# load the query image and show it queryImage = cv2.imread(args["dataset"] + "/" + args["query"]) # cv2.imshow("Query", queryImage) # print "query: %s" % (args["query"]),'\n<br/>' # describe the query in the same way that we did in # index.py -- a 3D RGB histogram with 8 bins per # channel desc = RGBHistogram([8, 8, 8]) queryFeatures = desc.describe(queryImage) # load the index perform the search #index = cPickle.loads(open(args["index"]).read()) s = SimilarImages() _index = s.findAll() index = {} for i in _index: features = i.get('features') # print cPickle.loads(features) # print cPickle.load(i.get('features')) index[i.get('name')] = cPickle.loads(features) # sys.exit(0) ; searcher = Searcher(index) results = searcher.search(queryFeatures) # initialize the two montages to display our results -- # we have a total of 25 images in the index, but let's only # display the top 10 results; 5 images per montage, with
# load the query image and show it queryImage = cv2.imread(args["dataset"] + "/" + args["query"]) # cv2.imshow("Query", queryImage) # print "query: %s" % (args["query"]),'\n<br/>' # describe the query in the same way that we did in # index.py -- a 3D RGB histogram with 8 bins per # channel desc = RGBHistogram([8, 8, 8]) queryFeatures = desc.describe(queryImage) # load the index perform the search # index = cPickle.loads(open(args["index"]).read()) s = SimilarImages() _index = s.findAll() index = {} for i in _index: features = i.get("features") # print cPickle.loads(features) # print cPickle.load(i.get('features')) index[i.get("name")] = cPickle.loads(features) # sys.exit(0) ; searcher = Searcher(index) results = searcher.search(queryFeatures) # initialize the two montages to display our results -- # we have a total of 25 images in the index, but let's only # display the top 10 results; 5 images per montage, with
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 # if(os.path.isfile(args["index"])): # # index = cPickle.loads(open(args["index"]).read()) # if(index.has_key(args["file"])): # print "has exist" # sys.exit(0) s = SimilarImages() i = s.findByName(args["file"]) if i != None: print "has exist" sys.exit(0) index = {} # initialize our image descriptor -- a 3D RGB histogram with # 8 bins per channel desc = RGBHistogram([8, 8, 8]) # load the image, describe it using our RGB histogram # descriptor, and update the index image = cv2.imread(args["dataset"] + "/" + args["file"])
"--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 # if(os.path.isfile(args["index"])): # # index = cPickle.loads(open(args["index"]).read()) # if(index.has_key(args["file"])): # print "has exist" # sys.exit(0) s = SimilarImages() i = s.findByName(args["file"]) if (i != None): print "has exist" sys.exit(0) index = {} # initialize our image descriptor -- a 3D RGB histogram with # 8 bins per channel desc = RGBHistogram([8, 8, 8]) # load the image, describe it using our RGB histogram # descriptor, and update the index image = cv2.imread(args["dataset"] + "/" + args["file"]) features = desc.describe(image)