diff options
Diffstat (limited to 'r_facelib/detection/align_trans.py')
| -rw-r--r-- | r_facelib/detection/align_trans.py | 219 |
1 files changed, 219 insertions, 0 deletions
diff --git a/r_facelib/detection/align_trans.py b/r_facelib/detection/align_trans.py new file mode 100644 index 0000000..0b7374a --- /dev/null +++ b/r_facelib/detection/align_trans.py @@ -0,0 +1,219 @@ +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
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