# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required = True,
help = "Path to the directory that contains the images 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 the index dictionary to store our our quantifed
# images, with the 'key' of the dictionary being the image
# filename and the 'value' our computed features
index = {}
# initialize our image descriptor -- a 3D RGB histogram with
# 8 bins per channel
desc = RGBHistogram([8, 8, 8])
# use glob to grab the image paths and loop over them
for imagePath in glob.glob(args["dataset"] + "/*.jpg"):
# extract our unique image ID (i.e. the filename)
k = imagePath[imagePath.rfind("/") + 1:];print("k "+k)
# load the image, describe it using our RGB histogram
# descriptor, and update the index
image = cv2.imread(imagePath)
features = desc.describe(image)
index[k] = features
# we are now done indexing our image -- now we can write our
# index to disk
f = open(args["index"], "w")
search = 0
directory = args["query"]
for filename in os.listdir(directory):
if filename.endswith(".png"):
queryPath = os.path.join(directory, filename)
print(queryPath)
queryImage = cv2.imread(queryPath)
queryImage = cv2.resize(queryImage, (450, 360))
cv2.putText(queryImage, queryPath, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
1.0, (0, 0, 255), 3)
cv2.imshow("Query", queryImage)
print("query: %s" % queryPath)
if search == 0:
if args["descriptor"] == "rgb":
desc = RGBHistogram([8, 8, 8])
queryFeatures = desc.describe(queryImage)
elif args["descriptor"] == "lbp":
desc = LocalBinaryPatterns(24, 8)
gray = cv2.cvtColor(queryImage, cv2.COLOR_BGR2GRAY)
queryFeatures = desc.describe(gray)
elif args["descriptor"] == "hog":
winSize = (64, 64)
blockSize = (16, 16)
blockStride = (8, 8)
cellSize = (8, 8)
nbins = 9
derivAperture = 1
winSigma = 4.
histogramNormType = 0
L2HysThreshold = 2.0000000000000001e-01
