Bagaimana cara menghapus objek lain dari gambar dengan menggunakan opencv?

Question

Saya mencoba mendeteksi garis kuning pada gambar berikut tetapi logo (warna kuning) akan ditandai juga. Pertanyaan saya adalah bagaimana cara menutupi logo?

Saya menggunakan kode standar Transformasi Hough. Kode saya adalah sebagai berikut:

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()

Answer 1

Anda dapat menggunakan pendekatan pencocokan template multi-skala.

Anda ingin menghapus gambar berikut:

• 1. Deteksi gambar

  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. Isi persegi panjang

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

  
  • 1. Terapkan Hough Transform

  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)