如何检测空间图案的边缘？

Question

我正在建造一台新机器，边缘检测有问题。我有一张纸放在一个圆筒上，如下图所示。如何检测本文的边缘？

我试图建立一个领导背景，但是这篇论文非常大。因此，我的机器没有足够的空间来运行。

Answer 1

您可以使用以下代码作为参考。在这里，我基本上是使用cv2.inRange函数从图像中分割浅绿色（不是深绿色，否则将检测到其中一个轴的边缘），最后将Canny边缘检测应用于灰度分割图像的版本，即cv2.Canny。

import cv2
import numpy as np 

img = cv2.imread('cylinder.png')

# convert to HSV color space
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# Threshold the HSV image to get green colors by defining range of green color in HSV
mask = cv2.inRange(img_hsv, (36,0,0), (55,255,255))

# Bitwise-AND mask and original image
res = cv2.bitwise_and(img, img, mask = mask)

# coverting image with green colored region of interest from HSV to RGB
img_hsv2bgr = cv2.cvtColor(res, cv2.COLOR_HSV2BGR)

# coverting image from RGB to GRAYSCALE
img_gray = cv2.cvtColor(img_hsv2bgr, cv2.COLOR_BGR2GRAY)

# canny edge detection
edges = cv2.Canny(img_gray, 100, 200)

cv2.imshow('Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

输出：

编辑：对上述代码进行了一些修改之后，例如将黄色部分与浅绿色一起分割，并在传递到cv2.Canny函数之前应用高斯模糊，可以提供比上面更好的输出。

代码：

# Threshold the HSV image to get both green and yellow colors by defining range of color in HSV
mask_green = cv2.inRange(img_hsv, (36,0,0), (55,255,255))
mask_yellow = cv2.inRange(img_hsv, (21, 39, 64), (38, 255, 255))
mask = cv2.bitwise_or(mask_green, mask_yellow)

# Bitwise-AND mask and original image
res = cv2.bitwise_and(img, img, mask = mask)

# coverting image with green colored region of interest from HSV to RGB
frame_hsv2bgr = cv2.cvtColor(res, cv2.COLOR_HSV2BGR)

# coverting image from RGB to GRAYSCALE
frame_gray = cv2.cvtColor(frame_hsv2bgr, cv2.COLOR_BGR2GRAY)

gaussian_blurred = cv2.GaussianBlur(frame_gray,(5, 3), 0)

# canny edge detection
edges = cv2.Canny(gaussian_blurred, 100, 200)

输出：