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2016-12-11 00:51:53 -0500 commented question Detecting and Extracting Rectangular based Structure

Unfortunately colorspace approach does not work as I have multiple images with different colors. None of the methods seems to be perfect in isolating the object of interest. I can share the images. Appreciate if further advice can be provided on the code and the approach. I can share more images if required.

2016-12-09 04:46:43 -0500 received badge  Enthusiast
2016-12-01 05:20:47 -0500 commented question Detecting and Extracting Rectangular based Structure

Vintez. Thanks for your reply. I will try the Gaussian Blur effect for noise removal. I also want advice on whether this is the best method to detect such object in the image. What about colorspace segmentation approach

2016-12-01 04:30:03 -0500 asked a question Detecting and Extracting Rectangular based Structure

I am currently working on a python script to extract specific rectangular feature from an image that has multiple objects.

I would like to extract each object individually as seen below. The problem I am having is that my code works onlly on clear images with no background noise and high resolution. The image on the right does not get detected at all for some reason as its noisier, the edges are rough and it's lower resolution image.

Appreciate any help I can get with this.

image description

import numpy as np
import cv2 
from matplotlib import pyplot as plt
import os


mypath='path\\images'
onlyfiles = [ f for f in os.listdir(mypath) if os.path.isfile(os.path.join(mypath,f)) ]
images = np.empty(len(onlyfiles), dtype=object)
for n in range(0, len(onlyfiles)):
   images[n] = cv2.imread( os.path.join(mypath,onlyfiles[n]) )

   gwash = images[n] #import image

   gwashBW = cv2.cvtColor(gwash, cv2.COLOR_RGB2GRAY) #change to grayscale

   height = np.size(gwash, 0)
   width = np.size(gwash, 1)

   ret,thresh1 = cv2.threshold(gwashBW ,41,255,cv2.THRESH_BINARY) 


   kernel = np.ones((1,1),np.uint8) 

   erosion = cv2.erode(thresh1, kernel,iterations = 31) 
   opening = cv2.morphologyEx(erosion, cv2.MORPH_OPEN, kernel)
   closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel) 

   _,contours, hierarchy = cv2.findContours(closing,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) 

   areas = [] #list to hold all areas

  for i,contour in enumerate(contours):
      ar = cv2.contourArea(contour)
      areas.append(ar)
      cnt = contour
      (x, y, w, h) = cv2.boundingRect(cnt)
       if cv2.contourArea(cnt) > 60000 and cv2.contourArea(cnt) < (height*width):
          if hierarchy[0,i,3] == -1:
             cv2.rectangle(gwash, (x,y), (x+w,y+h), (255, 0, 0), 12)


  plt.subplot2grid((2,5),(0,n)),plt.imshow(gwash)
  plt.title('Extraction'), plt.xticks([]), plt.yticks([])


plt.show()