Python调用已训练好的caffe模型进行分类

# set up Python environment: numpy for numerical routines, and matplotlib for plotting
import numpy as np
import matplotlib.pyplot as plt
# display plots in this notebook
%matplotlib inline

# set display defaults
plt.rcParams['figure.figsize'] = (10, 10)        # large images
plt.rcParams['image.interpolation'] = 'nearest'  # don't interpolate: show square pixels
plt.rcParams['image.cmap'] = 'gray'  # use grayscale output rather than a (potentially misleading) color heatmap

# The caffe module needs to be on the Python path;
#  we'll add it here explicitly.
import sys
caffe_root = '/home/ubuntu/caffe/'  # this file should be run from {caffe_root}/examples (otherwise change this line)
sys.path.insert(0, caffe_root + 'python')

import caffe
# If you get "No module named _caffe", either you have not built pycaffe or you have the wrong path.

caffe.set_mode_gpu()

model_def = './deploy.prototxt'
model_pretrained = './snapshot_iter_6720.caffemodel'
# load the mean ImageNet image (as distributed with Caffe) for subtraction
MEAN_PROTO_PATH = './mean.binaryproto'   #这里是二进制文件，而不是Python的npy文件
blob = caffe.proto.caffe_pb2.BlobProto()
data = open(MEAN_PROTO_PATH, 'rb' ).read()
blob.ParseFromString(data)
array = np.array(caffe.io.blobproto_to_array(blob))# 将blob中的均值转换成numpy格式，array的shape （mean_number，channel, hight, width）
mu = array[0]
mean = mu.mean(1).mean(1)  # average over pixels to obtain the mean (BGR) pixel values

net = caffe.Classifier(model_def, model_pretrained,mean=mean,
channel_swap=(2,1,0),#RGB通道与BGR
raw_scale=255,#把图片归一化到0~1之间
image_dims=(256, 256))#设置输入图片的大小

label_list=['BAC','Caocx2','MUC','NEG','RBC','SPURM','WBC','XIAOYUAN','YEA','YISHUI']
input_image = caffe.io.load_image('1.jpg')#读取图片

#显示原图片，以及分类预测结果
prediction = net.predict([input_image])#图片分类
str_gender=label_list[prediction[0].argmax()]
print str_gender

plt.imshow(input_image)
plt.title(str_gender)
plt.show()

BAC