In this tutorial we read the pair of images using imread
, compare them visually
and process using OpenPIV. Here the import is using directly the basic functions and methods
from openpiv.tools import imread, save, display_vector_field
from openpiv.process import extended_search_area_piv, get_coordinates
from openpiv.validation import sig2noise_val
from openpiv.filters import replace_outliers
from openpiv.scaling import uniform
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
frame_a = imread( 'exp1_001_a.bmp' )
frame_b = imread( 'exp1_001_b.bmp' )
fig,ax = plt.subplots(1,2)
ax[0].imshow(frame_a,cmap=plt.cm.gray)
ax[1].imshow(frame_b,cmap=plt.cm.gray)
<matplotlib.image.AxesImage at 0x1101172e8>
winsize = 24 # pixels
searchsize = 64 # pixels, search in image B
overlap = 12 # pixels
dt = 0.02 # sec
u0, v0, sig2noise = 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 = get_coordinates( image_size=frame_a.shape, window_size=winsize, overlap=overlap )
u1, v1, mask = sig2noise_val( u0, v0, sig2noise, threshold = 1.3 )
u2, v2 = replace_outliers( u1, v1, method='localmean', max_iter=10, kernel_size=2)
x, y, u3, v3 = uniform(x, y, u2, v2, scaling_factor = 96.52 )
save(x, y, u3, v3, mask, 'exp1_001.txt' )
display_vector_field('exp1_001.txt', scale=100, width=0.0025)