# -*- coding:utf-8 -*- # 用于模型的单张图像分类操作 import os os.environ['GLOG_minloglevel'] = '2' # 将caffe的输出log信息不显示，必须放到import caffe前 import caffe # caffe 模块 from caffe.proto import caffe_pb2 from google.protobuf import text_format import numpy as np import cv2 import matplotlib.pyplot as plt import time # 分类单张图像img def classification(img, net, transformer, synset_words): im = caffe.io.load_image(img) # 导入输入图像 net.blobs['data'].data[...] = transformer.preprocess('data', im) start = time.clock() # 执行测试 net.forward() end = time.clock() print('classification time: %f s' % (end - start)) # 查看目标检测结果 labels = np.loadtxt(synset_words, str, delimiter='\t') category = net.blobs['prob'].data[0].argmax() class_str = labels[int(category)].split(',') class_name = class_str[0] # text_font = cv2.cv.InitFont(cv2.cv.CV_FONT_HERSHEY_SCRIPT_SIMPLEX, 1, 1, 0, 3, 8) cv2.putText(im, class_name, (0, im.shape[0]), cv2.cv.CV_FONT_HERSHEY_SIMPLEX, 1, (55, 255, 155), 2) # 显示结果 plt.imshow(im, 'brg') plt.show() #CPU或GPU模型转换 caffe.set_mode_cpu() #caffe.set_device(0) #caffe.set_mode_gpu() caffe_root = '../../' # 网络参数（权重）文件 caffemodel = caffe_root + 'models/bvlc_alexnet/bvlc_alexnet.caffemodel' # 网络实施结构配置文件 deploy = caffe_root + 'models/bvlc_alexnet/deploy.prototxt' img_root = caffe_root + 'data/VOCdevkit/VOC2007/JPEGImages/' synset_words = caffe_root + 'data/ilsvrc12/synset_words.txt' # 网络实施分类 net = caffe.Net(deploy, # 定义模型结构 caffemodel, # 包含了模型的训练权值 caffe.TEST) # 使用测试模式(不执行dropout) # 加载ImageNet图像均值 (随着Caffe一起发布的) mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy') mu = mu.mean(1).mean(1) # 对所有像素值取平均以此获取BGR的均值像素值 # 图像预处理 transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape}) transformer.set_transpose('data', (2,0,1)) transformer.set_mean('data', mu) transformer.set_raw_scale('data', 255) transformer.set_channel_swap('data', (2,1,0)) # 处理图像 while 1: img_num = raw_input("Enter Img Number: ") if img_num == '': break img = img_root + '{:0>6}'.format(img_num) + '.jpg' classification(img,net,transformer,synset_words)