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I'm trying to write an app for wild leopard classification and conservation in South Asia. For this, I have the main challenge to identify the leopards by their spot pattern in the forehead.

The current approach I used was,

1. Store the known leopard forehead images as a base list
2. Get the user-provided leopard image and crop the forehead of the leopard
3. identify the keypoints using the SIFT algorithm
4. Use FLANN matcher to get KNN matches
5. Select good matches based on the ratio threshold

Sample code below:

img1 = cv.bilateralFilter(baseImg, 9, 75, 75)
img2 = cv.bilateralFilter(userImage, 9, 75, 75)

detector = cv.xfeatures2d_SIFT.create()
keypoints1, descriptors1 = detector.detectAndCompute(img1, None)
keypoints2, descriptors2 = detector.detectAndCompute(img2, None)

# FLANN parameters
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)  # or pass empty dictionary
matcher = cv.FlannBasedMatcher(index_params, search_params)

knn_matches = matcher.knnMatch(descriptors1, descriptors2, 2)

allmatchpointcount = len(knn_matches)
ratio_thresh = 0.7
good_matches = []

for m, n in knn_matches:
    if m.distance < ratio_thresh * n.distance:
        good_matches.append(m)


goodmatchpointcount = len(good_matches)
print("Good match count : ", goodmatchpointcount)
matchsuccesspercentage = goodmatchpointcount/allmatchpointcount*100
print("Match percentage : ", matchsuccesspercentage)

Problems I have with this approach:

1. The method has a medium-low success rate and tends to break when there is a new user image.
2. The user images are sometimes taken from different angles where some key patterns are not visible or warped.
3. The user image quality affects the match result significantly.

I'm trying to write an app for wild leopard classification and conservation in South Asia. For this, I have the main challenge to identify the leopards by their spot pattern in the forehead.

The current approach I used was,

1. Store the known leopard forehead images as a base list
2. Get the user-provided leopard image and crop the forehead of the leopard
3. identify the keypoints using the SIFT algorithm
4. Use FLANN matcher to get KNN matches
5. Select good matches based on the ratio threshold

Sample code below:

img1 = cv.bilateralFilter(baseImg, 9, 75, 75)
img2 = cv.bilateralFilter(userImage, 9, 75, 75)

detector = cv.xfeatures2d_SIFT.create()
keypoints1, descriptors1 = detector.detectAndCompute(img1, None)
keypoints2, descriptors2 = detector.detectAndCompute(img2, None)

# FLANN parameters
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)  # or pass empty dictionary
matcher = cv.FlannBasedMatcher(index_params, search_params)

knn_matches = matcher.knnMatch(descriptors1, descriptors2, 2)

allmatchpointcount = len(knn_matches)
ratio_thresh = 0.7
good_matches = []

for m, n in knn_matches:
    if m.distance < ratio_thresh * n.distance:
        good_matches.append(m)


goodmatchpointcount = len(good_matches)
print("Good match count : ", goodmatchpointcount)
matchsuccesspercentage = goodmatchpointcount/allmatchpointcount*100
print("Match percentage : ", matchsuccesspercentage)

Problems I have with this approach:

1. The method has a medium-low success rate and tends to break when there is a new user image.
2. The user images are sometimes taken from different angles where some key patterns are not visible or warped.
3. The user image quality affects the match result significantly.

I'm trying to write an app for wild leopard classification and conservation in South Asia. For this, I have the main challenge to identify the leopards by their spot pattern in the forehead.

The current approach I used was,

1. Store the known leopard forehead images as a base list
2. Get the user-provided leopard image and crop the forehead of the leopard
3. identify the keypoints using the SIFT algorithm
4. Use FLANN matcher to get KNN matches
5. Select good matches based on the ratio threshold

Sample code below:

img1 = cv.bilateralFilter(baseImg, 9, 75, 75)
img2 = cv.bilateralFilter(userImage, 9, 75, 75)

detector = cv.xfeatures2d_SIFT.create()
keypoints1, descriptors1 = detector.detectAndCompute(img1, None)
keypoints2, descriptors2 = detector.detectAndCompute(img2, None)

# FLANN parameters
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)  # or pass empty dictionary
matcher = cv.FlannBasedMatcher(index_params, search_params)

knn_matches = matcher.knnMatch(descriptors1, descriptors2, 2)

allmatchpointcount = len(knn_matches)
ratio_thresh = 0.7
good_matches = []

for m, n in knn_matches:
    if m.distance < ratio_thresh * n.distance:
        good_matches.append(m)


goodmatchpointcount = len(good_matches)
print("Good match count : ", goodmatchpointcount)
matchsuccesspercentage = goodmatchpointcount/allmatchpointcount*100
print("Match percentage : ", matchsuccesspercentage)

Problems I have with this approach:

1. The method has a medium-low success rate and tends to break when there is a new user image.
2. The user images are sometimes taken from different angles where some key patterns are not visible or warped.
3. The user image quality affects the match result significantly.

I appreciate any suggestions to get this improved in any manner.

Example user image: (Base image)

Base Image

Above is matching to below: (Incorrect pattern matched)

Matching incorrect pattern