#!/usr/bin/env python # coding: utf-8 # # 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, pyprocess, validation, filters, scaling import numpy as np import matplotlib.pyplot as plt get_ipython().run_line_magic('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) # In[4]: winsize = 32 # pixels, interrogation window size in frame A searchsize = 38 # pixels, search in image B overlap = 12 # pixels, 50% overlap dt = 0.02 # sec, time interval between pulses u0, v0, sig2noise = pyprocess.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 = pyprocess.get_coordinates( image_size=frame_a.shape, search_area_size=searchsize, overlap=overlap ) # In[6]: u1, v1, mask = validation.sig2noise_val( u0, v0, sig2noise, threshold = 1.05 ) # if you need more detailed look, first create a histogram of sig2noise # plt.hist(sig2noise.flatten()) # to see where is a reasonable limit # In[7]: # filter out outliers that are very different from the # neighbours u2, v2 = filters.replace_outliers( u1, v1, method='localmean', max_iter=3, kernel_size=3) # In[8]: # convert x,y to mm # convert u,v to mm/sec x, y, u3, v3 = scaling.uniform(x, y, u2, v2, scaling_factor = 96.52 ) # 96.52 microns/pixel # 0,0 shall be bottom left, positive rotation rate is counterclockwise x, y, u3, v3 = tools.transform_coordinates(x, y, u3, v3) # In[9]: #save in the simple ASCII table format tools.save(x, y, u3, v3, mask, 'exp1_001.txt' ) # In[10]: fig, ax = plt.subplots(figsize=(8,8)) tools.display_vector_field('exp1_001.txt', ax=ax, scaling_factor=96.52, scale=50, # scale defines here the arrow length width=0.0035, # width is the thickness of the arrow on_img=True, # overlay on the image image_name='../test1/exp1_001_a.bmp'); # ## One could also use some shortcuts # In[11]: from openpiv import piv piv.simple_piv(frame_a, frame_b); # In[12]: piv.piv_example(); # In[ ]: