From 2fe6db4f5a0eb451691bb5d7b47feaf7a8a7bf31 Mon Sep 17 00:00:00 2001
From: syan <lsy17096535@users.noreply.github.com>
Date: Wed, 24 Jan 2018 13:26:23 +0800
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diff --git a/HyperLPR中文车牌识别安装(Windows7-x64,-Python3.5版).md b/HyperLPR中文车牌识别安装(Windows7-x64,-Python3.5版).md
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@@ -1 +1,67 @@
-Welcome to the HyperLPR wiki!
+[TOC]
+
+# 1. 简介
+HyperLPR是一个使用深度学习针对对中文车牌识别的实现，与较为流行的开源的EasyPR相比，它的检测速度和鲁棒性和多场景的适应性都要好于目前开源的EasyPR。本安装针对Window 7 x64，Python3.5版本。
+# 2. 安装依赖
+基本要求：
+* 使用Python版的程序，比C++的准确率高，包含的车牌类型多
+* 操作系统Windows 7 x64
+* cpu版本
+* Keras使用Tensorflow作为后端
+* Python 3.5对Tensorflow适应性比较好
+## 2.1. 安装Anaconda
+* 使用清华大学开源软件镜像站[Anaconda](https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/)
+* 选择[Anaconda3-4.2.0-Windows-x86_64.exe](https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-4.2.0-Windows-x86_64.exe)下载，其Python版本是3.5
+* Anaconda自带了Numpy，Scipy，Scikit-image，PIL
+## 2.2. 安装Tensorflow和Keras
+* "开始"-“程序”-“Anaconda3”-“Anaconda Prompt”命令行打开
+* 安装cpu版本的tensorflow，命令行输入
+```
+pip install tensorflow
+```
+* 安装Keras，命令行输入
+```
+pip install Keras
+```
+
+## 2.3. 安装OpenCV 3.3
+* 使用 whl 文件进行安装，[opencv_python‑3.4.0+contrib‑cp35‑cp35m‑win_amd64.whl](https://download.lfd.uci.edu/pythonlibs/n1rrk3iq/opencv_python-3.4.0+contrib-cp35-cp35m-win_amd64.whl)，若不能下载，请在[链接](https://www.lfd.uci.edu/~gohlke/pythonlibs/)查找OpenCV
+* 将opencv_python‑3.4.0+contrib‑cp35‑cp35m‑win_amd64.whl放到某个目录下，"开始"-“程序”-“Anaconda3”-“Anaconda Prompt”命令行打开，定位到文件目录下，命令行输入
+```
+pip install opencv_python‑3.4.0+contrib‑cp35‑cp35m‑win_amd64.whl
+```
+# 3.PyCharm开发Python程序
+* 使用免费的Community版本，下载最新的[PyCharm](https://www.jetbrains.com/pycharm/download/#section=windows)。
+* 使用PyCharm的git下载HyperLPR
+* 复制batch.py为batch_py3.py，修改代码如下，支持中文字符路径
+```
+#coding=utf-8
+import os
+from hyperlpr_py3 import pipline as pp
+
+import cv2
+
+import numpy as np
+
+parent = "D:\data\license_plate_images"
+
+for filename in os.listdir(parent):
+    print(filename)
+    path = os.path.join(parent, filename)
+    print(path)
+    if path.endswith(".jpg") or path.endswith(".png"):
+        image = cv2.imdecode(np.fromfile(path, dtype=np.uint8), -1)
+
+        image, res = pp.SimpleRecognizePlate(image)
+        cv2.imshow("image", image)
+        cv2.waitKey(0)
+```
+* 运行batch_py3.py，发现"hyperlpr_py3/Segmentation.py"第113行有错误
+```
+median = (data[size//2]+data[size//2-1])/2
+```
+修改为
+```
+median = (data[size//2]+data[size//2-1])//2
+```
+