def extract_query(infile_list):
featureID = 0
imageID = 0
NUMBER_OF_TABLES = 20
totalDescriptors = 0
f = open('./pig/query.csv', 'w')
for file_name in infile_list:
img = cv2.imread(file_name, 0)
kp = orb.detect(img, None)
kp, des = orb.compute(img, kp)
numDescriptors = des.shape[0]
totalDescriptors = totalDescriptors + numDescriptors
contig = numpy.ascontiguousarray(des, dtype=numpy.uint8)
hashes = lsh.hash(contig)
# for each descriptor, get the 20 hash values
cnt = 0
for descriptor in xrange(0, numDescriptors):
f.write("%u, %u, " % (featureID, imageID))
for hash in xrange(0, NUMBER_OF_TABLES):
f.write("%u, " % (hashes[cnt]))
cnt = cnt + 1
f.write("\n")
featureID = featureID + 1
imageID = imageID + 1
print "Wrote %u descriptor records\n" % (totalDescriptors)
f.close()
kp = orb.detect(img, None)
# compute the descriptors for the keypoints and store in a matrix
kp, des = orb.compute(img, kp)
# get the number of descriptors returned (number of rows)
numDescriptors = des.shape[0]
# keep track of the total number of descriptors processed
totalDescriptors = totalDescriptors + numDescriptors
# LSH requires access to a contiguous array for matrix processing
contig = numpy.ascontiguousarray(des, dtype=numpy.uint8)
# generate the hash values for all features in the descriptor matrix
hashes = lsh.hash(contig)
cnt = 0
# for each descriptor
for descriptor in xrange(0, numDescriptors):
# write out the FeatureID and ImageID
f.write("%u, %u, " % (featureID, imageID))
# for each hash table
for hash in xrange(0, NUMBER_OF_TABLES):
# write out hash value for this image in this hash tab;e
f.write("%u, " % (hashes[cnt]))
cnt = cnt + 1
f.write("\n")
featureID = featureID + 1
imageID = imageID + 1
kp = orb.detect(img, None)
# compute the descriptors for the keypoints and store in a matrix
kp, des = orb.compute(img, kp)
# get the number of descriptors returned (number of rows)
numDescriptors = des.shape[0]
# keep track of the total number of descriptors processed
totalDescriptors = totalDescriptors + numDescriptors
# LSH requires access to a contiguous array for matrix processing
contig = numpy.ascontiguousarray(des, dtype=numpy.uint8)
# generate the hash values for all features in the descriptor matrix
hashes = lsh.hash(contig)
cnt = 0
# for each descriptor
for descriptor in xrange(0, numDescriptors):
# write out the FeatureID and ImageID
f.write("%u, %u, " % (featureID, imageID))
# for each hash table
for hash in xrange(0, NUMBER_OF_TABLES):
# write out hash value for this image in this hash tab;e
f.write("%u, " % (hashes[cnt]))
cnt = cnt + 1
f.write("\n")
featureID = featureID + 1
imageID = imageID + 1
