Python调用已训练好的caffe模型进行分类

最新推荐文章于 2023-12-16 15:53:55 发布

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#python #caffe #deep learning

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python作为强大的解释型语言，其提供的库函数能够方便快速的实现常用的功能。本文用python调用caffe模型。

1.Setup

# 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

2.Load caffe

# 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.

3.Import Net

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

4.Classifier

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()

结果如下：

BAC