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README
Apache-2.0

常用二维码识别程序 QrCodeFind

【前言简介】

  • 基于百度 Paddle 飞浆AI图片目标检测框架开发
  • 能识别主流网络上流行的二维码(普通二维码,创意二维码,腾迅小程序,抖音码)
  • 简单易用,与docker一起部署,运行环境不用配置,docker拉下来即能使用!
  • 性能优秀,在 CPU i5 8G内存情况下,平均每张图片 0.065 秒
  • 基于 web 接口,返回 json 结果。
  • 能返回直观的目录检测结果,返回 具体位置框,置信度,框大小。

【模型和语料】

  语料训练量约2000张各类的二维码识图片语料
  • 二维码的尺寸基本上只要大于64*64 ,均能正常识别
  • 本程序能自动缓存已经下载过的图片,不用重复下载,有脚本程序定时任务每半小时把过期的缓存图片清理掉
  • 平均每张图片识别时间 < 0.1秒
  • 采用百度自研 PP-PicoDet 网络

【docker结构说明】

  • 本二维码识别的 docker 已经安装好相关的环境,实现开箱即用!
  • 所有的程序结构说明,所有程序都在 /root/
/root/
  |
  |- QrCodeFind/ ## 程序主体
          |
          |- tmp_dir/   ## 主要是接口的临时存在图片的目录,半小时删除过期的图片
          |
          |- py_ktools-master/ ## 这是自己的一个 python 的工具类
          |
          |- logs   ## 存在访问接口的日志,只保存最近七天的数据
          |
          |- model_file_l_416  ##模型准确率最高,速度较慢,平均0.15秒一张图片
          |
          |- model_file_m_416  ##模型准确率次之,速度较快,平均0.06秒一张图片
          |
          |- ppdet ## 百度 PP-PicoDet 模型结构
          |
          |- test_img ## 是待被批量测试的图片
          |
          |- test_img_output ## 输出被检测后的图片
          |
          |- deploy  ## 程序接口部署的目录(只列出主要)
                |
                |- config
                |     |
                |     |- picodet_qr_code.yml #接口的配置文件
                |
                |- python
                      |
                      |- infer_qrcode.py #接口启动的主文章



【程序部署】

有两种方式部署程序

  1. 可以使用 git clone --depth=1 https://gitee.com/kernaling/qr-code-find.git 然后自己安装其他组件,其中还需要自己拉取 py_ktools

  2. docker方式,强烈推荐使用此方式!

  • 基于 docker debian 直接线上拉取启动即可使用,开箱即用!
  • 拉取镜像: docker search qr-code-find
  • 马上启动: docker run -d -p 5001:5001 --name qr-code-find -it huangjiaqideepin/qr-code-find /bin/bash

【如何运行】

  第一个测试脚本程序
  • 确保准确前要完成的工作: 拉取 QrCodeFind 的 docker 并成功启动完其 docker后,进入此 docker 内部
  1. 执行以下命令,进入 qr-code-find
  #  cd /root/qr-code-find

  2. 执行以下命令,启动一个测试程序
  #  python3 deploy/python/infer.py --model_dir=./model_file_m_416 --image_dir=test_img --output_dir=test_img_output --enable_mkldnn=True --enable_mkldnn_bfloat16=True --cpu_threads=4

    就会有以下输出:
    class_id:3, confidence:0.9582, left_top:[96.20,105.00],right_bottom:[304.34,311.58]
    save result to: test_img_output/0c97d7ce97fa0b5356e82b43b4d7479d.png
    Test iter 12
    class_id:0, confidence:0.9727, left_top:[66.61,118.66],right_bottom:[234.74,288.66]
    save result to: test_img_output/01dba7a94a514f4fa191c015304fffba.png
    Test iter 13
    class_id:2, confidence:0.9842, left_top:[10.61,13.72],right_bottom:[465.95,463.47]
    save result to: test_img_output/2c93772f30aebb10d290757df5f9aef3.png
    Test iter 14
    ------------------ Inference Time Info ----------------------
    total_time(ms): 1130.3000000000002, img_num: 15
    average latency time(ms): 75.35, QPS: 13.270813
    preprocess_time(ms): 25.00, inference_time(ms): 50.30, postprocess_time(ms): 0.00

  3. 完成后,会在 test_img_output 下产生结果,各图片的二维码也会被画上分类和框框,置信度等

【如何运行】

   建立第一个实用的接口程序
  • 同理,先保证把 qr-code-find 拉取,并成功启动后,进入其 docker 内部

  • 进入 # cd /root/qr-code-find/

    1. 执行启动命令 # ./shell/start.sh
    2. 执行停止命令 # ./shell/stop.sh
  • 启动后,可以尝试查看日志

    1. 查看日志 # tail -f /root/logs/info.log
  • 访问接口

    1. 访问接口 # curl 'http://127.0.0.1:5001/qr_code_find?url=http://127.0.0.1:5001/2f857d70da946709d1ea11fbd95fab72.png&test_img=1&rate=0.6'
    2. 参数说明
      url: 表示对应需要分析的图片url,下载的图片会缓存到 tmp_dir 中,可以写脚本把其定时删除缓存
      rate: 表示置信度取值0~1之间,如:rate=0.6 表示置信度超过0.6才会显示出来。
      test_img:表示是否返回分析后为输出返回二维码框框的URL。
      ### [注意!!] ###
      test_img 此参数只用于图片演示测试,真实分析环境中请取消此参数,因为会影响性能!
  1. 返回的json 分析
  {
  "stat": 1, ## 状态,只有 stat=1 才会正常状态
  "data": [{  ## 如果一张图片有多个二维码,则数组会多个
    "class_id": 0,  ## 类别id
    "class_name": "qr_common", ## 表示 普通二维码
    "score": 0.9278374314308167, ## 表示是置信度
    "x_min": 203,  ## 位置,左上角 x
    "y_min": 323,  ## 位置,左上角 y
    "x_max": 279,  ## 位置,右下角 x
    "y_max": 397   ## 位置,右下角 y
  }, {
    "class_id": 0,
    "class_name": "qr_common",
    "score": 0.9244049787521362,
    "x_min": 52,
    "y_min": 328,
    "x_max": 124,
    "y_max": 399
  }, {
    "class_id": 0,
    "class_name": "qr_common",
    "score": 0.9178856015205383,
    "x_min": 360,
    "y_min": 324,
    "x_max": 432,
    "y_max": 396
  }],
  "test_img_url": "http://127.0.0.1:5001/d70d1553acfb6fc0b8c78b2b69da1f0b.jpg",  ### 此参数只传入 test_img=1 才会返回,用于演示分析图片二维码标记状态,仅用于测试。
  "msg": "",
  "time": 91  ## 耗用时(下载图片和分析图片总时间)
}
  1. 测试接口性能 执行并返回以下信息 # ab -n 100 -c 4 'http://127.0.0.1:5001/qr_code_find?url=http://127.0.0.1:5001/2f857d70da946709d1ea11fbd95fab72.png'
  Concurrency Level:      4
  Time taken for tests:   8.388 seconds
  Complete requests:      100
  Failed requests:        0
  Total transferred:      55900 bytes
  HTML transferred:       43100 bytes
  Requests per second:    11.92 [#/sec] (mean) ## 
  Time per request:       335.534 [ms] (mean)
  Time per request:       83.884 [ms] (mean, across all concurrent requests)
  Transfer rate:          6.51 [Kbytes/sec] received

  Connection Times (ms)
                min  mean[+/-sd] median   max
  Connect:        0    0   0.0      0       0
  Processing:   194  334  68.9    324     527
  Waiting:      193  334  68.7    324     527
  Total:        194  335  68.9    324     527
  1. 以上参数表示 4条线程,测试 100 次
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