"--index",
required=True,
help="Path to where we stored our index")
ap.add_argument("-q", "--query", required=True, help="Path to query image")
args = vars(ap.parse_args())
queryImage = cv2.imread(args["query"])
cv2.imshow("Query", queryImage)
print("query: {}".format(args["query"]))
desc = RGBHistogram([8, 8, 8])
queryFeatures = desc.describe(queryImage)
index = pickle.loads(open(args["index"], "rb").read())
searcher = Searcher(index)
results = searcher.search(queryFeatures)
montageA = np.zeros((166 * 5, 400, 3), dtype="uint8")
montageB = np.zeros((166 * 5, 400, 3), dtype="uint8")
for j in range(0, 10):
(score, imageName) = results[j]
path = os.path.join(args["dataset"], imageName)
result = cv2.imread(path)
print("\t{}. {} : {:.3f}".format(j + 1, imageName, score))
if j < 5:
montageA[j * 166:(j + 1) * 166, :] = result
else:
montageB[(j - 5) * 166:((j - 5) + 1) * 166, :] = result
ap.add_argument("-r",
"--result-path",
required=True,
help="Path to the result path")
args = vars(ap.parse_args())
# initialize the image descriptor
cd = ColorDescriptor((8, 12, 3))
# load the query image and describe it
query = cv2.imread(args["query"])
features = cd.describe(query)
# perform the search
searcher = Searcher(args["index"])
results = searcher.search(features)
print(results)
# display the query
cv2.imshow("Query", query)
query = cv2.resize(query, (400, 400))
results_arr = []
# loop over the results
for (score, resultID) in results:
# load the result image and display it
result = cv2.imread(args["result_path"] + "/" + resultID)
result = cv2.resize(result, (400, 400))
results_arr.append(result)
#im = cv2.resize(result, (1000, 800))
#imstack = np.hstack(imstack, im)