linux系统安装caffe

sudo apt-get update
sudo apt-get install -y build-essential cmake git pkg-config
sudo apt-get install -y libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev
sudo apt-get install -y libhdf5-serial-dev protobuf-compiler
sudo apt-get install -y libgflags-dev libgoogle-glog-dev liblmdb-dev
sudo apt-get install -y python-dev python-numpy python-pip

git clone https://github.com/BVLC/caffe.git
cd caffe

cp Makefile.config.example Makefile.config

# Uncomment to use CUDA
USE_CUDA := 1

make all -j$(nproc)
make test -j$(nproc)
make runtest -j$(nproc)

make pycaffe -j$(nproc)

export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH

import caffe
import numpy as np
from PIL import Image

# 加载模型和权重
model_def = 'path/to/deploy.prototxt'
model_weights = 'path/to/model.caffemodel'

net = caffe.Net(model_def, model_weights, caffe.TEST)

# 加载并预处理图像
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))
transformer.set_mean('data', np.array([104,117,123]))  # 根据数据集调整均值
transformer.set_raw_scale('data', 255)
transformer.set_channel_swap('data', (2,1,0))

image = caffe.io.load_image('path/to/image.jpg')
transformed_image = transformer.preprocess('data', image)

# 设置输入数据并进行前向传播
net.blobs['data'].data[...] = transformed_image
output = net.forward()

# 输出分类结果
output_prob = output['prob'][0]
print('Predicted class is:', output_prob.argmax())