使用 OpenCV 对彩色背景进行边缘检测

问题描述

我正在使用以下代码检测给定文档的边缘.

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

然后我可以通过从中找到最大轮廓来从这个 edges 中找到文档.

我的问题是我可以从以下图片中找到文档:

但不是来自以下图片:

如何改进这种边缘检测?

解决方案

我使用 Python，但主要思想是一样的.

如果直接对img2做cvtColor:bgr->gray，那肯定失败.因为灰色变得难以区分区域:

相关答案:

1. 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导入 cv2将 numpy 导入为 np##(1) 读入 bgr 空间img = cv2.imread("test2.jpg")##(2) 转换为hsv-space，然后拆分通道hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)h,s,v = cv2.split(hsv)##(3) 使用自适应方法(`THRESH_OTSU`)或固定阈值对 S 通道进行阈值处理th, threshed = cv2.threshold(s, 50, 255, cv2.THRESH_BINARY_INV)##(4) 在threshed S上找到所有的外部轮廓cnts = cv2.findContours(threshed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]画布 = img.copy()#cv2.drawContours(canvas, cnts, -1, (0,255,0), 1)##排序并选择最大的轮廓cnts = sorted(cnts, key = cv2.contourArea)cnt = cnts[-1]## 近似轮廓，所以得到角点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)## 好的，你可以看到结果为 tag(6)cv2.imwrite("detected.png", canvas)

   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:

   but not from following pic:

   How can I improve this edge detection?

   解决方案

   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:

   ---

   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:

   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)
   

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