mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
fixed error "Failed to get convolution algorithm" on some systems
fixed error "dll load failed" on some systems Expanded eyebrows line of face masks. It does not affect mask of FAN-x converter mode.
This commit is contained in:
iperov
parent
582c974851
commit
b72d5a3f9a
15 changed files with 367 additions and 222 deletions
|
|
@ -109,10 +109,19 @@ landmarks_68_3D = np.array( [
|
|||
[0.205322 , 31.408738 , -21.903670 ],
|
||||
[-7.198266 , 30.844876 , -20.328022 ] ], dtype=np.float32)
|
||||
|
||||
def transform_points(points, mat, invert=False):
|
||||
if invert:
|
||||
mat = cv2.invertAffineTransform (mat)
|
||||
points = np.expand_dims(points, axis=1)
|
||||
points = cv2.transform(points, mat, points.shape)
|
||||
points = np.squeeze(points)
|
||||
return points
|
||||
|
||||
def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
|
||||
if not isinstance(image_landmarks, np.ndarray):
|
||||
image_landmarks = np.array (image_landmarks)
|
||||
|
||||
"""
|
||||
if face_type == FaceType.AVATAR:
|
||||
centroid = np.mean (image_landmarks, axis=0)
|
||||
|
||||
|
|
@ -128,76 +137,79 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
|
|||
mat = mat * scale * (output_size / 3)
|
||||
mat[:,2] += output_size / 2
|
||||
else:
|
||||
if face_type == FaceType.HALF:
|
||||
padding = 0
|
||||
elif face_type == FaceType.FULL:
|
||||
padding = (output_size / 64) * 12
|
||||
elif face_type == FaceType.HEAD:
|
||||
padding = (output_size / 64) * 24
|
||||
else:
|
||||
raise ValueError ('wrong face_type: ', face_type)
|
||||
"""
|
||||
remove_align = False
|
||||
if face_type == FaceType.FULL_NO_ALIGN:
|
||||
face_type = FaceType.FULL
|
||||
remove_align = True
|
||||
|
||||
if face_type == FaceType.HALF:
|
||||
padding = 0
|
||||
elif face_type == FaceType.FULL:
|
||||
padding = (output_size / 64) * 12
|
||||
elif face_type == FaceType.HEAD:
|
||||
padding = (output_size / 64) * 24
|
||||
else:
|
||||
raise ValueError ('wrong face_type: ', face_type)
|
||||
|
||||
mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
|
||||
mat = mat * (output_size - 2 * padding)
|
||||
mat[:,2] += padding
|
||||
mat *= (1 / scale)
|
||||
mat[:,2] += -output_size*( ( (1 / scale) - 1.0 ) / 2 )
|
||||
mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
|
||||
mat = mat * (output_size - 2 * padding)
|
||||
mat[:,2] += padding
|
||||
mat *= (1 / scale)
|
||||
mat[:,2] += -output_size*( ( (1 / scale) - 1.0 ) / 2 )
|
||||
|
||||
if remove_align:
|
||||
bbox = transform_points ( [ (0,0), (0,output_size-1), (output_size-1, output_size-1), (output_size-1,0) ], mat, True)
|
||||
area = mathlib.polygon_area(bbox[:,0], bbox[:,1] )
|
||||
side = math.sqrt(area) / 2
|
||||
center = transform_points ( [(output_size/2,output_size/2)], mat, True)
|
||||
|
||||
pts1 = np.float32([ center+[-side,-side], center+[side,-side], center+[-side,side] ])
|
||||
pts2 = np.float32([[0,0],[output_size-1,0],[0,output_size-1]])
|
||||
mat = cv2.getAffineTransform(pts1,pts2)
|
||||
|
||||
return mat
|
||||
|
||||
def transform_points(points, mat, invert=False):
|
||||
if invert:
|
||||
mat = cv2.invertAffineTransform (mat)
|
||||
points = np.expand_dims(points, axis=1)
|
||||
points = cv2.transform(points, mat, points.shape)
|
||||
points = np.squeeze(points)
|
||||
return points
|
||||
|
||||
|
||||
def get_image_hull_mask (image_shape, image_landmarks, ie_polys=None):
|
||||
if len(image_landmarks) != 68:
|
||||
raise Exception('get_image_hull_mask works only with 68 landmarks')
|
||||
int_lmrks = np.array(image_landmarks, dtype=np.int)
|
||||
int_lmrks = np.array(image_landmarks.copy(), dtype=np.int)
|
||||
|
||||
hull_mask = np.zeros(image_shape[0:2]+(1,),dtype=np.float32)
|
||||
|
||||
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
|
||||
np.concatenate ( (int_lmrks[0:9],
|
||||
int_lmrks[17:18]))) , (1,) )
|
||||
# #nose
|
||||
ml_pnt = (int_lmrks[36] + int_lmrks[0]) // 2
|
||||
mr_pnt = (int_lmrks[16] + int_lmrks[45]) // 2
|
||||
|
||||
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
|
||||
np.concatenate ( (int_lmrks[8:17],
|
||||
int_lmrks[26:27]))) , (1,) )
|
||||
# mid points between the mid points and eye
|
||||
ql_pnt = (int_lmrks[36] + ml_pnt) // 2
|
||||
qr_pnt = (int_lmrks[45] + mr_pnt) // 2
|
||||
|
||||
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
|
||||
np.concatenate ( (int_lmrks[17:20],
|
||||
int_lmrks[8:9]))) , (1,) )
|
||||
# Top of the eye arrays
|
||||
bot_l = np.array((ql_pnt, int_lmrks[36], int_lmrks[37], int_lmrks[38], int_lmrks[39]))
|
||||
bot_r = np.array((int_lmrks[42], int_lmrks[43], int_lmrks[44], int_lmrks[45], qr_pnt))
|
||||
|
||||
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
|
||||
np.concatenate ( (int_lmrks[24:27],
|
||||
int_lmrks[8:9]))) , (1,) )
|
||||
# Eyebrow arrays
|
||||
top_l = int_lmrks[17:22]
|
||||
top_r = int_lmrks[22:27]
|
||||
|
||||
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
|
||||
np.concatenate ( (int_lmrks[19:25],
|
||||
int_lmrks[8:9],
|
||||
))) , (1,) )
|
||||
# Adjust eyebrow arrays
|
||||
int_lmrks[17:22] = top_l + ((top_l - bot_l) // 2)
|
||||
int_lmrks[22:27] = top_r + ((top_r - bot_r) // 2)
|
||||
|
||||
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
|
||||
np.concatenate ( (int_lmrks[17:22],
|
||||
int_lmrks[27:28],
|
||||
int_lmrks[31:36],
|
||||
int_lmrks[8:9]
|
||||
))) , (1,) )
|
||||
r_jaw = (int_lmrks[0:9], int_lmrks[17:18])
|
||||
l_jaw = (int_lmrks[8:17], int_lmrks[26:27])
|
||||
r_cheek = (int_lmrks[17:20], int_lmrks[8:9])
|
||||
l_cheek = (int_lmrks[24:27], int_lmrks[8:9])
|
||||
nose_ridge = (int_lmrks[19:25], int_lmrks[8:9],)
|
||||
r_eye = (int_lmrks[17:22], int_lmrks[27:28], int_lmrks[31:36], int_lmrks[8:9])
|
||||
l_eye = (int_lmrks[22:27], int_lmrks[27:28], int_lmrks[31:36], int_lmrks[8:9])
|
||||
nose = (int_lmrks[27:31], int_lmrks[31:36])
|
||||
parts = [r_jaw, l_jaw, r_cheek, l_cheek, nose_ridge, r_eye, l_eye, nose]
|
||||
|
||||
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
|
||||
np.concatenate ( (int_lmrks[22:27],
|
||||
int_lmrks[27:28],
|
||||
int_lmrks[31:36],
|
||||
int_lmrks[8:9]
|
||||
))) , (1,) )
|
||||
|
||||
#nose
|
||||
cv2.fillConvexPoly( hull_mask, cv2.convexHull(int_lmrks[27:36]), (1,) )
|
||||
for item in parts:
|
||||
merged = np.concatenate(item)
|
||||
cv2.fillConvexPoly(hull_mask, cv2.convexHull(merged), 1)
|
||||
|
||||
if ie_polys is not None:
|
||||
ie_polys.overlay_mask(hull_mask)
|
||||
|
|
@ -309,7 +321,7 @@ def draw_landmarks (image, image_landmarks, color=(0,255,0), transparent_mask=Fa
|
|||
mask = get_image_hull_mask (image.shape, image_landmarks, ie_polys)
|
||||
image[...] = ( image * (1-mask) + image * mask / 2 )[...]
|
||||
|
||||
def draw_rect_landmarks (image, rect, image_landmarks, face_size, face_type, transparent_mask=False, ie_polys=None, landmarks_color=(0,255,0) ):
|
||||
def draw_rect_landmarks (image, rect, image_landmarks, face_size, face_type, transparent_mask=False, ie_polys=None, landmarks_color=(0,255,0)):
|
||||
draw_landmarks(image, image_landmarks, color=landmarks_color, transparent_mask=transparent_mask, ie_polys=ie_polys)
|
||||
imagelib.draw_rect (image, rect, (255,0,0), 2 )
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue