Edge detection on colored background using OpenCV

Question

I am using following code to detect edges from given document.

private Mat edgeDetection(Mat src) {
    Mat edges = new Mat();
    Imgproc.cvtColor(src, edge         


        

Answer 1

In OpenCV there is function called dilate this will darker the lines. so try the code like below.

private Mat edgeDetection(Mat src) {
    Mat edges = new Mat();
    Imgproc.cvtColor(src, edges, Imgproc.COLOR_BGR2GRAY);
    Imgproc.dilate(edges, edges, Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(10, 10)));
    Imgproc.GaussianBlur(edges, edges, new Size(5, 5), 0);
    Imgproc.Canny(edges, edges, 15, 15 * 3);
    return edges;
}

Answer 2

I use Python, but the main idea is the same.

If you directly do cvtColor: bgr -> gray for img2, then you must fail. Because the gray becames difficulty to distinguish the regions:

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Related answers:

1. How to detect colored patches in an image using OpenCV?
2. Edge detection on colored background using OpenCV
3. OpenCV C++/Obj-C: Detecting a sheet of paper / Square Detection

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In your image, the paper is white, while the background is colored. So, it's better to detect the paper is Saturation(饱和度) channel in HSV color space. For HSV, refer to https://en.wikipedia.org/wiki/HSL_and_HSV#Saturation.

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Main steps:

1. Read into BGR
2. Convert the image from bgr to hsv space
3. Threshold the S channel
4. Then find the max external contour(or do Canny, or HoughLines as you like, I choose findContours), approx to get the corners.

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This is the first result:

This is the second result:

The Python code(Python 3.5 + OpenCV 3.3):

#!/usr/bin/python3
# 2017.12.20 10:47:28 CST
# 2017.12.20 11:29:30 CST

import cv2
import numpy as np

##(1) read into  bgr-space
img = cv2.imread("test2.jpg")

##(2) convert to hsv-space, then split the channels
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(hsv)

##(3) threshold the S channel using adaptive method(`THRESH_OTSU`) or fixed thresh
th, threshed = cv2.threshold(s, 50, 255, cv2.THRESH_BINARY_INV)

##(4) find all the external contours on the threshed S
cnts = cv2.findContours(threshed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
canvas  = img.copy()
#cv2.drawContours(canvas, cnts, -1, (0,255,0), 1)

## sort and choose the largest contour
cnts = sorted(cnts, key = cv2.contourArea)
cnt = cnts[-1]

## approx the contour, so the get the corner points
arclen = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.02* arclen, True)
cv2.drawContours(canvas, [cnt], -1, (255,0,0), 1, cv2.LINE_AA)
cv2.drawContours(canvas, [approx], -1, (0, 0, 255), 1, cv2.LINE_AA)

## Ok, you can see the result as tag(6)
cv2.imwrite("detected.png", canvas)