TableOfContents

Transformations in EMAN2

The Transform3D Class

EMAN2 uses the [http://blake.bcm.edu/eman2/doxygen_html/classEMAN_1_1Transform3D.html Transform3D] class for storing/managing Euler angles and translations. At any time a Transform3D ($$T3D) object defines a group of 3 affine transformations that are applied in a specific order, namely

$$T3D \equiv T_{post} R T_{pre}$$

Where $$T_{pre}  is a pre translation, $$R$$ is a rotation and $$T_{post}  is a post translation. The Transform3D object stores these transformations internally in a 4x4 matrix, as is commonly the case in computer graphics applications that use homogeneous coordinate systems (i.e. OpenGL). In these approaches the 4x4 transformation matrix $$T3D$$ is constructed in this way

$$T3D = [[R,\hat{t}],[\hat{0}^T,1]]$$

Where R is a $$3x3$$ rotation matrix and $$\hat{t}=(dx,dy,dz)^T$$ is a post translation. In this approach a 3D point $$\hat{p}=(x,y,z)^T$$ as represented in homogeneous coordinates as a 4D vector $$\hat{p_{hc}}=(x,y,z,1)^T$$ and is multiplied against the matrix $$M$$ to produce the result of applying the transformation

$$ T3D \hat{p}_{hc} = ( R\hat{p} +  \hat{t}, 1 )^T $$

In this way the result of applying a Transform3D to a vector is literally a rotation follow by a translation. The Transform3D allows for both pre and post translation and stores the cumulative result internally

$$T3D = T_{post} R T_{pre} = [[I,\hat{t}_{post}],[\hat{0}^T,1]] [[R,\hat{0}],[\hat{0}^T,1]] [[I,\hat{t}_{pre}],[\hat{0}^T,1]] = [[R,R\hat{t}_{pre}+\hat{t}_{post}],[\hat{0}^T,1]]$$

Constructing a Transform3D Object In Python

In Python you can construct a Transform3D object in a number of ways

   1 from EMAN2 import Transform3D
   2 t = Transform3D() # t is the identity
   3 t = Transfrom3D(EULER_EMAN,25,45,65) # EULER_EMAN rotation convention uses the az, alt, phi 
   4 t = Transform3D(EULER_SPIDER,24,44,64) # EULER_SPIDER rotation convention uses the phi, theta, psi convention
   5 t = Transform3D(25,45,65) # EULER_EMAN convention used by default, arguments are taken as az, alt, phi
   6 t = Transform3D(Vec3f(1,2,3),25,45,65,Vec3f(4,5,6)) # Specify a pre trans, followed by EULER_EMAN convention rotations az, alt, phi, followed by the post trans
   7 t = Transform3D(25,45,65,Vec3f(4,5,6)) # EULER_EMAN convention rotations az, alt, phi, followed by the post trans
   8 t = Transform3D(1,0,0,0,1,0,0,0,1) # Explicitly setting the nine members of the rotation matrix, row wise.
   9 s = Transform3D(t) # copy constructor

Setting and Getting Transform3D Rotations and Translation Attributes

You can set the pre and post translations, as well as the rotations, directly from Python

   1 from EMAN2 import Transform3D
   2 t = Transform3D()
   3 # setting the rotations
   4 t.set_rotation(25,45,65) # EULER_EMAN convention rotations az, alt, phi
   5 t.set_rotation(EULER_SPIDER,24,44,64) # EULER_SPIDER rotation convention uses the phi, theta, psi convention
   6 t.set_rotation(EULER_EMAN, {"az":25,"alt":45,"phi":65}) # Optional dictionary style approach
   7 t.set_rotation(1,0,0,0,1,0,0,0,1) # Explicitly set the nine members of the rotation matrix, row wise.
   8 # setting translations
   9 t.set_pretrans(1,2,3)# pre translation dx, dy, dz
  10 t.set_pretrans(Vec3f(1,2,3)) # also takes Vec3f argument
  11 t.set_pretrans([1,2,3]) # also takes tuple argument
  12 t.set_posttrans(4,5,6)# post translation dx, dy, dz
  13 t.set_posttrans(Vec3f(4,5,6)) # also takes Vec3f argument
  14 t.set_posttrans([4,5,6]) # also takes tuple argument

Multiplication

Transform3D Times a Transform3D

The main thing to consider when multiplying two Transform3D objects is what will be the ultimate result of asking for the pre_trans and post_trans vectors from Python. To answer this question we look at the details

$$T3D_{2} T3D_{1} = T_{2,post} R_{2} T_{2,pre} T_{1,post} R_{1} T_{1,pre} = T_{2,post} R_{2} T_{2,pre}[[R_{1},R_{1}\hat{t}_{1,pre}+\hat{t}_{1,post}],[\hat{0}^T,1]]$$

$$ = T_{2,post} R_{2} [[R_{1},R_{1}\hat{t}_{1,pre}+\hat{t}_{1,post}+\hat{t}_{2,pre}],[\hat{0}^T,1]]$$

The translation in right column is now what will be returned when the Transform3D object is asked for its pre_translation vector from python. Similarly, the post translation vector of $$T3D_{2}$$ will now be returned by a call to get_postrans. To complete the details, internally the Transform3D object will look like

$$ T3D_{2} T3D_{1} = [[ R_{2}R_{1},R_{2}(R_{1}\hat{t}_{1,pre}+\hat{t}_{1,post}+\hat{t}_{2,pre})+\hat{t}_{2,post}],[\hat{0}^T,1]]$$

You can get these attributes using similar syntax

   1 from EMAN2 import Transform3D
   2 t = Transform3D(Vec3f(1,2,3),25,45,65,Vec3f(4,5,6)) # Specify a pre trans, followed by EULER_EMAN convention rotations az, alt, phi, followed by the post trans
   3 # get rotations
   4 dictionary = t.get_rotation(EULER_EMAN) # returns a dictionary with keys "az", "alt" and "phi"
   5 dictionary = t.get_rotation(EULER_SPIDER) # returns a dictionary with keys "phi", "theta" and "psi"
   6 # get translations
   7 vector = t.get_pretrans() # Returns a Vec3f object containing the translation
   8 vector = t.get_posttrans() # Returns a Vec3f object containing the translation

Rotations

A rotation of a pixel coordinate at [x,y,z] about the y axis looks like

4x4 Rotation matrix x homog pix coord

In general a rotation matrix will look like this

4x4 general rotation matrix

Note that only the upper left 3x3 block is used to store the rotation information

Translations

Translation of a pixel coordinate at [x,y,z] by [dx,dy,dz] looks like

4x4 depiction of translation matrix x homog pix coord

This shows how the homogeneous coordinate representation is used to achieve translation

Rotations and Translations

Rotation

Need MathML

Transforming an EMData object

An EMData object may be transformed using the following syntax

   1 e = EMData("mydata.hdf")
   2 t = Transform3D(EULER_EMAN,10,23,0) # three angles in the EMAN convention are az=10,alt=23,phi=0. 
   3 # The convention may also be EULER_SPIDER, EULER_IMAGIC, EULER_MRC, EULER_SPIN, EULER_XYZ
   4 t.set_pretrans(1,1,1)
   5 t.set_postrans(2,2,2)
   6 e.rotate_translate(t)

This next section will look better once we get mathml working

A pixel given at coordinate vector v = [x,y,z]^T will be transformed using the following"

$$vhat = T_{post} R T_{pre} v$$

Where the rotation matrix R and associated conventions are defined in Baldwin and Penczek 2007.

Transformations and projections

Say the data model is a 3D map denoted M(x,y,z) and a projection is to be generated in a particular direction. The model may also be pre and/or post translated as part of the projection process. The translation information along with the direction of the projection is to be stored in a Transform3D object T, and the projection is to be generated according to or equivalently to the following

$$p(x,y) = int T M(x,y,z)  dz$$

That is, the projection is generated by first transforming the 3D map M by the Transform3D object, and proceeded by taking line integrals along z.

Transformations and recontructors

In order to insert a projection as generated in the conventional way (above) into a 3D volume in the correct orientation, one must invert the Transform3D object that was used to generate the projection prior to slice insertion.