from Image import ImageObject
from Histogram import rgb_histogram
path = "images/"
image = ImageObject(path + "eu.png")
image.load_image()
#image.set_size(512, 512)
sample_grid = int(input("Enter an even value for sample grid: "))
while sample_grid % 2 != 0:
sample_grid = int(input("Enter an even value for sample grid: "))
level = int(input("Enter an even value for levels of RGB: "))
while level % 2 != 0:
level = int(input("Enter an even value for levels of RGB: "))
image.discretize(sample_grid)
image.quantize(level)
rgb_histogram(image)
image.save_image(
path + f"color/color_quantize{level}_discretize{sample_grid}_cat.png")
image.show_image()
from Image import ImageObject from Histogram import gray_histogram from copy import deepcopy import matplotlib.pylab as plt path = "images/" image = ImageObject(path + "greyscale.png") image.load_image() image.set_size(512, 512) image.set_image_to_gray() image.save_image(path + "pb.png") old_image = deepcopy(image) image.equalization() image.save_image(path + "equalization/equalization.png") old_image.show_image() gray_histogram(old_image, 'r') image.show_image() gray_histogram(image, 'gray') plt.show()
from Image import ImageObject from Histogram import gray_histogram from copy import deepcopy import matplotlib.pylab as plt path = "images/" image = ImageObject(path + "greyscale.png") image.load_image() image.set_size(512, 512) image.set_image_to_gray() image.save_image(path + "pb.png") old_image = deepcopy(image) image.stretch(0.001) image.save_image(path + "stretch/normal_stretch.png") image.show_image() gray_histogram(image, 'b') image.stretch(0.125) image.save_image(path + "stretch/stretch25.png") old_image.show_image() gray_histogram(old_image, 'r') image.show_image() gray_histogram(image, 'gray') plt.show()
from Image import ImageObject
from Histogram import gray_histogram
from copy import deepcopy
import matplotlib.pylab as plt
path = "images/"
image = ImageObject(path + "greyscale.png")
image.load_image()
image.set_size(512, 512)
image.set_image_to_gray()
image.save_image(path + "pb.png")
old_image = deepcopy(image)
print("""What operation do you want to perform?
1.Sum
2.Subtraction
3.Multiplication
4.Division""")
option = 0
while not int(option) in range(1, 5):
option = int(input(""))
highlight = float(input("Enter a highlight value: "))
if option == 1:
image.linearSum(highlight)
name = "linears/sum_h"
image.save_image(path + name + f"{int(highlight)}.png")
if option == 2:
image.linearSub(highlight)
name = "linears/sub_h"
image.save_image(path + name + f"{int(highlight)}.png")
from Image import ImageObject
import numpy as np
path = "images/"
image = ImageObject(path + "greyscale.png")
image.load_image()
image.set_size(512, 512)
image.set_image_to_gray()
image.save_image(path + "pb.png")
weighted_average = [[1.5, 2.0, 2.5], [2.0, 4.0, 2.0], [1.5, 2.0, 1.5]]
laplace = [[0.0, -1.0, 0.0], [-1.0, 4.0, -1.0], [0.0, -1.0, 0.0]]
sharpen = [[-1.0, -1.0, -1.0], [-1.0, 9.0, -1.0], [-1.0, -1.0, -1.0]]
border_detection = [[-1.0, -1.0, -1.0], [-1.0, 8.0, -1.0], [-1.0, -1.0, -1.0]]
sobel_a = np.square([[-1.0, -2.0, -1.0], [0.0, 0.0, 0.0],
[1.0, 2.0, 1.0]]) #primeira matriz elevada ao quadrado
sobel_b = np.square([[-1.0, 0.0, -1.0], [-2.0, 0.0, 2.0],
[-1.0, 0.0, 1.0]]) #segunda matriz elevada ao quadrado
final_sobel = np.sqrt(
sobel_a +
sobel_b) #resultado final é a raiz da soma das matrizes anteriores
def saturate(value):
if value > 255:
return 255
if value < 0:
return 0
return value
from Image import ImageObject
from Histogram import gray_histogram
path = "images/"
image = ImageObject(path + "cat.png")
image.load_image()
image.set_size(512, 512)
image.set_image_to_gray()
image.save_image(path + "_pb.png")
sample_grid = int(input("Enter an even value for sample grid: "))
while sample_grid % 2 != 0:
sample_grid = int(input("Enter an even value for sample grid: "))
level = int(input("Enter an even value for levels of gray: "))
while level % 2 != 0:
level = int(input("Enter an even value for levels of gray: "))
image.gray_discretize(sample_grid)
image.gray_quantize(level)
gray_histogram(image)
image.save_image(path +
f"/gray/gray_quantize{level}_discretize{sample_grid}_cat.png")
image.show_image()
from Image import ImageObject from Histogram import gray_histogram from copy import deepcopy import matplotlib.pylab as plt path = "images/" image = ImageObject(path + "greyscale.png") image.load_image() image.set_size(512, 512) image.set_image_to_gray() image.save_image(path + "pb.png") old_image = deepcopy(image) image.transformation() image.save_image(path + "linears/transformed.png") old_image.show_image() gray_histogram(old_image, 'r') image.show_image() gray_histogram(image, 'gray') plt.show()