OpenPIV tutorial 1¶

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

In [1]:

from openpiv import tools, process, validation, filters, scaling 

import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook

In [2]:

frame_a  = tools.imread( '../test1/exp1_001_a.bmp' )
frame_b  = tools.imread( '../test1/exp1_001_b.bmp' )

In [3]:

fig,ax = plt.subplots(1,2)
ax[0].imshow(frame_a,cmap=plt.cm.gray)
ax[1].imshow(frame_b,cmap=plt.cm.gray)

Out[3]:

<matplotlib.image.AxesImage at 0x1252279b0>

In [4]:

winsize = 24 # pixels
searchsize = 64  # pixels, search in image B
overlap = 12 # pixels
dt = 0.02 # sec


u0, v0, sig2noise = process.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' )

In [5]:

x, y = process.get_coordinates( image_size=frame_a.shape, window_size=winsize, overlap=overlap )

In [6]:

u1, v1, mask = validation.sig2noise_val( u0, v0, sig2noise, threshold = 1.3 )

In [7]:

u2, v2 = filters.replace_outliers( u1, v1, method='localmean', max_iter=10, kernel_size=2)

In [8]:

x, y, u3, v3 = scaling.uniform(x, y, u2, v2, scaling_factor = 96.52 )

In [9]:

tools.save(x, y, u3, v3, mask, 'exp1_001.txt' )

In [10]:

tools.display_vector_field('exp1_001.txt', scale=100, width=0.0025)