From b4c7512a737663bd7378bf9a1993a26cfcbbcd12 Mon Sep 17 00:00:00 2001
From: Hazel Noack <hazel@webcontact.de>
Date: Wed, 7 May 2025 12:02:54 +0200
Subject: [PATCH] feat: detect color edges

---
 deblur/deblur_2d.py | 89 ++++++++++++++++++---------------------------
 1 file changed, 36 insertions(+), 53 deletions(-)

diff --git a/deblur/deblur_2d.py b/deblur/deblur_2d.py
index 3aca925..ed40b88 100644
--- a/deblur/deblur_2d.py
+++ b/deblur/deblur_2d.py
@@ -5,6 +5,8 @@ from scipy.sparse.linalg import spsolve
 import cv2
 import matplotlib.pyplot as plt
 from pathlib import Path
+from scipy.ndimage import correlate
+
 
 """
 https://setosa.io/ev/image-kernels/
@@ -130,67 +132,48 @@ def get_mask(image_file):
 
 
 
+def color_edge_detection(img, threshold=30):
+    # Load image
+    img_lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
+
+    # Split Lab channels
+    L, A, B = cv2.split(img_lab)
+
+    # Compute gradients using Sobel for each channel
+    def gradient_magnitude(channel):
+        gx = cv2.Sobel(channel, cv2.CV_64F, 1, 0, ksize=3)
+        gy = cv2.Sobel(channel, cv2.CV_64F, 0, 1, ksize=3)
+        return gx, gy
+
+    gxL, gyL = gradient_magnitude(L)
+    gxA, gyA = gradient_magnitude(A)
+    gxB, gyB = gradient_magnitude(B)
+
+    # Combine gradients across channels
+    gx_total = gxL**2 + gxA**2 + gxB**2
+    gy_total = gyL**2 + gyA**2 + gyB**2
+    magnitude = np.sqrt(gx_total + gy_total)
+
+    # Normalize and threshold
+    magnitude = cv2.normalize(magnitude, None, 0, 255, cv2.NORM_MINMAX)
+    edges = (magnitude > threshold).astype(np.uint8) * 255
+
+    return edges
+
+
 
 def deconvolution(image_file):
     
-    image = cv2.imread(image_file, 0)
-    # image = cv2.resize(image, (200, 200), interpolation= cv2.INTER_LINEAR)
+    blurred = cv2.imread(image_file)
+    # blurred = cv2.resize(blurred, (200, 200), interpolation= cv2.INTER_LINEAR)
     mask = get_mask(image_file)
 
-
-    # Define 2D image and kernel
+    edges = color_edge_detection(blurred, threshold=8)
+    # edges = cv2.bitwise_and(edges, edges, mask=mask)
+    show(edges)
 
 
-    kernel = np.array([
-        [1, 1, 1],
-        [1, 1, 1],
-        [1, 1, 1]
-    ], dtype=np.float32) 
-    kernel /= kernel.sum()  # Normalize
 
-    print(kernel)
-    return
-
-    # Perform 2D convolution (blurring)
-
-    h, w = image.shape
-    kh, kw = kernel.shape
-    pad_h, pad_w = kh // 2, kw // 2
-
-
-    show(image)
-
-    print("Original image:\n", image)
-    print("\nBlurred image:\n", image)
-
-    print("\nBuilding linear system for deconvolution...")
-
-    # Step 2: Build sparse matrix A
-    N = h * w
-    A = lil_matrix((N, N), dtype=np.float32)
-    b = image.flatten()
-
-    def index(y, x):
-        return y * w + x
-
-    for y in range(h):
-        for x in range(w):
-            row_idx = index(y, x)
-            for ky in range(kh):
-                for kx in range(kw):
-                    iy = y + ky - pad_h
-                    ix = x + kx - pad_w
-                    if 0 <= iy < h and 0 <= ix < w:
-                        col_idx = index(iy, ix)
-                        A[row_idx, col_idx] += kernel[ky, kx]
-
-    # Step 3: Solve the sparse system A * x = b
-    x = spsolve(A.tocsr(), b)
-    deblurred = x.reshape((h, w))
-
-    print("\nDeblurred image:\n", np.round(deblurred, 2))
-
-    show(deblurred)
 
 if __name__ == "__main__":
     img_file = "assets/real_test.jpg"