mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
Merger:
added smooth_rect option default is ON. Decreases jitter of predicting rect by using temporal interpolation. You can disable this option if you have problems with dynamic scenes.
This commit is contained in:
Colombo
parent
e0a55ff1c3
commit
814da70577
6 changed files with 236 additions and 66 deletions
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@ -249,77 +249,162 @@ def transform_points(points, mat, invert=False):
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points = np.squeeze(points)
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return points
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def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0, full_face_align_top=True):
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def get_transform_mat_data (image_landmarks, face_type, scale=1.0):
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if not isinstance(image_landmarks, np.ndarray):
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image_landmarks = np.array (image_landmarks)
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padding, remove_align = FaceType_to_padding_remove_align.get(face_type, 0.0)
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# estimate landmarks transform from global space to local aligned space with bounds [0..1]
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mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
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# get corner points in global space
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l_p = transform_points ( np.float32([(0,0),(1,0),(1,1),(0,1),(0.5,0.5)]) , mat, True)
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l_c = l_p[4]
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# calc diagonal vectors between corners in global space
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tb_diag_vec = (l_p[2]-l_p[0]).astype(np.float32)
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tb_diag_vec /= npla.norm(tb_diag_vec)
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bt_diag_vec = (l_p[1]-l_p[3]).astype(np.float32)
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bt_diag_vec /= npla.norm(bt_diag_vec)
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# calc modifier of diagonal vectors for scale and padding value
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padding, _ = FaceType_to_padding_remove_align.get(face_type, 0.0)
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mod = (1.0 / scale)* ( npla.norm(l_p[0]-l_p[2])*(padding*np.sqrt(2.0) + 0.5) )
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return l_c, tb_diag_vec, bt_diag_vec, mod
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def get_transform_mat_by_data (l_c, tb_diag_vec, bt_diag_vec, mod, output_size, face_type):
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_, remove_align = FaceType_to_padding_remove_align.get(face_type, 0.0)
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# calc 3 points in global space to estimate 2d affine transform
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if not remove_align:
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l_t = np.array( [ np.round( l_c - tb_diag_vec*mod ),
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np.round( l_c + bt_diag_vec*mod ),
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np.round( l_c + tb_diag_vec*mod ) ] )
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else:
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# remove_align - face will be centered in the frame but not aligned
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l_t = np.array( [ np.round( l_c - tb_diag_vec*mod ),
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np.round( l_c + bt_diag_vec*mod ),
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np.round( l_c + tb_diag_vec*mod ),
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np.round( l_c - bt_diag_vec*mod ),
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] )
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# get area of face square in global space
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area = mathlib.polygon_area(l_t[:,0], l_t[:,1] )
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# calc side of square
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side = np.float32(math.sqrt(area) / 2)
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# calc 3 points with unrotated square
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l_t = np.array( [ np.round( l_c + [-side,-side] ),
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np.round( l_c + [ side,-side] ),
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np.round( l_c + [ side, side] ) ] )
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# calc affine transform from 3 global space points to 3 local space points size of 'output_size'
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pts2 = np.float32(( (0,0),(output_size,0),(output_size,output_size) ))
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mat = cv2.getAffineTransform(l_t,pts2)
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#if remove_align:
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# bbox = transform_points ( [ (0,0), (0,output_size), (output_size, output_size), (output_size,0) ], mat, True)
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# #import code
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# #code.interact(local=dict(globals(), **locals()))
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# area = mathlib.polygon_area(bbox[:,0], bbox[:,1] )
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# side = math.sqrt(area) / 2
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# center = transform_points ( [(output_size/2,output_size/2)], mat, True)
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# pts1 = np.float32(( center+[-side,-side], center+[side,-side], center+[side,-side] ))
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# pts2 = np.float32([[0,0],[output_size,0],[0,output_size]])
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# mat = cv2.getAffineTransform(pts1,pts2)
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return mat
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#if full_face_align_top and (face_type == FaceType.FULL or face_type == FaceType.FULL_NO_ALIGN):
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# #lmrks2 = expand_eyebrows(image_landmarks)
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# #lmrks2_ = transform_points( [ lmrks2[19], lmrks2[24] ], mat, False )
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# #y_diff = np.float32( (0,np.min(lmrks2_[:,1])) )
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# #y_diff = transform_points( [ np.float32( (0,0) ), y_diff], mat, True)
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# #y_diff = y_diff[1]-y_diff[0]
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#
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# x_diff = np.float32((0,0))
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#
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# lmrks2_ = transform_points( [ image_landmarks[0], image_landmarks[16] ], mat, False )
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# if lmrks2_[0,0] < 0:
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# x_diff = lmrks2_[0,0]
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# x_diff = transform_points( [ np.float32( (0,0) ), np.float32((x_diff,0)) ], mat, True)
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# x_diff = x_diff[1]-x_diff[0]
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# elif lmrks2_[1,0] >= output_size:
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# x_diff = lmrks2_[1,0]-(output_size-1)
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# x_diff = transform_points( [ np.float32( (0,0) ), np.float32((x_diff,0)) ], mat, True)
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# x_diff = x_diff[1]-x_diff[0]
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#
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# mat = cv2.getAffineTransform( l_t+y_diff+x_diff ,pts2)
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def get_averaged_transform_mat (img_landmarks,
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img_landmarks_prev,
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img_landmarks_next,
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average_frame_count,
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average_center_frame_count,
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output_size, face_type, scale=1.0):
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l_c_list = []
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tb_diag_vec_list = []
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bt_diag_vec_list = []
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mod_list = []
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count = max(average_frame_count,average_center_frame_count)
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for i in range ( -count, count+1, 1 ):
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if i < 0:
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lmrks = img_landmarks_prev[i] if -i < len(img_landmarks_prev) else None
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elif i > 0:
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lmrks = img_landmarks_next[i] if i < len(img_landmarks_next) else None
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else:
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lmrks = img_landmarks
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if lmrks is None:
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continue
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l_c, tb_diag_vec, bt_diag_vec, mod = get_transform_mat_data (lmrks, face_type, scale=scale)
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if i >= -average_frame_count and i <= average_frame_count:
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tb_diag_vec_list.append(tb_diag_vec)
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bt_diag_vec_list.append(bt_diag_vec)
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mod_list.append(mod)
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if i >= -average_center_frame_count and i <= average_center_frame_count:
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l_c_list.append(l_c)
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tb_diag_vec = np.mean( np.array(tb_diag_vec_list), axis=0 )
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bt_diag_vec = np.mean( np.array(bt_diag_vec_list), axis=0 )
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mod = np.mean( np.array(mod_list), axis=0 )
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l_c = np.mean( np.array(l_c_list), axis=0 )
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return get_transform_mat_by_data (l_c, tb_diag_vec, bt_diag_vec, mod, output_size, face_type)
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def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
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l_c, tb_diag_vec, bt_diag_vec, mod = get_transform_mat_data (image_landmarks, face_type, scale=scale)
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return get_transform_mat_by_data (l_c, tb_diag_vec, bt_diag_vec, mod, output_size, face_type)
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"""
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def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
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if not isinstance(image_landmarks, np.ndarray):
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image_landmarks = np.array (image_landmarks)
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# get face padding value for FaceType
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padding, remove_align = FaceType_to_padding_remove_align.get(face_type, 0.0)
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# estimate landmarks transform from global space to local aligned space with bounds [0..1]
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mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
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# get corner points in global space
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l_p = transform_points ( np.float32([(0,0),(1,0),(1,1),(0,1),(0.5,0.5)]) , mat, True)
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l_c = l_p[4]
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# calc diagonal vectors between corners in global space
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tb_diag_vec = (l_p[2]-l_p[0]).astype(np.float32)
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tb_diag_vec /= npla.norm(tb_diag_vec)
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bt_diag_vec = (l_p[1]-l_p[3]).astype(np.float32)
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bt_diag_vec /= npla.norm(bt_diag_vec)
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# calc modifier of diagonal vectors for scale and padding value
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mod = (1.0 / scale)* ( npla.norm(l_p[0]-l_p[2])*(padding*np.sqrt(2.0) + 0.5) )
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# calc 3 points in global space to estimate 2d affine transform
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if not remove_align:
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l_t = np.array( [ np.round( l_c - tb_diag_vec*mod ),
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np.round( l_c + bt_diag_vec*mod ),
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np.round( l_c + tb_diag_vec*mod ) ] )
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else:
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# remove_align - face will be centered in the frame but not aligned
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l_t = np.array( [ np.round( l_c - tb_diag_vec*mod ),
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np.round( l_c + bt_diag_vec*mod ),
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np.round( l_c + tb_diag_vec*mod ),
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np.round( l_c - bt_diag_vec*mod ),
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] )
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# get area of face square in global space
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area = mathlib.polygon_area(l_t[:,0], l_t[:,1] )
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# calc side of square
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side = np.float32(math.sqrt(area) / 2)
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# calc 3 points with unrotated square
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l_t = np.array( [ np.round( l_c + [-side,-side] ),
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np.round( l_c + [ side,-side] ),
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np.round( l_c + [ side, side] ) ] )
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# calc affine transform from 3 global space points to 3 local space points size of 'output_size'
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pts2 = np.float32(( (0,0),(output_size,0),(output_size,output_size) ))
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mat = cv2.getAffineTransform(l_t,pts2)
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return mat
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"""
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def expand_eyebrows(lmrks, eyebrows_expand_mod=1.0):
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if len(lmrks) != 68:
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raise Exception('works only with 68 landmarks')
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@ -710,3 +795,35 @@ def estimate_pitch_yaw_roll(aligned_256px_landmarks):
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roll = np.clip ( roll, -math.pi, math.pi )
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return -pitch, yaw, roll
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#if remove_align:
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# bbox = transform_points ( [ (0,0), (0,output_size), (output_size, output_size), (output_size,0) ], mat, True)
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# #import code
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# #code.interact(local=dict(globals(), **locals()))
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# area = mathlib.polygon_area(bbox[:,0], bbox[:,1] )
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# side = math.sqrt(area) / 2
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# center = transform_points ( [(output_size/2,output_size/2)], mat, True)
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# pts1 = np.float32(( center+[-side,-side], center+[side,-side], center+[side,-side] ))
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# pts2 = np.float32([[0,0],[output_size,0],[0,output_size]])
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# mat = cv2.getAffineTransform(pts1,pts2)
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#if full_face_align_top and (face_type == FaceType.FULL or face_type == FaceType.FULL_NO_ALIGN):
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# #lmrks2 = expand_eyebrows(image_landmarks)
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# #lmrks2_ = transform_points( [ lmrks2[19], lmrks2[24] ], mat, False )
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# #y_diff = np.float32( (0,np.min(lmrks2_[:,1])) )
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# #y_diff = transform_points( [ np.float32( (0,0) ), y_diff], mat, True)
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# #y_diff = y_diff[1]-y_diff[0]
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#
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# x_diff = np.float32((0,0))
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#
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# lmrks2_ = transform_points( [ image_landmarks[0], image_landmarks[16] ], mat, False )
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# if lmrks2_[0,0] < 0:
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# x_diff = lmrks2_[0,0]
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# x_diff = transform_points( [ np.float32( (0,0) ), np.float32((x_diff,0)) ], mat, True)
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# x_diff = x_diff[1]-x_diff[0]
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# elif lmrks2_[1,0] >= output_size:
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# x_diff = lmrks2_[1,0]-(output_size-1)
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# x_diff = transform_points( [ np.float32( (0,0) ), np.float32((x_diff,0)) ], mat, True)
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# x_diff = x_diff[1]-x_diff[0]
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#
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# mat = cv2.getAffineTransform( l_t+y_diff+x_diff ,pts2)
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@ -148,7 +148,7 @@ class MergeSubprocessor(Subprocessor):
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cfg.fanseg_extract_func = self.fanseg_extract_func
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try:
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final_img = MergeMasked (self.predictor_func, self.predictor_input_shape, cfg, frame_info)
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final_img = MergeMasked (self.predictor_func, self.predictor_input_shape, cfg, frame_info, pf.prev_temporal_frame_infos, pf.next_temporal_frame_infos)
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except Exception as e:
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e_str = traceback.format_exc()
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if 'MemoryError' in e_str:
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@ -387,6 +387,7 @@ class MergeSubprocessor(Subprocessor):
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'z' : lambda cfg,shift_pressed: cfg.toggle_masked_hist_match(),
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'x' : lambda cfg,shift_pressed: cfg.toggle_mask_mode(),
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'c' : lambda cfg,shift_pressed: cfg.toggle_color_transfer_mode(),
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'b' : lambda cfg,shift_pressed: cfg.toggle_smooth_rect(),
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'n' : lambda cfg,shift_pressed: cfg.toggle_sharpen_mode(),
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}
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self.masked_keys = list(self.masked_keys_funcs.keys())
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@ -674,6 +675,7 @@ def main (model_class_name=None,
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input_path_image_paths = pathex.get_image_paths(input_path)
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if cfg.type == MergerConfig.TYPE_MASKED:
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if not aligned_path.exists():
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io.log_err('Aligned directory not found. Please ensure it exists.')
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@ -741,11 +743,34 @@ def main (model_class_name=None,
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io.log_info ("Use 'recover original filename' to determine the exact duplicates.")
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io.log_info ("")
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frames = [ MergeSubprocessor.Frame( frame_info=FrameInfo(filepath=Path(p),
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landmarks_list=alignments.get(Path(p).stem, None)
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)
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)
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for p in input_path_image_paths ]
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filesdata = []
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for filepath in io.progress_bar_generator(input_path_image_paths, "Collecting info"):
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filepath=Path(filepath)
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filesdata += [ FrameInfo(filepath=filepath, landmarks_list=alignments.get(filepath.stem, None)) ]
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frames = []
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filesdata_len = len(filesdata)
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for i in range(len(filesdata)):
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frame_info = filesdata[i]
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if multiple_faces_detected:
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prev_temporal_frame_infos = None
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next_temporal_frame_infos = None
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else:
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prev_temporal_frame_infos = []
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next_temporal_frame_infos = []
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for t in range (1,6):
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prev_frame_info = filesdata[ max(i -t, 0) ]
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next_frame_info = filesdata[ min(i +t, filesdata_len-1 )]
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prev_temporal_frame_infos.insert (0, prev_frame_info )
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next_temporal_frame_infos.append ( next_frame_info )
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frames.append ( MergeSubprocessor.Frame(prev_temporal_frame_infos=prev_temporal_frame_infos,
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frame_info=frame_info,
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next_temporal_frame_infos=next_temporal_frame_infos) )
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if multiple_faces_detected:
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io.log_info ("Warning: multiple faces detected. Motion blur will not be used.")
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@ -783,6 +808,8 @@ def main (model_class_name=None,
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elif cfg.type == MergerConfig.TYPE_FACE_AVATAR:
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pass
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"""
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filesdata = []
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for filepath in io.progress_bar_generator(input_path_image_paths, "Collecting info"):
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filepath = Path(filepath)
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@ -812,7 +839,7 @@ def main (model_class_name=None,
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frames.append ( MergeSubprocessor.Frame(prev_temporal_frame_infos=prev_temporal_frame_infos,
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frame_info=frame_info,
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next_temporal_frame_infos=next_temporal_frame_infos) )
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"""
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if len(frames) == 0:
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io.log_info ("No frames to merge in input_dir.")
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else:
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Before Width: | Height: | Size: 307 KiB After Width: | Height: | Size: 312 KiB |
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@ -8,7 +8,7 @@ from facelib import FaceType, LandmarksProcessor
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from core.interact import interact as io
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from core.cv2ex import *
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def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmarks):
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def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmarks, img_landmarks_prev, img_landmarks_next):
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img_size = img_bgr.shape[1], img_bgr.shape[0]
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img_face_mask_a = LandmarksProcessor.get_image_hull_mask (img_bgr.shape, img_face_landmarks)
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@ -18,20 +18,29 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
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out_img = img_bgr.copy()
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out_merging_mask_a = None
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mask_subres = 4
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input_size = predictor_input_shape[0]
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mask_subres_size = input_size*4
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output_size = input_size
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if cfg.super_resolution_power != 0:
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if cfg.super_resolution_power != 0 or cfg.smooth_rect:
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output_size *= 4
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face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type)
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face_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale )
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if cfg.smooth_rect:
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average_frame_count=5
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average_center_frame_count=1
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else:
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average_frame_count=0
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average_center_frame_count=0
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def get_transform_mat(*args, **kwargs):
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return LandmarksProcessor.get_averaged_transform_mat (img_face_landmarks, img_landmarks_prev, img_landmarks_next, average_frame_count, average_center_frame_count, *args, **kwargs)
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face_mat = get_transform_mat (output_size, face_type=cfg.face_type)
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face_output_mat = get_transform_mat (output_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale)
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if mask_subres_size == output_size:
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face_mask_output_mat = face_output_mat
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else:
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face_mask_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, mask_subres_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale )
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face_mask_output_mat = get_transform_mat (mask_subres_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale)
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dst_face_bgr = cv2.warpAffine( img_bgr , face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
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dst_face_bgr = np.clip(dst_face_bgr, 0, 1)
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|
|
@ -56,11 +65,13 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
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if cfg.super_resolution_power != 0:
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prd_face_bgr_enhanced = cfg.superres_func(prd_face_bgr)
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mod = cfg.super_resolution_power / 100.0
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prd_face_bgr = cv2.resize(prd_face_bgr, (output_size,output_size))*(1.0-mod) + \
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prd_face_bgr_enhanced*mod
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prd_face_bgr = cv2.resize(prd_face_bgr, (output_size,output_size))*(1.0-mod) + prd_face_bgr_enhanced*mod
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prd_face_bgr = np.clip(prd_face_bgr, 0, 1)
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elif cfg.smooth_rect:
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prd_face_bgr = cv2.resize(prd_face_bgr, (output_size,output_size), cv2.INTER_CUBIC)
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prd_face_bgr = np.clip(prd_face_bgr, 0, 1)
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if cfg.super_resolution_power != 0 or cfg.smooth_rect:
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||||
if predictor_masked:
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prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC)
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else:
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|
|
@ -77,14 +88,14 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
|
|||
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if cfg.mask_mode >= 4 and cfg.mask_mode <= 7:
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||||
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full_face_fanseg_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, cfg.fanseg_input_size, face_type=FaceType.FULL)
|
||||
full_face_fanseg_mat = get_transform_mat (cfg.fanseg_input_size, face_type=FaceType.FULL)
|
||||
dst_face_fanseg_bgr = cv2.warpAffine(img_bgr, full_face_fanseg_mat, (cfg.fanseg_input_size,)*2, flags=cv2.INTER_CUBIC )
|
||||
dst_face_fanseg_mask = cfg.fanseg_extract_func( FaceType.FULL, dst_face_fanseg_bgr )
|
||||
|
||||
if cfg.face_type == FaceType.FULL:
|
||||
FAN_dst_face_mask_a_0 = cv2.resize (dst_face_fanseg_mask, (output_size,output_size), cv2.INTER_CUBIC)
|
||||
else:
|
||||
face_fanseg_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, cfg.fanseg_input_size, face_type=cfg.face_type)
|
||||
face_fanseg_mat = get_transform_mat (cfg.fanseg_input_size, face_type=cfg.face_type)
|
||||
|
||||
fanseg_rect_corner_pts = np.array ( [ [0,0], [cfg.fanseg_input_size-1,0], [0,cfg.fanseg_input_size-1] ], dtype=np.float32 )
|
||||
a = LandmarksProcessor.transform_points (fanseg_rect_corner_pts, face_fanseg_mat, invert=True )
|
||||
|
|
@ -106,13 +117,12 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
|
|||
|
||||
prd_face_mask_a_0[ prd_face_mask_a_0 < (1.0/255.0) ] = 0.0 # get rid of noise
|
||||
|
||||
|
||||
# process mask in local predicted space
|
||||
if 'raw' not in cfg.mode:
|
||||
# resize to mask_subres_size
|
||||
if prd_face_mask_a_0.shape[0] != mask_subres_size:
|
||||
prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (mask_subres_size, mask_subres_size), cv2.INTER_CUBIC)
|
||||
|
||||
# process mask in local predicted space
|
||||
if 'raw' not in cfg.mode:
|
||||
# add zero pad
|
||||
prd_face_mask_a_0 = np.pad (prd_face_mask_a_0, input_size)
|
||||
|
||||
|
|
@ -281,7 +291,7 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
|
|||
k_size = int(frame_info.motion_power*cfg_mp)
|
||||
if k_size >= 1:
|
||||
k_size = np.clip (k_size+1, 2, 50)
|
||||
if cfg.super_resolution_power != 0:
|
||||
if cfg.super_resolution_power != 0 or cfg.smooth_rect:
|
||||
k_size *= 2
|
||||
out_face_bgr = imagelib.LinearMotionBlur (out_face_bgr, k_size , frame_info.motion_deg)
|
||||
|
||||
|
|
@ -321,14 +331,20 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
|
|||
return out_img, out_merging_mask_a
|
||||
|
||||
|
||||
def MergeMasked (predictor_func, predictor_input_shape, cfg, frame_info):
|
||||
def MergeMasked (predictor_func, predictor_input_shape, cfg, frame_info, prev_temporal_frame_infos=None, next_temporal_frame_infos=None):
|
||||
img_bgr_uint8 = cv2_imread(frame_info.filepath)
|
||||
img_bgr_uint8 = imagelib.normalize_channels (img_bgr_uint8, 3)
|
||||
img_bgr = img_bgr_uint8.astype(np.float32) / 255.0
|
||||
|
||||
|
||||
outs = []
|
||||
for face_num, img_landmarks in enumerate( frame_info.landmarks_list ):
|
||||
out_img, out_img_merging_mask = MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img_bgr_uint8, img_bgr, img_landmarks)
|
||||
img_landmarks_prev = [ x.landmarks_list[0] for x in prev_temporal_frame_infos if len(x.landmarks_list) != 0] \
|
||||
if prev_temporal_frame_infos is not None else []
|
||||
img_landmarks_next = [ x.landmarks_list[0] for x in next_temporal_frame_infos if len(x.landmarks_list) != 0] \
|
||||
if next_temporal_frame_infos is not None else []
|
||||
|
||||
out_img, out_img_merging_mask = MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img_bgr_uint8, img_bgr, img_landmarks, img_landmarks_prev, img_landmarks_next)
|
||||
outs += [ (out_img, out_img_merging_mask) ]
|
||||
|
||||
#Combining multiple face outputs
|
||||
|
|
|
|||
|
|
@ -119,6 +119,7 @@ class MergerConfigMasked(MergerConfig):
|
|||
output_face_scale = 0,
|
||||
super_resolution_power = 0,
|
||||
color_transfer_mode = ctm_str_dict['rct'],
|
||||
smooth_rect = True,
|
||||
image_denoise_power = 0,
|
||||
bicubic_degrade_power = 0,
|
||||
color_degrade_power = 0,
|
||||
|
|
@ -148,6 +149,7 @@ class MergerConfigMasked(MergerConfig):
|
|||
self.output_face_scale = output_face_scale
|
||||
self.super_resolution_power = super_resolution_power
|
||||
self.color_transfer_mode = color_transfer_mode
|
||||
self.smooth_rect = smooth_rect
|
||||
self.image_denoise_power = image_denoise_power
|
||||
self.bicubic_degrade_power = bicubic_degrade_power
|
||||
self.color_degrade_power = color_degrade_power
|
||||
|
|
@ -188,6 +190,9 @@ class MergerConfigMasked(MergerConfig):
|
|||
def toggle_color_transfer_mode(self):
|
||||
self.color_transfer_mode = (self.color_transfer_mode+1) % ( max(ctm_dict.keys())+1 )
|
||||
|
||||
def toggle_smooth_rect(self):
|
||||
self.smooth_rect = not self.smooth_rect
|
||||
|
||||
def add_super_resolution_power(self, diff):
|
||||
self.super_resolution_power = np.clip ( self.super_resolution_power+diff , 0, 100)
|
||||
|
||||
|
|
@ -241,6 +246,8 @@ class MergerConfigMasked(MergerConfig):
|
|||
self.color_transfer_mode = io.input_str ( "Color transfer to predicted face", None, valid_list=list(ctm_str_dict.keys())[1:] )
|
||||
self.color_transfer_mode = ctm_str_dict[self.color_transfer_mode]
|
||||
|
||||
self.smooth_rect = io.input_bool("Smooth rect?", True, help_message="Decreases jitter of predicting rect by using temporal interpolation. You can disable this option if you have problems with dynamic scenes.")
|
||||
|
||||
super().ask_settings()
|
||||
|
||||
self.super_resolution_power = np.clip ( io.input_int ("Choose super resolution power", 0, add_info="0..100", help_message="Enhance details by applying superresolution network."), 0, 100)
|
||||
|
|
@ -266,6 +273,7 @@ class MergerConfigMasked(MergerConfig):
|
|||
self.motion_blur_power == other.motion_blur_power and \
|
||||
self.output_face_scale == other.output_face_scale and \
|
||||
self.color_transfer_mode == other.color_transfer_mode and \
|
||||
self.smooth_rect == other.smooth_rect and \
|
||||
self.super_resolution_power == other.super_resolution_power and \
|
||||
self.image_denoise_power == other.image_denoise_power and \
|
||||
self.bicubic_degrade_power == other.bicubic_degrade_power and \
|
||||
|
|
@ -300,6 +308,8 @@ class MergerConfigMasked(MergerConfig):
|
|||
if 'raw' not in self.mode:
|
||||
r += f"""color_transfer_mode: {ctm_dict[self.color_transfer_mode]}\n"""
|
||||
|
||||
r += f"""smooth_rect: {self.smooth_rect}\n"""
|
||||
|
||||
r += super().to_string(filename)
|
||||
r += f"""super_resolution_power: {self.super_resolution_power}\n"""
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue