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Use KMeans in Sklearn to do clustering and find the nearest cluster centroid for each SIFT feature; Use cv 2 . xfeatures 2 d .
Use KMeans in Sklearn to do clustering and find the nearest cluster centroid for each SIFT feature;
Use cvxfeaturesdSIFTcreate to create a SIFT object;
Use cvKeypoint to generate key points;
Use sift.compute to compute SIFT descriptors given densely sampled keypoints.
Be mindful of RAM usage. Try to make the code more memory efficient, otherwise it could easily exceed RAM limits in Colab, at which point your session will crash.
If your RAM is going to run out of space, use gccollect for the garbage collector to collect unused objects in memory to free some space.
Store data or features as NumPy arrays instead of lists. Computation on NumPy arrays is much more efficient than lists.
from sklearn import neighbors
nprandom.seed
##########WRITE YOUR CODE HERE##########
# The following steps are just for your reference
# You can write in your own way
#
# # densely sample keypoints
# def samplekpshape stride, size:
# return kp
#
# # extract vocabulary of SIFT features
# def extractvocabularyrawdata, keypoint:
# return vocabulary
#
# # extract Bag of SIFT Representation of images
# def extractfeatrawdata, vocabulary, keypoint:
# return feat
#
# # sample dense keypoints
# skp samplekptraindatashapetraindatashape
# vocabulary extractvocabularytraindata, skp
# trainfeat extractfeattraindata, vocabulary, skp
# testfeat extractfeattestdata, vocabulary, skp
trainfeat
testfeat
##########END OF CODE##########
# this block should generate
# trainfeat and testfeat corresponding to traindata and testdata
In python please and thank you!
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