if not is_exist(layer, 'envarea'): fieldDefn = ogr.FieldDefn('envarea', ogr.OFTReal) layer.CreateField(fieldDefn) numFeatures = layer.GetFeatureCount() print 'Total region count:', numFeatures #test img = None TEST = False if TEST == True: feature = layer.GetNextFeature() img, env, maxlen, envarea = getRegion(g_raster, feature) scores = net.classify(img, False) is_car = net.top_k_prediction(scores, 2) if is_car[1][0] == 'car': print "Woh...a car..." raw_input("enter any character break:") break else: # loop through the regions and predict them pbar = progressbar.ProgressBar(maxval=numFeatures).start() cnt = 0 feature = layer.GetNextFeature() while feature: # 获取对应的图像样本 img, env, maxlen, envarea= getRegion(g_raster, feature) scores = net.classify(img, False)
if len(sys.argv) == 1: car = smalldata.car() else: print "Using " + sys.argv[1] car = imread(sys.argv[1]) Decaf = False if Decaf: from kitnet import DecafNet as KitNet kit_net = KitNet() # print car.shape car = car.reshape((40,40,1)) scores = kit_net.classify(car) print 'Is car ? prediction:', kit_net.top_k_prediction(scores, 1) car_conv3 = kit_net.feature("conv3_neuron_cudanet_out") #conv3_cudanet_out mid_convs = car_conv3.reshape((car_conv3.shape[0],-1)) else: os.chdir("E:/2013/cuda-convnet/trunk") # sys.path.append("E:/2013/cuda-convnet/trunk") from show_pred import model as car_model scores = car_model.show_predictions(car) print 'Is car ? prediction:', scores[-1] mid_convs = car_model.get_features(car) os.chdir(os.path.dirname(os.path.realpath(__file__))) # visualize.draw_net_to_file(net._net, "decafnet.png") #bug! # print 'Network structure written to decafnet.png' net = DecafNet()