def test_mask_coordinates():
test_directory = os.path.split(os.path.abspath(__file__))[0]
img = rgb2gray(rgba2rgb(imread(os.path.join(test_directory, "moon.png"))))
img1, mask = dynamic_masking(img_as_float(img), method="intensity")
mask_coords = mask_coordinates(mask, 1.5, 3)
assert (np.allclose(
mask_coords,
np.array([[127., 17.], [101., 16.], [78., 22.], [69., 28.], [51., 48.],
[43., 70.], [43., 90.], [48., 108.],
[57.,
127.]]))) # it has to fail so we remember to make a test
def test_multi_pass_circ():
""" test fot the multipass """
settings.windowsizes = (64, 64, 16)
settings.overlap = (32, 32, 8)
settings.num_iterations = 2
settings.interpolation_order = 3
settings.validation_first_pass = True
settings.sig2noise_validate = False
# ettings.show_all_plots = True
x, y, u, v, s2n = windef.first_pass(
frame_a,
frame_b,
settings,
)
print("first pass\n")
print("\n", x, y, u, v, s2n)
assert np.allclose(u, shift_u, atol = threshold)
assert np.allclose(v, shift_v, atol = threshold)
if settings.image_mask:
image_mask = np.logical_and(mask_a, mask_b)
mask_coords = preprocess.mask_coordinates(image_mask)
# mark those points on the grid of PIV inside the mask
grid_mask = preprocess.prepare_mask_on_grid(x,y,mask_coords)
# mask the velocity
u = np.ma.masked_array(u, mask=grid_mask)
v = np.ma.masked_array(v, mask=grid_mask)
else:
mask_coords = []
u = np.ma.masked_array(u, mask=np.ma.nomask)
v = np.ma.masked_array(v, mask=np.ma.nomask)
for i in range(1,settings.num_iterations):
x, y, u, v, s2n, _ = windef.multipass_img_deform(
frame_a,
frame_b,
i,
x,
y,
u,
v,
settings
)
print(f"Pass {i}\n")
print(x)
print(y)
print(u)
print(v)
print(s2n)
assert np.mean(np.abs(u - shift_u)) < threshold
assert np.mean(np.abs(v - shift_v)) < threshold
def func(args):
"""A function to process each image pair."""
# this line is REQUIRED for multiprocessing to work
# always use it in your custom function
file_a, file_b, counter = args
# counter2=str(counter2)
#####################
# Here goes you code
#####################
" read images into numpy arrays"
frame_a = imread(os.path.join(settings.filepath_images, file_a))
frame_b = imread(os.path.join(settings.filepath_images, file_b))
# Miguel: I just had a quick look, and I do not understand the reason
# for this step.
# I propose to remove it.
# frame_a = (frame_a*1024).astype(np.int32)
# frame_b = (frame_b*1024).astype(np.int32)
" crop to ROI"
if settings.ROI == "full":
frame_a = frame_a
frame_b = frame_b
else:
frame_a = frame_a[settings.ROI[0]:settings.ROI[1],
settings.ROI[2]:settings.ROI[3]]
frame_b = frame_b[settings.ROI[0]:settings.ROI[1],
settings.ROI[2]:settings.ROI[3]]
if settings.invert is True:
frame_a = invert(frame_a)
frame_b = invert(frame_b)
if settings.show_all_plots:
fig, ax = plt.subplots(1, 1)
ax.imshow(frame_a, cmap=plt.get_cmap('Reds'))
ax.imshow(frame_b, cmap=plt.get_cmap('Blues'), alpha=.5)
plt.show()
if settings.dynamic_masking_method in ("edge", "intensity"):
frame_a, mask_a = preprocess.dynamic_masking(
frame_a,
method=settings.dynamic_masking_method,
filter_size=settings.dynamic_masking_filter_size,
threshold=settings.dynamic_masking_threshold,
)
frame_b, mask_b = preprocess.dynamic_masking(
frame_b,
method=settings.dynamic_masking_method,
filter_size=settings.dynamic_masking_filter_size,
threshold=settings.dynamic_masking_threshold,
)
# "first pass"
x, y, u, v, s2n = first_pass(frame_a, frame_b, settings)
if settings.show_all_plots:
plt.figure()
plt.quiver(x, y, u, -v, color='b')
# plt.gca().invert_yaxis()
# plt.gca().set_aspect(1.)
# plt.title('after first pass, invert')
# plt.show()
# " Image masking "
if settings.image_mask:
image_mask = np.logical_and(mask_a, mask_b)
mask_coords = preprocess.mask_coordinates(image_mask)
# mark those points on the grid of PIV inside the mask
grid_mask = preprocess.prepare_mask_on_grid(x, y, mask_coords)
# mask the velocity
u = np.ma.masked_array(u, mask=grid_mask)
v = np.ma.masked_array(v, mask=grid_mask)
else:
mask_coords = []
u = np.ma.masked_array(u, mask=np.ma.nomask)
v = np.ma.masked_array(v, mask=np.ma.nomask)
if settings.validation_first_pass:
u, v, mask = validation.typical_validation(u, v, s2n, settings)
if settings.show_all_plots:
# plt.figure()
plt.quiver(x, y, u, -v, color='r')
plt.gca().invert_yaxis()
plt.gca().set_aspect(1.)
plt.title('after first pass validation new, inverted')
plt.show()
# "filter to replace the values that where marked by the validation"
if settings.num_iterations == 1 and settings.replace_vectors:
# for multi-pass we cannot have holes in the data
# after the first pass
u, v = filters.replace_outliers(
u,
v,
method=settings.filter_method,
max_iter=settings.max_filter_iteration,
kernel_size=settings.filter_kernel_size,
)
# don't even check if it's true or false
elif settings.num_iterations > 1:
u, v = filters.replace_outliers(
u,
v,
method=settings.filter_method,
max_iter=settings.max_filter_iteration,
kernel_size=settings.filter_kernel_size,
)
# "adding masks to add the effect of all the validations"
if settings.smoothn:
u, dummy_u1, dummy_u2, dummy_u3 = smoothn.smoothn(
u, s=settings.smoothn_p)
v, dummy_v1, dummy_v2, dummy_v3 = smoothn.smoothn(
v, s=settings.smoothn_p)
if settings.image_mask:
grid_mask = preprocess.prepare_mask_on_grid(x, y, mask_coords)
u = np.ma.masked_array(u, mask=grid_mask)
v = np.ma.masked_array(v, mask=grid_mask)
else:
u = np.ma.masked_array(u, np.ma.nomask)
v = np.ma.masked_array(v, np.ma.nomask)
if settings.show_all_plots:
plt.figure()
plt.quiver(x, y, u, -v)
plt.gca().invert_yaxis()
plt.gca().set_aspect(1.)
plt.title('before multi pass, inverted')
plt.show()
if not isinstance(u, np.ma.MaskedArray):
raise ValueError("Expected masked array")
""" Multi pass """
for i in range(1, settings.num_iterations):
if not isinstance(u, np.ma.MaskedArray):
raise ValueError("Expected masked array")
x, y, u, v, s2n, mask = multipass_img_deform(
frame_a,
frame_b,
i,
x,
y,
u,
v,
settings,
mask_coords=mask_coords)
# If the smoothing is active, we do it at each pass
# but not the last one
if settings.smoothn is True and i < settings.num_iterations - 1:
u, dummy_u1, dummy_u2, dummy_u3 = smoothn.smoothn(
u, s=settings.smoothn_p)
v, dummy_v1, dummy_v2, dummy_v3 = smoothn.smoothn(
v, s=settings.smoothn_p)
if not isinstance(u, np.ma.MaskedArray):
raise ValueError('not a masked array anymore')
if hasattr(settings, 'image_mask') and settings.image_mask:
grid_mask = preprocess.prepare_mask_on_grid(x, y, mask_coords)
u = np.ma.masked_array(u, mask=grid_mask)
v = np.ma.masked_array(v, mask=grid_mask)
else:
u = np.ma.masked_array(u, np.ma.nomask)
v = np.ma.masked_array(v, np.ma.nomask)
if settings.show_all_plots:
plt.figure()
plt.quiver(x, y, u, -1 * v, color='r')
plt.gca().set_aspect(1.)
plt.gca().invert_yaxis()
plt.title('end of the multipass, invert')
plt.show()
if settings.show_all_plots and settings.num_iterations > 1:
plt.figure()
plt.quiver(x, y, u, -v)
plt.gca().invert_yaxis()
plt.gca().set_aspect(1.)
plt.title('after multi pass, before saving, inverted')
plt.show()
# we now use only 0s instead of the image
# masked regions.
# we could do Nan, not sure what is best
u = u.filled(0.)
v = v.filled(0.)
# "scales the results pixel-> meter"
x, y, u, v = scaling.uniform(x,
y,
u,
v,
scaling_factor=settings.scaling_factor)
if settings.image_mask:
grid_mask = preprocess.prepare_mask_on_grid(x, y, mask_coords)
u = np.ma.masked_array(u, mask=grid_mask)
v = np.ma.masked_array(v, mask=grid_mask)
else:
u = np.ma.masked_array(u, np.ma.nomask)
v = np.ma.masked_array(v, np.ma.nomask)
# before saving we conver to the "physically relevant"
# right-hand coordinate system with 0,0 at the bottom left
# x to the right, y upwards
# and so u,v
x, y, u, v = transform_coordinates(x, y, u, v)
# import pdb; pdb.set_trace()
# "save to a file"
tools.save(x,
y,
u,
v,
mask,
os.path.join(save_path, "field_A%03d.txt" % counter),
delimiter="\t")
# "some other stuff that one might want to use"
if settings.show_plot or settings.save_plot:
Name = os.path.join(save_path, "Image_A%03d.png" % counter)
fig, _ = display_vector_field(
os.path.join(save_path, "field_A%03d.txt" % counter),
scale=settings.scale_plot,
)
if settings.save_plot is True:
fig.savefig(Name)
if settings.show_plot is True:
plt.show()
print(f"Image Pair {counter + 1}")
print(file_a.rsplit('/')[-1], file_b.rsplit('/')[-1])
# In[7]: # let's combine the two masks if the body is slightly moving image_mask = np.logical_and(image_mask_a, image_mask_b) plt.imshow(image_mask) # ## Exract coordinates of the mask as a list of coordinates of a polygon # In[8]: mask_coords = mask_coordinates(image_mask) # ## Run the first pass # # We use typically the most robust approach: linear correlation (with zero padding) # and normalized correlation function (0..1) # In[9]: # In order to convert the image mask to the data mask in x,y # coordinates, we have to either run first pass or # use get_coordinates # Since we do not know how to use the image_mask in the # first pass with the vectorized correlations, i.e. how to