Added unsharp mask filter

src/filters.py

@@ -13,20 +13,23 @@ class filter:
     '''
     Parent class for all the filters
     '''
+
     def __init__(self, img):
         self.img = img
 
 
 class convolve(filter):
     ''' Convolve using custom kernel, 
-    if no kernel is given, use default 3x3 kernel for averaging'''  
+    if no kernel is given, use default 3x3 kernel for averaging
+    '''
+
     def __init__(self, img):
         super().__init__(img)
 
     def apply(self, params):
-        # Set default values
         ksize = int(params["ksize"]) if params["ksize"] else 3
-        kernel = np.array(params["kernel"]) if params["kernel"] else np.ones((ksize, ksize), np.float32) / np.sqrt(ksize)
+        kernel = np.array(params["kernel"]) if params["kernel"] else np.ones(
+            (ksize, ksize), np.float32) / np.sqrt(ksize)
 
         #print("with params: " + " ksize: " + str(ksize) + " kernel: \n" + str(kernel))
         self.img = cv.filter2D(self.img, -1, kernel)
@@ -37,7 +40,6 @@ class blur(filter):
         super().__init__(img)
 
     def apply(self, params):
-        # Set default values
         if(params["anchor"]):
             try:
                 anchor = tuple(map(int, params["anchor"].split(',')))
@@ -47,16 +49,15 @@ class blur(filter):
             anchor = (-1, -1)
         ksize = int(params["ksize"]) if params["ksize"] else 3
 
-        
         #print("with params: " + " ksize: " + str(ksize) + " anchor: " + str(anchor))
         self.img = cv.blur(self.img, ksize=(ksize, ksize), anchor=anchor)
 
+
 class gaussian(filter):
     def __init__(self, img):
         super().__init__(img)
 
     def apply(self, params):
-        # Set default values
         ksize = int(params["ksize"]) if params["ksize"] else 3
         sigmaX = float(params["sigmaX"]) if params["sigmaX"] else 0
         sigmaY = float(params["sigmaY"]) if params["sigmaY"] else 0
@@ -70,11 +71,10 @@ class median(filter):
         super().__init__(img)
 
     def apply(self, params):
-        # Set default values
         ksize = int(params["ksize"]) if params["ksize"] else 3
 
         #print("with params: " + " ksize: " + str(ksize))
-        self.img = cv.medianBlur(self.img, ksize)
+        self.img = cv.medianBlur(np.uint8(self.img), ksize)
 
 
 class bilateral(filter):
@@ -96,10 +96,11 @@ class denoise(filter):
         super().__init__(img)
 
     def apply(self, params):
-        # Set default values
         h = int(params["h"]) if params["h"] else 20
-        tWS = int(params["templateWindowSize"]) if params["templateWindowSize"] else 7
+        tWS = int(params["templateWindowSize"]
-        sWS = int(params["searchWindowSize"]) if params["searchWindowSize"] else 21
+                  ) if params["templateWindowSize"] else 7
+        sWS = int(params["searchWindowSize"]
+                  ) if params["searchWindowSize"] else 21
 
         #print("with params: " + " h: " + str(h) + " tWS: " + str(tWS) + " sWS: " + str(sWS))
         self.img = np.uint8(self.img)
@@ -111,7 +112,6 @@ class sharpen(filter):
         super().__init__(img)
 
     def apply(self, params):
-        # Set default values
         kernel = np.matrix(params["kernel"]) if params["kernel"] else np.array(
             [[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
 
@@ -119,17 +119,36 @@ class sharpen(filter):
         self.img = cv.filter2D(self.img, ddepth=-1, kernel=kernel)
 
 
-class morph(filter):
+class unsharp_mask(filter):
-    ''' General morphological operations.
+    ''' Unsharp mask filter. 
-
+    
-    Can be used with MORPH_OPEN, MORPH_CLOSE, MORPH_DILATE, MORPH_ERODE as op.
+    First blur the image a little bit, then calculate Laplacian of the image to get the edges.
+    Scale the Laplacian and subtract it from the original image.
     '''
+
     def __init__(self, img):
         super().__init__(img)
 
     def apply(self, params):
-        # Set default values
+        strength = float(params["strength"]) if params["strength"] else 1.0
-        kernel = np.matrix(params["kernel"]) if params["kernel"] else np.ones((3, 3), np.uint8)
+        ksize = int(params["ksize"]) if params["ksize"] else 3
+        blurred = cv.medianBlur(np.uint8(self.img), ksize)
+        lap = cv.Laplacian(blurred, cv.CV_32F)
+        self.img = blurred - strength*lap
+
+
+class morph(filter):
+    ''' General morphological operations.
+
+    Can be used with MORPH_OPEN, MORPH_CLOSE, MORPH_DILATE, MORPH_ERODE and more as 'op'.
+    '''
+
+    def __init__(self, img):
+        super().__init__(img)
+
+    def apply(self, params):
+        kernel = np.matrix(params["kernel"]) if params["kernel"] else np.ones(
+            (3, 3), np.uint8)
         iterations = int(params["iterations"]) if params["iterations"] else 1
         op = getattr(cv, params["op"]) if params["op"] else cv.MORPH_OPEN
         if(params["anchor"]):
@@ -140,7 +159,6 @@ class morph(filter):
         else:
             anchor = (-1, -1)
 
-
         #print("with params: " + " kernel: \n" + str(kernel) + " anchor: " + str(anchor) + " iterations: " + str(iterations) + " op: " + str(op))
         self.img = cv.morphologyEx(
             self.img, op=op, kernel=kernel, anchor=anchor, iterations=iterations)