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Administrator
stable-diffusion-webui
Commits
3e6c2420
Commit
3e6c2420
authored
Oct 25, 2022
by
captin411
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improve debug markers, fix algo weighting
parent
1be5933b
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1 changed file
with
129 additions
and
78 deletions
+129
-78
autocrop.py
modules/textual_inversion/autocrop.py
+129
-78
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modules/textual_inversion/autocrop.py
View file @
3e6c2420
import
cv2
import
cv2
import
os
from
collections
import
defaultdict
from
collections
import
defaultdict
from
math
import
log
,
sqrt
from
math
import
log
,
sqrt
import
numpy
as
np
import
numpy
as
np
...
@@ -26,19 +27,9 @@ def crop_image(im, settings):
...
@@ -26,19 +27,9 @@ def crop_image(im, settings):
scale_by
=
settings
.
crop_height
/
im
.
height
scale_by
=
settings
.
crop_height
/
im
.
height
im
=
im
.
resize
((
int
(
im
.
width
*
scale_by
),
int
(
im
.
height
*
scale_by
)))
im
=
im
.
resize
((
int
(
im
.
width
*
scale_by
),
int
(
im
.
height
*
scale_by
)))
im_debug
=
im
.
copy
()
if
im
.
width
==
settings
.
crop_width
and
im
.
height
==
settings
.
crop_height
:
focus
=
focal_point
(
im_debug
,
settings
)
if
settings
.
annotate_image
:
d
=
ImageDraw
.
Draw
(
im
)
rect
=
[
0
,
0
,
im
.
width
,
im
.
height
]
rect
[
2
]
-=
1
rect
[
3
]
-=
1
d
.
rectangle
(
rect
,
outline
=
GREEN
)
if
settings
.
destop_view_image
:
im
.
show
()
return
im
focus
=
focal_point
(
im
,
settings
)
# take the focal point and turn it into crop coordinates that try to center over the focal
# take the focal point and turn it into crop coordinates that try to center over the focal
# point but then get adjusted back into the frame
# point but then get adjusted back into the frame
...
@@ -62,62 +53,118 @@ def crop_image(im, settings):
...
@@ -62,62 +53,118 @@ def crop_image(im, settings):
crop
=
[
x1
,
y1
,
x2
,
y2
]
crop
=
[
x1
,
y1
,
x2
,
y2
]
results
=
[]
results
.
append
(
im
.
crop
(
tuple
(
crop
)))
if
settings
.
annotate_image
:
if
settings
.
annotate_image
:
d
=
ImageDraw
.
Draw
(
im
)
d
=
ImageDraw
.
Draw
(
im
_debug
)
rect
=
list
(
crop
)
rect
=
list
(
crop
)
rect
[
2
]
-=
1
rect
[
2
]
-=
1
rect
[
3
]
-=
1
rect
[
3
]
-=
1
d
.
rectangle
(
rect
,
outline
=
GREEN
)
d
.
rectangle
(
rect
,
outline
=
GREEN
)
results
.
append
(
im_debug
)
if
settings
.
destop_view_image
:
if
settings
.
destop_view_image
:
im
.
show
()
im
_debug
.
show
()
return
im
.
crop
(
tuple
(
crop
))
return
results
def
focal_point
(
im
,
settings
):
def
focal_point
(
im
,
settings
):
corner_points
=
image_corner_points
(
im
,
settings
)
corner_points
=
image_corner_points
(
im
,
settings
)
entropy_points
=
image_entropy_points
(
im
,
settings
)
entropy_points
=
image_entropy_points
(
im
,
settings
)
face_points
=
image_face_points
(
im
,
settings
)
face_points
=
image_face_points
(
im
,
settings
)
total_points
=
len
(
corner_points
)
+
len
(
entropy_points
)
+
len
(
face_points
)
corner_weight
=
settings
.
corner_points_weight
entropy_weight
=
settings
.
entropy_points_weight
face_weight
=
settings
.
face_points_weight
weight_pref_total
=
corner_weight
+
entropy_weight
+
face_weight
# weight things
pois
=
[]
pois
=
[]
if
weight_pref_total
==
0
or
total_points
==
0
:
return
pois
pois
.
extend
(
weight_pref_total
=
0
[
PointOfInterest
(
p
.
x
,
p
.
y
,
weight
=
p
.
weight
*
(
(
corner_weight
/
weight_pref_total
)
/
(
len
(
corner_points
)
/
total_points
)
))
for
p
in
corner_points
]
if
len
(
corner_points
)
>
0
:
)
weight_pref_total
+=
settings
.
corner_points_weight
pois
.
extend
(
if
len
(
entropy_points
)
>
0
:
[
PointOfInterest
(
p
.
x
,
p
.
y
,
weight
=
p
.
weight
*
(
(
entropy_weight
/
weight_pref_total
)
/
(
len
(
entropy_points
)
/
total_points
)
))
for
p
in
entropy_points
]
weight_pref_total
+=
settings
.
entropy_points_weight
)
if
len
(
face_points
)
>
0
:
pois
.
extend
(
weight_pref_total
+=
settings
.
face_points_weight
[
PointOfInterest
(
p
.
x
,
p
.
y
,
weight
=
p
.
weight
*
(
(
face_weight
/
weight_pref_total
)
/
(
len
(
face_points
)
/
total_points
)
))
for
p
in
face_points
]
)
corner_centroid
=
None
if
len
(
corner_points
)
>
0
:
corner_centroid
=
centroid
(
corner_points
)
corner_centroid
.
weight
=
settings
.
corner_points_weight
/
weight_pref_total
pois
.
append
(
corner_centroid
)
entropy_centroid
=
None
if
len
(
entropy_points
)
>
0
:
entropy_centroid
=
centroid
(
entropy_points
)
entropy_centroid
.
weight
=
settings
.
entropy_points_weight
/
weight_pref_total
pois
.
append
(
entropy_centroid
)
face_centroid
=
None
if
len
(
face_points
)
>
0
:
face_centroid
=
centroid
(
face_points
)
face_centroid
.
weight
=
settings
.
face_points_weight
/
weight_pref_total
pois
.
append
(
face_centroid
)
average_point
=
poi_average
(
pois
,
settings
)
average_point
=
poi_average
(
pois
,
settings
)
if
settings
.
annotate_image
:
if
settings
.
annotate_image
:
d
=
ImageDraw
.
Draw
(
im
)
d
=
ImageDraw
.
Draw
(
im
)
for
f
in
face_points
:
max_size
=
min
(
im
.
width
,
im
.
height
)
*
0.07
d
.
rectangle
(
f
.
bounding
(
f
.
size
),
outline
=
RED
)
if
corner_centroid
is
not
None
:
color
=
BLUE
box
=
corner_centroid
.
bounding
(
max_size
*
corner_centroid
.
weight
)
d
.
text
((
box
[
0
],
box
[
1
]
-
15
),
"Edge:
%.02
f"
%
corner_centroid
.
weight
,
fill
=
color
)
d
.
ellipse
(
box
,
outline
=
color
)
if
len
(
corner_points
)
>
1
:
for
f
in
corner_points
:
d
.
rectangle
(
f
.
bounding
(
4
),
outline
=
color
)
if
entropy_centroid
is
not
None
:
color
=
"#ff0"
box
=
entropy_centroid
.
bounding
(
max_size
*
entropy_centroid
.
weight
)
d
.
text
((
box
[
0
],
box
[
1
]
-
15
),
"Entropy:
%.02
f"
%
entropy_centroid
.
weight
,
fill
=
color
)
d
.
ellipse
(
box
,
outline
=
color
)
if
len
(
entropy_points
)
>
1
:
for
f
in
entropy_points
:
for
f
in
entropy_points
:
d
.
rectangle
(
f
.
bounding
(
30
),
outline
=
BLUE
)
d
.
rectangle
(
f
.
bounding
(
4
),
outline
=
color
)
for
poi
in
pois
:
if
face_centroid
is
not
None
:
w
=
max
(
4
,
4
*
0.5
*
sqrt
(
poi
.
weight
))
color
=
RED
d
.
ellipse
(
poi
.
bounding
(
w
),
fill
=
BLUE
)
box
=
face_centroid
.
bounding
(
max_size
*
face_centroid
.
weight
)
d
.
ellipse
(
average_point
.
bounding
(
25
),
outline
=
GREEN
)
d
.
text
((
box
[
0
],
box
[
1
]
-
15
),
"Face:
%.02
f"
%
face_centroid
.
weight
,
fill
=
color
)
d
.
ellipse
(
box
,
outline
=
color
)
if
len
(
face_points
)
>
1
:
for
f
in
face_points
:
d
.
rectangle
(
f
.
bounding
(
4
),
outline
=
color
)
d
.
ellipse
(
average_point
.
bounding
(
max_size
),
outline
=
GREEN
)
return
average_point
return
average_point
def
image_face_points
(
im
,
settings
):
def
image_face_points
(
im
,
settings
):
if
settings
.
dnn_model_path
is
not
None
:
detector
=
cv2
.
FaceDetectorYN
.
create
(
settings
.
dnn_model_path
,
""
,
(
im
.
width
,
im
.
height
),
0.8
,
# score threshold
0.3
,
# nms threshold
5000
# keep top k before nms
)
faces
=
detector
.
detect
(
np
.
array
(
im
))
results
=
[]
if
faces
[
1
]
is
not
None
:
for
face
in
faces
[
1
]:
x
=
face
[
0
]
y
=
face
[
1
]
w
=
face
[
2
]
h
=
face
[
3
]
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
size
=
w
,
weight
=
1
/
len
(
faces
[
1
])
)
)
return
results
else
:
np_im
=
np
.
array
(
im
)
np_im
=
np
.
array
(
im
)
gray
=
cv2
.
cvtColor
(
np_im
,
cv2
.
COLOR_BGR2GRAY
)
gray
=
cv2
.
cvtColor
(
np_im
,
cv2
.
COLOR_BGR2GRAY
)
...
@@ -131,9 +178,7 @@ def image_face_points(im, settings):
...
@@ -131,9 +178,7 @@ def image_face_points(im, settings):
[
f
'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml'
,
0.05
],
[
f
'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml'
,
0.05
],
[
f
'{cv2.data.haarcascades}haarcascade_upperbody.xml'
,
0.05
]
[
f
'{cv2.data.haarcascades}haarcascade_upperbody.xml'
,
0.05
]
]
]
for
t
in
tries
:
for
t
in
tries
:
# print(t[0])
classifier
=
cv2
.
CascadeClassifier
(
t
[
0
])
classifier
=
cv2
.
CascadeClassifier
(
t
[
0
])
minsize
=
int
(
min
(
im
.
width
,
im
.
height
)
*
t
[
1
])
# at least N percent of the smallest side
minsize
=
int
(
min
(
im
.
width
,
im
.
height
)
*
t
[
1
])
# at least N percent of the smallest side
try
:
try
:
...
@@ -144,7 +189,7 @@ def image_face_points(im, settings):
...
@@ -144,7 +189,7 @@ def image_face_points(im, settings):
if
len
(
faces
)
>
0
:
if
len
(
faces
)
>
0
:
rects
=
[[
f
[
0
],
f
[
1
],
f
[
0
]
+
f
[
2
],
f
[
1
]
+
f
[
3
]]
for
f
in
faces
]
rects
=
[[
f
[
0
],
f
[
1
],
f
[
0
]
+
f
[
2
],
f
[
1
]
+
f
[
3
]]
for
f
in
faces
]
return
[
PointOfInterest
((
r
[
0
]
+
r
[
2
])
//
2
,
(
r
[
1
]
+
r
[
3
])
//
2
,
size
=
abs
(
r
[
0
]
-
r
[
2
]
))
for
r
in
rects
]
return
[
PointOfInterest
((
r
[
0
]
+
r
[
2
])
//
2
,
(
r
[
1
]
+
r
[
3
])
//
2
,
size
=
abs
(
r
[
0
]
-
r
[
2
]),
weight
=
1
/
len
(
rects
))
for
r
in
rects
]
return
[]
return
[]
...
@@ -161,7 +206,7 @@ def image_corner_points(im, settings):
...
@@ -161,7 +206,7 @@ def image_corner_points(im, settings):
np_im
,
np_im
,
maxCorners
=
100
,
maxCorners
=
100
,
qualityLevel
=
0.04
,
qualityLevel
=
0.04
,
minDistance
=
min
(
grayscale
.
width
,
grayscale
.
height
)
*
0.0
7
,
minDistance
=
min
(
grayscale
.
width
,
grayscale
.
height
)
*
0.0
3
,
useHarrisDetector
=
False
,
useHarrisDetector
=
False
,
)
)
...
@@ -171,7 +216,7 @@ def image_corner_points(im, settings):
...
@@ -171,7 +216,7 @@ def image_corner_points(im, settings):
focal_points
=
[]
focal_points
=
[]
for
point
in
points
:
for
point
in
points
:
x
,
y
=
point
.
ravel
()
x
,
y
=
point
.
ravel
()
focal_points
.
append
(
PointOfInterest
(
x
,
y
,
size
=
4
))
focal_points
.
append
(
PointOfInterest
(
x
,
y
,
size
=
4
,
weight
=
1
/
len
(
points
)
))
return
focal_points
return
focal_points
...
@@ -205,17 +250,22 @@ def image_entropy_points(im, settings):
...
@@ -205,17 +250,22 @@ def image_entropy_points(im, settings):
x_mid
=
int
(
crop_best
[
0
]
+
settings
.
crop_width
/
2
)
x_mid
=
int
(
crop_best
[
0
]
+
settings
.
crop_width
/
2
)
y_mid
=
int
(
crop_best
[
1
]
+
settings
.
crop_height
/
2
)
y_mid
=
int
(
crop_best
[
1
]
+
settings
.
crop_height
/
2
)
return
[
PointOfInterest
(
x_mid
,
y_mid
,
size
=
25
)]
return
[
PointOfInterest
(
x_mid
,
y_mid
,
size
=
25
,
weight
=
1.0
)]
def
image_entropy
(
im
):
def
image_entropy
(
im
):
# greyscale image entropy
# greyscale image entropy
#
band = np.asarray(im.convert("L"))
band
=
np
.
asarray
(
im
.
convert
(
"L"
))
band
=
np
.
asarray
(
im
.
convert
(
"1"
),
dtype
=
np
.
uint8
)
#
band = np.asarray(im.convert("1"), dtype=np.uint8)
hist
,
_
=
np
.
histogram
(
band
,
bins
=
range
(
0
,
256
))
hist
,
_
=
np
.
histogram
(
band
,
bins
=
range
(
0
,
256
))
hist
=
hist
[
hist
>
0
]
hist
=
hist
[
hist
>
0
]
return
-
np
.
log2
(
hist
/
hist
.
sum
())
.
sum
()
return
-
np
.
log2
(
hist
/
hist
.
sum
())
.
sum
()
def
centroid
(
pois
):
x
=
[
poi
.
x
for
poi
in
pois
]
y
=
[
poi
.
y
for
poi
in
pois
]
return
PointOfInterest
(
sum
(
x
)
/
len
(
pois
),
sum
(
y
)
/
len
(
pois
))
def
poi_average
(
pois
,
settings
):
def
poi_average
(
pois
,
settings
):
weight
=
0.0
weight
=
0.0
...
@@ -260,11 +310,12 @@ class PointOfInterest:
...
@@ -260,11 +310,12 @@ class PointOfInterest:
class
Settings
:
class
Settings
:
def
__init__
(
self
,
crop_width
=
512
,
crop_height
=
512
,
corner_points_weight
=
0.5
,
entropy_points_weight
=
0.5
,
face_points_weight
=
0.5
,
annotate_image
=
False
):
def
__init__
(
self
,
crop_width
=
512
,
crop_height
=
512
,
corner_points_weight
=
0.5
,
entropy_points_weight
=
0.5
,
face_points_weight
=
0.5
,
annotate_image
=
False
,
dnn_model_path
=
None
):
self
.
crop_width
=
crop_width
self
.
crop_width
=
crop_width
self
.
crop_height
=
crop_height
self
.
crop_height
=
crop_height
self
.
corner_points_weight
=
corner_points_weight
self
.
corner_points_weight
=
corner_points_weight
self
.
entropy_points_weight
=
entropy_points_weight
self
.
entropy_points_weight
=
entropy_points_weight
self
.
face_points_weight
=
entropy
_points_weight
self
.
face_points_weight
=
face
_points_weight
self
.
annotate_image
=
annotate_image
self
.
annotate_image
=
annotate_image
self
.
destop_view_image
=
False
self
.
destop_view_image
=
False
self
.
dnn_model_path
=
dnn_model_path
\ No newline at end of file
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