我正在使用以下代码来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;
}
然后我可以通过查找此中最大的轮廓来找到此edges
中的文档。
我的问题是我可以从以下图片中找到该文件:
但不是来自以下图片:
如何改善此边缘检测?
答案 0 :(得分:10)
如果你直接做cvtColor:bgr - >灰色的img2,然后你必须失败。因为灰色难以区分区域:
相关答案:
在您的图片中,论文为white
,背景为colored
。因此,最好在Saturation(饱和度)
中检测论文是HSV color space
频道。对于HSV,请参阅https://en.wikipedia.org/wiki/HSL_and_HSV#Saturation。
BGR
bgr
转换为hsv
空间Canny
或HoughLines
,我选择findContours
),大约找到角落。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)
答案 1 :(得分:1)
在OpenCV中有一个名为dilate
的功能,这会使线条变暗。所以尝试下面的代码。
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;
}