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s184400
BachelorDeeplearning
Commits
34b4b7a1
Commit
34b4b7a1
authored
4 years ago
by
pjtka
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34b4b7a1
import
numpy
as
np
from
PIL
import
Image
import
matplotlib.pyplot
as
plt
import
cv2
from
scipy
import
signal
plt
.
close
(
'
all
'
)
def
dilation33
(
image
):
# Makes a 3 by 3 dilation of the a 2d image, program crashes if not provided as such
y_height
,
x_height
=
image
.
shape
out_image
=
np
.
zeros
((
y_height
,
x_height
,
3
))
out_image
[:,:,
0
]
=
np
.
row_stack
((
image
[
1
:,:],
image
[
-
1
,:]))
out_image
[:,:,
1
]
=
image
out_image
[:,:,
2
]
=
np
.
row_stack
((
image
[
0
,:],
image
[:(
y_height
-
1
),:]))
out_image2
=
np
.
max
(
out_image
,
axis
=
2
)
out_image
[:,:,
0
]
=
np
.
column_stack
(([
image
[:,
1
:],
image
[:,
-
1
]]))
out_image
[:,:,
1
]
=
out_image2
out_image
[:,:,
2
]
=
np
.
column_stack
(([
image
[:,
0
],
image
[:,
0
:(
x_height
-
1
)]]))
out_image
=
np
.
max
(
out_image
,
axis
=
2
)
return
out_image
"""
test = np.random.normal(100,7, (50,50))
plt.figure(0)
plt.imshow(test)
test = dilation33(test)
plt.figure(1)
plt.imshow(test)
plt.show()
"""
def
fill_border
(
image
,
border_width
):
dimension
=
1
if
len
(
image
.
shape
)
==
2
:
y_height
,
x_height
=
image
.
shape
out_image
=
np
.
zeros
((
y_height
+
border_width
*
2
,
x_height
+
border_width
*
2
))
else
:
y_height
,
x_height
,
dimension
=
image
.
shape
out_image
=
np
.
zeros
((
y_height
+
border_width
*
2
,
x_height
+
border_width
*
2
,
dimension
))
border_mat
=
np
.
ones
((
border_width
,
border_width
))
if
dimension
==
1
:
out_image
[:
border_width
,
:
border_width
]
=
border_mat
*
image
[
0
,
0
]
out_image
[
border_width
+
y_height
:
2
*
border_width
+
y_height
,
:
border_width
]
=
border_mat
*
image
[
y_height
-
1
,
0
]
out_image
[:
border_width
,
border_width
+
x_height
:
2
*
border_width
+
x_height
]
=
border_mat
*
image
[
0
,
x_height
-
1
]
out_image
[
border_width
+
y_height
:
2
*
border_width
+
y_height
,
border_width
+
x_height
:
2
*
border_width
+
x_height
]
=
border_mat
*
image
[
y_height
-
1
,
x_height
-
1
]
# Setting the inner values equal to original image
out_image
[
border_width
:
border_width
+
y_height
,
border_width
:
border_width
+
x_height
]
=
image
[:,
:]
# Copying and extending the values of the outer rows and columns of the original image
out_image
[:
border_width
,
border_width
:
border_width
+
x_height
]
=
np
.
tile
(
image
[
0
,
:],
(
border_width
,
1
))
out_image
[
border_width
+
y_height
:
2
*
border_width
+
y_height
,
border_width
:
border_width
+
x_height
]
=
np
.
tile
(
image
[
y_height
-
1
,
:],
(
border_width
,
1
))
out_image
[
border_width
:
border_width
+
y_height
,
:
border_width
]
=
np
.
transpose
(
np
.
tile
(
image
[:,
0
],
(
border_width
,
1
)))
out_image
[
border_width
:
border_width
+
y_height
,
border_width
+
x_height
:
2
*
border_width
+
x_height
]
=
np
.
transpose
(
np
.
tile
(
image
[:,
x_height
-
1
],
(
border_width
,
1
)))
else
:
for
i
in
range
(
dimension
):
# Setting entire corners equal to corner values in image
out_image
[:
border_width
,:
border_width
,
i
]
=
border_mat
*
image
[
0
,
0
,
i
]
out_image
[
border_width
+
y_height
:
2
*
border_width
+
y_height
,:
border_width
,
i
]
=
border_mat
*
image
[
y_height
-
1
,
0
,
i
]
out_image
[:
border_width
,
border_width
+
x_height
:
2
*
border_width
+
x_height
,
i
]
=
border_mat
*
image
[
0
,
x_height
-
1
,
i
]
out_image
[
border_width
+
y_height
:
2
*
border_width
+
y_height
,
border_width
+
x_height
:
2
*
border_width
+
x_height
,
i
]
=
border_mat
*
image
[
y_height
-
1
,
x_height
-
1
,
i
]
# Setting the inner values equal to original image
out_image
[
border_width
:
border_width
+
y_height
,
border_width
:
border_width
+
x_height
,
i
]
=
image
[:,:,
i
]
# Copying and extending the values of the outer rows and columns of the original image
out_image
[:
border_width
,
border_width
:
border_width
+
x_height
,
i
]
=
np
.
tile
(
image
[
0
,:,
i
],(
border_width
,
1
))
out_image
[
border_width
+
y_height
:
2
*
border_width
+
y_height
,
border_width
:
border_width
+
x_height
,
i
]
=
np
.
tile
(
image
[
y_height
-
1
,:,
i
],(
border_width
,
1
))
out_image
[
border_width
:
border_width
+
y_height
,:
border_width
,
i
]
=
np
.
transpose
(
np
.
tile
(
image
[:,
0
,
i
],(
border_width
,
1
)))
out_image
[
border_width
:
border_width
+
y_height
,
border_width
+
x_height
:
2
*
border_width
+
x_height
,
i
]
=
np
.
transpose
(
np
.
tile
(
image
[:,
x_height
-
1
,
i
],(
border_width
,
1
)))
return
out_image
"""
Test of function on normal distributed data
test_im2 = np.random.normal(100,7,(50,50))
plt.figure()
plt.imshow(test_im2)
fill_border_test = fill_border(test_im2,3)
plt.figure()
plt.imshow(fill_border_test)
plt.show()
"""
def
gaussian_derivative
(
image
,
sigma
,
i_order
,
j_order
):
# Calculates the Gaussian derivative of the i'th order and of the j'th order along the second axis
maximum_sigma
=
float
(
3
)
filter_size
=
int
(
maximum_sigma
*
sigma
+
0.5
)
# unclear as to the point of this
image
=
fill_border
(
image
,
filter_size
)
x
=
np
.
asarray
([
i
for
i
in
range
(
-
filter_size
,
filter_size
+
1
)])
gaussian_distribution
=
1
/
(
np
.
sqrt
(
2
*
np
.
pi
)
*
sigma
)
*
np
.
exp
((
x
**
2
)
/
(
-
2
*
sigma
**
2
))
# Gauss=1/(sqrt(2 * pi) * sigma)* exp((x.^2)/(-2 * sigma * sigma) );
# first making the gaussian in convolution in the x direction
if
i_order
==
0
:
gaussian
=
gaussian_distribution
/
np
.
sum
(
gaussian_distribution
)
elif
i_order
==
1
:
gaussian
=
-
(
x
/
sigma
**
2
)
*
gaussian_distribution
gaussian
=
gaussian
/
(
np
.
sum
(
x
*
gaussian
))
elif
i_order
==
2
:
gaussian
=
(
x
**
2
/
sigma
**
4
-
1
/
sigma
**
2
)
*
gaussian_distribution
gaussian
=
gaussian
-
sum
(
gaussian
)
/
(
len
(
x
))
#shape of x may also be used but has only one dimension
gaussian
=
gaussian
/
np
.
sum
(
0.5
*
x
*
x
*
gaussian
)
out_image
=
np
.
apply_along_axis
(
lambda
m
:
signal
.
convolve
(
m
,
gaussian
,
mode
=
'
valid
'
),
axis
=
1
,
arr
=
image
)
# subsequently in the y direction
if
j_order
==
0
:
gaussian
=
gaussian_distribution
/
np
.
sum
(
gaussian_distribution
)
elif
j_order
==
1
:
gaussian
=
-
(
x
/
sigma
**
2
)
*
gaussian_distribution
gaussian
=
gaussian
/
(
np
.
sum
(
x
*
gaussian
))
elif
j_order
==
2
:
gaussian
=
(
x
**
2
/
sigma
**
4
-
1
/
sigma
**
2
)
*
gaussian_distribution
gaussian
=
gaussian
-
np
.
sum
(
gaussian
)
/
(
len
(
x
))
# shape of x may also be used but has only one dimension
gaussian
=
gaussian
/
np
.
sum
(
0.5
*
x
*
x
*
gaussian
)
out_image
=
np
.
apply_along_axis
(
lambda
m
:
signal
.
convolve
(
m
,
gaussian
,
mode
=
'
valid
'
),
axis
=
0
,
arr
=
out_image
)
return
out_image
# test on normally distributed data
"""
test_img = np.random.normal(0,1,[100,100])
plt.figure(0)
plt.imshow(test_img)
test_img = gaussian_derivative(test_img,2,0,2)
plt.figure(1)
plt.imshow(test_img)
plt.show()
"""
def
norm_derivative
(
image
,
sigma
,
order
=
1
):
R
=
image
[:,
:,
0
]
G
=
image
[:,
:,
1
]
B
=
image
[:,
:,
2
]
if
order
==
1
:
Rx
=
gaussian_derivative
(
R
,
sigma
,
order
,
0
)
Ry
=
gaussian_derivative
(
R
,
sigma
,
0
,
order
)
Rw
=
np
.
sqrt
(
Rx
**
2
+
Ry
**
2
)
Gx
=
gaussian_derivative
(
G
,
sigma
,
order
,
0
)
Gy
=
gaussian_derivative
(
G
,
sigma
,
0
,
order
)
Gw
=
np
.
sqrt
(
Gx
**
2
+
Gy
**
2
)
Bx
=
gaussian_derivative
(
B
,
sigma
,
order
,
0
)
By
=
gaussian_derivative
(
B
,
sigma
,
0
,
order
)
Bw
=
np
.
sqrt
(
Bx
**
2
+
By
**
2
)
elif
order
==
2
:
Rx
=
gaussian_derivative
(
R
,
sigma
,
order
,
0
)
Ry
=
gaussian_derivative
(
R
,
sigma
,
0
,
order
)
Rxy
=
gaussian_derivative
(
R
,
sigma
,
order
//
2
,
order
//
2
)
Rw
=
np
.
sqrt
(
Rx
**
2
+
Ry
**
2
+
4
*
Rxy
**
2
)
Gx
=
gaussian_derivative
(
G
,
sigma
,
order
,
0
)
Gy
=
gaussian_derivative
(
G
,
sigma
,
0
,
order
)
Gxy
=
gaussian_derivative
(
G
,
sigma
,
order
//
2
,
order
//
2
)
Gw
=
np
.
sqrt
(
Gx
**
2
+
Gy
**
2
+
4
*
Gxy
**
2
)
Bx
=
gaussian_derivative
(
B
,
sigma
,
order
,
0
)
By
=
gaussian_derivative
(
B
,
sigma
,
0
,
order
)
Bxy
=
gaussian_derivative
(
B
,
sigma
,
order
//
2
,
order
//
2
)
Bw
=
np
.
sqrt
(
Bx
**
2
+
By
**
2
+
4
*
Bxy
**
2
)
return
Rw
,
Gw
,
Bw
def
set_border
(
image
,
width
,
method
=
0
):
y_height
,
x_height
=
image
.
shape
temp
=
np
.
ones
((
y_height
,
x_height
))
y
,
x
=
np
.
meshgrid
(
np
.
arange
(
0
,
y_height
),
np
.
arange
(
0
,
x_height
),
indexing
=
'
ij
'
)
temp
=
temp
*
((
x
<
(
x_height
-
width
))
*
(
x
>
width
))
temp
=
temp
*
((
y
<
(
y_height
-
width
))
*
(
y
>
width
))
out
=
temp
*
image
if
method
==
1
:
out
=
out
+
(
np
.
sum
(
out
)
/
np
.
sum
(
temp
))
*
(
np
.
ones
((
y_height
,
x_height
))
-
temp
)
return
out
def
general_color_constancy
(
image
,
gaussian_differentiation
=
0
,
minkowski_norm
=
1
,
sigma
=
1
,
mask_image
=
0
):
y_height
,
x_height
,
dimension
=
image
.
shape
if
mask_image
==
0
:
mask_image
=
np
.
zeros
((
y_height
,
x_height
))
#Removing saturated points
saturation_threshold
=
255
mask_image2
=
mask_image
+
(
dilation33
(
np
.
max
(
image
,
axis
=
2
))
>=
saturation_threshold
).
astype
(
int
)
mask_image2
=
(
mask_image2
==
0
).
astype
(
int
)
mask_image2
=
set_border
(
mask_image2
,
sigma
+
1
)
out_image
=
np
.
copy
(
image
)
if
gaussian_differentiation
==
0
:
if
sigma
!=
0
:
image
=
gaussian_derivative
(
image
,
sigma
,
0
,
0
)
elif
gaussian_differentiation
>
0
:
Rx
,
Gx
,
Bx
=
norm_derivative
(
image
,
sigma
,
gaussian_differentiation
)
image
[:,
:,
0
]
=
Rx
image
[:,
:,
1
]
=
Gx
image
[:,
:,
2
]
=
Bx
image
=
np
.
abs
(
image
)
if
minkowski_norm
!=
-
1
:
#Minkowski norm = (1, infinity [
kleur
=
np
.
power
(
image
,
minkowski_norm
)
white_R
=
np
.
power
(
np
.
sum
(
kleur
[:,
:,
0
]
*
mask_image2
),
1
/
minkowski_norm
)
white_G
=
np
.
power
(
np
.
sum
(
kleur
[:,
:,
1
]
*
mask_image2
),
1
/
minkowski_norm
)
white_B
=
np
.
power
(
np
.
sum
(
kleur
[:,
:,
2
]
*
mask_image2
),
1
/
minkowski_norm
)
som
=
np
.
sqrt
(
white_R
**
2
+
white_G
**
2
+
white_B
**
2
)
white_R
=
white_R
/
som
white_G
=
white_G
/
som
white_B
=
white_B
/
som
else
:
#Minkowski norm is infinite, hence the max algorithm is applied
R
=
image
[:,
:,
0
]
G
=
image
[:,
:,
1
]
B
=
image
[:,
:,
2
]
white_R
=
np
.
max
(
R
*
mask_image2
)
white_G
=
np
.
max
(
G
*
mask_image2
)
white_B
=
np
.
max
(
B
*
mask_image2
)
som
=
np
.
sqrt
(
white_R
**
2
+
white_G
**
2
+
white_B
**
2
)
white_R
=
white_R
/
som
white_G
=
white_G
/
som
white_B
=
white_B
/
som
out_image
[:,
:,
0
]
=
out_image
[:,
:,
0
]
/
(
white_R
*
np
.
sqrt
(
3
))
out_image
[:,
:,
1
]
=
out_image
[:,
:,
1
]
/
(
white_G
*
np
.
sqrt
(
3
))
out_image
[:,
:,
2
]
=
out_image
[:,
:,
2
]
/
(
white_B
*
np
.
sqrt
(
3
))
return
white_R
,
white_G
,
white_B
,
out_image
"""
#test_img = np.random.normal(100, 20, size=(20, 20, 3))
test_img = cv2.imread(r
'
C:\Users\Bruger\Pictures\melanomasTest.jpg
'
, 1)
plt.figure(0)
#im = Image.fromarray(test_img.astype(
'
uint8
'
)).convert(
'
RGB
'
)
im_rgb = cv2.cvtColor(test_img, cv2.COLOR_BGR2RGB)
plt.imshow(im_rgb)
R, G, B, test_img1 = general_color_constancy(im_rgb, gaussian_differentiation=1, minkowski_norm=3, sigma=5)
plt.figure(1)
im1 = Image.fromarray(test_img1.astype(
'
uint8
'
)).convert(
'
RGB
'
)
plt.imshow(im1)
plt.show()
"""
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