用java实施的电子商务平台太少了，使用spring cloud技术构建的b2b2c电子商务平台更少，大型企业分布式互联网电子商务平台，推出PC+微信+APP+云服务的云商平台系统，其中包括B2B、B2C、C2C、O2O、新零售、直播电商等子平台。需要JAVA Spring Cloud大型企业分布式微服务云构建的B2B2C电子商务平台源码 一零三八七七四六二六。
技术解决方案
开发语言： java、j2ee
数据库：mysql
JDK支持版本： JDK1.6、JDK1.7、JDK1.8版本
核心技术：分布式、云服务、微服务、服务编排等。
核心架构： 使用Spring Cloud分布式微服务云架构进行服务化开发，所有模块功能完全解耦，提供服务发现、注册、配置中心、消息总线、负载均衡、断路器、数据监控等。
技术列表：
Spring Cloud Config
配置管理工具包，让你可以把配置放到远程服务器，集中化管理集群配置，目前支持本地存储、Git以及Subversion
Spring Cloud Bus
事件、消息总线，用于在集群（例如，配置变化事件）中传播状态变化，可与Spring Cloud Config联合实现热部署
Eureka
云端服务发现，一个基于 REST 的服务，用于定位服务，以实现云端中间层服务发现和故障转移。
Hystrix
熔断器，容错管理工具，旨在通过熔断机制控制服务和第三方库的节点,从而对延迟和故障提供更强大的容错能力。
Zuul
Zuul 是在云平台上提供动态路由,监控,弹性,安全等边缘服务的框架。Zuul 相当于是设备和 Netflix 流应用的 Web 网站后端所有请求的前门。
Spring Cloud Security
基于spring security的安全工具包，为你的应用程序添加安全控制。
Feign
Feign是一种声明式、模板化的HTTP客户端。
Four Steps to Deep Learning
System Setup
Image Recognition
Object Detection
Segmentation
CUDA
一种并行计算技术
编写自己的图像识别程序.
https://github.com/dusty-nv/jetson-inference/tree/master/examples/my-recognition
/*
* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*/
// include imageNet header for image recognition
#include <jetson-inference/imageNet.h>
// include loadImage header for loading images
#include <jetson-utils/loadImage.h>
// main entry point
int main( int argc, char** argv )
{
// a command line argument containing the image filename is expected,
// so make sure we have at least 2 args (the first arg is the program)
if( argc < 2 )
{
printf("my-recognition: expected image filename as argument\n");
printf("example usage: ./my-recognition my_image.jpg\n");
return 0;
}
// retrieve the image filename from the array of command line args
const char* imgFilename = argv[1];
// these variables will be used to store the image data and dimensions
// the image data will be stored in shared CPU/GPU memory, so there are
// pointers for the CPU and GPU (both reference the same physical memory)
float* imgCPU = NULL; // CPU pointer to floating-point RGBA image data
float* imgCUDA = NULL; // GPU pointer to floating-point RGBA image data
int imgWidth = 0; // width of the image (in pixels)
int imgHeight = 0; // height of the image (in pixels)
// load the image from disk as float4 RGBA (32 bits per channel, 128 bits per pixel)
if( !loadImageRGBA(imgFilename, (float4**)&imgCPU, (float4**)&imgCUDA, &imgWidth, &imgHeight) )
{
printf("failed to load image '%s'\n", imgFilename);
return 0;
}
// load the GoogleNet image recognition network with TensorRT
// you can use imageNet::ALEXNET to load AlexNet model instead
imageNet* net = imageNet::Create(imageNet::GOOGLENET);
// check to make sure that the network model loaded properly
if( !net )
{
printf("failed to load image recognition network\n");
return 0;
}
// this variable will store the confidence of the classification (between 0 and 1)
float confidence = 0.0;
// classify the image with TensorRT on the GPU (hence we use the CUDA pointer)
// this will return the index of the object class that the image was recognized as (or -1 on error)
const int classIndex = net->Classify(imgCUDA, imgWidth, imgHeight, &confidence);
// make sure a valid classification result was returned
if( classIndex >= 0 )
{
// retrieve the name/description of the object class index
const char* classDescription = net->GetClassDesc(classIndex);
// print out the classification results
printf("image is recognized as '%s' (class #%i) with %f%% confidence\n",
classDescription, classIndex, confidence * 100.0f);
}
else
{
// if Classify() returned < 0, an error occurred
printf("failed to classify image\n");
}
// free the network's resources before shutting down
delete net;
// this is the end of the example!
return 0;
}
载入图像 loadImageRGBA
加载的图像存储于共享内存，映射到cpu和gpu.实际的内存里的image只有1份,cpu/gpu pointer指向的都是同一份物理内存。
The loaded image will be stored in shared memory that's mapped to both the CPU and GPU. There are two pointers available for access in the CPU and GPU address spaces, but there is really only one copy of the image in memory. Both the CPU and GPU pointers resolve to the same physical memory, without needing to perform memory copies (i.e. cudaMemcpy()).
载入神经网络模型
imageNet::Create()
GOOGLENET是一个预先训练好的模型,使用的数据集是ImageNet(注意不是imageNet对象).类别有1000个,包括了动植物,常见生活用品等.
// load the GoogleNet image recognition network with TensorRT
// you can use imageNet::ALEXNET to load AlexNet model instead
imageNet* net = imageNet::Create(imageNet::GOOGLENET);
// check to make sure that the network model loaded properly
if( !net )
{
printf("failed to load image recognition network\n");
return 0;
}
对图片进行分类
Classify返回的是类别对应的index
//this variable will store the confidence of the classification (www.hengtongyoule.com/ between 0 and 1)
float confidence = 0.0;
// classify the image with TensorRT on the GPU (hence we use the CUDA pointer)
// this will return the index of the object class that the image was recognized as (www.tianjiuyule178.com or -1 on error)
const int classIndex = net->Classify(imgCUDA,www.gaozhuoyiqi.com imgWidth, imgHeight, &confidence);
解释结果
// make sure a valid classification result was returned
if( classIndex >= 0 )
{
// retrieve the name/description of the object class index
const char* classDescription = net->GetClassDesc(classIndex);
// print out the classification results
printf("image is recognized as '%s'www.qwert888.com/ (class #%i) with %f%% confidence\n",
classDescription, classIndex, confidence * 100.0f);
}
else
{
// if Classify() returned <www.zhongyiyul.cn 0, an error occurred
printf("failed to classify image\n");
}
These descriptions of the 1000 classes are parsed from ilsvrc12_synset_words.txt when the network gets loaded (this file was previously downloaded when the jetson-inference repo was built).
退出
程序退出前要释放掉资源
// free the network's resources before shutting down
delete net;
// this is the end of the example!
return 0;
}
cmake文件
# require CMake 2.8 or greater
cmake_minimum_required(VERSION 2.8)
# declare my-recognition project
project(my-recognition)
# import jetson-inference and jetson-utils packages.
# note that if you didn't do "sudo make install"
# while building jetson-inference, this will error.
find_package(jetson-utils)
find_package(jetson-inference)
# CUDA and Qt4 are required
find_package(CUDA)
find_package(Qt4)
# setup Qt4 for build
include(${QT_USE_FILE})
add_definitions(${QT_DEFINITIONS})
# compile the my-recognition program
cuda_add_executable(my-www.yunshengyule178.com recognition my-recognition.cpp)
# link my-recognition to jetson-inference library
target_link_libraries(my-recognition jetson-inference)
没什么要特别说的,主要的依赖如下:
find_package(jetson-utils)
find_package(jetson-inference)
target_link_libraries(my-recognition jetson-inference)
实时图片识别
上面的代码展示的是本地图片的识别,这一节给出实时的摄像头拍摄图片识别的demo.
iamgenet-camera