utils/face_alignment.py

import cv2
import numpy as np


def umeyama(src, dst, estimate_scale):
    num = src.shape[0]
    dim = src.shape[1]
    src_mean = src.mean(axis=0)
    dst_mean = dst.mean(axis=0)
    src_demean = src - src_mean
    dst_demean = dst - dst_mean
    A = np.dot(dst_demean.T, src_demean) / num
    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)
    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] = np.dot(U, V)
        else:
            s = d[dim - 1]
            d[dim - 1] = -1
            T[:dim, :dim] = np.dot(U, np.dot(np.diag(d), V))
            d[dim - 1] = s
    else:
        T[:dim, :dim] = np.dot(U, np.dot(np.diag(d), V.T))
    if estimate_scale:
        scale = 1.0 / src_demean.var(axis=0).sum() * np.dot(S, d)
    else:
        scale = 1.0
    T[:dim, dim] = dst_mean - scale * np.dot(T[:dim, :dim], src_mean.T)
    T[:dim, :dim] *= scale
    return T


arcface_dst = np.array(
    [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366],
     [41.5493, 92.3655], [70.7299, 92.2041]],
    dtype=np.float32)


def estimate_norm(lmk, image_size=112, mode='arcface'):
    assert lmk.shape == (5, 2)
    assert image_size % 112 == 0 or image_size % 128 == 0
    if image_size % 112 == 0:
        ratio = float(image_size) / 112.0
        diff_x = 0
    else:
        ratio = float(image_size) / 128.0
        diff_x = 8.0 * ratio
    dst = arcface_dst * ratio
    dst[:, 0] += diff_x
    M = umeyama(lmk, dst, True)[0:2, :]
    return M


def norm_crop2(img, landmark, image_size=112, mode='arcface'):
    M = estimate_norm(landmark, image_size, mode)
    warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0, borderMode=cv2.BORDER_REPLICATE)
    return warped, M


def get_cropped_head(img, landmark, scale=1.4):
    # it is ugly but works :D
    center = np.mean(landmark, axis=0)
    landmark = center + (landmark - center) * scale
    M = estimate_norm(landmark, 128, mode='arcface')
    warped = cv2.warpAffine(img, M/0.25, (512, 512), borderValue=0.0)
    return warped