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

# In[1]:


# %load red_Cell.py
from openpiv import tools, pyprocess, scaling, filters, \
                    validation, process
import numpy as np
from skimage import data
import matplotlib.pyplot as plt

from scipy import ndimage
from skimage import feature
from PIL import Image
from pylab import *
get_ipython().run_line_magic('matplotlib', 'inline')
from skimage.color import rgb2gray
from skimage import img_as_uint

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


# for whatever reason the shape of frame_a is (3, 284, 256)
# so we first tranpose to the RGB image and then convert to the gray scale

frame_a = img_as_uint(rgb2gray(frame_a))
frame_b = img_as_uint(rgb2gray(frame_b))
plt.imshow(np.c_[frame_a,frame_b],cmap=plt.cm.gray)


# In[2]:


# frame_a.dtype


# In[3]:


#u, v, sig2noise = openpiv.process.extended_search_area_piv( frame_a, frame_b, window_size=24, overlap=12, dt=0.02, search_area_size=64, sig2noise_method='peak2peak' )
u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a, frame_b, window_size=32, overlap=8, dt=.1, sig2noise_method='peak2peak' )
x, y = pyprocess.get_coordinates( image_size=frame_a.shape, window_size=32, overlap=8 )


# In[4]:


u, v, mask = validation.sig2noise_val( u, v, sig2noise, threshold = 1.3 )
u, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2)
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 96.52 )
quiver(x,y,u,v)


# In[5]:


tools.save(x, y, u, v, mask, 'Y4-S3_Camera000398.txt' )
tools.display_vector_field('Y4-S3_Camera000398.txt', scale=3, width=0.0125)
# frame_vectors  = io.imshow(vectors)


# In[6]:


x,y,u,v, mask = process.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[7]:


tools.save(x, y, u, v, zeros_like(u), 'Y4-S3_Camera000398.txt' )
tools.display_vector_field('Y4-S3_Camera000398.txt', scale=300, width=0.005)


# In[8]:


x,y,u,v, mask = process.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[9]:


tools.save(x, y, u, v, zeros_like(u), 'Y4-S3_Camera000398.txt' )
tools.display_vector_field('Y4-S3_Camera000398.txt', scale=300, width=0.005)


# In[ ]: