if __name__ == '__main__':
"""A simple demo showing how to run decafnet."""
from decaf.util import smalldata, visualize
logging.getLogger().setLevel(logging.INFO)
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]
img = r.ReadAsArray(cx ,cy , w, h)
img = img.swapaxes(0,2).swapaxes(0,1)
img = rgb2gray(img)
img = resize(img, (in_size, in_size), mode='wrap')
return img_as_ubyte(img.reshape((in_size, in_size,1))), (cx,cy,w,h), max_len, env_area
#io.imsave("segementation/%s_%s_%s_%s.png" % \
# (lu_offset_x, lu_offset_y, w, h), img)
#tmp = cv2.imread("segementation/%s_%s_%s_%s.png" % \
# (lu_offset_x, lu_offset_y, w, h))
#return cv2.resize(tmp, (256,256), interpolation=cv2.INTER_LINEAR)
#return resize(img, (256,256))
# 加载 decaf 和 classifier
from kitnet import DecafNet as KitNet
#from kit_angle_net import DecafNet as AngleNet
net = KitNet()
#angle = AngleNet()
# 读取栅格图像
gdal.AllRegister()
if len(sys.argv) != 3:
print "Usage: segement_detection.py path_image_folder path_shp_folder"
sys.exit()
else:
image_folder = sys.argv[1]
shp_folder = sys.argv[2]
#start = int(sys.argv[3])
#end = int(sys.argv[4])
shp_list = []
def load_net(net_file=None, meta_file=None):
global NET, IMG, GRAY
logging.info("Loading DecafNet...")
NET = DecafNet(net_file,meta_file)
logging.info("Loading default image...")