这是机器未来的第4篇文章

写在前面：
? 博客简介：专注AIoT领域，追逐未来时代的脉搏，记录路途中的技术成长！
? 专栏简介：记录博主从0到1掌握物体检测工作流的过程，具备自定义物体检测器的能力
? 面向人群：具备深度学习理论基础的学生或初级开发者
? 专栏计划：接下来会逐步发布跨入人工智能的系列博文，敬请期待
? Python零基础快速入门系列
? 快速入门Python数据科学系列
? 人工智能开发环境搭建系列
? 机器学习系列
? 物体检测快速入门系列
? 自动驾驶物体检测系列
? ......

@[toc]

1. 概述

tensorflow object detection api一个框架，它可以很容易地构建、训练和部署对象检测模型，并且是一个提供了众多基于COCO数据集、Kitti数据集、Open Images数据集、AVA v2.1数据集和iNaturalist物种检测数据集上提供预先训练的对象检测模型集合。

kites_detections_output

tensorflow object detection api是目前最主流的目标检测框架之一，主流的目标检测模型如图所示：

snipaste20220513_094828

2. 预置条件

为了顺利按照本手册安装tensroflow object detection api，请参考Windows部署Docker GPU深度学习开发环境安装必备的工具。

若自行创建安装条件，请确保已经满足以下条件

? 支持python3.8以上版本
? 支持cuda、cudnn（可选）
? 支持git

本手册使用docker运行环境。

3. 安装步骤

3.1 Docker环境

3.1.1 启动docker

启动docker桌面客户端，如图所示：

3.1.2 启动容器

在windows平台可以启动命令行工具或者windows terminal工具(App Store下载)，这里使用terminal工具。

输入以下命令，查看当前存在的images列表

PS?C:\Users\xxxxx>?docker?images
REPOSITORY???????????????TAG?????????????????IMAGE?ID???????CREATED???????SIZE
docker/getting-started???latest??????????????bd9a9f733898???5?weeks?ago???28.8MB
tensorflow/tensorflow????2.8.0-gpu-jupyter???cc9a9ae2a5af???6?weeks?ago???5.99GB

可以看到之前安装的tensorflow-2.8.0-gpu-jupyter镜像，现在基于这个镜像启动容器

docker?run?--gpus?all?-itd?-v?e:/dockerdir/docker_work/:/home/zhou/?-p?8888:8888?-p?6006:6006?--ipc=host?cc9a9ae2a5af??jupyter?notebook?--no-browser?--ip=0.0.0.0?--allow-root?--NotebookApp.token=?--notebook-dir='/home/zhou/'

命令释义：docker run：表示基于镜像启动容器 --gpus all：不加此选项,nvidia-smi命令会不可用 -i: 交互式操作。-t: 终端。-d:后台运行，需要使用【docker exec -it 容器id /bin/bash】进入容器 -v e:/dockerdir/docker_work/:/home/zhou/:将windows平台的e:/dockerdir/docker_work/目录映射到docker的ubuntu系统的/home/zhou/目录下，实现windows平台和docker系统的文件共享 -p 8888:8888 -p 6006:6006：表示将windows系统的8888、6006端口映射到docker的8888、6006端口，这两个端口分别为jupyter notebook和tensorboard的访问端口 --ipc=host:用于多个容器之间的通讯 cc9a9ae2a5af:tensorflow-2.8.0-gpu-jupyter镜像的IMAGE ID jupyter notebook --no-browser --ip=0.0.0.0 --allow-root --NotebookApp.token= --notebook-dir='/home/zhou/': docker开机启动命令，这里启动jupyter

3.1.3 使用vscode访问docker container

启动vscode后，选择docker工具栏，在启动的容器上，右键选择附着到VsCode

3.1.4 更换docker容器ubuntu系统的安装源为国内源

在vscode软件界面上，选择【文件】-【打开文件夹】，选择根目录【/】，找到【/etc/apt/sources.list】,将ubuntu的安装源全部切换为aliyun源，具体操作为：将【archive.ubuntu.com】修改为【mirrors.aliyun.com】即可，修改后如下：

#?See?http://help.ubuntu.com/community/UpgradeNotes?for?how?to?upgrade?to
#?newer?versions?of?the?distribution.
deb?http://mirrors.aliyun.com/ubuntu/?focal?main?restricted
#?deb-src?http://mirrors.aliyun.com/ubuntu/?focal?main?restricted
##?Major?bug?fix?updates?produced?after?the?final?release?of?the
##?distribution.
deb?http://mirrors.aliyun.com/ubuntu/?focal-updates?main?restricted
#?deb-src?http://mirrors.aliyun.com/ubuntu/?focal-updates?main?restricted
##?N.B.?software?from?this?repository?is?ENTIRELY?UNSUPPORTED?by?the?Ubuntu
##?team.?Also,?please?note?that?software?in?universe?WILL?NOT?receive?any
##?review?or?updates?from?the?Ubuntu?security?team.
deb?http://mirrors.aliyun.com/ubuntu/?focal?universe
#?deb-src?http://mirrors.aliyun.com/ubuntu/?focal?universe
deb?http://mirrors.aliyun.com/ubuntu/?focal-updates?universe
#?deb-src?http://mirrors.aliyun.com/ubuntu/?focal-updates?universe
##?N.B.?software?from?this?repository?is?ENTIRELY?UNSUPPORTED?by?the?Ubuntu
##?team,?and?may?not?be?under?a?free?licence.?Please?satisfy?yourself?as?to
##?your?rights?to?use?the?software.?Also,?please?note?that?software?in
##?multiverse?WILL?NOT?receive?any?review?or?updates?from?the?Ubuntu
##?security?team.
deb?http://mirrors.aliyun.com/ubuntu/?focal?multiverse
#?deb-src?http://mirrors.aliyun.com/ubuntu/?focal?multiverse
deb?http://mirrors.aliyun.com/ubuntu/?focal-updates?multiverse
#?deb-src?http://mirrors.aliyun.com/ubuntu/?focal-updates?multiverse
##?N.B.?software?from?this?repository?may?not?have?been?tested?as
##?extensively?as?that?contained?in?the?main?release,?although?it?includes
##?newer?versions?of?some?applications?which?may?provide?useful?features.
##?Also,?please?note?that?software?in?backports?WILL?NOT?receive?any?review
##?or?updates?from?the?Ubuntu?security?team.
deb?http://mirrors.aliyun.com/ubuntu/?focal-backports?main?restricted?universe?multiverse
#?deb-src?http://mirrors.aliyun.com/ubuntu/?focal-backports?main?restricted?universe?multiverse
##?Uncomment?the?following?two?lines?to?add?software?from?Canonical's
##?'partner'?repository.
##?This?software?is?not?part?of?Ubuntu,?but?is?offered?by?Canonical?and?the
##?respective?vendors?as?a?service?to?Ubuntu?users.
#?deb?http://archive.canonical.com/ubuntu?focal?partner
#?deb-src?http://archive.canonical.com/ubuntu?focal?partner
deb?http://security.ubuntu.com/ubuntu/?focal-security?main?restricted
#?deb-src?http://security.ubuntu.com/ubuntu/?focal-security?main?restricted
deb?http://security.ubuntu.com/ubuntu/?focal-security?universe
#?deb-src?http://security.ubuntu.com/ubuntu/?focal-security?universe
deb?http://security.ubuntu.com/ubuntu/?focal-security?multiverse
#?deb-src?http://security.ubuntu.com/ubuntu/?focal-security?multiverse

? 执行如下命令，更新配置

apt-get?update;apt-get?-f?install;?apt-get?upgrade

? 更多aliyun的源配置访问：阿里云安装源传送门

3.1.5 验证GPU是否加载成功（在电脑有Nvidia显卡的情况下）

? 输入nvidia-smi查看GPU使用情况，nvcc -V查询cuda版本

root@cc58e655b170:/home/zhou#?nvidia-smi
Tue?Mar?22?15:08:57?2022???????
+-----------------------------------------------------------------------------+
|?NVIDIA-SMI?470.85???????Driver?Version:?472.47???????CUDA?Version:?11.4?????|
|-------------------------------+----------------------+----------------------+
|?GPU??Name????????Persistence-M|?Bus-Id????????Disp.A?|?Volatile?Uncorr.?ECC?|
|?Fan??Temp??Perf??Pwr:Usage/Cap|?????????Memory-Usage?|?GPU-Util??Compute?M.?|
|???????????????????????????????|??????????????????????|???????????????MIG?M.?|
|===============================+======================+======================|
|???0??NVIDIA?GeForce?...??Off??|?00000000:01:00.0?Off?|??????????????????N/A?|
|?N/A???48C????P8?????9W?/??N/A?|????153MiB?/??6144MiB?|????ERR!??????Default?|
|???????????????????????????????|??????????????????????|??????????????????N/A?|
+-------------------------------+----------------------+----------------------+
???????????????????????????????????????????????????????????????????????????????
+-----------------------------------------------------------------------------+
|?Processes:??????????????????????????????????????????????????????????????????|
|??GPU???GI???CI????????PID???Type???Process?name??????????????????GPU?Memory?|
|????????ID???ID???????????????????????????????????????????????????Usage??????|
|=============================================================================|
|??No?running?processes?found?????????????????????????????????????????????????|
+-----------------------------------------------------------------------------+
root@cc58e655b170:/home/zhou#?nvcc?-V
nvcc:?NVIDIA?(R)?Cuda?compiler?driver
Copyright?(c)?2005-2021?NVIDIA?Corporation
Built?on?Sun_Feb_14_21:12:58_PST_2021
Cuda?compilation?tools,?release?11.2,?V11.2.152
Build?cuda_11.2.r11.2/compiler.29618528_0

从nvcc -V的日志，可以看出cuda版本为11.2

? 输入以下命令，查询cuDNN版本

python?-c?"import?tensorflow?as?tf;print(tf.reduce_sum(tf.random.normal([1000,?1000])))"

输出结果如下：

root@cc58e655b170:/usr#?python?-c?"import?tensorflow?as?tf;print(tf.reduce_sum(tf.random.normal([1000,?1000])))"
2022-03-22?15:26:13.281719:?I?tensorflow/core/common_runtime/gpu/gpu_device.cc:1525]?Created?device?/job:localhost/replica:0/task:0/device:GPU:0?with?3951?MB?memory:??->?device:?0,?name:?NVIDIA?GeForce?GTX?1660?Ti,?pci?bus?id:?0000:01:00.0,?compute?capability:?7.5
tf.Tensor(-2613.715,?shape=(),?dtype=float32)

从输出日志，可以看到GPU：NVIDIA GeForce GTX 1660 Ti已经加载到docker，cuDNN版本为7.5

3.2 Windows开发环境

同Docker环境，验证cuda和cuDNN安装情况。

3.3 下载tensorflow object detection api项目源码

? 在home/zhou目录下创建tensorflow的目录

cd?/home/zhou;?mkdir?tensorflow;?cd?tensorflow

? 下载源码

git?clone?https://github.com/tensorflow/models.git

下载完毕后，默认文件名名称为models-master, 将文件名重命名为models,保持文件名和平台一致

mv?models-matser?models

如果网速不好，直接下载zip压缩包吧

下载完毕后的文档结构如图所示：

tensorflow/
└─?models/
???├─?community/
???├─?official/
???├─?orbit/
???├─?research/
???└──?...

3.4 安装配置protobuf

Tensorflow对象检测API使用Protobufs来配置模型和训练参数。在使用框架之前，必须下载并编译Protobuf库。

cd?/home/zhou

? 下载protobuf 这里下载的已经预编译好的protobuf

wget?-c?https://github.com/protocolbuffers/protobuf/releases/download/v3.19.4/protoc-3.19.4-linux-x86_64.zip

? 解压先执行mkdir protoc-3.19.4创建目录，然后执行unzip protoc-3.19.4-linux-x86_64.zip -d protoc-3.19.4/解压到制定目录protoc-3.19.4

root@cc58e655b170:/home/zhou#?mkdir?protoc-3.19.4
root@cc58e655b170:/home/zhou#?unzip?protoc-3.19.4-linux-x86_64.zip?-d?protoc-3.19.4/
Archive:??protoc-3.19.4-linux-x86_64.zip
???creating:?protoc-3.19.4/include/
???creating:?protoc-3.19.4/include/google/
???creating:?protoc-3.19.4/include/google/protobuf/
??inflating:?protoc-3.19.4/include/google/protobuf/wrappers.proto??
??inflating:?protoc-3.19.4/include/google/protobuf/source_context.proto??
??inflating:?protoc-3.19.4/include/google/protobuf/struct.proto??
??inflating:?protoc-3.19.4/include/google/protobuf/any.proto??
??inflating:?protoc-3.19.4/include/google/protobuf/api.proto??
??inflating:?protoc-3.19.4/include/google/protobuf/descriptor.proto??
???creating:?protoc-3.19.4/include/google/protobuf/compiler/
??inflating:?protoc-3.19.4/include/google/protobuf/compiler/plugin.proto??
??inflating:?protoc-3.19.4/include/google/protobuf/timestamp.proto??
??inflating:?protoc-3.19.4/include/google/protobuf/field_mask.proto??
??inflating:?protoc-3.19.4/include/google/protobuf/empty.proto??
??inflating:?protoc-3.19.4/include/google/protobuf/duration.proto??
??inflating:?protoc-3.19.4/include/google/protobuf/type.proto??
???creating:?protoc-3.19.4/bin/
??inflating:?protoc-3.19.4/bin/protoc??
??inflating:?protoc-3.19.4/readme.txt??

? 配置protoc 在~/.bashrc文件的末尾添加如下代码

export?PATH=$PATH:/home/zhou/protoc-3.19.4/bin

执行如下命令，使其生效

source?~/.bashrc

执行echo $PATH查看是否生效

root@cc58e655b170:/home/zhou/protoc-3.19.4/bin#?echo?$PATH
/home/zhou/protoc-3.19.4/bin:/home/zhou/protoc-3.19.4/bin:/home/zhou/protoc-3.19.4/bin:/root/.vscode-server/bin/c722ca6c7eed3d7987c0d5c3df5c45f6b15e77d1/bin/remote-cli:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/home/zhou/protoc-3.19.4/bin

可以看到protoc的安装目录/home/zhou/protoc-3.19.4/bin已经添加到PATH了。

3.5 将proto后缀文件转换为python可识别格式

cd?/home/zhou/tensorflow/models/research/

?ls?object_detection/protos/

?
4
转换proto文件格式为python可识别序列化文件

protoc?object_detection/protos/*.proto?--python_out=.

? 转换后，如下所示

?ls?object_detection/protos/

3.6 安装coco api

从TensorFlow 2.x开始, pycocotools包被列为对象检测API的依赖项。理想情况下，这个包应该在安装对象检测API时安装，如下面安装对象检测API一节所述，但是由于各种原因，安装可能会失败，因此更简单的方法是提前安装这个包，在这种情况下，稍后的安装将被跳过。

pip?install?cython
pip?install?git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI

默认指标是基于Pascal VOC评估中使用的那些指标。要使用COCO对象检测指标，在配置文件的eval_config消息中添加metrics_set: "coco_detection_metrics"。要使用COCO实例分割度量，在配置文件的eval_config消息中添加metrics_set: "coco_mask_metrics"。

3.7 安装object detection api

? 当前的工作路径应为

root@cc58e655b170:/home/zhou/tensorflow/models/research#?pwd
/home/zhou/tensorflow/models/research

? 安装object detection api

cp?object_detection/packages/tf2/setup.py?.
python?-m?pip?install?--use-feature=2020-resolver?.

安装过程会持续一段时间，安装完毕后，可以执行如下代码，测试安装是否完成。

python?object_detection/builders/model_builder_tf2_test.py

输出如下：

......
I0322?16:48:09.677789?140205126002496?efficientnet_model.py:144]?round_filter?input=192?output=384
I0322?16:48:10.876914?140205126002496?efficientnet_model.py:144]?round_filter?input=192?output=384
I0322?16:48:10.877072?140205126002496?efficientnet_model.py:144]?round_filter?input=320?output=640
I0322?16:48:11.294571?140205126002496?efficientnet_model.py:144]?round_filter?input=1280?output=2560
I0322?16:48:11.337533?140205126002496?efficientnet_model.py:454]?Building?model?efficientnet?with?params?ModelConfig(width_coefficient=2.0,?depth_coefficient=3.1,?resolution=600,?dropout_rate=0.5,?blocks=(BlockConfig(input_filters=32,?output_filters=16,?kernel_size=3,?num_repeat=1,?expand_ratio=1,?strides=(1,?1),?se_ratio=0.25,?id_skip=True,?fused_conv=False,?conv_type='depthwise'),?BlockConfig(input_filters=16,?output_filters=24,?kernel_size=3,?num_repeat=2,?expand_ratio=6,?strides=(2,?2),?se_ratio=0.25,?id_skip=True,?fused_conv=False,?conv_type='depthwise'),?BlockConfig(input_filters=24,?output_filters=40,?kernel_size=5,?num_repeat=2,?expand_ratio=6,?strides=(2,?2),?se_ratio=0.25,?id_skip=True,?fused_conv=False,?conv_type='depthwise'),?BlockConfig(input_filters=40,?output_filters=80,?kernel_size=3,?num_repeat=3,?expand_ratio=6,?strides=(2,?2),?se_ratio=0.25,?id_skip=True,?fused_conv=False,?conv_type='depthwise'),?BlockConfig(input_filters=80,?output_filters=112,?kernel_size=5,?num_repeat=3,?expand_ratio=6,?strides=(1,?1),?se_ratio=0.25,?id_skip=True,?fused_conv=False,?conv_type='depthwise'),?BlockConfig(input_filters=112,?output_filters=192,?kernel_size=5,?num_repeat=4,?expand_ratio=6,?strides=(2,?2),?se_ratio=0.25,?id_skip=True,?fused_conv=False,?conv_type='depthwise'),?BlockConfig(input_filters=192,?output_filters=320,?kernel_size=3,?num_repeat=1,?expand_ratio=6,?strides=(1,?1),?se_ratio=0.25,?id_skip=True,?fused_conv=False,?conv_type='depthwise')),?stem_base_filters=32,?top_base_filters=1280,?activation='simple_swish',?batch_norm='default',?bn_momentum=0.99,?bn_epsilon=0.001,?weight_decay=5e-06,?drop_connect_rate=0.2,?depth_divisor=8,?min_depth=None,?use_se=True,?input_channels=3,?num_classes=1000,?model_name='efficientnet',?rescale_input=False,?data_format='channels_last',?dtype='float32')
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config):?33.12s
I0322?16:48:11.521103?140205126002496?test_util.py:2373]?time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config):?33.12s
[???????OK?]?ModelBuilderTF2Test.test_create_ssd_models_from_config
[?RUN??????]?ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update):?0.0s
I0322?16:48:11.532667?140205126002496?test_util.py:2373]?time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update):?0.0s
[???????OK?]?ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
[?RUN??????]?ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold):?0.0s
I0322?16:48:11.535152?140205126002496?test_util.py:2373]?time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold):?0.0s
[???????OK?]?ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
[?RUN??????]?ModelBuilderTF2Test.test_invalid_model_config_proto
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto):?0.0s
I0322?16:48:11.535965?140205126002496?test_util.py:2373]?time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto):?0.0s
[???????OK?]?ModelBuilderTF2Test.test_invalid_model_config_proto
[?RUN??????]?ModelBuilderTF2Test.test_invalid_second_stage_batch_size
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size):?0.0s
I0322?16:48:11.539124?140205126002496?test_util.py:2373]?time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size):?0.0s
[???????OK?]?ModelBuilderTF2Test.test_invalid_second_stage_batch_size
[?RUN??????]?ModelBuilderTF2Test.test_session
[??SKIPPED?]?ModelBuilderTF2Test.test_session
[?RUN??????]?ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor):?0.0s
I0322?16:48:11.542018?140205126002496?test_util.py:2373]?time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor):?0.0s
[???????OK?]?ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
[?RUN??????]?ModelBuilderTF2Test.test_unknown_meta_architecture
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture):?0.0s
I0322?16:48:11.543226?140205126002496?test_util.py:2373]?time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture):?0.0s
[???????OK?]?ModelBuilderTF2Test.test_unknown_meta_architecture
[?RUN??????]?ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor):?0.0s
I0322?16:48:11.545147?140205126002496?test_util.py:2373]?time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor):?0.0s
[???????OK?]?ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
----------------------------------------------------------------------
Ran?24?tests?in?42.982s
OK?(skipped=1)

看到结果为OK,则表示安装成功，接下来就可以开始物体检测之旅了。

? 《物体检测快速入门系列》快速导航：

? 物体检测快速入门系列(1)-基于Tensorflow2.x Object Detection API构建自定义物体检测器
? 物体检测快速入门系列(2)-Windows部署GPU深度学习开发环境
? 物体检测快速入门系列(3)-Windows部署Docker GPU深度学习开发环境
? 物体检测快速入门系列(4)-TensorFlow 2.x Object Detection API快速安装手册

一文掌握物体检测库TensorFlow 2.x Object Detection安装

1. 概述

2. 预置条件

3. 安装步骤

3.1 Docker环境

3.1.1 启动docker

3.1.2 启动容器

3.1.3 使用vscode访问docker container

3.1.4 更换docker容器ubuntu系统的安装源为国内源

3.1.5 验证GPU是否加载成功（在电脑有Nvidia显卡的情况下）

3.2 Windows开发环境

3.3 下载tensorflow object detection api项目源码

3.4 安装配置protobuf

3.5 将proto后缀文件转换为python可识别格式

3.6 安装coco api

3.7 安装object detection api

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一文掌握物体检测库TensorFlow 2.x Object Detection安装

1. 概述

2. 预置条件

3. 安装步骤

3.1 Docker环境

3.1.1 启动docker

3.1.2 启动容器

3.1.3 使用vscode访问docker container

3.1.4 更换docker容器ubuntu系统的安装源为国内源

3.1.5 验证GPU是否加载成功（在电脑有Nvidia显卡的情况下）

3.2 Windows开发环境

3.3 下载tensorflow object detection api项目源码

3.4 安装配置protobuf

3.5 将proto后缀文件转换为python可识别格式

3.6 安装coco api

3.7 安装object detection api

相关推荐

取消回复欢迎 你 发表评论:

一个小时多点，完成scrapy爬取官方网站新房的数据，50块到手

C语言char同时读取多个输入字符并打印

爬虫项目:实现京东全网爬虫 京东爬虫跳过登录页

C语言总结_格式化打印函数、字符串、运算符

信息系统安全:软件系统安全

嵌入式开发C语言编程的那些编程思路与技巧

volatile很难?由浅入深怼到CPU汇编，彻底搞清楚它的底层原理

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爬虫项目:实现京东全网爬虫京东爬虫跳过登录页