def test(intercept, featureWeight): testFeatures = getFeatures(0) region = testFeatures.region score = [] for i in range(len(region)): score.append({}) fOut = open("../txt/result.txt",'w') for i in range(len(region)): for query in region[i]: score[i][query] = {} for url in region[i][query]: score[i][query][url] = \ dot_product(region[i][query][url],featureWeight) + intercept url_sorted_by_value = OrderedDict(sorted(score[i][query].items(), key=lambda x: x[1])) url_sorted_by_value_r = list(reversed(url_sorted_by_value)) out = str(query) + " " + str(i) + " " for u in url_sorted_by_value_r: out = out + str(u) + " " out = out+ "\n" fOut.write(out)
def consolidate_features(base_graphs, Gcollab_delta, k): features = {} Gcollab = base_graphs[graphutils.Graph.COLLAB] feature_graphs = graphutils.split_feat_graphs(base_graphs) for node in Gcollab.Nodes(): nodeID = node.GetId() for neighborID in graphutils.getKHopN(Gcollab, nodeID, k): if nodeID > neighborID: # swap nodeID = neighborID + nodeID neighborID = nodeID - neighborID nodeID = nodeID - neighborID if (nodeID, neighborID) in features: continue features[(nodeID, neighborID)] = [] for graph in feature_graphs: features = getFeatures(Gcollab, Gcollab_delta, graph, features) return features
def consolidate_features(base_graphs, Gcollab_delta, k): features = {} Gcollab = base_graphs[graphutils.Graph.COLLAB] feature_graphs = graphutils.split_feat_graphs(base_graphs) for node in Gcollab.Nodes(): nodeID= node.GetId() for neighborID in graphutils.getKHopN(Gcollab, nodeID, k): if nodeID > neighborID: # swap nodeID= neighborID + nodeID neighborID= nodeID - neighborID nodeID= nodeID - neighborID if (nodeID, neighborID) in features: continue features[(nodeID, neighborID)]= [] for graph in feature_graphs: features = getFeatures(Gcollab, Gcollab_delta, graph, features) return features
import matplotlib.pyplot as plt if __name__ == '__main__': # setup video capture cap = cv2.VideoCapture("input_videos/Easy.mp4") ret,img1 = cap.read() ret,img2 = cap.read() cap.release() maxCorners = 20 qualityLevel = 0.01 minDistance = 8 bbox_list = [] bbox_pts = [] bbox_list, bbox_pts, new_img = getBoundingBox(img1, bbox_list, bbox_pts) img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) startXs, startYs, _ = getFeatures(img1_gray, bbox_list, maxCorners, qualityLevel, minDistance) newXs = startXs + 5 newYs = startYs + 7 print(startXs, startYs) print(newXs, newYs) #nnewXs, nnewYs = estimateAllTranslation(newXs, newYs, img2, img3) #print(len(newXs[0])) #print(len(newYs[0])) plt.figure() plt.imshow(img2) #plt.plot(newYs, newXs, 'w+') plt.axis('off') plt.show()
import time import commands from getFeatures import * from featureProcess import getAverage from featureProcess import normalize from rank import * print '##################################' print '# Program start #' print '##################################' print '-Stage 1- extract features..' trainStruct = getFeatures(1) print '-Stage 2- processing features..' trainStruct = getAverage(trainStruct) print '-Stage 3- normalizing features..' trainStruct = normalize(trainStruct) print '-Stage 4- generating training set for Weka..' arffGen(trainStruct.region) time.sleep(1) print '-Stage 5- getting trained model parameters..' beta = commands.getoutput("java WekaTester trainClickThrough.arff").split('\n') intercept = float(beta[0]) beta = beta[1:len(beta)-1] for i,item in enumerate(beta): beta[i] = float(item) print beta[i] print '-Stage 6- get test set..' test(intercept,beta)