face_vid2vid/GPEN/align_faces.py

# -*- coding: utf-8 -*-
"""
Created on Mon Apr 24 15:43:29 2017
@author: zhaoy
"""
"""
@Modified by yangxy (yangtao9009@gmail.com)
"""
import cv2
import numpy as np
from skimage import transform as trans

# reference facial points, a list of coordinates (x,y)
REFERENCE_FACIAL_POINTS = [
    [30.29459953, 51.69630051],
    [65.53179932, 51.50139999],
    [48.02519989, 71.73660278],
    [33.54930115, 92.3655014],
    [62.72990036, 92.20410156],
]

DEFAULT_CROP_SIZE = (96, 112)


def _umeyama(src, dst, estimate_scale=True, scale=1.0):
    """Estimate N-D similarity transformation with or without scaling.
    Parameters
    ----------
    src : (M, N) array
        Source coordinates.
    dst : (M, N) array
        Destination coordinates.
    estimate_scale : bool
        Whether to estimate scaling factor.
    Returns
    -------
    T : (N + 1, N + 1)
        The homogeneous similarity transformation matrix. The matrix contains
        NaN values only if the problem == not well-conditioned.
    References
    ----------
    .. [1] "Least-squares estimation of transformation parameters between two
            point patterns", Shinji Umeyama, PAMI 1991, :DOI:`10.1109/34.88573`
    """

    num = src.shape[0]
    dim = src.shape[1]

    # Compute mean of src and dst.
    src_mean = src.mean(axis=0)
    dst_mean = dst.mean(axis=0)

    # Subtract mean from src and dst.
    src_demean = src - src_mean
    dst_demean = dst - dst_mean

    # Eq. (38).
    A = dst_demean.T @ src_demean / num

    # Eq. (39).
    d = np.ones((dim,), dtype=np.double)
    if np.linalg.det(A) < 0:
        d[dim - 1] = -1

    T = np.eye(dim + 1, dtype=np.double)

    U, S, V = np.linalg.svd(A)

    # Eq. (40) and (43).
    rank = np.linalg.matrix_rank(A)
    if rank == 0:
        return np.nan * T
    elif rank == dim - 1:
        if np.linalg.det(U) * np.linalg.det(V) > 0:
            T[:dim, :dim] = U @ V
        else:
            s = d[dim - 1]
            d[dim - 1] = -1
            T[:dim, :dim] = U @ np.diag(d) @ V
            d[dim - 1] = s
    else:
        T[:dim, :dim] = U @ np.diag(d) @ V

    if estimate_scale:
        # Eq. (41) and (42).
        scale = 1.0 / src_demean.var(axis=0).sum() * (S @ d)
    else:
        scale = scale

    T[:dim, dim] = dst_mean - scale * (T[:dim, :dim] @ src_mean.T)
    T[:dim, :dim] *= scale

    return T, scale


class FaceWarpException(Exception):
    def __str__(self):
        return "In File {}:{}".format(__file__, super.__str__(self))


def get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False):
    tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
    tmp_crop_size = np.array(DEFAULT_CROP_SIZE)

    # 0) make the inner region a square
    if default_square:
        size_diff = max(tmp_crop_size) - tmp_crop_size
        tmp_5pts += size_diff / 2
        tmp_crop_size += size_diff

    if output_size and output_size[0] == tmp_crop_size[0] and output_size[1] == tmp_crop_size[1]:
        print("output_size == DEFAULT_CROP_SIZE {}: return default reference points".format(tmp_crop_size))
        return tmp_5pts

    if inner_padding_factor == 0 and outer_padding == (0, 0):
        if output_size is None:
            print("No paddings to do: return default reference points")
            return tmp_5pts
        else:
            raise FaceWarpException("No paddings to do, output_size must be None or {}".format(tmp_crop_size))

    # check output size
    if not (0 <= inner_padding_factor <= 1.0):
        raise FaceWarpException("Not (0 <= inner_padding_factor <= 1.0)")

    if (inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) and output_size is None:
        output_size = tmp_crop_size * (1 + inner_padding_factor * 2).astype(np.int32)
        output_size += np.array(outer_padding)
        print("              deduced from paddings, output_size = ", output_size)

    if not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1]):
        raise FaceWarpException("Not (outer_padding[0] < output_size[0]" "and outer_padding[1] < output_size[1])")

    # 1) pad the inner region according inner_padding_factor
    # print('---> STEP1: pad the inner region according inner_padding_factor')
    if inner_padding_factor > 0:
        size_diff = tmp_crop_size * inner_padding_factor * 2
        tmp_5pts += size_diff / 2
        tmp_crop_size += np.round(size_diff).astype(np.int32)

    # print('              crop_size = ', tmp_crop_size)
    # print('              reference_5pts = ', tmp_5pts)

    # 2) resize the padded inner region
    # print('---> STEP2: resize the padded inner region')
    size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
    # print('              crop_size = ', tmp_crop_size)
    # print('              size_bf_outer_pad = ', size_bf_outer_pad)

    if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:
        raise FaceWarpException("Must have (output_size - outer_padding)" "= some_scale * (crop_size * (1.0 + inner_padding_factor)")

    scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
    # print('              resize scale_factor = ', scale_factor)
    tmp_5pts = tmp_5pts * scale_factor
    #    size_diff = tmp_crop_size * (scale_factor - min(scale_factor))
    #    tmp_5pts = tmp_5pts + size_diff / 2
    tmp_crop_size = size_bf_outer_pad
    # print('              crop_size = ', tmp_crop_size)
    # print('              reference_5pts = ', tmp_5pts)

    # 3) add outer_padding to make output_size
    reference_5point = tmp_5pts + np.array(outer_padding)
    tmp_crop_size = output_size
    # print('---> STEP3: add outer_padding to make output_size')
    # print('              crop_size = ', tmp_crop_size)
    # print('              reference_5pts = ', tmp_5pts)
    #
    # print('===> end get_reference_facial_points\n')

    return reference_5point


def get_affine_transform_matrix(src_pts, dst_pts):
    tfm = np.float32([[1, 0, 0], [0, 1, 0]])
    n_pts = src_pts.shape[0]
    ones = np.ones((n_pts, 1), src_pts.dtype)
    src_pts_ = np.hstack([src_pts, ones])
    dst_pts_ = np.hstack([dst_pts, ones])

    A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)

    if rank == 3:
        tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]], [A[0, 1], A[1, 1], A[2, 1]]])
    elif rank == 2:
        tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]])

    return tfm


def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type="smilarity"):  # smilarity cv2_affine affine
    if reference_pts is None:
        if crop_size[0] == 96 and crop_size[1] == 112:
            reference_pts = REFERENCE_FACIAL_POINTS
        else:
            default_square = False
            inner_padding_factor = 0
            outer_padding = (0, 0)
            output_size = crop_size

            reference_pts = get_reference_facial_points(output_size, inner_padding_factor, outer_padding, default_square)
    ref_pts = np.float32(reference_pts)
    ref_pts_shp = ref_pts.shape
    if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
        raise FaceWarpException("reference_pts.shape must be (K,2) or (2,K) and K>2")

    if ref_pts_shp[0] == 2:
        ref_pts = ref_pts.T

    src_pts = np.float32(facial_pts)
    src_pts_shp = src_pts.shape
    if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
        raise FaceWarpException("facial_pts.shape must be (K,2) or (2,K) and K>2")

    if src_pts_shp[0] == 2:
        src_pts = src_pts.T

    if src_pts.shape != ref_pts.shape:
        raise FaceWarpException("facial_pts and reference_pts must have the same shape")

    if align_type == "cv2_affine":
        tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
        tfm_inv = cv2.getAffineTransform(ref_pts[0:3], src_pts[0:3])
    elif align_type == "affine":
        tfm = get_affine_transform_matrix(src_pts, ref_pts)
        tfm_inv = get_affine_transform_matrix(ref_pts, src_pts)
    else:
        params, scale = _umeyama(src_pts, ref_pts)
        tfm = params[:2, :]

        params, _ = _umeyama(ref_pts, src_pts, False, scale=1.0 / scale)
        tfm_inv = params[:2, :]

    face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]), flags=3)

    return face_img, tfm_inv