def process_image(img):
# Loader.print_image(img)
# print("[DEBUG] Showing VISUAL denoising algorithm comparison")
# denoising_comparison(img)
# print("[DEBUG] Showing HISTOGRAM denoising algorithm comparison")
# denoising_comparison(img, True)
# DENOISING IMAGE
denoised_img = smooth.median_filter(img, 9)
denoised_img = smooth.median_filter(denoised_img, 7)
# PRINT DENOISED IMAGE AND HISTOGRAM
#Loader.print_image(denoised_img)
# Loader.hist_and_cumsum(denoised_img)
# thresholding_comparison(denoised_img)
th_img = thresh.apply_thresholding_algorithm(denoised_img,
thresh.THRESH_TRIANGLE)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 5))
stretched = cv2.morphologyEx(th_img, cv2.MORPH_ERODE, kernel)
back, front = thresh.get_regions(denoised_img, stretched)
# Loader.hist_compare([back, front], ["Back", "Front"])
#Loader.print_image(front)
eq = Loader.equalization(front.astype("uint8"))
eq = bright_and_contrast(eq, 2.8, 80)
eq = smooth.gaussian(eq, 2.5)
Loader.print_image(eq)
# Loader.hist_and_cumsum(eq)
# EDGE DETECTION
#edgesFunctions(eq) # Comparison of different edge detection method
edges = edg.laplacian_of_gaussian(eq, 2)
Loader.print_image(edges)
# Fill the cornea area with white pixels
dilated = fill_cornea(edges)
#Loader.print_image(dilated)
#Calculate distances in the cornea-lens region
#lineas = dw.find_vertical_lines(dilated)
#diferencias,posiciones = dw.calculate_differences(lineas)
#dw.draw_graph_distance(diferencias, posiciones)
#output_image = dw.lines_image(lineas, img)
# Surround the córnea area and lens edges with visible and thin line
(i, contornos, jerarquia) = cv2.findContours(dilated, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = []
for c in contornos:
if cv2.contourArea(c) < 1000:
continue
else:
cnts.append(c)
cv2.drawContours(img, cnts, -1, (0, 0, 255), 3)
#Loader.print_image(img)
return img
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
return outputCanvas
if __name__ == '__main__':
print("[DEBUG] Load image from local sources")
# img = Loader.load_image("test/th.jpeg")
# Loader.print_image(stretched)
# Loader.print_image(smoothed_thresholded)
# 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":
