[ { "path": "MobileNetSSD_demo/.gitignore", "content": "*.iml\n.gradle\n/local.properties\n/.idea/caches/build_file_checksums.ser\n/.idea/libraries\n/.idea/modules.xml\n/.idea/workspace.xml\n.DS_Store\n/build\n/captures\n.externalNativeBuild\n" }, { "path": "MobileNetSSD_demo/.idea/codeStyles/Project.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "MobileNetSSD_demo/.idea/gradle.xml", "content": "\n\n \n \n \n" }, { "path": "MobileNetSSD_demo/.idea/misc.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n" }, { "path": "MobileNetSSD_demo/app/.gitignore", "content": "/build\n" }, { "path": "MobileNetSSD_demo/app/CMakeLists.txt", "content": "# For more information about using CMake with Android Studio, read the\n# documentation: https://d.android.com/studio/projects/add-native-code.html\n\n# Sets the minimum version of CMake required to build the native library.\n\ncmake_minimum_required(VERSION 3.4.1)\n\n# Creates and names a library, sets it as either STATIC\n# or SHARED, and provides the relative paths to its source code.\n# You can define multiple libraries, and CMake builds them for you.\n# Gradle automatically packages shared libraries with your APK.\n\n##需要添加\nset(ncnn_lib ${CMAKE_SOURCE_DIR}/src/main/jniLibs/armeabi-v7a/libncnn.a)\nadd_library (ncnn_lib STATIC IMPORTED)\nset_target_properties(ncnn_lib PROPERTIES IMPORTED_LOCATION ${ncnn_lib})\n\nadd_library( # Sets the name of the library.\n MobileNetssd ## 为生成.so的文字最好直接和.c名字一样,需要更改\n\n # Sets the library as a shared library.\n SHARED\n\n # Provides a relative path to your source file(s).\n src/main/cpp/MobileNetssd.cpp)##cpp文件的name\n\n# Searches for a specified prebuilt library and stores the path as a\n# variable. Because CMake includes system libraries in the search path by\n# default, you only need to specify the name of the public NDK library\n# you want to add. CMake verifies that the library exists before\n# completing its build.\n\nfind_library( # Sets the name of the path variable.\n log-lib\n\n # Specifies the name of the NDK library that\n # you want CMake to locate.\n log)\n\n# Specifies libraries CMake should link to your target library. You\n# can link multiple libraries, such as libraries you define in this\n# build script, prebuilt third-party libraries, or system libraries.\n\ntarget_link_libraries( # Specifies the target library.\n ##以下三个都要添加\n MobileNetssd #和上面一样\n ncnn_lib #这个ncnn的lib的add\n jnigraphics #这个jni也需要add\n\n # Links the target library to the log library\n # included in the NDK.\n ${log-lib})" }, { "path": "MobileNetSSD_demo/app/build.gradle", "content": "apply plugin: 'com.android.application'\n\nandroid {\n compileSdkVersion 28\n defaultConfig {\n applicationId \"com.example.che.mobilenetssd_demo\"\n minSdkVersion 15\n targetSdkVersion 28\n versionCode 1\n versionName \"1.0\"\n testInstrumentationRunner \"android.support.test.runner.AndroidJUnitRunner\"\n externalNativeBuild {\n cmake {\n cppFlags \"-std=c++11 -fopenmp\"//c++,多线程 需要添加need to add\n abiFilters \"armeabi-v7a\" // 手机的硬件架构，基本所有的硬件都适配\n }\n }\n }\n buildTypes {\n release {\n minifyEnabled false\n proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'\n }\n }\n externalNativeBuild {\n cmake {\n path \"CMakeLists.txt\"\n }\n }\n\n // 需要添加 把 .a文件导入, .a为 ncnn make intall生成的里面的.a文件\n sourceSets {\n main {\n jniLibs.srcDirs = [\"src/main/jniLibs\"]\n jni.srcDirs = ['src/cpp']\n }\n }\n}\n\ndependencies {\n implementation fileTree(dir: 'libs', include: ['*.jar'])\n implementation 'com.android.support:appcompat-v7:28.0.0'\n implementation 'com.android.support.constraint:constraint-layout:1.1.3'\n testImplementation 'junit:junit:4.12'\n implementation 'com.github.bumptech.glide:glide:4.3.1' // need to add增加图片类 bumptech，build自动红线消失\n androidTestImplementation 'com.android.support.test:runner:1.0.2'\n androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'\n}\n" }, { "path": "MobileNetSSD_demo/app/proguard-rules.pro", "content": "# Add project specific ProGuard rules here.\n# You can control the set of applied configuration files using the\n# proguardFiles setting in build.gradle.\n#\n# For more details, see\n# http://developer.android.com/guide/developing/tools/proguard.html\n\n# If your project uses WebView with JS, uncomment the following\n# and specify the fully qualified class name to the JavaScript interface\n# class:\n#-keepclassmembers class fqcn.of.javascript.interface.for.webview {\n# public *;\n#}\n\n# Uncomment this to preserve the line number information for\n# debugging stack traces.\n#-keepattributes SourceFile,LineNumberTable\n\n# If you keep the line number information, uncomment this to\n# hide the original source file name.\n#-renamesourcefileattribute SourceFile\n" }, { "path": "MobileNetSSD_demo/app/src/androidTest/java/com/example/che/mobilenetssd_demo/ExampleInstrumentedTest.java", "content": "package com.example.che.mobilenetssd_demo;\n\nimport android.content.Context;\nimport android.support.test.InstrumentationRegistry;\nimport android.support.test.runner.AndroidJUnit4;\n\nimport org.junit.Test;\nimport org.junit.runner.RunWith;\n\nimport static org.junit.Assert.*;\n\n/**\n * Instrumented test, which will execute on an Android device.\n *\n * @see Testing documentation\n */\n@RunWith(AndroidJUnit4.class)\npublic class ExampleInstrumentedTest {\n @Test\n public void useAppContext() {\n // Context of the app under test.\n Context appContext = InstrumentationRegistry.getTargetContext();\n\n assertEquals(\"com.example.che.mobilenetssd_demo\", appContext.getPackageName());\n }\n}\n" }, { "path": "MobileNetSSD_demo/app/src/main/AndroidManifest.xml", "content": "\n\n\n \n \n \n\n \n \n \n \n\n \n \n \n \n\n" }, { "path": "MobileNetSSD_demo/app/src/main/assets/words.txt", "content": "background\naeroplane\nbicycle\nbird\nboat\nbottle\nbus\ncar\ncat\nchair\ncow\ndiningtable\ndog\nhorse\nmotorbike\nperson\npottedplant\nsheep\nsofa\ntrain\ntvmonitor" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/MobileNetSSD_deploy.id.h", "content": "#ifndef NCNN_INCLUDE_GUARD_MobileNetSSD_deploy_id_h\n#define NCNN_INCLUDE_GUARD_MobileNetSSD_deploy_id_h\nnamespace MobileNetSSD_deploy_param_id {\nconst int LAYER_input = 0;\nconst int BLOB_data = 0;\nconst int LAYER_splitncnn_0 = 1;\nconst int BLOB_data_splitncnn_0 = 1;\nconst int BLOB_data_splitncnn_1 = 2;\nconst int BLOB_data_splitncnn_2 = 3;\nconst int BLOB_data_splitncnn_3 = 4;\nconst int BLOB_data_splitncnn_4 = 5;\nconst int BLOB_data_splitncnn_5 = 6;\nconst int BLOB_data_splitncnn_6 = 7;\nconst int LAYER_conv0 = 2;\nconst int BLOB_conv0 = 8;\nconst int LAYER_conv0_relu = 3;\nconst int BLOB_conv0_conv0_relu = 9;\nconst int LAYER_conv1_dw = 4;\nconst int BLOB_conv1_dw = 10;\nconst int LAYER_conv1_dw_relu = 5;\nconst int BLOB_conv1_dw_conv1_dw_relu = 11;\nconst int LAYER_conv1 = 6;\nconst int BLOB_conv1 = 12;\nconst int LAYER_conv1_relu = 7;\nconst int BLOB_conv1_conv1_relu = 13;\nconst int LAYER_conv2_dw = 8;\nconst int BLOB_conv2_dw = 14;\nconst int LAYER_conv2_dw_relu = 9;\nconst int BLOB_conv2_dw_conv2_dw_relu = 15;\nconst int LAYER_conv2 = 10;\nconst int BLOB_conv2 = 16;\nconst int LAYER_conv2_relu = 11;\nconst int BLOB_conv2_conv2_relu = 17;\nconst int LAYER_conv3_dw = 12;\nconst int BLOB_conv3_dw = 18;\nconst int LAYER_conv3_dw_relu = 13;\nconst int BLOB_conv3_dw_conv3_dw_relu = 19;\nconst int LAYER_conv3 = 14;\nconst int BLOB_conv3 = 20;\nconst int LAYER_conv3_relu = 15;\nconst int BLOB_conv3_conv3_relu = 21;\nconst int LAYER_conv4_dw = 16;\nconst int BLOB_conv4_dw = 22;\nconst int LAYER_conv4_dw_relu = 17;\nconst int BLOB_conv4_dw_conv4_dw_relu = 23;\nconst int LAYER_conv4 = 18;\nconst int BLOB_conv4 = 24;\nconst int LAYER_conv4_relu = 19;\nconst int BLOB_conv4_conv4_relu = 25;\nconst int LAYER_conv5_dw = 20;\nconst int BLOB_conv5_dw = 26;\nconst int LAYER_conv5_dw_relu = 21;\nconst int BLOB_conv5_dw_conv5_dw_relu = 27;\nconst int LAYER_conv5 = 22;\nconst int BLOB_conv5 = 28;\nconst int LAYER_conv5_relu = 23;\nconst int BLOB_conv5_conv5_relu = 29;\nconst int LAYER_conv6_dw = 24;\nconst int BLOB_conv6_dw = 30;\nconst int LAYER_conv6_dw_relu = 25;\nconst int BLOB_conv6_dw_conv6_dw_relu = 31;\nconst int LAYER_conv6 = 26;\nconst int BLOB_conv6 = 32;\nconst int LAYER_conv6_relu = 27;\nconst int BLOB_conv6_conv6_relu = 33;\nconst int LAYER_conv7_dw = 28;\nconst int BLOB_conv7_dw = 34;\nconst int LAYER_conv7_dw_relu = 29;\nconst int BLOB_conv7_dw_conv7_dw_relu = 35;\nconst int LAYER_conv7 = 30;\nconst int BLOB_conv7 = 36;\nconst int LAYER_conv7_relu = 31;\nconst int BLOB_conv7_conv7_relu = 37;\nconst int LAYER_conv8_dw = 32;\nconst int BLOB_conv8_dw = 38;\nconst int LAYER_conv8_dw_relu = 33;\nconst int BLOB_conv8_dw_conv8_dw_relu = 39;\nconst int LAYER_conv8 = 34;\nconst int BLOB_conv8 = 40;\nconst int LAYER_conv8_relu = 35;\nconst int BLOB_conv8_conv8_relu = 41;\nconst int LAYER_conv9_dw = 36;\nconst int BLOB_conv9_dw = 42;\nconst int LAYER_conv9_dw_relu = 37;\nconst int BLOB_conv9_dw_conv9_dw_relu = 43;\nconst int LAYER_conv9 = 38;\nconst int BLOB_conv9 = 44;\nconst int LAYER_conv9_relu = 39;\nconst int BLOB_conv9_conv9_relu = 45;\nconst int LAYER_conv10_dw = 40;\nconst int BLOB_conv10_dw = 46;\nconst int LAYER_conv10_dw_relu = 41;\nconst int BLOB_conv10_dw_conv10_dw_relu = 47;\nconst int LAYER_conv10 = 42;\nconst int BLOB_conv10 = 48;\nconst int LAYER_conv10_relu = 43;\nconst int BLOB_conv10_conv10_relu = 49;\nconst int LAYER_conv11_dw = 44;\nconst int BLOB_conv11_dw = 50;\nconst int LAYER_conv11_dw_relu = 45;\nconst int BLOB_conv11_dw_conv11_dw_relu = 51;\nconst int LAYER_conv11 = 46;\nconst int BLOB_conv11 = 52;\nconst int LAYER_conv11_relu = 47;\nconst int BLOB_conv11_conv11_relu = 53;\nconst int LAYER_splitncnn_1 = 48;\nconst int BLOB_conv11_conv11_relu_splitncnn_0 = 54;\nconst int BLOB_conv11_conv11_relu_splitncnn_1 = 55;\nconst int BLOB_conv11_conv11_relu_splitncnn_2 = 56;\nconst int BLOB_conv11_conv11_relu_splitncnn_3 = 57;\nconst int LAYER_conv12_dw = 49;\nconst int BLOB_conv12_dw = 58;\nconst int LAYER_conv12_dw_relu = 50;\nconst int BLOB_conv12_dw_conv12_dw_relu = 59;\nconst int LAYER_conv12 = 51;\nconst int BLOB_conv12 = 60;\nconst int LAYER_conv12_relu = 52;\nconst int BLOB_conv12_conv12_relu = 61;\nconst int LAYER_conv13_dw = 53;\nconst int BLOB_conv13_dw = 62;\nconst int LAYER_conv13_dw_relu = 54;\nconst int BLOB_conv13_dw_conv13_dw_relu = 63;\nconst int LAYER_conv13 = 55;\nconst int BLOB_conv13 = 64;\nconst int LAYER_conv13_relu = 56;\nconst int BLOB_conv13_conv13_relu = 65;\nconst int LAYER_splitncnn_2 = 57;\nconst int BLOB_conv13_conv13_relu_splitncnn_0 = 66;\nconst int BLOB_conv13_conv13_relu_splitncnn_1 = 67;\nconst int BLOB_conv13_conv13_relu_splitncnn_2 = 68;\nconst int BLOB_conv13_conv13_relu_splitncnn_3 = 69;\nconst int LAYER_conv14_1 = 58;\nconst int BLOB_conv14_1 = 70;\nconst int LAYER_conv14_1_relu = 59;\nconst int BLOB_conv14_1_conv14_1_relu = 71;\nconst int LAYER_conv14_2 = 60;\nconst int BLOB_conv14_2 = 72;\nconst int LAYER_conv14_2_relu = 61;\nconst int BLOB_conv14_2_conv14_2_relu = 73;\nconst int LAYER_splitncnn_3 = 62;\nconst int BLOB_conv14_2_conv14_2_relu_splitncnn_0 = 74;\nconst int BLOB_conv14_2_conv14_2_relu_splitncnn_1 = 75;\nconst int BLOB_conv14_2_conv14_2_relu_splitncnn_2 = 76;\nconst int BLOB_conv14_2_conv14_2_relu_splitncnn_3 = 77;\nconst int LAYER_conv15_1 = 63;\nconst int BLOB_conv15_1 = 78;\nconst int LAYER_conv15_1_relu = 64;\nconst int BLOB_conv15_1_conv15_1_relu = 79;\nconst int LAYER_conv15_2 = 65;\nconst int BLOB_conv15_2 = 80;\nconst int LAYER_conv15_2_relu = 66;\nconst int BLOB_conv15_2_conv15_2_relu = 81;\nconst int LAYER_splitncnn_4 = 67;\nconst int BLOB_conv15_2_conv15_2_relu_splitncnn_0 = 82;\nconst int BLOB_conv15_2_conv15_2_relu_splitncnn_1 = 83;\nconst int BLOB_conv15_2_conv15_2_relu_splitncnn_2 = 84;\nconst int BLOB_conv15_2_conv15_2_relu_splitncnn_3 = 85;\nconst int LAYER_conv16_1 = 68;\nconst int BLOB_conv16_1 = 86;\nconst int LAYER_conv16_1_relu = 69;\nconst int BLOB_conv16_1_conv16_1_relu = 87;\nconst int LAYER_conv16_2 = 70;\nconst int BLOB_conv16_2 = 88;\nconst int LAYER_conv16_2_relu = 71;\nconst int BLOB_conv16_2_conv16_2_relu = 89;\nconst int LAYER_splitncnn_5 = 72;\nconst int BLOB_conv16_2_conv16_2_relu_splitncnn_0 = 90;\nconst int BLOB_conv16_2_conv16_2_relu_splitncnn_1 = 91;\nconst int BLOB_conv16_2_conv16_2_relu_splitncnn_2 = 92;\nconst int BLOB_conv16_2_conv16_2_relu_splitncnn_3 = 93;\nconst int LAYER_conv17_1 = 73;\nconst int BLOB_conv17_1 = 94;\nconst int LAYER_conv17_1_relu = 74;\nconst int BLOB_conv17_1_conv17_1_relu = 95;\nconst int LAYER_conv17_2 = 75;\nconst int BLOB_conv17_2 = 96;\nconst int LAYER_conv17_2_relu = 76;\nconst int BLOB_conv17_2_conv17_2_relu = 97;\nconst int LAYER_splitncnn_6 = 77;\nconst int BLOB_conv17_2_conv17_2_relu_splitncnn_0 = 98;\nconst int BLOB_conv17_2_conv17_2_relu_splitncnn_1 = 99;\nconst int BLOB_conv17_2_conv17_2_relu_splitncnn_2 = 100;\nconst int LAYER_conv11_mbox_loc = 78;\nconst int BLOB_conv11_mbox_loc = 101;\nconst int LAYER_conv11_mbox_loc_perm = 79;\nconst int BLOB_conv11_mbox_loc_perm = 102;\nconst int LAYER_conv11_mbox_loc_flat = 80;\nconst int BLOB_conv11_mbox_loc_flat = 103;\nconst int LAYER_conv11_mbox_conf = 81;\nconst int BLOB_conv11_mbox_conf = 104;\nconst int LAYER_conv11_mbox_conf_perm = 82;\nconst int BLOB_conv11_mbox_conf_perm = 105;\nconst int LAYER_conv11_mbox_conf_flat = 83;\nconst int BLOB_conv11_mbox_conf_flat = 106;\nconst int LAYER_conv11_mbox_priorbox = 84;\nconst int BLOB_conv11_mbox_priorbox = 107;\nconst int LAYER_conv13_mbox_loc = 85;\nconst int BLOB_conv13_mbox_loc = 108;\nconst int LAYER_conv13_mbox_loc_perm = 86;\nconst int BLOB_conv13_mbox_loc_perm = 109;\nconst int LAYER_conv13_mbox_loc_flat = 87;\nconst int BLOB_conv13_mbox_loc_flat = 110;\nconst int LAYER_conv13_mbox_conf = 88;\nconst int BLOB_conv13_mbox_conf = 111;\nconst int LAYER_conv13_mbox_conf_perm = 89;\nconst int BLOB_conv13_mbox_conf_perm = 112;\nconst int LAYER_conv13_mbox_conf_flat = 90;\nconst int BLOB_conv13_mbox_conf_flat = 113;\nconst int LAYER_conv13_mbox_priorbox = 91;\nconst int BLOB_conv13_mbox_priorbox = 114;\nconst int LAYER_conv14_2_mbox_loc = 92;\nconst int BLOB_conv14_2_mbox_loc = 115;\nconst int LAYER_conv14_2_mbox_loc_perm = 93;\nconst int BLOB_conv14_2_mbox_loc_perm = 116;\nconst int LAYER_conv14_2_mbox_loc_flat = 94;\nconst int BLOB_conv14_2_mbox_loc_flat = 117;\nconst int LAYER_conv14_2_mbox_conf = 95;\nconst int BLOB_conv14_2_mbox_conf = 118;\nconst int LAYER_conv14_2_mbox_conf_perm = 96;\nconst int BLOB_conv14_2_mbox_conf_perm = 119;\nconst int LAYER_conv14_2_mbox_conf_flat = 97;\nconst int BLOB_conv14_2_mbox_conf_flat = 120;\nconst int LAYER_conv14_2_mbox_priorbox = 98;\nconst int BLOB_conv14_2_mbox_priorbox = 121;\nconst int LAYER_conv15_2_mbox_loc = 99;\nconst int BLOB_conv15_2_mbox_loc = 122;\nconst int LAYER_conv15_2_mbox_loc_perm = 100;\nconst int BLOB_conv15_2_mbox_loc_perm = 123;\nconst int LAYER_conv15_2_mbox_loc_flat = 101;\nconst int BLOB_conv15_2_mbox_loc_flat = 124;\nconst int LAYER_conv15_2_mbox_conf = 102;\nconst int BLOB_conv15_2_mbox_conf = 125;\nconst int LAYER_conv15_2_mbox_conf_perm = 103;\nconst int BLOB_conv15_2_mbox_conf_perm = 126;\nconst int LAYER_conv15_2_mbox_conf_flat = 104;\nconst int BLOB_conv15_2_mbox_conf_flat = 127;\nconst int LAYER_conv15_2_mbox_priorbox = 105;\nconst int BLOB_conv15_2_mbox_priorbox = 128;\nconst int LAYER_conv16_2_mbox_loc = 106;\nconst int BLOB_conv16_2_mbox_loc = 129;\nconst int LAYER_conv16_2_mbox_loc_perm = 107;\nconst int BLOB_conv16_2_mbox_loc_perm = 130;\nconst int LAYER_conv16_2_mbox_loc_flat = 108;\nconst int BLOB_conv16_2_mbox_loc_flat = 131;\nconst int LAYER_conv16_2_mbox_conf = 109;\nconst int BLOB_conv16_2_mbox_conf = 132;\nconst int LAYER_conv16_2_mbox_conf_perm = 110;\nconst int BLOB_conv16_2_mbox_conf_perm = 133;\nconst int LAYER_conv16_2_mbox_conf_flat = 111;\nconst int BLOB_conv16_2_mbox_conf_flat = 134;\nconst int LAYER_conv16_2_mbox_priorbox = 112;\nconst int BLOB_conv16_2_mbox_priorbox = 135;\nconst int LAYER_conv17_2_mbox_loc = 113;\nconst int BLOB_conv17_2_mbox_loc = 136;\nconst int LAYER_conv17_2_mbox_loc_perm = 114;\nconst int BLOB_conv17_2_mbox_loc_perm = 137;\nconst int LAYER_conv17_2_mbox_loc_flat = 115;\nconst int BLOB_conv17_2_mbox_loc_flat = 138;\nconst int LAYER_conv17_2_mbox_conf = 116;\nconst int BLOB_conv17_2_mbox_conf = 139;\nconst int LAYER_conv17_2_mbox_conf_perm = 117;\nconst int BLOB_conv17_2_mbox_conf_perm = 140;\nconst int LAYER_conv17_2_mbox_conf_flat = 118;\nconst int BLOB_conv17_2_mbox_conf_flat = 141;\nconst int LAYER_conv17_2_mbox_priorbox = 119;\nconst int BLOB_conv17_2_mbox_priorbox = 142;\nconst int LAYER_mbox_loc = 120;\nconst int BLOB_mbox_loc = 143;\nconst int LAYER_mbox_conf = 121;\nconst int BLOB_mbox_conf = 144;\nconst int LAYER_mbox_priorbox = 122;\nconst int BLOB_mbox_priorbox = 145;\nconst int LAYER_mbox_conf_reshape = 123;\nconst int BLOB_mbox_conf_reshape = 146;\nconst int LAYER_mbox_conf_softmax = 124;\nconst int BLOB_mbox_conf_softmax = 147;\nconst int LAYER_mbox_conf_flatten = 125;\nconst int BLOB_mbox_conf_flatten = 148;\nconst int LAYER_detection_out = 126;\nconst int BLOB_detection_out = 149;\n} // namespace MobileNetSSD_deploy_param_id\n#endif // NCNN_INCLUDE_GUARD_MobileNetSSD_deploy_id_h\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/MobileNetssd.cpp", "content": "#include \n#include \n#include \n#include \n#include \n\n// ncnn\n#include \"include/opencv.h\"\n#include \"MobileNetSSD_deploy.id.h\" //这里看成自己的id.h\n#include \n#include \n#include \"include/net.h\"\n\nstatic ncnn::UnlockedPoolAllocator g_blob_pool_allocator;\nstatic ncnn::PoolAllocator g_workspace_pool_allocator;\n\nstatic ncnn::Mat ncnn_param;\nstatic ncnn::Mat ncnn_bin;\nstatic ncnn::Net ncnn_net;\n\nextern \"C\" {\n\n\n// public native boolean Init(byte[] words,byte[] param, byte[] bin);　　原函数形式（c++） 以下形式为ndk的c++形式\nJNIEXPORT jboolean JNICALL\nJava_com_example_che_mobilenetssd_1demo_MobileNetssd_Init(JNIEnv *env, jobject obj, jbyteArray param, jbyteArray bin) {\n __android_log_print(ANDROID_LOG_DEBUG, \"MobileNetssd\", \"enter the jni func\");\n // init param\n {\n int len = env->GetArrayLength(param);\n ncnn_param.create(len, (size_t) 1u);\n env->GetByteArrayRegion(param, 0, len, (jbyte *) ncnn_param);\n int ret = ncnn_net.load_param((const unsigned char *) ncnn_param);\n __android_log_print(ANDROID_LOG_DEBUG, \"MobileNetssd\", \"load_param %d %d\", ret, len);\n }\n\n // init bin\n {\n int len = env->GetArrayLength(bin);\n ncnn_bin.create(len, (size_t) 1u);\n env->GetByteArrayRegion(bin, 0, len, (jbyte *) ncnn_bin);\n int ret = ncnn_net.load_model((const unsigned char *) ncnn_bin);\n __android_log_print(ANDROID_LOG_DEBUG, \"MobileNetssd\", \"load_model %d %d\", ret, len);\n }\n\n ncnn::Option opt;\n opt.lightmode = true;\n opt.num_threads = 4; //线程 这里可以修改\n opt.blob_allocator = &g_blob_pool_allocator;\n opt.workspace_allocator = &g_workspace_pool_allocator;\n\n ncnn::set_default_option(opt);\n\n return JNI_TRUE;\n}\n\n// public native String Detect(Bitmap bitmap);\nJNIEXPORT jfloatArray JNICALL Java_com_example_che_mobilenetssd_1demo_MobileNetssd_Detect(JNIEnv* env, jobject thiz, jobject bitmap)\n{\n // ncnn from bitmap\n ncnn::Mat in;\n {\n AndroidBitmapInfo info;\n AndroidBitmap_getInfo(env, bitmap, &info);\n// int origin_w = info.width;\n// int origin_h = info.height;\n// int width = 300;\n// int height = 300;\n int width = info.width;\n int height = info.height;\n if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888)\n return NULL;\n\n void* indata;\n AndroidBitmap_lockPixels(env, bitmap, &indata);\n // 把像素转换成data，并指定通道顺序\n // 因为图像预处理每个网络层输入的数据格式不一样一般为300*300 128*128等等所以这类需要一个resize的操作可以在cpp中写，也可以是java读入图片时有个resize操作\n// in = ncnn::Mat::from_pixels_resize((const unsigned char*)indata, ncnn::Mat::PIXEL_RGBA2RGB, origin_w, origin_h, width, height);\n\n in = ncnn::Mat::from_pixels((const unsigned char*)indata, ncnn::Mat::PIXEL_RGBA2RGB, width, height);\n\n // 下面一行为debug代码\n //__android_log_print(ANDROID_LOG_DEBUG, \"MobilenetssdJniIn\", \"Mobilenetssd_predict_has_input1, in.w: %d; in.h: %d\", in.w, in.h);\n AndroidBitmap_unlockPixels(env, bitmap);\n }\n\n // ncnn_net\n std::vector cls_scores;\n {\n // 减去均值和乘上比例（这个数据和前面的归一化图片预处理形式一一对应）\n const float mean_vals[3] = {127.5f, 127.5f, 127.5f};\n const float scale[3] = {0.007843f, 0.007843f, 0.007843f};\n\n in.substract_mean_normalize(mean_vals, scale);// 归一化\n\n ncnn::Extractor ex = ncnn_net.create_extractor();//前向传播\n\n // 如果不加密是使用ex.input(\"data\", in);\n // BLOB_data在id.h文件中可见，相当于datainput网络层的id\n ex.input(MobileNetSSD_deploy_param_id::BLOB_data, in);\n //ex.set_num_threads(4); 和上面一样一个对象\n\n ncnn::Mat out;\n // 如果时不加密是使用ex.extract(\"prob\", out);\n //BLOB_detection_out.h文件中可见，相当于dataout网络层的id,输出检测的结果数据\n ex.extract(MobileNetSSD_deploy_param_id::BLOB_detection_out, out);\n\n int output_wsize = out.w;\n int output_hsize = out.h;\n\n //输出整理\n jfloat *output[output_wsize * output_hsize]; // float类型\n for(int i = 0; i< out.h; i++) {\n for (int j = 0; j < out.w; j++) {\n output[i*output_wsize + j] = &out.row(i)[j];\n }\n }\n //建立float数组 长度为 output_wsize * output_hsize,如果只是ouput_size相当于只有一行的out的数据那就是一个object检测数据\n jfloatArray jOutputData = env->NewFloatArray(output_wsize * output_hsize);\n if (jOutputData == nullptr) return nullptr;\n env->SetFloatArrayRegion(jOutputData, 0, output_wsize * output_hsize,\n reinterpret_cast(*output));\n return jOutputData;\n }\n}\n}\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/include/allocator.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_ALLOCATOR_H\n#define NCNN_ALLOCATOR_H\n\n#ifdef _WIN32\n#define WIN32_LEAN_AND_MEAN\n#include \n#else\n#include \n#endif\n\n#include \n#include \n\nnamespace ncnn {\n\n// the alignment of all the allocated buffers\n#define MALLOC_ALIGN 16\n\n// Aligns a pointer to the specified number of bytes\n// ptr Aligned pointer\n// n Alignment size that must be a power of two\ntemplate static inline _Tp* alignPtr(_Tp* ptr, int n=(int)sizeof(_Tp))\n{\n return (_Tp*)(((size_t)ptr + n-1) & -n);\n}\n\n// Aligns a buffer size to the specified number of bytes\n// The function returns the minimum number that is greater or equal to sz and is divisible by n\n// sz Buffer size to align\n// n Alignment size that must be a power of two\nstatic inline size_t alignSize(size_t sz, int n)\n{\n return (sz + n-1) & -n;\n}\n\nstatic inline void* fastMalloc(size_t size)\n{\n unsigned char* udata = (unsigned char*)malloc(size + sizeof(void*) + MALLOC_ALIGN);\n if (!udata)\n return 0;\n unsigned char** adata = alignPtr((unsigned char**)udata + 1, MALLOC_ALIGN);\n adata[-1] = udata;\n return adata;\n}\n\nstatic inline void fastFree(void* ptr)\n{\n if (ptr)\n {\n unsigned char* udata = ((unsigned char**)ptr)[-1];\n free(udata);\n }\n}\n\n// exchange-add operation for atomic operations on reference counters\n#if defined __INTEL_COMPILER && !(defined WIN32 || defined _WIN32)\n// atomic increment on the linux version of the Intel(tm) compiler\n# define NCNN_XADD(addr, delta) (int)_InterlockedExchangeAdd(const_cast(reinterpret_cast(addr)), delta)\n#elif defined __GNUC__\n# if defined __clang__ && __clang_major__ >= 3 && !defined __ANDROID__ && !defined __EMSCRIPTEN__ && !defined(__CUDACC__)\n# ifdef __ATOMIC_ACQ_REL\n# define NCNN_XADD(addr, delta) __c11_atomic_fetch_add((_Atomic(int)*)(addr), delta, __ATOMIC_ACQ_REL)\n# else\n# define NCNN_XADD(addr, delta) __atomic_fetch_add((_Atomic(int)*)(addr), delta, 4)\n# endif\n# else\n# if defined __ATOMIC_ACQ_REL && !defined __clang__\n// version for gcc >= 4.7\n# define NCNN_XADD(addr, delta) (int)__atomic_fetch_add((unsigned*)(addr), (unsigned)(delta), __ATOMIC_ACQ_REL)\n# else\n# define NCNN_XADD(addr, delta) (int)__sync_fetch_and_add((unsigned*)(addr), (unsigned)(delta))\n# endif\n# endif\n#elif defined _MSC_VER && !defined RC_INVOKED\n# include \n# define NCNN_XADD(addr, delta) (int)_InterlockedExchangeAdd((long volatile*)addr, delta)\n#else\n// thread-unsafe branch\nstatic inline int NCNN_XADD(int* addr, int delta) { int tmp = *addr; *addr += delta; return tmp; }\n#endif\n\n#ifdef _WIN32\nclass Mutex\n{\npublic:\n Mutex() { InitializeSRWLock(&srwlock); }\n ~Mutex() {}\n void lock() { AcquireSRWLockExclusive(&srwlock); }\n void unlock() { ReleaseSRWLockExclusive(&srwlock); }\nprivate:\n // NOTE SRWLock is available from windows vista\n SRWLOCK srwlock;\n};\n#else // _WIN32\nclass Mutex\n{\npublic:\n Mutex() { pthread_mutex_init(&mutex, 0); }\n ~Mutex() { pthread_mutex_destroy(&mutex); }\n void lock() { pthread_mutex_lock(&mutex); }\n void unlock() { pthread_mutex_unlock(&mutex); }\nprivate:\n pthread_mutex_t mutex;\n};\n#endif // _WIN32\n\nclass Allocator\n{\npublic:\n virtual ~Allocator() = 0;\n virtual void* fastMalloc(size_t size) = 0;\n virtual void fastFree(void* ptr) = 0;\n};\n\nclass PoolAllocator : public Allocator\n{\npublic:\n PoolAllocator();\n ~PoolAllocator();\n\n // ratio range 0 ~ 1\n // default cr = 0.75\n void set_size_compare_ratio(float scr);\n\n // release all budgets immediately\n void clear();\n\n virtual void* fastMalloc(size_t size);\n virtual void fastFree(void* ptr);\n\nprivate:\n Mutex budgets_lock;\n Mutex payouts_lock;\n unsigned int size_compare_ratio;// 0~256\n std::list< std::pair > budgets;\n std::list< std::pair > payouts;\n};\n\nclass UnlockedPoolAllocator : public Allocator\n{\npublic:\n UnlockedPoolAllocator();\n ~UnlockedPoolAllocator();\n\n // ratio range 0 ~ 1\n // default cr = 0.75\n void set_size_compare_ratio(float scr);\n\n // release all budgets immediately\n void clear();\n\n virtual void* fastMalloc(size_t size);\n virtual void fastFree(void* ptr);\n\nprivate:\n unsigned int size_compare_ratio;// 0~256\n std::list< std::pair > budgets;\n std::list< std::pair > payouts;\n};\n\n} // namespace ncnn\n\n#endif // NCNN_ALLOCATOR_H\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/include/benchmark.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_BENCHMARK_H\n#define NCNN_BENCHMARK_H\n\n#include \"platform.h\"\n#include \"mat.h\"\n#include \"layer.h\"\n\nnamespace ncnn {\n\n// get now timestamp in ms\ndouble get_current_time();\n\n#if NCNN_BENCHMARK\n\nvoid benchmark(const Layer* layer, double start, double end);\nvoid benchmark(const Layer* layer, const Mat& bottom_blob, Mat& top_blob, double start, double end);\n\n#endif // NCNN_BENCHMARK\n\n} // namespace ncnn\n\n#endif // NCNN_BENCHMARK_H\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/include/blob.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_BLOB_H\n#define NCNN_BLOB_H\n\n#include \n#include \n#include \"platform.h\"\n\nnamespace ncnn {\n\nclass Blob\n{\npublic:\n // empty\n Blob();\n\npublic:\n#if NCNN_STRING\n // blob name\n std::string name;\n#endif // NCNN_STRING\n // layer index which produce this blob as output\n int producer;\n // layer index which need this blob as input\n std::vector consumers;\n};\n\n} // namespace ncnn\n\n#endif // NCNN_BLOB_H\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/include/cpu.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_CPU_H\n#define NCNN_CPU_H\n\nnamespace ncnn {\n\n// test optional cpu features\n// neon = armv7 neon or aarch64 asimd\nint cpu_support_arm_neon();\n// vfpv4 = armv7 fp16 + fma\nint cpu_support_arm_vfpv4();\n// asimdhp = aarch64 asimd half precision\nint cpu_support_arm_asimdhp();\n\n// cpu info\nint get_cpu_count();\n\n// bind all threads on little clusters if powersave enabled\n// affacts HMP arch cpu like ARM big.LITTLE\n// only implemented on android at the moment\n// switching powersave is expensive and not thread-safe\n// 0 = all cores enabled(default)\n// 1 = only little clusters enabled\n// 2 = only big clusters enabled\n// return 0 if success for setter function\nint get_cpu_powersave();\nint set_cpu_powersave(int powersave);\n\n// misc function wrapper for openmp routines\nint get_omp_num_threads();\nvoid set_omp_num_threads(int num_threads);\n\nint get_omp_dynamic();\nvoid set_omp_dynamic(int dynamic);\n\n} // namespace ncnn\n\n#endif // NCNN_CPU_H\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/include/layer.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_LAYER_H\n#define NCNN_LAYER_H\n\n#include \n#include \n#include \n#include \"mat.h\"\n#include \"modelbin.h\"\n#include \"paramdict.h\"\n#include \"platform.h\"\n\nnamespace ncnn {\n\nclass Allocator;\nclass Option\n{\npublic:\n // default option\n Option();\n\npublic:\n // light mode\n // intermediate blob will be recycled when enabled\n // enabled by default\n bool lightmode;\n\n // thread count\n // default value is the one returned by get_cpu_count()\n int num_threads;\n\n // blob memory allocator\n Allocator* blob_allocator;\n\n // workspace memory allocator\n Allocator* workspace_allocator;\n};\n\n// the global default option\nconst Option& get_default_option();\nint set_default_option(const Option& opt);\n\nclass Layer\n{\npublic:\n // empty\n Layer();\n // virtual destructor\n virtual ~Layer();\n\n // load layer specific parameter from parsed dict\n // return 0 if success\n virtual int load_param(const ParamDict& pd);\n\n // load layer specific weight data from model binary\n // return 0 if success\n virtual int load_model(const ModelBin& mb);\n\npublic:\n // one input and one output blob\n bool one_blob_only;\n\n // support inplace inference\n bool support_inplace;\n\npublic:\n // implement inference\n // return 0 if success\n virtual int forward(const std::vector& bottom_blobs, std::vector& top_blobs, const Option& opt = get_default_option()) const;\n virtual int forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt = get_default_option()) const;\n\n // implement inplace inference\n // return 0 if success\n virtual int forward_inplace(std::vector& bottom_top_blobs, const Option& opt = get_default_option()) const;\n virtual int forward_inplace(Mat& bottom_top_blob, const Option& opt = get_default_option()) const;\n\npublic:\n#if NCNN_STRING\n // layer type name\n std::string type;\n // layer name\n std::string name;\n#endif // NCNN_STRING\n // blob index which this layer needs as input\n std::vector bottoms;\n // blob index which this layer produces as output\n std::vector tops;\n};\n\n// layer factory function\ntypedef Layer* (*layer_creator_func)();\n\nstruct layer_registry_entry\n{\n#if NCNN_STRING\n // layer type name\n const char* name;\n#endif // NCNN_STRING\n // layer factory entry\n layer_creator_func creator;\n};\n\n#if NCNN_STRING\n// get layer type from type name\nint layer_to_index(const char* type);\n// create layer from type name\nLayer* create_layer(const char* type);\n#endif // NCNN_STRING\n// create layer from layer type\nLayer* create_layer(int index);\n\n#define DEFINE_LAYER_CREATOR(name) \\\n ::ncnn::Layer* name##_layer_creator() { return new name; }\n\n} // namespace ncnn\n\n#endif // NCNN_LAYER_H\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/include/layer_type.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_LAYER_TYPE_H\n#define NCNN_LAYER_TYPE_H\n\nnamespace ncnn {\n\nnamespace LayerType {\nenum\n{\n#include \"layer_type_enum.h\"\n CustomBit = (1<<8),\n};\n} // namespace LayerType\n\n} // namespace ncnn\n\n#endif // NCNN_LAYER_TYPE_H\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/include/layer_type_enum.h", "content": "// Layer Type Enum header\n//\n// This file is auto-generated by cmake, don't edit it.\n\nAbsVal = 0,\nArgMax = 1,\nBatchNorm = 2,\nBias = 3,\nBNLL = 4,\nConcat = 5,\nConvolution = 6,\nCrop = 7,\nDeconvolution = 8,\nDropout = 9,\nEltwise = 10,\nELU = 11,\nEmbed = 12,\nExp = 13,\nFlatten = 14,\nInnerProduct = 15,\nInput = 16,\nLog = 17,\nLRN = 18,\nMemoryData = 19,\nMVN = 20,\nPooling = 21,\nPower = 22,\nPReLU = 23,\nProposal = 24,\nReduction = 25,\nReLU = 26,\nReshape = 27,\nROIPooling = 28,\nScale = 29,\nSigmoid = 30,\nSlice = 31,\nSoftmax = 32,\nSplit = 33,\nSPP = 34,\nTanH = 35,\nThreshold = 36,\nTile = 37,\nRNN = 38,\nLSTM = 39,\nBinaryOp = 40,\nUnaryOp = 41,\nConvolutionDepthWise = 42,\nPadding = 43,\nSqueeze = 44,\nExpandDims = 45,\nNormalize = 46,\nPermute = 47,\nPriorBox = 48,\nDetectionOutput = 49,\nInterp = 50,\nDeconvolutionDepthWise = 51,\nShuffleChannel = 52,\nInstanceNorm = 53,\nClip = 54,\nReorg = 55,\nYoloDetectionOutput = 56,\nQuantize = 57,\nDequantize = 58,\nYolov3DetectionOutput = 59,\nPSROIPooling = 60,\nROIAlign = 61,\n\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/include/mat.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_MAT_H\n#define NCNN_MAT_H\n\n#include \n#include \n#if __ARM_NEON\n#include \n#endif\n#include \"allocator.h\"\n#include \"platform.h\"\n\nnamespace ncnn {\n\n// the three dimension matrix\nclass Mat\n{\npublic:\n // empty\n Mat();\n // vec\n Mat(int w, size_t elemsize = 4u, Allocator* allocator = 0);\n // image\n Mat(int w, int h, size_t elemsize = 4u, Allocator* allocator = 0);\n // dim\n Mat(int w, int h, int c, size_t elemsize = 4u, Allocator* allocator = 0);\n // copy\n Mat(const Mat& m);\n // external vec\n Mat(int w, void* data, size_t elemsize = 4u, Allocator* allocator = 0);\n // external image\n Mat(int w, int h, void* data, size_t elemsize = 4u, Allocator* allocator = 0);\n // external dim\n Mat(int w, int h, int c, void* data, size_t elemsize = 4u, Allocator* allocator = 0);\n // release\n ~Mat();\n // assign\n Mat& operator=(const Mat& m);\n // set all\n void fill(float v);\n void fill(int v);\n template void fill(T v);\n // deep copy\n Mat clone(Allocator* allocator = 0) const;\n // reshape vec\n Mat reshape(int w, Allocator* allocator = 0) const;\n // reshape image\n Mat reshape(int w, int h, Allocator* allocator = 0) const;\n // reshape dim\n Mat reshape(int w, int h, int c, Allocator* allocator = 0) const;\n // allocate vec\n void create(int w, size_t elemsize = 4u, Allocator* allocator = 0);\n // allocate image\n void create(int w, int h, size_t elemsize = 4u, Allocator* allocator = 0);\n // allocate dim\n void create(int w, int h, int c, size_t elemsize = 4u, Allocator* allocator = 0);\n // refcount++\n void addref();\n // refcount--\n void release();\n\n bool empty() const;\n size_t total() const;\n\n // data reference\n Mat channel(int c);\n const Mat channel(int c) const;\n float* row(int y);\n const float* row(int y) const;\n template T* row(int y);\n template const T* row(int y) const;\n\n // range reference\n Mat channel_range(int c, int channels);\n const Mat channel_range(int c, int channels) const;\n Mat row_range(int y, int rows);\n const Mat row_range(int y, int rows) const;\n Mat range(int x, int n);\n const Mat range(int x, int n) const;\n\n // access raw data\n template operator T*();\n template operator const T*() const;\n\n // convenient access float vec element\n float& operator[](int i);\n const float& operator[](int i) const;\n\n#if NCNN_PIXEL\n enum\n {\n PIXEL_CONVERT_SHIFT = 16,\n PIXEL_FORMAT_MASK = 0x0000ffff,\n PIXEL_CONVERT_MASK = 0xffff0000,\n\n PIXEL_RGB = 1,\n PIXEL_BGR = (1 << 1),\n PIXEL_GRAY = (1 << 2),\n PIXEL_RGBA = (1 << 3),\n\n PIXEL_RGB2BGR = PIXEL_RGB | (PIXEL_BGR << PIXEL_CONVERT_SHIFT),\n PIXEL_RGB2GRAY = PIXEL_RGB | (PIXEL_GRAY << PIXEL_CONVERT_SHIFT),\n\n PIXEL_BGR2RGB = PIXEL_BGR | (PIXEL_RGB << PIXEL_CONVERT_SHIFT),\n PIXEL_BGR2GRAY = PIXEL_BGR | (PIXEL_GRAY << PIXEL_CONVERT_SHIFT),\n\n PIXEL_GRAY2RGB = PIXEL_GRAY | (PIXEL_RGB << PIXEL_CONVERT_SHIFT),\n PIXEL_GRAY2BGR = PIXEL_GRAY | (PIXEL_BGR << PIXEL_CONVERT_SHIFT),\n\n PIXEL_RGBA2RGB = PIXEL_RGBA | (PIXEL_RGB << PIXEL_CONVERT_SHIFT),\n PIXEL_RGBA2BGR = PIXEL_RGBA | (PIXEL_BGR << PIXEL_CONVERT_SHIFT),\n PIXEL_RGBA2GRAY = PIXEL_RGBA | (PIXEL_GRAY << PIXEL_CONVERT_SHIFT),\n };\n // convenient construct from pixel data\n static Mat from_pixels(const unsigned char* pixels, int type, int w, int h, Allocator* allocator = 0);\n // convenient construct from pixel data and resize to specific size\n static Mat from_pixels_resize(const unsigned char* pixels, int type, int w, int h, int target_width, int target_height, Allocator* allocator = 0);\n\n // convenient export to pixel data\n void to_pixels(unsigned char* pixels, int type) const;\n // convenient export to pixel data and resize to specific size\n void to_pixels_resize(unsigned char* pixels, int type, int target_width, int target_height) const;\n#endif // NCNN_PIXEL\n\n // substract channel-wise mean values, then multiply by normalize values, pass 0 to skip\n void substract_mean_normalize(const float* mean_vals, const float* norm_vals);\n\n // convenient construct from half precisoin floating point data\n static Mat from_float16(const unsigned short* data, int size);\n\n // pointer to the data\n void* data;\n\n // pointer to the reference counter\n // when points to user-allocated data, the pointer is NULL\n int* refcount;\n\n // element size in bytes\n // 4 = float32/int32\n // 2 = float16\n // 1 = int8/uint8\n // 0 = empty\n size_t elemsize;\n\n // the allocator\n Allocator* allocator;\n\n // the dimensionality\n int dims;\n\n int w;\n int h;\n int c;\n\n size_t cstep;\n};\n\n// misc function\n#if NCNN_PIXEL\n// convert yuv420sp(nv21) to rgb, the fast approximate version\nvoid yuv420sp2rgb(const unsigned char* yuv420sp, int w, int h, unsigned char* rgb);\n// image pixel bilinear resize\nvoid resize_bilinear_c1(const unsigned char* src, int srcw, int srch, unsigned char* dst, int w, int h);\nvoid resize_bilinear_c2(const unsigned char* src, int srcw, int srch, unsigned char* dst, int w, int h);\nvoid resize_bilinear_c3(const unsigned char* src, int srcw, int srch, unsigned char* dst, int w, int h);\nvoid resize_bilinear_c4(const unsigned char* src, int srcw, int srch, unsigned char* dst, int w, int h);\n// image pixel bilinear resize, convenient wrapper for yuv420sp(nv21)\nvoid resize_bilinear_yuv420sp(const unsigned char* src, int srcw, int srch, unsigned char* dst, int w, int h);\n#endif // NCNN_PIXEL\n\n// mat process\nenum\n{\n BORDER_CONSTANT = 0,\n BORDER_REPLICATE = 1,\n};\nvoid copy_make_border(const Mat& src, Mat& dst, int top, int bottom, int left, int right, int type, float v, Allocator* allocator = 0, int num_threads = 1);\nvoid copy_cut_border(const Mat& src, Mat& dst, int top, int bottom, int left, int right, Allocator* allocator = 0, int num_threads = 1);\nvoid resize_bilinear(const Mat& src, Mat& dst, int w, int h, Allocator* allocator = 0, int num_threads = 1);\n\ninline Mat::Mat()\n : data(0), refcount(0), elemsize(0), allocator(0), dims(0), w(0), h(0), c(0), cstep(0)\n{\n}\n\ninline Mat::Mat(int _w, size_t _elemsize, Allocator* allocator)\n : data(0), refcount(0), dims(0)\n{\n create(_w, _elemsize, allocator);\n}\n\ninline Mat::Mat(int _w, int _h, size_t _elemsize, Allocator* allocator)\n : data(0), refcount(0), dims(0)\n{\n create(_w, _h, _elemsize, allocator);\n}\n\ninline Mat::Mat(int _w, int _h, int _c, size_t _elemsize, Allocator* allocator)\n : data(0), refcount(0), dims(0)\n{\n create(_w, _h, _c, _elemsize, allocator);\n}\n\ninline Mat::Mat(const Mat& m)\n : data(m.data), refcount(m.refcount), elemsize(m.elemsize), allocator(m.allocator), dims(m.dims)\n{\n if (refcount)\n NCNN_XADD(refcount, 1);\n\n w = m.w;\n h = m.h;\n c = m.c;\n\n cstep = m.cstep;\n}\n\ninline Mat::Mat(int _w, void* _data, size_t _elemsize, Allocator* _allocator)\n : data(_data), refcount(0), elemsize(_elemsize), allocator(_allocator), dims(1)\n{\n w = _w;\n h = 1;\n c = 1;\n\n cstep = w;\n}\n\ninline Mat::Mat(int _w, int _h, void* _data, size_t _elemsize, Allocator* _allocator)\n : data(_data), refcount(0), elemsize(_elemsize), allocator(_allocator), dims(2)\n{\n w = _w;\n h = _h;\n c = 1;\n\n cstep = w * h;\n}\n\ninline Mat::Mat(int _w, int _h, int _c, void* _data, size_t _elemsize, Allocator* _allocator)\n : data(_data), refcount(0), elemsize(_elemsize), allocator(_allocator), dims(3)\n{\n w = _w;\n h = _h;\n c = _c;\n\n cstep = alignSize(w * h * elemsize, 16) / elemsize;\n}\n\ninline Mat::~Mat()\n{\n release();\n}\n\ninline Mat& Mat::operator=(const Mat& m)\n{\n if (this == &m)\n return *this;\n\n if (m.refcount)\n NCNN_XADD(m.refcount, 1);\n\n release();\n\n data = m.data;\n refcount = m.refcount;\n elemsize = m.elemsize;\n allocator = m.allocator;\n\n dims = m.dims;\n w = m.w;\n h = m.h;\n c = m.c;\n\n cstep = m.cstep;\n\n return *this;\n}\n\ninline void Mat::fill(float _v)\n{\n int size = total();\n float* ptr = (float*)data;\n\n#if __ARM_NEON\n int nn = size >> 2;\n int remain = size - (nn << 2);\n#else\n int remain = size;\n#endif // __ARM_NEON\n\n#if __ARM_NEON\n float32x4_t _c = vdupq_n_f32(_v);\n#if __aarch64__\n if (nn > 0)\n {\n asm volatile (\n \"0: \\n\"\n \"subs %w0, %w0, #1 \\n\"\n \"st1 {%4.4s}, [%1], #16 \\n\"\n \"bne 0b \\n\"\n : \"=r\"(nn), // %0\n \"=r\"(ptr) // %1\n : \"0\"(nn),\n \"1\"(ptr),\n \"w\"(_c) // %4\n : \"cc\", \"memory\"\n );\n }\n#else\n if (nn > 0)\n {\n asm volatile(\n \"0: \\n\"\n \"subs %0, #1 \\n\"\n \"vst1.f32 {%e4-%f4}, [%1 :128]!\\n\"\n \"bne 0b \\n\"\n : \"=r\"(nn), // %0\n \"=r\"(ptr) // %1\n : \"0\"(nn),\n \"1\"(ptr),\n \"w\"(_c) // %4\n : \"cc\", \"memory\"\n );\n }\n#endif // __aarch64__\n#endif // __ARM_NEON\n for (; remain>0; remain--)\n {\n *ptr++ = _v;\n }\n}\n\ninline void Mat::fill(int _v)\n{\n int size = total();\n int* ptr = (int*)data;\n\n#if __ARM_NEON\n int nn = size >> 2;\n int remain = size - (nn << 2);\n#else\n int remain = size;\n#endif // __ARM_NEON\n\n#if __ARM_NEON\n int32x4_t _c = vdupq_n_s32(_v);\n#if __aarch64__\n if (nn > 0)\n {\n asm volatile (\n \"0: \\n\"\n \"subs %w0, %w0, #1 \\n\"\n \"st1 {%4.4s}, [%1], #16 \\n\"\n \"bne 0b \\n\"\n : \"=r\"(nn), // %0\n \"=r\"(ptr) // %1\n : \"0\"(nn),\n \"1\"(ptr),\n \"w\"(_c) // %4\n : \"cc\", \"memory\"\n );\n }\n#else\n if (nn > 0)\n {\n asm volatile(\n \"0: \\n\"\n \"subs %0, #1 \\n\"\n \"vst1.s32 {%e4-%f4}, [%1 :128]!\\n\"\n \"bne 0b \\n\"\n : \"=r\"(nn), // %0\n \"=r\"(ptr) // %1\n : \"0\"(nn),\n \"1\"(ptr),\n \"w\"(_c) // %4\n : \"cc\", \"memory\"\n );\n }\n#endif // __aarch64__\n#endif // __ARM_NEON\n for (; remain>0; remain--)\n {\n *ptr++ = _v;\n }\n}\n\ntemplate \ninline void Mat::fill(T _v)\n{\n int size = total();\n T* ptr = (T*)data;\n for (int i=0; i 0)\n {\n memcpy(m.data, data, total() * elemsize);\n }\n\n return m;\n}\n\ninline Mat Mat::reshape(int _w, Allocator* allocator) const\n{\n if (w * h * c != _w)\n return Mat();\n\n if (dims == 3 && cstep != (size_t)w * h)\n {\n Mat m;\n m.create(_w, elemsize, allocator);\n\n // flatten\n for (int i=0; i 0)\n {\n size_t totalsize = alignSize(total() * elemsize, 4);\n if (allocator)\n data = allocator->fastMalloc(totalsize + (int)sizeof(*refcount));\n else\n data = fastMalloc(totalsize + (int)sizeof(*refcount));\n refcount = (int*)(((unsigned char*)data) + totalsize);\n *refcount = 1;\n }\n}\n\ninline void Mat::create(int _w, int _h, size_t _elemsize, Allocator* _allocator)\n{\n if (dims == 2 && w == _w && h == _h && elemsize == _elemsize && allocator == _allocator)\n return;\n\n release();\n\n elemsize = _elemsize;\n allocator = _allocator;\n\n dims = 2;\n w = _w;\n h = _h;\n c = 1;\n\n cstep = w * h;\n\n if (total() > 0)\n {\n size_t totalsize = alignSize(total() * elemsize, 4);\n if (allocator)\n data = allocator->fastMalloc(totalsize + (int)sizeof(*refcount));\n else\n data = fastMalloc(totalsize + (int)sizeof(*refcount));\n refcount = (int*)(((unsigned char*)data) + totalsize);\n *refcount = 1;\n }\n}\n\ninline void Mat::create(int _w, int _h, int _c, size_t _elemsize, Allocator* _allocator)\n{\n if (dims == 3 && w == _w && h == _h && c == _c && elemsize == _elemsize && allocator == _allocator)\n return;\n\n release();\n\n elemsize = _elemsize;\n allocator = _allocator;\n\n dims = 3;\n w = _w;\n h = _h;\n c = _c;\n\n cstep = alignSize(w * h * elemsize, 16) / elemsize;\n\n if (total() > 0)\n {\n size_t totalsize = alignSize(total() * elemsize, 4);\n if (allocator)\n data = allocator->fastMalloc(totalsize + (int)sizeof(*refcount));\n else\n data = fastMalloc(totalsize + (int)sizeof(*refcount));\n refcount = (int*)(((unsigned char*)data) + totalsize);\n *refcount = 1;\n }\n}\n\ninline void Mat::addref()\n{\n if (refcount)\n NCNN_XADD(refcount, 1);\n}\n\ninline void Mat::release()\n{\n if (refcount && NCNN_XADD(refcount, -1) == 1)\n {\n if (allocator)\n allocator->fastFree(data);\n else\n fastFree(data);\n }\n\n data = 0;\n\n elemsize = 0;\n\n dims = 0;\n w = 0;\n h = 0;\n c = 0;\n\n cstep = 0;\n\n refcount = 0;\n}\n\ninline bool Mat::empty() const\n{\n return data == 0 || total() == 0;\n}\n\ninline size_t Mat::total() const\n{\n return cstep * c;\n}\n\ninline Mat Mat::channel(int c)\n{\n return Mat(w, h, (unsigned char*)data + cstep * c * elemsize, elemsize, allocator);\n}\n\ninline const Mat Mat::channel(int c) const\n{\n return Mat(w, h, (unsigned char*)data + cstep * c * elemsize, elemsize, allocator);\n}\n\ninline float* Mat::row(int y)\n{\n return (float*)data + w * y;\n}\n\ninline const float* Mat::row(int y) const\n{\n return (const float*)data + w * y;\n}\n\ntemplate \ninline T* Mat::row(int y)\n{\n return (T*)data + w * y;\n}\n\ntemplate \ninline const T* Mat::row(int y) const\n{\n return (const T*)data + w * y;\n}\n\ninline Mat Mat::channel_range(int _c, int channels)\n{\n return Mat(w, h, channels, (unsigned char*)data + cstep * _c * elemsize, elemsize, allocator);\n}\n\ninline const Mat Mat::channel_range(int _c, int channels) const\n{\n return Mat(w, h, channels, (unsigned char*)data + cstep * _c * elemsize, elemsize, allocator);\n}\n\ninline Mat Mat::row_range(int y, int rows)\n{\n return Mat(w, rows, (unsigned char*)data + w * y * elemsize, elemsize, allocator);\n}\n\ninline const Mat Mat::row_range(int y, int rows) const\n{\n return Mat(w, rows, (unsigned char*)data + w * y * elemsize, elemsize, allocator);\n}\n\ninline Mat Mat::range(int x, int n)\n{\n return Mat(n, (unsigned char*)data + x * elemsize, elemsize, allocator);\n}\n\ninline const Mat Mat::range(int x, int n) const\n{\n return Mat(n, (unsigned char*)data + x * elemsize, elemsize, allocator);\n}\n\ntemplate \ninline Mat::operator T*()\n{\n return (T*)data;\n}\n\ntemplate \ninline Mat::operator const T*() const\n{\n return (const T*)data;\n}\n\ninline float& Mat::operator[](int i)\n{\n return ((float*)data)[i];\n}\n\ninline const float& Mat::operator[](int i) const\n{\n return ((const float*)data)[i];\n}\n\n} // namespace ncnn\n\n#endif // NCNN_MAT_H\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/include/modelbin.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_MODELBIN_H\n#define NCNN_MODELBIN_H\n\n#include \n#include \"mat.h\"\n#include \"platform.h\"\n\nnamespace ncnn {\n\nclass Net;\nclass ModelBin\n{\npublic:\n // element type\n // 0 = auto\n // 1 = float32\n // 2 = float16\n // 3 = int8\n // load vec\n virtual Mat load(int w, int type) const = 0;\n // load image\n virtual Mat load(int w, int h, int type) const;\n // load dim\n virtual Mat load(int w, int h, int c, int type) const;\n};\n\n#if NCNN_STDIO\nclass ModelBinFromStdio : public ModelBin\n{\npublic:\n // construct from file\n ModelBinFromStdio(FILE* binfp);\n\n virtual Mat load(int w, int type) const;\n\nprotected:\n FILE* binfp;\n};\n#endif // NCNN_STDIO\n\nclass ModelBinFromMemory : public ModelBin\n{\npublic:\n // construct from external memory\n ModelBinFromMemory(const unsigned char*& mem);\n\n virtual Mat load(int w, int type) const;\n\nprotected:\n const unsigned char*& mem;\n};\n\nclass ModelBinFromMatArray : public ModelBin\n{\npublic:\n // construct from weight blob array\n ModelBinFromMatArray(const Mat* weights);\n\n virtual Mat load(int w, int type) const;\n\nprotected:\n mutable const Mat* weights;\n};\n\n} // namespace ncnn\n\n#endif // NCNN_MODELBIN_H\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/include/net.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_NET_H\n#define NCNN_NET_H\n\n#include \n#include \n#include \"blob.h\"\n#include \"layer.h\"\n#include \"mat.h\"\n#include \"platform.h\"\n\nnamespace ncnn {\n\nclass Extractor;\nclass Net\n{\npublic:\n // empty init\n Net();\n // clear and destroy\n ~Net();\n\n#if NCNN_STRING\n // register custom layer by layer type name\n // return 0 if success\n int register_custom_layer(const char* type, layer_creator_func creator);\n#endif // NCNN_STRING\n // register custom layer by layer type\n // return 0 if success\n int register_custom_layer(int index, layer_creator_func creator);\n\n#if NCNN_STDIO\n#if NCNN_STRING\n // load network structure from plain param file\n // return 0 if success\n int load_param(FILE* fp);\n int load_param(const char* protopath);\n int load_param_mem(const char* mem);\n#endif // NCNN_STRING\n // load network structure from binary param file\n // return 0 if success\n int load_param_bin(FILE* fp);\n int load_param_bin(const char* protopath);\n\n // load network weight data from model file\n // return 0 if success\n int load_model(FILE* fp);\n int load_model(const char* modelpath);\n#endif // NCNN_STDIO\n\n // load network structure from external memory\n // memory pointer must be 32-bit aligned\n // return bytes consumed\n int load_param(const unsigned char* mem);\n\n // reference network weight data from external memory\n // weight data is not copied but referenced\n // so external memory should be retained when used\n // memory pointer must be 32-bit aligned\n // return bytes consumed\n int load_model(const unsigned char* mem);\n\n // unload network structure and weight data\n void clear();\n\n // construct an Extractor from network\n Extractor create_extractor() const;\n\npublic:\n // enable winograd convolution optimization\n // improve convolution 3x3 stride1 performace, may consume more memory\n // changes should be applied before loading network structure and weight\n // enabled by default\n int use_winograd_convolution;\n\n // enable sgemm convolution optimization\n // improve convolution 1x1 stride1 performace, may consume more memory\n // changes should be applied before loading network structure and weight\n // enabled by default\n int use_sgemm_convolution;\n\n // enable quantized int8 inference\n // use low-precision int8 path for quantized model\n // changes should be applied before loading network structure and weight\n // enabled by default\n int use_int8_inference;\n\nprotected:\n friend class Extractor;\n#if NCNN_STRING\n int find_blob_index_by_name(const char* name) const;\n int find_layer_index_by_name(const char* name) const;\n int custom_layer_to_index(const char* type);\n Layer* create_custom_layer(const char* type);\n#endif // NCNN_STRING\n Layer* create_custom_layer(int index);\n int forward_layer(int layer_index, std::vector& blob_mats, Option& opt) const;\n\nprotected:\n std::vector blobs;\n std::vector layers;\n\n std::vector custom_layer_registry;\n};\n\nclass Extractor\n{\npublic:\n // enable light mode\n // intermediate blob will be recycled when enabled\n // enabled by default\n void set_light_mode(bool enable);\n\n // set thread count for this extractor\n // this will overwrite the global setting\n // default count is system depended\n void set_num_threads(int num_threads);\n\n // set blob memory allocator\n void set_blob_allocator(Allocator* allocator);\n\n // set workspace memory allocator\n void set_workspace_allocator(Allocator* allocator);\n\n#if NCNN_STRING\n // set input by blob name\n // return 0 if success\n int input(const char* blob_name, const Mat& in);\n\n // get result by blob name\n // return 0 if success\n int extract(const char* blob_name, Mat& feat);\n#endif // NCNN_STRING\n\n // set input by blob index\n // return 0 if success\n int input(int blob_index, const Mat& in);\n\n // get result by blob index\n // return 0 if success\n int extract(int blob_index, Mat& feat);\n\nprotected:\n friend Extractor Net::create_extractor() const;\n Extractor(const Net* net, int blob_count);\n\nprivate:\n const Net* net;\n std::vector blob_mats;\n Option opt;\n};\n\n} // namespace ncnn\n\n#endif // NCNN_NET_H\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/include/opencv.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_OPENCV_H\n#define NCNN_OPENCV_H\n\n#include \"platform.h\"\n\n#if NCNN_OPENCV\n\n#include \n#include \n#include \"mat.h\"\n\n// minimal opencv style data structure implementation\nnamespace cv\n{\n\nstruct Size\n{\n Size() : width(0), height(0) {}\n Size(int _w, int _h) : width(_w), height(_h) {}\n\n int width;\n int height;\n};\n\ntemplate\nstruct Rect_\n{\n Rect_() : x(0), y(0), width(0), height(0) {}\n Rect_(_Tp _x, _Tp _y, _Tp _w, _Tp _h) : x(_x), y(_y), width(_w), height(_h) {}\n\n _Tp x;\n _Tp y;\n _Tp width;\n _Tp height;\n\n // area\n _Tp area() const\n {\n return width * height;\n }\n};\n\ntemplate static inline Rect_<_Tp>& operator &= ( Rect_<_Tp>& a, const Rect_<_Tp>& b )\n{\n _Tp x1 = std::max(a.x, b.x), y1 = std::max(a.y, b.y);\n a.width = std::min(a.x + a.width, b.x + b.width) - x1;\n a.height = std::min(a.y + a.height, b.y + b.height) - y1;\n a.x = x1; a.y = y1;\n if( a.width <= 0 || a.height <= 0 )\n a = Rect_<_Tp>();\n return a;\n}\n\ntemplate static inline Rect_<_Tp>& operator |= ( Rect_<_Tp>& a, const Rect_<_Tp>& b )\n{\n _Tp x1 = std::min(a.x, b.x), y1 = std::min(a.y, b.y);\n a.width = std::max(a.x + a.width, b.x + b.width) - x1;\n a.height = std::max(a.y + a.height, b.y + b.height) - y1;\n a.x = x1; a.y = y1;\n return a;\n}\n\ntemplate static inline Rect_<_Tp> operator & (const Rect_<_Tp>& a, const Rect_<_Tp>& b)\n{\n Rect_<_Tp> c = a;\n return c &= b;\n}\n\ntemplate static inline Rect_<_Tp> operator | (const Rect_<_Tp>& a, const Rect_<_Tp>& b)\n{\n Rect_<_Tp> c = a;\n return c |= b;\n}\n\ntypedef Rect_ Rect;\ntypedef Rect_ Rect2f;\n\ntemplate\nstruct Point_\n{\n Point_() : x(0), y(0) {}\n Point_(_Tp _x, _Tp _y) : x(_x), y(_y) {}\n\n _Tp x;\n _Tp y;\n};\n\ntypedef Point_ Point;\ntypedef Point_ Point2f;\n\n#define CV_8UC1 1\n#define CV_8UC3 3\n#define CV_8UC4 4\n#define CV_32FC1 4\n\nstruct Mat\n{\n Mat() : data(0), refcount(0), rows(0), cols(0), c(0) {}\n\n Mat(int _rows, int _cols, int flags) : data(0), refcount(0)\n {\n create(_rows, _cols, flags);\n }\n\n // copy\n Mat(const Mat& m) : data(m.data), refcount(m.refcount)\n {\n if (refcount)\n NCNN_XADD(refcount, 1);\n\n rows = m.rows;\n cols = m.cols;\n c = m.c;\n }\n\n Mat(int _rows, int _cols, int flags, void* _data) : data((unsigned char*)_data), refcount(0)\n {\n rows = _rows;\n cols = _cols;\n c = flags;\n }\n\n ~Mat()\n {\n release();\n }\n\n // assign\n Mat& operator=(const Mat& m)\n {\n if (this == &m)\n return *this;\n\n if (m.refcount)\n NCNN_XADD(m.refcount, 1);\n\n release();\n\n data = m.data;\n refcount = m.refcount;\n\n rows = m.rows;\n cols = m.cols;\n c = m.c;\n\n return *this;\n }\n\n void create(int _rows, int _cols, int flags)\n {\n release();\n\n rows = _rows;\n cols = _cols;\n c = flags;\n\n if (total() > 0)\n {\n // refcount address must be aligned, so we expand totalsize here\n size_t totalsize = (total() + 3) >> 2 << 2;\n data = (unsigned char*)ncnn::fastMalloc(totalsize + (int)sizeof(*refcount));\n refcount = (int*)(((unsigned char*)data) + totalsize);\n *refcount = 1;\n }\n }\n\n void release()\n {\n if (refcount && NCNN_XADD(refcount, -1) == 1)\n ncnn::fastFree(data);\n\n data = 0;\n\n rows = 0;\n cols = 0;\n c = 0;\n\n refcount = 0;\n }\n\n Mat clone() const\n {\n if (empty())\n return Mat();\n\n Mat m(rows, cols, c);\n\n if (total() > 0)\n {\n memcpy(m.data, data, total());\n }\n\n return m;\n }\n\n bool empty() const { return data == 0 || total() == 0; }\n\n int channels() const { return c; }\n\n size_t total() const { return cols * rows * c; }\n\n const unsigned char* ptr(int y) const { return data + y * cols * c; }\n\n unsigned char* ptr(int y) { return data + y * cols * c; }\n\n // roi\n Mat operator()( const Rect& roi ) const\n {\n if (empty())\n return Mat();\n\n Mat m(roi.height, roi.width, c);\n\n int sy = roi.y;\n for (int y = 0; y < roi.height; y++)\n {\n const unsigned char* sptr = ptr(sy) + roi.x * c;\n unsigned char* dptr = m.ptr(y);\n memcpy(dptr, sptr, roi.width * c);\n sy++;\n }\n\n return m;\n }\n\n unsigned char* data;\n\n // pointer to the reference counter;\n // when points to user-allocated data, the pointer is NULL\n int* refcount;\n\n int rows;\n int cols;\n\n int c;\n\n};\n\n#define CV_LOAD_IMAGE_GRAYSCALE 1\n#define CV_LOAD_IMAGE_COLOR 3\nMat imread(const std::string& path, int flags);\nvoid imwrite(const std::string& path, const Mat& m);\n\n#if NCNN_PIXEL\nvoid resize(const Mat& src, Mat& dst, const Size& size, float sw = 0.f, float sh = 0.f, int flags = 0);\n#endif // NCNN_PIXEL\n\n} // namespace cv\n\n#endif // NCNN_OPENCV\n\n#endif // NCNN_OPENCV_H\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/include/paramdict.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_PARAMDICT_H\n#define NCNN_PARAMDICT_H\n\n#include \n#include \"mat.h\"\n#include \"platform.h\"\n\n// at most 20 parameters\n#define NCNN_MAX_PARAM_COUNT 20\n\nnamespace ncnn {\n\nclass Net;\nclass ParamDict\n{\npublic:\n // empty\n ParamDict();\n\n // get int\n int get(int id, int def) const;\n // get float\n float get(int id, float def) const;\n // get array\n Mat get(int id, const Mat& def) const;\n\n // set int\n void set(int id, int i);\n // set float\n void set(int id, float f);\n // set array\n void set(int id, const Mat& v);\n\npublic:\n int use_winograd_convolution;\n int use_sgemm_convolution;\n int use_int8_inference;\n\nprotected:\n friend class Net;\n\n void clear();\n\n#if NCNN_STDIO\n#if NCNN_STRING\n int load_param(FILE* fp);\n int load_param_mem(const char*& mem);\n#endif // NCNN_STRING\n int load_param_bin(FILE* fp);\n#endif // NCNN_STDIO\n int load_param(const unsigned char*& mem);\n\nprotected:\n struct\n {\n int loaded;\n union { int i; float f; };\n Mat v;\n } params[NCNN_MAX_PARAM_COUNT];\n};\n\n} // namespace ncnn\n\n#endif // NCNN_PARAMDICT_H\n" }, { "path": "MobileNetSSD_demo/app/src/main/cpp/include/platform.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_PLATFORM_H\n#define NCNN_PLATFORM_H\n\n#define NCNN_STDIO 1\n#define NCNN_STRING 1\n#define NCNN_OPENCV 0\n#define NCNN_BENCHMARK 0\n#define NCNN_PIXEL 1\n#define NCNN_PIXEL_ROTATE 0\n\n#endif // NCNN_PLATFORM_H\n" }, { "path": "MobileNetSSD_demo/app/src/main/java/com/example/che/mobilenetssd_demo/MainActivity.java", "content": "package com.example.che.mobilenetssd_demo;\n\nimport android.Manifest;\nimport android.app.Activity;\nimport android.content.Intent;\nimport android.content.pm.PackageManager;\nimport android.content.res.AssetManager;\nimport android.graphics.Bitmap;\nimport android.graphics.Canvas;\nimport android.graphics.Color;\nimport android.graphics.Paint;\nimport android.net.Uri;\nimport android.support.annotation.NonNull;\nimport android.support.annotation.Nullable;\nimport android.support.v4.app.ActivityCompat;\nimport android.support.v4.content.ContextCompat;\nimport android.support.v7.app.AppCompatActivity;\nimport android.os.Bundle;\nimport android.text.method.ScrollingMovementMethod;\nimport android.util.Log;\nimport android.view.View;\nimport android.widget.Button;\nimport android.widget.ImageView;\nimport android.widget.TextView;\nimport android.widget.Toast;\n\nimport java.io.BufferedReader;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.InputStreamReader;\nimport java.util.ArrayList;\nimport java.util.Arrays;\nimport java.util.List;\n\n\nimport com.bumptech.glide.Glide;\nimport com.bumptech.glide.load.engine.DiskCacheStrategy;\nimport com.bumptech.glide.request.RequestOptions;\n\n\npublic class MainActivity extends AppCompatActivity {\n\n private static final String TAG = MainActivity.class.getName();\n private static final int USE_PHOTO = 1001;\n private String camera_image_path;\n private ImageView show_image;\n private TextView result_text;\n private boolean load_result = false;\n private int[] ddims = {1, 3, 300, 300}; //这里的维度的值要和train model的input 一一对应\n private int model_index = 1;\n private List resultLabel = new ArrayList<>();\n private MobileNetssd mobileNetssd = new MobileNetssd(); //java接口实例化　下面直接利用java函数调用NDK c++函数\n\n @Override\n protected void onCreate(Bundle savedInstanceState) {\n super.onCreate(savedInstanceState);\n setContentView(R.layout.activity_main);\n try\n {\n initMobileNetSSD();\n } catch (IOException e) {\n Log.e(\"MainActivity\", \"initMobileNetSSD error\");\n }\n init_view();\n readCacheLabelFromLocalFile();\n}\n\n /**\n *\n * MobileNetssd初始化，也就是把model文件进行加载\n */\n private void initMobileNetSSD() throws IOException {\n byte[] param = null;\n byte[] bin = null;\n {\n //用io流读取二进制文件，最后存入到byte[]数组中\n InputStream assetsInputStream = getAssets().open(\"MobileNetSSD_deploy.param.bin\");// param： 网络结构文件\n int available = assetsInputStream.available();\n param = new byte[available];\n int byteCode = assetsInputStream.read(param);\n assetsInputStream.close();\n }\n {\n //用io流读取二进制文件，最后存入到byte上，转换为int型\n InputStream assetsInputStream = getAssets().open(\"MobileNetSSD_deploy.bin\");//bin： model文件\n int available = assetsInputStream.available();\n bin = new byte[available];\n int byteCode = assetsInputStream.read(bin);\n assetsInputStream.close();\n }\n\n load_result = mobileNetssd.Init(param, bin);// 再将文件传入java的NDK接口(c++ 代码中的init接口 )\n Log.d(\"load model\", \"MobileNetSSD_load_model_result:\" + load_result);\n }\n\n\n // initialize view\n private void init_view() {\n request_permissions();\n show_image = (ImageView) findViewById(R.id.show_image);\n result_text = (TextView) findViewById(R.id.result_text);\n result_text.setMovementMethod(ScrollingMovementMethod.getInstance());\n Button use_photo = (Button) findViewById(R.id.use_photo);\n // use photo click\n use_photo.setOnClickListener(new View.OnClickListener() {\n @Override\n public void onClick(View view) {\n if (!load_result) {\n Toast.makeText(MainActivity.this, \"never load model\", Toast.LENGTH_SHORT).show();\n return;\n }\n PhotoUtil.use_photo(MainActivity.this, USE_PHOTO);\n }\n });\n }\n\n // load label's name\n private void readCacheLabelFromLocalFile() {\n try {\n AssetManager assetManager = getApplicationContext().getAssets();\n BufferedReader reader = new BufferedReader(new InputStreamReader(assetManager.open(\"words.txt\")));//这里是label的文件\n String readLine = null;\n while ((readLine = reader.readLine()) != null) {\n resultLabel.add(readLine);\n }\n reader.close();\n } catch (Exception e) {\n Log.e(\"labelCache\", \"error \" + e);\n }\n }\n\n\n protected void onActivityResult(int requestCode, int resultCode, @Nullable Intent data) {\n String image_path;\n RequestOptions options = new RequestOptions().skipMemoryCache(true).diskCacheStrategy(DiskCacheStrategy.NONE);\n if (resultCode == Activity.RESULT_OK) {\n switch (requestCode) {\n case USE_PHOTO:\n if (data == null) {\n Log.w(TAG, \"user photo data is null\");\n return;\n }\n Uri image_uri = data.getData();\n\n //Glide.with(MainActivity.this).load(image_uri).apply(options).into(show_image);\n\n // get image path from uri\n image_path = PhotoUtil.get_path_from_URI(MainActivity.this, image_uri);\n // predict image\n predict_image(image_path);\n break;\n }\n }\n }\n\n // predict image\n private void predict_image(String image_path) {\n // picture to float array\n Bitmap bmp = PhotoUtil.getScaleBitmap(image_path);\n Bitmap rgba = bmp.copy(Bitmap.Config.ARGB_8888, true);\n // resize\n Bitmap input_bmp = Bitmap.createScaledBitmap(rgba, ddims[2], ddims[3], false);\n try {\n // Data format conversion takes too long\n // Log.d(\"inputData\", Arrays.toString(inputData));\n long start = System.currentTimeMillis();\n // get predict result\n float[] result = mobileNetssd.Detect(input_bmp);\n // time end\n long end = System.currentTimeMillis();\n Log.d(TAG, \"origin predict result:\" + Arrays.toString(result));\n long time = end - start;\n Log.d(\"result length\", \"length of result: \" + String.valueOf(result.length));\n // show predict result and time\n // float[] r = get_max_result(result);\n\n String show_text = \"result：\" + Arrays.toString(result) + \"\\nname：\" + resultLabel.get((int) result[0]) + \"\\nprobability：\" + result[1] + \"\\ntime：\" + time + \"ms\" ;\n result_text.setText(show_text);\n\n // 画布配置\n Canvas canvas = new Canvas(rgba);\n //图像上画矩形\n Paint paint = new Paint();\n paint.setColor(Color.RED);\n paint.setStyle(Paint.Style.STROKE);//不填充\n paint.setStrokeWidth(5); //线的宽度\n\n\n float get_finalresult[][] = TwoArry(result);\n Log.d(\"zhuanhuan\",get_finalresult+\"\");\n int object_num = 0;\n int num = result.length/6;// number of object\n //continue to draw rect\n for(object_num = 0; object_num < num; object_num++){\n Log.d(TAG, \"haha :\" + Arrays.toString(get_finalresult));\n // 画框\n paint.setColor(Color.RED);\n paint.setStyle(Paint.Style.STROKE);//不填充\n paint.setStrokeWidth(5); //线的宽度\n canvas.drawRect(get_finalresult[object_num][2] * rgba.getWidth(), get_finalresult[object_num][3] * rgba.getHeight(),\n get_finalresult[object_num][4] * rgba.getWidth(), get_finalresult[object_num][5] * rgba.getHeight(), paint);\n\n paint.setColor(Color.YELLOW);\n paint.setStyle(Paint.Style.FILL);//不填充\n paint.setStrokeWidth(1); //线的宽度\n canvas.drawText(resultLabel.get((int) get_finalresult[object_num][0]) + \"\\n\" + get_finalresult[object_num][1],\n get_finalresult[object_num][2]*rgba.getWidth(),get_finalresult[object_num][3]*rgba.getHeight(),paint);\n }\n\n show_image.setImageBitmap(rgba);\n\n\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n\n //一维数组转化为二维数组\n public static float[][] TwoArry(float[] inputfloat){\n int n = inputfloat.length;\n int num = inputfloat.length/6;\n float[][] outputfloat = new float[num][6];\n int k = 0;\n for(int i = 0; i < num ; i++)\n {\n int j = 0;\n\n while(j<6)\n {\n outputfloat[i][j] = inputfloat[k];\n k++;\n j++;\n }\n\n }\n\n return outputfloat;\n }\n\n /*\n // get max probability label\n private float[] get_max_result(float[] result) {\n int num_rs = result.length / 6;\n float maxProp = result[1];\n int maxI = 0;\n for(int i = 1; i permissionList = new ArrayList<>();\n if (ContextCompat.checkSelfPermission(this, Manifest.permission.CAMERA) != PackageManager.PERMISSION_GRANTED) {\n permissionList.add(Manifest.permission.CAMERA);\n }\n if (ContextCompat.checkSelfPermission(this, Manifest.permission.WRITE_EXTERNAL_STORAGE) != PackageManager.PERMISSION_GRANTED) {\n permissionList.add(Manifest.permission.WRITE_EXTERNAL_STORAGE);\n }\n if (ContextCompat.checkSelfPermission(this, Manifest.permission.READ_EXTERNAL_STORAGE) != PackageManager.PERMISSION_GRANTED) {\n permissionList.add(Manifest.permission.READ_EXTERNAL_STORAGE);\n }\n // if list is not empty will request permissions\n if (!permissionList.isEmpty()) {\n ActivityCompat.requestPermissions(this, permissionList.toArray(new String[permissionList.size()]), 1);\n }\n }\n\n @Override\n public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions, @NonNull int[] grantResults) {\n super.onRequestPermissionsResult(requestCode, permissions, grantResults);\n switch (requestCode) {\n case 1:\n if (grantResults.length > 0) {\n for (int i = 0; i < grantResults.length; i++) {\n int grantResult = grantResults[i];\n if (grantResult == PackageManager.PERMISSION_DENIED) {\n String s = permissions[i];\n Toast.makeText(this, s + \"permission was denied\", Toast.LENGTH_SHORT).show();\n }\n }\n }\n break;\n }\n }\n\n\n\n}\n\n" }, { "path": "MobileNetSSD_demo/app/src/main/java/com/example/che/mobilenetssd_demo/MobileNetssd.java", "content": "package com.example.che.mobilenetssd_demo;\n\nimport android.graphics.Bitmap;\n\n/**\n * MobileNetssd的java接口，与本地c++代码相呼应 native为本地 此文件与 MobileNetssd.cpp相呼应\n */\npublic class MobileNetssd {\n\n public native boolean Init(byte[] param, byte[] bin); // 初始化函数\n public native float[] Detect(Bitmap bitmap); // 检测函数\n // Used to load the 'native-lib' library on application startup.\n static {\n System.loadLibrary(\"MobileNetssd\");\n }\n}\n\n" }, { "path": "MobileNetSSD_demo/app/src/main/java/com/example/che/mobilenetssd_demo/PhotoUtil.java", "content": "package com.example.che.mobilenetssd_demo;\n\nimport android.app.Activity;\nimport android.content.Context;\nimport android.content.Intent;\nimport android.database.Cursor;\nimport android.graphics.Bitmap;\nimport android.graphics.BitmapFactory;\nimport android.net.Uri;\nimport android.provider.MediaStore;\n\npublic class PhotoUtil {\n // get picture in photo\n public static void use_photo(Activity activity, int requestCode) {\n Intent intent = new Intent(Intent.ACTION_PICK);\n intent.setType(\"image/*\");\n activity.startActivityForResult(intent, requestCode);\n }\n\n // get photo from Uri\n public static String get_path_from_URI(Context context, Uri uri) {\n String result;\n Cursor cursor = context.getContentResolver().query(uri, null, null, null, null);\n if (cursor == null) {\n result = uri.getPath();\n } else {\n cursor.moveToFirst();\n int idx = cursor.getColumnIndex(MediaStore.Images.ImageColumns.DATA);\n result = cursor.getString(idx);\n cursor.close();\n }\n return result;\n }\n\n // compress picture\n public static Bitmap getScaleBitmap(String filePath) {\n BitmapFactory.Options opt = new BitmapFactory.Options();\n opt.inJustDecodeBounds = true;\n BitmapFactory.decodeFile(filePath, opt);\n\n int bmpWidth = opt.outWidth;\n int bmpHeight = opt.outHeight;\n\n int maxSize = 500;\n\n // compress picture with inSampleSize\n opt.inSampleSize = 1;\n while (true) {\n if (bmpWidth / opt.inSampleSize < maxSize || bmpHeight / opt.inSampleSize < maxSize) {\n break;\n }\n opt.inSampleSize *= 2;\n }\n opt.inJustDecodeBounds = false;\n return BitmapFactory.decodeFile(filePath, opt);\n }\n}\n\n" }, { "path": "MobileNetSSD_demo/app/src/main/res/drawable/ic_launcher_background.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "MobileNetSSD_demo/app/src/main/res/drawable-v24/ic_launcher_foreground.xml", "content": "\n \n \n \n \n \n \n \n \n \n\n" }, { "path": "MobileNetSSD_demo/app/src/main/res/layout/activity_main.xml", "content": "\n\n \n \n \n \n \n\n\n" }, { "path": "MobileNetSSD_demo/app/src/main/res/mipmap-anydpi-v26/ic_launcher.xml", "content": "\n\n \n \n" }, { "path": "MobileNetSSD_demo/app/src/main/res/mipmap-anydpi-v26/ic_launcher_round.xml", "content": "\n\n \n \n" }, { "path": "MobileNetSSD_demo/app/src/main/res/values/colors.xml", "content": "\n\n #008577\n #00574B\n #D81B60\n\n" }, { "path": "MobileNetSSD_demo/app/src/main/res/values/strings.xml", "content": "\n MobileNetSSD_demo\n\n" }, { "path": "MobileNetSSD_demo/app/src/main/res/values/styles.xml", "content": "\n\n \n \n\n\n" }, { "path": "MobileNetSSD_demo/app/src/test/java/com/example/che/mobilenetssd_demo/ExampleUnitTest.java", "content": "package com.example.che.mobilenetssd_demo;\n\nimport org.junit.Test;\n\nimport static org.junit.Assert.*;\n\n/**\n * Example local unit test, which will execute on the development machine (host).\n *\n * @see Testing documentation\n */\npublic class ExampleUnitTest {\n @Test\n public void addition_isCorrect() {\n assertEquals(4, 2 + 2);\n }\n}" }, { "path": "MobileNetSSD_demo/build.gradle", "content": "// Top-level build file where you can add configuration options common to all sub-projects/modules.\n\nbuildscript {\n \n repositories {\n google()\n jcenter()\n }\n dependencies {\n classpath 'com.android.tools.build:gradle:3.2.1'\n \n\n // NOTE: Do not place your application dependencies here; they belong\n // in the individual module build.gradle files\n }\n}\n\nallprojects {\n repositories {\n google()\n jcenter()\n }\n}\n\ntask clean(type: Delete) {\n delete rootProject.buildDir\n}\n" }, { "path": "MobileNetSSD_demo/gradle/wrapper/gradle-wrapper.properties", "content": "distributionBase=GRADLE_USER_HOME\ndistributionPath=wrapper/dists\ndistributionUrl=https\\://services.gradle.org/distributions/gradle-4.6-all.zip\nzipStoreBase=GRADLE_USER_HOME\nzipStorePath=wrapper/dists\n" }, { "path": "MobileNetSSD_demo/gradle.properties", "content": "# Project-wide Gradle settings.\n# IDE (e.g. Android Studio) users:\n# Gradle settings configured through the IDE *will override*\n# any settings specified in this file.\n# For more details on how to configure your build environment visit\n# http://www.gradle.org/docs/current/userguide/build_environment.html\n# Specifies the JVM arguments used for the daemon process.\n# The setting is particularly useful for tweaking memory settings.\norg.gradle.jvmargs=-Xmx1536m\n# When configured, Gradle will run in incubating parallel mode.\n# This option should only be used with decoupled projects. More details, visit\n# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects\n# org.gradle.parallel=true\n\n\n" }, { "path": "MobileNetSSD_demo/gradlew", "content": "#!/usr/bin/env sh\n\n##############################################################################\n##\n## Gradle start up script for UN*X\n##\n##############################################################################\n\n# Attempt to set APP_HOME\n# Resolve links: $0 may be a link\nPRG=\"$0\"\n# Need this for relative symlinks.\nwhile [ -h \"$PRG\" ] ; do\n ls=`ls -ld \"$PRG\"`\n link=`expr \"$ls\" : '.*-> \\(.*\\)$'`\n if expr \"$link\" : '/.*' > /dev/null; then\n PRG=\"$link\"\n else\n PRG=`dirname \"$PRG\"`\"/$link\"\n fi\ndone\nSAVED=\"`pwd`\"\ncd \"`dirname \\\"$PRG\\\"`/\" >/dev/null\nAPP_HOME=\"`pwd -P`\"\ncd \"$SAVED\" >/dev/null\n\nAPP_NAME=\"Gradle\"\nAPP_BASE_NAME=`basename \"$0\"`\n\n# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.\nDEFAULT_JVM_OPTS=\"\"\n\n# Use the maximum available, or set MAX_FD != -1 to use that value.\nMAX_FD=\"maximum\"\n\nwarn () {\n echo \"$*\"\n}\n\ndie () {\n echo\n echo \"$*\"\n echo\n exit 1\n}\n\n# OS specific support (must be 'true' or 'false').\ncygwin=false\nmsys=false\ndarwin=false\nnonstop=false\ncase \"`uname`\" in\n CYGWIN* )\n cygwin=true\n ;;\n Darwin* )\n darwin=true\n ;;\n MINGW* )\n msys=true\n ;;\n NONSTOP* )\n nonstop=true\n ;;\nesac\n\nCLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar\n\n# Determine the Java command to use to start the JVM.\nif [ -n \"$JAVA_HOME\" ] ; then\n if [ -x \"$JAVA_HOME/jre/sh/java\" ] ; then\n # IBM's JDK on AIX uses strange locations for the executables\n JAVACMD=\"$JAVA_HOME/jre/sh/java\"\n else\n JAVACMD=\"$JAVA_HOME/bin/java\"\n fi\n if [ ! -x \"$JAVACMD\" ] ; then\n die \"ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME\n\nPlease set the JAVA_HOME variable in your environment to match the\nlocation of your Java installation.\"\n fi\nelse\n JAVACMD=\"java\"\n which java >/dev/null 2>&1 || die \"ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.\n\nPlease set the JAVA_HOME variable in your environment to match the\nlocation of your Java installation.\"\nfi\n\n# Increase the maximum file descriptors if we can.\nif [ \"$cygwin\" = \"false\" -a \"$darwin\" = \"false\" -a \"$nonstop\" = \"false\" ] ; then\n MAX_FD_LIMIT=`ulimit -H -n`\n if [ $? -eq 0 ] ; then\n if [ \"$MAX_FD\" = \"maximum\" -o \"$MAX_FD\" = \"max\" ] ; then\n MAX_FD=\"$MAX_FD_LIMIT\"\n fi\n ulimit -n $MAX_FD\n if [ $? -ne 0 ] ; then\n warn \"Could not set maximum file descriptor limit: $MAX_FD\"\n fi\n else\n warn \"Could not query maximum file descriptor limit: $MAX_FD_LIMIT\"\n fi\nfi\n\n# For Darwin, add options to specify how the application appears in the dock\nif $darwin; then\n GRADLE_OPTS=\"$GRADLE_OPTS \\\"-Xdock:name=$APP_NAME\\\" \\\"-Xdock:icon=$APP_HOME/media/gradle.icns\\\"\"\nfi\n\n# For Cygwin, switch paths to Windows format before running java\nif $cygwin ; then\n APP_HOME=`cygpath --path --mixed \"$APP_HOME\"`\n CLASSPATH=`cygpath --path --mixed \"$CLASSPATH\"`\n JAVACMD=`cygpath --unix \"$JAVACMD\"`\n\n # We build the pattern for arguments to be converted via cygpath\n ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`\n SEP=\"\"\n for dir in $ROOTDIRSRAW ; do\n ROOTDIRS=\"$ROOTDIRS$SEP$dir\"\n SEP=\"|\"\n done\n OURCYGPATTERN=\"(^($ROOTDIRS))\"\n # Add a user-defined pattern to the cygpath arguments\n if [ \"$GRADLE_CYGPATTERN\" != \"\" ] ; then\n OURCYGPATTERN=\"$OURCYGPATTERN|($GRADLE_CYGPATTERN)\"\n fi\n # Now convert the arguments - kludge to limit ourselves to /bin/sh\n i=0\n for arg in \"$@\" ; do\n CHECK=`echo \"$arg\"|egrep -c \"$OURCYGPATTERN\" -`\n CHECK2=`echo \"$arg\"|egrep -c \"^-\"` ### Determine if an option\n\n if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then ### Added a condition\n eval `echo args$i`=`cygpath --path --ignore --mixed \"$arg\"`\n else\n eval `echo args$i`=\"\\\"$arg\\\"\"\n fi\n i=$((i+1))\n done\n case $i in\n (0) set -- ;;\n (1) set -- \"$args0\" ;;\n (2) set -- \"$args0\" \"$args1\" ;;\n (3) set -- \"$args0\" \"$args1\" \"$args2\" ;;\n (4) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" ;;\n (5) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" ;;\n (6) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" ;;\n (7) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" \"$args6\" ;;\n (8) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" \"$args6\" \"$args7\" ;;\n (9) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" \"$args6\" \"$args7\" \"$args8\" ;;\n esac\nfi\n\n# Escape application args\nsave () {\n for i do printf %s\\\\n \"$i\" | sed \"s/'/'\\\\\\\\''/g;1s/^/'/;\\$s/\\$/' \\\\\\\\/\" ; done\n echo \" \"\n}\nAPP_ARGS=$(save \"$@\")\n\n# Collect all arguments for the java command, following the shell quoting and substitution rules\neval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS \"\\\"-Dorg.gradle.appname=$APP_BASE_NAME\\\"\" -classpath \"\\\"$CLASSPATH\\\"\" org.gradle.wrapper.GradleWrapperMain \"$APP_ARGS\"\n\n# by default we should be in the correct project dir, but when run from Finder on Mac, the cwd is wrong\nif [ \"$(uname)\" = \"Darwin\" ] && [ \"$HOME\" = \"$PWD\" ]; then\n cd \"$(dirname \"$0\")\"\nfi\n\nexec \"$JAVACMD\" \"$@\"\n" }, { "path": "MobileNetSSD_demo/gradlew.bat", "content": "@if \"%DEBUG%\" == \"\" @echo off\r\n@rem ##########################################################################\r\n@rem\r\n@rem Gradle startup script for Windows\r\n@rem\r\n@rem ##########################################################################\r\n\r\n@rem Set local scope for the variables with windows NT shell\r\nif \"%OS%\"==\"Windows_NT\" setlocal\r\n\r\nset DIRNAME=%~dp0\r\nif \"%DIRNAME%\" == \"\" set DIRNAME=.\r\nset APP_BASE_NAME=%~n0\r\nset APP_HOME=%DIRNAME%\r\n\r\n@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.\r\nset DEFAULT_JVM_OPTS=\r\n\r\n@rem Find java.exe\r\nif defined JAVA_HOME goto findJavaFromJavaHome\r\n\r\nset JAVA_EXE=java.exe\r\n%JAVA_EXE% -version >NUL 2>&1\r\nif \"%ERRORLEVEL%\" == \"0\" goto init\r\n\r\necho.\r\necho ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.\r\necho.\r\necho Please set the JAVA_HOME variable in your environment to match the\r\necho location of your Java installation.\r\n\r\ngoto fail\r\n\r\n:findJavaFromJavaHome\r\nset JAVA_HOME=%JAVA_HOME:\"=%\r\nset JAVA_EXE=%JAVA_HOME%/bin/java.exe\r\n\r\nif exist \"%JAVA_EXE%\" goto init\r\n\r\necho.\r\necho ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%\r\necho.\r\necho Please set the JAVA_HOME variable in your environment to match the\r\necho location of your Java installation.\r\n\r\ngoto fail\r\n\r\n:init\r\n@rem Get command-line arguments, handling Windows variants\r\n\r\nif not \"%OS%\" == \"Windows_NT\" goto win9xME_args\r\n\r\n:win9xME_args\r\n@rem Slurp the command line arguments.\r\nset CMD_LINE_ARGS=\r\nset _SKIP=2\r\n\r\n:win9xME_args_slurp\r\nif \"x%~1\" == \"x\" goto execute\r\n\r\nset CMD_LINE_ARGS=%*\r\n\r\n:execute\r\n@rem Setup the command line\r\n\r\nset CLASSPATH=%APP_HOME%\\gradle\\wrapper\\gradle-wrapper.jar\r\n\r\n@rem Execute Gradle\r\n\"%JAVA_EXE%\" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% \"-Dorg.gradle.appname=%APP_BASE_NAME%\" -classpath \"%CLASSPATH%\" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%\r\n\r\n:end\r\n@rem End local scope for the variables with windows NT shell\r\nif \"%ERRORLEVEL%\"==\"0\" goto mainEnd\r\n\r\n:fail\r\nrem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of\r\nrem the _cmd.exe /c_ return code!\r\nif not \"\" == \"%GRADLE_EXIT_CONSOLE%\" exit 1\r\nexit /b 1\r\n\r\n:mainEnd\r\nif \"%OS%\"==\"Windows_NT\" endlocal\r\n\r\n:omega\r\n" }, { "path": "MobileNetSSD_demo/settings.gradle", "content": "include ':app'\n" }, { "path": "MobileNetSSD_demo_single/.gitignore", "content": "*.iml\n.gradle\n/local.properties\n/.idea/caches/build_file_checksums.ser\n/.idea/libraries\n/.idea/modules.xml\n/.idea/workspace.xml\n.DS_Store\n/build\n/captures\n.externalNativeBuild\n" }, { "path": "MobileNetSSD_demo_single/.idea/codeStyles/Project.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "MobileNetSSD_demo_single/.idea/gradle.xml", "content": "\n\n \n \n \n" }, { "path": "MobileNetSSD_demo_single/.idea/misc.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n" }, { "path": "MobileNetSSD_demo_single/app/.gitignore", "content": "/build\n" }, { "path": "MobileNetSSD_demo_single/app/CMakeLists.txt", "content": "# For more information about using CMake with Android Studio, read the\n# documentation: https://d.android.com/studio/projects/add-native-code.html\n\n# Sets the minimum version of CMake required to build the native library.\n\ncmake_minimum_required(VERSION 3.4.1)\n\n# Creates and names a library, sets it as either STATIC\n# or SHARED, and provides the relative paths to its source code.\n# You can define multiple libraries, and CMake builds them for you.\n# Gradle automatically packages shared libraries with your APK.\n\n##需要添加\nset(ncnn_lib ${CMAKE_SOURCE_DIR}/src/main/jniLibs/armeabi-v7a/libncnn.a)\nadd_library (ncnn_lib STATIC IMPORTED)\nset_target_properties(ncnn_lib PROPERTIES IMPORTED_LOCATION ${ncnn_lib})\n\nadd_library( # Sets the name of the library.\n MobileNetssd ## 为生成.so的文字最好直接和.c名字一样,需要更改\n\n # Sets the library as a shared library.\n SHARED\n\n # Provides a relative path to your source file(s).\n src/main/cpp/MobileNetssd.cpp)##cpp文件的name\n\n# Searches for a specified prebuilt library and stores the path as a\n# variable. Because CMake includes system libraries in the search path by\n# default, you only need to specify the name of the public NDK library\n# you want to add. CMake verifies that the library exists before\n# completing its build.\n\nfind_library( # Sets the name of the path variable.\n log-lib\n\n # Specifies the name of the NDK library that\n # you want CMake to locate.\n log)\n\n# Specifies libraries CMake should link to your target library. You\n# can link multiple libraries, such as libraries you define in this\n# build script, prebuilt third-party libraries, or system libraries.\n\ntarget_link_libraries( # Specifies the target library.\n ##以下三个都要添加\n MobileNetssd #和上面一样\n ncnn_lib #这个ncnn的lib的add\n jnigraphics #这个jni也需要add\n\n # Links the target library to the log library\n # included in the NDK.\n ${log-lib})" }, { "path": "MobileNetSSD_demo_single/app/build.gradle", "content": "apply plugin: 'com.android.application'\n\nandroid {\n compileSdkVersion 28\n defaultConfig {\n applicationId \"com.example.che.mobilenetssd_demo\"\n minSdkVersion 15\n targetSdkVersion 28\n versionCode 1\n versionName \"1.0\"\n testInstrumentationRunner \"android.support.test.runner.AndroidJUnitRunner\"\n externalNativeBuild {\n cmake {\n cppFlags \"-std=c++11 -fopenmp\"//c++,多线程 需要添加need to add\n abiFilters \"armeabi-v7a\" // 手机的硬件架构，基本所有的硬件都适配\n }\n }\n }\n buildTypes {\n release {\n minifyEnabled false\n proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'\n }\n }\n externalNativeBuild {\n cmake {\n path \"CMakeLists.txt\"\n }\n }\n\n // 需要添加 把 .a文件导入, .a为 ncnn make intall生成的里面的.a文件\n sourceSets {\n main {\n jniLibs.srcDirs = [\"src/main/jniLibs\"]\n jni.srcDirs = ['src/cpp']\n }\n }\n}\n\ndependencies {\n implementation fileTree(dir: 'libs', include: ['*.jar'])\n implementation 'com.android.support:appcompat-v7:28.0.0'\n implementation 'com.android.support.constraint:constraint-layout:1.1.3'\n testImplementation 'junit:junit:4.12'\n implementation 'com.github.bumptech.glide:glide:4.3.1' // need to add增加图片类 bumptech，build自动红线消失\n androidTestImplementation 'com.android.support.test:runner:1.0.2'\n androidTestImplementation 'com.android.support.test.espresso:espresso-core:3.0.2'\n}\n" }, { "path": "MobileNetSSD_demo_single/app/proguard-rules.pro", "content": "# Add project specific ProGuard rules here.\n# You can control the set of applied configuration files using the\n# proguardFiles setting in build.gradle.\n#\n# For more details, see\n# http://developer.android.com/guide/developing/tools/proguard.html\n\n# If your project uses WebView with JS, uncomment the following\n# and specify the fully qualified class name to the JavaScript interface\n# class:\n#-keepclassmembers class fqcn.of.javascript.interface.for.webview {\n# public *;\n#}\n\n# Uncomment this to preserve the line number information for\n# debugging stack traces.\n#-keepattributes SourceFile,LineNumberTable\n\n# If you keep the line number information, uncomment this to\n# hide the original source file name.\n#-renamesourcefileattribute SourceFile\n" }, { "path": "MobileNetSSD_demo_single/app/src/androidTest/java/com/example/che/mobilenetssd_demo/ExampleInstrumentedTest.java", "content": "package com.example.che.mobilenetssd_demo;\n\nimport android.content.Context;\nimport android.support.test.InstrumentationRegistry;\nimport android.support.test.runner.AndroidJUnit4;\n\nimport org.junit.Test;\nimport org.junit.runner.RunWith;\n\nimport static org.junit.Assert.*;\n\n/**\n * Instrumented test, which will execute on an Android device.\n *\n * @see Testing documentation\n */\n@RunWith(AndroidJUnit4.class)\npublic class ExampleInstrumentedTest {\n @Test\n public void useAppContext() {\n // Context of the app under test.\n Context appContext = InstrumentationRegistry.getTargetContext();\n\n assertEquals(\"com.example.che.mobilenetssd_demo\", appContext.getPackageName());\n }\n}\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/AndroidManifest.xml", "content": "\n\n\n \n \n \n\n \n \n \n \n\n \n \n \n \n\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/assets/words.txt", "content": "background\naeroplane\nbicycle\nbird\nboat\nbottle\nbus\ncar\ncat\nchair\ncow\ndiningtable\ndog\nhorse\nmotorbike\nperson\npottedplant\nsheep\nsofa\ntrain\ntvmonitor" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/MobileNetSSD_deploy.id.h", "content": "#ifndef NCNN_INCLUDE_GUARD_MobileNetSSD_deploy_id_h\n#define NCNN_INCLUDE_GUARD_MobileNetSSD_deploy_id_h\nnamespace MobileNetSSD_deploy_param_id {\nconst int LAYER_input = 0;\nconst int BLOB_data = 0;\nconst int LAYER_splitncnn_0 = 1;\nconst int BLOB_data_splitncnn_0 = 1;\nconst int BLOB_data_splitncnn_1 = 2;\nconst int BLOB_data_splitncnn_2 = 3;\nconst int BLOB_data_splitncnn_3 = 4;\nconst int BLOB_data_splitncnn_4 = 5;\nconst int BLOB_data_splitncnn_5 = 6;\nconst int BLOB_data_splitncnn_6 = 7;\nconst int LAYER_conv0 = 2;\nconst int BLOB_conv0 = 8;\nconst int LAYER_conv0_relu = 3;\nconst int BLOB_conv0_conv0_relu = 9;\nconst int LAYER_conv1_dw = 4;\nconst int BLOB_conv1_dw = 10;\nconst int LAYER_conv1_dw_relu = 5;\nconst int BLOB_conv1_dw_conv1_dw_relu = 11;\nconst int LAYER_conv1 = 6;\nconst int BLOB_conv1 = 12;\nconst int LAYER_conv1_relu = 7;\nconst int BLOB_conv1_conv1_relu = 13;\nconst int LAYER_conv2_dw = 8;\nconst int BLOB_conv2_dw = 14;\nconst int LAYER_conv2_dw_relu = 9;\nconst int BLOB_conv2_dw_conv2_dw_relu = 15;\nconst int LAYER_conv2 = 10;\nconst int BLOB_conv2 = 16;\nconst int LAYER_conv2_relu = 11;\nconst int BLOB_conv2_conv2_relu = 17;\nconst int LAYER_conv3_dw = 12;\nconst int BLOB_conv3_dw = 18;\nconst int LAYER_conv3_dw_relu = 13;\nconst int BLOB_conv3_dw_conv3_dw_relu = 19;\nconst int LAYER_conv3 = 14;\nconst int BLOB_conv3 = 20;\nconst int LAYER_conv3_relu = 15;\nconst int BLOB_conv3_conv3_relu = 21;\nconst int LAYER_conv4_dw = 16;\nconst int BLOB_conv4_dw = 22;\nconst int LAYER_conv4_dw_relu = 17;\nconst int BLOB_conv4_dw_conv4_dw_relu = 23;\nconst int LAYER_conv4 = 18;\nconst int BLOB_conv4 = 24;\nconst int LAYER_conv4_relu = 19;\nconst int BLOB_conv4_conv4_relu = 25;\nconst int LAYER_conv5_dw = 20;\nconst int BLOB_conv5_dw = 26;\nconst int LAYER_conv5_dw_relu = 21;\nconst int BLOB_conv5_dw_conv5_dw_relu = 27;\nconst int LAYER_conv5 = 22;\nconst int BLOB_conv5 = 28;\nconst int LAYER_conv5_relu = 23;\nconst int BLOB_conv5_conv5_relu = 29;\nconst int LAYER_conv6_dw = 24;\nconst int BLOB_conv6_dw = 30;\nconst int LAYER_conv6_dw_relu = 25;\nconst int BLOB_conv6_dw_conv6_dw_relu = 31;\nconst int LAYER_conv6 = 26;\nconst int BLOB_conv6 = 32;\nconst int LAYER_conv6_relu = 27;\nconst int BLOB_conv6_conv6_relu = 33;\nconst int LAYER_conv7_dw = 28;\nconst int BLOB_conv7_dw = 34;\nconst int LAYER_conv7_dw_relu = 29;\nconst int BLOB_conv7_dw_conv7_dw_relu = 35;\nconst int LAYER_conv7 = 30;\nconst int BLOB_conv7 = 36;\nconst int LAYER_conv7_relu = 31;\nconst int BLOB_conv7_conv7_relu = 37;\nconst int LAYER_conv8_dw = 32;\nconst int BLOB_conv8_dw = 38;\nconst int LAYER_conv8_dw_relu = 33;\nconst int BLOB_conv8_dw_conv8_dw_relu = 39;\nconst int LAYER_conv8 = 34;\nconst int BLOB_conv8 = 40;\nconst int LAYER_conv8_relu = 35;\nconst int BLOB_conv8_conv8_relu = 41;\nconst int LAYER_conv9_dw = 36;\nconst int BLOB_conv9_dw = 42;\nconst int LAYER_conv9_dw_relu = 37;\nconst int BLOB_conv9_dw_conv9_dw_relu = 43;\nconst int LAYER_conv9 = 38;\nconst int BLOB_conv9 = 44;\nconst int LAYER_conv9_relu = 39;\nconst int BLOB_conv9_conv9_relu = 45;\nconst int LAYER_conv10_dw = 40;\nconst int BLOB_conv10_dw = 46;\nconst int LAYER_conv10_dw_relu = 41;\nconst int BLOB_conv10_dw_conv10_dw_relu = 47;\nconst int LAYER_conv10 = 42;\nconst int BLOB_conv10 = 48;\nconst int LAYER_conv10_relu = 43;\nconst int BLOB_conv10_conv10_relu = 49;\nconst int LAYER_conv11_dw = 44;\nconst int BLOB_conv11_dw = 50;\nconst int LAYER_conv11_dw_relu = 45;\nconst int BLOB_conv11_dw_conv11_dw_relu = 51;\nconst int LAYER_conv11 = 46;\nconst int BLOB_conv11 = 52;\nconst int LAYER_conv11_relu = 47;\nconst int BLOB_conv11_conv11_relu = 53;\nconst int LAYER_splitncnn_1 = 48;\nconst int BLOB_conv11_conv11_relu_splitncnn_0 = 54;\nconst int BLOB_conv11_conv11_relu_splitncnn_1 = 55;\nconst int BLOB_conv11_conv11_relu_splitncnn_2 = 56;\nconst int BLOB_conv11_conv11_relu_splitncnn_3 = 57;\nconst int LAYER_conv12_dw = 49;\nconst int BLOB_conv12_dw = 58;\nconst int LAYER_conv12_dw_relu = 50;\nconst int BLOB_conv12_dw_conv12_dw_relu = 59;\nconst int LAYER_conv12 = 51;\nconst int BLOB_conv12 = 60;\nconst int LAYER_conv12_relu = 52;\nconst int BLOB_conv12_conv12_relu = 61;\nconst int LAYER_conv13_dw = 53;\nconst int BLOB_conv13_dw = 62;\nconst int LAYER_conv13_dw_relu = 54;\nconst int BLOB_conv13_dw_conv13_dw_relu = 63;\nconst int LAYER_conv13 = 55;\nconst int BLOB_conv13 = 64;\nconst int LAYER_conv13_relu = 56;\nconst int BLOB_conv13_conv13_relu = 65;\nconst int LAYER_splitncnn_2 = 57;\nconst int BLOB_conv13_conv13_relu_splitncnn_0 = 66;\nconst int BLOB_conv13_conv13_relu_splitncnn_1 = 67;\nconst int BLOB_conv13_conv13_relu_splitncnn_2 = 68;\nconst int BLOB_conv13_conv13_relu_splitncnn_3 = 69;\nconst int LAYER_conv14_1 = 58;\nconst int BLOB_conv14_1 = 70;\nconst int LAYER_conv14_1_relu = 59;\nconst int BLOB_conv14_1_conv14_1_relu = 71;\nconst int LAYER_conv14_2 = 60;\nconst int BLOB_conv14_2 = 72;\nconst int LAYER_conv14_2_relu = 61;\nconst int BLOB_conv14_2_conv14_2_relu = 73;\nconst int LAYER_splitncnn_3 = 62;\nconst int BLOB_conv14_2_conv14_2_relu_splitncnn_0 = 74;\nconst int BLOB_conv14_2_conv14_2_relu_splitncnn_1 = 75;\nconst int BLOB_conv14_2_conv14_2_relu_splitncnn_2 = 76;\nconst int BLOB_conv14_2_conv14_2_relu_splitncnn_3 = 77;\nconst int LAYER_conv15_1 = 63;\nconst int BLOB_conv15_1 = 78;\nconst int LAYER_conv15_1_relu = 64;\nconst int BLOB_conv15_1_conv15_1_relu = 79;\nconst int LAYER_conv15_2 = 65;\nconst int BLOB_conv15_2 = 80;\nconst int LAYER_conv15_2_relu = 66;\nconst int BLOB_conv15_2_conv15_2_relu = 81;\nconst int LAYER_splitncnn_4 = 67;\nconst int BLOB_conv15_2_conv15_2_relu_splitncnn_0 = 82;\nconst int BLOB_conv15_2_conv15_2_relu_splitncnn_1 = 83;\nconst int BLOB_conv15_2_conv15_2_relu_splitncnn_2 = 84;\nconst int BLOB_conv15_2_conv15_2_relu_splitncnn_3 = 85;\nconst int LAYER_conv16_1 = 68;\nconst int BLOB_conv16_1 = 86;\nconst int LAYER_conv16_1_relu = 69;\nconst int BLOB_conv16_1_conv16_1_relu = 87;\nconst int LAYER_conv16_2 = 70;\nconst int BLOB_conv16_2 = 88;\nconst int LAYER_conv16_2_relu = 71;\nconst int BLOB_conv16_2_conv16_2_relu = 89;\nconst int LAYER_splitncnn_5 = 72;\nconst int BLOB_conv16_2_conv16_2_relu_splitncnn_0 = 90;\nconst int BLOB_conv16_2_conv16_2_relu_splitncnn_1 = 91;\nconst int BLOB_conv16_2_conv16_2_relu_splitncnn_2 = 92;\nconst int BLOB_conv16_2_conv16_2_relu_splitncnn_3 = 93;\nconst int LAYER_conv17_1 = 73;\nconst int BLOB_conv17_1 = 94;\nconst int LAYER_conv17_1_relu = 74;\nconst int BLOB_conv17_1_conv17_1_relu = 95;\nconst int LAYER_conv17_2 = 75;\nconst int BLOB_conv17_2 = 96;\nconst int LAYER_conv17_2_relu = 76;\nconst int BLOB_conv17_2_conv17_2_relu = 97;\nconst int LAYER_splitncnn_6 = 77;\nconst int BLOB_conv17_2_conv17_2_relu_splitncnn_0 = 98;\nconst int BLOB_conv17_2_conv17_2_relu_splitncnn_1 = 99;\nconst int BLOB_conv17_2_conv17_2_relu_splitncnn_2 = 100;\nconst int LAYER_conv11_mbox_loc = 78;\nconst int BLOB_conv11_mbox_loc = 101;\nconst int LAYER_conv11_mbox_loc_perm = 79;\nconst int BLOB_conv11_mbox_loc_perm = 102;\nconst int LAYER_conv11_mbox_loc_flat = 80;\nconst int BLOB_conv11_mbox_loc_flat = 103;\nconst int LAYER_conv11_mbox_conf = 81;\nconst int BLOB_conv11_mbox_conf = 104;\nconst int LAYER_conv11_mbox_conf_perm = 82;\nconst int BLOB_conv11_mbox_conf_perm = 105;\nconst int LAYER_conv11_mbox_conf_flat = 83;\nconst int BLOB_conv11_mbox_conf_flat = 106;\nconst int LAYER_conv11_mbox_priorbox = 84;\nconst int BLOB_conv11_mbox_priorbox = 107;\nconst int LAYER_conv13_mbox_loc = 85;\nconst int BLOB_conv13_mbox_loc = 108;\nconst int LAYER_conv13_mbox_loc_perm = 86;\nconst int BLOB_conv13_mbox_loc_perm = 109;\nconst int LAYER_conv13_mbox_loc_flat = 87;\nconst int BLOB_conv13_mbox_loc_flat = 110;\nconst int LAYER_conv13_mbox_conf = 88;\nconst int BLOB_conv13_mbox_conf = 111;\nconst int LAYER_conv13_mbox_conf_perm = 89;\nconst int BLOB_conv13_mbox_conf_perm = 112;\nconst int LAYER_conv13_mbox_conf_flat = 90;\nconst int BLOB_conv13_mbox_conf_flat = 113;\nconst int LAYER_conv13_mbox_priorbox = 91;\nconst int BLOB_conv13_mbox_priorbox = 114;\nconst int LAYER_conv14_2_mbox_loc = 92;\nconst int BLOB_conv14_2_mbox_loc = 115;\nconst int LAYER_conv14_2_mbox_loc_perm = 93;\nconst int BLOB_conv14_2_mbox_loc_perm = 116;\nconst int LAYER_conv14_2_mbox_loc_flat = 94;\nconst int BLOB_conv14_2_mbox_loc_flat = 117;\nconst int LAYER_conv14_2_mbox_conf = 95;\nconst int BLOB_conv14_2_mbox_conf = 118;\nconst int LAYER_conv14_2_mbox_conf_perm = 96;\nconst int BLOB_conv14_2_mbox_conf_perm = 119;\nconst int LAYER_conv14_2_mbox_conf_flat = 97;\nconst int BLOB_conv14_2_mbox_conf_flat = 120;\nconst int LAYER_conv14_2_mbox_priorbox = 98;\nconst int BLOB_conv14_2_mbox_priorbox = 121;\nconst int LAYER_conv15_2_mbox_loc = 99;\nconst int BLOB_conv15_2_mbox_loc = 122;\nconst int LAYER_conv15_2_mbox_loc_perm = 100;\nconst int BLOB_conv15_2_mbox_loc_perm = 123;\nconst int LAYER_conv15_2_mbox_loc_flat = 101;\nconst int BLOB_conv15_2_mbox_loc_flat = 124;\nconst int LAYER_conv15_2_mbox_conf = 102;\nconst int BLOB_conv15_2_mbox_conf = 125;\nconst int LAYER_conv15_2_mbox_conf_perm = 103;\nconst int BLOB_conv15_2_mbox_conf_perm = 126;\nconst int LAYER_conv15_2_mbox_conf_flat = 104;\nconst int BLOB_conv15_2_mbox_conf_flat = 127;\nconst int LAYER_conv15_2_mbox_priorbox = 105;\nconst int BLOB_conv15_2_mbox_priorbox = 128;\nconst int LAYER_conv16_2_mbox_loc = 106;\nconst int BLOB_conv16_2_mbox_loc = 129;\nconst int LAYER_conv16_2_mbox_loc_perm = 107;\nconst int BLOB_conv16_2_mbox_loc_perm = 130;\nconst int LAYER_conv16_2_mbox_loc_flat = 108;\nconst int BLOB_conv16_2_mbox_loc_flat = 131;\nconst int LAYER_conv16_2_mbox_conf = 109;\nconst int BLOB_conv16_2_mbox_conf = 132;\nconst int LAYER_conv16_2_mbox_conf_perm = 110;\nconst int BLOB_conv16_2_mbox_conf_perm = 133;\nconst int LAYER_conv16_2_mbox_conf_flat = 111;\nconst int BLOB_conv16_2_mbox_conf_flat = 134;\nconst int LAYER_conv16_2_mbox_priorbox = 112;\nconst int BLOB_conv16_2_mbox_priorbox = 135;\nconst int LAYER_conv17_2_mbox_loc = 113;\nconst int BLOB_conv17_2_mbox_loc = 136;\nconst int LAYER_conv17_2_mbox_loc_perm = 114;\nconst int BLOB_conv17_2_mbox_loc_perm = 137;\nconst int LAYER_conv17_2_mbox_loc_flat = 115;\nconst int BLOB_conv17_2_mbox_loc_flat = 138;\nconst int LAYER_conv17_2_mbox_conf = 116;\nconst int BLOB_conv17_2_mbox_conf = 139;\nconst int LAYER_conv17_2_mbox_conf_perm = 117;\nconst int BLOB_conv17_2_mbox_conf_perm = 140;\nconst int LAYER_conv17_2_mbox_conf_flat = 118;\nconst int BLOB_conv17_2_mbox_conf_flat = 141;\nconst int LAYER_conv17_2_mbox_priorbox = 119;\nconst int BLOB_conv17_2_mbox_priorbox = 142;\nconst int LAYER_mbox_loc = 120;\nconst int BLOB_mbox_loc = 143;\nconst int LAYER_mbox_conf = 121;\nconst int BLOB_mbox_conf = 144;\nconst int LAYER_mbox_priorbox = 122;\nconst int BLOB_mbox_priorbox = 145;\nconst int LAYER_mbox_conf_reshape = 123;\nconst int BLOB_mbox_conf_reshape = 146;\nconst int LAYER_mbox_conf_softmax = 124;\nconst int BLOB_mbox_conf_softmax = 147;\nconst int LAYER_mbox_conf_flatten = 125;\nconst int BLOB_mbox_conf_flatten = 148;\nconst int LAYER_detection_out = 126;\nconst int BLOB_detection_out = 149;\n} // namespace MobileNetSSD_deploy_param_id\n#endif // NCNN_INCLUDE_GUARD_MobileNetSSD_deploy_id_h\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/MobileNetssd.cpp", "content": "#include \n#include \n#include \n#include \n#include \n\n// ncnn\n#include \"include/opencv.h\"\n#include \"MobileNetSSD_deploy.id.h\" //这里看成自己的id.h\n#include \n#include \n#include \"include/net.h\"\n\nstatic ncnn::UnlockedPoolAllocator g_blob_pool_allocator;\nstatic ncnn::PoolAllocator g_workspace_pool_allocator;\n\nstatic ncnn::Mat ncnn_param;\nstatic ncnn::Mat ncnn_bin;\nstatic ncnn::Net ncnn_net;\n\nextern \"C\" {\n\n\n// public native boolean Init(byte[] words,byte[] param, byte[] bin);　　原函数形式（c++） 以下形式为ndk的c++形式\nJNIEXPORT jboolean JNICALL\nJava_com_example_che_mobilenetssd_1demo_MobileNetssd_Init(JNIEnv *env, jobject obj, jbyteArray param, jbyteArray bin) {\n __android_log_print(ANDROID_LOG_DEBUG, \"MobileNetssd\", \"enter the jni func\");\n // init param\n {\n int len = env->GetArrayLength(param);\n ncnn_param.create(len, (size_t) 1u);\n env->GetByteArrayRegion(param, 0, len, (jbyte *) ncnn_param);\n int ret = ncnn_net.load_param((const unsigned char *) ncnn_param);\n __android_log_print(ANDROID_LOG_DEBUG, \"MobileNetssd\", \"load_param %d %d\", ret, len);\n }\n\n // init bin\n {\n int len = env->GetArrayLength(bin);\n ncnn_bin.create(len, (size_t) 1u);\n env->GetByteArrayRegion(bin, 0, len, (jbyte *) ncnn_bin);\n int ret = ncnn_net.load_model((const unsigned char *) ncnn_bin);\n __android_log_print(ANDROID_LOG_DEBUG, \"MobileNetssd\", \"load_model %d %d\", ret, len);\n }\n\n ncnn::Option opt;\n opt.lightmode = true;\n opt.num_threads = 4; //线程 这里可以修改\n opt.blob_allocator = &g_blob_pool_allocator;\n opt.workspace_allocator = &g_workspace_pool_allocator;\n\n ncnn::set_default_option(opt);\n\n return JNI_TRUE;\n}\n\n// public native String Detect(Bitmap bitmap);\nJNIEXPORT jfloatArray JNICALL Java_com_example_che_mobilenetssd_1demo_MobileNetssd_Detect(JNIEnv* env, jobject thiz, jobject bitmap)\n{\n // ncnn from bitmap\n ncnn::Mat in;\n {\n AndroidBitmapInfo info;\n AndroidBitmap_getInfo(env, bitmap, &info);\n// int origin_w = info.width;\n// int origin_h = info.height;\n// int width = 300;\n// int height = 300;\n int width = info.width;\n int height = info.height;\n if (info.format != ANDROID_BITMAP_FORMAT_RGBA_8888)\n return NULL;\n\n void* indata;\n AndroidBitmap_lockPixels(env, bitmap, &indata);\n // 把像素转换成data，并指定通道顺序\n // 因为图像预处理每个网络层输入的数据格式不一样一般为300*300 128*128等等所以这类需要一个resize的操作可以在cpp中写，也可以是java读入图片时有个resize操作\n// in = ncnn::Mat::from_pixels_resize((const unsigned char*)indata, ncnn::Mat::PIXEL_RGBA2RGB, origin_w, origin_h, width, height);\n\n in = ncnn::Mat::from_pixels((const unsigned char*)indata, ncnn::Mat::PIXEL_RGBA2RGB, width, height);\n\n // 下面一行为debug代码\n //__android_log_print(ANDROID_LOG_DEBUG, \"MobilenetssdJniIn\", \"Mobilenetssd_predict_has_input1, in.w: %d; in.h: %d\", in.w, in.h);\n AndroidBitmap_unlockPixels(env, bitmap);\n }\n\n // ncnn_net\n std::vector cls_scores;\n {\n // 减去均值和乘上比例（这个数据和前面的归一化图片预处理形式一一对应）\n const float mean_vals[3] = {127.5f, 127.5f, 127.5f};\n const float scale[3] = {0.007843f, 0.007843f, 0.007843f};\n\n in.substract_mean_normalize(mean_vals, scale);// 归一化\n\n ncnn::Extractor ex = ncnn_net.create_extractor();//前向传播\n\n // 如果不加密是使用ex.input(\"data\", in);\n // BLOB_data在id.h文件中可见，相当于datainput网络层的id\n ex.input(MobileNetSSD_deploy_param_id::BLOB_data, in);\n //ex.set_num_threads(4); 和上面一样一个对象\n\n ncnn::Mat out;\n // 如果时不加密是使用ex.extract(\"prob\", out);\n //BLOB_detection_out.h文件中可见，相当于dataout网络层的id,输出检测的结果数据\n ex.extract(MobileNetSSD_deploy_param_id::BLOB_detection_out, out);\n\n int output_wsize = out.w;\n int output_hsize = out.h;\n\n //输出整理\n jfloat *output[output_wsize * output_hsize];\n for(int i = 0; i< out.h; i++) {\n for (int j = 0; j < out.w; j++) {\n output[i*output_wsize + j] = &out.row(i)[j];\n }\n }\n jfloatArray jOutputData = env->NewFloatArray(output_wsize);\n if (jOutputData == nullptr) return nullptr;\n env->SetFloatArrayRegion(jOutputData, 0, output_wsize * output_hsize,\n reinterpret_cast(*output));\n\n return jOutputData;\n }\n}\n}\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/include/allocator.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_ALLOCATOR_H\n#define NCNN_ALLOCATOR_H\n\n#ifdef _WIN32\n#define WIN32_LEAN_AND_MEAN\n#include \n#else\n#include \n#endif\n\n#include \n#include \n\nnamespace ncnn {\n\n// the alignment of all the allocated buffers\n#define MALLOC_ALIGN 16\n\n// Aligns a pointer to the specified number of bytes\n// ptr Aligned pointer\n// n Alignment size that must be a power of two\ntemplate static inline _Tp* alignPtr(_Tp* ptr, int n=(int)sizeof(_Tp))\n{\n return (_Tp*)(((size_t)ptr + n-1) & -n);\n}\n\n// Aligns a buffer size to the specified number of bytes\n// The function returns the minimum number that is greater or equal to sz and is divisible by n\n// sz Buffer size to align\n// n Alignment size that must be a power of two\nstatic inline size_t alignSize(size_t sz, int n)\n{\n return (sz + n-1) & -n;\n}\n\nstatic inline void* fastMalloc(size_t size)\n{\n unsigned char* udata = (unsigned char*)malloc(size + sizeof(void*) + MALLOC_ALIGN);\n if (!udata)\n return 0;\n unsigned char** adata = alignPtr((unsigned char**)udata + 1, MALLOC_ALIGN);\n adata[-1] = udata;\n return adata;\n}\n\nstatic inline void fastFree(void* ptr)\n{\n if (ptr)\n {\n unsigned char* udata = ((unsigned char**)ptr)[-1];\n free(udata);\n }\n}\n\n// exchange-add operation for atomic operations on reference counters\n#if defined __INTEL_COMPILER && !(defined WIN32 || defined _WIN32)\n// atomic increment on the linux version of the Intel(tm) compiler\n# define NCNN_XADD(addr, delta) (int)_InterlockedExchangeAdd(const_cast(reinterpret_cast(addr)), delta)\n#elif defined __GNUC__\n# if defined __clang__ && __clang_major__ >= 3 && !defined __ANDROID__ && !defined __EMSCRIPTEN__ && !defined(__CUDACC__)\n# ifdef __ATOMIC_ACQ_REL\n# define NCNN_XADD(addr, delta) __c11_atomic_fetch_add((_Atomic(int)*)(addr), delta, __ATOMIC_ACQ_REL)\n# else\n# define NCNN_XADD(addr, delta) __atomic_fetch_add((_Atomic(int)*)(addr), delta, 4)\n# endif\n# else\n# if defined __ATOMIC_ACQ_REL && !defined __clang__\n// version for gcc >= 4.7\n# define NCNN_XADD(addr, delta) (int)__atomic_fetch_add((unsigned*)(addr), (unsigned)(delta), __ATOMIC_ACQ_REL)\n# else\n# define NCNN_XADD(addr, delta) (int)__sync_fetch_and_add((unsigned*)(addr), (unsigned)(delta))\n# endif\n# endif\n#elif defined _MSC_VER && !defined RC_INVOKED\n# include \n# define NCNN_XADD(addr, delta) (int)_InterlockedExchangeAdd((long volatile*)addr, delta)\n#else\n// thread-unsafe branch\nstatic inline int NCNN_XADD(int* addr, int delta) { int tmp = *addr; *addr += delta; return tmp; }\n#endif\n\n#ifdef _WIN32\nclass Mutex\n{\npublic:\n Mutex() { InitializeSRWLock(&srwlock); }\n ~Mutex() {}\n void lock() { AcquireSRWLockExclusive(&srwlock); }\n void unlock() { ReleaseSRWLockExclusive(&srwlock); }\nprivate:\n // NOTE SRWLock is available from windows vista\n SRWLOCK srwlock;\n};\n#else // _WIN32\nclass Mutex\n{\npublic:\n Mutex() { pthread_mutex_init(&mutex, 0); }\n ~Mutex() { pthread_mutex_destroy(&mutex); }\n void lock() { pthread_mutex_lock(&mutex); }\n void unlock() { pthread_mutex_unlock(&mutex); }\nprivate:\n pthread_mutex_t mutex;\n};\n#endif // _WIN32\n\nclass Allocator\n{\npublic:\n virtual ~Allocator() = 0;\n virtual void* fastMalloc(size_t size) = 0;\n virtual void fastFree(void* ptr) = 0;\n};\n\nclass PoolAllocator : public Allocator\n{\npublic:\n PoolAllocator();\n ~PoolAllocator();\n\n // ratio range 0 ~ 1\n // default cr = 0.75\n void set_size_compare_ratio(float scr);\n\n // release all budgets immediately\n void clear();\n\n virtual void* fastMalloc(size_t size);\n virtual void fastFree(void* ptr);\n\nprivate:\n Mutex budgets_lock;\n Mutex payouts_lock;\n unsigned int size_compare_ratio;// 0~256\n std::list< std::pair > budgets;\n std::list< std::pair > payouts;\n};\n\nclass UnlockedPoolAllocator : public Allocator\n{\npublic:\n UnlockedPoolAllocator();\n ~UnlockedPoolAllocator();\n\n // ratio range 0 ~ 1\n // default cr = 0.75\n void set_size_compare_ratio(float scr);\n\n // release all budgets immediately\n void clear();\n\n virtual void* fastMalloc(size_t size);\n virtual void fastFree(void* ptr);\n\nprivate:\n unsigned int size_compare_ratio;// 0~256\n std::list< std::pair > budgets;\n std::list< std::pair > payouts;\n};\n\n} // namespace ncnn\n\n#endif // NCNN_ALLOCATOR_H\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/include/benchmark.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_BENCHMARK_H\n#define NCNN_BENCHMARK_H\n\n#include \"platform.h\"\n#include \"mat.h\"\n#include \"layer.h\"\n\nnamespace ncnn {\n\n// get now timestamp in ms\ndouble get_current_time();\n\n#if NCNN_BENCHMARK\n\nvoid benchmark(const Layer* layer, double start, double end);\nvoid benchmark(const Layer* layer, const Mat& bottom_blob, Mat& top_blob, double start, double end);\n\n#endif // NCNN_BENCHMARK\n\n} // namespace ncnn\n\n#endif // NCNN_BENCHMARK_H\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/include/blob.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_BLOB_H\n#define NCNN_BLOB_H\n\n#include \n#include \n#include \"platform.h\"\n\nnamespace ncnn {\n\nclass Blob\n{\npublic:\n // empty\n Blob();\n\npublic:\n#if NCNN_STRING\n // blob name\n std::string name;\n#endif // NCNN_STRING\n // layer index which produce this blob as output\n int producer;\n // layer index which need this blob as input\n std::vector consumers;\n};\n\n} // namespace ncnn\n\n#endif // NCNN_BLOB_H\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/include/cpu.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_CPU_H\n#define NCNN_CPU_H\n\nnamespace ncnn {\n\n// test optional cpu features\n// neon = armv7 neon or aarch64 asimd\nint cpu_support_arm_neon();\n// vfpv4 = armv7 fp16 + fma\nint cpu_support_arm_vfpv4();\n// asimdhp = aarch64 asimd half precision\nint cpu_support_arm_asimdhp();\n\n// cpu info\nint get_cpu_count();\n\n// bind all threads on little clusters if powersave enabled\n// affacts HMP arch cpu like ARM big.LITTLE\n// only implemented on android at the moment\n// switching powersave is expensive and not thread-safe\n// 0 = all cores enabled(default)\n// 1 = only little clusters enabled\n// 2 = only big clusters enabled\n// return 0 if success for setter function\nint get_cpu_powersave();\nint set_cpu_powersave(int powersave);\n\n// misc function wrapper for openmp routines\nint get_omp_num_threads();\nvoid set_omp_num_threads(int num_threads);\n\nint get_omp_dynamic();\nvoid set_omp_dynamic(int dynamic);\n\n} // namespace ncnn\n\n#endif // NCNN_CPU_H\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/include/layer.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_LAYER_H\n#define NCNN_LAYER_H\n\n#include \n#include \n#include \n#include \"mat.h\"\n#include \"modelbin.h\"\n#include \"paramdict.h\"\n#include \"platform.h\"\n\nnamespace ncnn {\n\nclass Allocator;\nclass Option\n{\npublic:\n // default option\n Option();\n\npublic:\n // light mode\n // intermediate blob will be recycled when enabled\n // enabled by default\n bool lightmode;\n\n // thread count\n // default value is the one returned by get_cpu_count()\n int num_threads;\n\n // blob memory allocator\n Allocator* blob_allocator;\n\n // workspace memory allocator\n Allocator* workspace_allocator;\n};\n\n// the global default option\nconst Option& get_default_option();\nint set_default_option(const Option& opt);\n\nclass Layer\n{\npublic:\n // empty\n Layer();\n // virtual destructor\n virtual ~Layer();\n\n // load layer specific parameter from parsed dict\n // return 0 if success\n virtual int load_param(const ParamDict& pd);\n\n // load layer specific weight data from model binary\n // return 0 if success\n virtual int load_model(const ModelBin& mb);\n\npublic:\n // one input and one output blob\n bool one_blob_only;\n\n // support inplace inference\n bool support_inplace;\n\npublic:\n // implement inference\n // return 0 if success\n virtual int forward(const std::vector& bottom_blobs, std::vector& top_blobs, const Option& opt = get_default_option()) const;\n virtual int forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt = get_default_option()) const;\n\n // implement inplace inference\n // return 0 if success\n virtual int forward_inplace(std::vector& bottom_top_blobs, const Option& opt = get_default_option()) const;\n virtual int forward_inplace(Mat& bottom_top_blob, const Option& opt = get_default_option()) const;\n\npublic:\n#if NCNN_STRING\n // layer type name\n std::string type;\n // layer name\n std::string name;\n#endif // NCNN_STRING\n // blob index which this layer needs as input\n std::vector bottoms;\n // blob index which this layer produces as output\n std::vector tops;\n};\n\n// layer factory function\ntypedef Layer* (*layer_creator_func)();\n\nstruct layer_registry_entry\n{\n#if NCNN_STRING\n // layer type name\n const char* name;\n#endif // NCNN_STRING\n // layer factory entry\n layer_creator_func creator;\n};\n\n#if NCNN_STRING\n// get layer type from type name\nint layer_to_index(const char* type);\n// create layer from type name\nLayer* create_layer(const char* type);\n#endif // NCNN_STRING\n// create layer from layer type\nLayer* create_layer(int index);\n\n#define DEFINE_LAYER_CREATOR(name) \\\n ::ncnn::Layer* name##_layer_creator() { return new name; }\n\n} // namespace ncnn\n\n#endif // NCNN_LAYER_H\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/include/layer_type.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_LAYER_TYPE_H\n#define NCNN_LAYER_TYPE_H\n\nnamespace ncnn {\n\nnamespace LayerType {\nenum\n{\n#include \"layer_type_enum.h\"\n CustomBit = (1<<8),\n};\n} // namespace LayerType\n\n} // namespace ncnn\n\n#endif // NCNN_LAYER_TYPE_H\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/include/layer_type_enum.h", "content": "// Layer Type Enum header\n//\n// This file is auto-generated by cmake, don't edit it.\n\nAbsVal = 0,\nArgMax = 1,\nBatchNorm = 2,\nBias = 3,\nBNLL = 4,\nConcat = 5,\nConvolution = 6,\nCrop = 7,\nDeconvolution = 8,\nDropout = 9,\nEltwise = 10,\nELU = 11,\nEmbed = 12,\nExp = 13,\nFlatten = 14,\nInnerProduct = 15,\nInput = 16,\nLog = 17,\nLRN = 18,\nMemoryData = 19,\nMVN = 20,\nPooling = 21,\nPower = 22,\nPReLU = 23,\nProposal = 24,\nReduction = 25,\nReLU = 26,\nReshape = 27,\nROIPooling = 28,\nScale = 29,\nSigmoid = 30,\nSlice = 31,\nSoftmax = 32,\nSplit = 33,\nSPP = 34,\nTanH = 35,\nThreshold = 36,\nTile = 37,\nRNN = 38,\nLSTM = 39,\nBinaryOp = 40,\nUnaryOp = 41,\nConvolutionDepthWise = 42,\nPadding = 43,\nSqueeze = 44,\nExpandDims = 45,\nNormalize = 46,\nPermute = 47,\nPriorBox = 48,\nDetectionOutput = 49,\nInterp = 50,\nDeconvolutionDepthWise = 51,\nShuffleChannel = 52,\nInstanceNorm = 53,\nClip = 54,\nReorg = 55,\nYoloDetectionOutput = 56,\nQuantize = 57,\nDequantize = 58,\nYolov3DetectionOutput = 59,\nPSROIPooling = 60,\nROIAlign = 61,\n\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/include/mat.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_MAT_H\n#define NCNN_MAT_H\n\n#include \n#include \n#if __ARM_NEON\n#include \n#endif\n#include \"allocator.h\"\n#include \"platform.h\"\n\nnamespace ncnn {\n\n// the three dimension matrix\nclass Mat\n{\npublic:\n // empty\n Mat();\n // vec\n Mat(int w, size_t elemsize = 4u, Allocator* allocator = 0);\n // image\n Mat(int w, int h, size_t elemsize = 4u, Allocator* allocator = 0);\n // dim\n Mat(int w, int h, int c, size_t elemsize = 4u, Allocator* allocator = 0);\n // copy\n Mat(const Mat& m);\n // external vec\n Mat(int w, void* data, size_t elemsize = 4u, Allocator* allocator = 0);\n // external image\n Mat(int w, int h, void* data, size_t elemsize = 4u, Allocator* allocator = 0);\n // external dim\n Mat(int w, int h, int c, void* data, size_t elemsize = 4u, Allocator* allocator = 0);\n // release\n ~Mat();\n // assign\n Mat& operator=(const Mat& m);\n // set all\n void fill(float v);\n void fill(int v);\n template void fill(T v);\n // deep copy\n Mat clone(Allocator* allocator = 0) const;\n // reshape vec\n Mat reshape(int w, Allocator* allocator = 0) const;\n // reshape image\n Mat reshape(int w, int h, Allocator* allocator = 0) const;\n // reshape dim\n Mat reshape(int w, int h, int c, Allocator* allocator = 0) const;\n // allocate vec\n void create(int w, size_t elemsize = 4u, Allocator* allocator = 0);\n // allocate image\n void create(int w, int h, size_t elemsize = 4u, Allocator* allocator = 0);\n // allocate dim\n void create(int w, int h, int c, size_t elemsize = 4u, Allocator* allocator = 0);\n // refcount++\n void addref();\n // refcount--\n void release();\n\n bool empty() const;\n size_t total() const;\n\n // data reference\n Mat channel(int c);\n const Mat channel(int c) const;\n float* row(int y);\n const float* row(int y) const;\n template T* row(int y);\n template const T* row(int y) const;\n\n // range reference\n Mat channel_range(int c, int channels);\n const Mat channel_range(int c, int channels) const;\n Mat row_range(int y, int rows);\n const Mat row_range(int y, int rows) const;\n Mat range(int x, int n);\n const Mat range(int x, int n) const;\n\n // access raw data\n template operator T*();\n template operator const T*() const;\n\n // convenient access float vec element\n float& operator[](int i);\n const float& operator[](int i) const;\n\n#if NCNN_PIXEL\n enum\n {\n PIXEL_CONVERT_SHIFT = 16,\n PIXEL_FORMAT_MASK = 0x0000ffff,\n PIXEL_CONVERT_MASK = 0xffff0000,\n\n PIXEL_RGB = 1,\n PIXEL_BGR = (1 << 1),\n PIXEL_GRAY = (1 << 2),\n PIXEL_RGBA = (1 << 3),\n\n PIXEL_RGB2BGR = PIXEL_RGB | (PIXEL_BGR << PIXEL_CONVERT_SHIFT),\n PIXEL_RGB2GRAY = PIXEL_RGB | (PIXEL_GRAY << PIXEL_CONVERT_SHIFT),\n\n PIXEL_BGR2RGB = PIXEL_BGR | (PIXEL_RGB << PIXEL_CONVERT_SHIFT),\n PIXEL_BGR2GRAY = PIXEL_BGR | (PIXEL_GRAY << PIXEL_CONVERT_SHIFT),\n\n PIXEL_GRAY2RGB = PIXEL_GRAY | (PIXEL_RGB << PIXEL_CONVERT_SHIFT),\n PIXEL_GRAY2BGR = PIXEL_GRAY | (PIXEL_BGR << PIXEL_CONVERT_SHIFT),\n\n PIXEL_RGBA2RGB = PIXEL_RGBA | (PIXEL_RGB << PIXEL_CONVERT_SHIFT),\n PIXEL_RGBA2BGR = PIXEL_RGBA | (PIXEL_BGR << PIXEL_CONVERT_SHIFT),\n PIXEL_RGBA2GRAY = PIXEL_RGBA | (PIXEL_GRAY << PIXEL_CONVERT_SHIFT),\n };\n // convenient construct from pixel data\n static Mat from_pixels(const unsigned char* pixels, int type, int w, int h, Allocator* allocator = 0);\n // convenient construct from pixel data and resize to specific size\n static Mat from_pixels_resize(const unsigned char* pixels, int type, int w, int h, int target_width, int target_height, Allocator* allocator = 0);\n\n // convenient export to pixel data\n void to_pixels(unsigned char* pixels, int type) const;\n // convenient export to pixel data and resize to specific size\n void to_pixels_resize(unsigned char* pixels, int type, int target_width, int target_height) const;\n#endif // NCNN_PIXEL\n\n // substract channel-wise mean values, then multiply by normalize values, pass 0 to skip\n void substract_mean_normalize(const float* mean_vals, const float* norm_vals);\n\n // convenient construct from half precisoin floating point data\n static Mat from_float16(const unsigned short* data, int size);\n\n // pointer to the data\n void* data;\n\n // pointer to the reference counter\n // when points to user-allocated data, the pointer is NULL\n int* refcount;\n\n // element size in bytes\n // 4 = float32/int32\n // 2 = float16\n // 1 = int8/uint8\n // 0 = empty\n size_t elemsize;\n\n // the allocator\n Allocator* allocator;\n\n // the dimensionality\n int dims;\n\n int w;\n int h;\n int c;\n\n size_t cstep;\n};\n\n// misc function\n#if NCNN_PIXEL\n// convert yuv420sp(nv21) to rgb, the fast approximate version\nvoid yuv420sp2rgb(const unsigned char* yuv420sp, int w, int h, unsigned char* rgb);\n// image pixel bilinear resize\nvoid resize_bilinear_c1(const unsigned char* src, int srcw, int srch, unsigned char* dst, int w, int h);\nvoid resize_bilinear_c2(const unsigned char* src, int srcw, int srch, unsigned char* dst, int w, int h);\nvoid resize_bilinear_c3(const unsigned char* src, int srcw, int srch, unsigned char* dst, int w, int h);\nvoid resize_bilinear_c4(const unsigned char* src, int srcw, int srch, unsigned char* dst, int w, int h);\n// image pixel bilinear resize, convenient wrapper for yuv420sp(nv21)\nvoid resize_bilinear_yuv420sp(const unsigned char* src, int srcw, int srch, unsigned char* dst, int w, int h);\n#endif // NCNN_PIXEL\n\n// mat process\nenum\n{\n BORDER_CONSTANT = 0,\n BORDER_REPLICATE = 1,\n};\nvoid copy_make_border(const Mat& src, Mat& dst, int top, int bottom, int left, int right, int type, float v, Allocator* allocator = 0, int num_threads = 1);\nvoid copy_cut_border(const Mat& src, Mat& dst, int top, int bottom, int left, int right, Allocator* allocator = 0, int num_threads = 1);\nvoid resize_bilinear(const Mat& src, Mat& dst, int w, int h, Allocator* allocator = 0, int num_threads = 1);\n\ninline Mat::Mat()\n : data(0), refcount(0), elemsize(0), allocator(0), dims(0), w(0), h(0), c(0), cstep(0)\n{\n}\n\ninline Mat::Mat(int _w, size_t _elemsize, Allocator* allocator)\n : data(0), refcount(0), dims(0)\n{\n create(_w, _elemsize, allocator);\n}\n\ninline Mat::Mat(int _w, int _h, size_t _elemsize, Allocator* allocator)\n : data(0), refcount(0), dims(0)\n{\n create(_w, _h, _elemsize, allocator);\n}\n\ninline Mat::Mat(int _w, int _h, int _c, size_t _elemsize, Allocator* allocator)\n : data(0), refcount(0), dims(0)\n{\n create(_w, _h, _c, _elemsize, allocator);\n}\n\ninline Mat::Mat(const Mat& m)\n : data(m.data), refcount(m.refcount), elemsize(m.elemsize), allocator(m.allocator), dims(m.dims)\n{\n if (refcount)\n NCNN_XADD(refcount, 1);\n\n w = m.w;\n h = m.h;\n c = m.c;\n\n cstep = m.cstep;\n}\n\ninline Mat::Mat(int _w, void* _data, size_t _elemsize, Allocator* _allocator)\n : data(_data), refcount(0), elemsize(_elemsize), allocator(_allocator), dims(1)\n{\n w = _w;\n h = 1;\n c = 1;\n\n cstep = w;\n}\n\ninline Mat::Mat(int _w, int _h, void* _data, size_t _elemsize, Allocator* _allocator)\n : data(_data), refcount(0), elemsize(_elemsize), allocator(_allocator), dims(2)\n{\n w = _w;\n h = _h;\n c = 1;\n\n cstep = w * h;\n}\n\ninline Mat::Mat(int _w, int _h, int _c, void* _data, size_t _elemsize, Allocator* _allocator)\n : data(_data), refcount(0), elemsize(_elemsize), allocator(_allocator), dims(3)\n{\n w = _w;\n h = _h;\n c = _c;\n\n cstep = alignSize(w * h * elemsize, 16) / elemsize;\n}\n\ninline Mat::~Mat()\n{\n release();\n}\n\ninline Mat& Mat::operator=(const Mat& m)\n{\n if (this == &m)\n return *this;\n\n if (m.refcount)\n NCNN_XADD(m.refcount, 1);\n\n release();\n\n data = m.data;\n refcount = m.refcount;\n elemsize = m.elemsize;\n allocator = m.allocator;\n\n dims = m.dims;\n w = m.w;\n h = m.h;\n c = m.c;\n\n cstep = m.cstep;\n\n return *this;\n}\n\ninline void Mat::fill(float _v)\n{\n int size = total();\n float* ptr = (float*)data;\n\n#if __ARM_NEON\n int nn = size >> 2;\n int remain = size - (nn << 2);\n#else\n int remain = size;\n#endif // __ARM_NEON\n\n#if __ARM_NEON\n float32x4_t _c = vdupq_n_f32(_v);\n#if __aarch64__\n if (nn > 0)\n {\n asm volatile (\n \"0: \\n\"\n \"subs %w0, %w0, #1 \\n\"\n \"st1 {%4.4s}, [%1], #16 \\n\"\n \"bne 0b \\n\"\n : \"=r\"(nn), // %0\n \"=r\"(ptr) // %1\n : \"0\"(nn),\n \"1\"(ptr),\n \"w\"(_c) // %4\n : \"cc\", \"memory\"\n );\n }\n#else\n if (nn > 0)\n {\n asm volatile(\n \"0: \\n\"\n \"subs %0, #1 \\n\"\n \"vst1.f32 {%e4-%f4}, [%1 :128]!\\n\"\n \"bne 0b \\n\"\n : \"=r\"(nn), // %0\n \"=r\"(ptr) // %1\n : \"0\"(nn),\n \"1\"(ptr),\n \"w\"(_c) // %4\n : \"cc\", \"memory\"\n );\n }\n#endif // __aarch64__\n#endif // __ARM_NEON\n for (; remain>0; remain--)\n {\n *ptr++ = _v;\n }\n}\n\ninline void Mat::fill(int _v)\n{\n int size = total();\n int* ptr = (int*)data;\n\n#if __ARM_NEON\n int nn = size >> 2;\n int remain = size - (nn << 2);\n#else\n int remain = size;\n#endif // __ARM_NEON\n\n#if __ARM_NEON\n int32x4_t _c = vdupq_n_s32(_v);\n#if __aarch64__\n if (nn > 0)\n {\n asm volatile (\n \"0: \\n\"\n \"subs %w0, %w0, #1 \\n\"\n \"st1 {%4.4s}, [%1], #16 \\n\"\n \"bne 0b \\n\"\n : \"=r\"(nn), // %0\n \"=r\"(ptr) // %1\n : \"0\"(nn),\n \"1\"(ptr),\n \"w\"(_c) // %4\n : \"cc\", \"memory\"\n );\n }\n#else\n if (nn > 0)\n {\n asm volatile(\n \"0: \\n\"\n \"subs %0, #1 \\n\"\n \"vst1.s32 {%e4-%f4}, [%1 :128]!\\n\"\n \"bne 0b \\n\"\n : \"=r\"(nn), // %0\n \"=r\"(ptr) // %1\n : \"0\"(nn),\n \"1\"(ptr),\n \"w\"(_c) // %4\n : \"cc\", \"memory\"\n );\n }\n#endif // __aarch64__\n#endif // __ARM_NEON\n for (; remain>0; remain--)\n {\n *ptr++ = _v;\n }\n}\n\ntemplate \ninline void Mat::fill(T _v)\n{\n int size = total();\n T* ptr = (T*)data;\n for (int i=0; i 0)\n {\n memcpy(m.data, data, total() * elemsize);\n }\n\n return m;\n}\n\ninline Mat Mat::reshape(int _w, Allocator* allocator) const\n{\n if (w * h * c != _w)\n return Mat();\n\n if (dims == 3 && cstep != (size_t)w * h)\n {\n Mat m;\n m.create(_w, elemsize, allocator);\n\n // flatten\n for (int i=0; i 0)\n {\n size_t totalsize = alignSize(total() * elemsize, 4);\n if (allocator)\n data = allocator->fastMalloc(totalsize + (int)sizeof(*refcount));\n else\n data = fastMalloc(totalsize + (int)sizeof(*refcount));\n refcount = (int*)(((unsigned char*)data) + totalsize);\n *refcount = 1;\n }\n}\n\ninline void Mat::create(int _w, int _h, size_t _elemsize, Allocator* _allocator)\n{\n if (dims == 2 && w == _w && h == _h && elemsize == _elemsize && allocator == _allocator)\n return;\n\n release();\n\n elemsize = _elemsize;\n allocator = _allocator;\n\n dims = 2;\n w = _w;\n h = _h;\n c = 1;\n\n cstep = w * h;\n\n if (total() > 0)\n {\n size_t totalsize = alignSize(total() * elemsize, 4);\n if (allocator)\n data = allocator->fastMalloc(totalsize + (int)sizeof(*refcount));\n else\n data = fastMalloc(totalsize + (int)sizeof(*refcount));\n refcount = (int*)(((unsigned char*)data) + totalsize);\n *refcount = 1;\n }\n}\n\ninline void Mat::create(int _w, int _h, int _c, size_t _elemsize, Allocator* _allocator)\n{\n if (dims == 3 && w == _w && h == _h && c == _c && elemsize == _elemsize && allocator == _allocator)\n return;\n\n release();\n\n elemsize = _elemsize;\n allocator = _allocator;\n\n dims = 3;\n w = _w;\n h = _h;\n c = _c;\n\n cstep = alignSize(w * h * elemsize, 16) / elemsize;\n\n if (total() > 0)\n {\n size_t totalsize = alignSize(total() * elemsize, 4);\n if (allocator)\n data = allocator->fastMalloc(totalsize + (int)sizeof(*refcount));\n else\n data = fastMalloc(totalsize + (int)sizeof(*refcount));\n refcount = (int*)(((unsigned char*)data) + totalsize);\n *refcount = 1;\n }\n}\n\ninline void Mat::addref()\n{\n if (refcount)\n NCNN_XADD(refcount, 1);\n}\n\ninline void Mat::release()\n{\n if (refcount && NCNN_XADD(refcount, -1) == 1)\n {\n if (allocator)\n allocator->fastFree(data);\n else\n fastFree(data);\n }\n\n data = 0;\n\n elemsize = 0;\n\n dims = 0;\n w = 0;\n h = 0;\n c = 0;\n\n cstep = 0;\n\n refcount = 0;\n}\n\ninline bool Mat::empty() const\n{\n return data == 0 || total() == 0;\n}\n\ninline size_t Mat::total() const\n{\n return cstep * c;\n}\n\ninline Mat Mat::channel(int c)\n{\n return Mat(w, h, (unsigned char*)data + cstep * c * elemsize, elemsize, allocator);\n}\n\ninline const Mat Mat::channel(int c) const\n{\n return Mat(w, h, (unsigned char*)data + cstep * c * elemsize, elemsize, allocator);\n}\n\ninline float* Mat::row(int y)\n{\n return (float*)data + w * y;\n}\n\ninline const float* Mat::row(int y) const\n{\n return (const float*)data + w * y;\n}\n\ntemplate \ninline T* Mat::row(int y)\n{\n return (T*)data + w * y;\n}\n\ntemplate \ninline const T* Mat::row(int y) const\n{\n return (const T*)data + w * y;\n}\n\ninline Mat Mat::channel_range(int _c, int channels)\n{\n return Mat(w, h, channels, (unsigned char*)data + cstep * _c * elemsize, elemsize, allocator);\n}\n\ninline const Mat Mat::channel_range(int _c, int channels) const\n{\n return Mat(w, h, channels, (unsigned char*)data + cstep * _c * elemsize, elemsize, allocator);\n}\n\ninline Mat Mat::row_range(int y, int rows)\n{\n return Mat(w, rows, (unsigned char*)data + w * y * elemsize, elemsize, allocator);\n}\n\ninline const Mat Mat::row_range(int y, int rows) const\n{\n return Mat(w, rows, (unsigned char*)data + w * y * elemsize, elemsize, allocator);\n}\n\ninline Mat Mat::range(int x, int n)\n{\n return Mat(n, (unsigned char*)data + x * elemsize, elemsize, allocator);\n}\n\ninline const Mat Mat::range(int x, int n) const\n{\n return Mat(n, (unsigned char*)data + x * elemsize, elemsize, allocator);\n}\n\ntemplate \ninline Mat::operator T*()\n{\n return (T*)data;\n}\n\ntemplate \ninline Mat::operator const T*() const\n{\n return (const T*)data;\n}\n\ninline float& Mat::operator[](int i)\n{\n return ((float*)data)[i];\n}\n\ninline const float& Mat::operator[](int i) const\n{\n return ((const float*)data)[i];\n}\n\n} // namespace ncnn\n\n#endif // NCNN_MAT_H\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/include/modelbin.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_MODELBIN_H\n#define NCNN_MODELBIN_H\n\n#include \n#include \"mat.h\"\n#include \"platform.h\"\n\nnamespace ncnn {\n\nclass Net;\nclass ModelBin\n{\npublic:\n // element type\n // 0 = auto\n // 1 = float32\n // 2 = float16\n // 3 = int8\n // load vec\n virtual Mat load(int w, int type) const = 0;\n // load image\n virtual Mat load(int w, int h, int type) const;\n // load dim\n virtual Mat load(int w, int h, int c, int type) const;\n};\n\n#if NCNN_STDIO\nclass ModelBinFromStdio : public ModelBin\n{\npublic:\n // construct from file\n ModelBinFromStdio(FILE* binfp);\n\n virtual Mat load(int w, int type) const;\n\nprotected:\n FILE* binfp;\n};\n#endif // NCNN_STDIO\n\nclass ModelBinFromMemory : public ModelBin\n{\npublic:\n // construct from external memory\n ModelBinFromMemory(const unsigned char*& mem);\n\n virtual Mat load(int w, int type) const;\n\nprotected:\n const unsigned char*& mem;\n};\n\nclass ModelBinFromMatArray : public ModelBin\n{\npublic:\n // construct from weight blob array\n ModelBinFromMatArray(const Mat* weights);\n\n virtual Mat load(int w, int type) const;\n\nprotected:\n mutable const Mat* weights;\n};\n\n} // namespace ncnn\n\n#endif // NCNN_MODELBIN_H\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/include/net.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_NET_H\n#define NCNN_NET_H\n\n#include \n#include \n#include \"blob.h\"\n#include \"layer.h\"\n#include \"mat.h\"\n#include \"platform.h\"\n\nnamespace ncnn {\n\nclass Extractor;\nclass Net\n{\npublic:\n // empty init\n Net();\n // clear and destroy\n ~Net();\n\n#if NCNN_STRING\n // register custom layer by layer type name\n // return 0 if success\n int register_custom_layer(const char* type, layer_creator_func creator);\n#endif // NCNN_STRING\n // register custom layer by layer type\n // return 0 if success\n int register_custom_layer(int index, layer_creator_func creator);\n\n#if NCNN_STDIO\n#if NCNN_STRING\n // load network structure from plain param file\n // return 0 if success\n int load_param(FILE* fp);\n int load_param(const char* protopath);\n int load_param_mem(const char* mem);\n#endif // NCNN_STRING\n // load network structure from binary param file\n // return 0 if success\n int load_param_bin(FILE* fp);\n int load_param_bin(const char* protopath);\n\n // load network weight data from model file\n // return 0 if success\n int load_model(FILE* fp);\n int load_model(const char* modelpath);\n#endif // NCNN_STDIO\n\n // load network structure from external memory\n // memory pointer must be 32-bit aligned\n // return bytes consumed\n int load_param(const unsigned char* mem);\n\n // reference network weight data from external memory\n // weight data is not copied but referenced\n // so external memory should be retained when used\n // memory pointer must be 32-bit aligned\n // return bytes consumed\n int load_model(const unsigned char* mem);\n\n // unload network structure and weight data\n void clear();\n\n // construct an Extractor from network\n Extractor create_extractor() const;\n\npublic:\n // enable winograd convolution optimization\n // improve convolution 3x3 stride1 performace, may consume more memory\n // changes should be applied before loading network structure and weight\n // enabled by default\n int use_winograd_convolution;\n\n // enable sgemm convolution optimization\n // improve convolution 1x1 stride1 performace, may consume more memory\n // changes should be applied before loading network structure and weight\n // enabled by default\n int use_sgemm_convolution;\n\n // enable quantized int8 inference\n // use low-precision int8 path for quantized model\n // changes should be applied before loading network structure and weight\n // enabled by default\n int use_int8_inference;\n\nprotected:\n friend class Extractor;\n#if NCNN_STRING\n int find_blob_index_by_name(const char* name) const;\n int find_layer_index_by_name(const char* name) const;\n int custom_layer_to_index(const char* type);\n Layer* create_custom_layer(const char* type);\n#endif // NCNN_STRING\n Layer* create_custom_layer(int index);\n int forward_layer(int layer_index, std::vector& blob_mats, Option& opt) const;\n\nprotected:\n std::vector blobs;\n std::vector layers;\n\n std::vector custom_layer_registry;\n};\n\nclass Extractor\n{\npublic:\n // enable light mode\n // intermediate blob will be recycled when enabled\n // enabled by default\n void set_light_mode(bool enable);\n\n // set thread count for this extractor\n // this will overwrite the global setting\n // default count is system depended\n void set_num_threads(int num_threads);\n\n // set blob memory allocator\n void set_blob_allocator(Allocator* allocator);\n\n // set workspace memory allocator\n void set_workspace_allocator(Allocator* allocator);\n\n#if NCNN_STRING\n // set input by blob name\n // return 0 if success\n int input(const char* blob_name, const Mat& in);\n\n // get result by blob name\n // return 0 if success\n int extract(const char* blob_name, Mat& feat);\n#endif // NCNN_STRING\n\n // set input by blob index\n // return 0 if success\n int input(int blob_index, const Mat& in);\n\n // get result by blob index\n // return 0 if success\n int extract(int blob_index, Mat& feat);\n\nprotected:\n friend Extractor Net::create_extractor() const;\n Extractor(const Net* net, int blob_count);\n\nprivate:\n const Net* net;\n std::vector blob_mats;\n Option opt;\n};\n\n} // namespace ncnn\n\n#endif // NCNN_NET_H\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/include/opencv.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_OPENCV_H\n#define NCNN_OPENCV_H\n\n#include \"platform.h\"\n\n#if NCNN_OPENCV\n\n#include \n#include \n#include \"mat.h\"\n\n// minimal opencv style data structure implementation\nnamespace cv\n{\n\nstruct Size\n{\n Size() : width(0), height(0) {}\n Size(int _w, int _h) : width(_w), height(_h) {}\n\n int width;\n int height;\n};\n\ntemplate\nstruct Rect_\n{\n Rect_() : x(0), y(0), width(0), height(0) {}\n Rect_(_Tp _x, _Tp _y, _Tp _w, _Tp _h) : x(_x), y(_y), width(_w), height(_h) {}\n\n _Tp x;\n _Tp y;\n _Tp width;\n _Tp height;\n\n // area\n _Tp area() const\n {\n return width * height;\n }\n};\n\ntemplate static inline Rect_<_Tp>& operator &= ( Rect_<_Tp>& a, const Rect_<_Tp>& b )\n{\n _Tp x1 = std::max(a.x, b.x), y1 = std::max(a.y, b.y);\n a.width = std::min(a.x + a.width, b.x + b.width) - x1;\n a.height = std::min(a.y + a.height, b.y + b.height) - y1;\n a.x = x1; a.y = y1;\n if( a.width <= 0 || a.height <= 0 )\n a = Rect_<_Tp>();\n return a;\n}\n\ntemplate static inline Rect_<_Tp>& operator |= ( Rect_<_Tp>& a, const Rect_<_Tp>& b )\n{\n _Tp x1 = std::min(a.x, b.x), y1 = std::min(a.y, b.y);\n a.width = std::max(a.x + a.width, b.x + b.width) - x1;\n a.height = std::max(a.y + a.height, b.y + b.height) - y1;\n a.x = x1; a.y = y1;\n return a;\n}\n\ntemplate static inline Rect_<_Tp> operator & (const Rect_<_Tp>& a, const Rect_<_Tp>& b)\n{\n Rect_<_Tp> c = a;\n return c &= b;\n}\n\ntemplate static inline Rect_<_Tp> operator | (const Rect_<_Tp>& a, const Rect_<_Tp>& b)\n{\n Rect_<_Tp> c = a;\n return c |= b;\n}\n\ntypedef Rect_ Rect;\ntypedef Rect_ Rect2f;\n\ntemplate\nstruct Point_\n{\n Point_() : x(0), y(0) {}\n Point_(_Tp _x, _Tp _y) : x(_x), y(_y) {}\n\n _Tp x;\n _Tp y;\n};\n\ntypedef Point_ Point;\ntypedef Point_ Point2f;\n\n#define CV_8UC1 1\n#define CV_8UC3 3\n#define CV_8UC4 4\n#define CV_32FC1 4\n\nstruct Mat\n{\n Mat() : data(0), refcount(0), rows(0), cols(0), c(0) {}\n\n Mat(int _rows, int _cols, int flags) : data(0), refcount(0)\n {\n create(_rows, _cols, flags);\n }\n\n // copy\n Mat(const Mat& m) : data(m.data), refcount(m.refcount)\n {\n if (refcount)\n NCNN_XADD(refcount, 1);\n\n rows = m.rows;\n cols = m.cols;\n c = m.c;\n }\n\n Mat(int _rows, int _cols, int flags, void* _data) : data((unsigned char*)_data), refcount(0)\n {\n rows = _rows;\n cols = _cols;\n c = flags;\n }\n\n ~Mat()\n {\n release();\n }\n\n // assign\n Mat& operator=(const Mat& m)\n {\n if (this == &m)\n return *this;\n\n if (m.refcount)\n NCNN_XADD(m.refcount, 1);\n\n release();\n\n data = m.data;\n refcount = m.refcount;\n\n rows = m.rows;\n cols = m.cols;\n c = m.c;\n\n return *this;\n }\n\n void create(int _rows, int _cols, int flags)\n {\n release();\n\n rows = _rows;\n cols = _cols;\n c = flags;\n\n if (total() > 0)\n {\n // refcount address must be aligned, so we expand totalsize here\n size_t totalsize = (total() + 3) >> 2 << 2;\n data = (unsigned char*)ncnn::fastMalloc(totalsize + (int)sizeof(*refcount));\n refcount = (int*)(((unsigned char*)data) + totalsize);\n *refcount = 1;\n }\n }\n\n void release()\n {\n if (refcount && NCNN_XADD(refcount, -1) == 1)\n ncnn::fastFree(data);\n\n data = 0;\n\n rows = 0;\n cols = 0;\n c = 0;\n\n refcount = 0;\n }\n\n Mat clone() const\n {\n if (empty())\n return Mat();\n\n Mat m(rows, cols, c);\n\n if (total() > 0)\n {\n memcpy(m.data, data, total());\n }\n\n return m;\n }\n\n bool empty() const { return data == 0 || total() == 0; }\n\n int channels() const { return c; }\n\n size_t total() const { return cols * rows * c; }\n\n const unsigned char* ptr(int y) const { return data + y * cols * c; }\n\n unsigned char* ptr(int y) { return data + y * cols * c; }\n\n // roi\n Mat operator()( const Rect& roi ) const\n {\n if (empty())\n return Mat();\n\n Mat m(roi.height, roi.width, c);\n\n int sy = roi.y;\n for (int y = 0; y < roi.height; y++)\n {\n const unsigned char* sptr = ptr(sy) + roi.x * c;\n unsigned char* dptr = m.ptr(y);\n memcpy(dptr, sptr, roi.width * c);\n sy++;\n }\n\n return m;\n }\n\n unsigned char* data;\n\n // pointer to the reference counter;\n // when points to user-allocated data, the pointer is NULL\n int* refcount;\n\n int rows;\n int cols;\n\n int c;\n\n};\n\n#define CV_LOAD_IMAGE_GRAYSCALE 1\n#define CV_LOAD_IMAGE_COLOR 3\nMat imread(const std::string& path, int flags);\nvoid imwrite(const std::string& path, const Mat& m);\n\n#if NCNN_PIXEL\nvoid resize(const Mat& src, Mat& dst, const Size& size, float sw = 0.f, float sh = 0.f, int flags = 0);\n#endif // NCNN_PIXEL\n\n} // namespace cv\n\n#endif // NCNN_OPENCV\n\n#endif // NCNN_OPENCV_H\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/include/paramdict.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_PARAMDICT_H\n#define NCNN_PARAMDICT_H\n\n#include \n#include \"mat.h\"\n#include \"platform.h\"\n\n// at most 20 parameters\n#define NCNN_MAX_PARAM_COUNT 20\n\nnamespace ncnn {\n\nclass Net;\nclass ParamDict\n{\npublic:\n // empty\n ParamDict();\n\n // get int\n int get(int id, int def) const;\n // get float\n float get(int id, float def) const;\n // get array\n Mat get(int id, const Mat& def) const;\n\n // set int\n void set(int id, int i);\n // set float\n void set(int id, float f);\n // set array\n void set(int id, const Mat& v);\n\npublic:\n int use_winograd_convolution;\n int use_sgemm_convolution;\n int use_int8_inference;\n\nprotected:\n friend class Net;\n\n void clear();\n\n#if NCNN_STDIO\n#if NCNN_STRING\n int load_param(FILE* fp);\n int load_param_mem(const char*& mem);\n#endif // NCNN_STRING\n int load_param_bin(FILE* fp);\n#endif // NCNN_STDIO\n int load_param(const unsigned char*& mem);\n\nprotected:\n struct\n {\n int loaded;\n union { int i; float f; };\n Mat v;\n } params[NCNN_MAX_PARAM_COUNT];\n};\n\n} // namespace ncnn\n\n#endif // NCNN_PARAMDICT_H\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/cpp/include/platform.h", "content": "// Tencent is pleased to support the open source community by making ncnn available.\n//\n// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.\n//\n// Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n// in compliance with the License. You may obtain a copy of the License at\n//\n// https://opensource.org/licenses/BSD-3-Clause\n//\n// Unless required by applicable law or agreed to in writing, software distributed\n// under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n// CONDITIONS OF ANY KIND, either express or implied. See the License for the\n// specific language governing permissions and limitations under the License.\n\n#ifndef NCNN_PLATFORM_H\n#define NCNN_PLATFORM_H\n\n#define NCNN_STDIO 1\n#define NCNN_STRING 1\n#define NCNN_OPENCV 0\n#define NCNN_BENCHMARK 0\n#define NCNN_PIXEL 1\n#define NCNN_PIXEL_ROTATE 0\n\n#endif // NCNN_PLATFORM_H\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/java/com/example/che/mobilenetssd_demo/MainActivity.java", "content": "package com.example.che.mobilenetssd_demo;\n\nimport android.Manifest;\nimport android.app.Activity;\nimport android.content.Intent;\nimport android.content.pm.PackageManager;\nimport android.content.res.AssetManager;\nimport android.graphics.Bitmap;\nimport android.graphics.Canvas;\nimport android.graphics.Color;\nimport android.graphics.Paint;\nimport android.net.Uri;\nimport android.support.annotation.NonNull;\nimport android.support.annotation.Nullable;\nimport android.support.v4.app.ActivityCompat;\nimport android.support.v4.content.ContextCompat;\nimport android.support.v7.app.AppCompatActivity;\nimport android.os.Bundle;\nimport android.text.method.ScrollingMovementMethod;\nimport android.util.Log;\nimport android.view.View;\nimport android.widget.Button;\nimport android.widget.ImageView;\nimport android.widget.TextView;\nimport android.widget.Toast;\n\nimport java.io.BufferedReader;\nimport java.io.IOException;\nimport java.io.InputStream;\nimport java.io.InputStreamReader;\nimport java.util.ArrayList;\nimport java.util.Arrays;\nimport java.util.List;\n\n\nimport com.bumptech.glide.Glide;\nimport com.bumptech.glide.load.engine.DiskCacheStrategy;\nimport com.bumptech.glide.request.RequestOptions;\n\n\npublic class MainActivity extends AppCompatActivity {\n\n private static final String TAG = MainActivity.class.getName();\n private static final int USE_PHOTO = 1001;\n private String camera_image_path;\n private ImageView show_image;\n private TextView result_text;\n private boolean load_result = false;\n private int[] ddims = {1, 3, 300, 300}; //这里的维度的值要和train model的input 一一对应\n private int model_index = 1;\n private List resultLabel = new ArrayList<>();\n private MobileNetssd mobileNetssd = new MobileNetssd(); //java接口实例化　下面直接利用java函数调用NDK c++函数\n\n @Override\n protected void onCreate(Bundle savedInstanceState) {\n super.onCreate(savedInstanceState);\n setContentView(R.layout.activity_main);\n try\n {\n initMobileNetSSD();\n } catch (IOException e) {\n Log.e(\"MainActivity\", \"initMobileNetSSD error\");\n }\n init_view();\n readCacheLabelFromLocalFile();\n}\n\n /**\n *\n * MobileNetssd初始化，也就是把model文件进行加载\n */\n private void initMobileNetSSD() throws IOException {\n byte[] param = null;\n byte[] bin = null;\n {\n //用io流读取二进制文件，最后存入到byte[]数组中\n InputStream assetsInputStream = getAssets().open(\"MobileNetSSD_deploy.param.bin\");// param： 网络结构文件\n int available = assetsInputStream.available();\n param = new byte[available];\n int byteCode = assetsInputStream.read(param);\n assetsInputStream.close();\n }\n {\n //用io流读取二进制文件，最后存入到byte上，转换为int型\n InputStream assetsInputStream = getAssets().open(\"MobileNetSSD_deploy.bin\");//bin： model文件\n int available = assetsInputStream.available();\n bin = new byte[available];\n int byteCode = assetsInputStream.read(bin);\n assetsInputStream.close();\n }\n\n load_result = mobileNetssd.Init(param, bin);// 再将文件传入java的NDK接口(c++ 代码中的init接口 )\n Log.d(\"load model\", \"MobileNetSSD_load_model_result:\" + load_result);\n }\n\n\n // initialize view\n private void init_view() {\n request_permissions();\n show_image = (ImageView) findViewById(R.id.show_image);\n result_text = (TextView) findViewById(R.id.result_text);\n result_text.setMovementMethod(ScrollingMovementMethod.getInstance());\n Button use_photo = (Button) findViewById(R.id.use_photo);\n // use photo click\n use_photo.setOnClickListener(new View.OnClickListener() {\n @Override\n public void onClick(View view) {\n if (!load_result) {\n Toast.makeText(MainActivity.this, \"never load model\", Toast.LENGTH_SHORT).show();\n return;\n }\n PhotoUtil.use_photo(MainActivity.this, USE_PHOTO);\n }\n });\n }\n\n // load label's name\n private void readCacheLabelFromLocalFile() {\n try {\n AssetManager assetManager = getApplicationContext().getAssets();\n BufferedReader reader = new BufferedReader(new InputStreamReader(assetManager.open(\"words.txt\")));//这里是label的文件\n String readLine = null;\n while ((readLine = reader.readLine()) != null) {\n resultLabel.add(readLine);\n }\n reader.close();\n } catch (Exception e) {\n Log.e(\"labelCache\", \"error \" + e);\n }\n }\n\n\n protected void onActivityResult(int requestCode, int resultCode, @Nullable Intent data) {\n String image_path;\n RequestOptions options = new RequestOptions().skipMemoryCache(true).diskCacheStrategy(DiskCacheStrategy.NONE);\n if (resultCode == Activity.RESULT_OK) {\n switch (requestCode) {\n case USE_PHOTO:\n if (data == null) {\n Log.w(TAG, \"user photo data is null\");\n return;\n }\n Uri image_uri = data.getData();\n\n //Glide.with(MainActivity.this).load(image_uri).apply(options).into(show_image);\n\n // get image path from uri\n image_path = PhotoUtil.get_path_from_URI(MainActivity.this, image_uri);\n // predict image\n predict_image(image_path);\n break;\n }\n }\n }\n\n // predict image\n private void predict_image(String image_path) {\n // picture to float array\n Bitmap bmp = PhotoUtil.getScaleBitmap(image_path);\n Bitmap rgba = bmp.copy(Bitmap.Config.ARGB_8888, true);\n // resize to 300*300\n Bitmap input_bmp = Bitmap.createScaledBitmap(rgba, ddims[2], ddims[3], false);\n try {\n // Data format conversion takes too long\n // Log.d(\"inputData\", Arrays.toString(inputData));\n long start = System.currentTimeMillis();\n // get predict result\n float[] result = mobileNetssd.Detect(input_bmp);\n // time end\n long end = System.currentTimeMillis();\n Log.d(TAG, \"origin predict result:\" + Arrays.toString(result));\n long time = end - start;\n Log.d(\"result length\", \"length of result: \" + String.valueOf(result.length));\n // show predict result and time\n float[] r = get_max_result(result);\n\n String show_text = \"result：\" + Arrays.toString(r) + \"\\nname：\" + resultLabel.get((int) r[0]) + \"\\nprobability：\" + r[1] + \"\\ntime：\" + time + \"ms\" ;\n result_text.setText(show_text);\n Canvas canvas = new Canvas(rgba);\n //图像上画矩形\n Paint paint = new Paint();\n paint.setColor(Color.RED);\n paint.setStyle(Paint.Style.STROKE);//不填充\n paint.setStrokeWidth(10); //线的宽度\n canvas.drawRect(r[2]*rgba.getWidth(), r[3]*rgba.getHeight(), r[4]*rgba.getWidth(), r[5]*rgba.getHeight(), paint);\n show_image.setImageBitmap(rgba);\n\n\n } catch (Exception e) {\n e.printStackTrace();\n }\n }\n\n // get max probability label\n private float[] get_max_result(float[] result) {\n int num_rs = result.length / 6;\n float maxProp = result[1];\n int maxI = 0;\n for(int i = 1; i permissionList = new ArrayList<>();\n if (ContextCompat.checkSelfPermission(this, Manifest.permission.CAMERA) != PackageManager.PERMISSION_GRANTED) {\n permissionList.add(Manifest.permission.CAMERA);\n }\n if (ContextCompat.checkSelfPermission(this, Manifest.permission.WRITE_EXTERNAL_STORAGE) != PackageManager.PERMISSION_GRANTED) {\n permissionList.add(Manifest.permission.WRITE_EXTERNAL_STORAGE);\n }\n if (ContextCompat.checkSelfPermission(this, Manifest.permission.READ_EXTERNAL_STORAGE) != PackageManager.PERMISSION_GRANTED) {\n permissionList.add(Manifest.permission.READ_EXTERNAL_STORAGE);\n }\n // if list is not empty will request permissions\n if (!permissionList.isEmpty()) {\n ActivityCompat.requestPermissions(this, permissionList.toArray(new String[permissionList.size()]), 1);\n }\n }\n\n @Override\n public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions, @NonNull int[] grantResults) {\n super.onRequestPermissionsResult(requestCode, permissions, grantResults);\n switch (requestCode) {\n case 1:\n if (grantResults.length > 0) {\n for (int i = 0; i < grantResults.length; i++) {\n int grantResult = grantResults[i];\n if (grantResult == PackageManager.PERMISSION_DENIED) {\n String s = permissions[i];\n Toast.makeText(this, s + \"permission was denied\", Toast.LENGTH_SHORT).show();\n }\n }\n }\n break;\n }\n }\n\n\n\n}\n\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/java/com/example/che/mobilenetssd_demo/MobileNetssd.java", "content": "package com.example.che.mobilenetssd_demo;\n\nimport android.graphics.Bitmap;\n\n/**\n * MobileNetssd的java接口，与本地c++代码相呼应 native为本地 此文件与 MobileNetssd.cpp相呼应\n */\npublic class MobileNetssd {\n\n public native boolean Init(byte[] param, byte[] bin); // 初始化函数\n public native float[] Detect(Bitmap bitmap); // 检测函数\n // Used to load the 'native-lib' library on application startup.\n static {\n System.loadLibrary(\"MobileNetssd\");\n }\n}\n\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/java/com/example/che/mobilenetssd_demo/PhotoUtil.java", "content": "package com.example.che.mobilenetssd_demo;\n\nimport android.app.Activity;\nimport android.content.Context;\nimport android.content.Intent;\nimport android.database.Cursor;\nimport android.graphics.Bitmap;\nimport android.graphics.BitmapFactory;\nimport android.net.Uri;\nimport android.provider.MediaStore;\n\npublic class PhotoUtil {\n // get picture in photo\n public static void use_photo(Activity activity, int requestCode) {\n Intent intent = new Intent(Intent.ACTION_PICK);\n intent.setType(\"image/*\");\n activity.startActivityForResult(intent, requestCode);\n }\n\n // get photo from Uri\n public static String get_path_from_URI(Context context, Uri uri) {\n String result;\n Cursor cursor = context.getContentResolver().query(uri, null, null, null, null);\n if (cursor == null) {\n result = uri.getPath();\n } else {\n cursor.moveToFirst();\n int idx = cursor.getColumnIndex(MediaStore.Images.ImageColumns.DATA);\n result = cursor.getString(idx);\n cursor.close();\n }\n return result;\n }\n\n // compress picture\n public static Bitmap getScaleBitmap(String filePath) {\n BitmapFactory.Options opt = new BitmapFactory.Options();\n opt.inJustDecodeBounds = true;\n BitmapFactory.decodeFile(filePath, opt);\n\n int bmpWidth = opt.outWidth;\n int bmpHeight = opt.outHeight;\n\n int maxSize = 500;\n\n // compress picture with inSampleSize\n opt.inSampleSize = 1;\n while (true) {\n if (bmpWidth / opt.inSampleSize < maxSize || bmpHeight / opt.inSampleSize < maxSize) {\n break;\n }\n opt.inSampleSize *= 2;\n }\n opt.inJustDecodeBounds = false;\n return BitmapFactory.decodeFile(filePath, opt);\n }\n}\n\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/res/drawable/ic_launcher_background.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/res/drawable-v24/ic_launcher_foreground.xml", "content": "\n \n \n \n \n \n \n \n \n \n\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/res/layout/activity_main.xml", "content": "\n\n \n \n \n \n \n\n\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/res/mipmap-anydpi-v26/ic_launcher.xml", "content": "\n\n \n \n" }, { "path": "MobileNetSSD_demo_single/app/src/main/res/mipmap-anydpi-v26/ic_launcher_round.xml", "content": "\n\n \n \n" }, { "path": "MobileNetSSD_demo_single/app/src/main/res/values/colors.xml", "content": "\n\n #008577\n #00574B\n #D81B60\n\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/res/values/strings.xml", "content": "\n MobileNetSSD_demo\n\n" }, { "path": "MobileNetSSD_demo_single/app/src/main/res/values/styles.xml", "content": "\n\n \n \n\n\n" }, { "path": "MobileNetSSD_demo_single/app/src/test/java/com/example/che/mobilenetssd_demo/ExampleUnitTest.java", "content": "package com.example.che.mobilenetssd_demo;\n\nimport org.junit.Test;\n\nimport static org.junit.Assert.*;\n\n/**\n * Example local unit test, which will execute on the development machine (host).\n *\n * @see Testing documentation\n */\npublic class ExampleUnitTest {\n @Test\n public void addition_isCorrect() {\n assertEquals(4, 2 + 2);\n }\n}" }, { "path": "MobileNetSSD_demo_single/build.gradle", "content": "// Top-level build file where you can add configuration options common to all sub-projects/modules.\n\nbuildscript {\n \n repositories {\n google()\n jcenter()\n }\n dependencies {\n classpath 'com.android.tools.build:gradle:3.2.1'\n \n\n // NOTE: Do not place your application dependencies here; they belong\n // in the individual module build.gradle files\n }\n}\n\nallprojects {\n repositories {\n google()\n jcenter()\n }\n}\n\ntask clean(type: Delete) {\n delete rootProject.buildDir\n}\n" }, { "path": "MobileNetSSD_demo_single/gradle/wrapper/gradle-wrapper.properties", "content": "distributionBase=GRADLE_USER_HOME\ndistributionPath=wrapper/dists\ndistributionUrl=https\\://services.gradle.org/distributions/gradle-4.6-all.zip\nzipStoreBase=GRADLE_USER_HOME\nzipStorePath=wrapper/dists\n" }, { "path": "MobileNetSSD_demo_single/gradle.properties", "content": "# Project-wide Gradle settings.\n# IDE (e.g. Android Studio) users:\n# Gradle settings configured through the IDE *will override*\n# any settings specified in this file.\n# For more details on how to configure your build environment visit\n# http://www.gradle.org/docs/current/userguide/build_environment.html\n# Specifies the JVM arguments used for the daemon process.\n# The setting is particularly useful for tweaking memory settings.\norg.gradle.jvmargs=-Xmx1536m\n# When configured, Gradle will run in incubating parallel mode.\n# This option should only be used with decoupled projects. More details, visit\n# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects\n# org.gradle.parallel=true\n\n\n" }, { "path": "MobileNetSSD_demo_single/gradlew", "content": "#!/usr/bin/env sh\n\n##############################################################################\n##\n## Gradle start up script for UN*X\n##\n##############################################################################\n\n# Attempt to set APP_HOME\n# Resolve links: $0 may be a link\nPRG=\"$0\"\n# Need this for relative symlinks.\nwhile [ -h \"$PRG\" ] ; do\n ls=`ls -ld \"$PRG\"`\n link=`expr \"$ls\" : '.*-> \\(.*\\)$'`\n if expr \"$link\" : '/.*' > /dev/null; then\n PRG=\"$link\"\n else\n PRG=`dirname \"$PRG\"`\"/$link\"\n fi\ndone\nSAVED=\"`pwd`\"\ncd \"`dirname \\\"$PRG\\\"`/\" >/dev/null\nAPP_HOME=\"`pwd -P`\"\ncd \"$SAVED\" >/dev/null\n\nAPP_NAME=\"Gradle\"\nAPP_BASE_NAME=`basename \"$0\"`\n\n# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.\nDEFAULT_JVM_OPTS=\"\"\n\n# Use the maximum available, or set MAX_FD != -1 to use that value.\nMAX_FD=\"maximum\"\n\nwarn () {\n echo \"$*\"\n}\n\ndie () {\n echo\n echo \"$*\"\n echo\n exit 1\n}\n\n# OS specific support (must be 'true' or 'false').\ncygwin=false\nmsys=false\ndarwin=false\nnonstop=false\ncase \"`uname`\" in\n CYGWIN* )\n cygwin=true\n ;;\n Darwin* )\n darwin=true\n ;;\n MINGW* )\n msys=true\n ;;\n NONSTOP* )\n nonstop=true\n ;;\nesac\n\nCLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar\n\n# Determine the Java command to use to start the JVM.\nif [ -n \"$JAVA_HOME\" ] ; then\n if [ -x \"$JAVA_HOME/jre/sh/java\" ] ; then\n # IBM's JDK on AIX uses strange locations for the executables\n JAVACMD=\"$JAVA_HOME/jre/sh/java\"\n else\n JAVACMD=\"$JAVA_HOME/bin/java\"\n fi\n if [ ! -x \"$JAVACMD\" ] ; then\n die \"ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME\n\nPlease set the JAVA_HOME variable in your environment to match the\nlocation of your Java installation.\"\n fi\nelse\n JAVACMD=\"java\"\n which java >/dev/null 2>&1 || die \"ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.\n\nPlease set the JAVA_HOME variable in your environment to match the\nlocation of your Java installation.\"\nfi\n\n# Increase the maximum file descriptors if we can.\nif [ \"$cygwin\" = \"false\" -a \"$darwin\" = \"false\" -a \"$nonstop\" = \"false\" ] ; then\n MAX_FD_LIMIT=`ulimit -H -n`\n if [ $? -eq 0 ] ; then\n if [ \"$MAX_FD\" = \"maximum\" -o \"$MAX_FD\" = \"max\" ] ; then\n MAX_FD=\"$MAX_FD_LIMIT\"\n fi\n ulimit -n $MAX_FD\n if [ $? -ne 0 ] ; then\n warn \"Could not set maximum file descriptor limit: $MAX_FD\"\n fi\n else\n warn \"Could not query maximum file descriptor limit: $MAX_FD_LIMIT\"\n fi\nfi\n\n# For Darwin, add options to specify how the application appears in the dock\nif $darwin; then\n GRADLE_OPTS=\"$GRADLE_OPTS \\\"-Xdock:name=$APP_NAME\\\" \\\"-Xdock:icon=$APP_HOME/media/gradle.icns\\\"\"\nfi\n\n# For Cygwin, switch paths to Windows format before running java\nif $cygwin ; then\n APP_HOME=`cygpath --path --mixed \"$APP_HOME\"`\n CLASSPATH=`cygpath --path --mixed \"$CLASSPATH\"`\n JAVACMD=`cygpath --unix \"$JAVACMD\"`\n\n # We build the pattern for arguments to be converted via cygpath\n ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`\n SEP=\"\"\n for dir in $ROOTDIRSRAW ; do\n ROOTDIRS=\"$ROOTDIRS$SEP$dir\"\n SEP=\"|\"\n done\n OURCYGPATTERN=\"(^($ROOTDIRS))\"\n # Add a user-defined pattern to the cygpath arguments\n if [ \"$GRADLE_CYGPATTERN\" != \"\" ] ; then\n OURCYGPATTERN=\"$OURCYGPATTERN|($GRADLE_CYGPATTERN)\"\n fi\n # Now convert the arguments - kludge to limit ourselves to /bin/sh\n i=0\n for arg in \"$@\" ; do\n CHECK=`echo \"$arg\"|egrep -c \"$OURCYGPATTERN\" -`\n CHECK2=`echo \"$arg\"|egrep -c \"^-\"` ### Determine if an option\n\n if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then ### Added a condition\n eval `echo args$i`=`cygpath --path --ignore --mixed \"$arg\"`\n else\n eval `echo args$i`=\"\\\"$arg\\\"\"\n fi\n i=$((i+1))\n done\n case $i in\n (0) set -- ;;\n (1) set -- \"$args0\" ;;\n (2) set -- \"$args0\" \"$args1\" ;;\n (3) set -- \"$args0\" \"$args1\" \"$args2\" ;;\n (4) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" ;;\n (5) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" ;;\n (6) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" ;;\n (7) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" \"$args6\" ;;\n (8) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" \"$args6\" \"$args7\" ;;\n (9) set -- \"$args0\" \"$args1\" \"$args2\" \"$args3\" \"$args4\" \"$args5\" \"$args6\" \"$args7\" \"$args8\" ;;\n esac\nfi\n\n# Escape application args\nsave () {\n for i do printf %s\\\\n \"$i\" | sed \"s/'/'\\\\\\\\''/g;1s/^/'/;\\$s/\\$/' \\\\\\\\/\" ; done\n echo \" \"\n}\nAPP_ARGS=$(save \"$@\")\n\n# Collect all arguments for the java command, following the shell quoting and substitution rules\neval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS \"\\\"-Dorg.gradle.appname=$APP_BASE_NAME\\\"\" -classpath \"\\\"$CLASSPATH\\\"\" org.gradle.wrapper.GradleWrapperMain \"$APP_ARGS\"\n\n# by default we should be in the correct project dir, but when run from Finder on Mac, the cwd is wrong\nif [ \"$(uname)\" = \"Darwin\" ] && [ \"$HOME\" = \"$PWD\" ]; then\n cd \"$(dirname \"$0\")\"\nfi\n\nexec \"$JAVACMD\" \"$@\"\n" }, { "path": "MobileNetSSD_demo_single/gradlew.bat", "content": "@if \"%DEBUG%\" == \"\" @echo off\r\n@rem ##########################################################################\r\n@rem\r\n@rem Gradle startup script for Windows\r\n@rem\r\n@rem ##########################################################################\r\n\r\n@rem Set local scope for the variables with windows NT shell\r\nif \"%OS%\"==\"Windows_NT\" setlocal\r\n\r\nset DIRNAME=%~dp0\r\nif \"%DIRNAME%\" == \"\" set DIRNAME=.\r\nset APP_BASE_NAME=%~n0\r\nset APP_HOME=%DIRNAME%\r\n\r\n@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.\r\nset DEFAULT_JVM_OPTS=\r\n\r\n@rem Find java.exe\r\nif defined JAVA_HOME goto findJavaFromJavaHome\r\n\r\nset JAVA_EXE=java.exe\r\n%JAVA_EXE% -version >NUL 2>&1\r\nif \"%ERRORLEVEL%\" == \"0\" goto init\r\n\r\necho.\r\necho ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.\r\necho.\r\necho Please set the JAVA_HOME variable in your environment to match the\r\necho location of your Java installation.\r\n\r\ngoto fail\r\n\r\n:findJavaFromJavaHome\r\nset JAVA_HOME=%JAVA_HOME:\"=%\r\nset JAVA_EXE=%JAVA_HOME%/bin/java.exe\r\n\r\nif exist \"%JAVA_EXE%\" goto init\r\n\r\necho.\r\necho ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%\r\necho.\r\necho Please set the JAVA_HOME variable in your environment to match the\r\necho location of your Java installation.\r\n\r\ngoto fail\r\n\r\n:init\r\n@rem Get command-line arguments, handling Windows variants\r\n\r\nif not \"%OS%\" == \"Windows_NT\" goto win9xME_args\r\n\r\n:win9xME_args\r\n@rem Slurp the command line arguments.\r\nset CMD_LINE_ARGS=\r\nset _SKIP=2\r\n\r\n:win9xME_args_slurp\r\nif \"x%~1\" == \"x\" goto execute\r\n\r\nset CMD_LINE_ARGS=%*\r\n\r\n:execute\r\n@rem Setup the command line\r\n\r\nset CLASSPATH=%APP_HOME%\\gradle\\wrapper\\gradle-wrapper.jar\r\n\r\n@rem Execute Gradle\r\n\"%JAVA_EXE%\" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% \"-Dorg.gradle.appname=%APP_BASE_NAME%\" -classpath \"%CLASSPATH%\" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%\r\n\r\n:end\r\n@rem End local scope for the variables with windows NT shell\r\nif \"%ERRORLEVEL%\"==\"0\" goto mainEnd\r\n\r\n:fail\r\nrem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of\r\nrem the _cmd.exe /c_ return code!\r\nif not \"\" == \"%GRADLE_EXIT_CONSOLE%\" exit 1\r\nexit /b 1\r\n\r\n:mainEnd\r\nif \"%OS%\"==\"Windows_NT\" endlocal\r\n\r\n:omega\r\n" }, { "path": "MobileNetSSD_demo_single/settings.gradle", "content": "include ':app'\n" }, { "path": "README.md", "content": "# ncnnforandroid_objectiondetection_Mobilenetssd\n利用Mobilenetssd目标检测框架,ncnn前向推理,android项目\n请看我的个人blog配套教程\n - [MobileNetSSD通过Ncnn前向推理框架在PC端的使用(目标检测 objection detection)](https://blog.csdn.net/qq_33431368/article/details/84990390)\n - [MobileNetSSD通过Ncnn前向推理框架在Android端的使用--Cmake编译(目标检测 objection detection)](https://blog.csdn.net/qq_33431368/article/details/85009758)\n - [MobileNetSSD通过Ncnn前向推理框架在Android端的使用--Cmake编译(目标检测 objection detection)补充篇章(多目标也可以显示)](https://blog.csdn.net/qq_33431368/article/details/85019234)\n" } ]