Update app.py

app.py

@@ -440,14 +440,15 @@ def optimize(v, d):
             # define criteria, number of clusters(K) and apply kmeans()
             criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 4, 1.0)
             ret,label,center=cv2.kmeans(f,l,None,criteria,4,cv2.KMEANS_RANDOM_CENTERS)
+            print(label)
             # Now convert back into uint8, and make original image
             center = np.uint8(center)
             res = center[label.flatten()]
             frame = res.reshape((frame.shape))
 
             depth = cv2.imread(depths[k]).astype(np.uint8)
-            sky = depth[(depth==0).all()]
-            dcolor.append(bincount(frame[sky>0]))
+            mask = cv2.cvtColor(depth, cv2.COLOR_RGB2GRAY)
+            dcolor.append(bincount(frame[mask==0]))
             print(dcolor[k])
             
             #mask = cv2.convertScaleAbs(cv2.Laplacian(cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY), ddepth, ksize=kernel_size))