import cv2
import numpy as np
from matplotlib import pyplot as plt


img = cv2.imread('80nm_updated/4_2.PNG', cv2.IMREAD_GRAYSCALE)
mask = np.zeros(img.shape, dtype=np.uint8)

h = mask.shape[0]
w = mask.shape[1]

for y in range(0, h):
    for x in range(0, w):
        if x > 18 and y > 18 and x < 30 and y < 30:  mask[y, x] = 255

clahe = cv2.createCLAHE(clipLimit=15, tileGridSize=(5, 5))

clahe2 = cv2.createCLAHE(clipLimit=25, tileGridSize=(1, 1))

kernal = np.ones((5, 5), np.uint8)

median = cv2.GaussianBlur(img, (3, 3), 3)  #
heq = median
median2 = cv2.medianBlur(heq, 3)
heq2 = clahe2.apply(median2)
median3 = cv2.GaussianBlur(heq2, (3, 3), 5)
median3 = cv2.medianBlur(median3, 3)

median3 = clahe.apply(median3)

_, thersh = cv2.threshold(median3, 228, 255, cv2.THRESH_BINARY)

maskedThresh = cv2.bitwise_and(thersh,thersh,mask=mask)

nlabels, labels, stats, centroids = cv2.connectedComponentsWithStats(maskedThresh)
sizes = stats[1:, -1]; nlabels = nlabels - 1
min_size = 5
filteredImg = np.zeros((labels.shape))
for i in range(0, nlabels):
    if sizes[i] >= min_size:
        filteredImg[labels == i + 1] = 255

titles = ['image','median','Heq','median2','heq2','median3','threshold','Final']
images = [img,median,heq,median2,heq2,median3,maskedThresh,filteredImg]

n_white_pix = np.sum(filteredImg == 255)
print("Positive" if n_white_pix>1 else "Negative")


for i in range(len(images)):
    plt.subplot(2, 4, i+1), plt.imshow(images[i], 'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])

plt.show()