Update app.py

app.py

@@ -422,18 +422,21 @@ def optimize(v, d):
     if v == True:
         ddepth = cv2.CV_16S
         kernel_size = 3
-        l = 4
+        l = 16
 
         dcolor = []
         for k, f in enumerate(frames):
             frame = cv2.imread(frames[k]).astype(np.uint8)
             
-            indices = np.arange(0,256)   # List of all colors 
-            divider = np.linspace(0,255,l+1)[1] # we get a divider
-            quantiz = np.int0(np.linspace(0,255,l)) # we get quantization colors
-            color_levels = np.clip(np.int0(indices/divider),0,l-1) # color levels 0,1,2..
-            palette = quantiz[color_levels]
-            frame = cv2.convertScaleAbs(palette[frame]).astype(np.uint8)
+            # convert to np.float32
+            f = np.float32(frame.reshape((-1,3)))
+            # define criteria, number of clusters(K) and apply kmeans()
+            criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+            ret,label,center=cv2.kmeans(f,l,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
+            # Now convert back into uint8, and make original image
+            center = np.uint8(center)
+            res = center[label.flatten()]
+            frame = res.reshape((frame.shape))
 
             dcolor.append(bincount(frame))
             print(dcolor[k])