小200行Python代码做了一个换脸程序

is because the above formulation assumes the matrix goes on the right

# (with row vectors) where as our solution requires the matrix to be on the

# left (with column vectors).

R = (U * Vt).T

return numpy.vstack([numpy.hstack(((s2 / s1) * R,

c2.T - (s2 / s1) * R * c1.T)),

numpy.matrix([0., 0., 1.])])

def read_im_and_landmarks(fname):

im = cv2.imread(fname, cv2.IMREAD_COLOR)

im = cv2.resize(im, (im.shape[1] * SCALE_FACTOR,

im.shape[0] * SCALE_FACTOR))

s = get_landmarks(im)

return im, s

def warp_im(im, M, dshape):

output_im = numpy.zeros(dshape, dtype=im.dtype)

cv2.warpAffine(im,

M[:2],

(dshape[1], dshape[0]),

dst=output_im,

borderMode=cv2.BORDER_TRANSPARENT,

flags=cv2.WARP_INVERSE_MAP)

return output_im

def correct_colours(im1, im2, landmarks1):

blur_amount = COLOUR_CORRECT_BLUR_FRAC * numpy.linalg.norm(

numpy.mean(landmarks1[LEFT_EYE_POINTS], axis=0) -

numpy.mean(landmarks1[RIGHT_EYE_POINTS], axis=0))

blur_amount = int(blur_amount)

if blur_amount % 2 == 0:

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