find contours of thresholded image

Question

I want to detect the contours of image. and check if these contours are in the other image or no

Answer 1

You can do a perspective warp to align one image to the other. Convert both to HSV and get the saturation channels. Then threshold them and get the absolute difference between them. Then get the contours and finally the bounding boxes of the contours, which are drawn on the first image. Here is how to do that with Python/OpenCV. Note, I do not have SIFT installed, so I will use ORB in its place.

Input 1 (reference image):

Input 2 (image to be warped/aligned):

import cv2
import numpy as np


MAX_FEATURES = 500
GOOD_MATCH_PERCENT = 0.15


def alignImages(im1, im2):

  # im2 is reference and im1 in to be warped to match im2
  # note: numbering is swapped in function

  # Convert images to grayscale
  im1Gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
  im2Gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)

  # Detect ORB features and compute descriptors.
  orb = cv2.ORB_create(MAX_FEATURES)
  keypoints1, descriptors1 = orb.detectAndCompute(im1Gray, None)
  keypoints2, descriptors2 = orb.detectAndCompute(im2Gray, None)

  # Match features.
  matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
  matches = matcher.match(descriptors1, descriptors2, None)

  # Sort matches by score
  matches.sort(key=lambda x: x.distance, reverse=False)

  # Remove not so good matches
  numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)
  matches = matches[:numGoodMatches]

  # Draw top matches
  imMatches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None)
  cv2.imwrite("pipes_matches.png", imMatches)

  # Extract location of good matches
  points1 = np.zeros((len(matches), 2), dtype=np.float32)
  points2 = np.zeros((len(matches), 2), dtype=np.float32)

  for i, match in enumerate(matches):
    points1[i, :] = keypoints1[match.queryIdx].pt
    points2[i, :] = keypoints2[match.trainIdx].pt

  # Find homography
  h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)

  # Use homography
  height, width, channels = im2.shape
  im1Reg = cv2.warpPerspective(im1, h, (width, height))

  return im1Reg, h


if __name__ == '__main__':

  # Read reference image
  refFilename = "pipes1.jpg"
  print("Reading reference image : ", refFilename)
  imReference = cv2.imread(refFilename, cv2.IMREAD_COLOR)

  # Read image to be aligned
  imFilename = "pipes2.jpg"
  print("Reading image to align : ", imFilename);  
  im = cv2.imread(imFilename, cv2.IMREAD_COLOR)

  # Aligned image will be stored in imReg. 
  # The estimated homography will be stored in h. 
  imReg, h = alignImages(im, imReference)

  # Print estimated homography
  print("Estimated homography : \n",  h)

  # Convert images to HSV and get saturation channel
  refSat = cv2.cvtColor(imReference, cv2.COLOR_BGR2HSV)[:,:,1]
  imSat = cv2.cvtColor(imReg, cv2.COLOR_BGR2HSV)[:,:,1]

  # Otsu threshold
  refThresh = cv2.threshold(refSat, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
  imThresh = cv2.threshold(imSat, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]

  # apply morphology open and close
  kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7,7))
  refThresh = cv2.morphologyEx(refThresh, cv2.MORPH_OPEN, kernel, iterations=1)
  refThresh = cv2.morphologyEx(refThresh, cv2.MORPH_CLOSE, kernel, iterations=1).astype(np.float64)
  imThresh = cv2.morphologyEx(imThresh, cv2.MORPH_OPEN, kernel, iterations=1).astype(np.float64)
  imThresh = cv2.morphologyEx(imThresh, cv2.MORPH_CLOSE, kernel, iterations=1)

  # get absolute difference between the two thresholded images
  diff = np.abs(cv2.add(imThresh,-refThresh))

  # apply morphology open to remove thin lines caused by slight misalignment of the two images
  kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (13,13))
  diff_cleaned = cv2.morphologyEx(diff, cv2.MORPH_OPEN, kernel, iterations=1).astype(np.uint8)

  # Filter using contour area and draw bounding boxes that do not touch the sides of the image
  cnts = cv2.findContours(diff_cleaned, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
  cnts = cnts[0] if len(cnts) == 2 else cnts[1]
  result = imReference.copy()
  for c in cnts:
      x,y,w,h = cv2.boundingRect(c)
      cv2.rectangle(result, (x, y), (x+w, y+h), (0, 0, 255), 2)

  # show images
  cv2.imshow('reference', imReference)
  cv2.imshow('image', im)
  cv2.imshow('image_aligned', imReg)
  cv2.imshow('refThresh', refThresh)
  cv2.imshow('imThresh', imThresh)
  cv2.imshow('diff', diff)
  cv2.imshow('diff_cleaned', diff_cleaned)
  cv2.imshow('result', result)
  cv2.waitKey()

  # save images
  cv2.imwrite('pipes2_aligned.jpg', imReg)
  cv2.imwrite('pipes_diff.png', diff_cleaned)
  cv2.imwrite('pipes_result.png', result)

Matches Image:

Image 2 (perspectively) aligned to Image 1:

Absolute difference image (cleaned):

Result: