How to whiten background and blaken grid in a same image - opencv

Question

I have an image like this. I wan to use HoughLine detection but the image is too dark to recognize the line. Is there a way that can whiten the background and blacken the grid? Is there any algorithms in openCV or python that I can apply? Thank you

Answer 1

I try to dilate the image first, then medianBlur it, so I get the background. Use the original gray image to sub the background, I get the frontground ( that is the grids). Then do some other steps, I get the result like this.
The code is as follow:
#!/usr/bin/python3
# 2017.10.04 19:37:43 CST
filename = "data/paper.png"
img = cv2.imread(filename)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
## do morph-dilate-op
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
dilated = cv2.morphologyEx(gray, cv2.MORPH_DILATE, kernel)
diff1 = 255 - cv2.subtract(dilated, gray)
## do medianBlur
median = cv2.medianBlur(dilated, 15)
diff2 = 255 - cv2.subtract(median, gray)
## do normalize
normed = cv2.normalize(diff2,None, 0, 255, cv2.NORM_MINMAX )
## save the result
dst = np.hstack((gray, normed))
cv2.imwrite("result_paper1.png", dst)
res = np.hstack((gray,dilated, diff1, median, diff2, normed))
cv2.imwrite("result_paper2.png", res)

Answer 2

You should try a form of localized adaptive thresholding.
In OpenCV this is called cv2.adaptiveThreshold.
See here: http://docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html for a python example.
The code (from the source above):
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('sudoku.png',0)
img = cv2.medianBlur(img,5)
ret,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
cv2.THRESH_BINARY,11,2)
th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
cv2.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 range(4):
plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
plt.show()

Answer 3

Your image has poor contrast and inconsistent light. You have to make some preprocessing (c++ code here):
cv::Mat img = cv::imread("E:\\Workspace\\KS\\excercise\\oBwBH.jpg", 0);
cv::Mat workingMat;
cv::GaussianBlur(img, workingMat, cv::Size(101, 101), 31, 31); //high blur to extract background light
img = img - 0.7*work; //adjust light level
cv::normalize(img, img, 0, 255, cv::NORM_MINMAX); \\use whole range
cv::medianBlur(img, img, 5); \\remove noise
cv::Canny(img, work, 100, 200); \\extract lines; you could do hough lines instead since it has canny inside.
Results (from left to right, canny has all lines, in preview there is some compression issue):