Post merge changes

src/methods/chaari_mohamed/fonctions.ipynb

-{
- "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"
-   ]
-  },
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-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "3eec062c",
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-   "execution_count": null,
-   "id": "ef4e59c8",
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src/methods/chaari_mohamed/fonctions.py

+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