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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-16 10:03:41 -07:00
removing smooth_rect option
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Colombo
parent
01376fd17c
commit
4f928074b9
6 changed files with 119 additions and 180 deletions
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@ -249,9 +249,7 @@ 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_data (image_landmarks, face_type, scale=1.0):
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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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@ -269,112 +267,9 @@ def get_transform_mat_data (image_landmarks, face_type, scale=1.0):
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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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return mat
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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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@ -402,9 +297,8 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
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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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@ -826,4 +720,102 @@ def estimate_pitch_yaw_roll(aligned_256px_landmarks):
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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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# mat = cv2.getAffineTransform( l_t+y_diff+x_diff ,pts2)
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"""
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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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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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