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Kourosh Gerayeli
sicom_image_analysis_project
Commits
221b1656
Commit
221b1656
authored
1 year ago
by
Matthieu Muller
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src/methods/chaari_mohamed/fonctions.ipynb
+0
-142
0 additions, 142 deletions
src/methods/chaari_mohamed/fonctions.ipynb
src/methods/chaari_mohamed/fonctions.py
+77
-0
77 additions, 0 deletions
src/methods/chaari_mohamed/fonctions.py
with
77 additions
and
142 deletions
src/methods/chaari_mohamed/fonctions.ipynb
deleted
100644 → 0
+
0
−
142
View file @
a959fb66
{
"cells": [
{
"cell_type": "code",
"execution_count": 24,
"id": "ec6da321",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.signal import convolve2d\n",
"from src.forward_model import CFA\n",
"\n",
"def bilinear_demosaicing(op: CFA, y: np.ndarray) -> np.ndarray:\n",
" \"\"\"\n",
" Bilinear demosaicing method.\n",
"\n",
" Args:\n",
" op (CFA): CFA operator.\n",
" y (np.ndarray): Mosaicked image.\n",
"\n",
" Returns:\n",
" np.ndarray: Demosaicked image.\n",
" \"\"\"\n",
" # Copie des valeurs directement connues pour chaque canal\n",
" red = y[:, :, 0]\n",
" green = y[:, :, 1]\n",
" blue = y[:, :, 2]\n",
"\n",
" # Création des masques pour chaque couleur selon le motif CFA\n",
" mask_red = (op.mask == 0) # Supposons que 0 correspond au rouge dans le masque\n",
" mask_green = (op.mask == 1) # Supposons que 1 correspond au vert\n",
" mask_blue = (op.mask == 2) # Supposons que 2 correspond au bleu\n",
"\n",
" # Interpolation bilinéaire pour le rouge et le bleu\n",
" # Note: np.multiply multiplie les éléments correspondants des tableaux, c'est pourquoi nous utilisons np.multiply au lieu de *\n",
" red_interp = convolve2d(np.multiply(red, mask_red), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')\n",
" blue_interp = convolve2d(np.multiply(blue, mask_blue), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')\n",
"\n",
" # Interpolation bilinéaire pour le vert\n",
" # Pour le vert, nous utilisons un autre noyau car il y a plus de pixels verts\n",
" green_interp = convolve2d(np.multiply(green, mask_green), [[0, 1/4, 0], [1/4, 1, 1/4], [0, 1/4, 0]], mode='same')\n",
"\n",
" # Création de l'image interpolée\n",
" demosaicked_image = np.stack((red_interp, green_interp, blue_interp), axis=-1)\n",
"\n",
" # Correction des valeurs interpolées: on réapplique les valeurs connues pour éviter le flou\n",
" demosaicked_image[:, :, 0][mask_red] = red[mask_red]\n",
" demosaicked_image[:, :, 1][mask_green] = green[mask_green]\n",
" demosaicked_image[:, :, 2][mask_blue] = blue[mask_blue]\n",
"\n",
" # Clip pour s'assurer que toutes les valeurs sont dans la plage [0, 1]\n",
" demosaicked_image = np.clip(demosaicked_image, 0, 1)\n",
"\n",
" return demosaicked_image\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "cf379598",
"metadata": {},
"outputs": [],
"source": [
"def quad_bayer_demosaicing(op: CFA, y: np.ndarray) -> np.ndarray:\n",
" \"\"\"\n",
" Demosaicing method for Quad Bayer CFA pattern.\n",
"\n",
" Args:\n",
" op (CFA): CFA operator.\n",
" y (np.ndarray): Mosaicked image.\n",
"\n",
" Returns:\n",
" np.ndarray: Demosaicked image.\n",
" \"\"\"\n",
" \n",
" # Interpolation bilinéaire pour chaque canal\n",
" red_interp = convolve2d(np.multiply(y[:, :, 0], op.mask == 0), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')\n",
" green_interp = convolve2d(np.multiply(y[:, :, 1], op.mask == 1), [[0, 1/4, 0], [1/4, 1, 1/4], [0, 1/4, 0]], mode='same')\n",
" blue_interp = convolve2d(np.multiply(y[:, :, 2], op.mask == 2), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')\n",
"\n",
" # Assemblage de l'image interpolée\n",
" demosaicked_image = np.stack((red_interp, green_interp, blue_interp), axis=-1)\n",
"\n",
" # Réapplication des valeurs connues\n",
" demosaicked_image[:, :, 0][op.mask == 0] = y[:, :, 0][op.mask == 0]\n",
" demosaicked_image[:, :, 1][op.mask == 1] = y[:, :, 1][op.mask == 1]\n",
" demosaicked_image[:, :, 2][op.mask == 2] = y[:, :, 2][op.mask == 2]\n",
"\n",
" # Clip des valeurs pour les maintenir dans la plage appropriée\n",
" demosaicked_image = np.clip(demosaicked_image, 0, 1)\n",
"\n",
" return demosaicked_image\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "3eec062c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "a6630396",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef4e59c8",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
%% Cell type:code id:ec6da321 tags:
```
python
import
numpy
as
np
from
scipy.signal
import
convolve2d
from
src.forward_model
import
CFA
def
bilinear_demosaicing
(
op
:
CFA
,
y
:
np
.
ndarray
)
->
np
.
ndarray
:
"""
Bilinear demosaicing method.
Args:
op (CFA): CFA operator.
y (np.ndarray): Mosaicked image.
Returns:
np.ndarray: Demosaicked image.
"""
# Copie des valeurs directement connues pour chaque canal
red
=
y
[:,
:,
0
]
green
=
y
[:,
:,
1
]
blue
=
y
[:,
:,
2
]
# Création des masques pour chaque couleur selon le motif CFA
mask_red
=
(
op
.
mask
==
0
)
# Supposons que 0 correspond au rouge dans le masque
mask_green
=
(
op
.
mask
==
1
)
# Supposons que 1 correspond au vert
mask_blue
=
(
op
.
mask
==
2
)
# Supposons que 2 correspond au bleu
# Interpolation bilinéaire pour le rouge et le bleu
# Note: np.multiply multiplie les éléments correspondants des tableaux, c'est pourquoi nous utilisons np.multiply au lieu de *
red_interp
=
convolve2d
(
np
.
multiply
(
red
,
mask_red
),
[[
1
/
4
,
1
/
2
,
1
/
4
],
[
1
/
2
,
1
,
1
/
2
],
[
1
/
4
,
1
/
2
,
1
/
4
]],
mode
=
'
same
'
)
blue_interp
=
convolve2d
(
np
.
multiply
(
blue
,
mask_blue
),
[[
1
/
4
,
1
/
2
,
1
/
4
],
[
1
/
2
,
1
,
1
/
2
],
[
1
/
4
,
1
/
2
,
1
/
4
]],
mode
=
'
same
'
)
# Interpolation bilinéaire pour le vert
# Pour le vert, nous utilisons un autre noyau car il y a plus de pixels verts
green_interp
=
convolve2d
(
np
.
multiply
(
green
,
mask_green
),
[[
0
,
1
/
4
,
0
],
[
1
/
4
,
1
,
1
/
4
],
[
0
,
1
/
4
,
0
]],
mode
=
'
same
'
)
# Création de l'image interpolée
demosaicked_image
=
np
.
stack
((
red_interp
,
green_interp
,
blue_interp
),
axis
=-
1
)
# Correction des valeurs interpolées: on réapplique les valeurs connues pour éviter le flou
demosaicked_image
[:,
:,
0
][
mask_red
]
=
red
[
mask_red
]
demosaicked_image
[:,
:,
1
][
mask_green
]
=
green
[
mask_green
]
demosaicked_image
[:,
:,
2
][
mask_blue
]
=
blue
[
mask_blue
]
# Clip pour s'assurer que toutes les valeurs sont dans la plage [0, 1]
demosaicked_image
=
np
.
clip
(
demosaicked_image
,
0
,
1
)
return
demosaicked_image
```
%% Cell type:code id:cf379598 tags:
```
python
def
quad_bayer_demosaicing
(
op
:
CFA
,
y
:
np
.
ndarray
)
->
np
.
ndarray
:
"""
Demosaicing method for Quad Bayer CFA pattern.
Args:
op (CFA): CFA operator.
y (np.ndarray): Mosaicked image.
Returns:
np.ndarray: Demosaicked image.
"""
# Interpolation bilinéaire pour chaque canal
red_interp
=
convolve2d
(
np
.
multiply
(
y
[:,
:,
0
],
op
.
mask
==
0
),
[[
1
/
4
,
1
/
2
,
1
/
4
],
[
1
/
2
,
1
,
1
/
2
],
[
1
/
4
,
1
/
2
,
1
/
4
]],
mode
=
'
same
'
)
green_interp
=
convolve2d
(
np
.
multiply
(
y
[:,
:,
1
],
op
.
mask
==
1
),
[[
0
,
1
/
4
,
0
],
[
1
/
4
,
1
,
1
/
4
],
[
0
,
1
/
4
,
0
]],
mode
=
'
same
'
)
blue_interp
=
convolve2d
(
np
.
multiply
(
y
[:,
:,
2
],
op
.
mask
==
2
),
[[
1
/
4
,
1
/
2
,
1
/
4
],
[
1
/
2
,
1
,
1
/
2
],
[
1
/
4
,
1
/
2
,
1
/
4
]],
mode
=
'
same
'
)
# Assemblage de l'image interpolée
demosaicked_image
=
np
.
stack
((
red_interp
,
green_interp
,
blue_interp
),
axis
=-
1
)
# Réapplication des valeurs connues
demosaicked_image
[:,
:,
0
][
op
.
mask
==
0
]
=
y
[:,
:,
0
][
op
.
mask
==
0
]
demosaicked_image
[:,
:,
1
][
op
.
mask
==
1
]
=
y
[:,
:,
1
][
op
.
mask
==
1
]
demosaicked_image
[:,
:,
2
][
op
.
mask
==
2
]
=
y
[:,
:,
2
][
op
.
mask
==
2
]
# Clip des valeurs pour les maintenir dans la plage appropriée
demosaicked_image
=
np
.
clip
(
demosaicked_image
,
0
,
1
)
return
demosaicked_image
```
%% Cell type:code id:3eec062c tags:
```
python
``
`
%%
Cell
type
:
code
id
:
a6630396
tags
:
```
python
```
%% Cell type:code id:ef4e59c8 tags:
```
python
```
This diff is collapsed.
Click to expand it.
src/methods/chaari_mohamed/fonctions.py
0 → 100644
+
77
−
0
View file @
221b1656
import
numpy
as
np
from
scipy.signal
import
convolve2d
from
src.forward_model
import
CFA
def
bilinear_demosaicing
(
op
:
CFA
,
y
:
np
.
ndarray
)
->
np
.
ndarray
:
"""
Bilinear demosaicing method.
Args:
op (CFA): CFA operator.
y (np.ndarray): Mosaicked image.
Returns:
np.ndarray: Demosaicked image.
"""
# Copie des valeurs directement connues pour chaque canal
red
=
y
[:,
:,
0
]
green
=
y
[:,
:,
1
]
blue
=
y
[:,
:,
2
]
# Création des masques pour chaque couleur selon le motif CFA
mask_red
=
(
op
.
mask
==
0
)
# Supposons que 0 correspond au rouge dans le masque
mask_green
=
(
op
.
mask
==
1
)
# Supposons que 1 correspond au vert
mask_blue
=
(
op
.
mask
==
2
)
# Supposons que 2 correspond au bleu
# Interpolation bilinéaire pour le rouge et le bleu
# Note: np.multiply multiplie les éléments correspondants des tableaux, c'est pourquoi nous utilisons np.multiply au lieu de *
red_interp
=
convolve2d
(
np
.
multiply
(
red
,
mask_red
),
[[
1
/
4
,
1
/
2
,
1
/
4
],
[
1
/
2
,
1
,
1
/
2
],
[
1
/
4
,
1
/
2
,
1
/
4
]],
mode
=
'
same
'
)
blue_interp
=
convolve2d
(
np
.
multiply
(
blue
,
mask_blue
),
[[
1
/
4
,
1
/
2
,
1
/
4
],
[
1
/
2
,
1
,
1
/
2
],
[
1
/
4
,
1
/
2
,
1
/
4
]],
mode
=
'
same
'
)
# Interpolation bilinéaire pour le vert
# Pour le vert, nous utilisons un autre noyau car il y a plus de pixels verts
green_interp
=
convolve2d
(
np
.
multiply
(
green
,
mask_green
),
[[
0
,
1
/
4
,
0
],
[
1
/
4
,
1
,
1
/
4
],
[
0
,
1
/
4
,
0
]],
mode
=
'
same
'
)
# Création de l'image interpolée
demosaicked_image
=
np
.
stack
((
red_interp
,
green_interp
,
blue_interp
),
axis
=-
1
)
# Correction des valeurs interpolées: on réapplique les valeurs connues pour éviter le flou
demosaicked_image
[:,
:,
0
][
mask_red
]
=
red
[
mask_red
]
demosaicked_image
[:,
:,
1
][
mask_green
]
=
green
[
mask_green
]
demosaicked_image
[:,
:,
2
][
mask_blue
]
=
blue
[
mask_blue
]
# Clip pour s'assurer que toutes les valeurs sont dans la plage [0, 1]
demosaicked_image
=
np
.
clip
(
demosaicked_image
,
0
,
1
)
return
demosaicked_image
def
quad_bayer_demosaicing
(
op
:
CFA
,
y
:
np
.
ndarray
)
->
np
.
ndarray
:
"""
Demosaicing method for Quad Bayer CFA pattern.
Args:
op (CFA): CFA operator.
y (np.ndarray): Mosaicked image.
Returns:
np.ndarray: Demosaicked image.
"""
# Interpolation bilinéaire pour chaque canal
red_interp
=
convolve2d
(
np
.
multiply
(
y
[:,
:,
0
],
op
.
mask
==
0
),
[[
1
/
4
,
1
/
2
,
1
/
4
],
[
1
/
2
,
1
,
1
/
2
],
[
1
/
4
,
1
/
2
,
1
/
4
]],
mode
=
'
same
'
)
green_interp
=
convolve2d
(
np
.
multiply
(
y
[:,
:,
1
],
op
.
mask
==
1
),
[[
0
,
1
/
4
,
0
],
[
1
/
4
,
1
,
1
/
4
],
[
0
,
1
/
4
,
0
]],
mode
=
'
same
'
)
blue_interp
=
convolve2d
(
np
.
multiply
(
y
[:,
:,
2
],
op
.
mask
==
2
),
[[
1
/
4
,
1
/
2
,
1
/
4
],
[
1
/
2
,
1
,
1
/
2
],
[
1
/
4
,
1
/
2
,
1
/
4
]],
mode
=
'
same
'
)
# Assemblage de l'image interpolée
demosaicked_image
=
np
.
stack
((
red_interp
,
green_interp
,
blue_interp
),
axis
=-
1
)
# Réapplication des valeurs connues
demosaicked_image
[:,
:,
0
][
op
.
mask
==
0
]
=
y
[:,
:,
0
][
op
.
mask
==
0
]
demosaicked_image
[:,
:,
1
][
op
.
mask
==
1
]
=
y
[:,
:,
1
][
op
.
mask
==
1
]
demosaicked_image
[:,
:,
2
][
op
.
mask
==
2
]
=
y
[:,
:,
2
][
op
.
mask
==
2
]
# Clip des valeurs pour les maintenir dans la plage appropriée
demosaicked_image
=
np
.
clip
(
demosaicked_image
,
0
,
1
)
return
demosaicked_image
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