Update app.py

app.py

@@ -9,9 +9,49 @@ def greet(image, in_contrast, in_brightness):
     # https://docs.opencv.org/4.x/d3/dc1/tutorial_basic_linear_transform.html
     new_image = cv2.convertScaleAbs(image, alpha=in_contrast, beta=in_brightness)
 
+    # We create our gabor filters, and then apply them to our image
+    gfilters = create_gaborfilter()
+    new_image = apply_filter(new_image, gfilters)
+ 
     return new_image
     
+def create_gaborfilter():
+    # This function is designed to produce a set of GaborFilters 
+    # an even distribution of theta values equally distributed amongst pi rad / 180 degree
+     
+    filters = []
+    num_filters = 16
+    ksize = 35  # The local area to evaluate
+    sigma = 3.0  # Larger Values produce more edges
+    lambd = 10.0
+    gamma = 0.5
+    psi = 0  # Offset value - lower generates cleaner results
+    for theta in np.arange(0, np.pi, np.pi / num_filters):  # Theta is the orientation for edge detection
+        kern = cv2.getGaborKernel((ksize, ksize), sigma, theta, lambd, gamma, psi, ktype=cv2.CV_64F)
+        kern /= 1.0 * kern.sum()  # Brightness normalization
+        filters.append(kern)
+    return filters
 
+def apply_filter(img, filters):
+# This general function is designed to apply filters to our image
+     
+    # First create a numpy array the same size as our input image
+    newimage = np.zeros_like(img)
+     
+    # Starting with a blank image, we loop through the images and apply our Gabor Filter
+    # On each iteration, we take the highest value (super impose), until we have the max value across all filters
+    # The final image is returned
+    depth = -1 # remain depth same as original image
+     
+    for kern in filters:  # Loop through the kernels in our GaborFilter
+        image_filter = cv2.filter2D(img, depth, kern)  #Apply filter to image
+         
+        # Using Numpy.maximum to compare our filter and cumulative image, taking the higher value (max)
+        np.maximum(newimage, image_filter, newimage)
+    return newimage
+
+
+    
 demo = gradio.Interface(
     fn=greet,
     inputs=['image', gradio.Slider(1,3), gradio.Slider(0, 100)],