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("processed {} images".format(i), msgType="[PROGRESS]")
"--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, local invariant descriptor, and 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)
"--images",
required=True,
help="Path to input images directory")
ap.add_argument("-c", "--codebook", required=True, help="Path to the codebook")
ap.add_argument("-m", "--model", required=True, help="Path to the classifier")
args = vars(ap.parse_args())
# initialize the keypoint detector, local invariant descriptor, and the descriptor
# pipeline
detector = FeatureDetector_create("GFTT")
descriptor = DescriptorExtractor_create("RootSIFT")
dad = DetectAndDescribe(detector, descriptor)
# load the codebook vocabulary and initialize the bag-of-visual-words transformer
vocab = pickle.loads(open(args["codebook"], "rb").read())
bovw = BagOfVisualWords(vocab)
# load the classifier
model = pickle.loads(open(args["model"], "rb").read())
num_tests = len(list(paths.list_images(args["images"])))
montage = ResultsMontage((240, 320), 3, num_tests)
# loop over the image paths
for imagePath in paths.list_images(args["images"]):
# load the image and prepare it from description
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = imutils.resize(gray, width=min(320, image.shape[1]))
# describe the image and classify it