test.py

import cv2
import numpy as np

I= cv2.imread('radiologie-hirntumor-vor-nach-op.jpg')
#I= cv2.imread('detect.jpg')
#I= cv2.imread('tumeur2.jpg')
#cv2.waitKey(0)

print('shape of  ',I.shape)
scale_percent = 50 # percentage taille de l'image original
width = int(I.shape[1] * scale_percent / 100)
height = int(I.shape[0] * scale_percent / 100)
dim = (width, height)

# resize image ---------------------------------------------------------------------------------
IR = cv2.resize(I, dim, interpolation = cv2.INTER_AREA)
cv2.imshow("Resized image",IR)
cv2.waitKey(0)

#reduction de bruiiiiiiiiiiiiiiiiiiiiiiiiiit
IR=cv2.medianBlur(src=IR, ksize= 9)

# binarized image -------------------------------------------------------------------------------------
th, I_th = cv2.threshold(IR, 156 , 255 , cv2.THRESH_BINARY)
# 'for second brain tumour image '   th, I_th = cv2.threshold(IR, 136 , 255 , cv2.THRESH_BINARY)
print('value of th is  : ',th)
#cv2.imshow('image binarized',I_th)

#edged image-------------------------------------------------------------------------------
edged = cv2.Canny(I_th , 30 , 200)
#finding contours--------------------------------------------------------------
contours, hierarchy = cv2.findContours(edged, 
    cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
#cv2.imshow('Canny Edges After Contouring', edged)
#cv2.waitKey(0)

print("Number of Contours found = " + str(len(contours)))
#drawing contours --------------------------------------------------------------------
cv2.drawContours(IR, contours ,-1, (0, 255, 0), 2)

kernel3 = np.array([[0, -1,  0],
                [-1,  5, -1],
                    [0, -1,  0]])
sharp_img = cv2.filter2D(src=IR, ddepth=-1, kernel=kernel3)

cv2.imshow('final image with Contours', IR)
cv2.waitKey(0)
#  choisir une des deux images sharp_img ou IR-----------------------------------

#cv2.imshow('final image with Contours 2', sharp_img)
#cv2.waitKey(0)