From 3732c6849c1b3313a77534541993f1dc393d95ac Mon Sep 17 00:00:00 2001
From: Samia Bouddahab <samia.bouddahab@grenoble-inp.org>
Date: Mon, 12 Feb 2024 07:44:40 +0100
Subject: [PATCH] Update src/methods/Samia_Bouddahab/function.py

---
 src/methods/Samia_Bouddahab/function.py | 94 +++++++++++++++++++++++++
 1 file changed, 94 insertions(+)
 create mode 100644 src/methods/Samia_Bouddahab/function.py

diff --git a/src/methods/Samia_Bouddahab/function.py b/src/methods/Samia_Bouddahab/function.py
new file mode 100644
index 0000000..794193a
--- /dev/null
+++ b/src/methods/Samia_Bouddahab/function.py
@@ -0,0 +1,94 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Feb 12 07:38:51 2024
+
+@author: etudiant
+"""
+import numpy as np
+from scipy.signal import convolve2d
+from src.forward_model import CFA
+from scipy.ndimage import convolve
+
+
+def malvar_demosaicing(op: CFA, y: np.ndarray) -> np.ndarray:
+    """
+    Performs a malvar interpolation.
+
+    Args:
+        op (CFA): CFA operator.
+        y (np.ndarray): Mosaicked image.
+
+    Returns:
+        np.ndarray: Demosaicked image.
+    """
+    z = op.adjoint(y)
+    # Definnig Masks
+    R_m = op.mask[:, :, 0]
+    G_m = op.mask[:, :, 1]
+    B_m = op.mask[:, :, 2]
+
+    GR_GB = np.array([
+        [0.0, 0.0, -1.0, 0.0, 0.0],
+        [0.0, 0.0, 2.0, 0.0, 0.0],
+        [-1.0, 2.0, 4.0, 2.0, -1.0],
+        [0.0, 0.0, 2.0, 0.0, 0.0],
+        [0.0, 0.0, -1.0, 0.0, 0.0],
+    ]) / 8
+
+    Rg_RB_Bg_BR = np.array(
+        [
+            [0.0, 0.0, 0.5, 0.0, 0.0],
+            [0.0, -1.0, 0.0, -1.0, 0.0],
+            [-1.0, 4.0, 5.0, 4.0, -1.0],
+            [0.0, -1.0, 0.0, -1.0, 0.0],
+            [0.0, 0.0, 0.5, 0.0, 0.0],
+        ]) / 8
+
+    Rg_BR_Bg_RB = np.transpose(Rg_RB_Bg_BR)
+
+    Rb_BB_Br_RR = np.array(
+        [
+            [0.0, 0.0, -1.5, 0.0, 0.0],
+            [0.0, 2.0, 0.0, 2.0, 0.0],
+            [-1.5, 0.0, 6.0, 0.0, -1.5],
+            [0.0, 2.0, 0.0, 2.0, 0.0],
+            [0.0, 0.0, -1.5, 0.0, 0.0],
+        ]) / 8
+
+    R = y * R_m
+    G = y * G_m
+    B = y * B_m
+
+    del G_m
+
+    G = np.where(np.logical_or(R_m == 1, B_m == 1), convolve(y, GR_GB), G)
+
+    RBg_RBBR = convolve(y, Rg_RB_Bg_BR)
+    RBg_BRRB = convolve(y, Rg_BR_Bg_RB)
+    RBgr_BBRR = convolve(y, Rb_BB_Br_RR)
+
+    del GR_GB, Rg_RB_Bg_BR, Rg_BR_Bg_RB, Rb_BB_Br_RR
+
+    # Red rows.
+    R_r = np.transpose(np.any(R_m == 1, axis=1)[None]) * np.ones(R.shape)
+    # Red columns.
+    R_c = np.any(R_m == 1, axis=0)[None] * np.ones(R.shape)
+    # Blue rows.
+    B_r = np.transpose(np.any(B_m == 1, axis=1)[None]) * np.ones(B.shape)
+    # Blue columns
+    B_c = np.any(B_m == 1, axis=0)[None] * np.ones(B.shape)
+
+    del R_m, B_m
+
+    R = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_RBBR, R)
+    R = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_BRRB, R)
+
+    B = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_RBBR, B)
+    B = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_BRRB, B)
+
+    R = np.where(np.logical_and(B_r == 1, B_c == 1), RBgr_BBRR, R)
+    B = np.where(np.logical_and(R_r == 1, R_c == 1), RBgr_BBRR, B)
+
+    del RBg_RBBR, RBg_BRRB, RBgr_BBRR, R_r, R_c, B_r, B_c
+    # Combine channels
+    return np.clip(np.stack((R, G, B), axis=-1), 0, 1)
-- 
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