bloodvesselsegmentationfinal.py

# -*- coding: utf-8 -*-
"""bloodvesselsegmentationfinal.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1ZrQR3EcvBOfmYmDmTekA8njpaoRvmVul
"""

# Commented out IPython magic to ensure Python compatibility.
import cv2
import numpy as np
# %matplotlib inline

from google.colab import files
dff = files.upload()

adv = cv2.imread('severe.tif',1) #read the image as colour

from google.colab.patches import cv2_imshow
sev = cv2.resize(adv,(250,200))
cv2_imshow(sev)

#use add weighted function - (img, alpha (contrast), img, beta (brightness), gamma
out = cv2.addWeighted( sev, 2, sev, 0,0)
cv2_imshow(out)

gray = cv2.cvtColor(out, cv2.COLOR_BGR2GRAY)
cv2_imshow(gray)

#cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst]) 
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,31,3)
cv2_imshow(thresh)

from google.colab import files
df = files.upload()

m = cv2.imread('0-35.tif',1)
m = cv2.resize(m,(250,200))
cv2_imshow(m)

outmild = cv2.addWeighted( m, 2.5,m, 0,0)
cv2_imshow(outmild)

#BGR to gray-scale
graym = cv2.cvtColor(outmild,cv2.COLOR_BGR2GRAY)
cv2_imshow(graym)

#apply adaptive thresholding 
thresh2 = cv2.adaptiveThreshold(graym, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,31,3)
cv2_imshow(thresh2)

img3 = cv2.hconcat([thresh2,thresh]) #horizontal concatenation 
cv2_imshow(img3) #display both images