idf = args["idf"]
else:
codebook = "codebook\\vocab.cpickle"
features_db = "features\\featuresquiz.hdf5"
bovw_db = "output\\bowv.hdf5"
maxBufferSize = 1000
idf = "output\\idf.pickle"
# load the codebook vocabulary and initialize the bag-of-visual-words transformer
vocab = cPickle.loads(open(codebook, "rb").read())
bovw = BagOfVisualWords(vocab)
# open the features database and initialize the bag-of-visual-words indexer
featuresDB = h5py.File(features_db, mode="r")
bi = BOVWIndexer(bovw.codebook.shape[0],
bovw_db,
estNumImages=featuresDB["image_ids"].shape[0],
maxBufferSize=maxBufferSize)
# loop over the image IDs and index
for (i,
(imageID,
offset)) in enumerate(zip(featuresDB["image_ids"], featuresDB["index"])):
# check to see if progress should be displayed
if i > 0 and i % 10 == 0:
bi._debug("processed {} images".format(i), msgType="[PROGRESS]")
# extract the feature vectors for the current image using the starting and
# ending offsets (while ignoring the keypoints) and then quantize the
# features to construct the bag-of-visual-words histogram
features = featuresDB["features"][offset[0]:offset[1]][:, 2:]
hist = bovw.describe(features)
ap.add_argument(
"-s",
"--max-buffer-size",
type=int,
default=500,
help="Maximum buffer size for # of features to be stored in memory")
args = vars(ap.parse_args())
# load the codebook vocabulary and initialize the bag-of-visual-words transformer
vocab = cPickle.loads(open(args["codebook"]).read())
bovw = BagOfVisualWords(vocab)
# open the features database and initialize the bag-of-visual-words indexer
featuresDB = h5py.File(args["features_db"], mode="r")
bi = BOVWIndexer(bovw.codebook.shape[0],
args["bovw_db"],
estNumImages=featuresDB["image_ids"].shape[0],
maxBufferSize=args["max_buffer_size"])
# loop over the image IDs and index
for (i,
(imageID,
offset)) in enumerate(zip(featuresDB["image_ids"], featuresDB["index"])):
# check to see if progress should be displayed
if i > 0 and i % 10 == 0:
bi._debug("processed {} images".format(i), msgType="[PROGRESS]")
# extract the feature vectors for the current image using the starting and
# ending offsets (while ignoring the keypoints) and then quantize the
# features to construct the bag-of-visual-words histogram
features = featuresDB["features"][offset[0]:offset[1]][:, 2:]
hist = bovw.describe(features)
ap.add_argument(
"-s",
"--max-buffer-size",
type=int,
default=500,
help="Maximum buffer size for # of features to be stored in memory")
args = vars(ap.parse_args())
# load the codebook vocabulary and initialize the bag-of-visual-words transformer
vocab = cPickle.loads(open(args["codebook"]).read())
bovw = BagOfVisualWords(vocab)
# open the features database and initialize the bag-of-visual-words indexer
featuresDB = h5py.File(args["features_db"], mode="r")
bi = BOVWIndexer(bovw.codebook.shape[0],
args["bovw_db"],
estNumImages=featuresDB["image_ids"].shape[0],
maxBufferSize=args["max_buffer_size"])
# loop over the image IDs and index
for (i,
(imageID,
offset)) in enumerate(zip(featuresDB["image_ids"], featuresDB["index"])):
# extract the feature vectors for the current image using the starting and
# ending offsets (while ignoring the keypoints) and then quantize the
# features to construct the bag-of-visual-words histogram
features = featuresDB["features"][offset[0]:offset[1]][:, 2:]
hist = bovw.describe(features)
# normalize the histogram such that it sums to one then add the
# bag-of-visual-words to the index
hist /= hist.sum()