def getQimage(dims, cc, color=(1.,1.,0.), factor = 4):
print(dims)
img = coords.coords2Image(dims, cc, factor, True)
colorimg = np.zeros((img.shape[0],img.shape[1],4),dtype=np.uint8)
mx = np.max(img)
colorimg[:,:,0] = color[2]*img*(255./mx) # blue = LSB
colorimg[:,:,1] = color[1]*img*(255./mx) # green
colorimg[:,:,2] = color[0]*img*(255./mx) # red
return colorimg
def loadImage(filename, color=(1.,1.,0.), factor=4): print "reading file %s" % filename dims, cc = coords.readfile(filename) img = coords.coords2Image(dims, cc, factor=factor) colorimg = np.zeros((img.shape[0],img.shape[1],4),dtype=np.uint8) mx = np.max(img) colorimg[:,:,0] = color[2]*img*(255./mx) # blue = LSB colorimg[:,:,1] = color[1]*img*(255./mx) # green colorimg[:,:,2] = color[0]*img*(255./mx) # red return colorimg, cc, dims
def doTransformationButton_clicked(self):
self.heatmatrix = self.transformcontroller.calculateTransform([self.m_npimages[0].shape[0]/self.m_factor, self.m_npimages[0].shape[1]/self.m_factor])
self.emit(QtCore.SIGNAL("hideBeads()"))
for i in range(1,len(self.m_npimages)): #Transforms the coordinates of the green according to the transformation
points = self.m_coords[i]
p_transformed = self.transformcontroller.doTransform(points[:,0:2], i)
points[:,0:2] = p_transformed
points = coords.cropROI(points, (0, self.m_dims[0][0]-1, 0, self.m_dims[0][1]-1))
img = coords.coords2Image(self.m_dims[i], points, self.m_factor)
colorimg = np.zeros((img.shape[0],img.shape[1],4),dtype=np.uint8)
mx = np.max(img)
colorimg[:,:,1] = 1*img*(255./mx) # green
self.m_npimages[i] = colorimg
self.recalculateResult()
def plt_overview(dimension, coords, roi):
# roi rectangle
mybox=np.array([[roi[0],roi[0], roi[1], roi[1], roi[0]],[roi[2],roi[3],roi[3],roi[2],roi[2]]])
#construct image from coordinates
zoom_factor = 8
im = cr.coords2Image(dimension, coords, zoom_factor)
#plot
plt.figure()
plt.title("Storm image: " + overviewfile)
plt.imshow(im)
plt.hot() #colormap
# rescale roi rectangle and plot it into the image
mybox=zoom_factor*mybox
plt.plot(mybox[0,...], mybox[1,...], '0.3')
plt.axis([0,dimension[0]*zoom_factor,0,dimension[1]*zoom_factor])
plt.colorbar()
if __name__ == "__main__":
import matplotlib.pyplot as plt
import coords
import scipy
file1 = 'data/HeLa5_10000z_3_polyL_actinmEOS2-steve.txt'
file1_landmarks = 'data/HeLa5_10000z_3_polyL_actinmEOS2-steve.txt_beads.txt'
file2 = 'data/HeLa5_10000z_3_polyL_ER647.txt'
file2_landmarks = 'data/HeLa5_10000z_3_polyL_ER647.txt_beads.txt'
thrash, landmarks1 = coords.readfile(file1_landmarks)
thrash, landmarks2 = coords.readfile(file2_landmarks)
landmarks1 = landmarks1[:,:2]
landmarks2 = landmarks2[:,:2]
trafo = affineMatrix2DFromCorrespondingPoints(landmarks2, landmarks1)
print("Transformation: \n", trafo)
dims, cc1 = coords.readfile(file1)
dims, cc2 = coords.readfile(file2)
cc2_ones = np.hstack([cc2[:,:2], np.ones((len(cc2),1))]) # add column with ones for affine trafo
cc2_transformed = np.dot(cc2_ones, trafo.T)[:,:2]
cc2_transformed = np.hstack([cc2_transformed,cc2[:,2:]]) # add intensity information again
im1 = coords.coords2Image(dims, cc1)
im2 = coords.coords2Image(dims, cc2_transformed)
im_color = np.dstack([im1/np.max(im1),im2/np.max(im2),np.zeros(im1.shape)])
scipy.misc.imsave(file1+"_processed.png", im_color)
plt.imshow(im_color)
plt.plot(landmarks1[:,0]*8, landmarks1[:,1]*8, 'wo', alpha=0.4, scalex=False, scaley=False)
plt.show()
