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 # images that are 400x166 pixels
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 # images that are 400x166 pixels