Update app.py

app.py

@@ -5,20 +5,18 @@ import numpy as np
 
 
 def inference(img):
-  #my_result = cv2.erode(img,(1,1))
-  #my_result = cv2.dilate(my_result,(1,1))
+  out = cv2.erode(img,(1,1))
+  out = cv2.dilate(my_result,(1,1))
   # https://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.remove_small_objects  
   # my_result = cv2.remove_small_objects(binarized.astype(bool), min_size=2, connectivity=2).astype(int)
 
-  img_bw = 255*(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) > 5).astype('uint8')
-
-  se1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
-  se2 = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
-  mask = cv2.morphologyEx(img_bw, cv2.MORPH_CLOSE, se1)
-  mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, se2)
-
-  mask = np.dstack([mask, mask, mask]) / 255
-  out = img * mask
+  #img_bw = 255*(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) > 5).astype('uint8')
+  #se1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
+  #se2 = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
+  #mask = cv2.morphologyEx(img_bw, cv2.MORPH_CLOSE, se1)
+  #mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, se2)
+  #mask = np.dstack([mask, mask, mask]) / 255
+  #out = img * mask
 
   return out