forked from mindspore-Ecosystem/mindspore
!6495 [MS][LITE]fix security error and change download link
Merge pull request !6495 from gongdaguo/fix_security_error
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Gitee
commit
0d0d34d4a7
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@ -25,6 +25,8 @@ graph_8bit_1021_combine.tflite
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lite-model_aiy_vision_classifier_insects_V1_3.tflite
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lite-model_aiy_vision_classifier_plants_V1_3.tflite
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lite-model_object_detection_mobile_object_labeler_v1_1.tflite
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lite-model_cropnet_classifier_cassava_disease_V1_1.tflite
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vision_classifier_fungi_mobile_V1_1_default_1.tflite
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detect.tflite
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ssd_mobilenet_v1_1_default_1.tflite
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object_detection_mobile_object_localizer_v1_1_default_1.tflite
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@ -126,11 +126,11 @@ target_link_libraries(
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)
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```
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* In this example, the download.gradle File configuration auto download MindSpore Lite version, placed in the 'app / src / main/cpp/mindspore_lite_x.x.x-minddata-arm64-cpu' directory.
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* In this example, the download.gradle File configuration auto download MindSpore Lite version, placed in the 'app/src/main/cpp/' directory.
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Note: if the automatic download fails, please manually download the relevant library files and put them in the corresponding location.
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MindSpore Lite version [MindSpore Lite version]( https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/libmindspore-lite.so)
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mindspore-lite-1.0.0-minddata-arm64-cpu.tar.gz [Download link](https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%201.0/mindspore-lite-1.0.0-minddata-arm64-cpu.tar.gz)
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### Downloading and Deploying a Model File
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@ -86,13 +86,13 @@ app
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### 配置MindSpore Lite依赖项
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Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通过MindSpore Lite源码编译生成`libmindspore-lite.so`库文件。
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Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通过MindSpore Lite[源码编译](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html)生成"mindspore-lite-X.X.X-mindata-armXX-cpu"库文件包（包含`libmindspore-lite.so`库文件和相关头文件，可包含多个兼容架构）。
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本示例中，build过程由download.gradle文件自动从华为服务器下载MindSpore Lite 版本文件，并放置在`app / src / main/cpp/mindspore_lite_x.x.x-minddata-arm64-cpu`目录下。
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本示例中，build过程由download.gradle文件自动从华为服务器下载MindSpore Lite 版本文件，并放置在`app / src / main/cpp/`目录下。
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* 注：若自动下载失败，请手动下载相关库文件并将其放在对应位置：
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MindSpore Lite版本 [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/libmindspore-lite.so)
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mindspore-lite-1.0.0-minddata-arm64-cpu.tar.gz [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%201.0/mindspore-lite-1.0.0-minddata-arm64-cpu.tar.gz)
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```
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@ -243,14 +243,16 @@ target_link_libraries(
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- 输出数据的后续处理。
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```cpp
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std::string ProcessRunnetResult(std::unordered_map<std::string,
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mindspore::tensor::MSTensor *> msOutputs, int runnetRet) {
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std::string ProcessRunnetResult(const int RET_CATEGORY_SUM, const char *const labels_name_map[],
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std::unordered_map<std::string, mindspore::tensor::MSTensor *> msOutputs) {
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// Get the branch of the model output.
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// Use iterators to get map elements.
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std::unordered_map<std::string, mindspore::tensor::MSTensor *>::iterator iter;
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iter = msOutputs.begin();
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// The mobilenetv2.ms model output just one branch.
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auto outputTensor = iter->second;
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int tensorNum = outputTensor->ElementsNum();
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MS_PRINT("Number of tensor elements:%d", tensorNum);
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@ -88,7 +88,7 @@ In this example, the download.gradle File configuration auto download library f
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Note: if the automatic download fails, please manually download the relevant library files and put them in the corresponding location.
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libmindspore-lite.so [libmindspore-lite.so]( https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/libmindspore-lite.so)
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mindspore-lite-1.0.0-minddata-arm64-cpu.tar.gz [Download link](https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%201.0/mindspore-lite-1.0.0-minddata-arm64-cpu.tar.gz)
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@ -27,7 +27,7 @@
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2. 连接Android设备，运行目标检测示例应用程序。
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通过USB连接Android设备调试，点击`Run 'app'`即可在你的设备上运行本示例项目。
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* 注：编译过程中Android Studio会自动下载MindSpore Lite、OpenCV、模型文件等相关依赖项，编译过程需做耐心等待。
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* 注：编译过程中Android Studio会自动下载MindSpore Lite、模型文件等相关依赖项，编译过程需做耐心等待。
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@ -85,9 +85,9 @@ app
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### 配置MindSpore Lite依赖项
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Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通过MindSpore Lite[源码编译](https://www.mindspore.cn/lite/docs/zh-CN/master/deploy.html)生成`libmindspore-lite.so`库文件。
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Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通过MindSpore Lite[源码编译](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html)生成"mindspore-lite-X.X.X-mindata-armXX-cpu"库文件包（包含`libmindspore-lite.so`库文件和相关头文件，可包含多个兼容架构）。
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在Android Studio中将编译完成的mindspore-lite-X.X.X-mindata-armXX-cpu压缩包（包含`libmindspore-lite.so`库文件和相关头文件，可包含多个兼容架构），解压之后放置在APP工程的`app/src/main/cpp`目录下，并在app的`build.gradle`文件中配置CMake编译支持，以及`arm64-v8a`和`armeabi-v7a`的编译支持，如下所示：
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在Android Studio中将编译完成的mindspore-lite-X.X.X-mindata-armXX-cpu压缩包，解压之后放置在APP工程的`app/src/main/cpp`目录下，并在app的`build.gradle`文件中配置CMake编译支持，以及`arm64-v8a`和`armeabi-v7a`的编译支持，如下所示：
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```
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android{
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defaultConfig{
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@ -130,7 +130,7 @@ target_link_libraries(
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* 注：若自动下载失败，请手动下载相关库文件并将其放在对应位置：
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* libmindspore-lite.so [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%200.7/libmindspore-lite.so)
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* mindspore-lite-1.0.0-minddata-arm64-cpu.tar.gz [下载链接](https://download.mindspore.cn/model_zoo/official/lite/lib/mindspore%20version%201.0/mindspore-lite-1.0.0-minddata-arm64-cpu.tar.gz)
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### 下载及部署模型文件
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@ -20,6 +20,7 @@
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#define MS_PRINT(format, ...) __android_log_print(ANDROID_LOG_INFO, "MSJNI", format, ##__VA_ARGS__)
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SSDModelUtil::~SSDModelUtil(void) {}
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/**
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* SSD model util constructor.
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@ -61,8 +62,7 @@ std::string SSDModelUtil::getDecodeResult(float *branchScores, float *branchBoxD
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}
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// NMS processing.
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ssd_boxes_decode(tmpBox, decodedBoxes);
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// const float nms_threshold = 0.6;
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ssd_boxes_decode(tmpBox, decodedBoxes, 0.1, 0.2, 1917);
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const float nms_threshold = 0.3;
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for (int i = 1; i < 81; i++) {
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std::vector<int> in_indexes;
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@ -26,6 +26,8 @@ class SSDModelUtil {
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// Constructor.
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SSDModelUtil(int srcImageWidth, int srcImgHeight);
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~SSDModelUtil();
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/**
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* Return the SSD model post-processing result.
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* @param branchScores
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@ -34,10 +36,6 @@ class SSDModelUtil {
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*/
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std::string getDecodeResult(float *branchScores, float *branchBoxData);
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// ============= variables =============.
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int inputImageHeight;
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int inputImageWidth;
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struct NormalBox {
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float y;
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float x;
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@ -64,7 +62,8 @@ class SSDModelUtil {
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private:
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std::vector<struct NormalBox> mDefaultBoxes;
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int inputImageHeight;
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int inputImageWidth;
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void getDefaultBoxes();
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@ -80,7 +79,6 @@ class SSDModelUtil {
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double IOU(float r1[4], float r2[4]);
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// ============= variables =============.
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struct network {
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int model_input_height = 300;
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