update default weights

1 files changed, 8 insertions, 8 deletions

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 01a92b12..9859974a 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -71,9 +71,9 @@ def crop_image(im, settings):
   return results

 

 def focal_point(im, settings):

-    corner_points = image_corner_points(im, settings)

-    entropy_points = image_entropy_points(im, settings)

-    face_points = image_face_points(im, settings)

+    corner_points = image_corner_points(im, settings) if settings.corner_points_weight > 0 else []

+    entropy_points = image_entropy_points(im, settings) if settings.entropy_points_weight > 0 else []

+    face_points = image_face_points(im, settings) if settings.face_points_weight > 0 else []

 

     pois = []

 

@@ -144,7 +144,7 @@ def image_face_points(im, settings):
           settings.dnn_model_path,

           "",

           (im.width, im.height),

-          0.8, # score threshold

+          0.9, # score threshold

           0.3, # nms threshold

           5000 # keep top k before nms

       )

@@ -159,7 +159,7 @@ def image_face_points(im, settings):
           results.append(

             PointOfInterest(

               int(x + (w * 0.5)), # face focus left/right is center

-              int(y + (h * 0)), # face focus up/down is close to the top of the head

+              int(y + (h * 0.33)), # face focus up/down is close to the top of the head

               size = w,

               weight = 1/len(faces[1])

             )

@@ -207,7 +207,7 @@ def image_corner_points(im, settings):
         np_im,

         maxCorners=100,

         qualityLevel=0.04,

-        minDistance=min(grayscale.width, grayscale.height)*0.03,

+        minDistance=min(grayscale.width, grayscale.height)*0.06,

         useHarrisDetector=False,

     )

 

@@ -256,8 +256,8 @@ def image_entropy_points(im, settings):
 

 def image_entropy(im):

     # greyscale image entropy

-    band = np.asarray(im.convert("L"))

-    # band = np.asarray(im.convert("1"), dtype=np.uint8)

+    # band = np.asarray(im.convert("L"))

+    band = np.asarray(im.convert("1"), dtype=np.uint8)

     hist, _ = np.histogram(band, bins=range(0, 256))

     hist = hist[hist > 0]

     return -np.log2(hist / hist.sum()).sum()