Update app.py

app.py

@@ -1,31 +1,19 @@
-import gradio
-import cv2
+import matplotlib.pyplot as plt
+from skimage import morphology
 import numpy as np
-
+import skimage
 
 
 def inference(img):
-  #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  
-  # 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
+  grayscale = skimage.color.rgb2gray(im)
+  binarized = np.where(grayscale>0.1, 1, 0)
+  processed = morphology.remove_small_objects(binarized.astype(bool), min_size=2, connectivity=2).astype(int)
+  out = processed
 
-  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY ) # grayscale
-  #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,221,3)
-  out = cv2.dilate(out,(55,55))
+  # black out pixels
+  #mask_x, mask_y = np.where(processed == 0)
+  #im[mask_x, mask_y, :3] = 0
 
-    
-  #kernel = cv2.getStructuringElement(cv2.MORPH_RECT, kernelSize)
-  #opening = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)
 
   return out
 
@@ -36,8 +24,8 @@ iface = gradio.Interface(
   fn=inference,
   inputs='image',
   outputs='image',
-  title='Noise Removal', 
-  description='Remove Noise with OpenCV and Adaptial Gaussian!',
+  title='Noise Removal w skimage', 
+  description='Remove Noise with skimage.morphology!',
   examples=["detail_with_lines_and_noise.jpg", "lama.webp", "dT4KW.png"])  
   #examples=["detail_with_lines_and_noise.jpg", "lama.webp", "test_lines.jpg","llama.jpg", "dT4KW.png"])