opti et simplification

function.py

@@ -21,16 +21,13 @@ def doTheInpainting(img,mask,radius):
         newTarget = None
         for emloop in range(1,iterEM+1):
             if (newTarget != None):
-                sourceToTarget.output = newTarget
                 targetToSource.input = newTarget
                 target = newTarget
                 newTarget = None
             for y in range(source.height):
                 for x in range(source.width):
                     if not source.containsMask(x,y,radius):
-                        sourceToTarget.field[y,x] = (x,y,0)
                         targetToSource.field[y,x] = (x,y,0)
-            sourceToTarget.minimize(iterNnf)
             targetToSource.minimize(iterNnf)
 
             upscaled = False
@@ -44,7 +41,6 @@ def doTheInpainting(img,mask,radius):
                 upscaled = False
 
             vote = np.zeros((newTarget.width, newTarget.height, 4))
-            ExpectationStep(sourceToTarget,True,vote,newSource,upscaled)
             ExpectationStep(targetToSource,False,vote,newSource,upscaled)
             MaximizationStep(newTarget, vote)
             result = cv2.resize(newTarget.image, (initial.width, initial.height), interpolation=cv2.INTER_AREA)

maskedImage.py

@@ -3,7 +3,7 @@ import numpy as np
 class MaskedImage:
     DSCALE = 10_000 # valeur arbitraire qui est le nombre max de la function de distance
     base = [1.0, 0.99, 0.96, 0.83, 0.38, 0.11, 0.02, 0.005, 0.0006, 0.0001, 0]
-    similarity = np.interp(np.linspace(0, 1, DSCALE + 1), np.linspace(0, 1, len(base)), base)
+    similarity = np.interp(np.linspace(0, 1, DSCALE), np.linspace(0, 1, len(base)), base)
 
     def __init__(self,image=None,mask=None,width=None,height=None):
         if image is not None:

nnf.py

@@ -68,7 +68,7 @@ class Nnf:
         zoneRecherche = max(self.output.height,self.output.width)
         while zoneRecherche>0:
             xp = self.field[y,x,0] + random.randint(0,2*zoneRecherche)-zoneRecherche
-            xp = self.field[y,x,1] + random.randint(0,2*zoneRecherche)-zoneRecherche
+            yp = self.field[y,x,1] + random.randint(0,2*zoneRecherche)-zoneRecherche
             xp = max(0,min(self.output.width-1,xp))
             yp = max(0,min(self.output.height-1,yp))
             dp = self.distance(x,y,xp,yp)
@@ -80,7 +80,7 @@ class Nnf:
         return distance(self.input,x,y,self.output,xp,yp,self.patchSize)
 
 def distance(source, xs, ys, target, xt, yt, patchSize):
-        ssd_max = 255 * 255
+        ssd_max = 255 ** 2
         distance, wsum = 0, ssd_max * (patchSize*2)**2
         for dy in range(-patchSize, patchSize):
             yks, ykt = ys + dy, yt + dy

selection.py

@@ -6,27 +6,30 @@ import numpy as np
 def onselect(eclick, erelease):
     x1, y1 = int(eclick.xdata), int(eclick.ydata)
     x2, y2 = int(erelease.xdata), int(erelease.ydata)
-    img[y1:y2,x1:x2] = (255,0,0)
-    mask[y1:y2,x1:x2] = True
+    img[y1:y2+1,x1:x2+1] = (255,0,0)
+    mask[y1:y2+1,x1:x2+1] = True
+    maskImg[y1:y2+1,x1:x2+1] = 255
     ax.imshow(img)
     plt.draw()
 
-
-
-img = read(input("lien relatif de l'image: "))
-img = img.copy()
+imgOr = read(input("lien relatif de l'image: "))
+img = imgOr.copy()
 
 rep = input("avez vous le mask de l'image ?(y:n)")
 yes = ["y","Y","yes","YES","Yes","o","O","oui","OUI"]
 height, width = img.shape[:2]
 if (rep in yes):
     maskImg = read(input("lien relatife de l'image: "))
+    if (len(maskImg.shape) == 3):
+        maskImg = maskImg[:,:,0]
     mask = maskImg == 255
     img[mask] = (255,0,0)
 else:
     fig, ax = plt.subplots()
+    plt.axis("off")
     ax.imshow(img)
     mask = np.zeros((height, width), dtype=bool)
+    maskImg = np.zeros((height, width), dtype=np.uint8)
     toggle_selector = RectangleSelector(ax, onselect,  useblit=True,
                                         button=[1], minspanx=5, minspany=5, spancoords='pixels',
                                         interactive=True)
@@ -42,7 +45,23 @@ plt.title("mask de l'image")
 plt.pause(1)
 plt.title("génération de l'image")
 
-img = doTheInpainting(img,mask,2)
-plt.imshow(img)
-plt.title("image final")
-plt.pause(20)
+res = doTheInpainting(img,mask,2)
+plt.ioff()
+
+if input("afficher le résultat avant après (y/n): ") in yes:
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+    ax1.imshow(imgOr)
+    ax1.set_title('Original')
+    ax1.axis("off")
+    ax2.imshow(res)
+    ax2.set_title('Result')
+    ax2.axis("off")
+    plt.show()
+
+if input("enregister le mask (y/n):") in yes:
+    save_path = input("Entrez le chemin d'enregistrement du mask: ")
+    plt.imsave(save_path, maskImg, cmap='gray')
+
+if input("enregister l'image produite (y/n):") in yes:
+    save_path = input("Entrez le chemin d'enregistrement de l'image: ")
+    plt.imsave(save_path, res)