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androidopencvedge-detection

Edge detection on colored background using OpenCV

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

private Mat edgeDetection(Mat src) {
    Mat edges = new Mat();
    Imgproc.cvtColor(src, edges, Imgproc.COLOR_BGR2GRAY);
    Imgproc.GaussianBlur(edges, edges, new Size(5, 5), 0);
    Imgproc.Canny(edges, edges, 10, 30);
    return edges;
}

And then I can find the document from this edges by finding largest contour from this.

My problem is I can find the document from following pic:

enter image description here

but not from following pic:

enter image description here

How can I improve this edge detection?

Solution

  • 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:

    enter image description here

    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

    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.

    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.

    This is the first result:

    enter image description here

    This is the second result:

    enter image description here

    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)