#!/usr/bin/env python
# coding: utf-8

# In[1]:


# import sys
# sys.path.append('/Users/alex/Documents/OpenPIV/openpiv-python')


import openpiv.tools
import openpiv.process
import openpiv.scaling

import numpy as np
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


frame_a  = openpiv.tools.imread( '/Users/alex/Downloads/Movie1_Scene1_Import VIDEO_079.png' )
frame_b  = openpiv.tools.imread( '/Users/alex/Downloads/Movie1_Scene1_Import VIDEO_080.png' )


# 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)


# In[4]:


winsize = 32 # pixels
searchsize = 64  # pixels, search in image B
overlap = 16 # pixels
dt = 1./30. # sec


u0, v0, sig2noise = openpiv.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 = openpiv.process.get_coordinates( image_size=frame_a.shape, window_size=winsize, overlap=overlap )


# In[6]:


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


# In[7]:


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


# In[8]:


x, y, u3, v3 = openpiv.scaling.uniform(x, y, u2, v2, scaling_factor = 5.9039*30 )


# In[9]:


openpiv.tools.save(x, y, u3, v3, mask, '/Users/alex/Downloads/Movie1_Scene1_Import VIDEO_080.txt' )


# In[10]:


openpiv.tools.display_vector_field('/Users/alex/Downloads/Movie1_Scene1_Import VIDEO_080.txt', scale=50, width=0.0025)


# In[ ]:


u0d, v0d =  openpiv.pyprocess.piv(frame_a, frame_b, window_size=winsize, overlap=overlap, dt=dt, corr_method='fft', subpixel_method='gaussian', sig2noise_method=None, nfftx=None, nffty=None, width=2)
openpiv.tools.save(x, y, u0d, v0d, mask, 'tmp.txt' )
openpiv.tools.display_vector_field('tmp.txt', scale=5000, width=0.0025)


# In[ ]:


u1d, v1d =  openpiv.pyprocess.piv(frame_a, frame_b, window_size=winsize, overlap=overlap, dt=dt, corr_method='direct', subpixel_method='gaussian', sig2noise_method=None, nfftx=None, nffty=None, width=2)
openpiv.tools.save(x, y, u1d, v1d, mask, 'tmpdirect.txt' )
openpiv.tools.display_vector_field('tmpdirect.txt', scale=5000, width=0.0025)


# In[ ]: