def align(src_img, src_pts, ref_pts, image_size, scale=1.0, transpose_input=False): w, h = image_size = tuple(image_size) # Actual offset = new center - old center (scaled) scale_ = max(w, h) * scale cx_ref = cy_ref = 0. offset_x = 0.5 * w - cx_ref * scale_ offset_y = 0.5 * h - cy_ref * scale_ s = np.array(src_pts).astype(np.float32).reshape([-1, 2]) r = np.array(ref_pts).astype(np.float32) * scale_ + np.array( [[offset_x, offset_y]]) if transpose_input: s = s.reshape([2, -1]).T tfm = get_similarity_transform_for_cv2(s, r) dst_img = cv2.warpAffine(src_img, tfm, image_size) s_new = np.concatenate([s.reshape([2, -1]), np.ones((1, s.shape[0]))]) s_new = np.matmul(tfm, s_new) s_new = s_new.reshape([-1]) if transpose_input else s_new.T.reshape([-1]) tfm = tfm.reshape([-1]) return dst_img, s_new, tfm
def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type='smilarity'): """ Function: ---------- apply affine transform 'trans' to uv Parameters: ---------- @src_img: 3x3 np.array input image @facial_pts: could be 1)a list of K coordinates (x,y) or 2) Kx2 or 2xK np.array each row or col is a pair of coordinates (x, y) @reference_pts: could be 1) a list of K coordinates (x,y) or 2) Kx2 or 2xK np.array each row or col is a pair of coordinates (x, y) or 3) None if None, use default reference facial points @crop_size: (w, h) output face image size @align_type: transform type, could be one of 1) 'similarity': use similarity transform 2) 'cv2_affine': use the first 3 points to do affine transform, by calling cv2.getAffineTransform() 3) 'affine': use all points to do affine transform Returns: ---------- @face_img: output face image with size (w, h) = @crop_size """ 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 # #print('--->src_pts:\n', src_pts # #print('--->ref_pts\n', ref_pts if src_pts.shape != ref_pts.shape: raise FaceWarpException( 'facial_pts and reference_pts must have the same shape') if align_type is 'cv2_affine': tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3]) # #print(('cv2.getAffineTransform() returns tfm=\n' + str(tfm)) elif align_type is 'affine': tfm = get_affine_transform_matrix(src_pts, ref_pts) # #print(('get_affine_transform_matrix() returns tfm=\n' + str(tfm)) else: tfm = get_similarity_transform_for_cv2(src_pts, ref_pts) # #print(('get_similarity_transform_for_cv2() returns tfm=\n' + str(tfm)) # #print('--->Transform matrix: ' # #print(('type(tfm):' + str(type(tfm))) # #print(('tfm.dtype:' + str(tfm.dtype)) # #print( tfm face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1])) return face_img