def test_process_extended_search_area():
""" test of the extended area PIV from Cython """
frame_a, frame_b = create_pair(image_size=64)
u, v, _ = piv(frame_a, frame_b, window_size=16,
search_area_size=32, dt=2., overlap=0)
assert np.allclose(u, shift_u/2., atol=threshold)
assert np.allclose(v, shift_v/2., atol=threshold)
def test_extended_search_area_overlap():
""" test of the extended area PIV with different overlap """
frame_a = np.zeros((64, 64))
frame_a = random_noise(frame_a)
frame_a = img_as_ubyte(frame_a)
frame_b = np.roll(np.roll(frame_a, 3, axis=1), 2, axis=0)
u, v = piv(frame_a, frame_b, window_size=16, search_size=32, overlap=8)
# print u,v
assert (np.max(np.abs(u - 3) + np.abs(v + 2)) <= 0.3)
def test_validation_peak2peak():
"""test of the simplest PIV run
default window_size = 32
"""
_, _, s2n = piv(frame_a,
frame_b,
window_size=32,
sig2noise_method="peak2peak")
assert np.allclose(np.min(s2n), 1.24009)
def test_extended_search_area_overlap():
""" test of the extended area PIV with different overlap """
frame_a = np.zeros((64,64))
frame_a = random_noise(frame_a)
frame_a = img_as_ubyte(frame_a)
frame_b = np.roll(np.roll(frame_a,3,axis=1),2,axis=0)
u,v = piv(frame_a,frame_b,window_size=16,search_size=32,overlap=8)
# print u,v
assert(np.max(np.abs(u-3)+np.abs(v+2)) <= 0.3)
def test_extended_search_area_sig2noise():
""" test of the extended area PIV with sig2peak """
frame_a = np.zeros((64,64))
frame_a = random_noise(frame_a)
frame_a = img_as_ubyte(frame_a)
frame_b = np.roll(np.roll(frame_a,3,axis=1),2,axis=0)
u,v,s2n = piv(frame_a,frame_b,window_size=16,search_size=32,
sig2noise_method='peak2peak')
assert(np.max(np.abs(u-3)+np.abs(v+2)) <= 0.2)
def test_validation_peak2mean():
"""test of the simplest PIV run
default window_size = 32
"""
_, _, s2n = piv(frame_a,
frame_b,
window_size=32,
sig2noise_method="peak2mean")
assert np.allclose(s2n.min(), 1.443882)
def test_piv_smaller_window():
""" test of the search area larger than the window """
frame_a = np.zeros((32, 32))
frame_a = random_noise(frame_a)
frame_a = img_as_ubyte(frame_a)
frame_b = np.roll(np.roll(frame_a, -3, axis=1), -2, axis=0)
u, v = piv(frame_a, frame_b, window_size=16, search_size=32)
# print u,v
assert (np.max(np.abs(u + 3)) < 0.2)
assert (np.max(np.abs(v - 2)) < 0.2)
def test_piv():
""" test of the simplest PIV run """
frame_a = np.zeros((32, 32))
frame_a = random_noise(frame_a)
frame_a = img_as_ubyte(frame_a)
frame_b = np.roll(np.roll(frame_a, 3, axis=1), 2, axis=0)
u, v = piv(frame_a, frame_b, window_size=32)
# print u,v
assert (np.max(np.abs(u - 3)) < 0.2)
assert (np.max(np.abs(v + 2)) < 0.2)
def test_piv():
""" test of the simplest PIV run """
frame_a = np.zeros((32,32))
frame_a = random_noise(frame_a)
frame_a = img_as_ubyte(frame_a)
frame_b = np.roll(np.roll(frame_a,3,axis=1),2,axis=0)
u,v = piv(frame_a,frame_b,window_size=32)
# print u,v
assert(np.max(np.abs(u-3)) < 0.2)
assert(np.max(np.abs(v+2)) < 0.2)
def test_piv_smaller_window():
""" test of the search area larger than the window """
frame_a = np.zeros((32,32))
frame_a = random_noise(frame_a)
frame_a = img_as_ubyte(frame_a)
frame_b = np.roll(np.roll(frame_a,-3,axis=1),-2,axis=0)
u,v = piv(frame_a,frame_b,window_size=16,search_size=32)
# print u,v
assert(np.max(np.abs(u+3)) < 0.2)
assert(np.max(np.abs(v-2)) < 0.2)
def test_extended_search_area_overlap():
""" test of the extended area PIV with different overlap """
frame_a, frame_b = create_pair(image_size=72)
u, v, _ = piv(frame_a, frame_b,
window_size=16,
search_area_size=32,
overlap=8)
print(f"\n u={u}\n v={v}\n")
assert np.allclose(u, shift_u, atol=threshold)
assert np.allclose(v, shift_v, atol=threshold)
def test_extended_search_area():
""" test of the extended area PIV with larger image """
frame_a, frame_b = create_pair(image_size=64, u=-3.5, v=-2.1)
u, v, _ = piv(frame_a, frame_b,
window_size=16,
search_area_size=32,
overlap=0)
assert np.allclose(u, -3.5, atol=threshold)
assert np.allclose(v, -2.1, atol=threshold)
def test_piv():
"""test of the simplest PIV run
default window_size = 32
"""
frame_a, frame_b = create_pair(image_size=32)
# extended_search_area_piv returns image based coordinate system
u, v, _ = piv(frame_a, frame_b, window_size=32)
print(u, v)
assert np.allclose(u, shift_u, atol=threshold)
assert np.allclose(v, shift_v, atol=threshold)
def test_extended_search_area_sig2noise():
""" test of the extended area PIV with sig2peak """
frame_a, frame_b = create_pair(image_size=64, u=-3.5, v=2.1)
u, v, s2n = piv(frame_a,
frame_b,
window_size=16,
search_area_size=32,
sig2noise_method="peak2peak",
subpixel_method="gaussian")
assert np.allclose(u, -3.5, atol=threshold)
assert np.allclose(v, 2.1, atol=threshold)
def test_extended_search_area_sig2noise():
""" test of the extended area PIV with sig2peak """
frame_a = np.zeros((64, 64))
frame_a = random_noise(frame_a)
frame_a = img_as_ubyte(frame_a)
frame_b = np.roll(np.roll(frame_a, 3, axis=1), 2, axis=0)
u, v, s2n = piv(frame_a,
frame_b,
window_size=16,
search_size=32,
sig2noise_method='peak2peak')
assert (np.max(np.abs(u - 3) + np.abs(v + 2)) <= 0.2)
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
u, v = process.extended_search_area_piv(frame_a.astype(np.int32),
frame_b.astype(np.int32),
window_size=24,
overlap=12,
search_area_size=32,
dt=1.)
plt.figure()
plt.quiver(u, v)
plt.axis('equal')
plt.show()
u1, v1 = pyprocess.piv(frame_a,
frame_b,
window_size=32,
search_size=48,
overlap=24)
# x, y = process.get_coordinates( image_size=frame_a.shape, window_size=24, overlap=0)
plt.figure()
plt.quiver(u1, v1, v1)
plt.axis('equal')
plt.show()
u2, v2 = pyprocess.piv(frame_a, frame_b, corr_method='direct', window_size=32)
# x, y = process.get_coordinates( image_size=frame_a.shape, window_size=24, overlap=0)
plt.figure()
def test_piv_smaller_window():
""" test of the search area larger than the window """
frame_a, frame_b = create_pair(image_size=32, u=-3.5, v=-2.1)
u, v, _ = piv(frame_a, frame_b, window_size=16, search_area_size=32)
assert np.allclose(u, -3.5, atol=threshold)
assert np.allclose(v, -2.1, atol=threshold)
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' )
x, y = process.get_coordinates( image_size=frame_a.shape, window_size=24, overlap=12 )
u1, v1, mask = validation.sig2noise_val( u1, v1, sig2noise, threshold = 1.3 )
u1, v1 = filters.replace_outliers( u1, v1, method='localmean', max_iter=10, kernel_size=2)
x, y, u1, v1 = scaling.uniform(x, y, u1, v1, scaling_factor = 96.52 )
tools.save(x, y, u1, v1, mask, 'exp1_001_1.txt' )
tools.display_vector_field('exp1_001_1.txt', scale=100, width=0.0025)
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)
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)
#frame_b = frame_b[:256,:]
plt.figure()
plt.imshow(np.c_[frame_a,frame_b],cmap=plt.cm.gray)
plt.show()
u, v = process.extended_search_area_piv(frame_a.astype(np.int32),
frame_b.astype(np.int32), window_size=24, overlap=12, search_area_size=32, dt = 1.)
plt.figure()
plt.quiver(u,v)
plt.axis('equal')
plt.show()
u1, v1 = pyprocess.piv(frame_a, frame_b, window_size=32, search_size=48, overlap=24)
# x, y = process.get_coordinates( image_size=frame_a.shape, window_size=24, overlap=0)
plt.figure()
plt.quiver(u1,v1,v1)
plt.axis('equal')
plt.show()
u2, v2 = pyprocess.piv(frame_a, frame_b, corr_method = 'direct', window_size=32)
# x, y = process.get_coordinates( image_size=frame_a.shape, window_size=24, overlap=0)
plt.figure()
plt.quiver(u2,v2,v2)
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 = 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')
x, y = process.get_coordinates(image_size=frame_a.shape,
window_size=24,
overlap=12)
u1, v1, mask = validation.sig2noise_val(u1, v1, sig2noise, threshold=1.3)
u1, v1 = filters.replace_outliers(u1,
v1,
method='localmean',
max_iter=10,
kernel_size=2)
