Affine.py 文件源码

def AffineFromPointsRANSAC(src,dst,new_size,filter=BILINEAR, normalize=True,tol=0.15):
    '''
    An affine transform that will rotate, translate, and scale to map one 
    set of points to the other. For example, to align eye coordinates in face images.

    Find a transform (a,b,tx,ty) such that it maps the source points to the 
    destination points::

        a*x1-b*y1+tx = x2
        b*x1+a*y1+ty = y2

    This method minimizes the squared error to find an optimal fit between the 
    points.  Instead of a LS solver the RANSAC solver is used to
    produce a transformation that is robust to outliers.

    @param src: a list of link.Points in the source image.
    @param dst: a list of link.Points in the destination image.
    @param new_size: new size for the image.
    @param filter: PIL filter to use.
    '''
    if normalize:
        # Normalize Points
        src_norm = AffineNormalizePoints(src)
        src = src_norm.transformPoints(src)
        dst_norm = AffineNormalizePoints(dst)
        dst = dst_norm.transformPoints(dst)

    # Compute the transformation parameters
    A = []
    b = []
    for i in range(len(src)):
        A.append([src[i].X(),-src[i].Y(),1,0])
        A.append([src[i].Y(), src[i].X(),0,1])
        b.append(dst[i].X())
        b.append(dst[i].Y())

    A = array(A)
    b = array(b)

    result = RANSAC(A,b,tol=tol,group=2)

    #print result,resids,rank,s 

    a,b,tx,ty = result    
    # Create the transform matrix
    matrix = array([[a,-b,tx],[b,a,ty],[0,0,1]],'d')

    if normalize:
        matrix = dot(dst_norm.inverse,dot(matrix,src_norm.matrix))

    return AffineTransform(matrix,new_size,filter)