使用 OpenCV 在彩色背景上进行边缘检测

Edge detection on colored background using OpenCV

我正在使用以下代码检测给定文档的边缘。

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;
}

然后我可以从这个 edges 中找到最大的轮廓来找到文档。

我的问题是我可以从下面的图片中找到文档:

但不是来自以下图片:

如何改进此边缘检测?

在 OpenCV 中有一个名为 dilate 的函数,这会使线条变暗。所以试试下面的代码。

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;
}

我用的是Python,但是主要思路是一样的

如果你直接对img2做cvtColor: bgr -> gray,那你一定会失败。因为灰色变得难以区分区域:


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在您的图像中,纸张为 white,而背景为 colored。所以,最好检测论文是HSV color space中的Saturation(饱和度)频道。 HSV参考https://en.wikipedia.org/wiki/HSL_and_HSV#Saturation.


主要步骤:

  1. 读入BGR
  2. 将图像从 bgr 转换为 hsv space
  3. S 通道阈值
  4. 然后求最大外轮廓(或者做Canny,或者HoughLines,我选findContours),近似得到角点。

这是第一个结果:

这是第二个结果:

Python代码(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)