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TP-IA-Syntax-Error
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VIGEOLAS-CHOURY Paul
TP-IA-Syntax-Error
Commits
bdd3b7fa
Commit
bdd3b7fa
authored
4 months ago
by
VIGEOLAS-CHOURY Paul
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Merge branch 'gris' into 'main'
Gris See merge request
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Gris
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2 changed files
TP.py
+52
-9
52 additions, 9 deletions
TP.py
TestTP.py
+2
-1
2 additions, 1 deletion
TestTP.py
with
54 additions
and
10 deletions
TP.py
+
52
−
9
View file @
bdd3b7fa
...
@@ -5,6 +5,8 @@ import pandas as pd
...
@@ -5,6 +5,8 @@ import pandas as pd
import
numpy
as
np
import
numpy
as
np
from
sklearn.metrics
import
accuracy_score
from
sklearn.metrics
import
accuracy_score
from
sklearn.model_selection
import
train_test_split
from
sklearn.model_selection
import
train_test_split
from
skimage.feature
import
graycomatrix
,
graycoprops
from
sklearn.naive_bayes
import
GaussianNB
def
buildSampleFromPath
(
path1
,
path2
,
size
=
0
):
def
buildSampleFromPath
(
path1
,
path2
,
size
=
0
):
...
@@ -29,9 +31,45 @@ def buildSampleFromPath(path1, path2, size=0):
...
@@ -29,9 +31,45 @@ def buildSampleFromPath(path1, path2, size=0):
return
S
return
S
def
computePixelBW
(
image
):
def
computePixelBW_histo
(
image_gl
):
return
np
.
array
(
image
.
convert
(
"
L
"
))
return
image_gl
.
histogram
()
def
compute_glcm_caracteristics
(
image_gl
):
image_arr
=
np
.
array
(
image_gl
)
#print(image_arr.shape)
glcm
=
graycomatrix
(
image_arr
,
distances
=
[
5
],
angles
=
[
0
],
levels
=
256
,
symmetric
=
True
,
normed
=
True
)
return
[
graycoprops
(
glcm
,
'
dissimilarity
'
)[
0
,
0
],
graycoprops
(
glcm
,
'
correlation
'
)[
0
,
0
],
graycoprops
(
glcm
,
'
contrast
'
)[
0
,
0
],
graycoprops
(
glcm
,
'
energy
'
)[
0
,
0
],
graycoprops
(
glcm
,
'
homogeneity
'
)[
0
,
0
]]
"""
def compute_glcm(image):
img_array = np.array(image)
distance, angle = 5, 0
glcm = np.zeros((256, 256))
offsets = (int(distance * np.cos(angle)), int(distance * np.sin(angle)))
for i in range(image.height - abs(offsets[1])):
for j in range(image.width - abs(offsets[0])):
px1 = img_array[i, j]
px2 = img_array[i + offsets[1], j + offsets[0]]
glcm[px1, px2] += 1
glcm /= np.sum(glcm)
return glcm
def extract_data_glcm(glcm):
contrast, energy, homogeneity = 0, 0, 0
for i in range(glcm.shape[0]):
for j in range(glcm.shape[1]):
contrast += (i - j) ** 2 * glcm[i, j]
energy += glcm[i, j]**2
homogeneity += (glcm[i, j] / (1 + abs(i - j)))
contrast = np.sum([[(i - j) ** 2 * glcm[i, j] for j in range(glcm.shape[1])] for i in range(glcm.shape[0])])
energy = np.sum(glcm ** 2)
homogeneity = np.sum(
[[(glcm[i, j] / (1 + abs(i - j))) for j in range(glcm.shape[1])] for i in range(glcm.shape[0])])
return [contrast, energy, homogeneity]
"""
def
computeDict
(
image_path
,
path
,
y_true_value
,
max_size
:
tuple
):
def
computeDict
(
image_path
,
path
,
y_true_value
,
max_size
:
tuple
):
"""
"""
...
@@ -48,13 +86,15 @@ def computeDict(image_path, path, y_true_value, max_size: tuple):
...
@@ -48,13 +86,15 @@ def computeDict(image_path, path, y_true_value, max_size: tuple):
image
=
image
.
convert
(
"
RGB
"
)
image
=
image
.
convert
(
"
RGB
"
)
resized
=
resizeImage
(
image
,
*
max_size
)
# On ne stocke pas resized image, on calcule tout avant de l'oublier
resized
=
resizeImage
(
image
,
*
max_size
)
# On ne stocke pas resized image, on calcule tout avant de l'oublier
image_gl
=
resized
.
convert
(
"
L
"
)
#print(computePixelBW_histo(resized))
return
{
"
name_path
"
:
full_path
,
return
{
"
name_path
"
:
full_path
,
#"resized_image": resized,
#"resized_image": resized,
"
X_histo
"
:
computeHisto
(
resized
),
"
X_histo
"
:
computeHisto
(
resized
),
"
X_pixelbw
"
:
computePixelBW
(
resized
),
"
X_pixelbw
"
:
computePixelBW_histo
(
resized
),
#"X_glcm_data": extract_data_glcm(compute_glcm(resized)),
"
X_glcm_data
"
:
compute_glcm_caracteristics
(
image_gl
),
"
y_true_class
"
:
y_true_value
,
"
y_true_class
"
:
y_true_value
,
"
y_predicted_class
"
:
None
}
"
y_predicted_class
"
:
None
}
...
@@ -93,7 +133,10 @@ def fitFromHisto(S, algo):
...
@@ -93,7 +133,10 @@ def fitFromHisto(S, algo):
S_train
,
S_test
,
y_train
,
y_test
=
train_test_split
(
S
,
y
,
test_size
=
0.2
,
random_state
=
42
)
S_train
,
S_test
,
y_train
,
y_test
=
train_test_split
(
S
,
y
,
test_size
=
0.2
,
random_state
=
42
)
X_train
=
np
.
array
([
np
.
array
(
l
[
"
X_histo
"
])
for
l
in
S_train
])
X_train
=
np
.
array
([
np
.
array
(
l
[
"
X_histo
"
]
+
l
[
"
X_glcm_data
"
])
for
l
in
S_train
])
#X_train = df[["X_histo", "X_pixelbw"]]
#print(X_train)
#print(len(X_train[0]))
algo
.
fit
(
X_train
,
y_train
)
algo
.
fit
(
X_train
,
y_train
)
...
@@ -109,7 +152,7 @@ def predictFromHisto(S, model, list_dict=True):
...
@@ -109,7 +152,7 @@ def predictFromHisto(S, model, list_dict=True):
:param list_dict: is the sample in list(dict)
:param list_dict: is the sample in list(dict)
:return: None
:return: None
"""
"""
tab
=
model
.
predict
(
np
.
array
([
x
[
"
X_histo
"
]
for
x
in
S
]))
tab
=
model
.
predict
(
np
.
array
([
x
[
"
X_histo
"
]
+
x
[
"
X_glcm_data
"
]
for
x
in
S
]))
if
list_dict
:
if
list_dict
:
for
i
in
range
(
len
(
S
)):
for
i
in
range
(
len
(
S
)):
S
[
i
][
"
y_predicted_class
"
]
=
tab
[
i
]
S
[
i
][
"
y_predicted_class
"
]
=
tab
[
i
]
...
...
This diff is collapsed.
Click to expand it.
TestTP.py
+
2
−
1
View file @
bdd3b7fa
...
@@ -2,6 +2,7 @@ from PIL import Image
...
@@ -2,6 +2,7 @@ from PIL import Image
from
sklearn.naive_bayes
import
GaussianNB
,
CategoricalNB
from
sklearn.naive_bayes
import
GaussianNB
,
CategoricalNB
from
sklearn.svm
import
SVC
from
sklearn.svm
import
SVC
import
TP
import
TP
from
xgboost
import
XGBClassifier
path1_t
=
"
./Init/Mer
"
path1_t
=
"
./Init/Mer
"
path2_t
=
"
./Init/Ailleurs
"
path2_t
=
"
./Init/Ailleurs
"
...
@@ -28,7 +29,7 @@ def test_sample():
...
@@ -28,7 +29,7 @@ def test_sample():
def
global_test
():
def
global_test
():
S
=
TP
.
buildSampleFromPath
(
path1_t
,
path2_t
)
S
=
TP
.
buildSampleFromPath
(
path1_t
,
path2_t
)
classifier
,
S_test
,
y_test
,
S_train
,
y_train
=
TP
.
fitFromHisto
(
S
,
SVC
())
classifier
,
S_test
,
y_test
,
S_train
,
y_train
=
TP
.
fitFromHisto
(
S
,
SVC
(
kernel
=
"
rbf
"
))
TP
.
predictFromHisto
(
S
,
classifier
)
TP
.
predictFromHisto
(
S
,
classifier
)
print
(
"
Erreur empirique :
"
,
TP
.
computeError
(
S_train
),
"
erreurs
"
)
print
(
"
Erreur empirique :
"
,
TP
.
computeError
(
S_train
),
"
erreurs
"
)
print
(
"
Erreur réelle :
"
,
TP
.
computeError
(
S_test
),
"
erreurs
"
)
print
(
"
Erreur réelle :
"
,
TP
.
computeError
(
S_test
),
"
erreurs
"
)
...
...
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Click to expand it.
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