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
# Loader.print_image(front)
for i in np.arange(1, 13):
img = Loader.load_image("im" + i.__str__() + ".jpeg")
print("Loaded image " + "im" + i.__str__() + ".jpeg")
# DENOISING IMAGE
denoised_img = smooth.median_filter(img, 9)
denoised_img = smooth.median_filter(denoised_img, 7)
# thresholding_comparison(denoised_img)
th_img = thresh.apply_thresholding_algorithm(denoised_img,
thresh.THRESH_TRIANGLE)
#Suavizamos los bordes marcados por la segmentacion
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 5))
stretched = cv2.morphologyEx(th_img, cv2.MORPH_ERODE, kernel)
smoothed_thresholded = smooth.max_filter(stretched, 5)
#Obtenemos las areas correspondientes a la imagen original, despues del segmentado
back, front = thresh.get_regions(denoised_img, stretched)
IMAGE_TYPE_CLASSIFICATION = define_image_properties(
smoothed_thresholded)
if IMAGE_TYPE_CLASSIFICATION == "A":
# RESOLUTION 1
print("Executing custom resolution, mode 1")
# EDGE DETECTION
#eq = Loader.equalization(front.astype("uint8"))
eq = Loader.bright_and_contrast(front.astype("uint8"), 1.0, 20)
eq = smooth.gaussian(eq, 1.5)
#Loader.print_image(eq)
edges = edg.laplacian_of_gaussian(eq, 2)
#Loader.print_image(edges)