def fill_cornea(edge_image):
k1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 10))
res1 = cv2.morphologyEx(edge_image, cv2.MORPH_DILATE, k1)
print("DILATED")
#Loader.print_image(res1)
enhance_black = smooth.min_filter(res1, 7)
print("BLACk")
#Loader.print_image(enhance_black)
k2 = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 5))
res2 = cv2.morphologyEx(enhance_black, cv2.MORPH_ERODE, k2)
print("eroded")
#Loader.print_image(res2)
res3 = smooth.max_filter(res2, 3)
res3 = smooth.gaussian(res3, 1.5)
print("dilated")
#Loader.print_image(res3)
return res3
def denoising_comparison(input_image, hist: bool = False):
# NON LOCAL MEAN DENOISING
nl_means_denoised_img = smooth.denoising_NlMeans(input_image)
# MEDIAN FILTER DENOISING
mean_denoised_img = smooth.median_filter(input_image, 9)
mean_denoised_img = smooth.median_filter(input_image, 9)
# GAUSSIAN DENOISING
gaussian_denoised = smooth.gaussian(input_image, 1.5)
# MINIMUM FILTER
minimum_denoised = smooth.min_filter(input_image, (5, 5))
# MAXIMUM FILTER
maximum_denoised = smooth.max_filter(input_image, (5, 5))
denoised_imgs = [
img, gaussian_denoised, mean_denoised_img, nl_means_denoised_img,
minimum_denoised, maximum_denoised
]
denoised_titles = [
"Original", "Denoised Gaussian", "Median Filtered", "NL Means Filter",
"Minimums Filter", "Maximums Filter"
]
Loader.hist_compare(denoised_imgs, denoised_titles, hist)
return denoised_imgs, denoised_titles
