Opencv find Contours on image

Question

I writing a code that detect book on image. First step is find the contour on the image but i have problem with some books. Sometimes I can't detect the contours correctly ( A book is a rectangle so just find 4 contours) beacouse the line is not appointed correctly and i have gap beatwen them as show on image. Is there a way to extend the detected edges ?

This is my code:

imgg = cv2.imread('\book.jpg')

gray = cv2.cvtColor(imgg, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(gray, 11, 17, 17)
edged = cv2.Canny(gray , 10, 250)


(cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
total = 0

#binary = cv2.bitwise_not(gray)

for c in cnts:

    area = cv2.contourArea(c)

    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.03 * peri, True)

    if (len(approx) == 4) and (area > 100000):

        cv2.drawContours(imgg, [approx], -1, (0, 255, 0), 4)

cv2.imshow('image',imgg)
cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

Edge detection often works poorly. Here you discard highly relevant information, which is the color contrast.

Binarization of the saturation component will be much more effective.

Answer 2

Here is a quick example of Thresholding, please remember to have a test.png in the same folder as the following script. Use it before applying findContours, should be a significant improvement. Otherwise google Otsu's Binarization .

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
img = cv.imread('test.png',0)
img = cv.medianBlur(img,5)
ret,th1 = cv.threshold(img,127,255,cv.THRESH_BINARY)
th2 = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C,\
            cv.THRESH_BINARY,11,2)
th3 = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,\
            cv.THRESH_BINARY,11,2)
titles = ['Original Image', 'Global Thresholding (v = 127)',
            'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
images = [img, th1, th2, th3]
for i in xrange(4):
    plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])
plt.show()