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| author | Grafting Rayman <156515434+GraftingRayman@users.noreply.github.com> | 2025-01-17 11:06:44 +0000 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2025-01-17 11:06:44 +0000 |
| commit | e6bd5af6a8e306a1cdef63402a77a980a04ad6e1 (patch) | |
| tree | d0732226bbc22feedad9e834b2218d7d0b0eff54 /r_facelib | |
| parent | 495ffc4777522e40941753e3b1b79c02f84b25b4 (diff) | |
| download | Comfyui-reactor-node-main.tar.gz | |
Diffstat (limited to 'r_facelib')
29 files changed, 4098 insertions, 0 deletions
diff --git a/r_facelib/__init__.py b/r_facelib/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/r_facelib/__init__.py diff --git a/r_facelib/detection/__init__.py b/r_facelib/detection/__init__.py new file mode 100644 index 0000000..3c953bd --- /dev/null +++ b/r_facelib/detection/__init__.py @@ -0,0 +1,102 @@ +import os
+import torch
+from torch import nn
+from copy import deepcopy
+import pathlib
+
+from r_facelib.utils import load_file_from_url
+from r_facelib.utils import download_pretrained_models
+from r_facelib.detection.yolov5face.models.common import Conv
+
+from .retinaface.retinaface import RetinaFace
+from .yolov5face.face_detector import YoloDetector
+
+
+def init_detection_model(model_name, half=False, device='cuda'):
+ if 'retinaface' in model_name:
+ model = init_retinaface_model(model_name, half, device)
+ elif 'YOLOv5' in model_name:
+ model = init_yolov5face_model(model_name, device)
+ else:
+ raise NotImplementedError(f'{model_name} is not implemented.')
+
+ return model
+
+
+def init_retinaface_model(model_name, half=False, device='cuda'):
+ if model_name == 'retinaface_resnet50':
+ model = RetinaFace(network_name='resnet50', half=half)
+ model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth'
+ elif model_name == 'retinaface_mobile0.25':
+ model = RetinaFace(network_name='mobile0.25', half=half)
+ model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth'
+ else:
+ raise NotImplementedError(f'{model_name} is not implemented.')
+
+ model_path = load_file_from_url(url=model_url, model_dir='../../models/facedetection', progress=True, file_name=None)
+ load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+ # remove unnecessary 'module.'
+ for k, v in deepcopy(load_net).items():
+ if k.startswith('module.'):
+ load_net[k[7:]] = v
+ load_net.pop(k)
+ model.load_state_dict(load_net, strict=True)
+ model.eval()
+ model = model.to(device)
+
+ return model
+
+
+def init_yolov5face_model(model_name, device='cuda'):
+ current_dir = str(pathlib.Path(__file__).parent.resolve())
+ if model_name == 'YOLOv5l':
+ model = YoloDetector(config_name=current_dir+'/yolov5face/models/yolov5l.yaml', device=device)
+ model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth'
+ elif model_name == 'YOLOv5n':
+ model = YoloDetector(config_name=current_dir+'/yolov5face/models/yolov5n.yaml', device=device)
+ model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5n-face.pth'
+ else:
+ raise NotImplementedError(f'{model_name} is not implemented.')
+
+ model_path = load_file_from_url(url=model_url, model_dir='../../models/facedetection', progress=True, file_name=None)
+ load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+ model.detector.load_state_dict(load_net, strict=True)
+ model.detector.eval()
+ model.detector = model.detector.to(device).float()
+
+ for m in model.detector.modules():
+ if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
+ m.inplace = True # pytorch 1.7.0 compatibility
+ elif isinstance(m, Conv):
+ m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
+
+ return model
+
+
+# Download from Google Drive
+# def init_yolov5face_model(model_name, device='cuda'):
+# if model_name == 'YOLOv5l':
+# model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5l.yaml', device=device)
+# f_id = {'yolov5l-face.pth': '131578zMA6B2x8VQHyHfa6GEPtulMCNzV'}
+# elif model_name == 'YOLOv5n':
+# model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5n.yaml', device=device)
+# f_id = {'yolov5n-face.pth': '1fhcpFvWZqghpGXjYPIne2sw1Fy4yhw6o'}
+# else:
+# raise NotImplementedError(f'{model_name} is not implemented.')
+
+# model_path = os.path.join('../../models/facedetection', list(f_id.keys())[0])
+# if not os.path.exists(model_path):
+# download_pretrained_models(file_ids=f_id, save_path_root='../../models/facedetection')
+
+# load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+# model.detector.load_state_dict(load_net, strict=True)
+# model.detector.eval()
+# model.detector = model.detector.to(device).float()
+
+# for m in model.detector.modules():
+# if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
+# m.inplace = True # pytorch 1.7.0 compatibility
+# elif isinstance(m, Conv):
+# m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
+
+# return model
\ No newline at end of file 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
diff --git a/r_facelib/detection/matlab_cp2tform.py b/r_facelib/detection/matlab_cp2tform.py new file mode 100644 index 0000000..b1014a8 --- /dev/null +++ b/r_facelib/detection/matlab_cp2tform.py @@ -0,0 +1,317 @@ +import numpy as np
+from numpy.linalg import inv, lstsq
+from numpy.linalg import matrix_rank as rank
+from numpy.linalg import norm
+
+
+class MatlabCp2tormException(Exception):
+
+ def __str__(self):
+ return 'In File {}:{}'.format(__file__, super.__str__(self))
+
+
+def tformfwd(trans, uv):
+ """
+ Function:
+ ----------
+ apply affine transform 'trans' to uv
+
+ Parameters:
+ ----------
+ @trans: 3x3 np.array
+ transform matrix
+ @uv: Kx2 np.array
+ each row is a pair of coordinates (x, y)
+
+ Returns:
+ ----------
+ @xy: Kx2 np.array
+ each row is a pair of transformed coordinates (x, y)
+ """
+ uv = np.hstack((uv, np.ones((uv.shape[0], 1))))
+ xy = np.dot(uv, trans)
+ xy = xy[:, 0:-1]
+ return xy
+
+
+def tforminv(trans, uv):
+ """
+ Function:
+ ----------
+ apply the inverse of affine transform 'trans' to uv
+
+ Parameters:
+ ----------
+ @trans: 3x3 np.array
+ transform matrix
+ @uv: Kx2 np.array
+ each row is a pair of coordinates (x, y)
+
+ Returns:
+ ----------
+ @xy: Kx2 np.array
+ each row is a pair of inverse-transformed coordinates (x, y)
+ """
+ Tinv = inv(trans)
+ xy = tformfwd(Tinv, uv)
+ return xy
+
+
+def findNonreflectiveSimilarity(uv, xy, options=None):
+ options = {'K': 2}
+
+ K = options['K']
+ M = xy.shape[0]
+ x = xy[:, 0].reshape((-1, 1)) # use reshape to keep a column vector
+ y = xy[:, 1].reshape((-1, 1)) # use reshape to keep a column vector
+
+ tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1))))
+ tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1))))
+ X = np.vstack((tmp1, tmp2))
+
+ u = uv[:, 0].reshape((-1, 1)) # use reshape to keep a column vector
+ v = uv[:, 1].reshape((-1, 1)) # use reshape to keep a column vector
+ U = np.vstack((u, v))
+
+ # We know that X * r = U
+ if rank(X) >= 2 * K:
+ r, _, _, _ = lstsq(X, U, rcond=-1)
+ r = np.squeeze(r)
+ else:
+ raise Exception('cp2tform:twoUniquePointsReq')
+ sc = r[0]
+ ss = r[1]
+ tx = r[2]
+ ty = r[3]
+
+ Tinv = np.array([[sc, -ss, 0], [ss, sc, 0], [tx, ty, 1]])
+ T = inv(Tinv)
+ T[:, 2] = np.array([0, 0, 1])
+
+ return T, Tinv
+
+
+def findSimilarity(uv, xy, options=None):
+ options = {'K': 2}
+
+ # uv = np.array(uv)
+ # xy = np.array(xy)
+
+ # Solve for trans1
+ trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options)
+
+ # Solve for trans2
+
+ # manually reflect the xy data across the Y-axis
+ xyR = xy
+ xyR[:, 0] = -1 * xyR[:, 0]
+
+ trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options)
+
+ # manually reflect the tform to undo the reflection done on xyR
+ TreflectY = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]])
+
+ trans2 = np.dot(trans2r, TreflectY)
+
+ # Figure out if trans1 or trans2 is better
+ xy1 = tformfwd(trans1, uv)
+ norm1 = norm(xy1 - xy)
+
+ xy2 = tformfwd(trans2, uv)
+ norm2 = norm(xy2 - xy)
+
+ if norm1 <= norm2:
+ return trans1, trans1_inv
+ else:
+ trans2_inv = inv(trans2)
+ return trans2, trans2_inv
+
+
+def get_similarity_transform(src_pts, dst_pts, reflective=True):
+ """
+ Function:
+ ----------
+ Find Similarity Transform Matrix 'trans':
+ u = src_pts[:, 0]
+ v = src_pts[:, 1]
+ x = dst_pts[:, 0]
+ y = dst_pts[:, 1]
+ [x, y, 1] = [u, v, 1] * trans
+
+ Parameters:
+ ----------
+ @src_pts: Kx2 np.array
+ source points, each row is a pair of coordinates (x, y)
+ @dst_pts: Kx2 np.array
+ destination points, each row is a pair of transformed
+ coordinates (x, y)
+ @reflective: True or False
+ if True:
+ use reflective similarity transform
+ else:
+ use non-reflective similarity transform
+
+ Returns:
+ ----------
+ @trans: 3x3 np.array
+ transform matrix from uv to xy
+ trans_inv: 3x3 np.array
+ inverse of trans, transform matrix from xy to uv
+ """
+
+ if reflective:
+ trans, trans_inv = findSimilarity(src_pts, dst_pts)
+ else:
+ trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts)
+
+ return trans, trans_inv
+
+
+def cvt_tform_mat_for_cv2(trans):
+ """
+ Function:
+ ----------
+ Convert Transform Matrix 'trans' into 'cv2_trans' which could be
+ directly used by cv2.warpAffine():
+ u = src_pts[:, 0]
+ v = src_pts[:, 1]
+ x = dst_pts[:, 0]
+ y = dst_pts[:, 1]
+ [x, y].T = cv_trans * [u, v, 1].T
+
+ Parameters:
+ ----------
+ @trans: 3x3 np.array
+ transform matrix from uv to xy
+
+ Returns:
+ ----------
+ @cv2_trans: 2x3 np.array
+ transform matrix from src_pts to dst_pts, could be directly used
+ for cv2.warpAffine()
+ """
+ cv2_trans = trans[:, 0:2].T
+
+ return cv2_trans
+
+
+def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True):
+ """
+ Function:
+ ----------
+ Find Similarity Transform Matrix 'cv2_trans' which could be
+ directly used by cv2.warpAffine():
+ u = src_pts[:, 0]
+ v = src_pts[:, 1]
+ x = dst_pts[:, 0]
+ y = dst_pts[:, 1]
+ [x, y].T = cv_trans * [u, v, 1].T
+
+ Parameters:
+ ----------
+ @src_pts: Kx2 np.array
+ source points, each row is a pair of coordinates (x, y)
+ @dst_pts: Kx2 np.array
+ destination points, each row is a pair of transformed
+ coordinates (x, y)
+ reflective: True or False
+ if True:
+ use reflective similarity transform
+ else:
+ use non-reflective similarity transform
+
+ Returns:
+ ----------
+ @cv2_trans: 2x3 np.array
+ transform matrix from src_pts to dst_pts, could be directly used
+ for cv2.warpAffine()
+ """
+ trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective)
+ cv2_trans = cvt_tform_mat_for_cv2(trans)
+
+ return cv2_trans
+
+
+if __name__ == '__main__':
+ """
+ u = [0, 6, -2]
+ v = [0, 3, 5]
+ x = [-1, 0, 4]
+ y = [-1, -10, 4]
+
+ # In Matlab, run:
+ #
+ # uv = [u'; v'];
+ # xy = [x'; y'];
+ # tform_sim=cp2tform(uv,xy,'similarity');
+ #
+ # trans = tform_sim.tdata.T
+ # ans =
+ # -0.0764 -1.6190 0
+ # 1.6190 -0.0764 0
+ # -3.2156 0.0290 1.0000
+ # trans_inv = tform_sim.tdata.Tinv
+ # ans =
+ #
+ # -0.0291 0.6163 0
+ # -0.6163 -0.0291 0
+ # -0.0756 1.9826 1.0000
+ # xy_m=tformfwd(tform_sim, u,v)
+ #
+ # xy_m =
+ #
+ # -3.2156 0.0290
+ # 1.1833 -9.9143
+ # 5.0323 2.8853
+ # uv_m=tforminv(tform_sim, x,y)
+ #
+ # uv_m =
+ #
+ # 0.5698 1.3953
+ # 6.0872 2.2733
+ # -2.6570 4.3314
+ """
+ u = [0, 6, -2]
+ v = [0, 3, 5]
+ x = [-1, 0, 4]
+ y = [-1, -10, 4]
+
+ uv = np.array((u, v)).T
+ xy = np.array((x, y)).T
+
+ print('\n--->uv:')
+ print(uv)
+ print('\n--->xy:')
+ print(xy)
+
+ trans, trans_inv = get_similarity_transform(uv, xy)
+
+ print('\n--->trans matrix:')
+ print(trans)
+
+ print('\n--->trans_inv matrix:')
+ print(trans_inv)
+
+ print('\n---> apply transform to uv')
+ print('\nxy_m = uv_augmented * trans')
+ uv_aug = np.hstack((uv, np.ones((uv.shape[0], 1))))
+ xy_m = np.dot(uv_aug, trans)
+ print(xy_m)
+
+ print('\nxy_m = tformfwd(trans, uv)')
+ xy_m = tformfwd(trans, uv)
+ print(xy_m)
+
+ print('\n---> apply inverse transform to xy')
+ print('\nuv_m = xy_augmented * trans_inv')
+ xy_aug = np.hstack((xy, np.ones((xy.shape[0], 1))))
+ uv_m = np.dot(xy_aug, trans_inv)
+ print(uv_m)
+
+ print('\nuv_m = tformfwd(trans_inv, xy)')
+ uv_m = tformfwd(trans_inv, xy)
+ print(uv_m)
+
+ uv_m = tforminv(trans, xy)
+ print('\nuv_m = tforminv(trans, xy)')
+ print(uv_m)
diff --git a/r_facelib/detection/retinaface/retinaface.py b/r_facelib/detection/retinaface/retinaface.py new file mode 100644 index 0000000..5d9770a --- /dev/null +++ b/r_facelib/detection/retinaface/retinaface.py @@ -0,0 +1,389 @@ +import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter
+
+from modules import shared
+
+from r_facelib.detection.align_trans import get_reference_facial_points, warp_and_crop_face
+from r_facelib.detection.retinaface.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head
+from r_facelib.detection.retinaface.retinaface_utils import (PriorBox, batched_decode, batched_decode_landm, decode, decode_landm,
+ py_cpu_nms)
+
+#device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+if torch.cuda.is_available():
+ device = torch.device('cuda')
+elif torch.backends.mps.is_available():
+ device = torch.device('mps')
+# elif hasattr(torch,'dml'):
+# device = torch.device('dml')
+elif hasattr(torch,'dml') or hasattr(torch,'privateuseone'): # AMD
+ if shared.cmd_opts is not None: # A1111
+ if shared.cmd_opts.device_id is not None:
+ device = torch.device(f'privateuseone:{shared.cmd_opts.device_id}')
+ else:
+ device = torch.device('privateuseone:0')
+ else:
+ device = torch.device('privateuseone:0')
+else:
+ device = torch.device('cpu')
+
+
+def generate_config(network_name):
+
+ cfg_mnet = {
+ 'name': 'mobilenet0.25',
+ 'min_sizes': [[16, 32], [64, 128], [256, 512]],
+ 'steps': [8, 16, 32],
+ 'variance': [0.1, 0.2],
+ 'clip': False,
+ 'loc_weight': 2.0,
+ 'gpu_train': True,
+ 'batch_size': 32,
+ 'ngpu': 1,
+ 'epoch': 250,
+ 'decay1': 190,
+ 'decay2': 220,
+ 'image_size': 640,
+ 'return_layers': {
+ 'stage1': 1,
+ 'stage2': 2,
+ 'stage3': 3
+ },
+ 'in_channel': 32,
+ 'out_channel': 64
+ }
+
+ cfg_re50 = {
+ 'name': 'Resnet50',
+ 'min_sizes': [[16, 32], [64, 128], [256, 512]],
+ 'steps': [8, 16, 32],
+ 'variance': [0.1, 0.2],
+ 'clip': False,
+ 'loc_weight': 2.0,
+ 'gpu_train': True,
+ 'batch_size': 24,
+ 'ngpu': 4,
+ 'epoch': 100,
+ 'decay1': 70,
+ 'decay2': 90,
+ 'image_size': 840,
+ 'return_layers': {
+ 'layer2': 1,
+ 'layer3': 2,
+ 'layer4': 3
+ },
+ 'in_channel': 256,
+ 'out_channel': 256
+ }
+
+ if network_name == 'mobile0.25':
+ return cfg_mnet
+ elif network_name == 'resnet50':
+ return cfg_re50
+ else:
+ raise NotImplementedError(f'network_name={network_name}')
+
+
+class RetinaFace(nn.Module):
+
+ def __init__(self, network_name='resnet50', half=False, phase='test'):
+ super(RetinaFace, self).__init__()
+ self.half_inference = half
+ cfg = generate_config(network_name)
+ self.backbone = cfg['name']
+
+ self.model_name = f'retinaface_{network_name}'
+ self.cfg = cfg
+ self.phase = phase
+ self.target_size, self.max_size = 1600, 2150
+ self.resize, self.scale, self.scale1 = 1., None, None
+ self.mean_tensor = torch.tensor([[[[104.]], [[117.]], [[123.]]]]).to(device)
+ self.reference = get_reference_facial_points(default_square=True)
+ # Build network.
+ backbone = None
+ if cfg['name'] == 'mobilenet0.25':
+ backbone = MobileNetV1()
+ self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])
+ elif cfg['name'] == 'Resnet50':
+ import torchvision.models as models
+ backbone = models.resnet50(pretrained=False)
+ self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])
+
+ in_channels_stage2 = cfg['in_channel']
+ in_channels_list = [
+ in_channels_stage2 * 2,
+ in_channels_stage2 * 4,
+ in_channels_stage2 * 8,
+ ]
+
+ out_channels = cfg['out_channel']
+ self.fpn = FPN(in_channels_list, out_channels)
+ self.ssh1 = SSH(out_channels, out_channels)
+ self.ssh2 = SSH(out_channels, out_channels)
+ self.ssh3 = SSH(out_channels, out_channels)
+
+ self.ClassHead = make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
+ self.BboxHead = make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
+ self.LandmarkHead = make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
+
+ self.to(device)
+ self.eval()
+ if self.half_inference:
+ self.half()
+
+ def forward(self, inputs):
+ self.to(device)
+ out = self.body(inputs)
+
+ if self.backbone == 'mobilenet0.25' or self.backbone == 'Resnet50':
+ out = list(out.values())
+ # FPN
+ fpn = self.fpn(out)
+
+ # SSH
+ feature1 = self.ssh1(fpn[0])
+ feature2 = self.ssh2(fpn[1])
+ feature3 = self.ssh3(fpn[2])
+ features = [feature1, feature2, feature3]
+
+ bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
+ classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)], dim=1)
+ tmp = [self.LandmarkHead[i](feature) for i, feature in enumerate(features)]
+ ldm_regressions = (torch.cat(tmp, dim=1))
+
+ if self.phase == 'train':
+ output = (bbox_regressions, classifications, ldm_regressions)
+ else:
+ output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
+ return output
+
+ def __detect_faces(self, inputs):
+ # get scale
+ height, width = inputs.shape[2:]
+ self.scale = torch.tensor([width, height, width, height], dtype=torch.float32).to(device)
+ tmp = [width, height, width, height, width, height, width, height, width, height]
+ self.scale1 = torch.tensor(tmp, dtype=torch.float32).to(device)
+
+ # forawrd
+ inputs = inputs.to(device)
+ if self.half_inference:
+ inputs = inputs.half()
+ loc, conf, landmarks = self(inputs)
+
+ # get priorbox
+ priorbox = PriorBox(self.cfg, image_size=inputs.shape[2:])
+ priors = priorbox.forward().to(device)
+
+ return loc, conf, landmarks, priors
+
+ # single image detection
+ def transform(self, image, use_origin_size):
+ # convert to opencv format
+ if isinstance(image, Image.Image):
+ image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
+ image = image.astype(np.float32)
+
+ # testing scale
+ im_size_min = np.min(image.shape[0:2])
+ im_size_max = np.max(image.shape[0:2])
+ resize = float(self.target_size) / float(im_size_min)
+
+ # prevent bigger axis from being more than max_size
+ if np.round(resize * im_size_max) > self.max_size:
+ resize = float(self.max_size) / float(im_size_max)
+ resize = 1 if use_origin_size else resize
+
+ # resize
+ if resize != 1:
+ image = cv2.resize(image, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
+
+ # convert to torch.tensor format
+ # image -= (104, 117, 123)
+ image = image.transpose(2, 0, 1)
+ image = torch.from_numpy(image).unsqueeze(0)
+
+ return image, resize
+
+ def detect_faces(
+ self,
+ image,
+ conf_threshold=0.8,
+ nms_threshold=0.4,
+ use_origin_size=True,
+ ):
+ """
+ Params:
+ imgs: BGR image
+ """
+ image, self.resize = self.transform(image, use_origin_size)
+ image = image.to(device)
+ if self.half_inference:
+ image = image.half()
+ image = image - self.mean_tensor
+
+ loc, conf, landmarks, priors = self.__detect_faces(image)
+
+ boxes = decode(loc.data.squeeze(0), priors.data, self.cfg['variance'])
+ boxes = boxes * self.scale / self.resize
+ boxes = boxes.cpu().numpy()
+
+ scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
+
+ landmarks = decode_landm(landmarks.squeeze(0), priors, self.cfg['variance'])
+ landmarks = landmarks * self.scale1 / self.resize
+ landmarks = landmarks.cpu().numpy()
+
+ # ignore low scores
+ inds = np.where(scores > conf_threshold)[0]
+ boxes, landmarks, scores = boxes[inds], landmarks[inds], scores[inds]
+
+ # sort
+ order = scores.argsort()[::-1]
+ boxes, landmarks, scores = boxes[order], landmarks[order], scores[order]
+
+ # do NMS
+ bounding_boxes = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
+ keep = py_cpu_nms(bounding_boxes, nms_threshold)
+ bounding_boxes, landmarks = bounding_boxes[keep, :], landmarks[keep]
+ # self.t['forward_pass'].toc()
+ # print(self.t['forward_pass'].average_time)
+ # import sys
+ # sys.stdout.flush()
+ return np.concatenate((bounding_boxes, landmarks), axis=1)
+
+ def __align_multi(self, image, boxes, landmarks, limit=None):
+
+ if len(boxes) < 1:
+ return [], []
+
+ if limit:
+ boxes = boxes[:limit]
+ landmarks = landmarks[:limit]
+
+ faces = []
+ for landmark in landmarks:
+ facial5points = [[landmark[2 * j], landmark[2 * j + 1]] for j in range(5)]
+
+ warped_face = warp_and_crop_face(np.array(image), facial5points, self.reference, crop_size=(112, 112))
+ faces.append(warped_face)
+
+ return np.concatenate((boxes, landmarks), axis=1), faces
+
+ def align_multi(self, img, conf_threshold=0.8, limit=None):
+
+ rlt = self.detect_faces(img, conf_threshold=conf_threshold)
+ boxes, landmarks = rlt[:, 0:5], rlt[:, 5:]
+
+ return self.__align_multi(img, boxes, landmarks, limit)
+
+ # batched detection
+ def batched_transform(self, frames, use_origin_size):
+ """
+ Arguments:
+ frames: a list of PIL.Image, or torch.Tensor(shape=[n, h, w, c],
+ type=np.float32, BGR format).
+ use_origin_size: whether to use origin size.
+ """
+ from_PIL = True if isinstance(frames[0], Image.Image) else False
+
+ # convert to opencv format
+ if from_PIL:
+ frames = [cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR) for frame in frames]
+ frames = np.asarray(frames, dtype=np.float32)
+
+ # testing scale
+ im_size_min = np.min(frames[0].shape[0:2])
+ im_size_max = np.max(frames[0].shape[0:2])
+ resize = float(self.target_size) / float(im_size_min)
+
+ # prevent bigger axis from being more than max_size
+ if np.round(resize * im_size_max) > self.max_size:
+ resize = float(self.max_size) / float(im_size_max)
+ resize = 1 if use_origin_size else resize
+
+ # resize
+ if resize != 1:
+ if not from_PIL:
+ frames = F.interpolate(frames, scale_factor=resize)
+ else:
+ frames = [
+ cv2.resize(frame, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
+ for frame in frames
+ ]
+
+ # convert to torch.tensor format
+ if not from_PIL:
+ frames = frames.transpose(1, 2).transpose(1, 3).contiguous()
+ else:
+ frames = frames.transpose((0, 3, 1, 2))
+ frames = torch.from_numpy(frames)
+
+ return frames, resize
+
+ def batched_detect_faces(self, frames, conf_threshold=0.8, nms_threshold=0.4, use_origin_size=True):
+ """
+ Arguments:
+ frames: a list of PIL.Image, or np.array(shape=[n, h, w, c],
+ type=np.uint8, BGR format).
+ conf_threshold: confidence threshold.
+ nms_threshold: nms threshold.
+ use_origin_size: whether to use origin size.
+ Returns:
+ final_bounding_boxes: list of np.array ([n_boxes, 5],
+ type=np.float32).
+ final_landmarks: list of np.array ([n_boxes, 10], type=np.float32).
+ """
+ # self.t['forward_pass'].tic()
+ frames, self.resize = self.batched_transform(frames, use_origin_size)
+ frames = frames.to(device)
+ frames = frames - self.mean_tensor
+
+ b_loc, b_conf, b_landmarks, priors = self.__detect_faces(frames)
+
+ final_bounding_boxes, final_landmarks = [], []
+
+ # decode
+ priors = priors.unsqueeze(0)
+ b_loc = batched_decode(b_loc, priors, self.cfg['variance']) * self.scale / self.resize
+ b_landmarks = batched_decode_landm(b_landmarks, priors, self.cfg['variance']) * self.scale1 / self.resize
+ b_conf = b_conf[:, :, 1]
+
+ # index for selection
+ b_indice = b_conf > conf_threshold
+
+ # concat
+ b_loc_and_conf = torch.cat((b_loc, b_conf.unsqueeze(-1)), dim=2).float()
+
+ for pred, landm, inds in zip(b_loc_and_conf, b_landmarks, b_indice):
+
+ # ignore low scores
+ pred, landm = pred[inds, :], landm[inds, :]
+ if pred.shape[0] == 0:
+ final_bounding_boxes.append(np.array([], dtype=np.float32))
+ final_landmarks.append(np.array([], dtype=np.float32))
+ continue
+
+ # sort
+ # order = score.argsort(descending=True)
+ # box, landm, score = box[order], landm[order], score[order]
+
+ # to CPU
+ bounding_boxes, landm = pred.cpu().numpy(), landm.cpu().numpy()
+
+ # NMS
+ keep = py_cpu_nms(bounding_boxes, nms_threshold)
+ bounding_boxes, landmarks = bounding_boxes[keep, :], landm[keep]
+
+ # append
+ final_bounding_boxes.append(bounding_boxes)
+ final_landmarks.append(landmarks)
+ # self.t['forward_pass'].toc(average=True)
+ # self.batch_time += self.t['forward_pass'].diff
+ # self.total_frame += len(frames)
+ # print(self.batch_time / self.total_frame)
+
+ return final_bounding_boxes, final_landmarks
diff --git a/r_facelib/detection/retinaface/retinaface_net.py b/r_facelib/detection/retinaface/retinaface_net.py new file mode 100644 index 0000000..c52535e --- /dev/null +++ b/r_facelib/detection/retinaface/retinaface_net.py @@ -0,0 +1,196 @@ +import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def conv_bn(inp, oup, stride=1, leaky=0):
+ return nn.Sequential(
+ nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup),
+ nn.LeakyReLU(negative_slope=leaky, inplace=True))
+
+
+def conv_bn_no_relu(inp, oup, stride):
+ return nn.Sequential(
+ nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
+ nn.BatchNorm2d(oup),
+ )
+
+
+def conv_bn1X1(inp, oup, stride, leaky=0):
+ return nn.Sequential(
+ nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False), nn.BatchNorm2d(oup),
+ nn.LeakyReLU(negative_slope=leaky, inplace=True))
+
+
+def conv_dw(inp, oup, stride, leaky=0.1):
+ return nn.Sequential(
+ nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
+ nn.BatchNorm2d(inp),
+ nn.LeakyReLU(negative_slope=leaky, inplace=True),
+ nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
+ nn.BatchNorm2d(oup),
+ nn.LeakyReLU(negative_slope=leaky, inplace=True),
+ )
+
+
+class SSH(nn.Module):
+
+ def __init__(self, in_channel, out_channel):
+ super(SSH, self).__init__()
+ assert out_channel % 4 == 0
+ leaky = 0
+ if (out_channel <= 64):
+ leaky = 0.1
+ self.conv3X3 = conv_bn_no_relu(in_channel, out_channel // 2, stride=1)
+
+ self.conv5X5_1 = conv_bn(in_channel, out_channel // 4, stride=1, leaky=leaky)
+ self.conv5X5_2 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
+
+ self.conv7X7_2 = conv_bn(out_channel // 4, out_channel // 4, stride=1, leaky=leaky)
+ self.conv7x7_3 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
+
+ def forward(self, input):
+ conv3X3 = self.conv3X3(input)
+
+ conv5X5_1 = self.conv5X5_1(input)
+ conv5X5 = self.conv5X5_2(conv5X5_1)
+
+ conv7X7_2 = self.conv7X7_2(conv5X5_1)
+ conv7X7 = self.conv7x7_3(conv7X7_2)
+
+ out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1)
+ out = F.relu(out)
+ return out
+
+
+class FPN(nn.Module):
+
+ def __init__(self, in_channels_list, out_channels):
+ super(FPN, self).__init__()
+ leaky = 0
+ if (out_channels <= 64):
+ leaky = 0.1
+ self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride=1, leaky=leaky)
+ self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride=1, leaky=leaky)
+ self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride=1, leaky=leaky)
+
+ self.merge1 = conv_bn(out_channels, out_channels, leaky=leaky)
+ self.merge2 = conv_bn(out_channels, out_channels, leaky=leaky)
+
+ def forward(self, input):
+ # names = list(input.keys())
+ # input = list(input.values())
+
+ output1 = self.output1(input[0])
+ output2 = self.output2(input[1])
+ output3 = self.output3(input[2])
+
+ up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode='nearest')
+ output2 = output2 + up3
+ output2 = self.merge2(output2)
+
+ up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode='nearest')
+ output1 = output1 + up2
+ output1 = self.merge1(output1)
+
+ out = [output1, output2, output3]
+ return out
+
+
+class MobileNetV1(nn.Module):
+
+ def __init__(self):
+ super(MobileNetV1, self).__init__()
+ self.stage1 = nn.Sequential(
+ conv_bn(3, 8, 2, leaky=0.1), # 3
+ conv_dw(8, 16, 1), # 7
+ conv_dw(16, 32, 2), # 11
+ conv_dw(32, 32, 1), # 19
+ conv_dw(32, 64, 2), # 27
+ conv_dw(64, 64, 1), # 43
+ )
+ self.stage2 = nn.Sequential(
+ conv_dw(64, 128, 2), # 43 + 16 = 59
+ conv_dw(128, 128, 1), # 59 + 32 = 91
+ conv_dw(128, 128, 1), # 91 + 32 = 123
+ conv_dw(128, 128, 1), # 123 + 32 = 155
+ conv_dw(128, 128, 1), # 155 + 32 = 187
+ conv_dw(128, 128, 1), # 187 + 32 = 219
+ )
+ self.stage3 = nn.Sequential(
+ conv_dw(128, 256, 2), # 219 +3 2 = 241
+ conv_dw(256, 256, 1), # 241 + 64 = 301
+ )
+ self.avg = nn.AdaptiveAvgPool2d((1, 1))
+ self.fc = nn.Linear(256, 1000)
+
+ def forward(self, x):
+ x = self.stage1(x)
+ x = self.stage2(x)
+ x = self.stage3(x)
+ x = self.avg(x)
+ # x = self.model(x)
+ x = x.view(-1, 256)
+ x = self.fc(x)
+ return x
+
+
+class ClassHead(nn.Module):
+
+ def __init__(self, inchannels=512, num_anchors=3):
+ super(ClassHead, self).__init__()
+ self.num_anchors = num_anchors
+ self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2, kernel_size=(1, 1), stride=1, padding=0)
+
+ def forward(self, x):
+ out = self.conv1x1(x)
+ out = out.permute(0, 2, 3, 1).contiguous()
+
+ return out.view(out.shape[0], -1, 2)
+
+
+class BboxHead(nn.Module):
+
+ def __init__(self, inchannels=512, num_anchors=3):
+ super(BboxHead, self).__init__()
+ self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(1, 1), stride=1, padding=0)
+
+ def forward(self, x):
+ out = self.conv1x1(x)
+ out = out.permute(0, 2, 3, 1).contiguous()
+
+ return out.view(out.shape[0], -1, 4)
+
+
+class LandmarkHead(nn.Module):
+
+ def __init__(self, inchannels=512, num_anchors=3):
+ super(LandmarkHead, self).__init__()
+ self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=(1, 1), stride=1, padding=0)
+
+ def forward(self, x):
+ out = self.conv1x1(x)
+ out = out.permute(0, 2, 3, 1).contiguous()
+
+ return out.view(out.shape[0], -1, 10)
+
+
+def make_class_head(fpn_num=3, inchannels=64, anchor_num=2):
+ classhead = nn.ModuleList()
+ for i in range(fpn_num):
+ classhead.append(ClassHead(inchannels, anchor_num))
+ return classhead
+
+
+def make_bbox_head(fpn_num=3, inchannels=64, anchor_num=2):
+ bboxhead = nn.ModuleList()
+ for i in range(fpn_num):
+ bboxhead.append(BboxHead(inchannels, anchor_num))
+ return bboxhead
+
+
+def make_landmark_head(fpn_num=3, inchannels=64, anchor_num=2):
+ landmarkhead = nn.ModuleList()
+ for i in range(fpn_num):
+ landmarkhead.append(LandmarkHead(inchannels, anchor_num))
+ return landmarkhead
diff --git a/r_facelib/detection/retinaface/retinaface_utils.py b/r_facelib/detection/retinaface/retinaface_utils.py new file mode 100644 index 0000000..f19e320 --- /dev/null +++ b/r_facelib/detection/retinaface/retinaface_utils.py @@ -0,0 +1,421 @@ +import numpy as np
+import torch
+import torchvision
+from itertools import product as product
+from math import ceil
+
+
+class PriorBox(object):
+
+ def __init__(self, cfg, image_size=None, phase='train'):
+ super(PriorBox, self).__init__()
+ self.min_sizes = cfg['min_sizes']
+ self.steps = cfg['steps']
+ self.clip = cfg['clip']
+ self.image_size = image_size
+ self.feature_maps = [[ceil(self.image_size[0] / step), ceil(self.image_size[1] / step)] for step in self.steps]
+ self.name = 's'
+
+ def forward(self):
+ anchors = []
+ for k, f in enumerate(self.feature_maps):
+ min_sizes = self.min_sizes[k]
+ for i, j in product(range(f[0]), range(f[1])):
+ for min_size in min_sizes:
+ s_kx = min_size / self.image_size[1]
+ s_ky = min_size / self.image_size[0]
+ dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
+ dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
+ for cy, cx in product(dense_cy, dense_cx):
+ anchors += [cx, cy, s_kx, s_ky]
+
+ # back to torch land
+ output = torch.Tensor(anchors).view(-1, 4)
+ if self.clip:
+ output.clamp_(max=1, min=0)
+ return output
+
+
+def py_cpu_nms(dets, thresh):
+ """Pure Python NMS baseline."""
+ keep = torchvision.ops.nms(
+ boxes=torch.Tensor(dets[:, :4]),
+ scores=torch.Tensor(dets[:, 4]),
+ iou_threshold=thresh,
+ )
+
+ return list(keep)
+
+
+def point_form(boxes):
+ """ Convert prior_boxes to (xmin, ymin, xmax, ymax)
+ representation for comparison to point form ground truth data.
+ Args:
+ boxes: (tensor) center-size default boxes from priorbox layers.
+ Return:
+ boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
+ """
+ return torch.cat(
+ (
+ boxes[:, :2] - boxes[:, 2:] / 2, # xmin, ymin
+ boxes[:, :2] + boxes[:, 2:] / 2),
+ 1) # xmax, ymax
+
+
+def center_size(boxes):
+ """ Convert prior_boxes to (cx, cy, w, h)
+ representation for comparison to center-size form ground truth data.
+ Args:
+ boxes: (tensor) point_form boxes
+ Return:
+ boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
+ """
+ return torch.cat(
+ (boxes[:, 2:] + boxes[:, :2]) / 2, # cx, cy
+ boxes[:, 2:] - boxes[:, :2],
+ 1) # w, h
+
+
+def intersect(box_a, box_b):
+ """ We resize both tensors to [A,B,2] without new malloc:
+ [A,2] -> [A,1,2] -> [A,B,2]
+ [B,2] -> [1,B,2] -> [A,B,2]
+ Then we compute the area of intersect between box_a and box_b.
+ Args:
+ box_a: (tensor) bounding boxes, Shape: [A,4].
+ box_b: (tensor) bounding boxes, Shape: [B,4].
+ Return:
+ (tensor) intersection area, Shape: [A,B].
+ """
+ A = box_a.size(0)
+ B = box_b.size(0)
+ max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
+ min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), box_b[:, :2].unsqueeze(0).expand(A, B, 2))
+ inter = torch.clamp((max_xy - min_xy), min=0)
+ return inter[:, :, 0] * inter[:, :, 1]
+
+
+def jaccard(box_a, box_b):
+ """Compute the jaccard overlap of two sets of boxes. The jaccard overlap
+ is simply the intersection over union of two boxes. Here we operate on
+ ground truth boxes and default boxes.
+ E.g.:
+ A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
+ Args:
+ box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]
+ box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]
+ Return:
+ jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
+ """
+ inter = intersect(box_a, box_b)
+ area_a = ((box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B]
+ area_b = ((box_b[:, 2] - box_b[:, 0]) * (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B]
+ union = area_a + area_b - inter
+ return inter / union # [A,B]
+
+
+def matrix_iou(a, b):
+ """
+ return iou of a and b, numpy version for data augenmentation
+ """
+ lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
+ rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
+
+ area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
+ area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
+ area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
+ return area_i / (area_a[:, np.newaxis] + area_b - area_i)
+
+
+def matrix_iof(a, b):
+ """
+ return iof of a and b, numpy version for data augenmentation
+ """
+ lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
+ rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
+
+ area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
+ area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
+ return area_i / np.maximum(area_a[:, np.newaxis], 1)
+
+
+def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):
+ """Match each prior box with the ground truth box of the highest jaccard
+ overlap, encode the bounding boxes, then return the matched indices
+ corresponding to both confidence and location preds.
+ Args:
+ threshold: (float) The overlap threshold used when matching boxes.
+ truths: (tensor) Ground truth boxes, Shape: [num_obj, 4].
+ priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
+ variances: (tensor) Variances corresponding to each prior coord,
+ Shape: [num_priors, 4].
+ labels: (tensor) All the class labels for the image, Shape: [num_obj].
+ landms: (tensor) Ground truth landms, Shape [num_obj, 10].
+ loc_t: (tensor) Tensor to be filled w/ encoded location targets.
+ conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
+ landm_t: (tensor) Tensor to be filled w/ encoded landm targets.
+ idx: (int) current batch index
+ Return:
+ The matched indices corresponding to 1)location 2)confidence
+ 3)landm preds.
+ """
+ # jaccard index
+ overlaps = jaccard(truths, point_form(priors))
+ # (Bipartite Matching)
+ # [1,num_objects] best prior for each ground truth
+ best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)
+
+ # ignore hard gt
+ valid_gt_idx = best_prior_overlap[:, 0] >= 0.2
+ best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]
+ if best_prior_idx_filter.shape[0] <= 0:
+ loc_t[idx] = 0
+ conf_t[idx] = 0
+ return
+
+ # [1,num_priors] best ground truth for each prior
+ best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)
+ best_truth_idx.squeeze_(0)
+ best_truth_overlap.squeeze_(0)
+ best_prior_idx.squeeze_(1)
+ best_prior_idx_filter.squeeze_(1)
+ best_prior_overlap.squeeze_(1)
+ best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2) # ensure best prior
+ # TODO refactor: index best_prior_idx with long tensor
+ # ensure every gt matches with its prior of max overlap
+ for j in range(best_prior_idx.size(0)): # 判别此anchor是预测哪一个boxes
+ best_truth_idx[best_prior_idx[j]] = j
+ matches = truths[best_truth_idx] # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来
+ conf = labels[best_truth_idx] # Shape: [num_priors] 此处为每一个anchor对应的label取出来
+ conf[best_truth_overlap < threshold] = 0 # label as background overlap<0.35的全部作为负样本
+ loc = encode(matches, priors, variances)
+
+ matches_landm = landms[best_truth_idx]
+ landm = encode_landm(matches_landm, priors, variances)
+ loc_t[idx] = loc # [num_priors,4] encoded offsets to learn
+ conf_t[idx] = conf # [num_priors] top class label for each prior
+ landm_t[idx] = landm
+
+
+def encode(matched, priors, variances):
+ """Encode the variances from the priorbox layers into the ground truth boxes
+ we have matched (based on jaccard overlap) with the prior boxes.
+ Args:
+ matched: (tensor) Coords of ground truth for each prior in point-form
+ Shape: [num_priors, 4].
+ priors: (tensor) Prior boxes in center-offset form
+ Shape: [num_priors,4].
+ variances: (list[float]) Variances of priorboxes
+ Return:
+ encoded boxes (tensor), Shape: [num_priors, 4]
+ """
+
+ # dist b/t match center and prior's center
+ g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
+ # encode variance
+ g_cxcy /= (variances[0] * priors[:, 2:])
+ # match wh / prior wh
+ g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
+ g_wh = torch.log(g_wh) / variances[1]
+ # return target for smooth_l1_loss
+ return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4]
+
+
+def encode_landm(matched, priors, variances):
+ """Encode the variances from the priorbox layers into the ground truth boxes
+ we have matched (based on jaccard overlap) with the prior boxes.
+ Args:
+ matched: (tensor) Coords of ground truth for each prior in point-form
+ Shape: [num_priors, 10].
+ priors: (tensor) Prior boxes in center-offset form
+ Shape: [num_priors,4].
+ variances: (list[float]) Variances of priorboxes
+ Return:
+ encoded landm (tensor), Shape: [num_priors, 10]
+ """
+
+ # dist b/t match center and prior's center
+ matched = torch.reshape(matched, (matched.size(0), 5, 2))
+ priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+ priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+ priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+ priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+ priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2)
+ g_cxcy = matched[:, :, :2] - priors[:, :, :2]
+ # encode variance
+ g_cxcy /= (variances[0] * priors[:, :, 2:])
+ # g_cxcy /= priors[:, :, 2:]
+ g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1)
+ # return target for smooth_l1_loss
+ return g_cxcy
+
+
+# Adapted from https://github.com/Hakuyume/chainer-ssd
+def decode(loc, priors, variances):
+ """Decode locations from predictions using priors to undo
+ the encoding we did for offset regression at train time.
+ Args:
+ loc (tensor): location predictions for loc layers,
+ Shape: [num_priors,4]
+ priors (tensor): Prior boxes in center-offset form.
+ Shape: [num_priors,4].
+ variances: (list[float]) Variances of priorboxes
+ Return:
+ decoded bounding box predictions
+ """
+
+ boxes = torch.cat((priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
+ priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
+ boxes[:, :2] -= boxes[:, 2:] / 2
+ boxes[:, 2:] += boxes[:, :2]
+ return boxes
+
+
+def decode_landm(pre, priors, variances):
+ """Decode landm from predictions using priors to undo
+ the encoding we did for offset regression at train time.
+ Args:
+ pre (tensor): landm predictions for loc layers,
+ Shape: [num_priors,10]
+ priors (tensor): Prior boxes in center-offset form.
+ Shape: [num_priors,4].
+ variances: (list[float]) Variances of priorboxes
+ Return:
+ decoded landm predictions
+ """
+ tmp = (
+ priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
+ priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],
+ priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],
+ priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],
+ priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],
+ )
+ landms = torch.cat(tmp, dim=1)
+ return landms
+
+
+def batched_decode(b_loc, priors, variances):
+ """Decode locations from predictions using priors to undo
+ the encoding we did for offset regression at train time.
+ Args:
+ b_loc (tensor): location predictions for loc layers,
+ Shape: [num_batches,num_priors,4]
+ priors (tensor): Prior boxes in center-offset form.
+ Shape: [1,num_priors,4].
+ variances: (list[float]) Variances of priorboxes
+ Return:
+ decoded bounding box predictions
+ """
+ boxes = (
+ priors[:, :, :2] + b_loc[:, :, :2] * variances[0] * priors[:, :, 2:],
+ priors[:, :, 2:] * torch.exp(b_loc[:, :, 2:] * variances[1]),
+ )
+ boxes = torch.cat(boxes, dim=2)
+
+ boxes[:, :, :2] -= boxes[:, :, 2:] / 2
+ boxes[:, :, 2:] += boxes[:, :, :2]
+ return boxes
+
+
+def batched_decode_landm(pre, priors, variances):
+ """Decode landm from predictions using priors to undo
+ the encoding we did for offset regression at train time.
+ Args:
+ pre (tensor): landm predictions for loc layers,
+ Shape: [num_batches,num_priors,10]
+ priors (tensor): Prior boxes in center-offset form.
+ Shape: [1,num_priors,4].
+ variances: (list[float]) Variances of priorboxes
+ Return:
+ decoded landm predictions
+ """
+ landms = (
+ priors[:, :, :2] + pre[:, :, :2] * variances[0] * priors[:, :, 2:],
+ priors[:, :, :2] + pre[:, :, 2:4] * variances[0] * priors[:, :, 2:],
+ priors[:, :, :2] + pre[:, :, 4:6] * variances[0] * priors[:, :, 2:],
+ priors[:, :, :2] + pre[:, :, 6:8] * variances[0] * priors[:, :, 2:],
+ priors[:, :, :2] + pre[:, :, 8:10] * variances[0] * priors[:, :, 2:],
+ )
+ landms = torch.cat(landms, dim=2)
+ return landms
+
+
+def log_sum_exp(x):
+ """Utility function for computing log_sum_exp while determining
+ This will be used to determine unaveraged confidence loss across
+ all examples in a batch.
+ Args:
+ x (Variable(tensor)): conf_preds from conf layers
+ """
+ x_max = x.data.max()
+ return torch.log(torch.sum(torch.exp(x - x_max), 1, keepdim=True)) + x_max
+
+
+# Original author: Francisco Massa:
+# https://github.com/fmassa/object-detection.torch
+# Ported to PyTorch by Max deGroot (02/01/2017)
+def nms(boxes, scores, overlap=0.5, top_k=200):
+ """Apply non-maximum suppression at test time to avoid detecting too many
+ overlapping bounding boxes for a given object.
+ Args:
+ boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
+ scores: (tensor) The class predscores for the img, Shape:[num_priors].
+ overlap: (float) The overlap thresh for suppressing unnecessary boxes.
+ top_k: (int) The Maximum number of box preds to consider.
+ Return:
+ The indices of the kept boxes with respect to num_priors.
+ """
+
+ keep = torch.Tensor(scores.size(0)).fill_(0).long()
+ if boxes.numel() == 0:
+ return keep
+ x1 = boxes[:, 0]
+ y1 = boxes[:, 1]
+ x2 = boxes[:, 2]
+ y2 = boxes[:, 3]
+ area = torch.mul(x2 - x1, y2 - y1)
+ v, idx = scores.sort(0) # sort in ascending order
+ # I = I[v >= 0.01]
+ idx = idx[-top_k:] # indices of the top-k largest vals
+ xx1 = boxes.new()
+ yy1 = boxes.new()
+ xx2 = boxes.new()
+ yy2 = boxes.new()
+ w = boxes.new()
+ h = boxes.new()
+
+ # keep = torch.Tensor()
+ count = 0
+ while idx.numel() > 0:
+ i = idx[-1] # index of current largest val
+ # keep.append(i)
+ keep[count] = i
+ count += 1
+ if idx.size(0) == 1:
+ break
+ idx = idx[:-1] # remove kept element from view
+ # load bboxes of next highest vals
+ torch.index_select(x1, 0, idx, out=xx1)
+ torch.index_select(y1, 0, idx, out=yy1)
+ torch.index_select(x2, 0, idx, out=xx2)
+ torch.index_select(y2, 0, idx, out=yy2)
+ # store element-wise max with next highest score
+ xx1 = torch.clamp(xx1, min=x1[i])
+ yy1 = torch.clamp(yy1, min=y1[i])
+ xx2 = torch.clamp(xx2, max=x2[i])
+ yy2 = torch.clamp(yy2, max=y2[i])
+ w.resize_as_(xx2)
+ h.resize_as_(yy2)
+ w = xx2 - xx1
+ h = yy2 - yy1
+ # check sizes of xx1 and xx2.. after each iteration
+ w = torch.clamp(w, min=0.0)
+ h = torch.clamp(h, min=0.0)
+ inter = w * h
+ # IoU = i / (area(a) + area(b) - i)
+ rem_areas = torch.index_select(area, 0, idx) # load remaining areas)
+ union = (rem_areas - inter) + area[i]
+ IoU = inter / union # store result in iou
+ # keep only elements with an IoU <= overlap
+ idx = idx[IoU.le(overlap)]
+ return keep, count
diff --git a/r_facelib/detection/yolov5face/__init__.py b/r_facelib/detection/yolov5face/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/r_facelib/detection/yolov5face/__init__.py diff --git a/r_facelib/detection/yolov5face/face_detector.py b/r_facelib/detection/yolov5face/face_detector.py new file mode 100644 index 0000000..ca6d8e3 --- /dev/null +++ b/r_facelib/detection/yolov5face/face_detector.py @@ -0,0 +1,141 @@ +import copy
+from pathlib import Path
+
+import cv2
+import numpy as np
+import torch
+from torch import torch_version
+
+from r_facelib.detection.yolov5face.models.common import Conv
+from r_facelib.detection.yolov5face.models.yolo import Model
+from r_facelib.detection.yolov5face.utils.datasets import letterbox
+from r_facelib.detection.yolov5face.utils.general import (
+ check_img_size,
+ non_max_suppression_face,
+ scale_coords,
+ scale_coords_landmarks,
+)
+
+print(f"Torch version: {torch.__version__}")
+IS_HIGH_VERSION = torch_version.__version__ >= "1.9.0"
+
+def isListempty(inList):
+ if isinstance(inList, list): # Is a list
+ return all(map(isListempty, inList))
+ return False # Not a list
+
+class YoloDetector:
+ def __init__(
+ self,
+ config_name,
+ min_face=10,
+ target_size=None,
+ device='cuda',
+ ):
+ """
+ config_name: name of .yaml config with network configuration from models/ folder.
+ min_face : minimal face size in pixels.
+ target_size : target size of smaller image axis (choose lower for faster work). e.g. 480, 720, 1080.
+ None for original resolution.
+ """
+ self._class_path = Path(__file__).parent.absolute()
+ self.target_size = target_size
+ self.min_face = min_face
+ self.detector = Model(cfg=config_name)
+ self.device = device
+
+
+ def _preprocess(self, imgs):
+ """
+ Preprocessing image before passing through the network. Resize and conversion to torch tensor.
+ """
+ pp_imgs = []
+ for img in imgs:
+ h0, w0 = img.shape[:2] # orig hw
+ if self.target_size:
+ r = self.target_size / min(h0, w0) # resize image to img_size
+ if r < 1:
+ img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_LINEAR)
+
+ imgsz = check_img_size(max(img.shape[:2]), s=self.detector.stride.max()) # check img_size
+ img = letterbox(img, new_shape=imgsz)[0]
+ pp_imgs.append(img)
+ pp_imgs = np.array(pp_imgs)
+ pp_imgs = pp_imgs.transpose(0, 3, 1, 2)
+ pp_imgs = torch.from_numpy(pp_imgs).to(self.device)
+ pp_imgs = pp_imgs.float() # uint8 to fp16/32
+ return pp_imgs / 255.0 # 0 - 255 to 0.0 - 1.0
+
+ def _postprocess(self, imgs, origimgs, pred, conf_thres, iou_thres):
+ """
+ Postprocessing of raw pytorch model output.
+ Returns:
+ bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2.
+ points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners).
+ """
+ bboxes = [[] for _ in range(len(origimgs))]
+ landmarks = [[] for _ in range(len(origimgs))]
+
+ pred = non_max_suppression_face(pred, conf_thres, iou_thres)
+
+ for image_id, origimg in enumerate(origimgs):
+ img_shape = origimg.shape
+ image_height, image_width = img_shape[:2]
+ gn = torch.tensor(img_shape)[[1, 0, 1, 0]] # normalization gain whwh
+ gn_lks = torch.tensor(img_shape)[[1, 0, 1, 0, 1, 0, 1, 0, 1, 0]] # normalization gain landmarks
+ det = pred[image_id].cpu()
+ scale_coords(imgs[image_id].shape[1:], det[:, :4], img_shape).round()
+ scale_coords_landmarks(imgs[image_id].shape[1:], det[:, 5:15], img_shape).round()
+
+ for j in range(det.size()[0]):
+ box = (det[j, :4].view(1, 4) / gn).view(-1).tolist()
+ box = list(
+ map(int, [box[0] * image_width, box[1] * image_height, box[2] * image_width, box[3] * image_height])
+ )
+ if box[3] - box[1] < self.min_face:
+ continue
+ lm = (det[j, 5:15].view(1, 10) / gn_lks).view(-1).tolist()
+ lm = list(map(int, [i * image_width if j % 2 == 0 else i * image_height for j, i in enumerate(lm)]))
+ lm = [lm[i : i + 2] for i in range(0, len(lm), 2)]
+ bboxes[image_id].append(box)
+ landmarks[image_id].append(lm)
+ return bboxes, landmarks
+
+ def detect_faces(self, imgs, conf_thres=0.7, iou_thres=0.5):
+ """
+ Get bbox coordinates and keypoints of faces on original image.
+ Params:
+ imgs: image or list of images to detect faces on with BGR order (convert to RGB order for inference)
+ conf_thres: confidence threshold for each prediction
+ iou_thres: threshold for NMS (filter of intersecting bboxes)
+ Returns:
+ bboxes: list of arrays with 4 coordinates of bounding boxes with format x1,y1,x2,y2.
+ points: list of arrays with coordinates of 5 facial keypoints (eyes, nose, lips corners).
+ """
+ # Pass input images through face detector
+ images = imgs if isinstance(imgs, list) else [imgs]
+ images = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in images]
+ origimgs = copy.deepcopy(images)
+
+ images = self._preprocess(images)
+
+ if IS_HIGH_VERSION:
+ with torch.inference_mode(): # for pytorch>=1.9
+ pred = self.detector(images)[0]
+ else:
+ with torch.no_grad(): # for pytorch<1.9
+ pred = self.detector(images)[0]
+
+ bboxes, points = self._postprocess(images, origimgs, pred, conf_thres, iou_thres)
+
+ # return bboxes, points
+ if not isListempty(points):
+ bboxes = np.array(bboxes).reshape(-1,4)
+ points = np.array(points).reshape(-1,10)
+ padding = bboxes[:,0].reshape(-1,1)
+ return np.concatenate((bboxes, padding, points), axis=1)
+ else:
+ return None
+
+ def __call__(self, *args):
+ return self.predict(*args)
diff --git a/r_facelib/detection/yolov5face/models/__init__.py b/r_facelib/detection/yolov5face/models/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/r_facelib/detection/yolov5face/models/__init__.py diff --git a/r_facelib/detection/yolov5face/models/common.py b/r_facelib/detection/yolov5face/models/common.py new file mode 100644 index 0000000..96894d5 --- /dev/null +++ b/r_facelib/detection/yolov5face/models/common.py @@ -0,0 +1,299 @@ +# This file contains modules common to various models
+
+import math
+
+import numpy as np
+import torch
+from torch import nn
+
+from r_facelib.detection.yolov5face.utils.datasets import letterbox
+from r_facelib.detection.yolov5face.utils.general import (
+ make_divisible,
+ non_max_suppression,
+ scale_coords,
+ xyxy2xywh,
+)
+
+
+def autopad(k, p=None): # kernel, padding
+ # Pad to 'same'
+ if p is None:
+ p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
+ return p
+
+
+def channel_shuffle(x, groups):
+ batchsize, num_channels, height, width = x.data.size()
+ channels_per_group = torch.div(num_channels, groups, rounding_mode="trunc")
+
+ # reshape
+ x = x.view(batchsize, groups, channels_per_group, height, width)
+ x = torch.transpose(x, 1, 2).contiguous()
+
+ # flatten
+ return x.view(batchsize, -1, height, width)
+
+
+def DWConv(c1, c2, k=1, s=1, act=True):
+ # Depthwise convolution
+ return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
+
+
+class Conv(nn.Module):
+ # Standard convolution
+ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
+ super().__init__()
+ self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
+ self.bn = nn.BatchNorm2d(c2)
+ self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
+
+ def forward(self, x):
+ return self.act(self.bn(self.conv(x)))
+
+ def fuseforward(self, x):
+ return self.act(self.conv(x))
+
+
+class StemBlock(nn.Module):
+ def __init__(self, c1, c2, k=3, s=2, p=None, g=1, act=True):
+ super().__init__()
+ self.stem_1 = Conv(c1, c2, k, s, p, g, act)
+ self.stem_2a = Conv(c2, c2 // 2, 1, 1, 0)
+ self.stem_2b = Conv(c2 // 2, c2, 3, 2, 1)
+ self.stem_2p = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
+ self.stem_3 = Conv(c2 * 2, c2, 1, 1, 0)
+
+ def forward(self, x):
+ stem_1_out = self.stem_1(x)
+ stem_2a_out = self.stem_2a(stem_1_out)
+ stem_2b_out = self.stem_2b(stem_2a_out)
+ stem_2p_out = self.stem_2p(stem_1_out)
+ return self.stem_3(torch.cat((stem_2b_out, stem_2p_out), 1))
+
+
+class Bottleneck(nn.Module):
+ # Standard bottleneck
+ def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = Conv(c_, c2, 3, 1, g=g)
+ self.add = shortcut and c1 == c2
+
+ def forward(self, x):
+ return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class BottleneckCSP(nn.Module):
+ # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
+ self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
+ self.cv4 = Conv(2 * c_, c2, 1, 1)
+ self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
+ self.act = nn.LeakyReLU(0.1, inplace=True)
+ self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+
+ def forward(self, x):
+ y1 = self.cv3(self.m(self.cv1(x)))
+ y2 = self.cv2(x)
+ return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
+
+
+class C3(nn.Module):
+ # CSP Bottleneck with 3 convolutions
+ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = Conv(c1, c_, 1, 1)
+ self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
+ self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
+
+ def forward(self, x):
+ return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
+
+
+class ShuffleV2Block(nn.Module):
+ def __init__(self, inp, oup, stride):
+ super().__init__()
+
+ if not 1 <= stride <= 3:
+ raise ValueError("illegal stride value")
+ self.stride = stride
+
+ branch_features = oup // 2
+
+ if self.stride > 1:
+ self.branch1 = nn.Sequential(
+ self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
+ nn.BatchNorm2d(inp),
+ nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
+ nn.BatchNorm2d(branch_features),
+ nn.SiLU(),
+ )
+ else:
+ self.branch1 = nn.Sequential()
+
+ self.branch2 = nn.Sequential(
+ nn.Conv2d(
+ inp if (self.stride > 1) else branch_features,
+ branch_features,
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ bias=False,
+ ),
+ nn.BatchNorm2d(branch_features),
+ nn.SiLU(),
+ self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
+ nn.BatchNorm2d(branch_features),
+ nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
+ nn.BatchNorm2d(branch_features),
+ nn.SiLU(),
+ )
+
+ @staticmethod
+ def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
+ return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
+
+ def forward(self, x):
+ if self.stride == 1:
+ x1, x2 = x.chunk(2, dim=1)
+ out = torch.cat((x1, self.branch2(x2)), dim=1)
+ else:
+ out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
+ out = channel_shuffle(out, 2)
+ return out
+
+
+class SPP(nn.Module):
+ # Spatial pyramid pooling layer used in YOLOv3-SPP
+ def __init__(self, c1, c2, k=(5, 9, 13)):
+ super().__init__()
+ c_ = c1 // 2 # hidden channels
+ self.cv1 = Conv(c1, c_, 1, 1)
+ self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
+ self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
+
+ def forward(self, x):
+ x = self.cv1(x)
+ return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
+
+
+class Focus(nn.Module):
+ # Focus wh information into c-space
+ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
+ super().__init__()
+ self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
+
+ def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
+ return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
+
+
+class Concat(nn.Module):
+ # Concatenate a list of tensors along dimension
+ def __init__(self, dimension=1):
+ super().__init__()
+ self.d = dimension
+
+ def forward(self, x):
+ return torch.cat(x, self.d)
+
+
+class NMS(nn.Module):
+ # Non-Maximum Suppression (NMS) module
+ conf = 0.25 # confidence threshold
+ iou = 0.45 # IoU threshold
+ classes = None # (optional list) filter by class
+
+ def forward(self, x):
+ return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
+
+
+class AutoShape(nn.Module):
+ # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+ img_size = 640 # inference size (pixels)
+ conf = 0.25 # NMS confidence threshold
+ iou = 0.45 # NMS IoU threshold
+ classes = None # (optional list) filter by class
+
+ def __init__(self, model):
+ super().__init__()
+ self.model = model.eval()
+
+ def autoshape(self):
+ print("autoShape already enabled, skipping... ") # model already converted to model.autoshape()
+ return self
+
+ def forward(self, imgs, size=640, augment=False, profile=False):
+ # Inference from various sources. For height=720, width=1280, RGB images example inputs are:
+ # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3)
+ # PIL: = Image.open('image.jpg') # HWC x(720,1280,3)
+ # numpy: = np.zeros((720,1280,3)) # HWC
+ # torch: = torch.zeros(16,3,720,1280) # BCHW
+ # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
+
+ p = next(self.model.parameters()) # for device and type
+ if isinstance(imgs, torch.Tensor): # torch
+ return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
+
+ # Pre-process
+ n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
+ shape0, shape1 = [], [] # image and inference shapes
+ for i, im in enumerate(imgs):
+ im = np.array(im) # to numpy
+ if im.shape[0] < 5: # image in CHW
+ im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
+ im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input
+ s = im.shape[:2] # HWC
+ shape0.append(s) # image shape
+ g = size / max(s) # gain
+ shape1.append([y * g for y in s])
+ imgs[i] = im # update
+ shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
+ x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
+ x = np.stack(x, 0) if n > 1 else x[0][None] # stack
+ x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
+ x = torch.from_numpy(x).to(p.device).type_as(p) / 255.0 # uint8 to fp16/32
+
+ # Inference
+ with torch.no_grad():
+ y = self.model(x, augment, profile)[0] # forward
+ y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
+
+ # Post-process
+ for i in range(n):
+ scale_coords(shape1, y[i][:, :4], shape0[i])
+
+ return Detections(imgs, y, self.names)
+
+
+class Detections:
+ # detections class for YOLOv5 inference results
+ def __init__(self, imgs, pred, names=None):
+ super().__init__()
+ d = pred[0].device # device
+ gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1.0, 1.0], device=d) for im in imgs] # normalizations
+ self.imgs = imgs # list of images as numpy arrays
+ self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
+ self.names = names # class names
+ self.xyxy = pred # xyxy pixels
+ self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
+ self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
+ self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
+ self.n = len(self.pred)
+
+ def __len__(self):
+ return self.n
+
+ def tolist(self):
+ # return a list of Detections objects, i.e. 'for result in results.tolist():'
+ x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)]
+ for d in x:
+ for k in ["imgs", "pred", "xyxy", "xyxyn", "xywh", "xywhn"]:
+ setattr(d, k, getattr(d, k)[0]) # pop out of list
+ return x
diff --git a/r_facelib/detection/yolov5face/models/experimental.py b/r_facelib/detection/yolov5face/models/experimental.py new file mode 100644 index 0000000..bdf7aea --- /dev/null +++ b/r_facelib/detection/yolov5face/models/experimental.py @@ -0,0 +1,45 @@ +# # This file contains experimental modules
+
+import numpy as np
+import torch
+from torch import nn
+
+from r_facelib.detection.yolov5face.models.common import Conv
+
+
+class CrossConv(nn.Module):
+ # Cross Convolution Downsample
+ def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
+ # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
+ super().__init__()
+ c_ = int(c2 * e) # hidden channels
+ self.cv1 = Conv(c1, c_, (1, k), (1, s))
+ self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
+ self.add = shortcut and c1 == c2
+
+ def forward(self, x):
+ return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+
+
+class MixConv2d(nn.Module):
+ # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
+ def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
+ super().__init__()
+ groups = len(k)
+ if equal_ch: # equal c_ per group
+ i = torch.linspace(0, groups - 1e-6, c2).floor() # c2 indices
+ c_ = [(i == g).sum() for g in range(groups)] # intermediate channels
+ else: # equal weight.numel() per group
+ b = [c2] + [0] * groups
+ a = np.eye(groups + 1, groups, k=-1)
+ a -= np.roll(a, 1, axis=1)
+ a *= np.array(k) ** 2
+ a[0] = 1
+ c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
+
+ self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
+ self.bn = nn.BatchNorm2d(c2)
+ self.act = nn.LeakyReLU(0.1, inplace=True)
+
+ def forward(self, x):
+ return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
diff --git a/r_facelib/detection/yolov5face/models/yolo.py b/r_facelib/detection/yolov5face/models/yolo.py new file mode 100644 index 0000000..02479dc --- /dev/null +++ b/r_facelib/detection/yolov5face/models/yolo.py @@ -0,0 +1,235 @@ +import math
+from copy import deepcopy
+from pathlib import Path
+
+import torch
+import yaml # for torch hub
+from torch import nn
+
+from r_facelib.detection.yolov5face.models.common import (
+ C3,
+ NMS,
+ SPP,
+ AutoShape,
+ Bottleneck,
+ BottleneckCSP,
+ Concat,
+ Conv,
+ DWConv,
+ Focus,
+ ShuffleV2Block,
+ StemBlock,
+)
+from r_facelib.detection.yolov5face.models.experimental import CrossConv, MixConv2d
+from r_facelib.detection.yolov5face.utils.autoanchor import check_anchor_order
+from r_facelib.detection.yolov5face.utils.general import make_divisible
+from r_facelib.detection.yolov5face.utils.torch_utils import copy_attr, fuse_conv_and_bn
+
+
+class Detect(nn.Module):
+ stride = None # strides computed during build
+ export = False # onnx export
+
+ def __init__(self, nc=80, anchors=(), ch=()): # detection layer
+ super().__init__()
+ self.nc = nc # number of classes
+ self.no = nc + 5 + 10 # number of outputs per anchor
+
+ self.nl = len(anchors) # number of detection layers
+ self.na = len(anchors[0]) // 2 # number of anchors
+ self.grid = [torch.zeros(1)] * self.nl # init grid
+ a = torch.tensor(anchors).float().view(self.nl, -1, 2)
+ self.register_buffer("anchors", a) # shape(nl,na,2)
+ self.register_buffer("anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
+ self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
+
+ def forward(self, x):
+ z = [] # inference output
+ if self.export:
+ for i in range(self.nl):
+ x[i] = self.m[i](x[i])
+ return x
+ for i in range(self.nl):
+ x[i] = self.m[i](x[i]) # conv
+ bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
+ x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
+
+ if not self.training: # inference
+ if self.grid[i].shape[2:4] != x[i].shape[2:4]:
+ self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
+
+ y = torch.full_like(x[i], 0)
+ y[..., [0, 1, 2, 3, 4, 15]] = x[i][..., [0, 1, 2, 3, 4, 15]].sigmoid()
+ y[..., 5:15] = x[i][..., 5:15]
+
+ y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy
+ y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
+
+ y[..., 5:7] = (
+ y[..., 5:7] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
+ ) # landmark x1 y1
+ y[..., 7:9] = (
+ y[..., 7:9] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
+ ) # landmark x2 y2
+ y[..., 9:11] = (
+ y[..., 9:11] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
+ ) # landmark x3 y3
+ y[..., 11:13] = (
+ y[..., 11:13] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
+ ) # landmark x4 y4
+ y[..., 13:15] = (
+ y[..., 13:15] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
+ ) # landmark x5 y5
+
+ z.append(y.view(bs, -1, self.no))
+
+ return x if self.training else (torch.cat(z, 1), x)
+
+ @staticmethod
+ def _make_grid(nx=20, ny=20):
+ # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)], indexing="ij") # for pytorch>=1.10
+ yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
+ return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
+
+
+class Model(nn.Module):
+ def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None): # model, input channels, number of classes
+ super().__init__()
+ self.yaml_file = Path(cfg).name
+ with Path(cfg).open(encoding="utf8") as f:
+ self.yaml = yaml.safe_load(f) # model dict
+
+ # Define model
+ ch = self.yaml["ch"] = self.yaml.get("ch", ch) # input channels
+ if nc and nc != self.yaml["nc"]:
+ self.yaml["nc"] = nc # override yaml value
+
+ self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
+ self.names = [str(i) for i in range(self.yaml["nc"])] # default names
+
+ # Build strides, anchors
+ m = self.model[-1] # Detect()
+ if isinstance(m, Detect):
+ s = 128 # 2x min stride
+ m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
+ m.anchors /= m.stride.view(-1, 1, 1)
+ check_anchor_order(m)
+ self.stride = m.stride
+ self._initialize_biases() # only run once
+
+ def forward(self, x):
+ return self.forward_once(x) # single-scale inference, train
+
+ def forward_once(self, x):
+ y = [] # outputs
+ for m in self.model:
+ if m.f != -1: # if not from previous layer
+ x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
+
+ x = m(x) # run
+ y.append(x if m.i in self.save else None) # save output
+
+ return x
+
+ def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
+ # https://arxiv.org/abs/1708.02002 section 3.3
+ m = self.model[-1] # Detect() module
+ for mi, s in zip(m.m, m.stride): # from
+ b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
+ b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
+ b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls
+ mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
+
+ def _print_biases(self):
+ m = self.model[-1] # Detect() module
+ for mi in m.m: # from
+ b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
+ print(("%6g Conv2d.bias:" + "%10.3g" * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
+
+ def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
+ print("Fusing layers... ")
+ for m in self.model.modules():
+ if isinstance(m, Conv) and hasattr(m, "bn"):
+ m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
+ delattr(m, "bn") # remove batchnorm
+ m.forward = m.fuseforward # update forward
+ elif type(m) is nn.Upsample:
+ m.recompute_scale_factor = None # torch 1.11.0 compatibility
+ return self
+
+ def nms(self, mode=True): # add or remove NMS module
+ present = isinstance(self.model[-1], NMS) # last layer is NMS
+ if mode and not present:
+ print("Adding NMS... ")
+ m = NMS() # module
+ m.f = -1 # from
+ m.i = self.model[-1].i + 1 # index
+ self.model.add_module(name=str(m.i), module=m) # add
+ self.eval()
+ elif not mode and present:
+ print("Removing NMS... ")
+ self.model = self.model[:-1] # remove
+ return self
+
+ def autoshape(self): # add autoShape module
+ print("Adding autoShape... ")
+ m = AutoShape(self) # wrap model
+ copy_attr(m, self, include=("yaml", "nc", "hyp", "names", "stride"), exclude=()) # copy attributes
+ return m
+
+
+def parse_model(d, ch): # model_dict, input_channels(3)
+ anchors, nc, gd, gw = d["anchors"], d["nc"], d["depth_multiple"], d["width_multiple"]
+ na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
+ no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
+
+ layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
+ for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args
+ m = eval(m) if isinstance(m, str) else m # eval strings
+ for j, a in enumerate(args):
+ try:
+ args[j] = eval(a) if isinstance(a, str) else a # eval strings
+ except:
+ pass
+
+ n = max(round(n * gd), 1) if n > 1 else n # depth gain
+ if m in [
+ Conv,
+ Bottleneck,
+ SPP,
+ DWConv,
+ MixConv2d,
+ Focus,
+ CrossConv,
+ BottleneckCSP,
+ C3,
+ ShuffleV2Block,
+ StemBlock,
+ ]:
+ c1, c2 = ch[f], args[0]
+
+ c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
+
+ args = [c1, c2, *args[1:]]
+ if m in [BottleneckCSP, C3]:
+ args.insert(2, n)
+ n = 1
+ elif m is nn.BatchNorm2d:
+ args = [ch[f]]
+ elif m is Concat:
+ c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
+ elif m is Detect:
+ args.append([ch[x + 1] for x in f])
+ if isinstance(args[1], int): # number of anchors
+ args[1] = [list(range(args[1] * 2))] * len(f)
+ else:
+ c2 = ch[f]
+
+ m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
+ t = str(m)[8:-2].replace("__main__.", "") # module type
+ np = sum(x.numel() for x in m_.parameters()) # number params
+ m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
+ save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
+ layers.append(m_)
+ ch.append(c2)
+ return nn.Sequential(*layers), sorted(save)
diff --git a/r_facelib/detection/yolov5face/models/yolov5l.yaml b/r_facelib/detection/yolov5face/models/yolov5l.yaml new file mode 100644 index 0000000..98a9e2c --- /dev/null +++ b/r_facelib/detection/yolov5face/models/yolov5l.yaml @@ -0,0 +1,47 @@ +# parameters
+nc: 1 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+
+# anchors
+anchors:
+ - [4,5, 8,10, 13,16] # P3/8
+ - [23,29, 43,55, 73,105] # P4/16
+ - [146,217, 231,300, 335,433] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, StemBlock, [64, 3, 2]], # 0-P1/2
+ [-1, 3, C3, [128]],
+ [-1, 1, Conv, [256, 3, 2]], # 2-P3/8
+ [-1, 9, C3, [256]],
+ [-1, 1, Conv, [512, 3, 2]], # 4-P4/16
+ [-1, 9, C3, [512]],
+ [-1, 1, Conv, [1024, 3, 2]], # 6-P5/32
+ [-1, 1, SPP, [1024, [3,5,7]]],
+ [-1, 3, C3, [1024, False]], # 8
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [512, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 5], 1, Concat, [1]], # cat backbone P4
+ [-1, 3, C3, [512, False]], # 12
+
+ [-1, 1, Conv, [256, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 3], 1, Concat, [1]], # cat backbone P3
+ [-1, 3, C3, [256, False]], # 16 (P3/8-small)
+
+ [-1, 1, Conv, [256, 3, 2]],
+ [[-1, 13], 1, Concat, [1]], # cat head P4
+ [-1, 3, C3, [512, False]], # 19 (P4/16-medium)
+
+ [-1, 1, Conv, [512, 3, 2]],
+ [[-1, 9], 1, Concat, [1]], # cat head P5
+ [-1, 3, C3, [1024, False]], # 22 (P5/32-large)
+
+ [[16, 19, 22], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
\ No newline at end of file diff --git a/r_facelib/detection/yolov5face/models/yolov5n.yaml b/r_facelib/detection/yolov5face/models/yolov5n.yaml new file mode 100644 index 0000000..0a03fb0 --- /dev/null +++ b/r_facelib/detection/yolov5face/models/yolov5n.yaml @@ -0,0 +1,45 @@ +# parameters
+nc: 1 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+
+# anchors
+anchors:
+ - [4,5, 8,10, 13,16] # P3/8
+ - [23,29, 43,55, 73,105] # P4/16
+ - [146,217, 231,300, 335,433] # P5/32
+
+# YOLOv5 backbone
+backbone:
+ # [from, number, module, args]
+ [[-1, 1, StemBlock, [32, 3, 2]], # 0-P2/4
+ [-1, 1, ShuffleV2Block, [128, 2]], # 1-P3/8
+ [-1, 3, ShuffleV2Block, [128, 1]], # 2
+ [-1, 1, ShuffleV2Block, [256, 2]], # 3-P4/16
+ [-1, 7, ShuffleV2Block, [256, 1]], # 4
+ [-1, 1, ShuffleV2Block, [512, 2]], # 5-P5/32
+ [-1, 3, ShuffleV2Block, [512, 1]], # 6
+ ]
+
+# YOLOv5 head
+head:
+ [[-1, 1, Conv, [128, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 4], 1, Concat, [1]], # cat backbone P4
+ [-1, 1, C3, [128, False]], # 10
+
+ [-1, 1, Conv, [128, 1, 1]],
+ [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+ [[-1, 2], 1, Concat, [1]], # cat backbone P3
+ [-1, 1, C3, [128, False]], # 14 (P3/8-small)
+
+ [-1, 1, Conv, [128, 3, 2]],
+ [[-1, 11], 1, Concat, [1]], # cat head P4
+ [-1, 1, C3, [128, False]], # 17 (P4/16-medium)
+
+ [-1, 1, Conv, [128, 3, 2]],
+ [[-1, 7], 1, Concat, [1]], # cat head P5
+ [-1, 1, C3, [128, False]], # 20 (P5/32-large)
+
+ [[14, 17, 20], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
+ ]
diff --git a/r_facelib/detection/yolov5face/utils/__init__.py b/r_facelib/detection/yolov5face/utils/__init__.py new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/r_facelib/detection/yolov5face/utils/__init__.py diff --git a/r_facelib/detection/yolov5face/utils/autoanchor.py b/r_facelib/detection/yolov5face/utils/autoanchor.py new file mode 100644 index 0000000..cb0de89 --- /dev/null +++ b/r_facelib/detection/yolov5face/utils/autoanchor.py @@ -0,0 +1,12 @@ +# Auto-anchor utils
+
+
+def check_anchor_order(m):
+ # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
+ a = m.anchor_grid.prod(-1).view(-1) # anchor area
+ da = a[-1] - a[0] # delta a
+ ds = m.stride[-1] - m.stride[0] # delta s
+ if da.sign() != ds.sign(): # same order
+ print("Reversing anchor order")
+ m.anchors[:] = m.anchors.flip(0)
+ m.anchor_grid[:] = m.anchor_grid.flip(0)
diff --git a/r_facelib/detection/yolov5face/utils/datasets.py b/r_facelib/detection/yolov5face/utils/datasets.py new file mode 100644 index 0000000..a72609b --- /dev/null +++ b/r_facelib/detection/yolov5face/utils/datasets.py @@ -0,0 +1,35 @@ +import cv2
+import numpy as np
+
+
+def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale_fill=False, scaleup=True):
+ # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
+ shape = img.shape[:2] # current shape [height, width]
+ if isinstance(new_shape, int):
+ new_shape = (new_shape, new_shape)
+
+ # Scale ratio (new / old)
+ r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+ if not scaleup: # only scale down, do not scale up (for better test mAP)
+ r = min(r, 1.0)
+
+ # Compute padding
+ ratio = r, r # width, height ratios
+ new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+ dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
+ if auto: # minimum rectangle
+ dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding
+ elif scale_fill: # stretch
+ dw, dh = 0.0, 0.0
+ new_unpad = (new_shape[1], new_shape[0])
+ ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
+
+ dw /= 2 # divide padding into 2 sides
+ dh /= 2
+
+ if shape[::-1] != new_unpad: # resize
+ img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+ top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+ left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+ img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
+ return img, ratio, (dw, dh)
diff --git a/r_facelib/detection/yolov5face/utils/extract_ckpt.py b/r_facelib/detection/yolov5face/utils/extract_ckpt.py new file mode 100644 index 0000000..e6bde00 --- /dev/null +++ b/r_facelib/detection/yolov5face/utils/extract_ckpt.py @@ -0,0 +1,5 @@ +import torch
+import sys
+sys.path.insert(0,'./facelib/detection/yolov5face')
+model = torch.load('facelib/detection/yolov5face/yolov5n-face.pt', map_location='cpu')['model']
+torch.save(model.state_dict(),'../../models/facedetection')
\ No newline at end of file diff --git a/r_facelib/detection/yolov5face/utils/general.py b/r_facelib/detection/yolov5face/utils/general.py new file mode 100644 index 0000000..618d2f3 --- /dev/null +++ b/r_facelib/detection/yolov5face/utils/general.py @@ -0,0 +1,271 @@ +import math
+import time
+
+import numpy as np
+import torch
+import torchvision
+
+
+def check_img_size(img_size, s=32):
+ # Verify img_size is a multiple of stride s
+ new_size = make_divisible(img_size, int(s)) # ceil gs-multiple
+ # if new_size != img_size:
+ # print(f"WARNING: --img-size {img_size:g} must be multiple of max stride {s:g}, updating to {new_size:g}")
+ return new_size
+
+
+def make_divisible(x, divisor):
+ # Returns x evenly divisible by divisor
+ return math.ceil(x / divisor) * divisor
+
+
+def xyxy2xywh(x):
+ # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
+ y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+ y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
+ y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
+ y[:, 2] = x[:, 2] - x[:, 0] # width
+ y[:, 3] = x[:, 3] - x[:, 1] # height
+ return y
+
+
+def xywh2xyxy(x):
+ # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+ y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+ y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
+ y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
+ y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
+ y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
+ return y
+
+
+def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
+ # Rescale coords (xyxy) from img1_shape to img0_shape
+ if ratio_pad is None: # calculate from img0_shape
+ gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
+ pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
+ else:
+ gain = ratio_pad[0][0]
+ pad = ratio_pad[1]
+
+ coords[:, [0, 2]] -= pad[0] # x padding
+ coords[:, [1, 3]] -= pad[1] # y padding
+ coords[:, :4] /= gain
+ clip_coords(coords, img0_shape)
+ return coords
+
+
+def clip_coords(boxes, img_shape):
+ # Clip bounding xyxy bounding boxes to image shape (height, width)
+ boxes[:, 0].clamp_(0, img_shape[1]) # x1
+ boxes[:, 1].clamp_(0, img_shape[0]) # y1
+ boxes[:, 2].clamp_(0, img_shape[1]) # x2
+ boxes[:, 3].clamp_(0, img_shape[0]) # y2
+
+
+def box_iou(box1, box2):
+ # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
+ """
+ Return intersection-over-union (Jaccard index) of boxes.
+ Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+ Arguments:
+ box1 (Tensor[N, 4])
+ box2 (Tensor[M, 4])
+ Returns:
+ iou (Tensor[N, M]): the NxM matrix containing the pairwise
+ IoU values for every element in boxes1 and boxes2
+ """
+
+ def box_area(box):
+ return (box[2] - box[0]) * (box[3] - box[1])
+
+ area1 = box_area(box1.T)
+ area2 = box_area(box2.T)
+
+ inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
+ return inter / (area1[:, None] + area2 - inter)
+
+
+def non_max_suppression_face(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
+ """Performs Non-Maximum Suppression (NMS) on inference results
+ Returns:
+ detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
+ """
+
+ nc = prediction.shape[2] - 15 # number of classes
+ xc = prediction[..., 4] > conf_thres # candidates
+
+ # Settings
+ # (pixels) maximum box width and height
+ max_wh = 4096
+ time_limit = 10.0 # seconds to quit after
+ redundant = True # require redundant detections
+ multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img)
+ merge = False # use merge-NMS
+
+ t = time.time()
+ output = [torch.zeros((0, 16), device=prediction.device)] * prediction.shape[0]
+ for xi, x in enumerate(prediction): # image index, image inference
+ # Apply constraints
+ x = x[xc[xi]] # confidence
+
+ # Cat apriori labels if autolabelling
+ if labels and len(labels[xi]):
+ label = labels[xi]
+ v = torch.zeros((len(label), nc + 15), device=x.device)
+ v[:, :4] = label[:, 1:5] # box
+ v[:, 4] = 1.0 # conf
+ v[range(len(label)), label[:, 0].long() + 15] = 1.0 # cls
+ x = torch.cat((x, v), 0)
+
+ # If none remain process next image
+ if not x.shape[0]:
+ continue
+
+ # Compute conf
+ x[:, 15:] *= x[:, 4:5] # conf = obj_conf * cls_conf
+
+ # Box (center x, center y, width, height) to (x1, y1, x2, y2)
+ box = xywh2xyxy(x[:, :4])
+
+ # Detections matrix nx6 (xyxy, conf, landmarks, cls)
+ if multi_label:
+ i, j = (x[:, 15:] > conf_thres).nonzero(as_tuple=False).T
+ x = torch.cat((box[i], x[i, j + 15, None], x[:, 5:15], j[:, None].float()), 1)
+ else: # best class only
+ conf, j = x[:, 15:].max(1, keepdim=True)
+ x = torch.cat((box, conf, x[:, 5:15], j.float()), 1)[conf.view(-1) > conf_thres]
+
+ # Filter by class
+ if classes is not None:
+ x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
+
+ # If none remain process next image
+ n = x.shape[0] # number of boxes
+ if not n:
+ continue
+
+ # Batched NMS
+ c = x[:, 15:16] * (0 if agnostic else max_wh) # classes
+ boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
+ i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
+
+ if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean)
+ # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+ iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
+ weights = iou * scores[None] # box weights
+ x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
+ if redundant:
+ i = i[iou.sum(1) > 1] # require redundancy
+
+ output[xi] = x[i]
+ if (time.time() - t) > time_limit:
+ break # time limit exceeded
+
+ return output
+
+
+def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):
+ """Performs Non-Maximum Suppression (NMS) on inference results
+
+ Returns:
+ detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
+ """
+
+ nc = prediction.shape[2] - 5 # number of classes
+ xc = prediction[..., 4] > conf_thres # candidates
+
+ # Settings
+ # (pixels) maximum box width and height
+ max_wh = 4096
+ time_limit = 10.0 # seconds to quit after
+ redundant = True # require redundant detections
+ multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img)
+ merge = False # use merge-NMS
+
+ t = time.time()
+ output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
+ for xi, x in enumerate(prediction): # image index, image inference
+ x = x[xc[xi]] # confidence
+
+ # Cat apriori labels if autolabelling
+ if labels and len(labels[xi]):
+ label_id = labels[xi]
+ v = torch.zeros((len(label_id), nc + 5), device=x.device)
+ v[:, :4] = label_id[:, 1:5] # box
+ v[:, 4] = 1.0 # conf
+ v[range(len(label_id)), label_id[:, 0].long() + 5] = 1.0 # cls
+ x = torch.cat((x, v), 0)
+
+ # If none remain process next image
+ if not x.shape[0]:
+ continue
+
+ # Compute conf
+ x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
+
+ # Box (center x, center y, width, height) to (x1, y1, x2, y2)
+ box = xywh2xyxy(x[:, :4])
+
+ # Detections matrix nx6 (xyxy, conf, cls)
+ if multi_label:
+ i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
+ x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
+ else: # best class only
+ conf, j = x[:, 5:].max(1, keepdim=True)
+ x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
+
+ # Filter by class
+ if classes is not None:
+ x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
+
+ # Check shape
+ n = x.shape[0] # number of boxes
+ if not n: # no boxes
+ continue
+
+ x = x[x[:, 4].argsort(descending=True)] # sort by confidence
+
+ # Batched NMS
+ c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
+ boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
+ i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
+ if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean)
+ # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+ iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
+ weights = iou * scores[None] # box weights
+ x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
+ if redundant:
+ i = i[iou.sum(1) > 1] # require redundancy
+
+ output[xi] = x[i]
+ if (time.time() - t) > time_limit:
+ print(f"WARNING: NMS time limit {time_limit}s exceeded")
+ break # time limit exceeded
+
+ return output
+
+
+def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None):
+ # Rescale coords (xyxy) from img1_shape to img0_shape
+ if ratio_pad is None: # calculate from img0_shape
+ gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
+ pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
+ else:
+ gain = ratio_pad[0][0]
+ pad = ratio_pad[1]
+
+ coords[:, [0, 2, 4, 6, 8]] -= pad[0] # x padding
+ coords[:, [1, 3, 5, 7, 9]] -= pad[1] # y padding
+ coords[:, :10] /= gain
+ coords[:, 0].clamp_(0, img0_shape[1]) # x1
+ coords[:, 1].clamp_(0, img0_shape[0]) # y1
+ coords[:, 2].clamp_(0, img0_shape[1]) # x2
+ coords[:, 3].clamp_(0, img0_shape[0]) # y2
+ coords[:, 4].clamp_(0, img0_shape[1]) # x3
+ coords[:, 5].clamp_(0, img0_shape[0]) # y3
+ coords[:, 6].clamp_(0, img0_shape[1]) # x4
+ coords[:, 7].clamp_(0, img0_shape[0]) # y4
+ coords[:, 8].clamp_(0, img0_shape[1]) # x5
+ coords[:, 9].clamp_(0, img0_shape[0]) # y5
+ return coords
diff --git a/r_facelib/detection/yolov5face/utils/torch_utils.py b/r_facelib/detection/yolov5face/utils/torch_utils.py new file mode 100644 index 0000000..f702962 --- /dev/null +++ b/r_facelib/detection/yolov5face/utils/torch_utils.py @@ -0,0 +1,40 @@ +import torch
+from torch import nn
+
+
+def fuse_conv_and_bn(conv, bn):
+ # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
+ fusedconv = (
+ nn.Conv2d(
+ conv.in_channels,
+ conv.out_channels,
+ kernel_size=conv.kernel_size,
+ stride=conv.stride,
+ padding=conv.padding,
+ groups=conv.groups,
+ bias=True,
+ )
+ .requires_grad_(False)
+ .to(conv.weight.device)
+ )
+
+ # prepare filters
+ w_conv = conv.weight.clone().view(conv.out_channels, -1)
+ w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
+ fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
+
+ # prepare spatial bias
+ b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
+ b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
+ fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
+
+ return fusedconv
+
+
+def copy_attr(a, b, include=(), exclude=()):
+ # Copy attributes from b to a, options to only include [...] and to exclude [...]
+ for k, v in b.__dict__.items():
+ if (include and k not in include) or k.startswith("_") or k in exclude:
+ continue
+
+ setattr(a, k, v)
diff --git a/r_facelib/parsing/__init__.py b/r_facelib/parsing/__init__.py new file mode 100644 index 0000000..e5aaa28 --- /dev/null +++ b/r_facelib/parsing/__init__.py @@ -0,0 +1,23 @@ +import torch
+
+from r_facelib.utils import load_file_from_url
+from .bisenet import BiSeNet
+from .parsenet import ParseNet
+
+
+def init_parsing_model(model_name='bisenet', half=False, device='cuda'):
+ if model_name == 'bisenet':
+ model = BiSeNet(num_class=19)
+ model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_bisenet.pth'
+ elif model_name == 'parsenet':
+ model = ParseNet(in_size=512, out_size=512, parsing_ch=19)
+ model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth'
+ else:
+ raise NotImplementedError(f'{model_name} is not implemented.')
+
+ model_path = load_file_from_url(url=model_url, model_dir='../../models/facedetection', progress=True, file_name=None)
+ load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+ model.load_state_dict(load_net, strict=True)
+ model.eval()
+ model = model.to(device)
+ return model
diff --git a/r_facelib/parsing/bisenet.py b/r_facelib/parsing/bisenet.py new file mode 100644 index 0000000..9e7a084 --- /dev/null +++ b/r_facelib/parsing/bisenet.py @@ -0,0 +1,140 @@ +import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .resnet import ResNet18
+
+
+class ConvBNReLU(nn.Module):
+
+ def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1):
+ super(ConvBNReLU, self).__init__()
+ self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=stride, padding=padding, bias=False)
+ self.bn = nn.BatchNorm2d(out_chan)
+
+ def forward(self, x):
+ x = self.conv(x)
+ x = F.relu(self.bn(x))
+ return x
+
+
+class BiSeNetOutput(nn.Module):
+
+ def __init__(self, in_chan, mid_chan, num_class):
+ super(BiSeNetOutput, self).__init__()
+ self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
+ self.conv_out = nn.Conv2d(mid_chan, num_class, kernel_size=1, bias=False)
+
+ def forward(self, x):
+ feat = self.conv(x)
+ out = self.conv_out(feat)
+ return out, feat
+
+
+class AttentionRefinementModule(nn.Module):
+
+ def __init__(self, in_chan, out_chan):
+ super(AttentionRefinementModule, self).__init__()
+ self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
+ self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size=1, bias=False)
+ self.bn_atten = nn.BatchNorm2d(out_chan)
+ self.sigmoid_atten = nn.Sigmoid()
+
+ def forward(self, x):
+ feat = self.conv(x)
+ atten = F.avg_pool2d(feat, feat.size()[2:])
+ atten = self.conv_atten(atten)
+ atten = self.bn_atten(atten)
+ atten = self.sigmoid_atten(atten)
+ out = torch.mul(feat, atten)
+ return out
+
+
+class ContextPath(nn.Module):
+
+ def __init__(self):
+ super(ContextPath, self).__init__()
+ self.resnet = ResNet18()
+ self.arm16 = AttentionRefinementModule(256, 128)
+ self.arm32 = AttentionRefinementModule(512, 128)
+ self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+ self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+ self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
+
+ def forward(self, x):
+ feat8, feat16, feat32 = self.resnet(x)
+ h8, w8 = feat8.size()[2:]
+ h16, w16 = feat16.size()[2:]
+ h32, w32 = feat32.size()[2:]
+
+ avg = F.avg_pool2d(feat32, feat32.size()[2:])
+ avg = self.conv_avg(avg)
+ avg_up = F.interpolate(avg, (h32, w32), mode='nearest')
+
+ feat32_arm = self.arm32(feat32)
+ feat32_sum = feat32_arm + avg_up
+ feat32_up = F.interpolate(feat32_sum, (h16, w16), mode='nearest')
+ feat32_up = self.conv_head32(feat32_up)
+
+ feat16_arm = self.arm16(feat16)
+ feat16_sum = feat16_arm + feat32_up
+ feat16_up = F.interpolate(feat16_sum, (h8, w8), mode='nearest')
+ feat16_up = self.conv_head16(feat16_up)
+
+ return feat8, feat16_up, feat32_up # x8, x8, x16
+
+
+class FeatureFusionModule(nn.Module):
+
+ def __init__(self, in_chan, out_chan):
+ super(FeatureFusionModule, self).__init__()
+ self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
+ self.conv1 = nn.Conv2d(out_chan, out_chan // 4, kernel_size=1, stride=1, padding=0, bias=False)
+ self.conv2 = nn.Conv2d(out_chan // 4, out_chan, kernel_size=1, stride=1, padding=0, bias=False)
+ self.relu = nn.ReLU(inplace=True)
+ self.sigmoid = nn.Sigmoid()
+
+ def forward(self, fsp, fcp):
+ fcat = torch.cat([fsp, fcp], dim=1)
+ feat = self.convblk(fcat)
+ atten = F.avg_pool2d(feat, feat.size()[2:])
+ atten = self.conv1(atten)
+ atten = self.relu(atten)
+ atten = self.conv2(atten)
+ atten = self.sigmoid(atten)
+ feat_atten = torch.mul(feat, atten)
+ feat_out = feat_atten + feat
+ return feat_out
+
+
+class BiSeNet(nn.Module):
+
+ def __init__(self, num_class):
+ super(BiSeNet, self).__init__()
+ self.cp = ContextPath()
+ self.ffm = FeatureFusionModule(256, 256)
+ self.conv_out = BiSeNetOutput(256, 256, num_class)
+ self.conv_out16 = BiSeNetOutput(128, 64, num_class)
+ self.conv_out32 = BiSeNetOutput(128, 64, num_class)
+
+ def forward(self, x, return_feat=False):
+ h, w = x.size()[2:]
+ feat_res8, feat_cp8, feat_cp16 = self.cp(x) # return res3b1 feature
+ feat_sp = feat_res8 # replace spatial path feature with res3b1 feature
+ feat_fuse = self.ffm(feat_sp, feat_cp8)
+
+ out, feat = self.conv_out(feat_fuse)
+ out16, feat16 = self.conv_out16(feat_cp8)
+ out32, feat32 = self.conv_out32(feat_cp16)
+
+ out = F.interpolate(out, (h, w), mode='bilinear', align_corners=True)
+ out16 = F.interpolate(out16, (h, w), mode='bilinear', align_corners=True)
+ out32 = F.interpolate(out32, (h, w), mode='bilinear', align_corners=True)
+
+ if return_feat:
+ feat = F.interpolate(feat, (h, w), mode='bilinear', align_corners=True)
+ feat16 = F.interpolate(feat16, (h, w), mode='bilinear', align_corners=True)
+ feat32 = F.interpolate(feat32, (h, w), mode='bilinear', align_corners=True)
+ return out, out16, out32, feat, feat16, feat32
+ else:
+ return out, out16, out32
diff --git a/r_facelib/parsing/parsenet.py b/r_facelib/parsing/parsenet.py new file mode 100644 index 0000000..2e80921 --- /dev/null +++ b/r_facelib/parsing/parsenet.py @@ -0,0 +1,194 @@ +"""Modified from https://github.com/chaofengc/PSFRGAN
+"""
+import numpy as np
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class NormLayer(nn.Module):
+ """Normalization Layers.
+
+ Args:
+ channels: input channels, for batch norm and instance norm.
+ input_size: input shape without batch size, for layer norm.
+ """
+
+ def __init__(self, channels, normalize_shape=None, norm_type='bn'):
+ super(NormLayer, self).__init__()
+ norm_type = norm_type.lower()
+ self.norm_type = norm_type
+ if norm_type == 'bn':
+ self.norm = nn.BatchNorm2d(channels, affine=True)
+ elif norm_type == 'in':
+ self.norm = nn.InstanceNorm2d(channels, affine=False)
+ elif norm_type == 'gn':
+ self.norm = nn.GroupNorm(32, channels, affine=True)
+ elif norm_type == 'pixel':
+ self.norm = lambda x: F.normalize(x, p=2, dim=1)
+ elif norm_type == 'layer':
+ self.norm = nn.LayerNorm(normalize_shape)
+ elif norm_type == 'none':
+ self.norm = lambda x: x * 1.0
+ else:
+ assert 1 == 0, f'Norm type {norm_type} not support.'
+
+ def forward(self, x, ref=None):
+ if self.norm_type == 'spade':
+ return self.norm(x, ref)
+ else:
+ return self.norm(x)
+
+
+class ReluLayer(nn.Module):
+ """Relu Layer.
+
+ Args:
+ relu type: type of relu layer, candidates are
+ - ReLU
+ - LeakyReLU: default relu slope 0.2
+ - PRelu
+ - SELU
+ - none: direct pass
+ """
+
+ def __init__(self, channels, relu_type='relu'):
+ super(ReluLayer, self).__init__()
+ relu_type = relu_type.lower()
+ if relu_type == 'relu':
+ self.func = nn.ReLU(True)
+ elif relu_type == 'leakyrelu':
+ self.func = nn.LeakyReLU(0.2, inplace=True)
+ elif relu_type == 'prelu':
+ self.func = nn.PReLU(channels)
+ elif relu_type == 'selu':
+ self.func = nn.SELU(True)
+ elif relu_type == 'none':
+ self.func = lambda x: x * 1.0
+ else:
+ assert 1 == 0, f'Relu type {relu_type} not support.'
+
+ def forward(self, x):
+ return self.func(x)
+
+
+class ConvLayer(nn.Module):
+
+ def __init__(self,
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ scale='none',
+ norm_type='none',
+ relu_type='none',
+ use_pad=True,
+ bias=True):
+ super(ConvLayer, self).__init__()
+ self.use_pad = use_pad
+ self.norm_type = norm_type
+ if norm_type in ['bn']:
+ bias = False
+
+ stride = 2 if scale == 'down' else 1
+
+ self.scale_func = lambda x: x
+ if scale == 'up':
+ self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest')
+
+ self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.) / 2)))
+ self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias)
+
+ self.relu = ReluLayer(out_channels, relu_type)
+ self.norm = NormLayer(out_channels, norm_type=norm_type)
+
+ def forward(self, x):
+ out = self.scale_func(x)
+ if self.use_pad:
+ out = self.reflection_pad(out)
+ out = self.conv2d(out)
+ out = self.norm(out)
+ out = self.relu(out)
+ return out
+
+
+class ResidualBlock(nn.Module):
+ """
+ Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html
+ """
+
+ def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'):
+ super(ResidualBlock, self).__init__()
+
+ if scale == 'none' and c_in == c_out:
+ self.shortcut_func = lambda x: x
+ else:
+ self.shortcut_func = ConvLayer(c_in, c_out, 3, scale)
+
+ scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']}
+ scale_conf = scale_config_dict[scale]
+
+ self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type)
+ self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none')
+
+ def forward(self, x):
+ identity = self.shortcut_func(x)
+
+ res = self.conv1(x)
+ res = self.conv2(res)
+ return identity + res
+
+
+class ParseNet(nn.Module):
+
+ def __init__(self,
+ in_size=128,
+ out_size=128,
+ min_feat_size=32,
+ base_ch=64,
+ parsing_ch=19,
+ res_depth=10,
+ relu_type='LeakyReLU',
+ norm_type='bn',
+ ch_range=[32, 256]):
+ super().__init__()
+ self.res_depth = res_depth
+ act_args = {'norm_type': norm_type, 'relu_type': relu_type}
+ min_ch, max_ch = ch_range
+
+ ch_clip = lambda x: max(min_ch, min(x, max_ch)) # noqa: E731
+ min_feat_size = min(in_size, min_feat_size)
+
+ down_steps = int(np.log2(in_size // min_feat_size))
+ up_steps = int(np.log2(out_size // min_feat_size))
+
+ # =============== define encoder-body-decoder ====================
+ self.encoder = []
+ self.encoder.append(ConvLayer(3, base_ch, 3, 1))
+ head_ch = base_ch
+ for i in range(down_steps):
+ cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2)
+ self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args))
+ head_ch = head_ch * 2
+
+ self.body = []
+ for i in range(res_depth):
+ self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args))
+
+ self.decoder = []
+ for i in range(up_steps):
+ cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2)
+ self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args))
+ head_ch = head_ch // 2
+
+ self.encoder = nn.Sequential(*self.encoder)
+ self.body = nn.Sequential(*self.body)
+ self.decoder = nn.Sequential(*self.decoder)
+ self.out_img_conv = ConvLayer(ch_clip(head_ch), 3)
+ self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch)
+
+ def forward(self, x):
+ feat = self.encoder(x)
+ x = feat + self.body(feat)
+ x = self.decoder(x)
+ out_img = self.out_img_conv(x)
+ out_mask = self.out_mask_conv(x)
+ return out_mask, out_img
diff --git a/r_facelib/parsing/resnet.py b/r_facelib/parsing/resnet.py new file mode 100644 index 0000000..e7cc283 --- /dev/null +++ b/r_facelib/parsing/resnet.py @@ -0,0 +1,69 @@ +import torch.nn as nn
+import torch.nn.functional as F
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+ """3x3 convolution with padding"""
+ return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+
+ def __init__(self, in_chan, out_chan, stride=1):
+ super(BasicBlock, self).__init__()
+ self.conv1 = conv3x3(in_chan, out_chan, stride)
+ self.bn1 = nn.BatchNorm2d(out_chan)
+ self.conv2 = conv3x3(out_chan, out_chan)
+ self.bn2 = nn.BatchNorm2d(out_chan)
+ self.relu = nn.ReLU(inplace=True)
+ self.downsample = None
+ if in_chan != out_chan or stride != 1:
+ self.downsample = nn.Sequential(
+ nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False),
+ nn.BatchNorm2d(out_chan),
+ )
+
+ def forward(self, x):
+ residual = self.conv1(x)
+ residual = F.relu(self.bn1(residual))
+ residual = self.conv2(residual)
+ residual = self.bn2(residual)
+
+ shortcut = x
+ if self.downsample is not None:
+ shortcut = self.downsample(x)
+
+ out = shortcut + residual
+ out = self.relu(out)
+ return out
+
+
+def create_layer_basic(in_chan, out_chan, bnum, stride=1):
+ layers = [BasicBlock(in_chan, out_chan, stride=stride)]
+ for i in range(bnum - 1):
+ layers.append(BasicBlock(out_chan, out_chan, stride=1))
+ return nn.Sequential(*layers)
+
+
+class ResNet18(nn.Module):
+
+ def __init__(self):
+ super(ResNet18, self).__init__()
+ self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+ self.bn1 = nn.BatchNorm2d(64)
+ self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
+ self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
+ self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
+ self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = F.relu(self.bn1(x))
+ x = self.maxpool(x)
+
+ x = self.layer1(x)
+ feat8 = self.layer2(x) # 1/8
+ feat16 = self.layer3(feat8) # 1/16
+ feat32 = self.layer4(feat16) # 1/32
+ return feat8, feat16, feat32
diff --git a/r_facelib/utils/__init__.py b/r_facelib/utils/__init__.py new file mode 100644 index 0000000..3397bda --- /dev/null +++ b/r_facelib/utils/__init__.py @@ -0,0 +1,7 @@ +from .face_utils import align_crop_face_landmarks, compute_increased_bbox, get_valid_bboxes, paste_face_back
+from .misc import img2tensor, load_file_from_url, download_pretrained_models, scandir
+
+__all__ = [
+ 'align_crop_face_landmarks', 'compute_increased_bbox', 'get_valid_bboxes', 'load_file_from_url',
+ 'download_pretrained_models', 'paste_face_back', 'img2tensor', 'scandir'
+]
diff --git a/r_facelib/utils/face_restoration_helper.py b/r_facelib/utils/face_restoration_helper.py new file mode 100644 index 0000000..1db75c9 --- /dev/null +++ b/r_facelib/utils/face_restoration_helper.py @@ -0,0 +1,455 @@ +import cv2
+import numpy as np
+import os
+import torch
+from torchvision.transforms.functional import normalize
+
+from r_facelib.detection import init_detection_model
+from r_facelib.parsing import init_parsing_model
+from r_facelib.utils.misc import img2tensor, imwrite
+
+
+def get_largest_face(det_faces, h, w):
+
+ def get_location(val, length):
+ if val < 0:
+ return 0
+ elif val > length:
+ return length
+ else:
+ return val
+
+ face_areas = []
+ for det_face in det_faces:
+ left = get_location(det_face[0], w)
+ right = get_location(det_face[2], w)
+ top = get_location(det_face[1], h)
+ bottom = get_location(det_face[3], h)
+ face_area = (right - left) * (bottom - top)
+ face_areas.append(face_area)
+ largest_idx = face_areas.index(max(face_areas))
+ return det_faces[largest_idx], largest_idx
+
+
+def get_center_face(det_faces, h=0, w=0, center=None):
+ if center is not None:
+ center = np.array(center)
+ else:
+ center = np.array([w / 2, h / 2])
+ center_dist = []
+ for det_face in det_faces:
+ face_center = np.array([(det_face[0] + det_face[2]) / 2, (det_face[1] + det_face[3]) / 2])
+ dist = np.linalg.norm(face_center - center)
+ center_dist.append(dist)
+ center_idx = center_dist.index(min(center_dist))
+ return det_faces[center_idx], center_idx
+
+
+class FaceRestoreHelper(object):
+ """Helper for the face restoration pipeline (base class)."""
+
+ def __init__(self,
+ upscale_factor,
+ face_size=512,
+ crop_ratio=(1, 1),
+ det_model='retinaface_resnet50',
+ save_ext='png',
+ template_3points=False,
+ pad_blur=False,
+ use_parse=False,
+ device=None):
+ self.template_3points = template_3points # improve robustness
+ self.upscale_factor = upscale_factor
+ # the cropped face ratio based on the square face
+ self.crop_ratio = crop_ratio # (h, w)
+ assert (self.crop_ratio[0] >= 1 and self.crop_ratio[1] >= 1), 'crop ration only supports >=1'
+ self.face_size = (int(face_size * self.crop_ratio[1]), int(face_size * self.crop_ratio[0]))
+
+ if self.template_3points:
+ self.face_template = np.array([[192, 240], [319, 240], [257, 371]])
+ else:
+ # standard 5 landmarks for FFHQ faces with 512 x 512
+ # facexlib
+ self.face_template = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935],
+ [201.26117, 371.41043], [313.08905, 371.15118]])
+
+ # dlib: left_eye: 36:41 right_eye: 42:47 nose: 30,32,33,34 left mouth corner: 48 right mouth corner: 54
+ # self.face_template = np.array([[193.65928, 242.98541], [318.32558, 243.06108], [255.67984, 328.82894],
+ # [198.22603, 372.82502], [313.91018, 372.75659]])
+
+
+ self.face_template = self.face_template * (face_size / 512.0)
+ if self.crop_ratio[0] > 1:
+ self.face_template[:, 1] += face_size * (self.crop_ratio[0] - 1) / 2
+ if self.crop_ratio[1] > 1:
+ self.face_template[:, 0] += face_size * (self.crop_ratio[1] - 1) / 2
+ self.save_ext = save_ext
+ self.pad_blur = pad_blur
+ if self.pad_blur is True:
+ self.template_3points = False
+
+ self.all_landmarks_5 = []
+ self.det_faces = []
+ self.affine_matrices = []
+ self.inverse_affine_matrices = []
+ self.cropped_faces = []
+ self.restored_faces = []
+ self.pad_input_imgs = []
+
+ if device is None:
+ self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ else:
+ self.device = device
+
+ # init face detection model
+ self.face_det = init_detection_model(det_model, half=False, device=self.device)
+
+ # init face parsing model
+ self.use_parse = use_parse
+ self.face_parse = init_parsing_model(model_name='parsenet', device=self.device)
+
+ def set_upscale_factor(self, upscale_factor):
+ self.upscale_factor = upscale_factor
+
+ def read_image(self, img):
+ """img can be image path or cv2 loaded image."""
+ # self.input_img is Numpy array, (h, w, c), BGR, uint8, [0, 255]
+ if isinstance(img, str):
+ img = cv2.imread(img)
+
+ if np.max(img) > 256: # 16-bit image
+ img = img / 65535 * 255
+ if len(img.shape) == 2: # gray image
+ img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+ elif img.shape[2] == 4: # BGRA image with alpha channel
+ img = img[:, :, 0:3]
+
+ self.input_img = img
+
+ if min(self.input_img.shape[:2])<512:
+ f = 512.0/min(self.input_img.shape[:2])
+ self.input_img = cv2.resize(self.input_img, (0,0), fx=f, fy=f, interpolation=cv2.INTER_LINEAR)
+
+ def get_face_landmarks_5(self,
+ only_keep_largest=False,
+ only_center_face=False,
+ resize=None,
+ blur_ratio=0.01,
+ eye_dist_threshold=None):
+ if resize is None:
+ scale = 1
+ input_img = self.input_img
+ else:
+ h, w = self.input_img.shape[0:2]
+ scale = resize / min(h, w)
+ scale = max(1, scale) # always scale up
+ h, w = int(h * scale), int(w * scale)
+ interp = cv2.INTER_AREA if scale < 1 else cv2.INTER_LINEAR
+ input_img = cv2.resize(self.input_img, (w, h), interpolation=interp)
+
+ with torch.no_grad():
+ bboxes = self.face_det.detect_faces(input_img)
+
+ if bboxes is None or bboxes.shape[0] == 0:
+ return 0
+ else:
+ bboxes = bboxes / scale
+
+ for bbox in bboxes:
+ # remove faces with too small eye distance: side faces or too small faces
+ eye_dist = np.linalg.norm([bbox[6] - bbox[8], bbox[7] - bbox[9]])
+ if eye_dist_threshold is not None and (eye_dist < eye_dist_threshold):
+ continue
+
+ if self.template_3points:
+ landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 11, 2)])
+ else:
+ landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 15, 2)])
+ self.all_landmarks_5.append(landmark)
+ self.det_faces.append(bbox[0:5])
+
+ if len(self.det_faces) == 0:
+ return 0
+ if only_keep_largest:
+ h, w, _ = self.input_img.shape
+ self.det_faces, largest_idx = get_largest_face(self.det_faces, h, w)
+ self.all_landmarks_5 = [self.all_landmarks_5[largest_idx]]
+ elif only_center_face:
+ h, w, _ = self.input_img.shape
+ self.det_faces, center_idx = get_center_face(self.det_faces, h, w)
+ self.all_landmarks_5 = [self.all_landmarks_5[center_idx]]
+
+ # pad blurry images
+ if self.pad_blur:
+ self.pad_input_imgs = []
+ for landmarks in self.all_landmarks_5:
+ # get landmarks
+ eye_left = landmarks[0, :]
+ eye_right = landmarks[1, :]
+ eye_avg = (eye_left + eye_right) * 0.5
+ mouth_avg = (landmarks[3, :] + landmarks[4, :]) * 0.5
+ eye_to_eye = eye_right - eye_left
+ eye_to_mouth = mouth_avg - eye_avg
+
+ # Get the oriented crop rectangle
+ # x: half width of the oriented crop rectangle
+ x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
+ # - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise
+ # norm with the hypotenuse: get the direction
+ x /= np.hypot(*x) # get the hypotenuse of a right triangle
+ rect_scale = 1.5
+ x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale)
+ # y: half height of the oriented crop rectangle
+ y = np.flipud(x) * [-1, 1]
+
+ # c: center
+ c = eye_avg + eye_to_mouth * 0.1
+ # quad: (left_top, left_bottom, right_bottom, right_top)
+ quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
+ # qsize: side length of the square
+ qsize = np.hypot(*x) * 2
+ border = max(int(np.rint(qsize * 0.1)), 3)
+
+ # get pad
+ # pad: (width_left, height_top, width_right, height_bottom)
+ pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
+ int(np.ceil(max(quad[:, 1]))))
+ pad = [
+ max(-pad[0] + border, 1),
+ max(-pad[1] + border, 1),
+ max(pad[2] - self.input_img.shape[0] + border, 1),
+ max(pad[3] - self.input_img.shape[1] + border, 1)
+ ]
+
+ if max(pad) > 1:
+ # pad image
+ pad_img = np.pad(self.input_img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
+ # modify landmark coords
+ landmarks[:, 0] += pad[0]
+ landmarks[:, 1] += pad[1]
+ # blur pad images
+ h, w, _ = pad_img.shape
+ y, x, _ = np.ogrid[:h, :w, :1]
+ mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0],
+ np.float32(w - 1 - x) / pad[2]),
+ 1.0 - np.minimum(np.float32(y) / pad[1],
+ np.float32(h - 1 - y) / pad[3]))
+ blur = int(qsize * blur_ratio)
+ if blur % 2 == 0:
+ blur += 1
+ blur_img = cv2.boxFilter(pad_img, 0, ksize=(blur, blur))
+ # blur_img = cv2.GaussianBlur(pad_img, (blur, blur), 0)
+
+ pad_img = pad_img.astype('float32')
+ pad_img += (blur_img - pad_img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
+ pad_img += (np.median(pad_img, axis=(0, 1)) - pad_img) * np.clip(mask, 0.0, 1.0)
+ pad_img = np.clip(pad_img, 0, 255) # float32, [0, 255]
+ self.pad_input_imgs.append(pad_img)
+ else:
+ self.pad_input_imgs.append(np.copy(self.input_img))
+
+ return len(self.all_landmarks_5)
+
+ def align_warp_face(self, save_cropped_path=None, border_mode='constant'):
+ """Align and warp faces with face template.
+ """
+ if self.pad_blur:
+ assert len(self.pad_input_imgs) == len(
+ self.all_landmarks_5), f'Mismatched samples: {len(self.pad_input_imgs)} and {len(self.all_landmarks_5)}'
+ for idx, landmark in enumerate(self.all_landmarks_5):
+ # use 5 landmarks to get affine matrix
+ # use cv2.LMEDS method for the equivalence to skimage transform
+ # ref: https://blog.csdn.net/yichxi/article/details/115827338
+ affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0]
+ self.affine_matrices.append(affine_matrix)
+ # warp and crop faces
+ if border_mode == 'constant':
+ border_mode = cv2.BORDER_CONSTANT
+ elif border_mode == 'reflect101':
+ border_mode = cv2.BORDER_REFLECT101
+ elif border_mode == 'reflect':
+ border_mode = cv2.BORDER_REFLECT
+ if self.pad_blur:
+ input_img = self.pad_input_imgs[idx]
+ else:
+ input_img = self.input_img
+ cropped_face = cv2.warpAffine(
+ input_img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132)) # gray
+ self.cropped_faces.append(cropped_face)
+ # save the cropped face
+ if save_cropped_path is not None:
+ path = os.path.splitext(save_cropped_path)[0]
+ save_path = f'{path}_{idx:02d}.{self.save_ext}'
+ imwrite(cropped_face, save_path)
+
+ def get_inverse_affine(self, save_inverse_affine_path=None):
+ """Get inverse affine matrix."""
+ for idx, affine_matrix in enumerate(self.affine_matrices):
+ inverse_affine = cv2.invertAffineTransform(affine_matrix)
+ inverse_affine *= self.upscale_factor
+ self.inverse_affine_matrices.append(inverse_affine)
+ # save inverse affine matrices
+ if save_inverse_affine_path is not None:
+ path, _ = os.path.splitext(save_inverse_affine_path)
+ save_path = f'{path}_{idx:02d}.pth'
+ torch.save(inverse_affine, save_path)
+
+
+ def add_restored_face(self, face):
+ self.restored_faces.append(face)
+
+
+ def paste_faces_to_input_image(self, save_path=None, upsample_img=None, draw_box=False, face_upsampler=None):
+ h, w, _ = self.input_img.shape
+ h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor)
+
+ if upsample_img is None:
+ # simply resize the background
+ # upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4)
+ upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LINEAR)
+ else:
+ upsample_img = cv2.resize(upsample_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4)
+
+ assert len(self.restored_faces) == len(
+ self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.')
+
+ inv_mask_borders = []
+ for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices):
+ if face_upsampler is not None:
+ restored_face = face_upsampler.enhance(restored_face, outscale=self.upscale_factor)[0]
+ inverse_affine /= self.upscale_factor
+ inverse_affine[:, 2] *= self.upscale_factor
+ face_size = (self.face_size[0]*self.upscale_factor, self.face_size[1]*self.upscale_factor)
+ else:
+ # Add an offset to inverse affine matrix, for more precise back alignment
+ if self.upscale_factor > 1:
+ extra_offset = 0.5 * self.upscale_factor
+ else:
+ extra_offset = 0
+ inverse_affine[:, 2] += extra_offset
+ face_size = self.face_size
+ inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up))
+
+ # if draw_box or not self.use_parse: # use square parse maps
+ # mask = np.ones(face_size, dtype=np.float32)
+ # inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
+ # # remove the black borders
+ # inv_mask_erosion = cv2.erode(
+ # inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8))
+ # pasted_face = inv_mask_erosion[:, :, None] * inv_restored
+ # total_face_area = np.sum(inv_mask_erosion) # // 3
+ # # add border
+ # if draw_box:
+ # h, w = face_size
+ # mask_border = np.ones((h, w, 3), dtype=np.float32)
+ # border = int(1400/np.sqrt(total_face_area))
+ # mask_border[border:h-border, border:w-border,:] = 0
+ # inv_mask_border = cv2.warpAffine(mask_border, inverse_affine, (w_up, h_up))
+ # inv_mask_borders.append(inv_mask_border)
+ # if not self.use_parse:
+ # # compute the fusion edge based on the area of face
+ # w_edge = int(total_face_area**0.5) // 20
+ # erosion_radius = w_edge * 2
+ # inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
+ # blur_size = w_edge * 2
+ # inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
+ # if len(upsample_img.shape) == 2: # upsample_img is gray image
+ # upsample_img = upsample_img[:, :, None]
+ # inv_soft_mask = inv_soft_mask[:, :, None]
+
+ # always use square mask
+ mask = np.ones(face_size, dtype=np.float32)
+ inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
+ # remove the black borders
+ inv_mask_erosion = cv2.erode(
+ inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8))
+ pasted_face = inv_mask_erosion[:, :, None] * inv_restored
+ total_face_area = np.sum(inv_mask_erosion) # // 3
+ # add border
+ if draw_box:
+ h, w = face_size
+ mask_border = np.ones((h, w, 3), dtype=np.float32)
+ border = int(1400/np.sqrt(total_face_area))
+ mask_border[border:h-border, border:w-border,:] = 0
+ inv_mask_border = cv2.warpAffine(mask_border, inverse_affine, (w_up, h_up))
+ inv_mask_borders.append(inv_mask_border)
+ # compute the fusion edge based on the area of face
+ w_edge = int(total_face_area**0.5) // 20
+ erosion_radius = w_edge * 2
+ inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
+ blur_size = w_edge * 2
+ inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
+ if len(upsample_img.shape) == 2: # upsample_img is gray image
+ upsample_img = upsample_img[:, :, None]
+ inv_soft_mask = inv_soft_mask[:, :, None]
+
+ # parse mask
+ if self.use_parse:
+ # inference
+ face_input = cv2.resize(restored_face, (512, 512), interpolation=cv2.INTER_LINEAR)
+ face_input = img2tensor(face_input.astype('float32') / 255., bgr2rgb=True, float32=True)
+ normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+ face_input = torch.unsqueeze(face_input, 0).to(self.device)
+ with torch.no_grad():
+ out = self.face_parse(face_input)[0]
+ out = out.argmax(dim=1).squeeze().cpu().numpy()
+
+ parse_mask = np.zeros(out.shape)
+ MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0]
+ for idx, color in enumerate(MASK_COLORMAP):
+ parse_mask[out == idx] = color
+ # blur the mask
+ parse_mask = cv2.GaussianBlur(parse_mask, (101, 101), 11)
+ parse_mask = cv2.GaussianBlur(parse_mask, (101, 101), 11)
+ # remove the black borders
+ thres = 10
+ parse_mask[:thres, :] = 0
+ parse_mask[-thres:, :] = 0
+ parse_mask[:, :thres] = 0
+ parse_mask[:, -thres:] = 0
+ parse_mask = parse_mask / 255.
+
+ parse_mask = cv2.resize(parse_mask, face_size)
+ parse_mask = cv2.warpAffine(parse_mask, inverse_affine, (w_up, h_up), flags=3)
+ inv_soft_parse_mask = parse_mask[:, :, None]
+ # pasted_face = inv_restored
+ fuse_mask = (inv_soft_parse_mask<inv_soft_mask).astype('int')
+ inv_soft_mask = inv_soft_parse_mask*fuse_mask + inv_soft_mask*(1-fuse_mask)
+
+ if len(upsample_img.shape) == 3 and upsample_img.shape[2] == 4: # alpha channel
+ alpha = upsample_img[:, :, 3:]
+ upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img[:, :, 0:3]
+ upsample_img = np.concatenate((upsample_img, alpha), axis=2)
+ else:
+ upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img
+
+ if np.max(upsample_img) > 256: # 16-bit image
+ upsample_img = upsample_img.astype(np.uint16)
+ else:
+ upsample_img = upsample_img.astype(np.uint8)
+
+ # draw bounding box
+ if draw_box:
+ # upsample_input_img = cv2.resize(input_img, (w_up, h_up))
+ img_color = np.ones([*upsample_img.shape], dtype=np.float32)
+ img_color[:,:,0] = 0
+ img_color[:,:,1] = 255
+ img_color[:,:,2] = 0
+ for inv_mask_border in inv_mask_borders:
+ upsample_img = inv_mask_border * img_color + (1 - inv_mask_border) * upsample_img
+ # upsample_input_img = inv_mask_border * img_color + (1 - inv_mask_border) * upsample_input_img
+
+ if save_path is not None:
+ path = os.path.splitext(save_path)[0]
+ save_path = f'{path}.{self.save_ext}'
+ imwrite(upsample_img, save_path)
+ return upsample_img
+
+ def clean_all(self):
+ self.all_landmarks_5 = []
+ self.restored_faces = []
+ self.affine_matrices = []
+ self.cropped_faces = []
+ self.inverse_affine_matrices = []
+ self.det_faces = []
+ self.pad_input_imgs = []
diff --git a/r_facelib/utils/face_utils.py b/r_facelib/utils/face_utils.py new file mode 100644 index 0000000..657ad25 --- /dev/null +++ b/r_facelib/utils/face_utils.py @@ -0,0 +1,248 @@ +import cv2
+import numpy as np
+import torch
+
+
+def compute_increased_bbox(bbox, increase_area, preserve_aspect=True):
+ left, top, right, bot = bbox
+ width = right - left
+ height = bot - top
+
+ if preserve_aspect:
+ width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width))
+ height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height))
+ else:
+ width_increase = height_increase = increase_area
+ left = int(left - width_increase * width)
+ top = int(top - height_increase * height)
+ right = int(right + width_increase * width)
+ bot = int(bot + height_increase * height)
+ return (left, top, right, bot)
+
+
+def get_valid_bboxes(bboxes, h, w):
+ left = max(bboxes[0], 0)
+ top = max(bboxes[1], 0)
+ right = min(bboxes[2], w)
+ bottom = min(bboxes[3], h)
+ return (left, top, right, bottom)
+
+
+def align_crop_face_landmarks(img,
+ landmarks,
+ output_size,
+ transform_size=None,
+ enable_padding=True,
+ return_inverse_affine=False,
+ shrink_ratio=(1, 1)):
+ """Align and crop face with landmarks.
+
+ The output_size and transform_size are based on width. The height is
+ adjusted based on shrink_ratio_h/shring_ration_w.
+
+ Modified from:
+ https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py
+
+ Args:
+ img (Numpy array): Input image.
+ landmarks (Numpy array): 5 or 68 or 98 landmarks.
+ output_size (int): Output face size.
+ transform_size (ing): Transform size. Usually the four time of
+ output_size.
+ enable_padding (float): Default: True.
+ shrink_ratio (float | tuple[float] | list[float]): Shring the whole
+ face for height and width (crop larger area). Default: (1, 1).
+
+ Returns:
+ (Numpy array): Cropped face.
+ """
+ lm_type = 'retinaface_5' # Options: dlib_5, retinaface_5
+
+ if isinstance(shrink_ratio, (float, int)):
+ shrink_ratio = (shrink_ratio, shrink_ratio)
+ if transform_size is None:
+ transform_size = output_size * 4
+
+ # Parse landmarks
+ lm = np.array(landmarks)
+ if lm.shape[0] == 5 and lm_type == 'retinaface_5':
+ eye_left = lm[0]
+ eye_right = lm[1]
+ mouth_avg = (lm[3] + lm[4]) * 0.5
+ elif lm.shape[0] == 5 and lm_type == 'dlib_5':
+ lm_eye_left = lm[2:4]
+ lm_eye_right = lm[0:2]
+ eye_left = np.mean(lm_eye_left, axis=0)
+ eye_right = np.mean(lm_eye_right, axis=0)
+ mouth_avg = lm[4]
+ elif lm.shape[0] == 68:
+ lm_eye_left = lm[36:42]
+ lm_eye_right = lm[42:48]
+ eye_left = np.mean(lm_eye_left, axis=0)
+ eye_right = np.mean(lm_eye_right, axis=0)
+ mouth_avg = (lm[48] + lm[54]) * 0.5
+ elif lm.shape[0] == 98:
+ lm_eye_left = lm[60:68]
+ lm_eye_right = lm[68:76]
+ eye_left = np.mean(lm_eye_left, axis=0)
+ eye_right = np.mean(lm_eye_right, axis=0)
+ mouth_avg = (lm[76] + lm[82]) * 0.5
+
+ eye_avg = (eye_left + eye_right) * 0.5
+ eye_to_eye = eye_right - eye_left
+ eye_to_mouth = mouth_avg - eye_avg
+
+ # Get the oriented crop rectangle
+ # x: half width of the oriented crop rectangle
+ x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
+ # - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise
+ # norm with the hypotenuse: get the direction
+ x /= np.hypot(*x) # get the hypotenuse of a right triangle
+ rect_scale = 1 # TODO: you can edit it to get larger rect
+ x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale)
+ # y: half height of the oriented crop rectangle
+ y = np.flipud(x) * [-1, 1]
+
+ x *= shrink_ratio[1] # width
+ y *= shrink_ratio[0] # height
+
+ # c: center
+ c = eye_avg + eye_to_mouth * 0.1
+ # quad: (left_top, left_bottom, right_bottom, right_top)
+ quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
+ # qsize: side length of the square
+ qsize = np.hypot(*x) * 2
+
+ quad_ori = np.copy(quad)
+ # Shrink, for large face
+ # TODO: do we really need shrink
+ shrink = int(np.floor(qsize / output_size * 0.5))
+ if shrink > 1:
+ h, w = img.shape[0:2]
+ rsize = (int(np.rint(float(w) / shrink)), int(np.rint(float(h) / shrink)))
+ img = cv2.resize(img, rsize, interpolation=cv2.INTER_AREA)
+ quad /= shrink
+ qsize /= shrink
+
+ # Crop
+ h, w = img.shape[0:2]
+ border = max(int(np.rint(qsize * 0.1)), 3)
+ crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
+ int(np.ceil(max(quad[:, 1]))))
+ crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, w), min(crop[3] + border, h))
+ if crop[2] - crop[0] < w or crop[3] - crop[1] < h:
+ img = img[crop[1]:crop[3], crop[0]:crop[2], :]
+ quad -= crop[0:2]
+
+ # Pad
+ # pad: (width_left, height_top, width_right, height_bottom)
+ h, w = img.shape[0:2]
+ pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
+ int(np.ceil(max(quad[:, 1]))))
+ pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - w + border, 0), max(pad[3] - h + border, 0))
+ if enable_padding and max(pad) > border - 4:
+ pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
+ img = np.pad(img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
+ h, w = img.shape[0:2]
+ y, x, _ = np.ogrid[:h, :w, :1]
+ mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0],
+ np.float32(w - 1 - x) / pad[2]),
+ 1.0 - np.minimum(np.float32(y) / pad[1],
+ np.float32(h - 1 - y) / pad[3]))
+ blur = int(qsize * 0.02)
+ if blur % 2 == 0:
+ blur += 1
+ blur_img = cv2.boxFilter(img, 0, ksize=(blur, blur))
+
+ img = img.astype('float32')
+ img += (blur_img - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
+ img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
+ img = np.clip(img, 0, 255) # float32, [0, 255]
+ quad += pad[:2]
+
+ # Transform use cv2
+ h_ratio = shrink_ratio[0] / shrink_ratio[1]
+ dst_h, dst_w = int(transform_size * h_ratio), transform_size
+ template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]])
+ # use cv2.LMEDS method for the equivalence to skimage transform
+ # ref: https://blog.csdn.net/yichxi/article/details/115827338
+ affine_matrix = cv2.estimateAffinePartial2D(quad, template, method=cv2.LMEDS)[0]
+ cropped_face = cv2.warpAffine(
+ img, affine_matrix, (dst_w, dst_h), borderMode=cv2.BORDER_CONSTANT, borderValue=(135, 133, 132)) # gray
+
+ if output_size < transform_size:
+ cropped_face = cv2.resize(
+ cropped_face, (output_size, int(output_size * h_ratio)), interpolation=cv2.INTER_LINEAR)
+
+ if return_inverse_affine:
+ dst_h, dst_w = int(output_size * h_ratio), output_size
+ template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]])
+ # use cv2.LMEDS method for the equivalence to skimage transform
+ # ref: https://blog.csdn.net/yichxi/article/details/115827338
+ affine_matrix = cv2.estimateAffinePartial2D(
+ quad_ori, np.array([[0, 0], [0, output_size], [dst_w, dst_h], [dst_w, 0]]), method=cv2.LMEDS)[0]
+ inverse_affine = cv2.invertAffineTransform(affine_matrix)
+ else:
+ inverse_affine = None
+ return cropped_face, inverse_affine
+
+
+def paste_face_back(img, face, inverse_affine):
+ h, w = img.shape[0:2]
+ face_h, face_w = face.shape[0:2]
+ inv_restored = cv2.warpAffine(face, inverse_affine, (w, h))
+ mask = np.ones((face_h, face_w, 3), dtype=np.float32)
+ inv_mask = cv2.warpAffine(mask, inverse_affine, (w, h))
+ # remove the black borders
+ inv_mask_erosion = cv2.erode(inv_mask, np.ones((2, 2), np.uint8))
+ inv_restored_remove_border = inv_mask_erosion * inv_restored
+ total_face_area = np.sum(inv_mask_erosion) // 3
+ # compute the fusion edge based on the area of face
+ w_edge = int(total_face_area**0.5) // 20
+ erosion_radius = w_edge * 2
+ inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
+ blur_size = w_edge * 2
+ inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
+ img = inv_soft_mask * inv_restored_remove_border + (1 - inv_soft_mask) * img
+ # float32, [0, 255]
+ return img
+
+
+if __name__ == '__main__':
+ import os
+
+ from custom_nodes.facerestore.facelib.detection import init_detection_model
+ from custom_nodes.facerestore.facelib.utils.face_restoration_helper import get_largest_face
+
+ img_path = '/home/wxt/datasets/ffhq/ffhq_wild/00009.png'
+ img_name = os.splitext(os.path.basename(img_path))[0]
+
+ # initialize model
+ det_net = init_detection_model('retinaface_resnet50', half=False)
+ img_ori = cv2.imread(img_path)
+ h, w = img_ori.shape[0:2]
+ # if larger than 800, scale it
+ scale = max(h / 800, w / 800)
+ if scale > 1:
+ img = cv2.resize(img_ori, (int(w / scale), int(h / scale)), interpolation=cv2.INTER_LINEAR)
+
+ with torch.no_grad():
+ bboxes = det_net.detect_faces(img, 0.97)
+ if scale > 1:
+ bboxes *= scale # the score is incorrect
+ bboxes = get_largest_face(bboxes, h, w)[0]
+
+ landmarks = np.array([[bboxes[i], bboxes[i + 1]] for i in range(5, 15, 2)])
+
+ cropped_face, inverse_affine = align_crop_face_landmarks(
+ img_ori,
+ landmarks,
+ output_size=512,
+ transform_size=None,
+ enable_padding=True,
+ return_inverse_affine=True,
+ shrink_ratio=(1, 1))
+
+ cv2.imwrite(f'tmp/{img_name}_cropeed_face.png', cropped_face)
+ img = paste_face_back(img_ori, cropped_face, inverse_affine)
+ cv2.imwrite(f'tmp/{img_name}_back.png', img)
diff --git a/r_facelib/utils/misc.py b/r_facelib/utils/misc.py new file mode 100644 index 0000000..6ea7c65 --- /dev/null +++ b/r_facelib/utils/misc.py @@ -0,0 +1,143 @@ +import cv2
+import os
+import os.path as osp
+import torch
+from torch.hub import download_url_to_file, get_dir
+from urllib.parse import urlparse
+# from basicsr.utils.download_util import download_file_from_google_drive
+#import gdown
+
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+
+def download_pretrained_models(file_ids, save_path_root):
+ os.makedirs(save_path_root, exist_ok=True)
+
+ for file_name, file_id in file_ids.items():
+ file_url = 'https://drive.google.com/uc?id='+file_id
+ save_path = osp.abspath(osp.join(save_path_root, file_name))
+ if osp.exists(save_path):
+ user_response = input(f'{file_name} already exist. Do you want to cover it? Y/N\n')
+ if user_response.lower() == 'y':
+ print(f'Covering {file_name} to {save_path}')
+ print("skipping gdown in facelib/utils/misc.py "+file_url)
+ #gdown.download(file_url, save_path, quiet=False)
+ # download_file_from_google_drive(file_id, save_path)
+ elif user_response.lower() == 'n':
+ print(f'Skipping {file_name}')
+ else:
+ raise ValueError('Wrong input. Only accepts Y/N.')
+ else:
+ print(f'Downloading {file_name} to {save_path}')
+ print("skipping gdown in facelib/utils/misc.py "+file_url)
+ #gdown.download(file_url, save_path, quiet=False)
+ # download_file_from_google_drive(file_id, save_path)
+
+
+def imwrite(img, file_path, params=None, auto_mkdir=True):
+ """Write image to file.
+
+ Args:
+ img (ndarray): Image array to be written.
+ file_path (str): Image file path.
+ params (None or list): Same as opencv's :func:`imwrite` interface.
+ auto_mkdir (bool): If the parent folder of `file_path` does not exist,
+ whether to create it automatically.
+
+ Returns:
+ bool: Successful or not.
+ """
+ if auto_mkdir:
+ dir_name = os.path.abspath(os.path.dirname(file_path))
+ os.makedirs(dir_name, exist_ok=True)
+ return cv2.imwrite(file_path, img, params)
+
+
+def img2tensor(imgs, bgr2rgb=True, float32=True):
+ """Numpy array to tensor.
+
+ Args:
+ imgs (list[ndarray] | ndarray): Input images.
+ bgr2rgb (bool): Whether to change bgr to rgb.
+ float32 (bool): Whether to change to float32.
+
+ Returns:
+ list[tensor] | tensor: Tensor images. If returned results only have
+ one element, just return tensor.
+ """
+
+ def _totensor(img, bgr2rgb, float32):
+ if img.shape[2] == 3 and bgr2rgb:
+ if img.dtype == 'float64':
+ img = img.astype('float32')
+ img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+ img = torch.from_numpy(img.transpose(2, 0, 1))
+ if float32:
+ img = img.float()
+ return img
+
+ if isinstance(imgs, list):
+ return [_totensor(img, bgr2rgb, float32) for img in imgs]
+ else:
+ return _totensor(imgs, bgr2rgb, float32)
+
+
+def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
+ """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
+ """
+ if model_dir is None:
+ hub_dir = get_dir()
+ model_dir = os.path.join(hub_dir, 'checkpoints')
+
+ os.makedirs(os.path.join(ROOT_DIR, model_dir), exist_ok=True)
+
+ parts = urlparse(url)
+ filename = os.path.basename(parts.path)
+ if file_name is not None:
+ filename = file_name
+ cached_file = os.path.abspath(os.path.join(ROOT_DIR, model_dir, filename))
+ if not os.path.exists(cached_file):
+ print(f'Downloading: "{url}" to {cached_file}\n')
+ download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
+ return cached_file
+
+
+def scandir(dir_path, suffix=None, recursive=False, full_path=False):
+ """Scan a directory to find the interested files.
+ Args:
+ dir_path (str): Path of the directory.
+ suffix (str | tuple(str), optional): File suffix that we are
+ interested in. Default: None.
+ recursive (bool, optional): If set to True, recursively scan the
+ directory. Default: False.
+ full_path (bool, optional): If set to True, include the dir_path.
+ Default: False.
+ Returns:
+ A generator for all the interested files with relative paths.
+ """
+
+ if (suffix is not None) and not isinstance(suffix, (str, tuple)):
+ raise TypeError('"suffix" must be a string or tuple of strings')
+
+ root = dir_path
+
+ def _scandir(dir_path, suffix, recursive):
+ for entry in os.scandir(dir_path):
+ if not entry.name.startswith('.') and entry.is_file():
+ if full_path:
+ return_path = entry.path
+ else:
+ return_path = osp.relpath(entry.path, root)
+
+ if suffix is None:
+ yield return_path
+ elif return_path.endswith(suffix):
+ yield return_path
+ else:
+ if recursive:
+ yield from _scandir(entry.path, suffix=suffix, recursive=recursive)
+ else:
+ continue
+
+ return _scandir(dir_path, suffix=suffix, recursive=recursive)
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