Spaces:
Runtime error
Runtime error
| """ | |
| Created on Mon Apr 24 15:43:29 2017 | |
| @author: zhaoy | |
| """ | |
| import cv2 | |
| import numpy as np | |
| from .matlab_cp2tform import get_similarity_transform_for_cv2 | |
| # reference facial points, a list of coordinates (x,y) | |
| dx = 1 | |
| dy = 1 | |
| REFERENCE_FACIAL_POINTS = [ | |
| [30.29459953 + dx, 51.69630051 + dy], # left eye | |
| [65.53179932 + dx, 51.50139999 + dy], # right eye | |
| [48.02519989 + dx, 71.73660278 + dy], # nose | |
| [33.54930115 + dx, 92.3655014 + dy], # left mouth | |
| [62.72990036 + dx, 92.20410156 + dy] # right mouth | |
| ] | |
| DEFAULT_CROP_SIZE = (96, 112) | |
| global FACIAL_POINTS | |
| 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 | |
| h_crop = tmp_crop_size[0] | |
| w_crop = tmp_crop_size[1] | |
| if (output_size): | |
| if (output_size[0] == h_crop and output_size[1] == w_crop): | |
| 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)') | |
| factor = inner_padding_factor > 0 or outer_padding[0] > 0 | |
| factor = factor or outer_padding[1] > 0 | |
| if (factor and output_size is None): | |
| output_size = tmp_crop_size * \ | |
| (1 + inner_padding_factor * 2).astype(np.int32) | |
| output_size += np.array(outer_padding) | |
| cond1 = outer_padding[0] < output_size[0] | |
| cond2 = outer_padding[1] < output_size[1] | |
| if not (cond1 and cond2): | |
| 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 | |
| # 3) add outer_padding to make output_size | |
| reference_5point = tmp_5pts + np.array(outer_padding) | |
| 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, | |
| ratio=0.84, | |
| reference_pts=None, | |
| crop_size=(96, 112), | |
| align_type='similarity' | |
| '', | |
| return_trans_inv=False): | |
| 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) | |
| factor = ratio | |
| ref_pts = (ref_pts - 112 / 2) * factor + 112 / 2 | |
| ref_pts *= crop_size[0] / 112. | |
| 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, ref_pts) | |
| tfm_inv = cv2.getAffineTransform(ref_pts, src_pts) | |
| elif align_type == 'affine': | |
| tfm = get_affine_transform_matrix(src_pts, ref_pts) | |
| tfm_inv = get_affine_transform_matrix(ref_pts, src_pts) | |
| else: | |
| tfm, tfm_inv = get_similarity_transform_for_cv2(src_pts, ref_pts) | |
| face_img = cv2.warpAffine( | |
| src_img, | |
| tfm, (crop_size[0], crop_size[1]), | |
| borderValue=(255, 255, 255)) | |
| if return_trans_inv: | |
| return face_img, tfm_inv | |
| else: | |
| return face_img | |