def processTest(testImageNumList, K, idfList, centers):
"""
This method used to process test images, and transform images to space vector model, and
return testImage list.
"""
testImages = []
processGap = len(testImageNumList)/10.0
processIndex = 0
for testImageIndex in xrange(len(testImageNumList)):
if testImageIndex == len(testImageNumList) - 1:
print ''
elif testImageIndex > processIndex*processGap:
sys.stdout.write('#')
processIndex = processIndex + 1
imageName = readImage.getImageName(testImageNumList[testImageIndex])
SIFTdescr = processSIFT.getSIFT(imageName)
# new train image object, with image name, testImageIndex and SIFT
testImageObject = Image.Image(imageName, testImageIndex, SIFTdescr)
testImages.append(testImageObject)
wordList = []
SIFTdescr = testImages[testImageIndex].SIFTdescr
# get distances between SIFT and centers
distances = getDistances(centers, SIFTdescr)
for siftIndex in xrange(len(testImages[testImageIndex].SIFTdescr)):
# minIndex, minValue = min(enumerate(distances[siftIndex], key = operator.itemgetter(1)))
minIndex = np.argmin(distances[siftIndex])
wordList.append(minIndex)
# save word list into test image
testImages[testImageIndex].setWordList(wordList)
# get tf list
tfList = getTFlist(wordList, K)
svm = getVSM(tfList, idfList)
testImages[testImageIndex].setVSM(svm)
# print svm
return testImages
def processTest(testImageNumList, K, idfList, centers):
"""
This method used to process test images, and transform images to space vector model, and
return testImage list.
"""
testImages = []
processGap = len(testImageNumList) / 10.0
processIndex = 0
for testImageIndex in xrange(len(testImageNumList)):
if testImageIndex == len(testImageNumList) - 1:
print ''
elif testImageIndex > processIndex * processGap:
sys.stdout.write('#')
processIndex = processIndex + 1
imageName = readImage.getImageName(testImageNumList[testImageIndex])
SIFTdescr = processSIFT.getSIFT(imageName)
# new train image object, with image name, testImageIndex and SIFT
testImageObject = Image.Image(imageName, testImageIndex, SIFTdescr)
testImages.append(testImageObject)
wordList = []
SIFTdescr = testImages[testImageIndex].SIFTdescr
# get distances between SIFT and centers
distances = getDistances(centers, SIFTdescr)
for siftIndex in xrange(len(testImages[testImageIndex].SIFTdescr)):
# minIndex, minValue = min(enumerate(distances[siftIndex], key = operator.itemgetter(1)))
minIndex = np.argmin(distances[siftIndex])
wordList.append(minIndex)
# save word list into test image
testImages[testImageIndex].setWordList(wordList)
# get tf list
tfList = getTFlist(wordList, K)
svm = getVSM(tfList, idfList)
testImages[testImageIndex].setVSM(svm)
# print svm
return testImages
def setTFList(imageList, centroids, K):
"""
This method used to set tf list to each image. We use centroids and compare
distance between sift descriptors in image, and assign these features to
visual words.
[image, image,..], [[int, int,...],...] => [image, image,..]
"""
processGap = len(imageList)/10.0
processIndex = 0
imageCount = 0
print 'PROCESS Images ',
for image in imageList:
if imageCount == len(imageList) - 1:
print '#'
elif imageCount > processIndex*processGap:
sys.stdout.write('#')
processIndex = processIndex + 1
imageCount = imageCount + 1
siftDescriptor = image.SIFTdescr
wordList = []
# get distances between SIFT and centers
distances = getDistances(centroids, siftDescriptor)
for siftIndex in xrange(len(siftDescriptor)):
minIndex = np.argmin(distances[siftIndex])
wordList.append(minIndex)
# save word list into test image
image.setWordList(wordList)
# get tf list
tfList = getTFlist(wordList, K)
image.settfList(tfList)
return imageList
