import openpiv.tools
import openpiv.process
import openpiv.scaling
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.
imshow(img_a)
<matplotlib.image.AxesImage at 0xbed6e90>
frame_a = img_a[220:420,:,0]
frame_b = img_b[220:420,:,0]
imshow(frame_a,cmap=cm.gray)
<matplotlib.image.AxesImage at 0xbf2fc90>
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)
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')
<matplotlib.colorbar.Colorbar instance at 0xcfad3a0>