"--dataset",
required=True,
help="Path to the directory that contains imgs to be indexed")
ap.add_argument("-i",
"--index",
required=True,
help="Path to where the computed index will be stored")
args = vars(ap.parse_args())
# initialize colour descriptor
cd = Main((8, 12, 3))
# open output index file for writing
output = open(args["index"], "w")
# use glob to grab img file and loop over time
for imagePath in glob.glob(args["dataset"] + "/*.jpg"):
# extract img ID from img path and load the img itself
imageID = imagePath[imagePath.rfind("\\") + 1:]
image = cv2.imread(imagePath)
# describe img
features = cd.describe(image)
# write features to file
features = [str(f) for f in features]
output.write("%s,%s\n" % (imageID, ",".join(features)))
# close the index file
output.close()
"--index",
required=True,
help="Path to where the computed index will be stored")
ap.add_argument("-q", "--query", required=True, help="Path to the query image")
ap.add_argument("-r",
"--result-path",
required=True,
help="Path to the result path")
args = vars(ap.parse_args())
# initialize img descriptor
cd = Main((8, 12, 3))
# load tge query image and describe
query = cv2.imread(args["query"])
features = cd.describe(query)
# perform search
searcher = Searcher(args["index"])
results = searcher.search(features)
# display query
# Create window with freedom of dimensions
cv2.namedWindow("Query", cv2.WINDOW_NORMAL)
cv2.imshow("Query", query)
# loop the results
for (score, resultID) in results:
result = cv2.imread(args["result_path"] + "/" + resultID)
cv2.namedWindow("Result", cv2.WINDOW_NORMAL)
cv2.imshow("Result", result)
