DIGIKEP

import cv2
import numpy as np

def addPointNoise(img, percentage, value):
    noise = np.copy(img)
    n = int(img.shape[0] * img.shape[1] * percentage)
    print(n)

    for k in range(1, n):
        i = np.random.randint(0, img.shape[1])
        j = np.random.randint(0, img.shape[0])

        if img.ndim == 2:
            noise[j, i] = value

        if img.ndim == 3:
            noise[j, i] = [value, value, value]

    return noise


def addSaltAndPepperNoise(img, percentage1, percentage2):
    n = addPointNoise(img, percentage1, 255) # Só
    n2 = addPointNoise(n, percentage2, 0) # Bors
    return n2


img = cv2.imread('Grape.jpg',cv2.IMREAD_GRAYSCALE);

imnoisegauss5x5 = cv2.GaussianBlur(img, (5, 5), sigmaX=2.0, sigmaY=2.0)

threshold, im_thresh = cv2.threshold(imnoisegauss5x5, 170, 255, cv2.THRESH_BINARY)

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
dst = cv2.dilate(im_thresh,kernel,iterations=1)
dst = cv2.erode(dst,kernel,iterations=2)
dst = cv2.dilate(dst,kernel,iterations=1)

dstinv = 255-dst

blurred = cv2.GaussianBlur(dstinv, (5, 5), 2.0)
edges = cv2.Canny(blurred, 100, 200, None, 5, True)

img_ocv_edge = cv2.cvtColor(img.copy(),cv2.COLOR_GRAY2BGR)
b, g, r = cv2.split(img_ocv_edge)
r = cv2.bitwise_and(r, ~edges)
g = cv2.bitwise_or(g, edges)
b = cv2.bitwise_and(b, ~edges)
img_ocv_edge = cv2.merge((b, g, r))

cv2.imwrite('done.jpg',img_ocv_edge)

cv2.imshow('img', img)
cv2.waitKey(0)
cv2.imshow('img', imnoisegauss5x5)
cv2.waitKey(0)
cv2.imshow('img', im_thresh)
cv2.waitKey(0)
cv2.imshow('img', dst)
cv2.waitKey(0)
cv2.imshow('img', dstinv)
cv2.waitKey(0)
cv2.imshow('img', edges)
cv2.waitKey(0)
cv2.imshow('img', img_ocv_edge)
cv2.waitKey(0)

cv2.destroyAllWindows()