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Check image dissimilar areas using SIFT

I want to see which areas in image are dissimilar. Attaching 2 images in which there is a difference in top left part. It shouldn't show the difference if some part is scaled, translated or skewed. I am trying to use SIFT matching to see the non matched points.image description image description.


import cv2 import numpy as np from matplotlib import pyplot as plt from scipy.spatial.distance import euclidean

img1 = cv2.imread('plant.png') img2 = cv2.imread('plantErase.png')

gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

sift = cv2.SIFT_create()

kp1, des1 = sift.detectAndCompute(gray1, None) kp2, des2 = sift.detectAndCompute(gray2, None)

im_with_keypoints1 = cv2.drawKeypoints(gray1, kp1, np.array([])) im_with_keypoints2 = cv2.drawKeypoints(gray2, kp2, np.array([]))

FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) search_params = dict(checks = 50)

flann = cv2.FlannBasedMatcher(index_params, search_params)

matches = flann.knnMatch(des1,des2,k=2)

MIN_MATCH_COUNT = 10

good = [] for m,n in matches: if m.distance < 0.7*n.distance: good.append(m)

if len(good)>MIN_MATCH_COUNT: src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2) dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)

kp1_matched=([ kp1[m.queryIdx] for m in good ])
kp2_matched=([ kp2[m.trainIdx] for m in good ])

kp1_miss_matched=[kp for kp in kp1 if kp not in kp1_matched]
kp2_miss_matched=[kp for kp in kp2 if kp not in kp2_matched]

img1_miss_matched_kp = cv2.drawKeypoints(img1,kp1_miss_matched, None,color=(255,0,0), flags=0)
plt.imshow(img1_miss_matched_kp),plt.show()

img2_miss_matched_kp = cv2.drawKeypoints(img2,kp2_miss_matched, None,color=(255,0,0), flags=0)
plt.imshow(img2_miss_matched_kp),plt.show()
click to hide/show revision 2
None

updated 2020-10-28 03:30:49 -0500

berak gravatar image

Check image dissimilar areas using SIFT

I want to see which areas in image are dissimilar. Attaching 2 images in which there is a difference in top left part. It shouldn't show the difference if some part is scaled, translated or skewed. I am trying to use SIFT matching to see the non matched points.image description image description.


import cv2
import numpy as np
from matplotlib import pyplot as plt
from scipy.spatial.distance import euclidean

euclidean img1 = cv2.imread('plant.png') img2 = cv2.imread('plantErase.png')

cv2.imread('plantErase.png') gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

cv2.COLOR_BGR2GRAY) sift = cv2.SIFT_create()

cv2.SIFT_create() kp1, des1 = sift.detectAndCompute(gray1, None) kp2, des2 = sift.detectAndCompute(gray2, None)

None) im_with_keypoints1 = cv2.drawKeypoints(gray1, kp1, np.array([])) im_with_keypoints2 = cv2.drawKeypoints(gray2, kp2, np.array([]))

np.array([])) FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) search_params = dict(checks = 50)

50) flann = cv2.FlannBasedMatcher(index_params, search_params)

search_params) matches = flann.knnMatch(des1,des2,k=2)

flann.knnMatch(des1,des2,k=2) MIN_MATCH_COUNT = 10

10 good = [] for m,n in matches: if m.distance < 0.7*n.distance: good.append(m)

good.append(m) if len(good)>MIN_MATCH_COUNT: src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2) dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)

]).reshape(-1,1,2)

    kp1_matched=([ kp1[m.queryIdx] for m in good ])
 kp2_matched=([ kp2[m.trainIdx] for m in good ])

 kp1_miss_matched=[kp for kp in kp1 if kp not in kp1_matched]
 kp2_miss_matched=[kp for kp in kp2 if kp not in kp2_matched]

 img1_miss_matched_kp = cv2.drawKeypoints(img1,kp1_miss_matched, None,color=(255,0,0), flags=0)
 plt.imshow(img1_miss_matched_kp),plt.show()

 img2_miss_matched_kp = cv2.drawKeypoints(img2,kp2_miss_matched, None,color=(255,0,0), flags=0)
 plt.imshow(img2_miss_matched_kp),plt.show()
click to hide/show revision 3
None

updated 2020-10-28 03:31:20 -0500

berak gravatar image

Check image dissimilar areas using SIFT

I want to see which areas in image are dissimilar. Attaching 2 images in which there is a difference in top left part. It shouldn't show the difference if some part is scaled, translated or skewed. I am trying to use SIFT matching to see the non matched points.points. image description image description.


import cv2
import numpy as np
from matplotlib import pyplot as plt
from scipy.spatial.distance import euclidean

img1 = cv2.imread('plant.png')
img2 = cv2.imread('plantErase.png')

gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

sift = cv2.SIFT_create()

kp1, des1 = sift.detectAndCompute(gray1, None)
kp2, des2 = sift.detectAndCompute(gray2, None)

im_with_keypoints1 = cv2.drawKeypoints(gray1, kp1, np.array([]))
im_with_keypoints2 = cv2.drawKeypoints(gray2, kp2, np.array([]))


FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)

flann = cv2.FlannBasedMatcher(index_params, search_params)

matches = flann.knnMatch(des1,des2,k=2)

MIN_MATCH_COUNT = 10

good = []
for m,n in matches:
    if m.distance < 0.7*n.distance:
        good.append(m)


if len(good)>MIN_MATCH_COUNT:
    src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
    dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)

    kp1_matched=([ kp1[m.queryIdx] for m in good ])
    kp2_matched=([ kp2[m.trainIdx] for m in good ])

    kp1_miss_matched=[kp for kp in kp1 if kp not in kp1_matched]
    kp2_miss_matched=[kp for kp in kp2 if kp not in kp2_matched]

    img1_miss_matched_kp = cv2.drawKeypoints(img1,kp1_miss_matched, None,color=(255,0,0), flags=0)
    plt.imshow(img1_miss_matched_kp),plt.show()

    img2_miss_matched_kp = cv2.drawKeypoints(img2,kp2_miss_matched, None,color=(255,0,0), flags=0)
    plt.imshow(img2_miss_matched_kp),plt.show()