"--relevant",
required=True,
help="Path to relevant dictionary")
ap.add_argument("-q", "--query", required=True, help="Path to the query image")
args = vars(ap.parse_args())
# initialize the keypoint detector and local invatiant descriptor
detector = FeatureDetector_create("SURF")
descriptor = DescriptorExtractor_create("RootSIFT")
dad = DetectAndDescribe(detector, descriptor)
# load the inverted document frequency array and codebook vocabulary,
# then initialize the bag-of-visual-words transformer
idf = pickle.loads(open(args["idf"], "rb").read())
vocab = pickle.loads(open(args["codebook"], "rb").read())
bovw = BagOfVisualWords(vocab)
# load the relevant queries dictionary and lookup the relevant results for the
# query image
relevant = json.loads(open(args["relevant"]).read())
queryFilename = args["query"][args["query"].rfind("/") + 1:]
queryRelevant = relevant[queryFilename]
# load the query image and process it
queryImage = cv2.imread(args["query"])
cv2.imshow("Query", imutils.resize(queryImage, width=320))
queryImage = imutils.resize(queryImage, width=320)
queryImage = cv2.cvtColor(queryImage, cv2.COLOR_BGR2GRAY)
# extract features from the query image and construct a bag-of-visual-words from it
(queryKps, queryDescs) = dad.describe(queryImage)
ap.add_argument(
"-b",
"--bovw-db",
required=True,
help="Path to where the bag-of-visual-words database will be stored")
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 = pickle.loads(open(args["codebook"], "rb").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(f"processed {i} images", msgType="[PROGRESS]")
# distance metric and inverted document frequency array
detector = FeatureDetector_create("SURF")
descriptor = DescriptorExtractor_create("RootSIFT")
dad = DetectAndDescribe(detector, descriptor)
distanceMetric = dists.chi2_distance
idf = None
# if the path to the inverted document frequency array was supplied, then load the
# idf array and update the distance metric
if args["idf"] is not None:
idf = pickle.loads(open(args["idf"], "rb").read())
distanceMetric = distance.cosine
# load the codebook vocabulary and initialize the bag-of-visual-words transformer
vocab = pickle.loads(open(args["codebook"], "rb").read())
bovw = BagOfVisualWords(vocab)
# connect to redis and initialize the searcher
redisDB = Redis(host="localhost", port=6379, db=0)
searcher = Searcher(redisDB, args["bovw_db"], args["features_db"], idf=idf, distanceMetric=distanceMetric)
spatialVerifier = SpatialVerifier(args["features_db"], idf, vocab)
# load the relevant queries dictionary
relevant = json.loads(open(args["relevant"]).read())
queryIDs = relevant.keys()
# initialize the accuracies list and timing list
accuracies = []
timings = []
# initialize the progressbar