Merge branch 'master' into 'master'

commit

See merge request !38

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"
+   ]
+  },
+  {
+   "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
+}

src/methods/chaari_mohamed/reconstruct.py

+"""The main file for the reconstruction.
+This file should NOT be modified except the body of the 'run_reconstruction' function.
+Students can call their functions (declared in others files of src/methods/your_name).
+"""
+
+
+import numpy as np
+
+from src.forward_model import CFA
+from src.methods.chaari_mohamed.fonctions import bilinear_demosaicing,quad_bayer_demosaicing
+
+
+def run_reconstruction(y: np.ndarray, cfa: str) -> np.ndarray:
+    """
+    Performs demosaicking on y based on the CFA pattern.
+
+    Args:
+        y (np.ndarray): Mosaicked image to be reconstructed.
+        cfa (str): Name of the CFA pattern. Can be 'bayer' or 'quad_bayer'.
+
+    Returns:
+        np.ndarray: Demosaicked image.
+    """
+    input_shape = (y.shape[0], y.shape[1], 3)
+    
+    op = CFA(cfa, input_shape)
+    
+    if cfa == 'bayer':
+        res = bilinear_demosaicing(op, y)
+    elif cfa == 'quad_bayer':
+        res = quad_bayer_demosaicing(op, y)
+    else:
+        raise ValueError("Unsupported CFA pattern. Supported patterns are 'bayer' and 'quad_bayer'.")
+    
+    return res
+
+
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+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller