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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. .

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()

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. .

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()