app.py

import gradio 
import cv2


    
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

def greet(image, in_contrast=1.15, in_brightness=20):
    in_contrast = float(in_contrast)
    in_brightness = float(in_brightness)
    # contrast [1.0-3.0]
    # brightness [0-100]
    # 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
    
iface = gradio.Interface(
    fn=greet,
    inputs=['image', gradio.Slider(1,3), gradio.Slider(0, 100)],
    outputs=['image'],
    examples=[['detail_with_lines_and_noise.jpg']],
)
iface.launch()