basic_model.py 文件源码

def applyLinearTransformToCoords(self, coords, angle, shear_x, shear_y, scale, \
                                   size_in, size_out):
    '''Apply the image transformation specified by three parameters to a list of
    coordinates. The anchor point of the transofrmation is the center of the tile.

    Args:
      x: list of coordinates.
      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.

    Returns:
      A list of transformed coordinates.

    '''
    s_in = (size_in, size_in)
    s_out = (size_out, size_out)
    c_in = .5 * np.asarray(s_in, dtype=np.float64).reshape((1, 2))
    c_out = .5 * np.asarray(s_out, dtype=np.float64).reshape((1, 2)) 

    M_rot = np.asarray([[math.cos(angle), -math.sin(angle)], \
                        [math.sin(angle),  math.cos(angle)]])
    M_shear = np.asarray([[1., shear_x], [shear_y, 1.]])
    M = np.dot(M_rot, M_shear)
    M *= scale  # Without translation, it does not matter whether scale is
                # applied first or last.

    coords = coords.astype(np.float64)
    coords -= c_in
    coords = np.dot(M.T, coords.T).T
    coords += c_out
    return np.round(coords).astype(np.int32)


  # tf augmentation methods
  # TODO https://github.com/tensorflow/benchmarks/blob/master/scripts/tf_cnn_benchmarks/preprocessing.py