deuxième test pour patchMatch et implémentation knn

knn.py

@@ -0,0 +1,90 @@
+from matplotlib.widgets import RectangleSelector
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+
+def doKnn(img,x1,y1,x2,y2):
+    
+    def getNotInBoundNeighbour(neighbour, x1,y1,x2,y2):
+        mask = np.logical_or(
+            np.logical_or(neighbour[:, 0] < y1, neighbour[:, 0] > y2),
+            np.logical_or(neighbour[:, 1] < x1, neighbour[:, 1] > x2)
+        )
+        return neighbour[mask]
+
+    def neighbourReelPixel(x,y):
+        tNeighbour = np.copy(neighbour)
+        tNeighbour = tNeighbour + np.array([y,x])
+        return tNeighbour
+    
+    def getAvgPixelFromNeighbour(neighbour):
+        return np.mean(img[neighbour[:,0],neighbour[:,1]], axis=0)
+    
+
+    neighbour = np.array([[-1,-1],[-1,0],[0,-1],[-1,1],[1,-1],[0,1],[1,0],[1,1]])
+    x1c = x1
+    y1c = y1
+    x2c = x2
+    y2c = y2
+    while x1 != x2 and y1 != y2:
+        for x in range(x1,x2):
+            currentNeighbour1 = neighbourReelPixel(x,y1)
+            currentNeighbour2 = neighbourReelPixel(x,y2)
+            currentNeighbour1 = getNotInBoundNeighbour(currentNeighbour1,x1,y1,x2,y2)
+            currentNeighbour2 = getNotInBoundNeighbour(currentNeighbour2,x1,y1,x2,y2)
+            currentColor1 = getAvgPixelFromNeighbour(currentNeighbour1)
+            currentColor2 = getAvgPixelFromNeighbour(currentNeighbour2)
+            img[y1,x] = currentColor1
+            img[y2,x] = currentColor2
+        for y in range(y1,y2):
+            currentNeighbour1 = neighbourReelPixel(x1,y)
+            currentNeighbour2 = neighbourReelPixel(x2,y)
+            currentNeighbour1 = getNotInBoundNeighbour(currentNeighbour1,x1,y1,x2,y2)
+            currentNeighbour2 = getNotInBoundNeighbour(currentNeighbour2,x1,y1,x2,y2)
+            currentColor1 = getAvgPixelFromNeighbour(currentNeighbour1)
+            currentColor2 = getAvgPixelFromNeighbour(currentNeighbour2)
+            img[y,x1] = currentColor1
+            img[y,x2] = currentColor2
+        x1 += 1
+        x2 -= 1
+        y1 += 1
+        y2 -= 1
+    
+    for x in range(x1c, x2c):
+        for y in range(y1c, y2c):
+            currentNeighbour = neighbourReelPixel(x, y)
+            currentNeighbour = getNotInBoundNeighbour(currentNeighbour,0,0,0,0)
+            currentColor = getAvgPixelFromNeighbour(currentNeighbour)
+            img[y, x] = currentColor
+    img[y1:y2,x1:x2] 
+    return img
+
+
+
+img = plt.imread('boat.png')
+
+
+if len(img.shape) == 2:
+    img = np.stack((img,)*3, axis=-1)
+
+
+
+def onselect(eclick, erelease):
+    x1, y1 = eclick.xdata, eclick.ydata
+    x2 = x1 + 150
+    x2, y2 = erelease.xdata, erelease.ydata
+
+    img_copy = np.copy(img)
+    res = doKnn(img_copy,int(x1),int(y1),int(x2),int(y2))
+    ax.imshow(res)
+    plt.draw()
+    print("drawed")
+
+fig, ax = plt.subplots()
+ax.imshow(img)
+toggle_selector = RectangleSelector(ax, onselect,  useblit=True,
+                                    button=[1], minspanx=5, minspany=5, spancoords='pixels',
+                                    interactive=True)
+plt.axis('off')
+plt.show()

patchMatch1.py

@@ -51,7 +51,7 @@ def patch_match(img, patch_size=3, iterations=1):
 
 
 # Load the image using matplotlib
-img = plt.imread('/home/UCA/lucastigli/patchMatch/boat.png')
+img = plt.imread('boat.png')
 
 
 def onselect(eclick, erelease):
@@ -60,7 +60,6 @@ def onselect(eclick, erelease):
     x2, y2 = erelease.xdata, erelease.ydata
 
     avg_color = np.mean(img, axis=(0, 1))
-
     img_copy = np.copy(img)
     img_copy[int(y1):int(y2), int(x1):int(x2)] = avg_color
     res = patch_match(img_copy)

patchMatch2.py

@@ -5,12 +5,19 @@ import numpy as np
 
 def patchMatch(img,x1,y1,x2,y2,patchSize=20, iterations=1000):
     height, width, _ = img.shape
-    img_copy = np.copy(img)
     xx1 = max(0, x1-patchSize)
     yy1 = max(0, y1-patchSize)
     xx2 = min(width, x2+patchSize)
     yy2 = min(height, y2+patchSize)
 
+    mask = np.ones((height, width), dtype=bool)
+    mask[yy1:yy2, xx1:xx2] = False
+    mask[y1:y2, x1:x2] = True
+    img[int(y1):int(y2), int(x1):int(x2)] = np.mean(img[~mask], axis=(0, 1))
+    img_copy = np.copy(img)
+
+    div = 10
+
     if (xx2-xx1 < patchSize or yy2-yy1 < patchSize):
         return img
 
@@ -26,7 +33,7 @@ def patchMatch(img,x1,y1,x2,y2,patchSize=20, iterations=1000):
         return np.sum((patch1 - patch2) ** 2)
     
     def gradientDescent(x, y, bestPatch, bestDistance):
-        neighbors = [(-1,0), (1,0), (0,-1), (0,1), (-1,-1), (-1,1), (1,-1), (1,1)]
+        neighbors = [(-div,0), (div,0), (0,-div), (0,div), (-div,-div), (-div,div), (div,-div), (div,div)]
         patch = img[y:y + patchSize, x:x + patchSize]
         hasChanged = True
         while hasChanged:
@@ -46,8 +53,8 @@ def patchMatch(img,x1,y1,x2,y2,patchSize=20, iterations=1000):
         return bestPatch, bestDistance
 
 
-    for x in range(xx1,xx2-patchSize,int(patchSize/10)):
-        for y in range(yy1,yy2-patchSize,int(patchSize/10)):
+    for x in range(xx1,xx2-patchSize,int(patchSize/div)):
+        for y in range(yy1,yy2-patchSize,int(patchSize/div)):
             px, py = getRandomPatch()
             bestPatch = [px, py]
             bestDistance = distance(img[y:y+patchSize,x:x+patchSize], img[py:py+patchSize,px:px+patchSize])
@@ -79,10 +86,7 @@ def onselect(eclick, erelease):
     x2 = x1 + 150
     x2, y2 = erelease.xdata, erelease.ydata
 
-    avg_color = np.mean(img, axis=(0, 1))
-
     img_copy = np.copy(img)
-    img_copy[int(y1):int(y2), int(x1):int(x2)] = avg_color
     res = patchMatch(img_copy,int(x1),int(y1),int(x2),int(y2))
     ax.imshow(res)
     plt.draw()