import cv2 import numpy as np from .matlab_cp2tform import get_similarity_transform_for_cv2 # 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) 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): """ Function: ---------- get reference 5 key points according to crop settings: 0. Set default crop_size: if default_square: crop_size = (112, 112) else: crop_size = (96, 112) 1. Pad the crop_size by inner_padding_factor in each side; 2. Resize crop_size into (output_size - outer_padding*2), pad into output_size with outer_padding; 3. Output reference_5point; Parameters: ---------- @output_size: (w, h) or None size of aligned face image @inner_padding_factor: (w_factor, h_factor) padding factor for inner (w, h) @outer_padding: (w_pad, h_pad) each row is a pair of coordinates (x, y) @default_square: True or False if True: default crop_size = (112, 112) else: default crop_size = (96, 112); !!! make sure, if output_size is not None: (output_size - outer_padding) = some_scale * (default crop_size * (1.0 + inner_padding_factor)) Returns: ---------- @reference_5point: 5x2 np.array each row is a pair of transformed coordinates (x, y) """ 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]): return tmp_5pts if (inner_padding_factor == 0 and outer_padding == (0, 0)): if output_size is None: 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) 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 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) # 2) resize the padded inner region size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2 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] 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 # 3) add outer_padding to make output_size reference_5point = tmp_5pts + np.array(outer_padding) tmp_crop_size = output_size return reference_5point def get_affine_transform_matrix(src_pts, dst_pts): """ Function: ---------- get affine transform matrix 'tfm' from src_pts to dst_pts Parameters: ---------- @src_pts: Kx2 np.array source points matrix, each row is a pair of coordinates (x, y) @dst_pts: Kx2 np.array destination points matrix, each row is a pair of coordinates (x, y) Returns: ---------- @tfm: 2x3 np.array transform matrix from src_pts to 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'): """ 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 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]) elif align_type == 'affine': tfm = get_affine_transform_matrix(src_pts, ref_pts) else: tfm = get_similarity_transform_for_cv2(src_pts, ref_pts) face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1])) return face_img