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clustering.py

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+import csv
+import cv2
+import glob
+import numpy as np
+import os
+import pandas as pd
+from matplotlib import pyplot as plt
+from PIL import Image
+from sklearn.cluster import DBSCAN
+
+
+def brainExtraction():
+    print("Brain Extraction Started")
+    cwd = os.getcwd()
+    pathToDataFolder = cwd+'\\testPatient'
+    task1 = '\Slices'
+    pathToTask1 = cwd+task1
+    if not os.path.exists(pathToTask1):
+        os.makedirs(pathToTask1)
+
+    for globalImage in glob.glob('%s\*_thresh.png' % pathToDataFolder):
+        words = globalImage.split('_')
+        words_len = len(words)
+        global_image_number = words[words_len-2]
+        org_img = cv2.imread(globalImage)
+        img_gray = cv2.cvtColor(org_img, cv2.COLOR_BGR2GRAY)
+        template = cv2.imread('template.png',0)
+        w, h = template.shape[::-1]
+
+        res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)
+        threshold = 0.8
+        loc = np.where( res >= threshold)
+        xstart = -1
+        xdiff = -10
+        ystart = -1
+        ydiff = -10
+
+        for pt in zip(*loc[::-1]):
+            xtemp = pt[0]
+            ytemp = pt[1]+h
+            if(xstart == -1):
+                xstart = xtemp
+            if(ystart == -1):
+                ystart = ytemp
+            if(xdiff == -10 and xstart != xtemp):
+                xdiff = xtemp - xstart
+            if(ydiff == -10 and ystart != ytemp):
+                ydiff = ytemp - ystart
+            if(xstart != -1 and ystart != -1 and xdiff != -10 and ydiff != -10):
+                break
+
+        pathToEachImageFolder = pathToTask1+"\\"+str(global_image_number)
+        if not os.path.exists(pathToEachImageFolder):
+            os.makedirs(pathToEachImageFolder)
+        os.chdir(pathToEachImageFolder)
+
+        img1 = Image.open(globalImage)
+        width, height = img1.size
+        loc_img_count = 0
+
+        for y0 in range(ystart, height, ydiff):
+            for x0 in range(xstart, width, xdiff):
+                if(x0+xdiff < width and y0+ydiff < height):
+                    box = (x0+5, y0,
+                        x0+xdiff if x0+xdiff < width else  width,
+                        y0+ydiff if y0+ydiff < height else height)
+                    img2 = img1.crop(box)
+                    extrema = img2.convert("L").getextrema()
+                    if extrema != (0, 0):
+                        loc_img_count = loc_img_count + 1
+                        img1.crop(box).save('%d.png' % loc_img_count)
+        os.chdir(cwd)
+    print("Brain Extraction Ended")
+
+def detectClusters():
+    print("Detecting Clusters Started")
+    cwd = os.getcwd()
+    task1 = '\Slices'
+    pathToTask1 = cwd+task1
+    task2 = '\Clusters'
+    pathToTask2 = cwd+task2
+    if not os.path.exists(pathToTask2):
+        os.makedirs(pathToTask2)
+
+    folderNum = 0
+    while 1:
+        folderNum = folderNum+1
+        pathToImageFolder = pathToTask1+"\\"+str(folderNum)
+        if os.path.exists(pathToImageFolder):
+            pathToEachImageFolder = pathToTask2+"\\"+str(folderNum)
+            if not os.path.exists(pathToEachImageFolder):
+                os.makedirs(pathToEachImageFolder)
+            os.chdir(pathToEachImageFolder)
+
+            if os.path.exists("CountReport.csv"):
+                os.remove("CountReport.csv")
+            f = open('CountReport.csv', 'w')
+            writer = csv.writer(f, delimiter = ',')
+            writer.writerow(["SliceNumber", "ClusterCount"])
+
+            for images in glob.glob('%s\*.png' % pathToImageFolder):
+                imageBaseName = os.path.basename(images)
+                words = imageBaseName.split('.')
+                imageNumber = words[0]
+
+                img = cv2.imread(images)
+                img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+                h, s, v = cv2.split(img_hsv)
+                cluster = cv2.threshold(s, 92, 255, cv2.THRESH_BINARY)[1]
+                imageName = imageNumber + '.png'
+                cv2.imwrite(imageName, cluster)
+
+                Y, X = np.where(cluster == 255)
+                zipped = np.column_stack((X,Y))
+                cnt=0
+
+                if len(zipped) > 0:
+                    clustering = DBSCAN(eps = 5, min_samples = 5).fit(zipped)
+                    labels=clustering.labels_
+                    ls, cs = np.unique(labels, return_counts = True)
+                    dic = dict(zip(ls, cs))
+                    idx = [i for i, label in enumerate(labels) if dic[label] > 135 and label >= 0]
+
+                    counts = np.bincount(labels[labels >= 0])
+                    for i in counts:
+                        if i > 135:
+                            cnt += 1
+                    #print(imageNumber, cnt)
+                    writer.writerow([imageNumber, cnt])
+                else:
+                    #print(imageNumber, cnt)
+                    writer.writerow([imageNumber, cnt])
+            f.close()
+            df = pd.read_csv("CountReport.csv")
+            df.sort_values(by=["SliceNumber"], axis = 0, ascending = True, inplace = True)
+            os.remove("CountReport.csv")
+            df.to_csv('CountReport.csv', index=False)
+        else:
+            break
+        os.chdir(cwd)
+    print("Detecting Clusters Ended")

test.py

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+from clustering import *
+
+brainExtraction()
+detectClusters()