Merge pull request #80 from justid/master

PHP版代码重构了一下，新增可信度要求和模型路径参数

.gitignore

@@ -6,3 +6,4 @@
 Prj-Linux/lpr/TEST_*
 Prj-Linux/*build*/
 *.pyc
+/Prj-PHP/build

Prj-PHP/CMakeLists.txt

@@ -1,21 +1,12 @@
 cmake_minimum_required(VERSION 3.6)
 project(platescan)
 
-set(OpenCV_DIR /usr/platescan/opencv-3.3.0/release/)
-#set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -g -O2 -std=c++11 -fpic -o")
-#set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
-
-set(CMAKE_CXX_STANDARD 11)
-
-set(PHP_CONFIG_DIR /etc/php.d/)
-set(PHP_CONFIG php-config)
-set(POST_COMPILE_COMMAND systemctl restart php-fpm)
-
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
 find_package(OpenCV 3.3.0 REQUIRED)
 include_directories( ${OpenCV_INCLUDE_DIRS})
 include_directories(include)
 
-
 add_library( # Sets the name of the library.
     platescan
 
@@ -42,10 +33,10 @@ target_link_libraries(
   ${OpenCV_LIBS}
 )
 
-execute_process(COMMAND ${PHP_CONFIG} --extension-dir
+execute_process(COMMAND php-config --extension-dir
         OUTPUT_VARIABLE LIBRARY_DIR)
 
-message("Test ${LIBRARY_DIR} failed.")
+message("PHP_EXTENSIONS_DIR ${LIBRARY_DIR}")
 
 add_custom_command(TARGET platescan
         POST_BUILD

Prj-PHP/README.MD

@@ -1,9 +1,29 @@
+
 # Prj-PHP
+
 HyperLPR 在PHP扩展程序中的实现，核心代码拷贝了 Prj-Linux 中庾金科大牛的代码。我做的这部分工作主要是配置编译成PHP扩展程序。
 
+## CPP 依赖
+
++ Opencv 3.3
++ PHPCPP
+
+## Linux/Mac 编译
+
+```bash
+mkdir build
+cd build
+cmake ../
+make -j
+```
+
+## DEMO
+
 ```php
-$path = realpath("demo.png"); 
-$res = platescan($path);
+// 也可编译完后直接在tests目录直接运行 php platescan.php
+$path = realpath("demo.png");   // 图片文件所在路径,realpath转为绝对路径
+$model = realpath("model");     // 模型资源所在文件夹
+$res = platescan($path, $model, 0.8);
 var_dump($res); // string(9) "苏ED0N19"
 
 ```
@@ -12,5 +32,19 @@ var_dump($res); // string(9) "苏ED0N19"
 
 ![image](./tests/demo.png)
 
+## 测试备忘
+
+经多次测试发现部分图片难以识别或者容易识别错误，经过筛选反推出如下规律：
+
+### 识别精度高的图片
+
++ 图片中包含有整个车身
++ 图片清晰度较高，不模糊
++ 图片拍摄角度为正面拍摄，车牌在图片中没有较大幅度的倾斜
++ 图片中的车牌在整个图片中所占位置较小
+
+### 识别精度低的图片
 
-#### 感谢各位大牛！
++ 图片模糊，程序未能裁切出正确的车牌，或者裁切后因图片太模糊而无法识别（此处应该可以通过训练更多样本解决）
++ 图片中的车牌在整个图片中占了1/3的位置甚至更高
++ 图片拍摄角度为俯角拍摄，车牌在图片中是倾斜着的

Prj-PHP/main.cpp

@@ -1,5 +1,6 @@
 //
 // Created by Coleflowers on 20/06/2018.
+// Updated by Justid on 02/07/2018.
 // 
 #include <phpcpp.h>
 #include <iostream>
@@ -10,88 +11,69 @@
 #include "PlateDetection.h"
 #include "FastDeskew.h"
 #include "FineMapping.h"
+#include "Pipeline.h"
 
-
-std::vector<std::string> chars{"京","沪","津","渝","冀","晋","蒙","辽","吉","黑","苏","浙","皖","闽","赣","鲁","豫","鄂","湘","粤","桂","琼","川","贵","云","藏","陕","甘","青","宁","新","0","1","2","3","4","5","6","7","8","9","A","B","C","D","E","F","G","H","J","K","L","M","N","P","Q","R","S","T","U","V","W","X","Y","Z"};
+/* 关闭原opencv报错输出 */
+int handleError( int status, const char* func_name,
+            const char* err_msg, const char* file_name,
+            int line, void* userdata )
+{
+    //Do nothing -- will suppress console output
+    return 0;   //Return value is not used
+}
 
 /**
  * 车牌图片识别
- * @param  imgpath 图片的绝对路径
+ * @params  imgpath 图片的绝对路径
+ * @params  modelpath 识别模型所在文件夹路径
+ * @params  confidence 可信度要求，可选参数，默认值0.75
  * @return  车牌号 图片路径的图片不存在的话 返回空值
  */
-cv::String scan(const char *imgpath){
-    // 读取图片获取车牌的粗略部分
-    cv::Mat image = cv::imread(imgpath); 
-    if(!image.data) { 
-        printf("No img data!\n");
-        return "";
-    }
-    pr::PlateDetection plateDetection("/usr/platescan/src/lpr/model/cascade.xml");
-    std::vector<pr::PlateInfo> plates;
-    plateDetection.plateDetectionRough(image,plates);
-    // todo 处理发现的每一个车牌信息
-    // 目前只处理了一组
-    cv::Mat img;
-    for(pr::PlateInfo platex:plates) {
-        // 暂时不保存了
-        //cv::imwrite("res/mmm.png",platex.getPlateImage());
-        img = platex.getPlateImage(); 
-    }
-   
-    // 车牌部分 
-    cv::Mat image_finemapping = pr::FineMapping::FineMappingVertical(img);
-    pr::FineMapping finemapper =  pr::FineMapping("/usr/platescan/src/lpr/model/HorizonalFinemapping.prototxt","/usr/platescan/src/lpr/model/HorizonalFinemapping.caffemodel");
-    
-    // 
-    image_finemapping = pr::fastdeskew(image_finemapping, 5);
-
-    // image_finemapping = finemapper.FineMappingHorizon(image_finemapping,0,-3);
-    // Android 代码里这个是 
-    // 改了之后部分图片数据精度发生变化 ，比如 字母精度有了，但是汉字又错了
-    image_finemapping = finemapper.FineMappingHorizon(image_finemapping,2,5);
-    // 暂时不保存了
-    //cv::imwrite("res/m222222222.png",image_finemapping);
-
-    // 设置尺寸 识别
-    cv::Mat demo = image_finemapping; 
-    cv::resize(demo,demo,cv::Size(136,36));
-    //cv::imwrite("res/m333333.png",demo);
+cv::String scan(std::string imgpath, std::string modelpath, double confidence){
+    cv::redirectError(handleError);
+    try {
+        pr::PipelinePR prc(modelpath+"/cascade.xml",
+                        modelpath+"/HorizonalFinemapping.prototxt",modelpath+"/HorizonalFinemapping.caffemodel",
+                        modelpath+"/Segmentation.prototxt",modelpath+"/Segmentation.caffemodel",
+                        modelpath+"/CharacterRecognization.prototxt",modelpath+"/CharacterRecognization.caffemodel",
+                        modelpath+"/SegmentationFree.prototxt",modelpath+"/SegmentationFree.caffemodel"
+                    );
+        cv::Mat image = cv::imread(imgpath);
+        std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(image,pr::SEGMENTATION_FREE_METHOD);
 
-    cv::Mat respones;
-    pr::PlateSegmentation plateSegmentation("/usr/platescan/src/lpr/model/Segmentation.prototxt","/usr/platescan/src/lpr/model/Segmentation.caffemodel");
-    pr::PlateInfo plate;
-    plate.setPlateImage(demo);
-    std::vector<cv::Rect> rects;
-    plateSegmentation.segmentPlatePipline(plate,1,rects);
-    plateSegmentation.ExtractRegions(plate,rects);
+        cv::String platenum = "";
+        for(auto st:res) {
+            if(st.confidence>confidence) {
+                platenum = st.getPlateName();
+                break;
+            }
+        }
 
-    pr::GeneralRecognizer *recognizer = new pr::CNNRecognizer("/usr/platescan/src/lpr/model/CharacterRecognization.prototxt","/usr/platescan/src/lpr/model/CharacterRecognization.caffemodel");
-    recognizer->SegmentBasedSequenceRecognition(plate);
-    //std::cout<<plate.decodePlateNormal(chars)<<std::endl;
-    plate.decodePlateNormal(chars);
-
-    cv::String txt = plate.getPlateName();
-    // printf("res:%s\n", txt.c_str());
+        return platenum;
+    }
+    catch( cv::Exception& e )
+    {
+        const char* err_msg = e.what();
+        throw Php::Exception(err_msg);
+    }
 
-    //
-    // 写入结果到文件
-    // FILE *fp;  
-    // fp = fopen(resSaveFile, "wb+");
-    // fprintf(fp, "%s", txt.c_str());  
-    // fclose(fp);
-    //
-    
-    delete(recognizer);
-    return txt;
 }
 
-Php::Value  myFunction(Php::Parameters &params) {
 
-   std::string str = params[0]; 
-   const char *str_c = str.c_str();
+Php::Value funcWrapper(Php::Parameters &params) {
+    // 图片路径
+    std::string img = params[0]; 
+    // 模型路径（文件夹）
+    std::string model = params[1]; 
+    // 可信度要求
+    double confidence = 0.75;
+
+    if (params.size() == 3){
+        confidence = (double)params[2];
+    }
 
-   cv::String res = scan(str_c);
-   return res.c_str();
+    cv::String res = scan(img, model, confidence);
+    return res.c_str();
 }
 
 
@@ -113,7 +95,11 @@ extern "C" {
         static Php::Extension extension("platescan", "1.0");
         
         // @todo    add your own functions, classes, namespaces to the extension
-        extension.add<myFunction>("platescan", {Php::ByRef("string", Php::Type::String)});       
+        extension.add<funcWrapper>("platescan", {
+            Php::ByVal("imgpath", Php::Type::String, true),
+            Php::ByVal("modelpath", Php::Type::String, true),
+            Php::ByVal("confidence", Php::Type::Float, false)
+        });       
  
         // return the extension
         return extension;

Prj-PHP/tests/model/CharacterRecognization.prototxt

@@ -0,0 +1,123 @@
+input: "data"
+input_dim: 1
+input_dim: 1
+input_dim: 30
+input_dim: 14
+layer {
+  name: "conv2d_1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv2d_1"
+  convolution_param {
+    num_output: 32
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "activation_1"
+  type: "ReLU"
+  bottom: "conv2d_1"
+  top: "activation_1"
+}
+layer {
+  name: "max_pooling2d_1"
+  type: "Pooling"
+  bottom: "activation_1"
+  top: "max_pooling2d_1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "conv2d_2"
+  type: "Convolution"
+  bottom: "max_pooling2d_1"
+  top: "conv2d_2"
+  convolution_param {
+    num_output: 64
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "activation_2"
+  type: "ReLU"
+  bottom: "conv2d_2"
+  top: "activation_2"
+}
+layer {
+  name: "max_pooling2d_2"
+  type: "Pooling"
+  bottom: "activation_2"
+  top: "max_pooling2d_2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "conv2d_3"
+  type: "Convolution"
+  bottom: "max_pooling2d_2"
+  top: "conv2d_3"
+  convolution_param {
+    num_output: 128
+    bias_term: true
+    pad: 0
+    kernel_size: 2
+    stride: 1
+  }
+}
+layer {
+  name: "activation_3"
+  type: "ReLU"
+  bottom: "conv2d_3"
+  top: "activation_3"
+}
+layer {
+  name: "flatten_1"
+  type: "Flatten"
+  bottom: "activation_3"
+  top: "flatten_1"
+}
+layer {
+  name: "dense_1"
+  type: "InnerProduct"
+  bottom: "flatten_1"
+  top: "dense_1"
+  inner_product_param {
+    num_output: 256
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "dense_1"
+  top: "relu2"
+}
+layer {
+  name: "dense2"
+  type: "InnerProduct"
+  bottom: "relu2"
+  top: "dense2"
+  inner_product_param {
+    num_output: 65
+  }
+}
+
+layer {
+  name: "prob"
+  type: "Softmax"
+  bottom: "dense2"
+  top: "prob"
+}

Prj-PHP/tests/model/HorizonalFinemapping.prototxt

@@ -0,0 +1,95 @@
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 16
+input_dim: 66
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  convolution_param {
+    num_output: 10
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "max_pooling2d_3"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "max_pooling2d_3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "max_pooling2d_3"
+  top: "conv2"
+  convolution_param {
+    num_output: 16
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "conv2"
+  top: "conv3"
+  convolution_param {
+    num_output: 32
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "flatten_2"
+  type: "Flatten"
+  bottom: "conv3"
+  top: "flatten_2"
+}
+layer {
+  name: "dense"
+  type: "InnerProduct"
+  bottom: "flatten_2"
+  top: "dense"
+  inner_product_param {
+    num_output: 2
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "dense"
+  top: "dense"
+}

Prj-PHP/tests/model/Segmentation.prototxt

@@ -0,0 +1,114 @@
+input: "data"
+input_dim: 1
+input_dim: 1
+input_dim: 22
+input_dim: 22
+layer {
+  name: "conv2d_12"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv2d_12"
+  convolution_param {
+    num_output: 16
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "activation_18"
+  type: "ReLU"
+  bottom: "conv2d_12"
+  top: "activation_18"
+}
+layer {
+  name: "max_pooling2d_10"
+  type: "Pooling"
+  bottom: "activation_18"
+  top: "max_pooling2d_10"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "conv2d_13"
+  type: "Convolution"
+  bottom: "max_pooling2d_10"
+  top: "conv2d_13"
+  convolution_param {
+    num_output: 16
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "activation_19"
+  type: "ReLU"
+  bottom: "conv2d_13"
+  top: "activation_19"
+}
+layer {
+  name: "max_pooling2d_11"
+  type: "Pooling"
+  bottom: "activation_19"
+  top: "max_pooling2d_11"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "flatten_6"
+  type: "Flatten"
+  bottom: "max_pooling2d_11"
+  top: "flatten_6"
+}
+layer {
+  name: "dense_9"
+  type: "InnerProduct"
+  bottom: "flatten_6"
+  top: "dense_9"
+  inner_product_param {
+    num_output: 256
+  }
+}
+layer {
+  name: "dropout_9"
+  type: "Dropout"
+  bottom: "dense_9"
+  top: "dropout_9"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "activation_20"
+  type: "ReLU"
+  bottom: "dropout_9"
+  top: "activation_20"
+}
+layer {
+  name: "dense_10"
+  type: "InnerProduct"
+  bottom: "activation_20"
+  top: "dense_10"
+  inner_product_param {
+    num_output: 3
+  }
+}
+
+
+  layer {
+  name: "prob"
+  type: "Softmax"
+  bottom: "dense_10"
+  top: "prob"
+}

Prj-PHP/tests/model/SegmentationFree.prototxt

@@ -0,0 +1,318 @@
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 160
+input_dim: 40
+layer {
+  name: "conv0"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv0"
+  convolution_param {
+    num_output: 32
+    bias_term: true
+    pad_h: 1
+    pad_w: 1
+    kernel_h: 3
+    kernel_w: 3
+    stride_h: 1
+    stride_w: 1
+  }
+}
+layer {
+  name: "bn0"
+  type: "BatchNorm"
+  bottom: "conv0"
+  top: "bn0"
+  batch_norm_param {
+    moving_average_fraction: 0.99
+    eps: 0.001
+  }
+}
+layer {
+  name: "bn0_scale"
+  type: "Scale"
+  bottom: "bn0"
+  top: "bn0"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu0"
+  type: "ReLU"
+  bottom: "bn0"
+  top: "bn0"
+}
+layer {
+  name: "pool0"
+  type: "Pooling"
+  bottom: "bn0"
+  top: "pool0"
+  pooling_param {
+    pool: MAX
+    kernel_h: 2
+    kernel_w: 2
+    stride_h: 2
+    stride_w: 2
+    pad_h: 0
+    pad_w: 0
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "pool0"
+  top: "conv1"
+  convolution_param {
+    num_output: 64
+    bias_term: true
+    pad_h: 1
+    pad_w: 1
+    kernel_h: 3
+    kernel_w: 3
+    stride_h: 1
+    stride_w: 1
+  }
+}
+layer {
+  name: "bn1"
+  type: "BatchNorm"
+  bottom: "conv1"
+  top: "bn1"
+  batch_norm_param {
+    moving_average_fraction: 0.99
+    eps: 0.001
+  }
+}
+layer {
+  name: "bn1_scale"
+  type: "Scale"
+  bottom: "bn1"
+  top: "bn1"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "bn1"
+  top: "bn1"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "bn1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_h: 2
+    kernel_w: 2
+    stride_h: 2
+    stride_w: 2
+    pad_h: 0
+    pad_w: 0
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  convolution_param {
+    num_output: 128
+    bias_term: true
+    pad_h: 1
+    pad_w: 1
+    kernel_h: 3
+    kernel_w: 3
+    stride_h: 1
+    stride_w: 1
+  }
+}
+layer {
+  name: "bn2"
+  type: "BatchNorm"
+  bottom: "conv2"
+  top: "bn2"
+  batch_norm_param {
+    moving_average_fraction: 0.99
+    eps: 0.001
+  }
+}
+layer {
+  name: "bn2_scale"
+  type: "Scale"
+  bottom: "bn2"
+  top: "bn2"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "bn2"
+  top: "bn2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "bn2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_h: 2
+    kernel_w: 2
+    stride_h: 2
+    stride_w: 2
+    pad_h: 0
+    pad_w: 0
+  }
+}
+layer {
+  name: "conv_512_15"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv_512_15"
+  convolution_param {
+    num_output: 512
+    bias_term: true
+    pad_h: 0
+    pad_w: 0
+    kernel_h: 1
+    kernel_w: 5
+    stride_h: 1
+    stride_w: 1
+  }
+}
+layer {
+  name: "batch_normalization_1"
+  type: "BatchNorm"
+  bottom: "conv_512_15"
+  top: "batch_normalization_1"
+  batch_norm_param {
+    moving_average_fraction: 0.99
+    eps: 0.001
+  }
+}
+layer {
+  name: "batch_normalization_1_scale"
+  type: "Scale"
+  bottom: "batch_normalization_1"
+  top: "batch_normalization_1"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "activation_1"
+  type: "ReLU"
+  bottom: "batch_normalization_1"
+  top: "batch_normalization_1"
+}
+layer {
+  name: "conv_512_51"
+  type: "Convolution"
+  bottom: "batch_normalization_1"
+  top: "conv_512_51"
+  convolution_param {
+    num_output: 512
+    bias_term: true
+    pad_h: 0
+    pad_w: 0
+    kernel_h: 5
+    kernel_w: 1
+    stride_h: 1
+    stride_w: 1
+  }
+}
+layer {
+  name: "batch_normalization_2"
+  type: "BatchNorm"
+  bottom: "conv_512_51"
+  top: "batch_normalization_2"
+  batch_norm_param {
+    moving_average_fraction: 0.99
+    eps: 0.001
+  }
+}
+layer {
+  name: "batch_normalization_2_scale"
+  type: "Scale"
+  bottom: "batch_normalization_2"
+  top: "batch_normalization_2"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "activation_2"
+  type: "ReLU"
+  bottom: "batch_normalization_2"
+  top: "batch_normalization_2"
+}
+layer {
+  name: "conv_1024_11"
+  type: "Convolution"
+  bottom: "batch_normalization_2"
+  top: "conv_1024_11"
+  convolution_param {
+    num_output: 1024
+    bias_term: true
+    pad_h: 0
+    pad_w: 0
+    kernel_h: 1
+    kernel_w: 1
+    stride_h: 1
+    stride_w: 1
+  }
+}
+layer {
+  name: "batch_normalization_3"
+  type: "BatchNorm"
+  bottom: "conv_1024_11"
+  top: "batch_normalization_3"
+  batch_norm_param {
+    moving_average_fraction: 0.99
+    eps: 0.001
+  }
+}
+layer {
+  name: "batch_normalization_3_scale"
+  type: "Scale"
+  bottom: "batch_normalization_3"
+  top: "batch_normalization_3"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "activation_3"
+  type: "ReLU"
+  bottom: "batch_normalization_3"
+  top: "batch_normalization_3"
+}
+layer {
+  name: "conv_class_11"
+  type: "Convolution"
+  bottom: "batch_normalization_3"
+  top: "conv_class_11"
+  convolution_param {
+    num_output: 84
+    bias_term: true
+    pad_h: 0
+    pad_w: 0
+    kernel_h: 1
+    kernel_w: 1
+    stride_h: 1
+    stride_w: 1
+  }
+}
+layer {
+  name: "prob"
+  type: "Softmax"
+  bottom: "conv_class_11"
+  top: "prob"
+}

Prj-PHP/tests/platescan.php

@@ -1,13 +1,13 @@
 <?php
 //
 // Created by Coleflowers on 20/06/2018.
+// Updated by JustID on 03/07/2018.
 // 
 
 /**
  * 车牌识别PHP扩展程序测试
  */
-$path = realpath("demo.png"); 
-$res = platescan($path);
+$res = platescan(realpath("demo.png"),realpath("model"),0.8);
 var_dump($res);
 
 ?>