Пример #1
0
 def rotate(self, theta):
     """ Theta in degrees.  Problem is that rotating grid result in negative 
     indicies in rr, cc.  Therefore, don't want to do image[rr,cc].  Returns 
     (2,N) indicies."""
     newgrid = rotate(column_array(self.grid), theta, center=self.midpoint)
     logger.warn("Rotations will lead to astry indicies")
     return unzip_array(astype_rint(newgrid))
Пример #2
0
 def rotate(self, theta):
     """ Theta in degrees.  Problem is that rotating grid result in negative 
     indicies in rr, cc.  Therefore, don't want to do image[rr,cc].  Returns 
     (2,N) indicies."""
     newgrid = rotate(column_array(self.grid), theta, center=self.midpoint)
     logger.warn("Rotations will lead to astry indicies")
     return unzip_array(astype_rint(newgrid))        
Пример #3
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    def centers(self):
        """ For now, returns indicies (IE TUPLE) instead of matrix.  Still is
        indexable with Image[cx, cy].  Takes atan2(x,y) relative to top left
        corner, result in a vector facing down, right.  Instead, we translate 
        opposite this (up, left), and then cut out centers that are negative.
        In short, we compute the vector from topleft corner to center, then 
        translate in opposite direction."""

        rxmax, rymax = self.shape

        # Vector from top left corner facing down/right to center
        thetadiag = math.degrees(math.atan(self.xspacing/self.yspacing))
        r = .5 * self.diagonalspacing
        # Translate towards up/left direction.
        thetadiag = thetadiag + 180

        cornerpairs = column_array(self.corners)
        translated = translate(cornerpairs, r, thetadiag)
        rr_cen, cc_cen = unzip_array(astype_rint(translated))

        # THIS IS HOW TO DEFINE 2D MASK PROPERLY
        mask = (rr_cen >= 0) & (rr_cen < rxmax) & (cc_cen > 0) & (cc_cen < rymax)
        return (rr_cen[mask], cc_cen[mask])
Пример #4
0
    def centers(self):
        """ For now, returns indicies (IE TUPLE) instead of matrix.  Still is
        indexable with Image[cx, cy].  Takes atan2(x,y) relative to top left
        corner, result in a vector facing down, right.  Instead, we translate 
        opposite this (up, left), and then cut out centers that are negative.
        In short, we compute the vector from topleft corner to center, then 
        translate in opposite direction."""

        rxmax, rymax = self.shape

        # Vector from top left corner facing down/right to center
        thetadiag = math.degrees(math.atan(self.xspacing / self.yspacing))
        r = .5 * self.diagonalspacing
        # Translate towards up/left direction.
        thetadiag = thetadiag + 180

        cornerpairs = column_array(self.corners)
        translated = translate(cornerpairs, r, thetadiag)
        rr_cen, cc_cen = unzip_array(astype_rint(translated))

        # THIS IS HOW TO DEFINE 2D MASK PROPERLY
        mask = (rr_cen >= 0) & (rr_cen < rxmax) & (cc_cen > 0) & (cc_cen <
                                                                  rymax)
        return (rr_cen[mask], cc_cen[mask])