OpenPIV Tutorial¶

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 inline

import imageio

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,figsize=(12,10))
ax[0].imshow(frame_a,cmap=plt.cm.gray)
ax[1].imshow(frame_b,cmap=plt.cm.gray)

Out[3]:

<matplotlib.image.AxesImage at 0x121ea3810>

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=50, width=0.0025)

Out[10]:

(<Figure size 432x288 with 1 Axes>,
 <matplotlib.axes._subplots.AxesSubplot at 0x1224ef050>)

In [17]:

# If you need a larger view:

fig, ax = plt.subplots(figsize=(12,12))
tools.display_vector_field('exp1_001.txt', ax=ax, scaling_factor=96.52, scale=50, width=0.0025, on_img=True, image_name='../test1/exp1_001_a.bmp');

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