In [1]:
import openpiv.tools
import openpiv.process
import openpiv.scaling
In [2]:
img_a  = openpiv.tools.imread( 'a.jpg' )
img_b  = openpiv.tools.imread( 'b.jpg' )

# cause a.jpg and b.jpg are color images, one needs to convert them to greyscale
# cause the image is too large and the flow is in a small region, we can crop it.
In [3]:
imshow(img_a)
Out[3]:
<matplotlib.image.AxesImage at 0xbed6e90>
In [4]:
frame_a = img_a[220:420,:,0]
frame_b = img_b[220:420,:,0]
imshow(frame_a,cmap=cm.gray)
Out[4]:
<matplotlib.image.AxesImage at 0xbf2fc90>
In [5]:
u, v, sig2noise = openpiv.process.extended_search_area_piv( frame_a, frame_b, window_size=32, overlap=16, dt=0.02, search_area_size=64, sig2noise_method='peak2peak' )

x, y = openpiv.process.get_coordinates( image_size=frame_a.shape, window_size=32, overlap=16 )

u, v, mask = openpiv.validation.sig2noise_val( u, v, sig2noise, threshold = 1.3 )

u, v = openpiv.filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2)

x, y, u, v = openpiv.scaling.uniform(x, y, u, v, scaling_factor = 1.0 )

openpiv.tools.save(x, y, u, v, mask, 'tutorial-part3.txt' )

# openpiv.tools.display_vector_field('tutorial-part3.txt', scale=100, width=0.0025)
In [18]:
ax = axes()
quiver(x,y,u,v,(u**2+v**2)**(0.5))
axis('tight')
ax.set_aspect(.9)
f = gcf()
f.set_size_inches(15,3)
colorbar(orientation='horizontal')
Out[18]:
<matplotlib.colorbar.Colorbar instance at 0xcfad3a0>
In [13]:
 
In [ ]: