remove gfpgan dep
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Qing
219
iopaint/plugins/facexlib/detection/align_trans.py
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219
iopaint/plugins/facexlib/detection/align_trans.py
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import cv2
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import numpy as np
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from .matlab_cp2tform import get_similarity_transform_for_cv2
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# reference facial points, a list of coordinates (x,y)
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REFERENCE_FACIAL_POINTS = [[30.29459953, 51.69630051], [65.53179932, 51.50139999], [48.02519989, 71.73660278],
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[33.54930115, 92.3655014], [62.72990036, 92.20410156]]
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DEFAULT_CROP_SIZE = (96, 112)
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class FaceWarpException(Exception):
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def __str__(self):
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return 'In File {}:{}'.format(__file__, super.__str__(self))
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def get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False):
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"""
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Function:
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----------
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get reference 5 key points according to crop settings:
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0. Set default crop_size:
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if default_square:
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crop_size = (112, 112)
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else:
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crop_size = (96, 112)
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1. Pad the crop_size by inner_padding_factor in each side;
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2. Resize crop_size into (output_size - outer_padding*2),
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pad into output_size with outer_padding;
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3. Output reference_5point;
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Parameters:
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----------
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@output_size: (w, h) or None
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size of aligned face image
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@inner_padding_factor: (w_factor, h_factor)
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padding factor for inner (w, h)
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@outer_padding: (w_pad, h_pad)
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each row is a pair of coordinates (x, y)
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@default_square: True or False
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if True:
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default crop_size = (112, 112)
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else:
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default crop_size = (96, 112);
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!!! make sure, if output_size is not None:
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(output_size - outer_padding)
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= some_scale * (default crop_size * (1.0 +
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inner_padding_factor))
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Returns:
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----------
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@reference_5point: 5x2 np.array
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each row is a pair of transformed coordinates (x, y)
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"""
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tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
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tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
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# 0) make the inner region a square
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if default_square:
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size_diff = max(tmp_crop_size) - tmp_crop_size
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tmp_5pts += size_diff / 2
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tmp_crop_size += size_diff
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if (output_size and output_size[0] == tmp_crop_size[0] and output_size[1] == tmp_crop_size[1]):
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return tmp_5pts
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if (inner_padding_factor == 0 and outer_padding == (0, 0)):
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if output_size is None:
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return tmp_5pts
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else:
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raise FaceWarpException('No paddings to do, output_size must be None or {}'.format(tmp_crop_size))
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# check output size
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if not (0 <= inner_padding_factor <= 1.0):
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raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
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if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) and output_size is None):
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output_size = tmp_crop_size * \
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(1 + inner_padding_factor * 2).astype(np.int32)
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output_size += np.array(outer_padding)
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if not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1]):
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raise FaceWarpException('Not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1])')
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# 1) pad the inner region according inner_padding_factor
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if inner_padding_factor > 0:
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size_diff = tmp_crop_size * inner_padding_factor * 2
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tmp_5pts += size_diff / 2
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tmp_crop_size += np.round(size_diff).astype(np.int32)
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# 2) resize the padded inner region
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size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
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if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:
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raise FaceWarpException('Must have (output_size - outer_padding)'
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'= some_scale * (crop_size * (1.0 + inner_padding_factor)')
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scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
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tmp_5pts = tmp_5pts * scale_factor
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# size_diff = tmp_crop_size * (scale_factor - min(scale_factor))
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# tmp_5pts = tmp_5pts + size_diff / 2
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tmp_crop_size = size_bf_outer_pad
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# 3) add outer_padding to make output_size
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reference_5point = tmp_5pts + np.array(outer_padding)
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tmp_crop_size = output_size
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return reference_5point
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def get_affine_transform_matrix(src_pts, dst_pts):
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"""
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Function:
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----------
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get affine transform matrix 'tfm' from src_pts to dst_pts
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Parameters:
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----------
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@src_pts: Kx2 np.array
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source points matrix, each row is a pair of coordinates (x, y)
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@dst_pts: Kx2 np.array
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destination points matrix, each row is a pair of coordinates (x, y)
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Returns:
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----------
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@tfm: 2x3 np.array
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transform matrix from src_pts to dst_pts
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"""
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tfm = np.float32([[1, 0, 0], [0, 1, 0]])
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n_pts = src_pts.shape[0]
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ones = np.ones((n_pts, 1), src_pts.dtype)
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src_pts_ = np.hstack([src_pts, ones])
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dst_pts_ = np.hstack([dst_pts, ones])
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A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
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if rank == 3:
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tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]], [A[0, 1], A[1, 1], A[2, 1]]])
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elif rank == 2:
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tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]])
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return tfm
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def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type='smilarity'):
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"""
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Function:
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----------
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apply affine transform 'trans' to uv
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Parameters:
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----------
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@src_img: 3x3 np.array
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input image
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@facial_pts: could be
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1)a list of K coordinates (x,y)
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or
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2) Kx2 or 2xK np.array
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each row or col is a pair of coordinates (x, y)
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@reference_pts: could be
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1) a list of K coordinates (x,y)
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or
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2) Kx2 or 2xK np.array
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each row or col is a pair of coordinates (x, y)
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or
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3) None
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if None, use default reference facial points
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@crop_size: (w, h)
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output face image size
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@align_type: transform type, could be one of
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1) 'similarity': use similarity transform
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2) 'cv2_affine': use the first 3 points to do affine transform,
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by calling cv2.getAffineTransform()
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3) 'affine': use all points to do affine transform
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Returns:
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----------
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@face_img: output face image with size (w, h) = @crop_size
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"""
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if reference_pts is None:
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if crop_size[0] == 96 and crop_size[1] == 112:
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reference_pts = REFERENCE_FACIAL_POINTS
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else:
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default_square = False
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inner_padding_factor = 0
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outer_padding = (0, 0)
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output_size = crop_size
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reference_pts = get_reference_facial_points(output_size, inner_padding_factor, outer_padding,
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default_square)
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ref_pts = np.float32(reference_pts)
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ref_pts_shp = ref_pts.shape
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if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
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raise FaceWarpException('reference_pts.shape must be (K,2) or (2,K) and K>2')
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if ref_pts_shp[0] == 2:
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ref_pts = ref_pts.T
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src_pts = np.float32(facial_pts)
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src_pts_shp = src_pts.shape
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if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
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raise FaceWarpException('facial_pts.shape must be (K,2) or (2,K) and K>2')
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if src_pts_shp[0] == 2:
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src_pts = src_pts.T
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if src_pts.shape != ref_pts.shape:
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raise FaceWarpException('facial_pts and reference_pts must have the same shape')
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if align_type == 'cv2_affine':
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tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
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elif align_type == 'affine':
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tfm = get_affine_transform_matrix(src_pts, ref_pts)
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else:
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tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
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face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
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return face_img
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