def applyLinearTransformToImage(self, image, angle, shear_x, shear_y, scale, size_out):
'''Apply the image transformation specified by three parameters.
Time it takes warping a 256 x 256 RGB image with various affine warping functions:
* 0.25ms cv2.warpImage, nearest interpolation
* 0.26ms cv2.warpImage, linear interpolation
* 5.11ms ndii.affine_transform, order=0
* 5.93ms skimage.transform._warps_cy._warp_fast, linear interpolation
Args:
x: 2D numpy array, a single image.
angle: Angle by which the image is rotated.
shear_x: Shearing factor along the x-axis by which the image is sheared.
shear_y: Shearing factor along the x-axis by which the image is sheared.
scale: Scaling factor by which the image is scaled.
channel_axis: Index of axis for channels in the input tensor.
Returns:
A tuple of transformed version of the input and the correction scale factor.
'''
# Positions of the image center before and after the transformation in
# pixel coordinates.
s_out = (size_out, size_out)
c_in = .5 * np.asarray(image.shape[:2], dtype=np.float64).reshape((2, 1))
c_out = .5 * np.asarray(s_out, dtype=np.float64).reshape((2, 1))
angle = -angle
M_rot_inv = np.asarray([[math.cos(angle), -math.sin(angle)], \
[math.sin(angle), math.cos(angle)]])
M_shear_inv = (1. / (shear_x * shear_y - 1.)) \
* np.asarray([[-1., shear_x], [shear_y, -1.]])
M_inv = np.dot(M_shear_inv, M_rot_inv) # First undo rotation, then shear.
M_inv /= scale
offset = c_in - np.dot(M_inv, c_out)
# cv2.warpAffine transform according to dst(p) = src(M_inv * p + offset).
# No need to reverse the channels because the channels are interpolated
# separately.
warped = cv2.warpAffine(image, np.concatenate((M_inv, offset), axis=1), s_out,
flags=cv2.INTER_LANCZOS4 | cv2.WARP_INVERSE_MAP)
# flags=cv2.INTER_CUBIC | cv2.WARP_INVERSE_MAP)
return warped
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