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

# In[1]:


# %load red_Cell.py
from openpiv import tools, pyprocess, scaling, filters, \
                    validation
from openpiv import widim

import numpy as np
import matplotlib.pyplot as plt

import imageio
from pylab import *
get_ipython().run_line_magic('matplotlib', 'inline')

from skimage import img_as_uint


# In[2]:


frame_a  = tools.imread('../test3/Y4-S3_Camera000398.tif')  
frame_b  = tools.imread('../test3/Y4-S3_Camera000399.tif')


# In[3]:


plt.imshow(np.c_[frame_a[40:,:-40],frame_b[40:,:-40]],cmap=plt.cm.gray)


# In[4]:


frame_a = frame_a[40:, :-40].astype(np.int32) # change of type for the Cython WiDIM
frame_b = frame_b[40:, :-40].astype(np.int32)


# In[ ]:





# In[5]:


# Use Python version, pyprocess:

u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a, frame_b, 
                                                     window_size=32, 
                                                     overlap=16,
                                                     search_area_size=32,
                                                     dt=.1, 
                                                     sig2noise_method='peak2peak',
                                                    normalized_correlation=True,
                                                    correlation_method = 'circular')
x, y = pyprocess.get_coordinates(image_size=frame_a.shape, 
                                 search_area_size=32, 
                                 overlap=16)


# In[6]:


plt.quiver(x,y,u,v,sig2noise)
plt.gca().invert_yaxis()
plt.colorbar()


# In[7]:


plt.hist(sig2noise.flatten());
p = percentile(sig2noise,5) # bottom 5%
plt.plot([p,p],[0,35],lw=2)


# In[8]:


u, v, mask = validation.sig2noise_val( u, v, sig2noise, threshold = p )
u, v = filters.replace_outliers( u, v, method='localmean', max_iter=1, kernel_size=2)
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 1. )

tools.save(x, y, u, v, sig2noise, mask, 'Y4-S3_Camera000398_a.txt' )


# In[9]:


# "natural" view without image
fig,ax = plt.subplots(2,1,figsize=(6,12))
ax[0].invert_yaxis()
ax[0].quiver(x,y,u,v)
ax[0].set_title(' Sort of natural view ')

ax[1].quiver(x,y,u,v)
ax[1].set_title('Quiver with 0,0 origin needs `negative` v for visualization purposes');
# plt.quiver(x,y,u,v)


# In[10]:


fig,ax = plt.subplots(figsize=(8,8))
tools.display_vector_field('Y4-S3_Camera000398_a.txt', 
                           on_img=True,
                           image_name='../test3/Y4-S3_Camera000398.tif',
                           scaling_factor=1.,
                          ax = ax)


# In[11]:


tools.display_vector_field('Y4-S3_Camera000398_a.txt')


# In[12]:


x,y,u,v, mask = widim.WiDIM(frame_a.astype(np.int32), 
                            frame_b.astype(np.int32), 
                            ones_like(frame_a).astype(np.int32), 
                            min_window_size=32, 
                            overlap_ratio=0.25, 
                            coarse_factor=0, 
                            dt=0.1, 
                            validation_method='mean_velocity', 
                            trust_1st_iter=0, 
                            validation_iter=0, 
                            tolerance=0.7, 
                            nb_iter_max=1, 
                            sig2noise_method='peak2peak')


# In[13]:


tools.save(x, y, u, v, 0*v, 0*v, 'Y4-S3_Camera000398_widim1.txt' )


# In[14]:


x,y,u,v, mask = widim.WiDIM(frame_a.astype(np.int32), 
                            frame_b.astype(np.int32), 
                            ones_like(frame_a).astype(np.int32), 
                            min_window_size=16, 
                            overlap_ratio=0.25, 
                            coarse_factor=2, 
                            dt=0.1, 
                            validation_method='mean_velocity', 
                            trust_1st_iter=1, 
                            validation_iter=2, 
                            tolerance=0.7, 
                            nb_iter_max=4, 
                            sig2noise_method='peak2peak')


# In[15]:


tools.save(x, y, u, v, 0*v, 0*v, 'Y4-S3_Camera000398_widim2.txt' )


# In[16]:


tools.display_vector_field('Y4-S3_Camera000398_widim1.txt', widim=True, scale=300, width=0.005)
tools.display_vector_field('Y4-S3_Camera000398_widim2.txt', widim=True, scale=300, width=0.005)
tools.display_vector_field('Y4-S3_Camera000398_a.txt', scale=300, 
                           width=0.005,scaling_factor=1.)


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