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extractor: fixes, optimizations,

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manual extractor: added 'a' option to switch accuracy mode
This commit is contained in:
iperov
2019-03-21 18:56:32 +04:00
parent 63c9be3d1f
commit 7a0cc56603
3 changed files with 121 additions and 64 deletions
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facelib/LandmarksExtractor.py
+1 -2
View File
@@ -46,7 +46,6 @@ class LandmarksExtractor(object):
predicted = self.keras_model.predict (image).transpose (0,3,1,2)
pts_img = self.get_pts_from_predict ( predicted[-1], center, scale)
pts_img = [ ( int(pt[0]), int(pt[1]) ) for pt in pts_img ]
landmarks.append (pts_img)
except:
landmarks.append (None)
@@ -118,4 +117,4 @@ class LandmarksExtractor(object):
c[i] += np.sign(diff)*0.25
c += 0.5
return [ self.transform (c[i], center, scale, a.shape[2]) for i in range(a.shape[0]) ]
return np.array( [ self.transform (c[i], center, scale, a.shape[2]) for i in range(a.shape[0]) ] )
facelib/LandmarksProcessor.py
+29 -26
View File
@@ -156,46 +156,47 @@ def transform_points(points, mat, invert=False):
def get_image_hull_mask (image_shape, image_landmarks):
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)
hull_mask = np.zeros(image_shape[0:2]+(1,),dtype=np.float32)
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
np.concatenate ( (image_landmarks[0:9],
image_landmarks[17:18]))) , (1,) )
np.concatenate ( (int_lmrks[0:9],
int_lmrks[17:18]))) , (1,) )
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
np.concatenate ( (image_landmarks[8:17],
image_landmarks[26:27]))) , (1,) )
np.concatenate ( (int_lmrks[8:17],
int_lmrks[26:27]))) , (1,) )
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
np.concatenate ( (image_landmarks[17:20],
image_landmarks[8:9]))) , (1,) )
np.concatenate ( (int_lmrks[17:20],
int_lmrks[8:9]))) , (1,) )
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
np.concatenate ( (image_landmarks[24:27],
image_landmarks[8:9]))) , (1,) )
np.concatenate ( (int_lmrks[24:27],
int_lmrks[8:9]))) , (1,) )
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
np.concatenate ( (image_landmarks[19:25],
image_landmarks[8:9],
np.concatenate ( (int_lmrks[19:25],
int_lmrks[8:9],
))) , (1,) )
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
np.concatenate ( (image_landmarks[17:22],
image_landmarks[27:28],
image_landmarks[31:36],
image_landmarks[8:9]
np.concatenate ( (int_lmrks[17:22],
int_lmrks[27:28],
int_lmrks[31:36],
int_lmrks[8:9]
))) , (1,) )
cv2.fillConvexPoly( hull_mask, cv2.convexHull(
np.concatenate ( (image_landmarks[22:27],
image_landmarks[27:28],
image_landmarks[31:36],
image_landmarks[8:9]
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(image_landmarks[27:36]), (1,) )
cv2.fillConvexPoly( hull_mask, cv2.convexHull(int_lmrks[27:36]), (1,) )
return hull_mask
@@ -285,13 +286,15 @@ def draw_landmarks (image, image_landmarks, color=(0,255,0), transparent_mask=Fa
if len(image_landmarks) != 68:
raise Exception('get_image_eye_mask works only with 68 landmarks')
jaw = image_landmarks[slice(*landmarks_68_pt["jaw"])]
right_eyebrow = image_landmarks[slice(*landmarks_68_pt["right_eyebrow"])]
left_eyebrow = image_landmarks[slice(*landmarks_68_pt["left_eyebrow"])]
mouth = image_landmarks[slice(*landmarks_68_pt["mouth"])]
right_eye = image_landmarks[slice(*landmarks_68_pt["right_eye"])]
left_eye = image_landmarks[slice(*landmarks_68_pt["left_eye"])]
nose = image_landmarks[slice(*landmarks_68_pt["nose"])]
int_lmrks = np.array(image_landmarks, dtype=np.int)
jaw = int_lmrks[slice(*landmarks_68_pt["jaw"])]
right_eyebrow = int_lmrks[slice(*landmarks_68_pt["right_eyebrow"])]
left_eyebrow = int_lmrks[slice(*landmarks_68_pt["left_eyebrow"])]
mouth = int_lmrks[slice(*landmarks_68_pt["mouth"])]
right_eye = int_lmrks[slice(*landmarks_68_pt["right_eye"])]
left_eye = int_lmrks[slice(*landmarks_68_pt["left_eye"])]
nose = int_lmrks[slice(*landmarks_68_pt["nose"])]
# open shapes
cv2.polylines(image, tuple(np.array([v]) for v in ( right_eyebrow, jaw, left_eyebrow, np.concatenate((nose, [nose[-6]])) )),