In [67]:
# %load red_Cell.py
import openpiv.tools
import openpiv.process
import openpiv.scaling
import numpy as np
from skimage import data, io, filter
import matplotlib.pyplot as plt
from scipy import ndimage
from skimage import feature
from PIL import Image
from pylab import *
%matplotlib inline
from skimage.color import rgb2gray
from skimage import img_as_uint
In [ ]:
In [68]:
frame_a = io.imread('/Users/alex/Downloads/1/PIC # 50015.tif')
frame_b = io.imread('/Users/alex/Downloads/1/PIC # 50016.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(transpose(frame_a,[1,2,0])))
# frame_b = img_as_uint(rgb2gray(transpose(frame_b,[1,2,0])))
#matshow ( frame_a, cmap=cm.Greys_r )
#mage = data.coins() # or any NumPy array!
io.imshow(np.c_[frame_a,frame_b])
Out[68]:
<matplotlib.image.AxesImage at 0x10bf267d0>
In [69]:
#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 = openpiv.pyprocess.piv( frame_a, frame_b, window_size=32, overlap=8, dt=.1, sig2noise_method='peak2peak' )
x, y = openpiv.process.get_coordinates( image_size=frame_a.shape, window_size=32, overlap=8 )
In [70]:
frame_a.dtype
Out[70]:
dtype('uint8')
In [71]:
u, v, mask = openpiv.validation.sig2noise_val( u, v, sig2noise, threshold = 1.3 )
u, v = openpiv.filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2)
x, y, u, v = openpiv.scaling.uniform(x, y, u, v, scaling_factor = 96.52 )
quiver(x,y,u,v)
Out[71]:
<matplotlib.quiver.Quiver at 0x10c1a5490>
In [88]:
openpiv.tools.save(x, y, u, v, mask, 'tmp.txt' )
openpiv.tools.display_vector_field('tmp.txt', scale=20, width=0.005, window_size = 8, on_img=True,image_name = '/Users/alex/Downloads/1/pic.tif')
In [149]:
u, v, sig2noise = openpiv.process.extended_search_area_piv(frame_a.astype(np.int32), frame_b.astype(np.int32), window_size=32, overlap = 8, dt=1.0, search_area_size=48, sig2noise_method='peak2peak' )
x, y = openpiv.process.get_coordinates( image_size=frame_a.shape, window_size=32, overlap=8 )
u, v, mask = openpiv.validation.sig2noise_val( u, v, sig2noise, threshold = 1.07)
u, v = openpiv.filters.replace_outliers( u, v, method='localmean', max_iter=2, kernel_size=1.5)
# x, y, u, v = openpiv.scaling.uniform(x, y, u, v, scaling_factor = 1.0)
openpiv.tools.save(x, y, u, v, mask, 'tmp.txt' )
# openpiv.tools.display_vector_field('tmp.txt', scale=10, width=0.005)
plt.figure(figsize=(16,12))
openpiv.tools.display_vector_field('tmp.txt', scale=100, width=0.005, window_size = True, on_img=True,image_name = '/Users/alex/Downloads/1/pic.tif')
<matplotlib.figure.Figure at 0x10f254110>
<matplotlib.figure.Figure at 0x10ec48750>
In [74]:
openpiv.tools.display_vector_field('tmp.txt', scale=10000, width=0.0021,window_size = 32, on_img=True,image_name = '/Users/alex/Downloads/1/PIC # 50016.tif')
In [75]:
openpiv.tools.display_vector_field?
In [150]:
openpiv.tools.display_vector_field??
In [158]:
a = np.loadtxt('tmp.txt')
fig=plt.figure(figsize=(16,12))
plt.hold(True)
im = imread('/Users/alex/Downloads/1/pic.tif')
# im = negative(im) #plot negative of the image for more clarity
# imsave('neg.tif', im)
# im = mpltimg.imread('neg.tif')
# xmax=np.amax(a[:,0])+window_size/(2*scaling_factor)
# ymax=np.amax(a[:,1])+window_size/(2*scaling_factor)
implot = plt.imshow(im, origin='lower', cmap="Greys_r")
# invalid = a[:,4].astype('bool')
#fig.canvas.set_window_title('Vector field, '+str(np.count_nonzero(invalid))+' wrong vectors')
# valid = ~invalid
# plt.quiver(a[invalid,0],a[invalid,1],a[invalid,2],a[invalid,3],color='r',**kw)
plt.quiver(a[:,0],a[:,1],a[:,2],a[:,3],color='y')
plt.draw()
plt.show()
plt.savefig('tmp.png',dpi=300)
<matplotlib.figure.Figure at 0x1038c0d50>
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