caffe 红绿灯识别

#coding=utf-8  #加载必要的库  import numpy as np   import sys,os   #设置当前目录  caffe_root = '/home/ubuntu/caffe/'   sys.path.insert(0, caffe_root + 'python')  import caffe  os.chdir(caffe_root)   net_file='/home/ubuntu/Downloads/deep-learning-traffic-lights-master/model/deploy.prototxt'  caffe_model='/home/ubuntu/Downloads/deep-learning-traffic-lights-master/model/train_squeezenet_scratch_trainval_manual_p2__iter_8000.caffemodel'  mean_file=caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'   net = caffe.Net(net_file,caffe_model,caffe.TEST)  transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})  transformer.set_transpose('data', (2,0,1))  transformer.set_mean('data', np.load(mean_file).mean(1).mean(1))  transformer.set_raw_scale('data', 255)   transformer.set_channel_swap('data', (2,1,0))   im=caffe.io.load_image('/home/ubuntu/Downloads/deep-learning-traffic-lights-master/4.jpg')  net.blobs['data'].data[...] = transformer.preprocess('data',im)  out = net.forward()    imagenet_labels_filename = '/home/ubuntu/Downloads/deep-learning-traffic-lights-master/synset_words.txt'labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\t')   top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]  for i in np.arange(top_k.size):      print top_k[i], labels[top_k[i]]

synset_words.txt
yelloredgreen