Update app.py

app.py

@@ -4,7 +4,7 @@ import numpy as np
 
 
 
-def inference(img):
+def inference(img,v1='121',v2='3'):
   #out = cv2.erode(img,(15,15))
   #out = cv2.dilate(out,(55,55))
   # https://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.remove_small_objects  
@@ -22,7 +22,8 @@ def inference(img):
   #out = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,133,9)
   #out = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,333,3)
   out = cv2.GaussianBlur( gray ,(5,5),0)
-  out = cv2.adaptiveThreshold( out ,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,121,1)
+  #  v1 121 , v2 1
+  out = cv2.adaptiveThreshold( out ,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,v1,v2)
   #out = cv2.dilate(out,(5,5))
   #out = blur = cv.GaussianBlur(img,(5,5),0)
   #out =  cv2.threshold(out,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
@@ -38,7 +39,7 @@ def inference(img):
 # For Spaces usage, head to https://huggingface.co/docs/hub/spaces
 iface = gradio.Interface(
   fn=inference,
-  inputs='image',
+  inputs=['image','v1','v2'],
   outputs='image',
   title='Noise Removal', 
   description='Remove Noise with OpenCV and Adaptial Gaussian!',