premier test de l'appli

app.py

@@ -0,0 +1,79 @@
+from matplotlib.widgets import RectangleSelector
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+
+
+def patch_match(img, patch_size=3, iterations=1):
+    height, width, _ = img.shape
+    offsets = np.zeros((height, width, 2), dtype=np.int32)
+
+    def random_offsets():
+        return np.random.randint(-patch_size, patch_size + 1, size=(height, width, 2))
+
+    def distance(patch1, patch2):
+        return np.sum((patch1 - patch2) ** 2)
+
+    def get_patch(x, y):
+        return img[max(0, y):min(height, max(0, y) + patch_size), max(0, x):min(width, max(0, x) + patch_size)]
+
+    offsets = random_offsets()
+
+    for _ in range(iterations):
+        for y in range(patch_size,height-patch_size*2):
+            for x in range(patch_size,width-patch_size*2):
+                best_offset = offsets[y, x]
+                best_distance = distance(get_patch(x, y), get_patch(x + best_offset[0], y + best_offset[1]))
+                for dy in range(-1, 2):
+                    for dx in range(-1, 2):
+                        if dx == 0 and dy == 0:
+                            continue
+                        new_offset = best_offset + [dx, dy]
+                        if (new_offset[0]+x>width-patch_size or new_offset[1]+y>height-patch_size):
+                            continue
+                        new_distance = distance(get_patch(x, y), get_patch(x + new_offset[0], y + new_offset[1]))
+                        if new_distance < best_distance:
+                            best_distance = new_distance
+                            best_offset = new_offset
+                offsets[y, x] = best_offset
+    
+    result = np.zeros_like(img)
+    for y in range(height):
+        for x in range(width):
+            offset = offsets[y, x]
+            result[y, x] = img[(y + offset[1]) % height, (x + offset[0]) % width]
+
+    return result
+
+
+
+
+
+# Load the image using matplotlib
+img = plt.imread('/home/UCA/lucastigli/patchMatch/boat.png')
+
+
+def onselect(eclick, erelease):
+    x1, y1 = eclick.xdata, eclick.ydata
+    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 = patch_match(img_copy)
+    ax.imshow(res)
+    plt.draw()
+    print("drawed")
+    #ax.imshow(img_copy)
+    #plt.draw()
+
+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()

app2.py

@@ -0,0 +1,97 @@
+from matplotlib.widgets import RectangleSelector
+import matplotlib.pyplot as plt
+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)
+
+    if (xx2-xx1 < patchSize or yy2-yy1 < patchSize):
+        return img
+
+    def getRandomPatch():
+        rx = np.random.randint(0, width - patchSize)
+        ry = np.random.randint(0, height - patchSize)
+        return rx, ry
+    
+    def fusion(patch1, patch2):
+        return (patch1*2+patch2) / 3
+    
+    def distance(patch1, patch2):
+        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)]
+        patch = img[y:y + patchSize, x:x + patchSize]
+        hasChanged = True
+        while hasChanged:
+            hasChanged = False
+            for nx, ny in neighbors:
+                cx = bestPatch[0] + nx
+                cy = bestPatch[1] + ny
+                if cx < 0 or cy < 0 or cx >= width - patchSize or cy >= height - patchSize:
+                    continue
+
+                neighborPatch = img[cy:cy + patchSize, cx:cx + patchSize]
+                neighborDist = distance(patch, neighborPatch)
+                if neighborDist < bestDistance:
+                    hasChanged = True
+                    bestPatch = [cx, cy]
+                    bestDistance = neighborDist
+        return bestPatch, bestDistance
+
+
+    for x in range(xx1,xx2-patchSize,int(patchSize/10)):
+        for y in range(yy1,yy2-patchSize,int(patchSize/10)):
+            px, py = getRandomPatch()
+            bestPatch = [px, py]
+            bestDistance = distance(img[y:y+patchSize,x:x+patchSize], img[py:py+patchSize,px:px+patchSize])
+            firstDistance = np.copy(bestDistance)
+            for _ in range(iterations):
+                px, py = getRandomPatch()
+                currentDistance = distance(img[y:y+patchSize,x:x+patchSize], img[py:py+patchSize,px:px+patchSize])
+                if currentDistance > firstDistance:
+                    continue
+                currentFirstDistance = np.copy(currentDistance)
+                currentPatch, bestDistance = gradientDescent(x, y, bestPatch, bestDistance)
+                if currentDistance < bestDistance:
+                    firstDistance = currentFirstDistance
+                    bestPatch = currentPatch
+                    bestDistance = currentDistance
+            img_copy[y:y+patchSize, x:x+patchSize] = fusion(img_copy[y:y+patchSize, x:x+patchSize],img[bestPatch[1]:bestPatch[1]+patchSize, bestPatch[0]:bestPatch[0]+patchSize])
+            img[y1:y2,x1:x2] = img_copy[y1:y2,x1:x2]
+    return img
+
+
+# Load the image using matplotlib
+img = plt.imread('simson.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
+
+    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()
+    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()