如何使用opencv从图中删除另一个对象? [英] How to delete the other object from figure by using opencv?

问题描述

我试图检测下图中的黄线，但徽标(黄色)也会被标记.我的问题是如何屏蔽徽标?

I tried to detect the yellow lines in the following picture but the logo(yellow color) will be marked as well. My question is that how to mask the logo?

我使用霍夫变换的标准代码.我的代码如下:

I use the standard code of the Hough Transform. My code is as follows:

import cv2
import numpy as np
img = cv2.imread('Road3.jpg')
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)


low_yellow=np.array([18, 94, 140])
up_yellow=np.array([48, 255, 255])
 mask=cv2.inRange(hsv, low_yellow, up_yellow)
 edges = cv2.Canny(mask,75,150)

 lines = cv2.HoughLinesP(edges,1,np.pi/180,50,maxLineGap=250)
  for line in lines:
 x1,y1,x2,y2 = line[0]
 cv2.line(img,(x1,y1),(x2,y2),(0,255,0),5)

 cv2.imshow('image', img)
 cv2.imshow("edges", edges)
 k = cv2.waitKey(0)
 cv2.destroyAllWindows()

推荐答案

您可以使用多尺度模板匹配方法.

You can use multi-scale template matching approach.

您要删除以下图片:

1. 检测图像

import cv2
import imutils
import numpy as np

template = cv2.imread("template/template.jpg")
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
template = cv2.Canny(template, 50, 200)
(h, w) = template.shape[:2]

image = cv2.imread('nnEGw.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
found = None

for scale in np.linspace(0.2, 1.0, 20)[::-1]:
     resized = imutils.resize(gray, width=int(gray.shape[1] * scale))
     r = gray.shape[1] / float(resized.shape[1])

     if resized.shape[0] < h or resized.shape[1] < w:
         break

     edged = cv2.Canny(resized, 50, 200)
     result = cv2.matchTemplate(edged, template, cv2.TM_CCOEFF)
     (_, maxVal, _, maxLoc) = cv2.minMaxLoc(result)

     if found is None or maxVal > found[0]:
         found = (maxVal, maxLoc, r)

(_, maxLoc, r) = found
(startX, startY) = (int(maxLoc[0] * r), int(maxLoc[1] * r))
(endX, endY) = (int((maxLoc[0] + w) * r), int((maxLoc[1] + h) * r))

cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2)
cv2.imwrite("result/edges2.png", image)

1. 填充矩形

cv2.rectangle(image, (startX, startY), (endX, endY), (255, 255, 255), -1)

1. 应用霍夫变换

hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

low_yellow = np.array([18, 94, 140])
up_yellow = np.array([48, 255, 255])
mask = cv2.inRange(hsv, low_yellow, up_yellow)
edges = cv2.Canny(mask, 75, 150)

lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 50, maxLineGap=250)
for line in lines:
    x1, y1, x2, y2 = line[0]
    cv2.line(image, (x1, y1), (x2, y2), (0, 255, 0), 5)

    # cv2.imshow('image', img)
    cv2.imwrite("result/edges3.png", edges)

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