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aima-python/tests/test_perception4e.py at master · samindaw/aima-python · GitHub
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test_perception4e.py
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import
random
import
pytest
from
perception4e
import
*
from
PIL
import
Image
import
numpy
as
np
import
os
random
.
seed
(
"aima-python"
)
def
test_array_normalization
():
assert
list
(
array_normalization
([
1
,
2
,
3
,
4
,
5
],
0
,
1
))
==
[
0
,
0.25
,
0.5
,
0.75
,
1
]
assert
list
(
array_normalization
([
1
,
2
,
3
,
4
,
5
],
1
,
2
))
==
[
1
,
1.25
,
1.5
,
1.75
,
2
]
def
test_sum_squared_difference
():
image
=
Image
.
open
(
os
.
path
.
abspath
(
"./images/broxrevised.png"
))
arr
=
np
.
asarray
(
image
)
arr1
=
arr
[
10
:
500
, :
514
]
arr2
=
arr
[
10
:
500
,
514
:
1028
]
assert
sum_squared_difference
(
arr1
,
arr1
)[
1
]
==
0
assert
sum_squared_difference
(
arr1
,
arr1
)[
0
]
==
(
0
,
0
)
assert
sum_squared_difference
(
arr1
,
arr2
)[
1
]
>
200000
def
test_gen_gray_scale_picture
():
assert
list
(
gen_gray_scale_picture
(
size
=
3
,
level
=
3
)[
0
])
==
[
0
,
125
,
250
]
assert
list
(
gen_gray_scale_picture
(
size
=
3
,
level
=
3
)[
1
])
==
[
125
,
125
,
250
]
assert
list
(
gen_gray_scale_picture
(
size
=
3
,
level
=
3
)[
2
])
==
[
250
,
250
,
250
]
assert
list
(
gen_gray_scale_picture
(
2
,
level
=
2
)[
0
])
==
[
0
,
250
]
assert
list
(
gen_gray_scale_picture
(
2
,
level
=
2
)[
1
])
==
[
250
,
250
]
def
test_generate_edge_weight
():
assert
generate_edge_weight
(
gray_scale_image
, (
0
,
0
), (
2
,
2
))
==
5
assert
generate_edge_weight
(
gray_scale_image
, (
1
,
0
), (
0
,
1
))
==
255
def
test_graph_bfs
():
graph
=
Graph
(
gray_scale_image
)
assert
not
graph
.
bfs
((
1
,
1
), (
0
,
0
), [])
parents
=
[]
assert
graph
.
bfs
((
0
,
0
), (
2
,
2
),
parents
)
assert
len
(
parents
)
==
8
def
test_graph_min_cut
():
image
=
gen_gray_scale_picture
(
size
=
3
,
level
=
2
)
graph
=
Graph
(
image
)
assert
len
(
graph
.
min_cut
((
0
,
0
), (
2
,
2
)))
==
4
image
=
gen_gray_scale_picture
(
size
=
10
,
level
=
2
)
graph
=
Graph
(
image
)
assert
len
(
graph
.
min_cut
((
0
,
0
), (
9
,
9
)))
==
10
def
test_gen_discs
():
discs
=
gen_discs
(
100
,
2
)
assert
len
(
discs
)
==
2
assert
len
(
discs
[
1
])
==
len
(
discs
[
0
])
==
8
def
test_simple_convnet
():
train
,
val
,
test
=
load_MINST
(
1000
,
100
,
10
)
model
=
simple_convnet
()
model
.
fit
(
train
[
0
],
train
[
1
],
validation_data
=
(
val
[
0
],
val
[
1
]),
epochs
=
5
,
verbose
=
2
,
batch_size
=
32
)
scores
=
model
.
evaluate
(
test
[
0
],
test
[
1
],
verbose
=
1
)
assert
scores
[
1
]
>
0.2
def
test_ROIPoolingLayer
():
# Create feature map input
feature_maps_shape
=
(
200
,
100
,
1
)
feature_map
=
np
.
ones
(
feature_maps_shape
,
dtype
=
'float32'
)
feature_map
[
200
-
1
,
100
-
3
,
0
]
=
50
roiss
=
np
.
asarray
([[
0.5
,
0.2
,
0.7
,
0.4
], [
0.0
,
0.0
,
1.0
,
1.0
]])
assert
pool_rois
(
feature_map
,
roiss
,
3
,
7
)[
0
].
tolist
()
==
[[
1
,
1
,
1
,
1
,
1
,
1
,
1
],
[
1
,
1
,
1
,
1
,
1
,
1
,
1
],
[
1
,
1
,
1
,
1
,
1
,
1
,
1
]]
assert
pool_rois
(
feature_map
,
roiss
,
3
,
7
)[
1
].
tolist
()
==
[[
1
,
1
,
1
,
1
,
1
,
1
,
1
],
[
1
,
1
,
1
,
1
,
1
,
1
,
1
],
[
1
,
1
,
1
,
1
,
1
,
1
,
50
]]
if
__name__
==
'__main__'
:
pytest
.
main
()
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