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()
