import cv2
import numpy as np
import os
import csv

countAll=0
countNeg=0

for filename in os.listdir("80nm_updated/80nm_dataset_2_mixedexperiments/80nm_dataset_2_mixedexperiments/train/pos"):
    if filename.endswith(".PNG") or filename.endswith(".png"):

        img = cv2.imread('80nm_updated/80nm_dataset_2_mixedexperiments/80nm_dataset_2_mixedexperiments/train/pos/'+filename, 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>12 and y>12 and x<36 and y<36:  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), 1)
        heq = median
        median2 = cv2.medianBlur(heq, 3)
        heq2 = clahe2.apply(median2)
        median3 = cv2.GaussianBlur(heq2, (3, 3), 1)
        median3 = cv2.medianBlur(median3, 5)


        median3 = clahe.apply(median3)


        _, thersh = cv2.threshold(median3, 230, 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 = 10
        filteredImg = np.zeros((labels.shape))
        for i in range(0, nlabels):
            if sizes[i] >= min_size:
                filteredImg[labels == i + 1] = 255

        n_white_pix = np.sum(filteredImg == 255)
        result="Positive" if n_white_pix>1 else "Negative"
        # print(result)
        countAll=countAll+1
        if n_white_pix>1:
            countNeg=countNeg+1
    else:
        continue
msg = "train_dataset2: Positive: %f" % float(((countNeg/countAll)*100))
msg += "%"
print(msg)


countAll=0
countNeg=0
for filename in os.listdir("80nm_updated/80nm_dataset_1_splitbyexperiment/80nm_dataset_1_splitbyexperiment/train/pos"):
    if filename.endswith(".PNG") or filename.endswith(".png"):

        img = cv2.imread('80nm_updated/80nm_dataset_1_splitbyexperiment/80nm_dataset_1_splitbyexperiment/train/pos/'+filename, 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>12 and y>12 and x<36 and y<36:  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), 1)
        heq = median
        median2 = cv2.medianBlur(heq, 3)
        heq2 = clahe2.apply(median2)
        median3 = cv2.GaussianBlur(heq2, (3, 3), 1)
        median3 = cv2.medianBlur(median3, 5)


        median3 = clahe.apply(median3)


        _, thersh = cv2.threshold(median3, 230, 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 = 10
        filteredImg = np.zeros((labels.shape))
        for i in range(0, nlabels):
            if sizes[i] >= min_size:
                filteredImg[labels == i + 1] = 255

        n_white_pix = np.sum(filteredImg == 255)
        result="Positive" if n_white_pix>1 else "Negative"
        # print(result)
        countAll=countAll+1
        if n_white_pix>1:
            countNeg=countNeg+1
    else:
        continue
msg = "train_dataset1: Positive: %f" % float(((countNeg/countAll)*100))
msg += "%"
print(msg)


countAll=0
countNeg=0
for filename in os.listdir("80nm_updated/80nm_dataset_2_mixedexperiments/80nm_dataset_2_mixedexperiments/train/neg"):
    if filename.endswith(".PNG") or filename.endswith(".png"):

        img = cv2.imread('80nm_updated/80nm_dataset_2_mixedexperiments/80nm_dataset_2_mixedexperiments/train/neg/'+filename, 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>12 and y>12 and x<36 and y<36:  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), 1)
        heq = median
        median2 = cv2.medianBlur(heq, 3)
        heq2 = clahe2.apply(median2)
        median3 = cv2.GaussianBlur(heq2, (3, 3), 1)
        median3 = cv2.medianBlur(median3, 5)


        median3 = clahe.apply(median3)


        _, thersh = cv2.threshold(median3, 230, 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 = 10
        filteredImg = np.zeros((labels.shape))
        for i in range(0, nlabels):
            if sizes[i] >= min_size:
                filteredImg[labels == i + 1] = 255

        n_white_pix = np.sum(filteredImg == 255)
        result="Positive" if n_white_pix>1 else "Negative"
        # print(result)
        countAll=countAll+1
        if n_white_pix<=1:
            countNeg=countNeg+1
    else:
        continue
msg = "train_dataset2: Negative: %f" % float(((countNeg/countAll)*100))
msg += "%"
print(msg)


countAll=0
countNeg=0
for filename in os.listdir("80nm_updated/80nm_dataset_1_splitbyexperiment/80nm_dataset_1_splitbyexperiment/train/neg"):
    if filename.endswith(".PNG") or filename.endswith(".png"):

        img = cv2.imread('80nm_updated/80nm_dataset_1_splitbyexperiment/80nm_dataset_1_splitbyexperiment/train/neg/'+filename, 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>12 and y>12 and x<36 and y<36:  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), 1)
        heq = median
        median2 = cv2.medianBlur(heq, 3)
        heq2 = clahe2.apply(median2)
        median3 = cv2.GaussianBlur(heq2, (3, 3), 1)
        median3 = cv2.medianBlur(median3, 5)


        median3 = clahe.apply(median3)


        _, thersh = cv2.threshold(median3, 230, 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 = 10
        filteredImg = np.zeros((labels.shape))
        for i in range(0, nlabels):
            if sizes[i] >= min_size:
                filteredImg[labels == i + 1] = 255

        n_white_pix = np.sum(filteredImg == 255)
        result="Positive" if n_white_pix>1 else "Negative"
        # print(result)
        countAll=countAll+1
        if n_white_pix<=1:
            countNeg=countNeg+1
    else:
        continue
msg = "train_dataset1: Negative: %f" % float(((countNeg/countAll)*100))
msg += "%"
print(msg)


countAll=0
countCorrect=0
for filename in os.listdir("80nm_updated/80nm_dataset_1_splitbyexperiment/80nm_dataset_1_splitbyexperiment/test"):
    if filename.endswith(".PNG") or filename.endswith(".png"):

        img = cv2.imread('80nm_updated/80nm_dataset_1_splitbyexperiment/80nm_dataset_1_splitbyexperiment/test/'+filename, 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>12 and y>12 and x<36 and y<36:  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), 1)
        heq = median
        median2 = cv2.medianBlur(heq, 3)
        heq2 = clahe2.apply(median2)
        median3 = cv2.GaussianBlur(heq2, (3, 3), 1)
        median3 = cv2.medianBlur(median3, 5)


        median3 = clahe.apply(median3)


        _, thersh = cv2.threshold(median3, 230, 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 = 10
        filteredImg = np.zeros((labels.shape))
        for i in range(0, nlabels):
            if sizes[i] >= min_size:
                filteredImg[labels == i + 1] = 255

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

        with open(
                '80nm_updated/80nm_dataset_1_splitbyexperiment/80nm_dataset_1_splitbyexperiment/annotations_test.csv') as file:
            reader = csv.reader(file)
            for row in reader:
                if row[0].split("/")[2] == filename:
                    # print(row[0].split("/")[2] + " : " + row[1])
                    if (row[1]=="0" and result=="Negative") or (row[1]=="1" and result=="Positive"):
                        countCorrect=countCorrect+1

    else:
        continue
msg = "test_dataset1: Correct: %f" % float(((countCorrect/countAll)*100))
msg += "%"
print(msg)

countAll=0
countCorrect=0
for filename in os.listdir("80nm_updated/80nm_dataset_2_mixedexperiments/80nm_dataset_2_mixedexperiments/test"):
    if filename.endswith(".PNG") or filename.endswith(".png"):

        img = cv2.imread('80nm_updated/80nm_dataset_2_mixedexperiments/80nm_dataset_2_mixedexperiments/test/'+filename, 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>12 and y>12 and x<36 and y<36:  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), 1)
        heq = median
        median2 = cv2.medianBlur(heq, 3)
        heq2 = clahe2.apply(median2)
        median3 = cv2.GaussianBlur(heq2, (3, 3), 1)
        median3 = cv2.medianBlur(median3, 5)


        median3 = clahe.apply(median3)


        _, thersh = cv2.threshold(median3, 230, 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 = 10
        filteredImg = np.zeros((labels.shape))
        for i in range(0, nlabels):
            if sizes[i] >= min_size:
                filteredImg[labels == i + 1] = 255

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

        with open(
                '80nm_updated/80nm_dataset_2_mixedexperiments/80nm_dataset_2_mixedexperiments/annotations_test.csv') as file:
            reader = csv.reader(file)
            for row in reader:
                if row[0].split("/")[2] == filename:
                    # print(row[0].split("/")[2] + " : " + row[1])
                    if (row[1]=="0" and result=="Negative") or (row[1]=="1" and result=="Positive"):
                        countCorrect=countCorrect+1

    else:
        continue
msg = "test_dataset2: Correct: %f" % float(((countCorrect/countAll)*100))
msg += "%"
print(msg)