import cv2
import numpy as np


img = cv2.imread('cebra.jpg', 0)
im = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
#gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#img = cv2.medianBlur(img,5)

th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
 cv2.THRESH_BINARY,11,2)
#cv2.imshow('Detector de Lineas', th2)
th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
 cv2.THRESH_BINARY,11,2)
#cv2.imshow('Detector de Lineas', th3)



ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
#cv2.imshow('Detector de Lineas', thresh1)
ret,thresh2 = cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV)
#cv2.imshow('Detector de Lineas', thresh2)
ret,thresh3 = cv2.threshold(img,127,255,cv2.THRESH_TRUNC+cv2.THRESH_OTSU)
#cv2.imshow('Detector de Lineas', thresh3)
ret,thresh4 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO)
#cv2.imshow('Detector de Lineas', thresh4)
ret,thresh5 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO_INV)
#cv2.imshow('Detector de Lineas', thresh5)





_, contours, h = cv2.findContours(thresh2, 1, 2)

cv2.drawContours(im, contours, -1, (0, 0, 255), -1 )
cv2.imshow('Detector de Lineas', im)
print(len(contours))

"""cnt  = contours
big_contour = []
max = 0
cont=0

for i in cnt:
    #if ((cv2.contourArea(i)) > 10):
    print("Contorno identificado")
    cont += 1
    #if(cont == 1):
    cv2.drawContours(im, i, -1, (0, 0, 255), -1 )

cv2.imshow('imagen', im)
#cv2.waitKey()"""

"""edges = cv2.Canny(thresh2, 150, 150, apertureSize = 3)


lines = cv2.HoughLines(edges, 1, np.pi/180, 80)
print(lines)

for line in lines:
    rho, theta = line[0]
    a = np.cos(theta)
    b = np.sin(theta)
    x0 = a*rho
    y0 = b*rho
    x1 = int(x0 + 1000*(-b))
    y1 = int(y0 + 1000*(a))
    x2 = int(x0 - 1000*(-b))
    y2 = int(y0 - 1000*(a))

    cv2.line(img, (x1,y1), (x2,y2), (0, 0, 255), 1, cv2.LINE_AA)"""

#cv2.imshow('Bordes de Imagen', edges)
#cv2.imshow('Detector de Lineas', img)
cv2.waitKey()
cv2.imwrite("cebra_11.jpg", img)