords : list of x,y coordinates (pixels) of the image mask,
default is an empty list
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 horizontal displacement for every interrogation
window [pixels]
u : 2d np.array
array containing the vertical displacement for every interrogation
window it returns values in [pixels]
s2n : 2D np.array of signal to noise ratio values
"""
if not isinstance(u_old, np.ma.MaskedArray):
raise ValueError('Expected masked array')
# calculate the y and y coordinates of the interrogation window centres.
# Hence, the
# edges must be extracted to provide the sufficient input. x_old and y_old
# are the coordinates of the old grid. x_int and y_int are the coordinates
# of the new grid
window_size = settings.windowsizes[current_iteration]
overlap = settings.overlap[current_iteration]
x, y = get_coordinates(frame_a.shape, window_size, overlap)
# The interpolation function dont like meshgrids as input.
# plus the coordinate system for y is now from top to bottom
# and RectBivariateSpline wants an increasing set
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, :]
# interpolating the displacements from the old grid onto the new grid
# y befor x because of numpy works row major
ip = RectBivariateSpline(y_old, x_old, u_old.filled(0.))
u_pre = ip(y_int, x_int)
ip2 = RectBivariateSpline(y_old, x_old, v_old.filled(0.))
v_pre = ip2(y_int, x_int)
# if settings.show_plot:
if settings.show_all_plots:
plt.figure()
plt.quiver(x_old, y_old, u_old, -1 * v_old, color='b')
plt.quiver(x_int, y_int, u_pre, -1 * v_pre, color='r', lw=2)
plt.gca().set_aspect(1.)
plt.gca().invert_yaxis()
plt.title('inside deform, invert')
plt.show()
# @TKauefer added another method to the windowdeformation, 'symmetric'
# splits the onto both frames, takes more effort due to additional
# interpolation however should deliver better results
old_frame_a = frame_a.copy()
old_frame_b = frame_b.copy()
# Image deformation has to occur in image coordinates
# therefore we need to convert the results of the
# previous pass which are stored in the physical units
# and so y from the get_coordinates
if settings.deformation_method == "symmetric":
# this one is doing the image deformation (see above)
x_new, y_new, ut, vt = create_deformation_field(
frame_a, x, y, u_pre, v_pre)
frame_a = scn.map_coordinates(frame_a,
((y_new - vt / 2, x_new - ut / 2)),
order=settings.interpolation_order,
mode='nearest')
frame_b = scn.map_coordinates(frame_b,
((y_new + vt / 2, x_new + ut / 2)),
order=settings.interpolation_order,
mode='nearest')
elif settings.deformation_method == "second image":
frame_b = deform_windows(
frame_b,
x,
y,
u_pre,
-v_pre,
interpolation_order=settings.interpolation_order)
else:
raise Exception("Deformation method is not valid.")
# if settings.show_plot:
if settings.show_all_plots:
if settings.deformation_method == 'symmetric':
plt.figure()
plt.imshow(frame_a - old_frame_a)
plt.show()
plt.figure()
plt.imshow(frame_b - old_frame_b)
plt.show()
# if do_sig2noise is True
# sig2noise_method = sig2noise_method
# else:
# sig2noise_method = None
# so we use here default circular not normalized correlation:
# if we did not want to validate every step, remove the method
if settings.sig2noise_validate is False:
settings.sig2noise_method = None
u, v, s2n = extended_search_area_piv(
frame_a,
frame_b,
window_size=window_size,
overlap=overlap,
width=settings.sig2noise_mask,
subpixel_method=settings.subpixel_method,
sig2noise_method=settings.sig2noise_method,
correlation_method=settings.correlation_method,
normalized_correlation=settings.normalized_correlation,
)
shapes = np.array(get_field_shape(frame_a.shape, window_size, overlap))
u = u.reshape(shapes)
v = v.reshape(shapes)
s2n = s2n.reshape(shapes)
u += u_pre
v += v_pre
# reapply the image mask to the new grid
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)
# validate in the multi-pass by default
u, v, mask = validation.typical_validation(u, v, s2n, settings)
if np.all(mask):
raise ValueError("Something happened in the validation")
if not isinstance(u, np.ma.MaskedArray):
raise ValueError('not a masked array anymore')
if settings.show_all_plots:
plt.figure()
nans = np.nonzero(mask)
plt.quiver(x[~nans], y[~nans], u[~nans], -v[~nans], color='b')
plt.quiver(x[nans], y[nans], u[nans], -v[nans], color='r')
plt.gca().invert_yaxis()
plt.gca().set_aspect(1.)
plt.title('After sig2noise, inverted')
plt.show()
# we have to replace outliers
u, v = filters.replace_outliers(
u,
v,
method=settings.filter_method,
max_iter=settings.max_filter_iteration,
kernel_size=settings.filter_kernel_size,
)
# reapply the image mask to the new grid
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, color='r')
plt.quiver(x, y, u_pre, -1 * v_pre, color='b')
plt.gca().invert_yaxis()
plt.gca().set_aspect(1.)
plt.title(' after replaced outliers, red, invert')
plt.show()
return x, y, u, v, s2n, mask
def first_pass(frame_a, frame_b, settings):
# window_size,
# overlap,
# iterations,
# correlation_method="circular",
# normalized_correlation=False,
# subpixel_method="gaussian",
# do_sig2noise=False,
# sig2noise_method="peak2peak",
# sig2noise_mask=2,
# settings):
"""
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, typically it is 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
"""
# if do_sig2noise is False or iterations != 1:
# sig2noise_method = None # this indicates to get out nans
u, v, s2n = extended_search_area_piv(
frame_a,
frame_b,
window_size=settings.windowsizes[0],
overlap=settings.overlap[0],
search_area_size=settings.windowsizes[0],
width=settings.sig2noise_mask,
subpixel_method=settings.subpixel_method,
sig2noise_method=settings.sig2noise_method,
correlation_method=settings.correlation_method,
normalized_correlation=settings.normalized_correlation)
shapes = np.array(
get_field_shape(frame_a.shape, settings.windowsizes[0],
settings.overlap[0]))
u = u.reshape(shapes)
v = v.reshape(shapes)
s2n = s2n.reshape(shapes)
x, y = get_coordinates(frame_a.shape, settings.windowsizes[0],
settings.overlap[0])
return x, y, u, v, s2n
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
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
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)
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)
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),
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)
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_b.txt')
# %%
# "natural" view without image
fig, ax = plt.subplots(2, 1, figsize=(6, 12))
ax[0].invert_yaxis()
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()
