for nu in range(num_smoothes):
print strain.shape[1]
for ni in range(strain_number): # this loop may be simplied used Ax=B matrix assembly
print "the computation process for the strain smooth(%)" + str(ni*100.0/strain.shape[1])
connector = getConnector(ni,mesh_edge)
np.append(connector,ni) # including itself for smoothing
connecting_strains = getConnectorValue(strain, connector)
strain[:,ni] = np.mean(connecting_strains, 1)
if __name__ == "__main__":
print "this a unit test process!"
import SaveLoad
import SpatialSampling
import Visualization
import scipy.ndimage as ndimage
inputDVC = SaveLoad.DVCdata("Dir")
point_arr1 = inputDVC.getPoints1()
Sample_Density = 12
mesh2 = SpatialSampling.MeshDelaunay(point_arr1.T) # use the original point_arr1
mesh2.mesh()
mesh2.alpha_shape(Sample_Density)
mesh2.mesh_triangle() # find unique triangles
for dummy_i in range(1):
Mesh_optimizer = SpatialSampling.MeshOptimizer(point_arr1.T,mesh2.ntri)
Mesh_optimizer.edge_connector_smooth()
displacements = [inputDVC.getDisplacementX()[0],
inputDVC.getDisplacementY()[0],
inputDVC.getDisplacementZ()[0]]
"""
this module creates image transformation or project for two images' registration
"""
import SaveLoad
import Visulization
from scipy import ndimage
from scipy import interpolate
import numpy as np
import scipy.ndimage as ndimage
inputDVC = SaveLoad.DVCdata("/Users/junchaowei/Desktop/MultiStrainGage11182016/") # import the DVC database
################### save the displacement, and create interpolation ##############
inter_x = interpolate.LinearNDInterpolator(inputDVC.getPoints1(), inputDVC.getDisplacementX()[0], fill_value=0.0, rescale=True)
inter_y = interpolate.LinearNDInterpolator(inputDVC.getPoints1(), inputDVC.getDisplacementY()[0], fill_value=0.0, rescale=True)
inter_z = interpolate.LinearNDInterpolator(inputDVC.getPoints1(), inputDVC.getDisplacementZ()[0], fill_value=0.0, rescale=True)
image1 = inputDVC.getImage1()
image2 = inputDVC.getImage2()
sx, sy = image1[:,:,0].shape # get dimensionality of image
mx, my = np.meshgrid(np.linspace(1,sx,sx),np.linspace(1,sy,sy))
# meshgrid change the orientation of x,y: x becomes the horizontal axis, y becomes the vertical
# this change would affect the return statement of reshape.
Cord_xy = np.vstack([mx.flatten(), my.flatten()]) # column major vector
displacementDataX = np.zeros([sy,sx, image1.shape[2]])
displacementDataY = np.zeros([sy,sx, image1.shape[2]])
displacementDataZ = np.zeros([sy,sx, image1.shape[2]])
