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stable-diffusion-webui
Commits
1be5933b
Unverified
Commit
1be5933b
authored
Oct 23, 2022
by
captin411
Committed by
GitHub
Oct 23, 2022
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auto cropping now works with non square crops
parent
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autocrop.py
modules/textual_inversion/autocrop.py
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modules/textual_inversion/autocrop.py
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1be5933b
import
cv2
import
cv2
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
from
PIL
import
Image
,
ImageDraw
from
PIL
import
Image
,
ImageDraw
GREEN
=
"#0F0"
GREEN
=
"#0F0"
BLUE
=
"#00F"
BLUE
=
"#00F"
RED
=
"#F00"
RED
=
"#F00"
def
crop_image
(
im
,
settings
):
def
crop_image
(
im
,
settings
):
""" Intelligently crop an image to the subject matter """
""" Intelligently crop an image to the subject matter """
if
im
.
height
>
im
.
width
:
im
=
im
.
resize
((
settings
.
crop_width
,
settings
.
crop_height
*
im
.
height
//
im
.
width
))
scale_by
=
1
elif
im
.
width
>
im
.
height
:
if
is_landscape
(
im
.
width
,
im
.
height
):
im
=
im
.
resize
((
settings
.
crop_width
*
im
.
width
//
im
.
height
,
settings
.
crop_height
))
scale_by
=
settings
.
crop_height
/
im
.
height
else
:
elif
is_portrait
(
im
.
width
,
im
.
height
):
im
=
im
.
resize
((
settings
.
crop_width
,
settings
.
crop_height
))
scale_by
=
settings
.
crop_width
/
im
.
width
elif
is_square
(
im
.
width
,
im
.
height
):
if
im
.
height
==
im
.
width
:
if
is_square
(
settings
.
crop_width
,
settings
.
crop_height
):
return
im
scale_by
=
settings
.
crop_width
/
im
.
width
elif
is_landscape
(
settings
.
crop_width
,
settings
.
crop_height
):
focus
=
focal_point
(
im
,
settings
)
scale_by
=
settings
.
crop_width
/
im
.
width
elif
is_portrait
(
settings
.
crop_width
,
settings
.
crop_height
):
# take the focal point and turn it into crop coordinates that try to center over the focal
scale_by
=
settings
.
crop_height
/
im
.
height
# point but then get adjusted back into the frame
y_half
=
int
(
settings
.
crop_height
/
2
)
im
=
im
.
resize
((
int
(
im
.
width
*
scale_by
),
int
(
im
.
height
*
scale_by
)))
x_half
=
int
(
settings
.
crop_width
/
2
)
if
im
.
width
==
settings
.
crop_width
and
im
.
height
==
settings
.
crop_height
:
x1
=
focus
.
x
-
x_half
if
settings
.
annotate_image
:
if
x1
<
0
:
d
=
ImageDraw
.
Draw
(
im
)
x1
=
0
rect
=
[
0
,
0
,
im
.
width
,
im
.
height
]
elif
x1
+
settings
.
crop_width
>
im
.
width
:
rect
[
2
]
-=
1
x1
=
im
.
width
-
settings
.
crop_width
rect
[
3
]
-=
1
d
.
rectangle
(
rect
,
outline
=
GREEN
)
y1
=
focus
.
y
-
y_half
if
settings
.
destop_view_image
:
if
y1
<
0
:
im
.
show
()
y1
=
0
return
im
elif
y1
+
settings
.
crop_height
>
im
.
height
:
y1
=
im
.
height
-
settings
.
crop_height
focus
=
focal_point
(
im
,
settings
)
x2
=
x1
+
settings
.
crop_width
# take the focal point and turn it into crop coordinates that try to center over the focal
y2
=
y1
+
settings
.
crop_height
# point but then get adjusted back into the frame
y_half
=
int
(
settings
.
crop_height
/
2
)
crop
=
[
x1
,
y1
,
x2
,
y2
]
x_half
=
int
(
settings
.
crop_width
/
2
)
if
settings
.
annotate_image
:
x1
=
focus
.
x
-
x_half
d
=
ImageDraw
.
Draw
(
im
)
if
x1
<
0
:
rect
=
list
(
crop
)
x1
=
0
rect
[
2
]
-=
1
elif
x1
+
settings
.
crop_width
>
im
.
width
:
rect
[
3
]
-=
1
x1
=
im
.
width
-
settings
.
crop_width
d
.
rectangle
(
rect
,
outline
=
GREEN
)
if
settings
.
destop_view_image
:
y1
=
focus
.
y
-
y_half
im
.
show
()
if
y1
<
0
:
y1
=
0
return
im
.
crop
(
tuple
(
crop
))
elif
y1
+
settings
.
crop_height
>
im
.
height
:
y1
=
im
.
height
-
settings
.
crop_height
def
focal_point
(
im
,
settings
):
corner_points
=
image_corner_points
(
im
,
settings
)
x2
=
x1
+
settings
.
crop_width
entropy_points
=
image_entropy_points
(
im
,
settings
)
y2
=
y1
+
settings
.
crop_height
face_points
=
image_face_points
(
im
,
settings
)
crop
=
[
x1
,
y1
,
x2
,
y2
]
total_points
=
len
(
corner_points
)
+
len
(
entropy_points
)
+
len
(
face_points
)
if
settings
.
annotate_image
:
corner_weight
=
settings
.
corner_points_weight
d
=
ImageDraw
.
Draw
(
im
)
entropy_weight
=
settings
.
entropy_points_weight
rect
=
list
(
crop
)
face_weight
=
settings
.
face_points_weight
rect
[
2
]
-=
1
rect
[
3
]
-=
1
weight_pref_total
=
corner_weight
+
entropy_weight
+
face_weight
d
.
rectangle
(
rect
,
outline
=
GREEN
)
if
settings
.
destop_view_image
:
# weight things
im
.
show
()
pois
=
[]
if
weight_pref_total
==
0
or
total_points
==
0
:
return
im
.
crop
(
tuple
(
crop
))
return
pois
def
focal_point
(
im
,
settings
):
pois
.
extend
(
corner_points
=
image_corner_points
(
im
,
settings
)
[
PointOfInterest
(
p
.
x
,
p
.
y
,
weight
=
p
.
weight
*
(
(
corner_weight
/
weight_pref_total
)
/
(
len
(
corner_points
)
/
total_points
)
))
for
p
in
corner_points
]
entropy_points
=
image_entropy_points
(
im
,
settings
)
)
face_points
=
image_face_points
(
im
,
settings
)
pois
.
extend
(
[
PointOfInterest
(
p
.
x
,
p
.
y
,
weight
=
p
.
weight
*
(
(
entropy_weight
/
weight_pref_total
)
/
(
len
(
entropy_points
)
/
total_points
)
))
for
p
in
entropy_points
]
total_points
=
len
(
corner_points
)
+
len
(
entropy_points
)
+
len
(
face_points
)
)
pois
.
extend
(
corner_weight
=
settings
.
corner_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
]
entropy_weight
=
settings
.
entropy_points_weight
)
face_weight
=
settings
.
face_points_weight
average_point
=
poi_average
(
pois
,
settings
)
weight_pref_total
=
corner_weight
+
entropy_weight
+
face_weight
if
settings
.
annotate_image
:
# weight things
d
=
ImageDraw
.
Draw
(
im
)
pois
=
[]
for
f
in
face_points
:
if
weight_pref_total
==
0
or
total_points
==
0
:
d
.
rectangle
(
f
.
bounding
(
f
.
size
),
outline
=
RED
)
return
pois
for
f
in
entropy_points
:
d
.
rectangle
(
f
.
bounding
(
30
),
outline
=
BLUE
)
pois
.
extend
(
for
poi
in
pois
:
[
PointOfInterest
(
p
.
x
,
p
.
y
,
weight
=
p
.
weight
*
(
(
corner_weight
/
weight_pref_total
)
/
(
len
(
corner_points
)
/
total_points
)
))
for
p
in
corner_points
]
w
=
max
(
4
,
4
*
0.5
*
sqrt
(
poi
.
weight
))
)
d
.
ellipse
(
poi
.
bounding
(
w
),
fill
=
BLUE
)
pois
.
extend
(
d
.
ellipse
(
average_point
.
bounding
(
25
),
outline
=
GREEN
)
[
PointOfInterest
(
p
.
x
,
p
.
y
,
weight
=
p
.
weight
*
(
(
entropy_weight
/
weight_pref_total
)
/
(
len
(
entropy_points
)
/
total_points
)
))
for
p
in
entropy_points
]
)
return
average_point
pois
.
extend
(
[
PointOfInterest
(
p
.
x
,
p
.
y
,
weight
=
p
.
weight
*
(
(
face_weight
/
weight_pref_total
)
/
(
len
(
face_points
)
/
total_points
)
))
for
p
in
face_points
]
)
def
image_face_points
(
im
,
settings
):
np_im
=
np
.
array
(
im
)
average_point
=
poi_average
(
pois
,
settings
)
gray
=
cv2
.
cvtColor
(
np_im
,
cv2
.
COLOR_BGR2GRAY
)
if
settings
.
annotate_image
:
tries
=
[
d
=
ImageDraw
.
Draw
(
im
)
[
f
'{cv2.data.haarcascades}haarcascade_eye.xml'
,
0.01
],
for
f
in
face_points
:
[
f
'{cv2.data.haarcascades}haarcascade_frontalface_default.xml'
,
0.05
],
d
.
rectangle
(
f
.
bounding
(
f
.
size
),
outline
=
RED
)
[
f
'{cv2.data.haarcascades}haarcascade_profileface.xml'
,
0.05
],
for
f
in
entropy_points
:
[
f
'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml'
,
0.05
],
d
.
rectangle
(
f
.
bounding
(
30
),
outline
=
BLUE
)
[
f
'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml'
,
0.05
],
for
poi
in
pois
:
[
f
'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml'
,
0.05
],
w
=
max
(
4
,
4
*
0.5
*
sqrt
(
poi
.
weight
))
[
f
'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml'
,
0.05
],
d
.
ellipse
(
poi
.
bounding
(
w
),
fill
=
BLUE
)
[
f
'{cv2.data.haarcascades}haarcascade_upperbody.xml'
,
0.05
]
d
.
ellipse
(
average_point
.
bounding
(
25
),
outline
=
GREEN
)
]
return
average_point
for
t
in
tries
:
# print(t[0])
classifier
=
cv2
.
CascadeClassifier
(
t
[
0
])
def
image_face_points
(
im
,
settings
):
minsize
=
int
(
min
(
im
.
width
,
im
.
height
)
*
t
[
1
])
# at least N percent of the smallest side
np_im
=
np
.
array
(
im
)
try
:
gray
=
cv2
.
cvtColor
(
np_im
,
cv2
.
COLOR_BGR2GRAY
)
faces
=
classifier
.
detectMultiScale
(
gray
,
scaleFactor
=
1.1
,
minNeighbors
=
7
,
minSize
=
(
minsize
,
minsize
),
flags
=
cv2
.
CASCADE_SCALE_IMAGE
)
tries
=
[
except
:
[
f
'{cv2.data.haarcascades}haarcascade_eye.xml'
,
0.01
],
continue
[
f
'{cv2.data.haarcascades}haarcascade_frontalface_default.xml'
,
0.05
],
[
f
'{cv2.data.haarcascades}haarcascade_profileface.xml'
,
0.05
],
if
len
(
faces
)
>
0
:
[
f
'{cv2.data.haarcascades}haarcascade_frontalface_alt.xml'
,
0.05
],
rects
=
[[
f
[
0
],
f
[
1
],
f
[
0
]
+
f
[
2
],
f
[
1
]
+
f
[
3
]]
for
f
in
faces
]
[
f
'{cv2.data.haarcascades}haarcascade_frontalface_alt2.xml'
,
0.05
],
return
[
PointOfInterest
((
r
[
0
]
+
r
[
2
])
//
2
,
(
r
[
1
]
+
r
[
3
])
//
2
,
size
=
abs
(
r
[
0
]
-
r
[
2
]))
for
r
in
rects
]
[
f
'{cv2.data.haarcascades}haarcascade_frontalface_alt_tree.xml'
,
0.05
],
return
[]
[
f
'{cv2.data.haarcascades}haarcascade_eye_tree_eyeglasses.xml'
,
0.05
],
[
f
'{cv2.data.haarcascades}haarcascade_upperbody.xml'
,
0.05
]
]
def
image_corner_points
(
im
,
settings
):
grayscale
=
im
.
convert
(
"L"
)
for
t
in
tries
:
# print(t[0])
# naive attempt at preventing focal points from collecting at watermarks near the bottom
classifier
=
cv2
.
CascadeClassifier
(
t
[
0
])
gd
=
ImageDraw
.
Draw
(
grayscale
)
minsize
=
int
(
min
(
im
.
width
,
im
.
height
)
*
t
[
1
])
# at least N percent of the smallest side
gd
.
rectangle
([
0
,
im
.
height
*
.9
,
im
.
width
,
im
.
height
],
fill
=
"#999"
)
try
:
faces
=
classifier
.
detectMultiScale
(
gray
,
scaleFactor
=
1.1
,
np_im
=
np
.
array
(
grayscale
)
minNeighbors
=
7
,
minSize
=
(
minsize
,
minsize
),
flags
=
cv2
.
CASCADE_SCALE_IMAGE
)
except
:
points
=
cv2
.
goodFeaturesToTrack
(
continue
np_im
,
maxCorners
=
100
,
if
len
(
faces
)
>
0
:
qualityLevel
=
0.04
,
rects
=
[[
f
[
0
],
f
[
1
],
f
[
0
]
+
f
[
2
],
f
[
1
]
+
f
[
3
]]
for
f
in
faces
]
minDistance
=
min
(
grayscale
.
width
,
grayscale
.
height
)
*
0.07
,
return
[
PointOfInterest
((
r
[
0
]
+
r
[
2
])
//
2
,
(
r
[
1
]
+
r
[
3
])
//
2
,
size
=
abs
(
r
[
0
]
-
r
[
2
]))
for
r
in
rects
]
useHarrisDetector
=
False
,
return
[]
)
if
points
is
None
:
def
image_corner_points
(
im
,
settings
):
return
[]
grayscale
=
im
.
convert
(
"L"
)
focal_points
=
[]
# naive attempt at preventing focal points from collecting at watermarks near the bottom
for
point
in
points
:
gd
=
ImageDraw
.
Draw
(
grayscale
)
x
,
y
=
point
.
ravel
()
gd
.
rectangle
([
0
,
im
.
height
*
.9
,
im
.
width
,
im
.
height
],
fill
=
"#999"
)
focal_points
.
append
(
PointOfInterest
(
x
,
y
,
size
=
4
))
np_im
=
np
.
array
(
grayscale
)
return
focal_points
points
=
cv2
.
goodFeaturesToTrack
(
np_im
,
def
image_entropy_points
(
im
,
settings
):
maxCorners
=
100
,
landscape
=
im
.
height
<
im
.
width
qualityLevel
=
0.04
,
portrait
=
im
.
height
>
im
.
width
minDistance
=
min
(
grayscale
.
width
,
grayscale
.
height
)
*
0.07
,
if
landscape
:
useHarrisDetector
=
False
,
move_idx
=
[
0
,
2
]
)
move_max
=
im
.
size
[
0
]
elif
portrait
:
if
points
is
None
:
move_idx
=
[
1
,
3
]
return
[]
move_max
=
im
.
size
[
1
]
else
:
focal_points
=
[]
return
[]
for
point
in
points
:
x
,
y
=
point
.
ravel
()
e_max
=
0
focal_points
.
append
(
PointOfInterest
(
x
,
y
,
size
=
4
))
crop_current
=
[
0
,
0
,
settings
.
crop_width
,
settings
.
crop_height
]
crop_best
=
crop_current
return
focal_points
while
crop_current
[
move_idx
[
1
]]
<
move_max
:
crop
=
im
.
crop
(
tuple
(
crop_current
))
e
=
image_entropy
(
crop
)
def
image_entropy_points
(
im
,
settings
):
landscape
=
im
.
height
<
im
.
width
if
(
e
>
e_max
):
portrait
=
im
.
height
>
im
.
width
e_max
=
e
if
landscape
:
crop_best
=
list
(
crop_current
)
move_idx
=
[
0
,
2
]
move_max
=
im
.
size
[
0
]
crop_current
[
move_idx
[
0
]]
+=
4
elif
portrait
:
crop_current
[
move_idx
[
1
]]
+=
4
move_idx
=
[
1
,
3
]
move_max
=
im
.
size
[
1
]
x_mid
=
int
(
crop_best
[
0
]
+
settings
.
crop_width
/
2
)
else
:
y_mid
=
int
(
crop_best
[
1
]
+
settings
.
crop_height
/
2
)
return
[]
return
[
PointOfInterest
(
x_mid
,
y_mid
,
size
=
25
)]
e_max
=
0
crop_current
=
[
0
,
0
,
settings
.
crop_width
,
settings
.
crop_height
]
crop_best
=
crop_current
def
image_entropy
(
im
):
while
crop_current
[
move_idx
[
1
]]
<
move_max
:
# greyscale image entropy
crop
=
im
.
crop
(
tuple
(
crop_current
))
# band = np.asarray(im.convert("L"))
e
=
image_entropy
(
crop
)
band
=
np
.
asarray
(
im
.
convert
(
"1"
),
dtype
=
np
.
uint8
)
hist
,
_
=
np
.
histogram
(
band
,
bins
=
range
(
0
,
256
))
if
(
e
>
e_max
):
hist
=
hist
[
hist
>
0
]
e_max
=
e
return
-
np
.
log2
(
hist
/
hist
.
sum
())
.
sum
()
crop_best
=
list
(
crop_current
)
crop_current
[
move_idx
[
0
]]
+=
4
def
poi_average
(
pois
,
settings
):
crop_current
[
move_idx
[
1
]]
+=
4
weight
=
0.0
x
=
0.0
x_mid
=
int
(
crop_best
[
0
]
+
settings
.
crop_width
/
2
)
y
=
0.0
y_mid
=
int
(
crop_best
[
1
]
+
settings
.
crop_height
/
2
)
for
poi
in
pois
:
weight
+=
poi
.
weight
return
[
PointOfInterest
(
x_mid
,
y_mid
,
size
=
25
)]
x
+=
poi
.
x
*
poi
.
weight
y
+=
poi
.
y
*
poi
.
weight
avg_x
=
round
(
x
/
weight
)
def
image_entropy
(
im
):
avg_y
=
round
(
y
/
weight
)
# greyscale image entropy
# band = np.asarray(im.convert("L"))
return
PointOfInterest
(
avg_x
,
avg_y
)
band
=
np
.
asarray
(
im
.
convert
(
"1"
),
dtype
=
np
.
uint8
)
hist
,
_
=
np
.
histogram
(
band
,
bins
=
range
(
0
,
256
))
hist
=
hist
[
hist
>
0
]
class
PointOfInterest
:
return
-
np
.
log2
(
hist
/
hist
.
sum
())
.
sum
()
def
__init__
(
self
,
x
,
y
,
weight
=
1.0
,
size
=
10
):
self
.
x
=
x
self
.
y
=
y
def
poi_average
(
pois
,
settings
):
self
.
weight
=
weight
weight
=
0.0
self
.
size
=
size
x
=
0.0
y
=
0.0
def
bounding
(
self
,
size
):
for
poi
in
pois
:
return
[
weight
+=
poi
.
weight
self
.
x
-
size
//
2
,
x
+=
poi
.
x
*
poi
.
weight
self
.
y
-
size
//
2
,
y
+=
poi
.
y
*
poi
.
weight
self
.
x
+
size
//
2
,
avg_x
=
round
(
x
/
weight
)
self
.
y
+
size
//
2
avg_y
=
round
(
y
/
weight
)
]
return
PointOfInterest
(
avg_x
,
avg_y
)
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
is_landscape
(
w
,
h
):
self
.
crop_width
=
crop_width
return
w
>
h
self
.
crop_height
=
crop_height
self
.
corner_points_weight
=
corner_points_weight
self
.
entropy_points_weight
=
entropy_points_weight
def
is_portrait
(
w
,
h
):
self
.
face_points_weight
=
entropy_points_weight
return
h
>
w
self
.
annotate_image
=
annotate_image
def
is_square
(
w
,
h
):
return
w
==
h
class
PointOfInterest
:
def
__init__
(
self
,
x
,
y
,
weight
=
1.0
,
size
=
10
):
self
.
x
=
x
self
.
y
=
y
self
.
weight
=
weight
self
.
size
=
size
def
bounding
(
self
,
size
):
return
[
self
.
x
-
size
//
2
,
self
.
y
-
size
//
2
,
self
.
x
+
size
//
2
,
self
.
y
+
size
//
2
]
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
):
self
.
crop_width
=
crop_width
self
.
crop_height
=
crop_height
self
.
corner_points_weight
=
corner_points_weight
self
.
entropy_points_weight
=
entropy_points_weight
self
.
face_points_weight
=
entropy_points_weight
self
.
annotate_image
=
annotate_image
self
.
destop_view_image
=
False
self
.
destop_view_image
=
False
\ No newline at end of file
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