├── .github └── workflows │ ├── publish-ubuntu.yaml │ └── publish-windows.yaml ├── .gitignore ├── LICENSE ├── README.md ├── batch_work.py ├── custom_formatter.py ├── custom_qwidget.py ├── docs └── result.png ├── file_version_info.txt ├── models ├── detect.caffemodel ├── detect.prototxt ├── sr.caffemodel └── sr.prototxt ├── pyqt5_qr_scan.py ├── qrscan.ico ├── qrscan.png ├── requirements.txt ├── resources.py ├── resources.qrc ├── scripts ├── config_env.bat ├── config_env.sh ├── publish.bat └── publish.sh ├── sql_helper.py └── utils.py /.github/workflows/publish-ubuntu.yaml: -------------------------------------------------------------------------------- 1 | name: Ubuntu 2 | 3 | on: 4 | push: 5 | branches: 6 | - '*' 7 | tags: 8 | - '*' 9 | 10 | jobs: 11 | deploy: 12 | name: build and run 13 | runs-on: ubuntu-latest 14 | steps: 15 | - uses: actions/checkout@v3 16 | - uses: actions/setup-python@v4 17 | with: 18 | python-version: '3.10' 19 | architecture: 'x64' 20 | - name: download code and run 21 | env: 22 | TZ: Asia/Shanghai 23 | run: | 24 | sudo apt update && sudo apt install python3-pip python3-venv zip unzip 25 | mv scripts/config_env.sh ./ 26 | mv scripts/publish.sh ./ 27 | 28 | - name: Configure Environments 29 | run: | 30 | chmod +x config_env.sh 31 | ./config_env.sh 32 | 33 | - name: Set version 34 | if: contains(github.ref, 'tags/') 35 | run: | 36 | export VERSION="${{ github.ref_name }}" 37 | export VERSION="${VERSION#v}" 38 | export MAJOR="${VERSION%%.*}" 39 | export MINOR="${VERSION#*.}" 40 | export PATCH="${MINOR#*.}" 41 | export MINOR="${MINOR%%.*}" 42 | export HASH="${GITHUB_SHA::8}" 43 | export DATETIME=$(date +%Y%m%d%H%M) 44 | 45 | echo "VERSION=${MAJOR}.${MINOR}.${PATCH}" 46 | echo "ID=${HASH}_${DATETIME}" 47 | 48 | sed -i "s/V_MAJOR/${MAJOR}/g" file_version_info.txt 49 | sed -i "s/V_MINOR/${MINOR}/g" file_version_info.txt 50 | sed -i "s/V_PATCH/${PATCH}/g" file_version_info.txt 51 | sed -i "s/COMMIT_HASH/${HASH}/g" file_version_info.txt 52 | sed -i "s/YYYYMMDDHHMM/${DATETIME}/g" file_version_info.txt 53 | sed -i "s#setWindowTitle.*#setWindowTitle(\"图片二维码检测识别 ${{ github.server_url }}/${{ github.repository }}/releases/tag/${{ github.ref_name }}\")#g" custom_qwidget.py 54 | 55 | 56 | - name: Publish Release 57 | run: | 58 | chmod +x publish.sh 59 | ./publish.sh 60 | 61 | - name: Upload release file 62 | if: contains(github.ref, 'tags/') 63 | uses: actions/upload-artifact@v3 64 | with: 65 | name: release 66 | path: QrScan.zip 67 | retention-days: 1 68 | 69 | 70 | upload: 71 | name: Upload Ubuntu Release 72 | runs-on: ubuntu-latest 73 | needs: deploy 74 | # 只在tag时执行，即在自己终端运行以下代码后触发 75 | # git tag -a v0.1.0 -m "release 0.1.0 version" 76 | # git push origin --tags 77 | if: contains(github.ref, 'tags/') 78 | steps: 79 | - name: Download release file 80 | uses: actions/download-artifact@v3 81 | with: 82 | name: release 83 | - name: Create Ubuntu Release 84 | run: | 85 | mv QrScan.zip QrScan_linux_${{ github.ref_name }}.zip 86 | - name: Upload Ubuntu Release 87 | uses: softprops/action-gh-release@v1 88 | with: 89 | files: QrScan_linux_${{ github.ref_name }}.zip -------------------------------------------------------------------------------- /.github/workflows/publish-windows.yaml: -------------------------------------------------------------------------------- 1 | name: Windows 2 | 3 | on: 4 | push: 5 | branches: 6 | - '*' 7 | tags: 8 | - '*' 9 | 10 | jobs: 11 | deploy: 12 | name: build and run 13 | runs-on: windows-latest 14 | steps: 15 | - uses: actions/checkout@v3 16 | - uses: actions/setup-python@v4 17 | with: 18 | python-version: '3.10' 19 | architecture: 'x64' 20 | - name: download code and run 21 | env: 22 | TZ: Asia/Shanghai 23 | run: | 24 | move scripts/config_env.bat ./ 25 | move scripts/publish.bat ./ 26 | 27 | - name: Configure Environments 28 | run: | 29 | ./config_env.bat 30 | 31 | - name: Set version 32 | shell: bash 33 | run: | 34 | if [[ "${{ github.ref }}" == "refs/tags/"* ]]; then 35 | export VERSION="${{ github.ref_name }}" 36 | else 37 | export VERSION="v0.0.0" 38 | fi 39 | export VERSION="${VERSION#v}" 40 | export MAJOR="${VERSION%%.*}" 41 | export MINOR="${VERSION#*.}" 42 | export PATCH="${MINOR#*.}" 43 | export MINOR="${MINOR%%.*}" 44 | export HASH="${GITHUB_SHA::8}" 45 | export DATETIME=$(date +%Y%m%d%H%M) 46 | 47 | echo "VERSION=${MAJOR}.${MINOR}.${PATCH}" 48 | echo "ID=${HASH}_${DATETIME}" 49 | 50 | sed -i "s/V_MAJOR/${MAJOR}/g" file_version_info.txt 51 | sed -i "s/V_MINOR/${MINOR}/g" file_version_info.txt 52 | sed -i "s/V_PATCH/${PATCH}/g" file_version_info.txt 53 | sed -i "s/COMMIT_HASH/${HASH}/g" file_version_info.txt 54 | sed -i "s/YYYYMMDDHHMM/${DATETIME}/g" file_version_info.txt 55 | sed -i "s#setWindowTitle.*#setWindowTitle(\"图片二维码检测识别 ${{ github.server_url }}/${{ github.repository }}/releases/tag/${{ github.ref_name }}\")#g" custom_qwidget.py 56 | 57 | - name: Publish Release 58 | run: | 59 | ./publish.bat 60 | 61 | - name: Upload release file 62 | uses: actions/upload-artifact@v3 63 | with: 64 | name: release 65 | path: QrScan.zip 66 | retention-days: 1 67 | 68 | upload: 69 | name: Upload Windows Release 70 | runs-on: ubuntu-latest 71 | needs: deploy 72 | # 只在tag时执行，即在自己终端运行以下代码后触发 73 | # git tag -a v0.1.0 -m "release 0.1.0 version" 74 | # git push origin --tags 75 | if: contains(github.ref, 'tags/') 76 | steps: 77 | - name: Download release file 78 | uses: actions/download-artifact@v3 79 | with: 80 | name: release 81 | - name: Create Windows Release 82 | run: | 83 | mv QrScan.zip QrScan_windows_${{ github.ref_name }}.zip 84 | - name: Upload Windows Release 85 | uses: softprops/action-gh-release@v1 86 | with: 87 | files: QrScan_windows_${{ github.ref_name }}.zip -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | [Ll]og/ 6 | *.db 7 | *.bat 8 | *.sh 9 | *.zip 10 | 11 | # C extensions 12 | *.so 13 | 14 | # Distribution / packaging 15 | .Python 16 | bin/ 17 | build/ 18 | develop-eggs/ 19 | dist/ 20 | downloads/ 21 | eggs/ 22 | .eggs/ 23 | lib/ 24 | lib64/ 25 | parts/ 26 | sdist/ 27 | var/ 28 | wheels/ 29 | pip-wheel-metadata/ 30 | share/python-wheels/ 31 | *.egg-info/ 32 | .installed.cfg 33 | *.egg 34 | MANIFEST 35 | 36 | # PyInstaller 37 | # Usually these files are written by a python script from a template 38 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 39 | *.manifest 40 | *.spec 41 | 42 | # Installer logs 43 | pip-log.txt 44 | pip-delete-this-directory.txt 45 | 46 | # Unit test / coverage reports 47 | htmlcov/ 48 | .tox/ 49 | .nox/ 50 | .coverage 51 | .coverage.* 52 | .cache 53 | nosetests.xml 54 | coverage.xml 55 | *.cover 56 | *.py,cover 57 | .hypothesis/ 58 | .pytest_cache/ 59 | 60 | # Translations 61 | *.mo 62 | *.pot 63 | 64 | # Django stuff: 65 | *.log 66 | local_settings.py 67 | db.sqlite3 68 | db.sqlite3-journal 69 | 70 | # Flask stuff: 71 | instance/ 72 | .webassets-cache 73 | 74 | # Scrapy stuff: 75 | .scrapy 76 | 77 | # Sphinx documentation 78 | docs/_build/ 79 | 80 | # PyBuilder 81 | target/ 82 | 83 | # Jupyter Notebook 84 | .ipynb_checkpoints 85 | 86 | # IPython 87 | profile_default/ 88 | ipython_config.py 89 | 90 | # pyenv 91 | .python-version 92 | 93 | # pipenv 94 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 95 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 96 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 97 | # install all needed dependencies. 98 | #Pipfile.lock 99 | 100 | # PEP 582; 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # QrScan 2 | 二维码图片批量检测识别软件 3 | 支持常见的图片文件：`jpg`、`jpeg`、`png`、`bmp`、`tif`、`tiff`、`pbm`、`pgm`、`ppm`、`ras`等（识别二维码根据的是文件内容，即使扩展名为其他的，只要文件内容是图片编码都可识别） 4 | **Win7及以下系统**可能存在兼容性问题 5 | 6 | 软件截图 7 | 软件截图 8 | 9 | ## 1. 功能 10 | * 完全**离线**的软件，升级和更新在GitHub查看 11 | * 能够批量排查图片是否包含二维码以及识别二维码链接 12 | * 支持文件夹导入排查范围，自动遍历所有子文件夹 13 | * 支持拖放文件夹到程序的输入框 14 | * 支持选择对包含二维码图片进行的操作：**删除**、**剪切**、**识别** 15 | * 对于剪切操作，需要设置保存剪切文件的文件夹，遇到文件重名将会按时间戳重命名 16 | * 对于识别操作，需要设置保存二维码识别结果的文件夹，结果自动保存在该目录的`qrcode.csv`文件 17 | * 支持`启动`、`暂停`、`继续`、`停止`四种操作 18 | * 支持继续上次的任务（关闭软件前的任务未完成） 19 | * 支持实时日志显示与进度展示 20 | * 支持文件日志记录，默认保存在程序目录下的`log`文件夹，文件名格式为`年月日时分秒毫秒.txt` 21 | * 对于剪切和删除操作，默认会在日志文件夹中保存二维码识别结果，文件名格式为`年月日时分秒毫秒.csv` 22 | * 支持多进程极速检测识别 23 | 24 | ## 2. 下载软件 25 | ### 2.1. 使用已经编译成功的发布版软件 26 | 下载地址：[release](https://github.com/zfb132/QrScan/releases) 27 | 建议解压后的程序放置在**不需要管理员权限的目录**下，否则可能会出现无法写入日志文件的问题 28 | 若某一个版本出现问题，可以尝试下载其他版本 29 | 30 | ### 2.2. 从代码编译运行打包软件 31 | 根据本机系统平台的不同，选择不同的文件后缀名： `Windows`平台选择`.bat`，`Linux`平台选择`.sh` 32 | * 把`scripts/config_env`和`scripts/publish`移动到当前目录 33 | * 执行`config_env` 34 | * 在`Windows`系统此时可以通过命令`.\venv\Scripts\python.exe pyqt5_qr_scan.py`运行本软件；在`Linux`系统此时可以通过命令`venv/bin/python3 pyqt5_qr_scan.py`运行本软件 35 | * 如要打包软件（剥离python环境），则执行`publish`，最终会在当前目录得到一个`QrScan.zip`压缩包 36 | 37 | ## 3. 免责声明 38 | 一切下载及使用本软件时均被视为已经仔细阅读并完全同意以下条款： 39 | * 软件仅供个人学习与交流使用，严禁用于非法用途，转载需申请作者授权 40 | * 严禁未经书面许可用于商业用途 41 | * 使用本软件所存在的风险将完全由其本人承担，软件作者不承担任何责任 42 | * 软件注明之服务条款外，其它因不当使用软件而导致的任何意外、疏忽、合约毁坏、诽谤、版权或其他知识产权侵犯及其所造成的任何损失，软件作者不承担任何法律责任 43 | * 本声明未涉及的问题请参见国家有关法律法规，当本声明与国家有关法律法规冲突时，以国家法律法规为准 44 | * 本软件相关声明版权及其修改权、更新权和最终解释权均属软件作者所有 -------------------------------------------------------------------------------- /batch_work.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # author: 'zfb' 4 | # time: 2022-05-03 18:02 5 | 6 | import logging 7 | from datetime import datetime 8 | from cv2 import imdecode, cvtColor, IMREAD_UNCHANGED, COLOR_GRAY2RGB, COLOR_RGBA2RGB, wechat_qrcode_WeChatQRCode 9 | from numpy import fromfile, uint8, uint16 10 | from shutil import move 11 | from sys import exit 12 | 13 | from multiprocessing import Event, cpu_count, Pool 14 | from os.path import join, dirname, basename, exists, splitext, normpath 15 | from os import walk, remove 16 | from PyQt5.QtCore import QObject, pyqtSignal 17 | 18 | from sql_helper import insert_file, insert_status, clean_files_table, clean_status_table, get_all_files 19 | from utils import get_base_path 20 | 21 | def setup_event(event): 22 | global unpaused 23 | unpaused = event 24 | 25 | try: 26 | # 使用opencv的wechat_qrcode模块创建二维码识别器 27 | # https://github.com/WeChatCV/opencv_3rdparty/tree/wechat_qrcode 28 | model_base_path = join(get_base_path(), "models") 29 | detector = wechat_qrcode_WeChatQRCode( 30 | join(model_base_path, "detect.prototxt"), join(model_base_path, "detect.caffemodel"), 31 | join(model_base_path, "sr.prototxt"), join(model_base_path, "sr.caffemodel") 32 | ) 33 | # print("创建识别器成功") 34 | except Exception as e: 35 | print(repr(e)) 36 | print("初始化识别器失败！") 37 | exit(0) 38 | 39 | def convert_to_8bit_rgb(img): 40 | """ 41 | 将图像转换为8位RGB格式。 42 | 如果图像已经是8位RGB，则不进行任何操作。 43 | """ 44 | # 如果图像是灰度图，转换为RGB 45 | if len(img.shape) == 2: 46 | img = cvtColor(img, COLOR_GRAY2RGB) 47 | 48 | # 如果图像是16位深度，转换为8位 49 | elif img.dtype == uint16: 50 | img = (img / 256).astype(uint8) 51 | 52 | # 如果图像是RGBA，转换为RGB 53 | elif img.shape[2] == 4: 54 | img = cvtColor(img, COLOR_RGBA2RGB) 55 | 56 | return img 57 | 58 | def scan(img_name, cut_path, operation): 59 | ''' 60 | @param img_name 图片文件的名称 61 | @param cut_path 准备存放包含二维码图片的路径 62 | @operation 对包含二维码的图片要进行的操作。 63 | 'cut'表示剪切到cut_path文件夹，'delete'表示删除该图片，'decode'表示识别二维码 64 | @return [img_status, op_status, qrcode] 65 | 66 | img_status: 67 | None表示遇到未知问题 68 | 1表示输入文件是包含二维码的图片\n 69 | 2表示输入文件是不包含二维码的图片\n 70 | 3表示输入文件不是一个合法图片\n 71 | 4表示输入文件不是一个图片\n 72 | 73 | op_status: 74 | None表示遇到未知问题 75 | 1表示文件剪切成功 76 | 2表示文件剪切失败 77 | 3表示文件删除成功 78 | 4表示文件删除失败 79 | 5表示文件已有重名，添加时间戳后剪切成功 80 | 6表示文件有重名，且添加时间戳后也失败 81 | 7表示对文件不进行任何操作 82 | 8表示文件识别成功 83 | ''' 84 | img_status = None 85 | op_status = None 86 | # [图片名称, [二维码]] 87 | qrcode = None 88 | # 处理过程中的运行信息 89 | note = None 90 | full_name = img_name 91 | # imread不支持中文路径 92 | # img = imread(img_name) 93 | npfile = fromfile(img_name, dtype=uint8) 94 | if npfile.size == 0: 95 | note = f"空白文件: {img_name}" 96 | logging.error(note) 97 | return [4, op_status, [full_name, qrcode], note] 98 | img = imdecode(npfile, IMREAD_UNCHANGED) 99 | if img is None: 100 | note = f"不是图片: {img_name}" 101 | logging.warning(note) 102 | return [4, op_status, [full_name, qrcode], note] 103 | # if img.empty(): 104 | if img.size == 0 : 105 | note = f"空白图片: {img_name} 不是一个合法图片！" 106 | logging.error(note) 107 | return [3, op_status, [full_name, qrcode], note] 108 | try: 109 | img = convert_to_8bit_rgb(img) 110 | res, points = detector.detectAndDecode(img) 111 | except Exception as e: 112 | print(repr(e)) 113 | note = f"识别失败: {img_name} 识别失败！" 114 | return [None, op_status, [full_name, qrcode], note] 115 | # res=None res=() res=("") 116 | if res == None or len(res) < 1 or (len(res) == 1 and not res[0]): 117 | note = f"无二维码: {img_name}" 118 | logging.info(note) 119 | img_status = 2 120 | op_status = 7 121 | return [img_status, op_status, [full_name, qrcode], note] 122 | else: 123 | logging.debug(f"{img_name} 检测到二维码") 124 | img_status = 1 125 | if operation == 'cut': 126 | new_name = join(cut_path, basename(img_name)) 127 | file_exist = False 128 | try: 129 | if not exists(new_name): 130 | move(img_name, new_name) 131 | note = f"直接剪切: {img_name}--->{new_name}" 132 | logging.debug(note) 133 | op_status = 1 134 | else: 135 | file_exist = True 136 | logging.warning(f"文件{img_name}已存在！") 137 | except Exception as e: 138 | logging.error(repr(e)) 139 | note = f"剪切失败: {img_name} 直接剪切失败！" 140 | logging.error(note) 141 | op_status = 2 142 | if file_exist: 143 | time_str = datetime.now().strftime("_%Y%m%d%H%M%S%f") 144 | m = splitext(new_name) 145 | new_name = m[0] + time_str + m[1] 146 | try: 147 | move(img_name, new_name) 148 | note = f"重名剪切: {img_name}--->{new_name}" 149 | logging.debug(note) 150 | op_status = 5 151 | except Exception as e: 152 | logging.error(repr(e)) 153 | note = f"剪切失败: {img_name} 重命名后剪切失败！" 154 | logging.error(note) 155 | op_status = 6 156 | elif operation == 'delete': 157 | try: 158 | remove(img_name) 159 | note = f"删除成功: {img_name}" 160 | logging.debug(note) 161 | op_status = 3 162 | except Exception as e: 163 | logging.error(repr(e)) 164 | note = f"删除失败: {img_name}" 165 | logging.error(note) 166 | op_status = 4 167 | elif operation == 'decode': 168 | op_status = 8 169 | note = f"识别成功: {img_name}" 170 | qrcode = [full_name, res] 171 | return [img_status, op_status, qrcode, note] 172 | 173 | 174 | def scan_process(pathes, cut_path, operation): 175 | '''批次任务启动过程中的中转函数 176 | ''' 177 | results = [] 178 | for path in pathes: 179 | unpaused.wait() 180 | results.append(scan(path, cut_path, operation)) 181 | return results 182 | 183 | class BatchWork(QObject): 184 | '''具体的多进程工作处理的类，通过构造函数接收参数和任务处理的函数 185 | ''' 186 | # 信号量，负责实现外部进程更新UI进程的界面显示 187 | # UI进程的界面显示只能在UI主进程里面进行，所以这里触发信号，并传递参数 188 | # 前往信号对应的槽函数，进行处理 189 | # 该信号用于触发和传递任务的执行进度（并非实时，只能每批任务执行完毕，统一更新） 190 | notifyProgress = pyqtSignal(int) 191 | # 该信号用于指示目前的工作状态，控制按钮的启用与否（保证任务运行过程中不会再次运行） 192 | notifyStatus = pyqtSignal(bool) 193 | 194 | def __init__(self, myvar, func): 195 | '''通过构造函数的myvar变量接收参数\n 196 | 通过func函数接收具体用于任务处理的函数 197 | ''' 198 | self.work = func 199 | self.var = myvar[:3] 200 | self.log_file = myvar[3] 201 | self.is_first = myvar[4] 202 | super(BatchWork, self).__init__() 203 | 204 | def chunks(self, l, n): 205 | '''此函数用于配合实现列表分割 206 | ''' 207 | for i in range(0, len(l), n): 208 | yield l[i:i + n] 209 | 210 | def resize_list(self, data_list, group_size): 211 | '''将一维列表按照指定长度分割 212 | ''' 213 | data_chunks = list(self.chunks(data_list, group_size)) 214 | results = [] 215 | for i in range(len(data_chunks)): 216 | results.append(data_chunks[i]) 217 | return results 218 | 219 | def show_info(self, img_status, op_status, name): 220 | '''根据返回的状态码显示不同的日志内容 221 | ''' 222 | if img_status is None: 223 | logging.error(f"{name}出现未知错误！") 224 | return 225 | elif img_status == 1: 226 | logging.debug(f"{name}检测到二维码") 227 | elif img_status == 2: 228 | logging.info(f"{name}不包含二维码") 229 | elif img_status == 3: 230 | logging.warning(f"{name}不是一个合法图片！") 231 | return 232 | elif img_status == 4: 233 | logging.warning(f"{name}不是一个图片！") 234 | return 235 | else: 236 | logging.error(f"{name}程序出现bug！") 237 | return 238 | if op_status is None: 239 | logging.error(f"{name}出现未知错误！") 240 | elif op_status == 1: 241 | logging.debug(f"{name}剪切成功") 242 | elif op_status == 2: 243 | logging.error(f"{name}剪切失败！") 244 | elif op_status == 3: 245 | logging.debug(f"{name}删除成功") 246 | elif op_status == 4: 247 | logging.error(f"{name}删除失败！") 248 | elif op_status == 5: 249 | logging.debug(f"{name}重名，添加时间戳后剪切成功") 250 | elif op_status == 6: 251 | logging.error(f"{name}重名，添加时间戳后剪切仍然失败！") 252 | elif op_status == 7: 253 | pass 254 | elif op_status == 8: 255 | logging.debug(f"{name}识别二维码成功") 256 | else: 257 | logging.error(f"{name}程序出现bug！") 258 | 259 | def save_qrcode(self, path, qrcode, is_log=False): 260 | if is_log: 261 | pure_log_name = basename(self.log_file.name).split('.')[:-1] 262 | name = join(path, f"{'.'.join(pure_log_name)}.csv") 263 | else: 264 | name = join(path, "qrcode.csv") 265 | flag = False 266 | with open(name, "a", encoding="utf-8-sig", errors="replace") as f: 267 | for single_res in qrcode: 268 | if single_res is None: 269 | continue 270 | f.write(f'"{single_res[0]}",{",".join(single_res[1])}\n') 271 | flag = True 272 | if flag: 273 | logging.debug(f"识别二维码结果保存到文件{name}成功！") 274 | 275 | def filter_names(self, pathes): 276 | ''' 277 | 从数据库中读取已经识别过的文件，将其从pathes中删除 278 | ''' 279 | db_normpath_names = [x[0] for x in get_all_files()] 280 | # item in new_lists but not in db_normpath_names 281 | return list(set(pathes) - set(db_normpath_names)) 282 | 283 | def run(self): 284 | # 使用信号槽机制，禁用按钮，因为此时已经开始处理 285 | # 防止用户再次提交任务 286 | self.notifyStatus.emit(True) 287 | logging.info('开始进行检测') 288 | # 读取参数 289 | path, cut_path, operation = self.var 290 | # 传入的path和cut_path已经是normpath 291 | # path = os.path.normpath(path) 292 | # 读取path及其子目录下的所有文件（所有扩展名）并存入m*2*n列表 293 | pathes = [] 294 | for root, dirs, files in walk(path): 295 | # for dir_name in dirs: 296 | # rename_func(root, dir_name) 297 | for file_name in files: 298 | pathes.append(normpath(join(root, file_name))) 299 | if not self.is_first: 300 | logging.info("检测到上次运行未结束，将过滤已经识别过的文件") 301 | pathes = self.filter_names(pathes) 302 | logging.info(f"载入所有{len(pathes)}个文件成功！") 303 | # 根据CPU核心数量开启多进程，充分利用资源 304 | pro_cnt = cpu_count() 305 | # 按照每组100个进行分割 306 | # group_size = 100 307 | # if len(pathes) < 1000: 308 | # group_size = len(pathes)//(2*pro_cnt) 309 | # if group_size < 1: 310 | # group_size = 1 311 | # 还是默认8个吧，否则一个批次的任务执行时间太久，日志和进度条会好久才动 312 | group_size = 4 313 | # 把m*2*n的列表分割成新列表，新列表的前面t-1元素都是group_size*2*n 314 | # 最后一个元素[len(pathes)-(t-1)*group_size]X2Xn 315 | final_pathes = self.resize_list(pathes, group_size) 316 | # 最终创建进程的数量（一般远大于进程池的大小） 317 | num_procs = len(final_pathes) 318 | # 用于存放多进程返回结果的列表 319 | results = list(range(num_procs)) 320 | insert_status(operation, path, cut_path, 0) 321 | # 创建进程池 322 | self.event = Event() 323 | self.pool = Pool(processes=pro_cnt, initializer=setup_event, initargs=(self.event,)) 324 | # 开始逐个将进程加入进程池 325 | for i in range(num_procs): 326 | params = final_pathes[i] 327 | # 维持执行的进程总数为processes，当一个进程执行完毕后会添加新的进程进去 328 | results[i] = self.pool.apply_async(func=self.work, args=(params, cut_path, operation,)) 329 | logging.info(f"创建{num_procs}个进程，进程池大小：{pro_cnt}") 330 | self.pool.close() 331 | # unpause workers 332 | self.event.set() 333 | # 调用join之前，先调用close函数，否则会出错。 334 | # 执行完close后不会有新的进程加入到pool，join函数等待所有子进程结束 335 | # pool.join() 336 | # 用于统计含有二维码图片的数量 337 | qr_img_num = 0 338 | # 用于统计操作成功的次数（包含剪切成功、删除成功、添加时间戳后剪切成功） 339 | op_success_num = 0 340 | for i in range(len(results)): 341 | qrcode = [] 342 | # 每个批次执行完毕才会进来 343 | m = results[i].get() 344 | # 格式化输出结果 345 | for t in range(len(m)): 346 | img_status, op_status, qrcode_thread, note = m[t] 347 | name = qrcode_thread[0] 348 | insert_file(name) 349 | if img_status == 1: 350 | qr_img_num += 1 351 | # 剪切成功、删除成功、添加时间戳后剪切成功 352 | if op_status in [1,3,5,8]: 353 | op_success_num += 1 354 | qrcode.append(qrcode_thread) 355 | # 显示每个批次的日志 356 | self.show_info(img_status, op_status, name) 357 | # 写入日志文件 358 | if self.log_file: 359 | self.log_file.write(f"{note}\n") 360 | if self.log_file: 361 | self.log_file.flush() 362 | # 更新进度条（进度条的上限初始化为100） 363 | if operation == "decode": 364 | self.save_qrcode(cut_path, qrcode) 365 | else: 366 | if self.log_file: 367 | self.save_qrcode(join(get_base_path(), "log"), qrcode, self.log_file) 368 | self.notifyProgress.emit((i+1)*100//num_procs) 369 | # logging.info(f"进程{i}结束") 370 | logging.info(f"扫描任务结束：") 371 | logging.info(f"共计检测到{qr_img_num}个包含二维码的图片，其中{op_success_num}个文件执行操作成功") 372 | # 清空files.db 373 | clean_files_table() 374 | # 保存操作记录 375 | clean_status_table() 376 | if self.log_file: 377 | self.log_file.close() 378 | # 使用信号槽机制，启用按钮，因为任务已经处理完成 379 | # 用户可以再次提交任务或创建新任务 380 | self.notifyStatus.emit(False) 381 | -------------------------------------------------------------------------------- /custom_formatter.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # author: 'zfb' 4 | # time: 2022-05-03 18:02 5 | 6 | import logging 7 | 8 | from PyQt5.QtGui import QColor 9 | 10 | class CustomFormatter(logging.Formatter): 11 | '''自定义日志的格式化，保证QPlainTextEdit可以用不同颜色显示对应等级的日志 12 | ''' 13 | # 定义不同日志等级的格式和颜色 14 | FORMATS = { 15 | # critical专门用于显示其他信息，所以不需要时间日期 16 | logging.CRITICAL: ("[{asctime}]: {message}", "#000000"), 17 | logging.ERROR: ("[{asctime}] {levelname:-<8}--- {message}", QColor("red")), 18 | logging.DEBUG: ("[{asctime}] {levelname:-<8}--- {message}", "green"), 19 | logging.INFO: ("[{asctime}] {levelname:-<8}--- {message}", "#0000FF"), 20 | logging.WARNING: ('[{asctime}] {levelname:-<8}--- {message}', QColor(100, 100, 0)) 21 | } 22 | 23 | def __init__(self): 24 | '''把格式化设置为{}风格，而不是()%s，从子类修改父类 25 | ''' 26 | super().__init__(style="{") 27 | 28 | def format(self, record): 29 | '''继承logging.Formatter必须实现的方法 30 | ''' 31 | last_fmt = self._style._fmt 32 | opt = CustomFormatter.FORMATS.get(record.levelno) 33 | # 设置时间日期的格式化规则 34 | self.datefmt = "%Y-%m-%d %H:%M:%S" 35 | if opt: 36 | fmt, color = opt 37 | self._style._fmt = "{}".format(QColor(color).name(),fmt) 38 | res = logging.Formatter.format(self, record) 39 | self._style._fmt = last_fmt 40 | return res -------------------------------------------------------------------------------- /custom_qwidget.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # author: 'zfb' 4 | # time: 2022-05-03 18:02 5 | 6 | import logging 7 | 8 | from os.path import isdir, normpath, exists 9 | from PyQt5.QtCore import QObject, QThread, pyqtSignal 10 | from PyQt5.QtWidgets import (QApplication, QPlainTextEdit, QDialog, QGridLayout, 11 | QProgressBar, QPushButton, QRadioButton, QSizePolicy, QStyleFactory, 12 | QFileDialog, QMessageBox, QLineEdit, QVBoxLayout, QHBoxLayout, QGroupBox) 13 | 14 | from custom_formatter import CustomFormatter 15 | from batch_work import BatchWork, scan_process 16 | from os import makedirs, path 17 | from datetime import datetime 18 | 19 | from sql_helper import get_status, clean_files_table, clean_status_table 20 | from utils import get_base_path 21 | 22 | class QPlainTextEditLogger(QObject, logging.Handler): 23 | '''自定义Qt控件，继承自QObject，初始化时创建QPlainTextEdit控件\n 24 | 用于实现QPlainTextEdit的日志输出功能 25 | ''' 26 | # 信号量，负责实现外部进程更新UI进程的界面显示 27 | # UI进程的界面显示只能在UI主进程里面进行，所以这里触发信号，并传递参数 28 | # 前往信号对应的槽函数，进行处理 29 | # 该信号用于触发和传递日志信息 30 | new_record = pyqtSignal(object) 31 | 32 | def __init__(self, parent=None): 33 | super().__init__() 34 | self.widget = QPlainTextEdit(parent) 35 | self.widget.setReadOnly(True) 36 | 37 | def emit(self, record): 38 | msg = self.format(record) 39 | self.new_record.emit(msg) 40 | 41 | 42 | class QDropLineEdit(QLineEdit): 43 | '''用于实现拖放文件夹到编辑框的功能\n 44 | 自定义Qt控件，继承自QLineEdit 45 | ''' 46 | def dragEnterEvent(self, event): 47 | if event.mimeData().hasUrls(): 48 | event.acceptProposedAction() 49 | 50 | def dropEvent(self, event): 51 | md = event.mimeData() 52 | if md.hasUrls(): 53 | files = [url.toLocalFile() for url in md.urls()] 54 | # 只接受文件夹的拖放 55 | files = [x for x in files if isdir(normpath(x))] 56 | num = len(files) 57 | if num > 1: 58 | logging.warning(f"检测到多条数据，只读取第一个！") 59 | elif num == 0: 60 | logging.warning(f"请确保拖放文件夹而不是文件！") 61 | return 62 | self.setText(files[0]) 63 | event.acceptProposedAction() 64 | 65 | 66 | class QrDetectDialog(QDialog): 67 | '''Qt界面的主窗体，继承自QDialog 68 | ''' 69 | def __init__(self, parent=None): 70 | super(QrDetectDialog, self).__init__(parent) 71 | 72 | # 用于写入日志到QPlainTextEdit 73 | self.logger = QPlainTextEditLogger() 74 | logging.getLogger().addHandler(self.logger) 75 | # 设置日志的格式化类 76 | self.logger.setFormatter(CustomFormatter()) 77 | # 设置日志的记录等级，低于该等级的日志将不会输出 78 | logging.getLogger().setLevel(logging.DEBUG) 79 | # 信号槽绑定，绑定在QPlainTextEditLogger创建的new_record信号到该函数 80 | self.logger.new_record.connect(self.logger.widget.appendHtml) 81 | self._loadThread = None 82 | self.run_func = None 83 | self.log_file = None 84 | 85 | # 界面基本元素创建 86 | self.createBottomLeftGroupBox() 87 | self.createTopLeftGroupBox() 88 | self.createControlGroupBox() 89 | self.createRightGroupBox(self.logger.widget) 90 | self.createProgressBar() 91 | 92 | # 用于在pyqt5中的一个线程中，开启多进程 93 | self.runButton.clicked.connect(self.batch_work) 94 | self.pauseButton.clicked.connect(self.pause_batch_work) 95 | self.resumeButton.clicked.connect(self.resume_batch_work) 96 | self.stopButton.clicked.connect(self.stop_batch_work) 97 | self.radioButton1.clicked.connect(self.disableCutPathStatus) 98 | self.radioButton2.clicked.connect(self.clickCutRadioButton) 99 | self.radioButton3.clicked.connect(self.clickDecodeRadioButton) 100 | self.imgPathButton.clicked.connect(self.get_img_path) 101 | self.cutPathButton.clicked.connect(self.get_cut_path) 102 | 103 | # 主界面布局搭建，网格布局 104 | mainLayout = QGridLayout() 105 | # addWidget(QWidget, row: int, column: int, rowSpan: int, columnSpan: int) 106 | mainLayout.addWidget(self.topLeftGroupBox, 1, 0) 107 | mainLayout.addWidget(self.bottomLeftGroupBox, 0, 0) 108 | mainLayout.addWidget(self.controlGroupBox, 2, 0) 109 | mainLayout.addWidget(self.rightGroupBox, 0, 1, 3, 1) 110 | mainLayout.addWidget(self.progressBar, 3, 0, 1, 2) 111 | # 设置每一列的宽度比例，第0列的宽度为1；第1列的宽度为2 112 | mainLayout.setColumnStretch(0, 1) 113 | mainLayout.setColumnStretch(1, 2) 114 | # 设置每一行的宽度比例，第0行的宽度为1；第1行的宽度为2 115 | mainLayout.setRowStretch(0, 1) 116 | mainLayout.setRowStretch(1, 1) 117 | mainLayout.setRowStretch(2, 2) 118 | self.setLayout(mainLayout) 119 | 120 | self.setWindowTitle("图片二维码检测识别 github.com/zfb132/QrScan") 121 | self.changeStyle('WindowsVista') 122 | self.load_exists() 123 | 124 | def load_exists(self): 125 | self.is_first = True 126 | res = get_status() 127 | if res: 128 | self.is_first = False 129 | # 弹窗询问是否继续上次的操作 130 | reply = QMessageBox.question(self, '提示', '检测到上次未完成的操作，是否继续？', QMessageBox.Yes | QMessageBox.No, QMessageBox.Yes) 131 | if reply == QMessageBox.No: 132 | clean_files_table() 133 | clean_status_table() 134 | return 135 | operation, img_path, cut_path = res 136 | if operation == "delete": 137 | self.radioButton1.setChecked(True) 138 | self.radioButton2.setChecked(False) 139 | self.radioButton3.setChecked(False) 140 | elif operation == "cut": 141 | self.radioButton2.setChecked(True) 142 | self.radioButton1.setChecked(False) 143 | self.radioButton3.setChecked(False) 144 | elif operation == "decode": 145 | self.radioButton3.setChecked(True) 146 | self.radioButton1.setChecked(False) 147 | self.radioButton2.setChecked(False) 148 | if img_path: 149 | self.imgPathTextBox.setText(img_path) 150 | if cut_path: 151 | self.cutPathTextBox.setText(cut_path) 152 | logging.info(f"上次未完成的操作为：{operation}") 153 | logging.info(f"图片路径为：{img_path}") 154 | logging.info(f"保存路径为：{cut_path}") 155 | 156 | 157 | def set_run_func(self, func): 158 | self.run_func = func 159 | 160 | def get_img_path(self): 161 | directory = QFileDialog.getExistingDirectory(self, "选取图片所在的文件夹") 162 | self.imgPathTextBox.setText(directory) 163 | 164 | def get_cut_path(self): 165 | directory = QFileDialog.getExistingDirectory(self, "选取保存结果的文件夹") 166 | self.cutPathTextBox.setText(directory) 167 | 168 | def set_log_file(self): 169 | log_name = path.join(get_base_path(), "log", f"{datetime.now().strftime('%Y%m%d%H%M%S')}.txt") 170 | try: 171 | self.log_file = open(log_name, "w", encoding="utf-8") 172 | logging.info(f"日志文件{log_name}创建成功") 173 | except Exception as e: 174 | logging.warning(f"日志文件{log_name}创建失败") 175 | logging.warning(repr(e)) 176 | 177 | def batch_work(self): 178 | # 运行按钮的响应函数 179 | # 获取操作类型，cut还是delete 180 | operation = 'cut' 181 | if self.radioButton1.isChecked(): 182 | operation = 'delete' 183 | if self.radioButton2.isChecked(): 184 | operation = 'cut' 185 | if self.radioButton3.isChecked(): 186 | operation = 'decode' 187 | c1 = self.imgPathTextBox.text().strip() 188 | if not c1: 189 | QMessageBox.warning(self, '警告', f'请先设置图片所在文件夹！', QMessageBox.Yes) 190 | return 191 | # 保证路径都是标准格式 192 | img_path = normpath(c1) 193 | if not exists(img_path): 194 | QMessageBox.warning(self, '警告', f'不存在路径：{img_path}', QMessageBox.Yes) 195 | return 196 | cut_path = "" 197 | if operation != 'delete': 198 | c2 = self.cutPathTextBox.text().strip() 199 | if not c2: 200 | msg = "请先设置保存二维码图片的文件夹！" 201 | if operation == 'decode': 202 | msg = "请先设置保存二维码识别结果的文件夹！" 203 | QMessageBox.warning(self, '警告', f'{msg}', QMessageBox.Yes) 204 | return 205 | cut_path = normpath(c2) 206 | if not exists(cut_path): 207 | QMessageBox.warning(self, '警告', f'不存在路径：{img_path}', QMessageBox.Yes) 208 | return 209 | # 设置默认日志 210 | self.logger.widget.setPlainText("") 211 | logging.critical("作者：zfb") 212 | logging.critical("https://github.com/zfb132/QrScan") 213 | # 创建线程 214 | self._loadThread=QThread(parent=self) 215 | self.set_log_file() 216 | # vars = [1, 2, 3, 4, 5, 6]*6 217 | vars = [img_path, cut_path, operation, self.log_file, self.is_first] 218 | self.loadThread=BatchWork(vars, self.run_func) 219 | # 为BatchWork类的两个信号绑定槽函数 220 | self.loadThread.notifyProgress.connect(self.updateProgressBar) 221 | self.loadThread.notifyStatus.connect(self.updateButtonStatus) 222 | # 用于开辟新进程，不阻塞主界面 223 | self.loadThread.moveToThread(self._loadThread) 224 | self._loadThread.started.connect(self.loadThread.run) 225 | self._loadThread.start() 226 | 227 | def pause_batch_work(self): 228 | if self.loadThread: 229 | self.loadThread.event.clear() 230 | logging.critical("暂停运行！") 231 | self.resumeButton.setDisabled(False) 232 | self.pauseButton.setDisabled(True) 233 | 234 | def resume_batch_work(self): 235 | if self.loadThread: 236 | self.loadThread.event.set() 237 | logging.critical("继续运行！") 238 | self.resumeButton.setDisabled(True) 239 | self.pauseButton.setDisabled(False) 240 | 241 | def stop_batch_work(self): 242 | if self.loadThread: 243 | self.loadThread.pool.terminate() 244 | self.loadThread.pool.join() 245 | logging.critical("手动停止运行！") 246 | self.runButton.setDisabled(False) 247 | self.pauseButton.setDisabled(True) 248 | self.resumeButton.setDisabled(True) 249 | self.stopButton.setDisabled(True) 250 | # if self._loadThread: 251 | # self._loadThread.quit() 252 | # self._loadThread.wait() 253 | # self.imgPathTextBox.setText("AA") 254 | 255 | def changeStyle(self, styleName): 256 | QApplication.setStyle(QStyleFactory.create(styleName)) 257 | self.changePalette() 258 | 259 | def changePalette(self): 260 | QApplication.setPalette(QApplication.style().standardPalette()) 261 | 262 | def updateProgressBar(self, i): 263 | self.progressBar.setValue(i) 264 | 265 | def updateButtonStatus(self, status): 266 | self.runButton.setDisabled(status) 267 | self.pauseButton.setDisabled(not status) 268 | self.resumeButton.setDisabled(True) 269 | self.stopButton.setDisabled(not status) 270 | if not status: 271 | QMessageBox.information(self, '提示', '成功完成扫描！', QMessageBox.Yes) 272 | 273 | def disableCutPathStatus(self): 274 | self.cutPathButton.setDisabled(True) 275 | self.cutPathTextBox.setDisabled(True) 276 | 277 | def clickCutRadioButton(self): 278 | self.cutPathButton.setDisabled(False) 279 | self.cutPathTextBox.setDisabled(False) 280 | self.cutPathButton.setText("选择剪切图片文件夹") 281 | 282 | def clickDecodeRadioButton(self): 283 | self.cutPathButton.setDisabled(False) 284 | self.cutPathTextBox.setDisabled(False) 285 | self.cutPathButton.setText("选择保存二维码识别结果文件夹") 286 | 287 | def createBottomLeftGroupBox(self): 288 | self.bottomLeftGroupBox = QGroupBox("包含二维码的图片操作") 289 | 290 | self.radioButton1 = QRadioButton("删除") 291 | self.radioButton2 = QRadioButton("剪切") 292 | self.radioButton3 = QRadioButton("识别") 293 | self.radioButton2.setChecked(True) 294 | 295 | layout = QGridLayout() 296 | layout.addWidget(self.radioButton1,0,0,1,1) 297 | layout.addWidget(self.radioButton2,1,0,1,1) 298 | layout.addWidget(self.radioButton3,0,1,1,1) 299 | #layout.addStretch(1) 300 | self.bottomLeftGroupBox.setLayout(layout) 301 | 302 | def createTopLeftGroupBox(self): 303 | self.topLeftGroupBox = QGroupBox("设置路径") 304 | 305 | self.imgPathButton = QPushButton("选择原始图片文件夹") 306 | self.imgPathButton.setDefault(True) 307 | 308 | self.imgPathTextBox = QDropLineEdit("") 309 | self.cutPathTextBox = QDropLineEdit("") 310 | 311 | self.cutPathButton = QPushButton("选择剪切图片文件夹") 312 | self.cutPathButton.setDefault(True) 313 | 314 | layout = QVBoxLayout() 315 | layout.addWidget(self.imgPathButton) 316 | layout.addWidget(self.imgPathTextBox) 317 | layout.addWidget(self.cutPathButton) 318 | layout.addWidget(self.cutPathTextBox) 319 | 320 | self.topLeftGroupBox.setLayout(layout) 321 | 322 | def createControlGroupBox(self): 323 | self.controlGroupBox = QGroupBox("控制按钮") 324 | layout = QGridLayout() 325 | self.runButton = QPushButton("启动") 326 | self.runButton.setDefault(True) 327 | self.pauseButton = QPushButton("暂停") 328 | self.pauseButton.setDefault(False) 329 | self.pauseButton.setDisabled(True) 330 | self.resumeButton = QPushButton("继续") 331 | self.resumeButton.setDefault(False) 332 | self.resumeButton.setDisabled(True) 333 | self.stopButton = QPushButton("停止") 334 | self.stopButton.setDefault(False) 335 | self.stopButton.setDisabled(True) 336 | layout.addWidget(self.runButton, 0, 0) 337 | layout.addWidget(self.pauseButton, 0, 1) 338 | layout.addWidget(self.resumeButton, 1, 0) 339 | layout.addWidget(self.stopButton, 1, 1) 340 | self.controlGroupBox.setLayout(layout) 341 | 342 | def createRightGroupBox(self, widget): 343 | self.rightGroupBox = QGroupBox("运行日志") 344 | layout = QHBoxLayout() 345 | widget.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding) 346 | widget.setMaximumBlockCount(10000) 347 | widget.setPlainText("作者：zfb\nhttps://github.com/zfb132/QrScan") 348 | layout.addWidget(widget) 349 | #layout.addStretch(0) 350 | self.rightGroupBox.setLayout(layout) 351 | 352 | def createProgressBar(self): 353 | self.progressBar = QProgressBar() 354 | self.progressBar.setRange(0, 100) 355 | self.progressBar.setValue(0) -------------------------------------------------------------------------------- /docs/result.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zfb132/QrScan/dfc1e63ea0beaf57930810c73576f83e91cdc607/docs/result.png -------------------------------------------------------------------------------- /file_version_info.txt: -------------------------------------------------------------------------------- 1 | # UTF-8 2 | # 3 | # For more details about fixed file info 'ffi' see: 4 | # http://msdn.microsoft.com/en-us/library/ms646997.aspx 5 | VSVersionInfo( 6 | ffi=FixedFileInfo( 7 | # filevers and prodvers should be always a tuple with four items: (1, 2, 3, 4) 8 | # Set not needed items to zero 0. 9 | filevers=(V_MAJOR, V_MINOR, V_PATCH, 0), 10 | prodvers=(V_MAJOR, V_MINOR, V_PATCH, 0), 11 | # Contains a bitmask that specifies the valid bits 'flags'r 12 | mask=0x3f, 13 | # Contains a bitmask that specifies the Boolean attributes of the file. 14 | flags=0x0, 15 | # The operating system for which this file was designed. 16 | # 0x4 - NT and there is no need to change it. 17 | OS=0x40004, 18 | # The general type of file. 19 | # 0x1 - the file is an application. 20 | fileType=0x1, 21 | # The function of the file. 22 | # 0x0 - the function is not defined for this fileType 23 | subtype=0x0, 24 | # Creation date and time stamp. 25 | date=(0, 0) 26 | ), 27 | kids=[ 28 | StringFileInfo( 29 | [ 30 | StringTable( 31 | '040904B0', 32 | [StringStruct('CompanyName', 'WHU ZFB'), 33 | StringStruct('FileDescription', '二维码检测程序'), 34 | StringStruct('FileVersion', 'V_MAJOR.V_MINOR.V_PATCH.0 (COMMIT_HASH)'), 35 | StringStruct('InternalName', 'QrScan.Exe'), 36 | StringStruct('LegalCopyright', '© https://github.com/zfb132. All rights reserved.'), 37 | StringStruct('OriginalFilename', 'QrScan_COMMIT_HASH_YYYYMMDDHHMM'), 38 | StringStruct('ProductName', '二维码检测程序'), 39 | StringStruct('ProductVersion', 'V_MAJOR.V_MINOR.V_PATCH.0')]) 40 | ]), 41 | VarFileInfo([VarStruct('Translation', [2052, 1200])]) 42 | ] 43 | ) -------------------------------------------------------------------------------- /models/detect.caffemodel: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zfb132/QrScan/dfc1e63ea0beaf57930810c73576f83e91cdc607/models/detect.caffemodel -------------------------------------------------------------------------------- /models/detect.prototxt: -------------------------------------------------------------------------------- 1 | layer { 2 | name: "data" 3 | type: "Input" 4 | top: "data" 5 | input_param { 6 | shape { 7 | dim: 1 8 | dim: 1 9 | dim: 384 10 | dim: 384 11 | } 12 | } 13 | } 14 | layer { 15 | name: "data/bn" 16 | type: "BatchNorm" 17 | bottom: "data" 18 | top: "data" 19 | param { 20 | lr_mult: 0.0 21 | decay_mult: 0.0 22 | } 23 | param { 24 | lr_mult: 0.0 25 | decay_mult: 0.0 26 | } 27 | param { 28 | lr_mult: 0.0 29 | decay_mult: 0.0 30 | } 31 | } 32 | layer { 33 | name: "data/bn/scale" 34 | type: "Scale" 35 | bottom: "data" 36 | top: "data" 37 | param { 38 | lr_mult: 1.0 39 | decay_mult: 0.0 40 | } 41 | param { 42 | lr_mult: 1.0 43 | decay_mult: 0.0 44 | } 45 | scale_param { 46 | filler { 47 | type: "constant" 48 | value: 1.0 49 | } 50 | bias_term: true 51 | bias_filler { 52 | type: "constant" 53 | value: 0.0 54 | } 55 | } 56 | } 57 | layer { 58 | name: "stage1" 59 | type: "Convolution" 60 | bottom: "data" 61 | top: "stage1" 62 | param { 63 | lr_mult: 1.0 64 | decay_mult: 1.0 65 | } 66 | param { 67 | lr_mult: 1.0 68 | decay_mult: 0.0 69 | } 70 | convolution_param { 71 | num_output: 24 72 | bias_term: true 73 | pad: 1 74 | kernel_size: 3 75 | group: 1 76 | stride: 2 77 | weight_filler { 78 | type: "msra" 79 | } 80 | dilation: 1 81 | } 82 | } 83 | layer { 84 | name: "stage1/bn" 85 | type: "BatchNorm" 86 | bottom: "stage1" 87 | top: "stage1" 88 | param { 89 | lr_mult: 0.0 90 | decay_mult: 0.0 91 | } 92 | param { 93 | lr_mult: 0.0 94 | decay_mult: 0.0 95 | } 96 | param { 97 | lr_mult: 0.0 98 | decay_mult: 0.0 99 | } 100 | } 101 | layer { 102 | name: "stage1/bn/scale" 103 | type: "Scale" 104 | bottom: "stage1" 105 | top: "stage1" 106 | param { 107 | lr_mult: 1.0 108 | decay_mult: 0.0 109 | } 110 | param { 111 | lr_mult: 1.0 112 | decay_mult: 0.0 113 | } 114 | scale_param { 115 | filler { 116 | type: "constant" 117 | value: 1.0 118 | } 119 | bias_term: true 120 | bias_filler { 121 | type: "constant" 122 | value: 0.0 123 | } 124 | } 125 | } 126 | layer { 127 | name: "stage1/relu" 128 | type: "ReLU" 129 | bottom: "stage1" 130 | top: "stage1" 131 | } 132 | layer { 133 | name: "stage2" 134 | type: "Pooling" 135 | bottom: "stage1" 136 | top: "stage2" 137 | pooling_param { 138 | pool: MAX 139 | kernel_size: 3 140 | stride: 2 141 | pad: 0 142 | } 143 | } 144 | layer { 145 | name: "stage3_1/conv1" 146 | type: "Convolution" 147 | bottom: "stage2" 148 | top: "stage3_1/conv1" 149 | param { 150 | lr_mult: 1.0 151 | decay_mult: 1.0 152 | } 153 | convolution_param { 154 | num_output: 16 155 | pad: 0 156 | kernel_size: 1 157 | group: 1 158 | stride: 1 159 | weight_filler { 160 | type: "msra" 161 | } 162 | dilation: 1 163 | } 164 | } 165 | layer { 166 | name: "stage3_1/conv1/relu" 167 | type: "ReLU" 168 | bottom: "stage3_1/conv1" 169 | top: "stage3_1/conv1" 170 | } 171 | layer { 172 | name: "stage3_1/conv2" 173 | type: "Convolution" 174 | bottom: "stage3_1/conv1" 175 | top: "stage3_1/conv2" 176 | param { 177 | lr_mult: 1.0 178 | decay_mult: 1.0 179 | } 180 | convolution_param { 181 | num_output: 16 182 | pad: 1 183 | kernel_size: 3 184 | group: 16 185 | stride: 2 186 | weight_filler { 187 | type: "msra" 188 | } 189 | dilation: 1 190 | } 191 | } 192 | layer { 193 | name: "stage3_1/conv3" 194 | type: "Convolution" 195 | bottom: "stage3_1/conv2" 196 | top: "stage3_1/conv3" 197 | param { 198 | lr_mult: 1.0 199 | decay_mult: 1.0 200 | } 201 | convolution_param { 202 | num_output: 64 203 | pad: 0 204 | kernel_size: 1 205 | group: 1 206 | stride: 1 207 | weight_filler { 208 | type: "msra" 209 | } 210 | dilation: 1 211 | } 212 | } 213 | layer { 214 | name: "stage3_1/relu" 215 | type: "ReLU" 216 | bottom: "stage3_1/conv3" 217 | top: "stage3_1/conv3" 218 | } 219 | layer { 220 | name: "stage3_2/conv1" 221 | type: "Convolution" 222 | bottom: "stage3_1/conv3" 223 | top: "stage3_2/conv1" 224 | param { 225 | lr_mult: 1.0 226 | decay_mult: 1.0 227 | } 228 | convolution_param { 229 | num_output: 16 230 | pad: 0 231 | kernel_size: 1 232 | group: 1 233 | stride: 1 234 | weight_filler { 235 | type: "msra" 236 | } 237 | dilation: 1 238 | } 239 | } 240 | layer { 241 | name: "stage3_2/conv1/relu" 242 | type: "ReLU" 243 | bottom: "stage3_2/conv1" 244 | top: "stage3_2/conv1" 245 | } 246 | layer { 247 | name: "stage3_2/conv2" 248 | type: "Convolution" 249 | bottom: "stage3_2/conv1" 250 | top: "stage3_2/conv2" 251 | param { 252 | lr_mult: 1.0 253 | decay_mult: 1.0 254 | } 255 | convolution_param { 256 | num_output: 16 257 | pad: 1 258 | kernel_size: 3 259 | group: 16 260 | stride: 1 261 | weight_filler { 262 | type: "msra" 263 | } 264 | dilation: 1 265 | } 266 | } 267 | layer { 268 | name: "stage3_2/conv3" 269 | type: "Convolution" 270 | bottom: "stage3_2/conv2" 271 | top: "stage3_2/conv3" 272 | param { 273 | lr_mult: 1.0 274 | decay_mult: 1.0 275 | } 276 | convolution_param { 277 | num_output: 64 278 | pad: 0 279 | kernel_size: 1 280 | group: 1 281 | stride: 1 282 | weight_filler { 283 | type: "msra" 284 | } 285 | dilation: 1 286 | } 287 | } 288 | layer { 289 | name: "stage3_2/sum" 290 | type: "Eltwise" 291 | bottom: "stage3_1/conv3" 292 | bottom: "stage3_2/conv3" 293 | top: "stage3_2/sum" 294 | eltwise_param { 295 | operation: SUM 296 | } 297 | } 298 | layer { 299 | name: "stage3_2/relu" 300 | type: "ReLU" 301 | bottom: "stage3_2/sum" 302 | top: "stage3_2/sum" 303 | } 304 | layer { 305 | name: "stage3_3/conv1" 306 | type: "Convolution" 307 | bottom: "stage3_2/sum" 308 | top: "stage3_3/conv1" 309 | param { 310 | lr_mult: 1.0 311 | decay_mult: 1.0 312 | } 313 | convolution_param { 314 | num_output: 16 315 | pad: 0 316 | kernel_size: 1 317 | group: 1 318 | stride: 1 319 | weight_filler { 320 | type: "msra" 321 | } 322 | dilation: 1 323 | } 324 | } 325 | layer { 326 | name: "stage3_3/conv1/relu" 327 | type: "ReLU" 328 | bottom: "stage3_3/conv1" 329 | top: "stage3_3/conv1" 330 | } 331 | layer { 332 | name: "stage3_3/conv2" 333 | type: "Convolution" 334 | bottom: "stage3_3/conv1" 335 | top: "stage3_3/conv2" 336 | param { 337 | lr_mult: 1.0 338 | decay_mult: 1.0 339 | } 340 | convolution_param { 341 | num_output: 16 342 | pad: 1 343 | kernel_size: 3 344 | group: 16 345 | stride: 1 346 | weight_filler { 347 | type: "msra" 348 | } 349 | dilation: 1 350 | } 351 | } 352 | layer { 353 | name: "stage3_3/conv3" 354 | type: "Convolution" 355 | bottom: "stage3_3/conv2" 356 | top: "stage3_3/conv3" 357 | param { 358 | lr_mult: 1.0 359 | decay_mult: 1.0 360 | } 361 | convolution_param { 362 | num_output: 64 363 | pad: 0 364 | kernel_size: 1 365 | group: 1 366 | stride: 1 367 | weight_filler { 368 | type: "msra" 369 | } 370 | dilation: 1 371 | } 372 | } 373 | layer { 374 | name: "stage3_3/sum" 375 | type: "Eltwise" 376 | bottom: "stage3_2/sum" 377 | bottom: "stage3_3/conv3" 378 | top: "stage3_3/sum" 379 | eltwise_param { 380 | operation: SUM 381 | } 382 | } 383 | layer { 384 | name: "stage3_3/relu" 385 | type: "ReLU" 386 | bottom: "stage3_3/sum" 387 | top: "stage3_3/sum" 388 | } 389 | layer { 390 | name: "stage3_4/conv1" 391 | type: "Convolution" 392 | bottom: "stage3_3/sum" 393 | top: "stage3_4/conv1" 394 | param { 395 | lr_mult: 1.0 396 | decay_mult: 1.0 397 | } 398 | convolution_param { 399 | num_output: 16 400 | pad: 0 401 | kernel_size: 1 402 | group: 1 403 | stride: 1 404 | weight_filler { 405 | type: "msra" 406 | } 407 | dilation: 1 408 | } 409 | } 410 | layer { 411 | name: "stage3_4/conv1/relu" 412 | type: "ReLU" 413 | bottom: "stage3_4/conv1" 414 | top: "stage3_4/conv1" 415 | } 416 | layer { 417 | name: "stage3_4/conv2" 418 | type: "Convolution" 419 | bottom: "stage3_4/conv1" 420 | top: "stage3_4/conv2" 421 | param { 422 | lr_mult: 1.0 423 | decay_mult: 1.0 424 | } 425 | convolution_param { 426 | num_output: 16 427 | pad: 1 428 | kernel_size: 3 429 | group: 16 430 | stride: 1 431 | weight_filler { 432 | type: "msra" 433 | } 434 | dilation: 1 435 | } 436 | } 437 | layer { 438 | name: "stage3_4/conv3" 439 | type: "Convolution" 440 | bottom: "stage3_4/conv2" 441 | top: "stage3_4/conv3" 442 | param { 443 | lr_mult: 1.0 444 | decay_mult: 1.0 445 | } 446 | convolution_param { 447 | num_output: 64 448 | pad: 0 449 | kernel_size: 1 450 | group: 1 451 | stride: 1 452 | weight_filler { 453 | type: "msra" 454 | } 455 | dilation: 1 456 | } 457 | } 458 | layer { 459 | name: "stage3_4/sum" 460 | type: "Eltwise" 461 | bottom: "stage3_3/sum" 462 | bottom: "stage3_4/conv3" 463 | top: "stage3_4/sum" 464 | eltwise_param { 465 | operation: SUM 466 | } 467 | } 468 | layer { 469 | name: "stage3_4/relu" 470 | type: "ReLU" 471 | bottom: "stage3_4/sum" 472 | top: "stage3_4/sum" 473 | } 474 | layer { 475 | name: "stage4_1/conv1" 476 | type: "Convolution" 477 | bottom: "stage3_4/sum" 478 | top: "stage4_1/conv1" 479 | param { 480 | lr_mult: 1.0 481 | decay_mult: 1.0 482 | } 483 | convolution_param { 484 | num_output: 32 485 | pad: 0 486 | kernel_size: 1 487 | group: 1 488 | stride: 1 489 | weight_filler { 490 | type: "msra" 491 | } 492 | dilation: 1 493 | } 494 | } 495 | layer { 496 | name: "stage4_1/conv1/relu" 497 | type: "ReLU" 498 | bottom: "stage4_1/conv1" 499 | top: "stage4_1/conv1" 500 | } 501 | layer { 502 | name: "stage4_1/conv2" 503 | type: "Convolution" 504 | bottom: "stage4_1/conv1" 505 | top: "stage4_1/conv2" 506 | param { 507 | lr_mult: 1.0 508 | decay_mult: 1.0 509 | } 510 | convolution_param { 511 | num_output: 32 512 | pad: 1 513 | kernel_size: 3 514 | group: 32 515 | stride: 2 516 | weight_filler { 517 | type: "msra" 518 | } 519 | dilation: 1 520 | } 521 | } 522 | layer { 523 | name: "stage4_1/conv3" 524 | type: "Convolution" 525 | bottom: "stage4_1/conv2" 526 | top: "stage4_1/conv3" 527 | param { 528 | lr_mult: 1.0 529 | decay_mult: 1.0 530 | } 531 | convolution_param { 532 | num_output: 128 533 | pad: 0 534 | kernel_size: 1 535 | group: 1 536 | stride: 1 537 | weight_filler { 538 | type: "msra" 539 | } 540 | dilation: 1 541 | } 542 | } 543 | layer { 544 | name: "stage4_1/relu" 545 | type: "ReLU" 546 | bottom: "stage4_1/conv3" 547 | top: "stage4_1/conv3" 548 | } 549 | layer { 550 | name: "stage4_2/conv1" 551 | type: "Convolution" 552 | bottom: "stage4_1/conv3" 553 | top: "stage4_2/conv1" 554 | param { 555 | lr_mult: 1.0 556 | decay_mult: 1.0 557 | } 558 | convolution_param { 559 | num_output: 32 560 | pad: 0 561 | kernel_size: 1 562 | group: 1 563 | stride: 1 564 | weight_filler { 565 | type: "msra" 566 | } 567 | dilation: 1 568 | } 569 | } 570 | layer { 571 | name: "stage4_2/conv1/relu" 572 | type: "ReLU" 573 | bottom: "stage4_2/conv1" 574 | top: "stage4_2/conv1" 575 | } 576 | layer { 577 | name: "stage4_2/conv2" 578 | type: "Convolution" 579 | bottom: "stage4_2/conv1" 580 | top: "stage4_2/conv2" 581 | param { 582 | lr_mult: 1.0 583 | decay_mult: 1.0 584 | } 585 | convolution_param { 586 | num_output: 32 587 | pad: 1 588 | kernel_size: 3 589 | group: 32 590 | stride: 1 591 | weight_filler { 592 | type: "msra" 593 | } 594 | dilation: 1 595 | } 596 | } 597 | layer { 598 | name: "stage4_2/conv3" 599 | type: "Convolution" 600 | bottom: "stage4_2/conv2" 601 | top: "stage4_2/conv3" 602 | param { 603 | lr_mult: 1.0 604 | decay_mult: 1.0 605 | } 606 | convolution_param { 607 | num_output: 128 608 | pad: 0 609 | kernel_size: 1 610 | group: 1 611 | stride: 1 612 | weight_filler { 613 | type: "msra" 614 | } 615 | dilation: 1 616 | } 617 | } 618 | layer { 619 | name: "stage4_2/sum" 620 | type: "Eltwise" 621 | bottom: "stage4_1/conv3" 622 | bottom: "stage4_2/conv3" 623 | top: "stage4_2/sum" 624 | eltwise_param { 625 | operation: SUM 626 | } 627 | } 628 | layer { 629 | name: "stage4_2/relu" 630 | type: "ReLU" 631 | bottom: "stage4_2/sum" 632 | top: "stage4_2/sum" 633 | } 634 | layer { 635 | name: "stage4_3/conv1" 636 | type: "Convolution" 637 | bottom: "stage4_2/sum" 638 | top: "stage4_3/conv1" 639 | param { 640 | lr_mult: 1.0 641 | decay_mult: 1.0 642 | } 643 | convolution_param { 644 | num_output: 32 645 | pad: 0 646 | kernel_size: 1 647 | group: 1 648 | stride: 1 649 | weight_filler { 650 | type: "msra" 651 | } 652 | dilation: 1 653 | } 654 | } 655 | layer { 656 | name: "stage4_3/conv1/relu" 657 | type: "ReLU" 658 | bottom: "stage4_3/conv1" 659 | top: "stage4_3/conv1" 660 | } 661 | layer { 662 | name: "stage4_3/conv2" 663 | type: "Convolution" 664 | bottom: "stage4_3/conv1" 665 | top: "stage4_3/conv2" 666 | param { 667 | lr_mult: 1.0 668 | decay_mult: 1.0 669 | } 670 | convolution_param { 671 | num_output: 32 672 | pad: 1 673 | kernel_size: 3 674 | group: 32 675 | stride: 1 676 | weight_filler { 677 | type: "msra" 678 | } 679 | dilation: 1 680 | } 681 | } 682 | layer { 683 | name: "stage4_3/conv3" 684 | type: "Convolution" 685 | bottom: "stage4_3/conv2" 686 | top: "stage4_3/conv3" 687 | param { 688 | lr_mult: 1.0 689 | decay_mult: 1.0 690 | } 691 | convolution_param { 692 | num_output: 128 693 | pad: 0 694 | kernel_size: 1 695 | group: 1 696 | stride: 1 697 | weight_filler { 698 | type: "msra" 699 | } 700 | dilation: 1 701 | } 702 | } 703 | layer { 704 | name: "stage4_3/sum" 705 | type: "Eltwise" 706 | bottom: "stage4_2/sum" 707 | bottom: "stage4_3/conv3" 708 | top: "stage4_3/sum" 709 | eltwise_param { 710 | operation: SUM 711 | } 712 | } 713 | layer { 714 | name: "stage4_3/relu" 715 | type: "ReLU" 716 | bottom: "stage4_3/sum" 717 | top: "stage4_3/sum" 718 | } 719 | layer { 720 | name: "stage4_4/conv1" 721 | type: "Convolution" 722 | bottom: "stage4_3/sum" 723 | top: "stage4_4/conv1" 724 | param { 725 | lr_mult: 1.0 726 | decay_mult: 1.0 727 | } 728 | convolution_param { 729 | num_output: 32 730 | pad: 0 731 | kernel_size: 1 732 | group: 1 733 | stride: 1 734 | weight_filler { 735 | type: "msra" 736 | } 737 | dilation: 1 738 | } 739 | } 740 | layer { 741 | name: "stage4_4/conv1/relu" 742 | type: "ReLU" 743 | bottom: "stage4_4/conv1" 744 | top: "stage4_4/conv1" 745 | } 746 | layer { 747 | name: "stage4_4/conv2" 748 | type: "Convolution" 749 | bottom: "stage4_4/conv1" 750 | top: "stage4_4/conv2" 751 | param { 752 | lr_mult: 1.0 753 | decay_mult: 1.0 754 | } 755 | convolution_param { 756 | num_output: 32 757 | pad: 1 758 | kernel_size: 3 759 | group: 32 760 | stride: 1 761 | weight_filler { 762 | type: "msra" 763 | } 764 | dilation: 1 765 | } 766 | } 767 | layer { 768 | name: "stage4_4/conv3" 769 | type: "Convolution" 770 | bottom: "stage4_4/conv2" 771 | top: "stage4_4/conv3" 772 | param { 773 | lr_mult: 1.0 774 | decay_mult: 1.0 775 | } 776 | convolution_param { 777 | num_output: 128 778 | pad: 0 779 | kernel_size: 1 780 | group: 1 781 | stride: 1 782 | weight_filler { 783 | type: "msra" 784 | } 785 | dilation: 1 786 | } 787 | } 788 | layer { 789 | name: "stage4_4/sum" 790 | type: "Eltwise" 791 | bottom: "stage4_3/sum" 792 | bottom: "stage4_4/conv3" 793 | top: "stage4_4/sum" 794 | eltwise_param { 795 | operation: SUM 796 | } 797 | } 798 | layer { 799 | name: "stage4_4/relu" 800 | type: "ReLU" 801 | bottom: "stage4_4/sum" 802 | top: "stage4_4/sum" 803 | } 804 | layer { 805 | name: "stage4_5/conv1" 806 | type: "Convolution" 807 | bottom: "stage4_4/sum" 808 | top: "stage4_5/conv1" 809 | param { 810 | lr_mult: 1.0 811 | decay_mult: 1.0 812 | } 813 | convolution_param { 814 | num_output: 32 815 | pad: 0 816 | kernel_size: 1 817 | group: 1 818 | stride: 1 819 | weight_filler { 820 | type: "msra" 821 | } 822 | dilation: 1 823 | } 824 | } 825 | layer { 826 | name: "stage4_5/conv1/relu" 827 | type: "ReLU" 828 | bottom: "stage4_5/conv1" 829 | top: "stage4_5/conv1" 830 | } 831 | layer { 832 | name: "stage4_5/conv2" 833 | type: "Convolution" 834 | bottom: "stage4_5/conv1" 835 | top: "stage4_5/conv2" 836 | param { 837 | lr_mult: 1.0 838 | decay_mult: 1.0 839 | } 840 | convolution_param { 841 | num_output: 32 842 | pad: 1 843 | kernel_size: 3 844 | group: 32 845 | stride: 1 846 | weight_filler { 847 | type: "msra" 848 | } 849 | dilation: 1 850 | } 851 | } 852 | layer { 853 | name: "stage4_5/conv3" 854 | type: "Convolution" 855 | bottom: "stage4_5/conv2" 856 | top: "stage4_5/conv3" 857 | param { 858 | lr_mult: 1.0 859 | decay_mult: 1.0 860 | } 861 | convolution_param { 862 | num_output: 128 863 | pad: 0 864 | kernel_size: 1 865 | group: 1 866 | stride: 1 867 | weight_filler { 868 | type: "msra" 869 | } 870 | dilation: 1 871 | } 872 | } 873 | layer { 874 | name: "stage4_5/sum" 875 | type: "Eltwise" 876 | bottom: "stage4_4/sum" 877 | bottom: "stage4_5/conv3" 878 | top: "stage4_5/sum" 879 | eltwise_param { 880 | operation: SUM 881 | } 882 | } 883 | layer { 884 | name: "stage4_5/relu" 885 | type: "ReLU" 886 | bottom: "stage4_5/sum" 887 | top: "stage4_5/sum" 888 | } 889 | layer { 890 | name: "stage4_6/conv1" 891 | type: "Convolution" 892 | bottom: "stage4_5/sum" 893 | top: "stage4_6/conv1" 894 | param { 895 | lr_mult: 1.0 896 | decay_mult: 1.0 897 | } 898 | convolution_param { 899 | num_output: 32 900 | pad: 0 901 | kernel_size: 1 902 | group: 1 903 | stride: 1 904 | weight_filler { 905 | type: "msra" 906 | } 907 | dilation: 1 908 | } 909 | } 910 | layer { 911 | name: "stage4_6/conv1/relu" 912 | type: "ReLU" 913 | bottom: "stage4_6/conv1" 914 | top: "stage4_6/conv1" 915 | } 916 | layer { 917 | name: "stage4_6/conv2" 918 | type: "Convolution" 919 | bottom: "stage4_6/conv1" 920 | top: "stage4_6/conv2" 921 | param { 922 | lr_mult: 1.0 923 | decay_mult: 1.0 924 | } 925 | convolution_param { 926 | num_output: 32 927 | pad: 1 928 | kernel_size: 3 929 | group: 32 930 | stride: 1 931 | weight_filler { 932 | type: "msra" 933 | } 934 | dilation: 1 935 | } 936 | } 937 | layer { 938 | name: "stage4_6/conv3" 939 | type: "Convolution" 940 | bottom: "stage4_6/conv2" 941 | top: "stage4_6/conv3" 942 | param { 943 | lr_mult: 1.0 944 | decay_mult: 1.0 945 | } 946 | convolution_param { 947 | num_output: 128 948 | pad: 0 949 | kernel_size: 1 950 | group: 1 951 | stride: 1 952 | weight_filler { 953 | type: "msra" 954 | } 955 | dilation: 1 956 | } 957 | } 958 | layer { 959 | name: "stage4_6/sum" 960 | type: "Eltwise" 961 | bottom: "stage4_5/sum" 962 | bottom: "stage4_6/conv3" 963 | top: "stage4_6/sum" 964 | eltwise_param { 965 | operation: SUM 966 | } 967 | } 968 | layer { 969 | name: "stage4_6/relu" 970 | type: "ReLU" 971 | bottom: "stage4_6/sum" 972 | top: "stage4_6/sum" 973 | } 974 | layer { 975 | name: "stage4_7/conv1" 976 | type: "Convolution" 977 | bottom: "stage4_6/sum" 978 | top: "stage4_7/conv1" 979 | param { 980 | lr_mult: 1.0 981 | decay_mult: 1.0 982 | } 983 | convolution_param { 984 | num_output: 32 985 | pad: 0 986 | kernel_size: 1 987 | group: 1 988 | stride: 1 989 | weight_filler { 990 | type: "msra" 991 | } 992 | dilation: 1 993 | } 994 | } 995 | layer { 996 | name: "stage4_7/conv1/relu" 997 | type: "ReLU" 998 | bottom: "stage4_7/conv1" 999 | top: "stage4_7/conv1" 1000 | } 1001 | layer { 1002 | name: "stage4_7/conv2" 1003 | type: "Convolution" 1004 | bottom: "stage4_7/conv1" 1005 | top: "stage4_7/conv2" 1006 | param { 1007 | lr_mult: 1.0 1008 | decay_mult: 1.0 1009 | } 1010 | convolution_param { 1011 | num_output: 32 1012 | pad: 1 1013 | kernel_size: 3 1014 | group: 32 1015 | stride: 1 1016 | weight_filler { 1017 | type: "msra" 1018 | } 1019 | dilation: 1 1020 | } 1021 | } 1022 | layer { 1023 | name: "stage4_7/conv3" 1024 | type: "Convolution" 1025 | bottom: "stage4_7/conv2" 1026 | top: "stage4_7/conv3" 1027 | param { 1028 | lr_mult: 1.0 1029 | decay_mult: 1.0 1030 | } 1031 | convolution_param { 1032 | num_output: 128 1033 | pad: 0 1034 | kernel_size: 1 1035 | group: 1 1036 | stride: 1 1037 | weight_filler { 1038 | type: "msra" 1039 | } 1040 | dilation: 1 1041 | } 1042 | } 1043 | layer { 1044 | name: "stage4_7/sum" 1045 | type: "Eltwise" 1046 | bottom: "stage4_6/sum" 1047 | bottom: "stage4_7/conv3" 1048 | top: "stage4_7/sum" 1049 | eltwise_param { 1050 | operation: SUM 1051 | } 1052 | } 1053 | layer { 1054 | name: "stage4_7/relu" 1055 | type: "ReLU" 1056 | bottom: "stage4_7/sum" 1057 | top: "stage4_7/sum" 1058 | } 1059 | layer { 1060 | name: "stage4_8/conv1" 1061 | type: "Convolution" 1062 | bottom: "stage4_7/sum" 1063 | top: "stage4_8/conv1" 1064 | param { 1065 | lr_mult: 1.0 1066 | decay_mult: 1.0 1067 | } 1068 | convolution_param { 1069 | num_output: 32 1070 | pad: 0 1071 | kernel_size: 1 1072 | group: 1 1073 | stride: 1 1074 | weight_filler { 1075 | type: "msra" 1076 | } 1077 | dilation: 1 1078 | } 1079 | } 1080 | layer { 1081 | name: "stage4_8/conv1/relu" 1082 | type: "ReLU" 1083 | bottom: "stage4_8/conv1" 1084 | top: "stage4_8/conv1" 1085 | } 1086 | layer { 1087 | name: "stage4_8/conv2" 1088 | type: "Convolution" 1089 | bottom: "stage4_8/conv1" 1090 | top: "stage4_8/conv2" 1091 | param { 1092 | lr_mult: 1.0 1093 | decay_mult: 1.0 1094 | } 1095 | convolution_param { 1096 | num_output: 32 1097 | pad: 1 1098 | kernel_size: 3 1099 | group: 32 1100 | stride: 1 1101 | weight_filler { 1102 | type: "msra" 1103 | } 1104 | dilation: 1 1105 | } 1106 | } 1107 | layer { 1108 | name: "stage4_8/conv3" 1109 | type: "Convolution" 1110 | bottom: "stage4_8/conv2" 1111 | top: "stage4_8/conv3" 1112 | param { 1113 | lr_mult: 1.0 1114 | decay_mult: 1.0 1115 | } 1116 | convolution_param { 1117 | num_output: 128 1118 | pad: 0 1119 | kernel_size: 1 1120 | group: 1 1121 | stride: 1 1122 | weight_filler { 1123 | type: "msra" 1124 | } 1125 | dilation: 1 1126 | } 1127 | } 1128 | layer { 1129 | name: "stage4_8/sum" 1130 | type: "Eltwise" 1131 | bottom: "stage4_7/sum" 1132 | bottom: "stage4_8/conv3" 1133 | top: "stage4_8/sum" 1134 | eltwise_param { 1135 | operation: SUM 1136 | } 1137 | } 1138 | layer { 1139 | name: "stage4_8/relu" 1140 | type: "ReLU" 1141 | bottom: "stage4_8/sum" 1142 | top: "stage4_8/sum" 1143 | } 1144 | layer { 1145 | name: "stage5_1/conv1" 1146 | type: "Convolution" 1147 | bottom: "stage4_8/sum" 1148 | top: "stage5_1/conv1" 1149 | param { 1150 | lr_mult: 1.0 1151 | decay_mult: 1.0 1152 | } 1153 | convolution_param { 1154 | num_output: 32 1155 | pad: 0 1156 | kernel_size: 1 1157 | group: 1 1158 | stride: 1 1159 | weight_filler { 1160 | type: "msra" 1161 | } 1162 | dilation: 1 1163 | } 1164 | } 1165 | layer { 1166 | name: "stage5_1/conv1/relu" 1167 | type: "ReLU" 1168 | bottom: "stage5_1/conv1" 1169 | top: "stage5_1/conv1" 1170 | } 1171 | layer { 1172 | name: "stage5_1/conv2" 1173 | type: "Convolution" 1174 | bottom: "stage5_1/conv1" 1175 | top: "stage5_1/conv2" 1176 | param { 1177 | lr_mult: 1.0 1178 | decay_mult: 1.0 1179 | } 1180 | convolution_param { 1181 | num_output: 32 1182 | pad: 2 1183 | kernel_size: 3 1184 | group: 32 1185 | stride: 2 1186 | weight_filler { 1187 | type: "msra" 1188 | } 1189 | dilation: 2 1190 | } 1191 | } 1192 | layer { 1193 | name: "stage5_1/conv3" 1194 | type: "Convolution" 1195 | bottom: "stage5_1/conv2" 1196 | top: "stage5_1/conv3" 1197 | param { 1198 | lr_mult: 1.0 1199 | decay_mult: 1.0 1200 | } 1201 | convolution_param { 1202 | num_output: 128 1203 | pad: 0 1204 | kernel_size: 1 1205 | group: 1 1206 | stride: 1 1207 | weight_filler { 1208 | type: "msra" 1209 | } 1210 | dilation: 1 1211 | } 1212 | } 1213 | layer { 1214 | name: "stage5_1/relu" 1215 | type: "ReLU" 1216 | bottom: "stage5_1/conv3" 1217 | top: "stage5_1/conv3" 1218 | } 1219 | layer { 1220 | name: "stage5_2/conv1" 1221 | type: "Convolution" 1222 | bottom: "stage5_1/conv3" 1223 | top: "stage5_2/conv1" 1224 | param { 1225 | lr_mult: 1.0 1226 | decay_mult: 1.0 1227 | } 1228 | convolution_param { 1229 | num_output: 32 1230 | pad: 0 1231 | kernel_size: 1 1232 | group: 1 1233 | stride: 1 1234 | weight_filler { 1235 | type: "msra" 1236 | } 1237 | dilation: 1 1238 | } 1239 | } 1240 | layer { 1241 | name: "stage5_2/conv1/relu" 1242 | type: "ReLU" 1243 | bottom: "stage5_2/conv1" 1244 | top: "stage5_2/conv1" 1245 | } 1246 | layer { 1247 | name: "stage5_2/conv2" 1248 | type: "Convolution" 1249 | bottom: "stage5_2/conv1" 1250 | top: "stage5_2/conv2" 1251 | param { 1252 | lr_mult: 1.0 1253 | decay_mult: 1.0 1254 | } 1255 | convolution_param { 1256 | num_output: 32 1257 | pad: 2 1258 | kernel_size: 3 1259 | group: 32 1260 | stride: 1 1261 | weight_filler { 1262 | type: "msra" 1263 | } 1264 | dilation: 2 1265 | } 1266 | } 1267 | layer { 1268 | name: "stage5_2/conv3" 1269 | type: "Convolution" 1270 | bottom: "stage5_2/conv2" 1271 | top: "stage5_2/conv3" 1272 | param { 1273 | lr_mult: 1.0 1274 | decay_mult: 1.0 1275 | } 1276 | convolution_param { 1277 | num_output: 128 1278 | pad: 0 1279 | kernel_size: 1 1280 | group: 1 1281 | stride: 1 1282 | weight_filler { 1283 | type: "msra" 1284 | } 1285 | dilation: 1 1286 | } 1287 | } 1288 | layer { 1289 | name: "stage5_2/sum" 1290 | type: "Eltwise" 1291 | bottom: "stage5_1/conv3" 1292 | bottom: "stage5_2/conv3" 1293 | top: "stage5_2/sum" 1294 | eltwise_param { 1295 | operation: SUM 1296 | } 1297 | } 1298 | layer { 1299 | name: "stage5_2/relu" 1300 | type: "ReLU" 1301 | bottom: "stage5_2/sum" 1302 | top: "stage5_2/sum" 1303 | } 1304 | layer { 1305 | name: "stage5_3/conv1" 1306 | type: "Convolution" 1307 | bottom: "stage5_2/sum" 1308 | top: "stage5_3/conv1" 1309 | param { 1310 | lr_mult: 1.0 1311 | decay_mult: 1.0 1312 | } 1313 | convolution_param { 1314 | num_output: 32 1315 | pad: 0 1316 | kernel_size: 1 1317 | group: 1 1318 | stride: 1 1319 | weight_filler { 1320 | type: "msra" 1321 | } 1322 | dilation: 1 1323 | } 1324 | } 1325 | layer { 1326 | name: "stage5_3/conv1/relu" 1327 | type: "ReLU" 1328 | bottom: "stage5_3/conv1" 1329 | top: "stage5_3/conv1" 1330 | } 1331 | layer { 1332 | name: "stage5_3/conv2" 1333 | type: "Convolution" 1334 | bottom: "stage5_3/conv1" 1335 | top: "stage5_3/conv2" 1336 | param { 1337 | lr_mult: 1.0 1338 | decay_mult: 1.0 1339 | } 1340 | convolution_param { 1341 | num_output: 32 1342 | pad: 2 1343 | kernel_size: 3 1344 | group: 32 1345 | stride: 1 1346 | weight_filler { 1347 | type: "msra" 1348 | } 1349 | dilation: 2 1350 | } 1351 | } 1352 | layer { 1353 | name: "stage5_3/conv3" 1354 | type: "Convolution" 1355 | bottom: "stage5_3/conv2" 1356 | top: "stage5_3/conv3" 1357 | param { 1358 | lr_mult: 1.0 1359 | decay_mult: 1.0 1360 | } 1361 | convolution_param { 1362 | num_output: 128 1363 | pad: 0 1364 | kernel_size: 1 1365 | group: 1 1366 | stride: 1 1367 | weight_filler { 1368 | type: "msra" 1369 | } 1370 | dilation: 1 1371 | } 1372 | } 1373 | layer { 1374 | name: "stage5_3/sum" 1375 | type: "Eltwise" 1376 | bottom: "stage5_2/sum" 1377 | bottom: "stage5_3/conv3" 1378 | top: "stage5_3/sum" 1379 | eltwise_param { 1380 | operation: SUM 1381 | } 1382 | } 1383 | layer { 1384 | name: "stage5_3/relu" 1385 | type: "ReLU" 1386 | bottom: "stage5_3/sum" 1387 | top: "stage5_3/sum" 1388 | } 1389 | layer { 1390 | name: "stage5_4/conv1" 1391 | type: "Convolution" 1392 | bottom: "stage5_3/sum" 1393 | top: "stage5_4/conv1" 1394 | param { 1395 | lr_mult: 1.0 1396 | decay_mult: 1.0 1397 | } 1398 | convolution_param { 1399 | num_output: 32 1400 | pad: 0 1401 | kernel_size: 1 1402 | group: 1 1403 | stride: 1 1404 | weight_filler { 1405 | type: "msra" 1406 | } 1407 | dilation: 1 1408 | } 1409 | } 1410 | layer { 1411 | name: "stage5_4/conv1/relu" 1412 | type: "ReLU" 1413 | bottom: "stage5_4/conv1" 1414 | top: "stage5_4/conv1" 1415 | } 1416 | layer { 1417 | name: "stage5_4/conv2" 1418 | type: "Convolution" 1419 | bottom: "stage5_4/conv1" 1420 | top: "stage5_4/conv2" 1421 | param { 1422 | lr_mult: 1.0 1423 | decay_mult: 1.0 1424 | } 1425 | convolution_param { 1426 | num_output: 32 1427 | pad: 2 1428 | kernel_size: 3 1429 | group: 32 1430 | stride: 1 1431 | weight_filler { 1432 | type: "msra" 1433 | } 1434 | dilation: 2 1435 | } 1436 | } 1437 | layer { 1438 | name: "stage5_4/conv3" 1439 | type: "Convolution" 1440 | bottom: "stage5_4/conv2" 1441 | top: "stage5_4/conv3" 1442 | param { 1443 | lr_mult: 1.0 1444 | decay_mult: 1.0 1445 | } 1446 | convolution_param { 1447 | num_output: 128 1448 | pad: 0 1449 | kernel_size: 1 1450 | group: 1 1451 | stride: 1 1452 | weight_filler { 1453 | type: "msra" 1454 | } 1455 | dilation: 1 1456 | } 1457 | } 1458 | layer { 1459 | name: "stage5_4/sum" 1460 | type: "Eltwise" 1461 | bottom: "stage5_3/sum" 1462 | bottom: "stage5_4/conv3" 1463 | top: "stage5_4/sum" 1464 | eltwise_param { 1465 | operation: SUM 1466 | } 1467 | } 1468 | layer { 1469 | name: "stage5_4/relu" 1470 | type: "ReLU" 1471 | bottom: "stage5_4/sum" 1472 | top: "stage5_4/sum" 1473 | } 1474 | layer { 1475 | name: "stage6_1/conv4" 1476 | type: "Convolution" 1477 | bottom: "stage5_4/sum" 1478 | top: "stage6_1/conv4" 1479 | param { 1480 | lr_mult: 1.0 1481 | decay_mult: 1.0 1482 | } 1483 | convolution_param { 1484 | num_output: 128 1485 | pad: 0 1486 | kernel_size: 1 1487 | group: 1 1488 | stride: 1 1489 | weight_filler { 1490 | type: "msra" 1491 | } 1492 | dilation: 1 1493 | } 1494 | } 1495 | layer { 1496 | name: "stage6_1/conv1" 1497 | type: "Convolution" 1498 | bottom: "stage5_4/sum" 1499 | top: "stage6_1/conv1" 1500 | param { 1501 | lr_mult: 1.0 1502 | decay_mult: 1.0 1503 | } 1504 | convolution_param { 1505 | num_output: 32 1506 | pad: 0 1507 | kernel_size: 1 1508 | group: 1 1509 | stride: 1 1510 | weight_filler { 1511 | type: "msra" 1512 | } 1513 | dilation: 1 1514 | } 1515 | } 1516 | layer { 1517 | name: "stage6_1/conv1/relu" 1518 | type: "ReLU" 1519 | bottom: "stage6_1/conv1" 1520 | top: "stage6_1/conv1" 1521 | } 1522 | layer { 1523 | name: "stage6_1/conv2" 1524 | type: "Convolution" 1525 | bottom: "stage6_1/conv1" 1526 | top: "stage6_1/conv2" 1527 | param { 1528 | lr_mult: 1.0 1529 | decay_mult: 1.0 1530 | } 1531 | convolution_param { 1532 | num_output: 32 1533 | pad: 2 1534 | kernel_size: 3 1535 | group: 32 1536 | stride: 1 1537 | weight_filler { 1538 | type: "msra" 1539 | } 1540 | dilation: 2 1541 | } 1542 | } 1543 | layer { 1544 | name: "stage6_1/conv3" 1545 | type: "Convolution" 1546 | bottom: "stage6_1/conv2" 1547 | top: "stage6_1/conv3" 1548 | param { 1549 | lr_mult: 1.0 1550 | decay_mult: 1.0 1551 | } 1552 | convolution_param { 1553 | num_output: 128 1554 | pad: 0 1555 | kernel_size: 1 1556 | group: 1 1557 | stride: 1 1558 | weight_filler { 1559 | type: "msra" 1560 | } 1561 | dilation: 1 1562 | } 1563 | } 1564 | layer { 1565 | name: "stage6_1/sum" 1566 | type: "Eltwise" 1567 | bottom: "stage6_1/conv4" 1568 | bottom: "stage6_1/conv3" 1569 | top: "stage6_1/sum" 1570 | eltwise_param { 1571 | operation: SUM 1572 | } 1573 | } 1574 | layer { 1575 | name: "stage6_1/relu" 1576 | type: "ReLU" 1577 | bottom: "stage6_1/sum" 1578 | top: "stage6_1/sum" 1579 | } 1580 | layer { 1581 | name: "stage6_2/conv1" 1582 | type: "Convolution" 1583 | bottom: "stage6_1/sum" 1584 | top: "stage6_2/conv1" 1585 | param { 1586 | lr_mult: 1.0 1587 | decay_mult: 1.0 1588 | } 1589 | convolution_param { 1590 | num_output: 32 1591 | pad: 0 1592 | kernel_size: 1 1593 | group: 1 1594 | stride: 1 1595 | weight_filler { 1596 | type: "msra" 1597 | } 1598 | dilation: 1 1599 | } 1600 | } 1601 | layer { 1602 | name: "stage6_2/conv1/relu" 1603 | type: "ReLU" 1604 | bottom: "stage6_2/conv1" 1605 | top: "stage6_2/conv1" 1606 | } 1607 | layer { 1608 | name: "stage6_2/conv2" 1609 | type: "Convolution" 1610 | bottom: "stage6_2/conv1" 1611 | top: "stage6_2/conv2" 1612 | param { 1613 | lr_mult: 1.0 1614 | decay_mult: 1.0 1615 | } 1616 | convolution_param { 1617 | num_output: 32 1618 | pad: 2 1619 | kernel_size: 3 1620 | group: 32 1621 | stride: 1 1622 | weight_filler { 1623 | type: "msra" 1624 | } 1625 | dilation: 2 1626 | } 1627 | } 1628 | layer { 1629 | name: "stage6_2/conv3" 1630 | type: "Convolution" 1631 | bottom: "stage6_2/conv2" 1632 | top: "stage6_2/conv3" 1633 | param { 1634 | lr_mult: 1.0 1635 | decay_mult: 1.0 1636 | } 1637 | convolution_param { 1638 | num_output: 128 1639 | pad: 0 1640 | kernel_size: 1 1641 | group: 1 1642 | stride: 1 1643 | weight_filler { 1644 | type: "msra" 1645 | } 1646 | dilation: 1 1647 | } 1648 | } 1649 | layer { 1650 | name: "stage6_2/sum" 1651 | type: "Eltwise" 1652 | bottom: "stage6_1/sum" 1653 | bottom: "stage6_2/conv3" 1654 | top: "stage6_2/sum" 1655 | eltwise_param { 1656 | operation: SUM 1657 | } 1658 | } 1659 | layer { 1660 | name: "stage6_2/relu" 1661 | type: "ReLU" 1662 | bottom: "stage6_2/sum" 1663 | top: "stage6_2/sum" 1664 | } 1665 | layer { 1666 | name: "stage7_1/conv4" 1667 | type: "Convolution" 1668 | bottom: "stage6_2/sum" 1669 | top: "stage7_1/conv4" 1670 | param { 1671 | lr_mult: 1.0 1672 | decay_mult: 1.0 1673 | } 1674 | convolution_param { 1675 | num_output: 128 1676 | pad: 0 1677 | kernel_size: 1 1678 | group: 1 1679 | stride: 1 1680 | weight_filler { 1681 | type: "msra" 1682 | } 1683 | dilation: 1 1684 | } 1685 | } 1686 | layer { 1687 | name: "stage7_1/conv1" 1688 | type: "Convolution" 1689 | bottom: "stage6_2/sum" 1690 | top: "stage7_1/conv1" 1691 | param { 1692 | lr_mult: 1.0 1693 | decay_mult: 1.0 1694 | } 1695 | convolution_param { 1696 | num_output: 32 1697 | pad: 0 1698 | kernel_size: 1 1699 | group: 1 1700 | stride: 1 1701 | weight_filler { 1702 | type: "msra" 1703 | } 1704 | dilation: 1 1705 | } 1706 | } 1707 | layer { 1708 | name: "stage7_1/conv1/relu" 1709 | type: "ReLU" 1710 | bottom: "stage7_1/conv1" 1711 | top: "stage7_1/conv1" 1712 | } 1713 | layer { 1714 | name: "stage7_1/conv2" 1715 | type: "Convolution" 1716 | bottom: "stage7_1/conv1" 1717 | top: "stage7_1/conv2" 1718 | param { 1719 | lr_mult: 1.0 1720 | decay_mult: 1.0 1721 | } 1722 | convolution_param { 1723 | num_output: 32 1724 | pad: 2 1725 | kernel_size: 3 1726 | group: 32 1727 | stride: 1 1728 | weight_filler { 1729 | type: "msra" 1730 | } 1731 | dilation: 2 1732 | } 1733 | } 1734 | layer { 1735 | name: "stage7_1/conv3" 1736 | type: "Convolution" 1737 | bottom: "stage7_1/conv2" 1738 | top: "stage7_1/conv3" 1739 | param { 1740 | lr_mult: 1.0 1741 | decay_mult: 1.0 1742 | } 1743 | convolution_param { 1744 | num_output: 128 1745 | pad: 0 1746 | kernel_size: 1 1747 | group: 1 1748 | stride: 1 1749 | weight_filler { 1750 | type: "msra" 1751 | } 1752 | dilation: 1 1753 | } 1754 | } 1755 | layer { 1756 | name: "stage7_1/sum" 1757 | type: "Eltwise" 1758 | bottom: "stage7_1/conv4" 1759 | bottom: "stage7_1/conv3" 1760 | top: "stage7_1/sum" 1761 | eltwise_param { 1762 | operation: SUM 1763 | } 1764 | } 1765 | layer { 1766 | name: "stage7_1/relu" 1767 | type: "ReLU" 1768 | bottom: "stage7_1/sum" 1769 | top: "stage7_1/sum" 1770 | } 1771 | layer { 1772 | name: "stage7_2/conv1" 1773 | type: "Convolution" 1774 | bottom: "stage7_1/sum" 1775 | top: "stage7_2/conv1" 1776 | param { 1777 | lr_mult: 1.0 1778 | decay_mult: 1.0 1779 | } 1780 | convolution_param { 1781 | num_output: 32 1782 | pad: 0 1783 | kernel_size: 1 1784 | group: 1 1785 | stride: 1 1786 | weight_filler { 1787 | type: "msra" 1788 | } 1789 | dilation: 1 1790 | } 1791 | } 1792 | layer { 1793 | name: "stage7_2/conv1/relu" 1794 | type: "ReLU" 1795 | bottom: "stage7_2/conv1" 1796 | top: "stage7_2/conv1" 1797 | } 1798 | layer { 1799 | name: "stage7_2/conv2" 1800 | type: "Convolution" 1801 | bottom: "stage7_2/conv1" 1802 | top: "stage7_2/conv2" 1803 | param { 1804 | lr_mult: 1.0 1805 | decay_mult: 1.0 1806 | } 1807 | convolution_param { 1808 | num_output: 32 1809 | pad: 2 1810 | kernel_size: 3 1811 | group: 32 1812 | stride: 1 1813 | weight_filler { 1814 | type: "msra" 1815 | } 1816 | dilation: 2 1817 | } 1818 | } 1819 | layer { 1820 | name: "stage7_2/conv3" 1821 | type: "Convolution" 1822 | bottom: "stage7_2/conv2" 1823 | top: "stage7_2/conv3" 1824 | param { 1825 | lr_mult: 1.0 1826 | decay_mult: 1.0 1827 | } 1828 | convolution_param { 1829 | num_output: 128 1830 | pad: 0 1831 | kernel_size: 1 1832 | group: 1 1833 | stride: 1 1834 | weight_filler { 1835 | type: "msra" 1836 | } 1837 | dilation: 1 1838 | } 1839 | } 1840 | layer { 1841 | name: "stage7_2/sum" 1842 | type: "Eltwise" 1843 | bottom: "stage7_1/sum" 1844 | bottom: "stage7_2/conv3" 1845 | top: "stage7_2/sum" 1846 | eltwise_param { 1847 | operation: SUM 1848 | } 1849 | } 1850 | layer { 1851 | name: "stage7_2/relu" 1852 | type: "ReLU" 1853 | bottom: "stage7_2/sum" 1854 | top: "stage7_2/sum" 1855 | } 1856 | layer { 1857 | name: "stage8_1/conv4" 1858 | type: "Convolution" 1859 | bottom: "stage7_2/sum" 1860 | top: "stage8_1/conv4" 1861 | param { 1862 | lr_mult: 1.0 1863 | decay_mult: 1.0 1864 | } 1865 | convolution_param { 1866 | num_output: 128 1867 | pad: 0 1868 | kernel_size: 1 1869 | group: 1 1870 | stride: 1 1871 | weight_filler { 1872 | type: "msra" 1873 | } 1874 | dilation: 1 1875 | } 1876 | } 1877 | layer { 1878 | name: "stage8_1/conv1" 1879 | type: "Convolution" 1880 | bottom: "stage7_2/sum" 1881 | top: "stage8_1/conv1" 1882 | param { 1883 | lr_mult: 1.0 1884 | decay_mult: 1.0 1885 | } 1886 | convolution_param { 1887 | num_output: 32 1888 | pad: 0 1889 | kernel_size: 1 1890 | group: 1 1891 | stride: 1 1892 | weight_filler { 1893 | type: "msra" 1894 | } 1895 | dilation: 1 1896 | } 1897 | } 1898 | layer { 1899 | name: "stage8_1/conv1/relu" 1900 | type: "ReLU" 1901 | bottom: "stage8_1/conv1" 1902 | top: "stage8_1/conv1" 1903 | } 1904 | layer { 1905 | name: "stage8_1/conv2" 1906 | type: "Convolution" 1907 | bottom: "stage8_1/conv1" 1908 | top: "stage8_1/conv2" 1909 | param { 1910 | lr_mult: 1.0 1911 | decay_mult: 1.0 1912 | } 1913 | convolution_param { 1914 | num_output: 32 1915 | pad: 2 1916 | kernel_size: 3 1917 | group: 32 1918 | stride: 1 1919 | weight_filler { 1920 | type: "msra" 1921 | } 1922 | dilation: 2 1923 | } 1924 | } 1925 | layer { 1926 | name: "stage8_1/conv3" 1927 | type: "Convolution" 1928 | bottom: "stage8_1/conv2" 1929 | top: "stage8_1/conv3" 1930 | param { 1931 | lr_mult: 1.0 1932 | decay_mult: 1.0 1933 | } 1934 | convolution_param { 1935 | num_output: 128 1936 | pad: 0 1937 | kernel_size: 1 1938 | group: 1 1939 | stride: 1 1940 | weight_filler { 1941 | type: "msra" 1942 | } 1943 | dilation: 1 1944 | } 1945 | } 1946 | layer { 1947 | name: "stage8_1/sum" 1948 | type: "Eltwise" 1949 | bottom: "stage8_1/conv4" 1950 | bottom: "stage8_1/conv3" 1951 | top: "stage8_1/sum" 1952 | eltwise_param { 1953 | operation: SUM 1954 | } 1955 | } 1956 | layer { 1957 | name: "stage8_1/relu" 1958 | type: "ReLU" 1959 | bottom: "stage8_1/sum" 1960 | top: "stage8_1/sum" 1961 | } 1962 | layer { 1963 | name: "stage8_2/conv1" 1964 | type: "Convolution" 1965 | bottom: "stage8_1/sum" 1966 | top: "stage8_2/conv1" 1967 | param { 1968 | lr_mult: 1.0 1969 | decay_mult: 1.0 1970 | } 1971 | convolution_param { 1972 | num_output: 32 1973 | pad: 0 1974 | kernel_size: 1 1975 | group: 1 1976 | stride: 1 1977 | weight_filler { 1978 | type: "msra" 1979 | } 1980 | dilation: 1 1981 | } 1982 | } 1983 | layer { 1984 | name: "stage8_2/conv1/relu" 1985 | type: "ReLU" 1986 | bottom: "stage8_2/conv1" 1987 | top: "stage8_2/conv1" 1988 | } 1989 | layer { 1990 | name: "stage8_2/conv2" 1991 | type: "Convolution" 1992 | bottom: "stage8_2/conv1" 1993 | top: "stage8_2/conv2" 1994 | param { 1995 | lr_mult: 1.0 1996 | decay_mult: 1.0 1997 | } 1998 | convolution_param { 1999 | num_output: 32 2000 | pad: 2 2001 | kernel_size: 3 2002 | group: 32 2003 | stride: 1 2004 | weight_filler { 2005 | type: "msra" 2006 | } 2007 | dilation: 2 2008 | } 2009 | } 2010 | layer { 2011 | name: "stage8_2/conv3" 2012 | type: "Convolution" 2013 | bottom: "stage8_2/conv2" 2014 | top: "stage8_2/conv3" 2015 | param { 2016 | lr_mult: 1.0 2017 | decay_mult: 1.0 2018 | } 2019 | convolution_param { 2020 | num_output: 128 2021 | pad: 0 2022 | kernel_size: 1 2023 | group: 1 2024 | stride: 1 2025 | weight_filler { 2026 | type: "msra" 2027 | } 2028 | dilation: 1 2029 | } 2030 | } 2031 | layer { 2032 | name: "stage8_2/sum" 2033 | type: "Eltwise" 2034 | bottom: "stage8_1/sum" 2035 | bottom: "stage8_2/conv3" 2036 | top: "stage8_2/sum" 2037 | eltwise_param { 2038 | operation: SUM 2039 | } 2040 | } 2041 | layer { 2042 | name: "stage8_2/relu" 2043 | type: "ReLU" 2044 | bottom: "stage8_2/sum" 2045 | top: "stage8_2/sum" 2046 | } 2047 | layer { 2048 | name: "cls1/conv" 2049 | type: "Convolution" 2050 | bottom: "stage4_8/sum" 2051 | top: "cls1/conv" 2052 | param { 2053 | lr_mult: 1.0 2054 | decay_mult: 1.0 2055 | } 2056 | param { 2057 | lr_mult: 1.0 2058 | decay_mult: 0.0 2059 | } 2060 | convolution_param { 2061 | num_output: 12 2062 | bias_term: true 2063 | pad: 0 2064 | kernel_size: 1 2065 | group: 1 2066 | stride: 1 2067 | weight_filler { 2068 | type: "msra" 2069 | } 2070 | dilation: 1 2071 | } 2072 | } 2073 | layer { 2074 | name: "cls1/permute" 2075 | type: "Permute" 2076 | bottom: "cls1/conv" 2077 | top: "cls1/permute" 2078 | permute_param { 2079 | order: 0 2080 | order: 2 2081 | order: 3 2082 | order: 1 2083 | } 2084 | } 2085 | layer { 2086 | name: "cls1/flatten" 2087 | type: "Flatten" 2088 | bottom: "cls1/permute" 2089 | top: "cls1/flatten" 2090 | flatten_param { 2091 | axis: 1 2092 | } 2093 | } 2094 | layer { 2095 | name: "loc1/conv" 2096 | type: "Convolution" 2097 | bottom: "stage4_8/sum" 2098 | top: "loc1/conv" 2099 | param { 2100 | lr_mult: 1.0 2101 | decay_mult: 1.0 2102 | } 2103 | param { 2104 | lr_mult: 1.0 2105 | decay_mult: 0.0 2106 | } 2107 | convolution_param { 2108 | num_output: 24 2109 | bias_term: true 2110 | pad: 0 2111 | kernel_size: 1 2112 | group: 1 2113 | stride: 1 2114 | weight_filler { 2115 | type: "msra" 2116 | } 2117 | dilation: 1 2118 | } 2119 | } 2120 | layer { 2121 | name: "loc1/permute" 2122 | type: "Permute" 2123 | bottom: "loc1/conv" 2124 | top: "loc1/permute" 2125 | permute_param { 2126 | order: 0 2127 | order: 2 2128 | order: 3 2129 | order: 1 2130 | } 2131 | } 2132 | layer { 2133 | name: "loc1/flatten" 2134 | type: "Flatten" 2135 | bottom: "loc1/permute" 2136 | top: "loc1/flatten" 2137 | flatten_param { 2138 | axis: 1 2139 | } 2140 | } 2141 | layer { 2142 | name: "stage4_8/sum/prior_box" 2143 | type: "PriorBox" 2144 | bottom: "stage4_8/sum" 2145 | bottom: "data" 2146 | top: "stage4_8/sum/prior_box" 2147 | prior_box_param { 2148 | min_size: 50.0 2149 | max_size: 100.0 2150 | aspect_ratio: 2.0 2151 | aspect_ratio: 0.5 2152 | aspect_ratio: 3.0 2153 | aspect_ratio: 0.3333333432674408 2154 | flip: false 2155 | clip: false 2156 | variance: 0.10000000149011612 2157 | variance: 0.10000000149011612 2158 | variance: 0.20000000298023224 2159 | variance: 0.20000000298023224 2160 | step: 16.0 2161 | } 2162 | } 2163 | layer { 2164 | name: "cls2/conv" 2165 | type: "Convolution" 2166 | bottom: "stage5_4/sum" 2167 | top: "cls2/conv" 2168 | param { 2169 | lr_mult: 1.0 2170 | decay_mult: 1.0 2171 | } 2172 | param { 2173 | lr_mult: 1.0 2174 | decay_mult: 0.0 2175 | } 2176 | convolution_param { 2177 | num_output: 12 2178 | bias_term: true 2179 | pad: 0 2180 | kernel_size: 1 2181 | group: 1 2182 | stride: 1 2183 | weight_filler { 2184 | type: "msra" 2185 | } 2186 | dilation: 1 2187 | } 2188 | } 2189 | layer { 2190 | name: "cls2/permute" 2191 | type: "Permute" 2192 | bottom: "cls2/conv" 2193 | top: "cls2/permute" 2194 | permute_param { 2195 | order: 0 2196 | order: 2 2197 | order: 3 2198 | order: 1 2199 | } 2200 | } 2201 | layer { 2202 | name: "cls2/flatten" 2203 | type: "Flatten" 2204 | bottom: "cls2/permute" 2205 | top: "cls2/flatten" 2206 | flatten_param { 2207 | axis: 1 2208 | } 2209 | } 2210 | layer { 2211 | name: "loc2/conv" 2212 | type: "Convolution" 2213 | bottom: "stage5_4/sum" 2214 | top: "loc2/conv" 2215 | param { 2216 | lr_mult: 1.0 2217 | decay_mult: 1.0 2218 | } 2219 | param { 2220 | lr_mult: 1.0 2221 | decay_mult: 0.0 2222 | } 2223 | convolution_param { 2224 | num_output: 24 2225 | bias_term: true 2226 | pad: 0 2227 | kernel_size: 1 2228 | group: 1 2229 | stride: 1 2230 | weight_filler { 2231 | type: "msra" 2232 | } 2233 | dilation: 1 2234 | } 2235 | } 2236 | layer { 2237 | name: "loc2/permute" 2238 | type: "Permute" 2239 | bottom: "loc2/conv" 2240 | top: "loc2/permute" 2241 | permute_param { 2242 | order: 0 2243 | order: 2 2244 | order: 3 2245 | order: 1 2246 | } 2247 | } 2248 | layer { 2249 | name: "loc2/flatten" 2250 | type: "Flatten" 2251 | bottom: "loc2/permute" 2252 | top: "loc2/flatten" 2253 | flatten_param { 2254 | axis: 1 2255 | } 2256 | } 2257 | layer { 2258 | name: "stage5_4/sum/prior_box" 2259 | type: "PriorBox" 2260 | bottom: "stage5_4/sum" 2261 | bottom: "data" 2262 | top: "stage5_4/sum/prior_box" 2263 | prior_box_param { 2264 | min_size: 100.0 2265 | max_size: 150.0 2266 | aspect_ratio: 2.0 2267 | aspect_ratio: 0.5 2268 | aspect_ratio: 3.0 2269 | aspect_ratio: 0.3333333432674408 2270 | flip: false 2271 | clip: false 2272 | variance: 0.10000000149011612 2273 | variance: 0.10000000149011612 2274 | variance: 0.20000000298023224 2275 | variance: 0.20000000298023224 2276 | step: 32.0 2277 | } 2278 | } 2279 | layer { 2280 | name: "cls3/conv" 2281 | type: "Convolution" 2282 | bottom: "stage6_2/sum" 2283 | top: "cls3/conv" 2284 | param { 2285 | lr_mult: 1.0 2286 | decay_mult: 1.0 2287 | } 2288 | param { 2289 | lr_mult: 1.0 2290 | decay_mult: 0.0 2291 | } 2292 | convolution_param { 2293 | num_output: 12 2294 | bias_term: true 2295 | pad: 0 2296 | kernel_size: 1 2297 | group: 1 2298 | stride: 1 2299 | weight_filler { 2300 | type: "msra" 2301 | } 2302 | dilation: 1 2303 | } 2304 | } 2305 | layer { 2306 | name: "cls3/permute" 2307 | type: "Permute" 2308 | bottom: "cls3/conv" 2309 | top: "cls3/permute" 2310 | permute_param { 2311 | order: 0 2312 | order: 2 2313 | order: 3 2314 | order: 1 2315 | } 2316 | } 2317 | layer { 2318 | name: "cls3/flatten" 2319 | type: "Flatten" 2320 | bottom: "cls3/permute" 2321 | top: "cls3/flatten" 2322 | flatten_param { 2323 | axis: 1 2324 | } 2325 | } 2326 | layer { 2327 | name: "loc3/conv" 2328 | type: "Convolution" 2329 | bottom: "stage6_2/sum" 2330 | top: "loc3/conv" 2331 | param { 2332 | lr_mult: 1.0 2333 | decay_mult: 1.0 2334 | } 2335 | param { 2336 | lr_mult: 1.0 2337 | decay_mult: 0.0 2338 | } 2339 | convolution_param { 2340 | num_output: 24 2341 | bias_term: true 2342 | pad: 0 2343 | kernel_size: 1 2344 | group: 1 2345 | stride: 1 2346 | weight_filler { 2347 | type: "msra" 2348 | } 2349 | dilation: 1 2350 | } 2351 | } 2352 | layer { 2353 | name: "loc3/permute" 2354 | type: "Permute" 2355 | bottom: "loc3/conv" 2356 | top: "loc3/permute" 2357 | permute_param { 2358 | order: 0 2359 | order: 2 2360 | order: 3 2361 | order: 1 2362 | } 2363 | } 2364 | layer { 2365 | name: "loc3/flatten" 2366 | type: "Flatten" 2367 | bottom: "loc3/permute" 2368 | top: "loc3/flatten" 2369 | flatten_param { 2370 | axis: 1 2371 | } 2372 | } 2373 | layer { 2374 | name: "stage6_2/sum/prior_box" 2375 | type: "PriorBox" 2376 | bottom: "stage6_2/sum" 2377 | bottom: "data" 2378 | top: "stage6_2/sum/prior_box" 2379 | prior_box_param { 2380 | min_size: 150.0 2381 | max_size: 200.0 2382 | aspect_ratio: 2.0 2383 | aspect_ratio: 0.5 2384 | aspect_ratio: 3.0 2385 | aspect_ratio: 0.3333333432674408 2386 | flip: false 2387 | clip: false 2388 | variance: 0.10000000149011612 2389 | variance: 0.10000000149011612 2390 | variance: 0.20000000298023224 2391 | variance: 0.20000000298023224 2392 | step: 32.0 2393 | } 2394 | } 2395 | layer { 2396 | name: "cls4/conv" 2397 | type: "Convolution" 2398 | bottom: "stage7_2/sum" 2399 | top: "cls4/conv" 2400 | param { 2401 | lr_mult: 1.0 2402 | decay_mult: 1.0 2403 | } 2404 | param { 2405 | lr_mult: 1.0 2406 | decay_mult: 0.0 2407 | } 2408 | convolution_param { 2409 | num_output: 12 2410 | bias_term: true 2411 | pad: 0 2412 | kernel_size: 1 2413 | group: 1 2414 | stride: 1 2415 | weight_filler { 2416 | type: "msra" 2417 | } 2418 | dilation: 1 2419 | } 2420 | } 2421 | layer { 2422 | name: "cls4/permute" 2423 | type: "Permute" 2424 | bottom: "cls4/conv" 2425 | top: "cls4/permute" 2426 | permute_param { 2427 | order: 0 2428 | order: 2 2429 | order: 3 2430 | order: 1 2431 | } 2432 | } 2433 | layer { 2434 | name: "cls4/flatten" 2435 | type: "Flatten" 2436 | bottom: "cls4/permute" 2437 | top: "cls4/flatten" 2438 | flatten_param { 2439 | axis: 1 2440 | } 2441 | } 2442 | layer { 2443 | name: "loc4/conv" 2444 | type: "Convolution" 2445 | bottom: "stage7_2/sum" 2446 | top: "loc4/conv" 2447 | param { 2448 | lr_mult: 1.0 2449 | decay_mult: 1.0 2450 | } 2451 | param { 2452 | lr_mult: 1.0 2453 | decay_mult: 0.0 2454 | } 2455 | convolution_param { 2456 | num_output: 24 2457 | bias_term: true 2458 | pad: 0 2459 | kernel_size: 1 2460 | group: 1 2461 | stride: 1 2462 | weight_filler { 2463 | type: "msra" 2464 | } 2465 | dilation: 1 2466 | } 2467 | } 2468 | layer { 2469 | name: "loc4/permute" 2470 | type: "Permute" 2471 | bottom: "loc4/conv" 2472 | top: "loc4/permute" 2473 | permute_param { 2474 | order: 0 2475 | order: 2 2476 | order: 3 2477 | order: 1 2478 | } 2479 | } 2480 | layer { 2481 | name: "loc4/flatten" 2482 | type: "Flatten" 2483 | bottom: "loc4/permute" 2484 | top: "loc4/flatten" 2485 | flatten_param { 2486 | axis: 1 2487 | } 2488 | } 2489 | layer { 2490 | name: "stage7_2/sum/prior_box" 2491 | type: "PriorBox" 2492 | bottom: "stage7_2/sum" 2493 | bottom: "data" 2494 | top: "stage7_2/sum/prior_box" 2495 | prior_box_param { 2496 | min_size: 200.0 2497 | max_size: 300.0 2498 | aspect_ratio: 2.0 2499 | aspect_ratio: 0.5 2500 | aspect_ratio: 3.0 2501 | aspect_ratio: 0.3333333432674408 2502 | flip: false 2503 | clip: false 2504 | variance: 0.10000000149011612 2505 | variance: 0.10000000149011612 2506 | variance: 0.20000000298023224 2507 | variance: 0.20000000298023224 2508 | step: 32.0 2509 | } 2510 | } 2511 | layer { 2512 | name: "cls5/conv" 2513 | type: "Convolution" 2514 | bottom: "stage8_2/sum" 2515 | top: "cls5/conv" 2516 | param { 2517 | lr_mult: 1.0 2518 | decay_mult: 1.0 2519 | } 2520 | param { 2521 | lr_mult: 1.0 2522 | decay_mult: 0.0 2523 | } 2524 | convolution_param { 2525 | num_output: 12 2526 | bias_term: true 2527 | pad: 0 2528 | kernel_size: 1 2529 | group: 1 2530 | stride: 1 2531 | weight_filler { 2532 | type: "msra" 2533 | } 2534 | dilation: 1 2535 | } 2536 | } 2537 | layer { 2538 | name: "cls5/permute" 2539 | type: "Permute" 2540 | bottom: "cls5/conv" 2541 | top: "cls5/permute" 2542 | permute_param { 2543 | order: 0 2544 | order: 2 2545 | order: 3 2546 | order: 1 2547 | } 2548 | } 2549 | layer { 2550 | name: "cls5/flatten" 2551 | type: "Flatten" 2552 | bottom: "cls5/permute" 2553 | top: "cls5/flatten" 2554 | flatten_param { 2555 | axis: 1 2556 | } 2557 | } 2558 | layer { 2559 | name: "loc5/conv" 2560 | type: "Convolution" 2561 | bottom: "stage8_2/sum" 2562 | top: "loc5/conv" 2563 | param { 2564 | lr_mult: 1.0 2565 | decay_mult: 1.0 2566 | } 2567 | param { 2568 | lr_mult: 1.0 2569 | decay_mult: 0.0 2570 | } 2571 | convolution_param { 2572 | num_output: 24 2573 | bias_term: true 2574 | pad: 0 2575 | kernel_size: 1 2576 | group: 1 2577 | stride: 1 2578 | weight_filler { 2579 | type: "msra" 2580 | } 2581 | dilation: 1 2582 | } 2583 | } 2584 | layer { 2585 | name: "loc5/permute" 2586 | type: "Permute" 2587 | bottom: "loc5/conv" 2588 | top: "loc5/permute" 2589 | permute_param { 2590 | order: 0 2591 | order: 2 2592 | order: 3 2593 | order: 1 2594 | } 2595 | } 2596 | layer { 2597 | name: "loc5/flatten" 2598 | type: "Flatten" 2599 | bottom: "loc5/permute" 2600 | top: "loc5/flatten" 2601 | flatten_param { 2602 | axis: 1 2603 | } 2604 | } 2605 | layer { 2606 | name: "stage8_2/sum/prior_box" 2607 | type: "PriorBox" 2608 | bottom: "stage8_2/sum" 2609 | bottom: "data" 2610 | top: "stage8_2/sum/prior_box" 2611 | prior_box_param { 2612 | min_size: 300.0 2613 | max_size: 400.0 2614 | aspect_ratio: 2.0 2615 | aspect_ratio: 0.5 2616 | aspect_ratio: 3.0 2617 | aspect_ratio: 0.3333333432674408 2618 | flip: false 2619 | clip: false 2620 | variance: 0.10000000149011612 2621 | variance: 0.10000000149011612 2622 | variance: 0.20000000298023224 2623 | variance: 0.20000000298023224 2624 | step: 32.0 2625 | } 2626 | } 2627 | layer { 2628 | name: "mbox_conf" 2629 | type: "Concat" 2630 | bottom: "cls1/flatten" 2631 | bottom: "cls2/flatten" 2632 | bottom: "cls3/flatten" 2633 | bottom: "cls4/flatten" 2634 | bottom: "cls5/flatten" 2635 | top: "mbox_conf" 2636 | concat_param { 2637 | axis: 1 2638 | } 2639 | } 2640 | layer { 2641 | name: "mbox_loc" 2642 | type: "Concat" 2643 | bottom: "loc1/flatten" 2644 | bottom: "loc2/flatten" 2645 | bottom: "loc3/flatten" 2646 | bottom: "loc4/flatten" 2647 | bottom: "loc5/flatten" 2648 | top: "mbox_loc" 2649 | concat_param { 2650 | axis: 1 2651 | } 2652 | } 2653 | layer { 2654 | name: "mbox_priorbox" 2655 | type: "Concat" 2656 | bottom: "stage4_8/sum/prior_box" 2657 | bottom: "stage5_4/sum/prior_box" 2658 | bottom: "stage6_2/sum/prior_box" 2659 | bottom: "stage7_2/sum/prior_box" 2660 | bottom: "stage8_2/sum/prior_box" 2661 | top: "mbox_priorbox" 2662 | concat_param { 2663 | axis: 2 2664 | } 2665 | } 2666 | layer { 2667 | name: "mbox_conf_reshape" 2668 | type: "Reshape" 2669 | bottom: "mbox_conf" 2670 | top: "mbox_conf_reshape" 2671 | reshape_param { 2672 | shape { 2673 | dim: 0 2674 | dim: -1 2675 | dim: 2 2676 | } 2677 | } 2678 | } 2679 | layer { 2680 | name: "mbox_conf_softmax" 2681 | type: "Softmax" 2682 | bottom: "mbox_conf_reshape" 2683 | top: "mbox_conf_softmax" 2684 | softmax_param { 2685 | axis: 2 2686 | } 2687 | } 2688 | layer { 2689 | name: "mbox_conf_flatten" 2690 | type: "Flatten" 2691 | bottom: "mbox_conf_softmax" 2692 | top: "mbox_conf_flatten" 2693 | flatten_param { 2694 | axis: 1 2695 | } 2696 | } 2697 | layer { 2698 | name: "detection_output" 2699 | type: "DetectionOutput" 2700 | bottom: "mbox_loc" 2701 | bottom: "mbox_conf_flatten" 2702 | bottom: "mbox_priorbox" 2703 | top: "detection_output" 2704 | detection_output_param { 2705 | num_classes: 2 2706 | share_location: true 2707 | background_label_id: 0 2708 | nms_param { 2709 | nms_threshold: 0.44999998807907104 2710 | top_k: 100 2711 | } 2712 | code_type: CENTER_SIZE 2713 | keep_top_k: 100 2714 | confidence_threshold: 0.20000000298023224 2715 | } 2716 | } 2717 | -------------------------------------------------------------------------------- /models/sr.caffemodel: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zfb132/QrScan/dfc1e63ea0beaf57930810c73576f83e91cdc607/models/sr.caffemodel -------------------------------------------------------------------------------- /models/sr.prototxt: -------------------------------------------------------------------------------- 1 | layer { 2 | name: "data" 3 | type: "Input" 4 | top: "data" 5 | input_param { 6 | shape { 7 | dim: 1 8 | dim: 1 9 | dim: 224 10 | dim: 224 11 | } 12 | } 13 | } 14 | layer { 15 | name: "conv0" 16 | type: "Convolution" 17 | bottom: "data" 18 | top: "conv0" 19 | param { 20 | lr_mult: 1.0 21 | decay_mult: 1.0 22 | } 23 | param { 24 | lr_mult: 1.0 25 | decay_mult: 0.0 26 | } 27 | convolution_param { 28 | num_output: 32 29 | bias_term: true 30 | pad: 1 31 | kernel_size: 3 32 | group: 1 33 | stride: 1 34 | weight_filler { 35 | type: "msra" 36 | } 37 | } 38 | } 39 | layer { 40 | name: "conv0/lrelu" 41 | type: "ReLU" 42 | bottom: "conv0" 43 | top: "conv0" 44 | relu_param { 45 | negative_slope: 0.05000000074505806 46 | } 47 | } 48 | layer { 49 | name: "db1/reduce" 50 | type: "Convolution" 51 | bottom: "conv0" 52 | top: "db1/reduce" 53 | param { 54 | lr_mult: 1.0 55 | decay_mult: 1.0 56 | } 57 | param { 58 | lr_mult: 1.0 59 | decay_mult: 0.0 60 | } 61 | convolution_param { 62 | num_output: 8 63 | bias_term: true 64 | pad: 0 65 | kernel_size: 1 66 | group: 1 67 | stride: 1 68 | weight_filler { 69 | type: "msra" 70 | } 71 | } 72 | } 73 | layer { 74 | name: "db1/reduce/lrelu" 75 | type: "ReLU" 76 | bottom: "db1/reduce" 77 | top: "db1/reduce" 78 | relu_param { 79 | negative_slope: 0.05000000074505806 80 | } 81 | } 82 | layer { 83 | name: "db1/3x3" 84 | type: "Convolution" 85 | bottom: "db1/reduce" 86 | top: "db1/3x3" 87 | param { 88 | lr_mult: 1.0 89 | decay_mult: 1.0 90 | } 91 | param { 92 | lr_mult: 1.0 93 | decay_mult: 0.0 94 | } 95 | convolution_param { 96 | num_output: 8 97 | bias_term: true 98 | pad: 1 99 | kernel_size: 3 100 | group: 8 101 | stride: 1 102 | weight_filler { 103 | type: "msra" 104 | } 105 | } 106 | } 107 | layer { 108 | name: "db1/3x3/lrelu" 109 | type: "ReLU" 110 | bottom: "db1/3x3" 111 | top: "db1/3x3" 112 | relu_param { 113 | negative_slope: 0.05000000074505806 114 | } 115 | } 116 | layer { 117 | name: "db1/1x1" 118 | type: "Convolution" 119 | bottom: "db1/3x3" 120 | top: "db1/1x1" 121 | param { 122 | lr_mult: 1.0 123 | decay_mult: 1.0 124 | } 125 | param { 126 | lr_mult: 1.0 127 | decay_mult: 0.0 128 | } 129 | convolution_param { 130 | num_output: 32 131 | bias_term: true 132 | pad: 0 133 | kernel_size: 1 134 | group: 1 135 | stride: 1 136 | weight_filler { 137 | type: "msra" 138 | } 139 | } 140 | } 141 | layer { 142 | name: "db1/1x1/lrelu" 143 | type: "ReLU" 144 | bottom: "db1/1x1" 145 | top: "db1/1x1" 146 | relu_param { 147 | negative_slope: 0.05000000074505806 148 | } 149 | } 150 | layer { 151 | name: "db1/concat" 152 | type: "Concat" 153 | bottom: "conv0" 154 | bottom: "db1/1x1" 155 | top: "db1/concat" 156 | concat_param { 157 | axis: 1 158 | } 159 | } 160 | layer { 161 | name: "db2/reduce" 162 | type: "Convolution" 163 | bottom: "db1/concat" 164 | top: "db2/reduce" 165 | param { 166 | lr_mult: 1.0 167 | decay_mult: 1.0 168 | } 169 | param { 170 | lr_mult: 1.0 171 | decay_mult: 0.0 172 | } 173 | convolution_param { 174 | num_output: 8 175 | bias_term: true 176 | pad: 0 177 | kernel_size: 1 178 | group: 1 179 | stride: 1 180 | weight_filler { 181 | type: "msra" 182 | } 183 | } 184 | } 185 | layer { 186 | name: "db2/reduce/lrelu" 187 | type: "ReLU" 188 | bottom: "db2/reduce" 189 | top: "db2/reduce" 190 | relu_param { 191 | negative_slope: 0.05000000074505806 192 | } 193 | } 194 | layer { 195 | name: "db2/3x3" 196 | type: "Convolution" 197 | bottom: "db2/reduce" 198 | top: "db2/3x3" 199 | param { 200 | lr_mult: 1.0 201 | decay_mult: 1.0 202 | } 203 | param { 204 | lr_mult: 1.0 205 | decay_mult: 0.0 206 | } 207 | convolution_param { 208 | num_output: 8 209 | bias_term: true 210 | pad: 1 211 | kernel_size: 3 212 | group: 8 213 | stride: 1 214 | weight_filler { 215 | type: "msra" 216 | } 217 | } 218 | } 219 | layer { 220 | name: "db2/3x3/lrelu" 221 | type: "ReLU" 222 | bottom: "db2/3x3" 223 | top: "db2/3x3" 224 | relu_param { 225 | negative_slope: 0.05000000074505806 226 | } 227 | } 228 | layer { 229 | name: "db2/1x1" 230 | type: "Convolution" 231 | bottom: "db2/3x3" 232 | top: "db2/1x1" 233 | param { 234 | lr_mult: 1.0 235 | decay_mult: 1.0 236 | } 237 | param { 238 | lr_mult: 1.0 239 | decay_mult: 0.0 240 | } 241 | convolution_param { 242 | num_output: 32 243 | bias_term: true 244 | pad: 0 245 | kernel_size: 1 246 | group: 1 247 | stride: 1 248 | weight_filler { 249 | type: "msra" 250 | } 251 | } 252 | } 253 | layer { 254 | name: "db2/1x1/lrelu" 255 | type: "ReLU" 256 | bottom: "db2/1x1" 257 | top: "db2/1x1" 258 | relu_param { 259 | negative_slope: 0.05000000074505806 260 | } 261 | } 262 | layer { 263 | name: "db2/concat" 264 | type: "Concat" 265 | bottom: "db1/concat" 266 | bottom: "db2/1x1" 267 | top: "db2/concat" 268 | concat_param { 269 | axis: 1 270 | } 271 | } 272 | layer { 273 | name: "upsample/reduce" 274 | type: "Convolution" 275 | bottom: "db2/concat" 276 | top: "upsample/reduce" 277 | param { 278 | lr_mult: 1.0 279 | decay_mult: 1.0 280 | } 281 | param { 282 | lr_mult: 1.0 283 | decay_mult: 0.0 284 | } 285 | convolution_param { 286 | num_output: 32 287 | bias_term: true 288 | pad: 0 289 | kernel_size: 1 290 | group: 1 291 | stride: 1 292 | weight_filler { 293 | type: "msra" 294 | } 295 | } 296 | } 297 | layer { 298 | name: "upsample/reduce/lrelu" 299 | type: "ReLU" 300 | bottom: "upsample/reduce" 301 | top: "upsample/reduce" 302 | relu_param { 303 | negative_slope: 0.05000000074505806 304 | } 305 | } 306 | layer { 307 | name: "upsample/deconv" 308 | type: "Deconvolution" 309 | bottom: "upsample/reduce" 310 | top: "upsample/deconv" 311 | param { 312 | lr_mult: 1.0 313 | decay_mult: 1.0 314 | } 315 | param { 316 | lr_mult: 1.0 317 | decay_mult: 0.0 318 | } 319 | convolution_param { 320 | num_output: 32 321 | bias_term: true 322 | pad: 1 323 | kernel_size: 3 324 | group: 32 325 | stride: 2 326 | weight_filler { 327 | type: "msra" 328 | } 329 | } 330 | } 331 | layer { 332 | name: "upsample/lrelu" 333 | type: "ReLU" 334 | bottom: "upsample/deconv" 335 | top: "upsample/deconv" 336 | relu_param { 337 | negative_slope: 0.05000000074505806 338 | } 339 | } 340 | layer { 341 | name: "upsample/rec" 342 | type: "Convolution" 343 | bottom: "upsample/deconv" 344 | top: "upsample/rec" 345 | param { 346 | lr_mult: 1.0 347 | decay_mult: 1.0 348 | } 349 | param { 350 | lr_mult: 1.0 351 | decay_mult: 0.0 352 | } 353 | convolution_param { 354 | num_output: 1 355 | bias_term: true 356 | pad: 0 357 | kernel_size: 1 358 | group: 1 359 | stride: 1 360 | weight_filler { 361 | type: "msra" 362 | } 363 | } 364 | } 365 | layer { 366 | name: "nearest" 367 | type: "Deconvolution" 368 | bottom: "data" 369 | top: "nearest" 370 | param { 371 | lr_mult: 0.0 372 | decay_mult: 0.0 373 | } 374 | convolution_param { 375 | num_output: 1 376 | bias_term: false 377 | pad: 0 378 | kernel_size: 2 379 | group: 1 380 | stride: 2 381 | weight_filler { 382 | type: "constant" 383 | value: 1.0 384 | } 385 | } 386 | } 387 | layer { 388 | name: "Crop1" 389 | type: "Crop" 390 | bottom: "nearest" 391 | bottom: "upsample/rec" 392 | top: "Crop1" 393 | } 394 | layer { 395 | name: "fc" 396 | type: "Eltwise" 397 | bottom: "Crop1" 398 | bottom: "upsample/rec" 399 | top: "fc" 400 | eltwise_param { 401 | operation: SUM 402 | } 403 | } 404 | -------------------------------------------------------------------------------- /pyqt5_qr_scan.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # author: 'zfb' 4 | # time: 2022-02-20 11:18 5 | 6 | from PyQt5.QtCore import Qt 7 | from PyQt5.QtWidgets import QApplication 8 | from PyQt5.QtGui import QIcon 9 | 10 | from multiprocessing import freeze_support 11 | import sys 12 | import os 13 | import logging 14 | 15 | from custom_qwidget import QrDetectDialog, scan_process 16 | from sql_helper import create_files_table, create_status_table 17 | from utils import get_base_path 18 | 19 | # 用于把图片资源嵌入到Qt程序里面 20 | # 发布exe时就不需要附该文件了 21 | import resources 22 | 23 | def log_init(): 24 | try: 25 | # 当前程序的路径 26 | log_dir = os.path.join(get_base_path(), "log") 27 | if not os.path.exists(log_dir): 28 | os.makedirs(log_dir) 29 | logging.info(f"日志文件夹{log_dir}创建成功") 30 | else: 31 | logging.info(f"日志文件夹{log_dir}已存在") 32 | except Exception as e: 33 | logging.warning(f"日志文件{log_dir}创建失败") 34 | logging.warning(repr(e)) 35 | 36 | if __name__ == '__main__': 37 | # 如果用pyinstaller打包含有多进程的代码，这一行必须要 38 | # 且在最开始执行 39 | freeze_support() 40 | create_files_table() 41 | create_status_table() 42 | app = QApplication(sys.argv) 43 | detectDialog = QrDetectDialog() 44 | detectDialog.set_run_func(scan_process) 45 | detectDialog.setWindowFlags(Qt.WindowMinimizeButtonHint|Qt.WindowCloseButtonHint) 46 | detectDialog.setWindowIcon(QIcon(':/icons/icon.png')) 47 | screen = app.desktop() 48 | detectDialog.resize(screen.height(),screen.height()//2) 49 | fg = detectDialog.frameGeometry() 50 | sc = screen.availableGeometry().center() 51 | fg.moveCenter(sc) 52 | detectDialog.show() 53 | log_init() 54 | code = app.exec_() 55 | if detectDialog._loadThread: 56 | detectDialog._loadThread.quit() 57 | sys.exit(code) -------------------------------------------------------------------------------- /qrscan.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zfb132/QrScan/dfc1e63ea0beaf57930810c73576f83e91cdc607/qrscan.ico -------------------------------------------------------------------------------- /qrscan.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zfb132/QrScan/dfc1e63ea0beaf57930810c73576f83e91cdc607/qrscan.png -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pyqt5 2 | pyinstaller>=6.3 3 | opencv-python==4.6.0.66 4 | opencv-contrib-python==4.6.0.66 5 | pycryptodome 6 | tinyaes -------------------------------------------------------------------------------- /resources.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # Resource object code 4 | # 5 | # Created by: The Resource Compiler for PyQt5 (Qt v5.15.2) 6 | # 7 | # WARNING! All changes made in this file will be lost! 8 | 9 | from PyQt5 import QtCore 10 | 11 | qt_resource_data = b"\ 12 | \x00\x00\x29\xad\ 13 | \x89\ 14 | \x50\x4e\x47\x0d\x0a\x1a\x0a\x00\x00\x00\x0d\x49\x48\x44\x52\x00\ 15 | \x00\x01\x90\x00\x00\x01\x90\x08\x02\x00\x00\x00\x0f\xdd\xa1\x9b\ 16 | \x00\x00\x00\x04\x67\x41\x4d\x41\x00\x00\xb1\x8f\x0b\xfc\x61\x05\ 17 | \x00\x00\x00\x20\x63\x48\x52\x4d\x00\x00\x7a\x26\x00\x00\x80\x84\ 18 | \x00\x00\xfa\x00\x00\x00\x80\xe8\x00\x00\x75\x30\x00\x00\xea\x60\ 19 | \x00\x00\x3a\x98\x00\x00\x17\x70\x9c\xba\x51\x3c\x00\x00\x00\x06\ 20 | \x62\x4b\x47\x44\x00\xff\x00\xff\x00\xff\xa0\xbd\xa7\x93\x00\x00\ 21 | \x29\x26\x49\x44\x41\x54\x78\xda\xed\xdd\x7b\x70\x1c\xd5\x9d\x2f\ 22 | \xf0\xdf\xe9\xee\x79\x8f\x35\x7a\x58\xc2\x0f\x02\xb6\x85\x30\x18\ 23 | \x5b\x16\x08\x1c\xdb\xe2\xa6\x02\x75\x6d\x08\x31\x64\x79\x5e\x2a\ 24 | \xc4\xd9\x38\x0e\x7f\x00\x7b\x9d\x18\x16\x02\xb5\xe4\x02\x55\x81\ 25 | \x78\x8b\x64\x49\xa8\xdd\x4a\x15\x81\xc4\xd7\xd7\x09\xa9\x10\x02\ 26 | 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\x5d\x63\x8c\xb1\xec\x69\x15\xd2\x0a\x40\x39\xd9\xaf\xad\x10\xc4\ 69 | \x18\xd3\x35\x16\xf6\x32\x6e\xb0\xcd\xbb\xe3\xd7\xbf\xd0\xb5\xed\ 70 | \x50\x2a\xc3\x8b\x7b\xd1\xa4\x58\x23\x2c\x21\x44\xca\xa2\xff\xec\ 71 | \x48\xde\xf9\xc6\x89\x41\x4b\x94\xf9\xb5\x91\xa3\xaa\x53\x51\x25\ 72 | \x46\xfc\x20\xf8\x45\xe9\x95\x84\x27\xf4\x5f\x34\x92\x38\xae\x62\ 73 | \x81\x4d\x84\x20\x2e\x48\x0c\x73\xbb\x35\x67\x62\xc3\xff\x75\xea\ 74 | \x67\xb0\x53\x5f\xd8\x91\xb1\x65\xe8\xa4\xfb\xb5\xf6\xfe\xcc\x4d\ 75 | \x7f\xec\xfa\xd5\x75\xd3\xbf\x56\x1b\xf0\xeb\xc5\xaa\xde\x2a\x4a\ 76 | \x60\x09\x21\x12\xa6\xf8\xd3\x47\x89\x75\x6f\xf6\xa4\x0d\x16\x09\ 77 | \x68\x44\x4c\x08\xa2\x91\x51\x95\x4d\xae\x93\xbf\x44\x10\x91\xc9\ 78 | \xc9\x14\x82\x04\x33\x2c\xc1\x48\xc8\x39\xfc\x0a\xa5\x78\x34\xc1\ 79 | \x51\xd6\x00\xb6\xc8\x70\x1a\x48\xf0\x40\x92\x9b\x92\x95\xf5\x09\ 80 | \x22\x41\xcc\xd4\x19\x31\x61\x30\x66\x68\x82\x88\xb4\x33\x2e\xe6\ 81 | \x30\x36\xfc\x93\x01\x63\x2c\x12\xd0\xe2\x69\xb1\xf6\x95\xee\xa7\ 82 | \xaf\xad\xba\xe9\xa2\x60\xd0\x28\x4a\x66\x31\xdb\x83\x3d\x5b\xbc\ 83 | \xfb\xc2\xfe\xc1\x75\x6f\xf6\x70\xaf\xe6\x37\x68\x54\x59\xd5\xa9\ 84 | \xad\xe5\x42\x90\xa0\x34\x27\x66\x89\x80\xc6\xc2\x21\x6d\x71\x8d\ 85 | \x77\x7e\xa5\x67\x7e\x85\x11\xf1\x69\xba\x7c\xb5\x03\x8c\x28\x99\ 86 | \x11\x17\x4f\xf7\xcc\xaf\xf4\x18\x9a\x5c\x3d\x0c\x54\x34\x64\x89\ 87 | \xfd\x27\x32\xed\x7d\x99\xa0\x87\x49\xf5\x47\xd0\xe2\x14\x1d\xe2\ 88 | \x1f\xf7\x99\x1f\xf7\x66\x5a\xbb\xd2\xf1\x41\x9e\xe4\x42\xe8\xcc\ 89 | \xab\x11\x31\xd2\xc6\xbc\x06\x2d\x52\x26\x69\x69\xfe\xf4\xb5\x55\ 90 | \xb7\x5e\x1c\xf2\x15\x61\x9c\x65\x7f\x60\x99\x5c\xbc\xf1\x69\xf2\ 91 | \x1f\xff\xdc\x9d\x23\xad\xb2\x03\xe0\xa4\x29\x02\x44\xe7\x96\x1b\ 92 | \x37\xcd\x0f\x5d\x3b\xd7\x5f\x5b\x61\x84\x3d\x9a\xc6\x86\x47\x5e\ 93 | \xd2\x3a\x39\x24\x96\xb9\x8d\xa0\x06\x39\x4f\x04\x87\xdb\x76\xf2\ 94 | \x8c\x35\x9e\xe1\x1d\x7d\xe6\x9b\x9d\xa9\x3f\x7d\x3c\x78\xa8\xdf\ 95 | \x4c\x12\x05\x0c\xc6\x18\x65\x2f\x46\x8f\x95\x59\xff\xfb\x1b\xd5\ 96 | 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\xa3\x29\x9e\xcc\x70\x26\x88\x69\xa4\x69\x4c\x3b\x75\x4a\xef\x64\ 657 | \xb3\x26\x45\xe6\xb6\x81\x92\x58\xf6\x3f\xb2\x5d\x20\x11\x27\x7f\ 658 | \xc1\xe7\x82\x38\x17\x82\x93\x60\xe4\xd5\xd9\xb9\x41\xbd\xb6\xc2\ 659 | \xb8\xa6\x2e\x78\x63\x5d\x70\xa6\x83\x51\x95\xe5\x68\x60\x65\xcd\ 660 | \x0c\xe9\x77\x37\x4c\xfb\xc7\x4b\x42\x6d\xdd\x99\xbf\x74\xa6\xda\ 661 | \x8e\x0f\x75\xc7\xad\xae\x14\xef\x4d\x8b\xa4\xc5\x2d\x8b\x34\x59\ 662 | \x6b\x1a\x88\x88\xa7\xac\xd8\x10\xc7\x94\xcf\x60\x0b\x93\x53\x3c\ 663 | \xc5\xad\x14\x1f\xf4\x4a\xd7\xe5\x05\x11\x27\xd2\x75\x0a\xe8\x5a\ 664 | \xa5\x5f\xab\xf1\x6b\xd5\x61\xbd\xfe\x1c\xdf\xca\xb9\xfe\xfa\x6a\ 665 | \x4f\xc8\x91\x13\xc0\x33\xb9\x10\x58\x59\x21\x8f\xb6\x6c\x96\x6f\ 666 | \xd9\x2c\x5f\xc2\x14\x47\x06\xcc\x4f\xfa\xcc\xf6\xbe\x4c\x47\xbf\ 667 | \x19\x4d\xf1\xb4\x29\xb8\x25\x69\x09\xb0\x48\xf2\xda\x88\x81\xb2\ 668 | \x06\xb0\x85\x47\x63\x17\x55\x19\xff\x50\x1b\xd0\x43\x9a\x54\x43\ 669 | \x2c\xc6\x48\xd3\x99\xd7\x60\x11\xbf\x56\x5b\x6e\xd4\x55\x78\x2e\ 670 | \xac\x30\x66\x4d\x33\x82\x76\xcc\x1a\x5a\x50\xc3\xa4\x0a\x86\xec\ 671 | \x9d\x0a\x69\x8b\x2c\x59\xef\x59\xe0\x44\x3e\x9d\x79\x35\x9c\x1a\ 672 | \x82\x0d\xb2\xb7\x85\xa4\xb9\x90\xad\x0a\x99\x31\xd2\x19\xf3\xea\ 673 | \xc4\x24\xbb\x0a\x22\x57\x60\x01\x00\xe4\x20\x5b\xb2\x03\x00\x8c\ 674 | \x09\x81\x05\x00\xca\x40\x60\x01\x80\x32\x10\x58\x00\xa0\x0c\x04\ 675 | \x16\x00\x28\x03\x81\x05\x00\xca\x40\x60\x01\x80\x32\x10\x58\x00\ 676 | \xa0\x0c\x04\x16\x00\x28\x03\x81\x05\x00\xca\x40\x60\x01\x80\x32\ 677 | \x10\x58\x00\xa0\x0c\x04\x16\x00\x28\x03\x81\x05\x00\xca\x40\x60\ 678 | \x01\x80\x32\x10\x58\x00\xa0\x0c\x04\x16\x00\x28\x03\x81\x05\x00\ 679 | \xca\x40\x60\x01\x80\x32\xfe\x3f\x28\x3d\x2d\xf3\xa0\xbb\xe1\x64\ 680 | \x00\x00\x00\x00\x49\x45\x4e\x44\xae\x42\x60\x82\ 681 | " 682 | 683 | qt_resource_name = b"\ 684 | \x00\x05\ 685 | \x00\x6f\xa6\x53\ 686 | \x00\x69\ 687 | \x00\x63\x00\x6f\x00\x6e\x00\x73\ 688 | \x00\x08\ 689 | \x0a\x61\x5a\xa7\ 690 | \x00\x69\ 691 | \x00\x63\x00\x6f\x00\x6e\x00\x2e\x00\x70\x00\x6e\x00\x67\ 692 | " 693 | 694 | qt_resource_struct_v1 = b"\ 695 | \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x01\ 696 | \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x02\ 697 | \x00\x00\x00\x10\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\ 698 | " 699 | 700 | qt_resource_struct_v2 = b"\ 701 | \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x01\ 702 | \x00\x00\x00\x00\x00\x00\x00\x00\ 703 | \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x02\ 704 | \x00\x00\x00\x00\x00\x00\x00\x00\ 705 | \x00\x00\x00\x10\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\ 706 | \x00\x00\x01\x7f\x24\x90\xc2\x69\ 707 | " 708 | 709 | qt_version = [int(v) for v in QtCore.qVersion().split('.')] 710 | if qt_version < [5, 8, 0]: 711 | rcc_version = 1 712 | qt_resource_struct = qt_resource_struct_v1 713 | else: 714 | rcc_version = 2 715 | qt_resource_struct = qt_resource_struct_v2 716 | 717 | def qInitResources(): 718 | QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data) 719 | 720 | def qCleanupResources(): 721 | QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data) 722 | 723 | qInitResources() 724 | -------------------------------------------------------------------------------- /resources.qrc: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | qrscan.png 5 | 6 | -------------------------------------------------------------------------------- /scripts/config_env.bat: -------------------------------------------------------------------------------- 1 | @echo off 2 | color a 3 | echo Creating virtual environment: venv 4 | python -m venv venv 5 | echo Upgrade pip version 6 | .\venv\Scripts\python.exe -m pip install --upgrade pip 7 | .\venv\Scripts\pip.exe install -U setuptools wheel 8 | echo Installing pyqt5 pyqt5-stubs pywin32 pyinstaller opencv-python opencv-contrib-python 9 | .\venv\Scripts\pip.exe install -r requirements.txt -------------------------------------------------------------------------------- /scripts/config_env.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # script will exit when a command occurs error 4 | set -e 5 | 6 | echo Creating virtual environment: venv 7 | python3 -m venv venv 8 | echo Upgrade pip version 9 | venv/bin/python -m pip install --upgrade pip 10 | venv/bin/pip install -U setuptools wheel 11 | echo Installing pyqt5 pyqt5-stubs pywin32 pyinstaller opencv-python opencv-contrib-python 12 | venv/bin/pip install -r requirements.txt -------------------------------------------------------------------------------- /scripts/publish.bat: -------------------------------------------------------------------------------- 1 | @echo off 2 | REM Set color in terminal 3 | color a 4 | echo -----------------Publish PyQt project to exe------------------ 5 | echo Set environment variables 6 | REM Set current work dir 7 | set PRJ_PATH=%~dp0 8 | REM Set python code file name 9 | set PY_NAME=pyqt5_qr_scan 10 | REM Set virtual environment name 11 | set PYTHON_NAME=venv 12 | REM set path of pyinstall.exe 13 | set PYINSTALLER_FILE=%PRJ_PATH%\%PYTHON_NAME%\Scripts\pyinstaller.exe 14 | REM set lib of PyQT5 15 | set PYQT_PATH=%PRJ_PATH%\%PYTHON_NAME%\Lib\site-packages\PyQt5\Qt\bin 16 | set SPEC_PATH=%PRJ_PATH%\build 17 | set WORK_PATH=%PRJ_PATH%\build 18 | set DIST_PATH=%PRJ_PATH%\bin 19 | set ICON_FILE=%PRJ_PATH%\qrscan.ico 20 | set FINAL_PATH=QrScan 21 | 22 | rmdir /Q /S %WORK_PATH% 23 | rmdir /Q /S %DIST_PATH% 24 | del resources.py 25 | 26 | REM how to get version.txt template 27 | REM %PRJ_PATH%\%PYTHON_NAME%\Scripts\python.exe %PRJ_PATH%\%PYTHON_NAME%\Lib\site-packages\PyInstaller\utils\cliutils grab_version.py demo.exe 28 | 29 | REM compile images 30 | %PRJ_PATH%\%PYTHON_NAME%\Scripts\pyrcc5.exe resources.qrc -o resources.py 31 | 32 | echo Packing Analysis.py 33 | %PYINSTALLER_FILE% --paths=%PYQT_PATH% --specpath=%SPEC_PATH% --workpath=%WORK_PATH% --distpath=%DIST_PATH% --version-file %PRJ_PATH%\file_version_info.txt --icon=%ICON_FILE% -D -w --clean %PRJ_PATH%\%PY_NAME%.py -y 34 | 35 | echo Creating %FINAL_PATH% 36 | rename "%DIST_PATH%\%PY_NAME%" "%FINAL_PATH%" 37 | md %DIST_PATH%\%FINAL_PATH%\models 38 | 39 | REM xcopy /y "%DIST_PATH%/control" "%FINAL_PATH%\" /e 40 | xcopy /y models\ %DIST_PATH%\%FINAL_PATH%\models /e 41 | 42 | cd %DIST_PATH%\ 43 | tar.exe -a -c -f %FINAL_PATH%.zip %FINAL_PATH% 44 | move %FINAL_PATH%.zip %PRJ_PATH% 45 | cd %PRJ_PATH% 46 | 47 | REM pause -------------------------------------------------------------------------------- /scripts/publish.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # script will exit when a command occurs error 4 | set -e 5 | 6 | export PRJ_PATH=$(pwd) 7 | export PY_NAME=pyqt5_qr_scan 8 | export PYTHON_NAME=venv 9 | export PYINSTALLER_FILE=${PRJ_PATH}/${PYTHON_NAME}/bin/pyinstaller 10 | export PY_VER=python$(venv/bin/python3 -V | cut -d " " -f 2 | cut -d "." -f 1-2) 11 | export PYQT_PATH=${PRJ_PATH}/${PYTHON_NAME}/lib/${PY_VER}/site-packages/PyQt5/Qt5/lib 12 | export SPEC_PATH=${PRJ_PATH}/build 13 | export WORK_PATH=${PRJ_PATH}/build 14 | export DIST_PATH=${PRJ_PATH}/bin 15 | export ICON_FILE=${PRJ_PATH}/qrscan.ico 16 | export FINAL_PATH=QrScan 17 | 18 | rm -rf ${WORK_PATH} 19 | rm -rf ${DIST_PATH} 20 | 21 | rm -f resources.py 22 | 23 | ${PRJ_PATH}/${PYTHON_NAME}/bin/pyrcc5 resources.qrc -o resources.py 24 | 25 | ${PYINSTALLER_FILE} --paths=${PYQT_PATH} --specpath=${SPEC_PATH} --workpath=${WORK_PATH} --distpath=${DIST_PATH} --version-file ${PRJ_PATH}/file_version_info.txt --icon=${ICON_FILE} -D -w --clean ${PRJ_PATH}/${PY_NAME}.py -y 26 | 27 | mv ${DIST_PATH}/${PY_NAME} ${DIST_PATH}/${FINAL_PATH} 28 | 29 | cp -r models ${DIST_PATH}/${FINAL_PATH}/ 30 | 31 | cd ${DIST_PATH} 32 | zip -q -r ${FINAL_PATH}.zip ${FINAL_PATH} 33 | mv ${FINAL_PATH}.zip ${PRJ_PATH} 34 | cd .. 35 | -------------------------------------------------------------------------------- /sql_helper.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # author: 'zfb' 4 | # time: 2023-01-04 19:03 5 | 6 | import sqlite3 7 | 8 | table_files_name = "files.db" 9 | table_status_name = "status.db" 10 | 11 | def create_files_table(): 12 | ''' 13 | files表用于存放未正常结束的操作，已经处理过的图片（只保存最近的一次处理结果） 14 | ''' 15 | conn = sqlite3.connect(table_files_name) 16 | cur = conn.cursor() 17 | try: 18 | sql = """CREATE TABLE if not exists files( 19 | id integer primary key autoincrement, 20 | img_name varchar(1024) not null, 21 | timestamp DATE DEFAULT (datetime('now','localtime')) 22 | );""" 23 | cur.execute(sql) 24 | return True 25 | except Exception as e: 26 | print(e) 27 | return False 28 | finally: 29 | cur.close() 30 | conn.close() 31 | 32 | def create_status_table(db_name="status.db"): 33 | ''' 34 | status表用于上一次处理的状态 35 | ''' 36 | conn = sqlite3.connect(db_name) 37 | cur = conn.cursor() 38 | try: 39 | sql = """CREATE TABLE if not exists status( 40 | id integer primary key autoincrement, 41 | operation varchar(10) not null, 42 | img_path varchar(1024) not null, 43 | cut_path varchar(1024) default null, 44 | finished integer default 0, 45 | timestamp DATE DEFAULT (datetime('now','localtime')) 46 | );""" 47 | cur.execute(sql) 48 | return True 49 | except Exception as e: 50 | print(e) 51 | return False 52 | finally: 53 | cur.close() 54 | conn.close() 55 | 56 | def clean_files_table(): 57 | conn = sqlite3.connect(table_files_name, isolation_level=None) 58 | cur = conn.cursor() 59 | try: 60 | sql = "DELETE FROM files;" 61 | cur.execute(sql) 62 | cur.execute("VACUUM;") 63 | conn.commit() 64 | return True 65 | except Exception as e: 66 | print(e) 67 | return False 68 | finally: 69 | cur.close() 70 | conn.close() 71 | 72 | def insert_file(img_name): 73 | ''' 74 | 存放在files表中的图片，表示已经处理过了 75 | ''' 76 | conn = sqlite3.connect(table_files_name) 77 | cur = conn.cursor() 78 | try: 79 | sql = f"INSERT INTO files (img_name) VALUES ('{img_name}');" 80 | cur.execute(sql) 81 | conn.commit() 82 | return True 83 | except Exception as e: 84 | print(e) 85 | return False 86 | finally: 87 | cur.close() 88 | conn.close() 89 | 90 | def insert_status(operation, img_path, cut_path=None, finished=0): 91 | conn = sqlite3.connect(table_status_name) 92 | cur = conn.cursor() 93 | try: 94 | sql = f"INSERT INTO status (operation, img_path, cut_path, finished) VALUES ('{operation}', '{img_path}', '{cut_path}', {finished});" 95 | cur.execute(sql) 96 | conn.commit() 97 | return True 98 | except Exception as e: 99 | print(e) 100 | return False 101 | finally: 102 | cur.close() 103 | conn.close() 104 | 105 | def exist_file(img_name): 106 | conn = sqlite3.connect(table_files_name) 107 | cur = conn.cursor() 108 | try: 109 | sql = f"SELECT * FROM files WHERE img_name='{img_name}';" 110 | cur.execute(sql) 111 | result = cur.fetchall() 112 | if len(result) == 0: 113 | return False 114 | else: 115 | return True 116 | except Exception as e: 117 | print(e) 118 | return False 119 | finally: 120 | cur.close() 121 | conn.close() 122 | 123 | def get_all_files(): 124 | conn = sqlite3.connect(table_files_name) 125 | cur = conn.cursor() 126 | try: 127 | sql = "SELECT img_name FROM files;" 128 | cur.execute(sql) 129 | result = cur.fetchall() 130 | return result 131 | except Exception as e: 132 | print(e) 133 | return None 134 | finally: 135 | cur.close() 136 | conn.close() 137 | 138 | def get_status(): 139 | conn = sqlite3.connect(table_status_name) 140 | cur = conn.cursor() 141 | try: 142 | # 按id降序排列，取第一条 143 | sql = "SELECT operation, img_path, cut_path FROM status WHERE finished=0 ORDER BY id DESC LIMIT 1;" 144 | cur.execute(sql) 145 | result = cur.fetchall() 146 | if len(result) == 0: 147 | return None 148 | return result[0] 149 | except Exception as e: 150 | print(e) 151 | return None 152 | finally: 153 | cur.close() 154 | conn.close() 155 | 156 | def clean_status_table(): 157 | conn = sqlite3.connect(table_status_name, isolation_level=None) 158 | cur = conn.cursor() 159 | try: 160 | sql = "DELETE FROM status;" 161 | cur.execute(sql) 162 | cur.execute("VACUUM;") 163 | conn.commit() 164 | return True 165 | except Exception as e: 166 | print(e) 167 | return False 168 | finally: 169 | cur.close() 170 | conn.close() -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # author: 'zfb' 4 | # time: 2023-12-25 18:26 5 | 6 | import sys 7 | # from os import getcwd 8 | from os.path import dirname, abspath 9 | 10 | def get_base_path(): 11 | """ 12 | 获取当前运行目录，适应于Python和PyInstaller环境 13 | """ 14 | # path = getcwd() 15 | path = dirname(abspath(__file__)) 16 | if getattr(sys, 'frozen', False) and hasattr(sys, '_MEIPASS'): 17 | # 如果是PyInstaller环境 18 | # 可以使用 sys.executable 获取当前运行的exe路径 19 | # 也可以使用 sys._MEIPASS 获取当前运行的exe所在目录下的_internal文件夹的路径 20 | # 例如 D:\QrScan\bin\QrScan\_internal 21 | # 获取 _internal 文件夹的路径，然后使用 dirname 获取上一级目录 22 | path = dirname(sys._MEIPASS) 23 | return path --------------------------------------------------------------------------------