zhoufeng
ec7f9f395a
Signed-off-by: zhoufeng <zhoufeng54@huawei.com> |
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| .. | ||
| app | ||
| gradle/wrapper | ||
| images | ||
| .gitignore | ||
| README.en.md | ||
| README.md | ||
| build.gradle | ||
| gradle.properties | ||
| gradlew | ||
| gradlew.bat | ||
| settings.gradle | ||
README.en.md
Demo of Object Detection
The following describes how to use the MindSpore Lite C++ APIs (Android JNIs) and MindSpore Lite object detection models to perform on-device inference, detect the content captured by a device camera, and display the most possible detection result on the application's image preview screen.
Running Dependencies
- Android Studio 3.2 or later (Android 4.0 or later is recommended.)
Building and Running
-
Load the sample source code to Android Studio.
Start Android Studio, click
File > Settings > System Settings > Android SDK, and select the correspondingSDK Tools. As shown in the following figure, select an SDK and clickOK. Android Studio automatically installs the SDK.
Android SDK Tools is the default installation. You can see this by unchecking the
Hide Obsolete Packagesbox.If you have any Android Studio configuration problem when trying this demo, please refer to item 4 to resolve it.
-
Connect to an Android device and runs this application.
Connect to the Android device through a USB cable for debugging. Click
Run 'app'to run the sample project on your device.
Android Studio will automatically download MindSpore Lite, model files and other dependencies during the compilation process. Please be patient during this process.
For details about how to connect the Android Studio to a device for debugging, see https://developer.android.com/studio/run/device?hl=zh-cn.
The mobile phone needs to be turn on "USB debugging mode" before Android Studio can recognize the mobile phone. Huawei mobile phones generally turn on "USB debugging model" in Settings -> system and update -> developer Options -> USB debugging.
-
Continue the installation on the Android device. After the installation is complete, you can view the content captured by a camera and the inference result.
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The solutions of configuration problems:
4.1 Problems of NDK, CMake, JDK Tools:
If the tools installed in Android Studio are not recognized, you can re-download and install them from the corresponding official website, and configure the path.
4.2 NDK version does not match:
Open
Android SDK, clickShow Package Details, and select the appropriate NDK version according to the error message.
4.3 Problem of Android Studio version:
Update the Android Studio version in
Toolbar - Help - Checkout for Updates.4.4 Gradle dependencies installed too slowly:
As shown in the picture, open the Demo root directory
build. Gradlefile, then add huawei mirror source address:maven {url 'https://developer.huawei.com/repo/'}, modify the classpath to 4.0.0 and clicksync. Once the download is complete, restore the classpath version and synchronize it again.
Detailed Description of the Sample Program
This object detection sample program on the Android device includes a Java layer and a JNI layer. At the Java layer, the Android Camera 2 API is used to enable a camera to obtain image frames and process images. At the JNI layer, the model inference process is completed .
Configuring MindSpore Lite Dependencies
When MindSpore C++ APIs are called at the Android JNI layer, related library files are required. You can use MindSpore Lite source code compilation to generate the MindSpore Lite version. In this case, you need to use the compile command of generate with image preprocessing module.
In this example, the build process automatically downloads the mindspore-lite-1.0.1-runtime-arm64-cpu by the app/download.gradle file and saves in the app/src/main/cpp directory.
Note: if the automatic download fails, please manually download the relevant library files and put them in the corresponding location.
mindspore-lite-1.0.1-runtime-arm64-cpu.tar.gz Download link
android{
defaultConfig{
externalNativeBuild{
cmake{
arguments "-DANDROID_STL=c++_shared"
}
}
ndk{
abiFilters 'arm64-v8a'
}
}
}
Create a link to the .so library file in the app/CMakeLists.txt file:
# Set MindSpore Lite Dependencies.
set(MINDSPORELITE_VERSION mindspore-lite-1.0.1-runtime-arm64-cpu)
include_directories(${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION})
add_library(mindspore-lite SHARED IMPORTED )
add_library(minddata-lite SHARED IMPORTED )
set_target_properties(mindspore-lite PROPERTIES IMPORTED_LOCATION
${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libmindspore-lite.so)
set_target_properties(minddata-lite PROPERTIES IMPORTED_LOCATION
${CMAKE_SOURCE_DIR}/src/main/cpp/${MINDSPORELITE_VERSION}/lib/libminddata-lite.so)
# Link target library.
target_link_libraries(
...
mindspore-lite
minddata-lite
...
)
Downloading and Deploying a Model File
In this example, the download.gradle File configuration auto download ssd.msand placed in the 'app / libs / arm64-v8a' directory.
Note: if the automatic download fails, please manually download the relevant library files and put them in the corresponding location.
ssd.ms ssd.ms
Compiling On-Device Inference Code
Call MindSpore Lite C++ APIs at the JNI layer to implement on-device inference.
The inference code process is as follows. For details about the complete code, see src/cpp/MindSporeNetnative.cpp.
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Load the MindSpore Lite model file and build the context, session, and computational graph for inference.
- Load a model file. Create and configure the context for model inference.
// Buffer is the model data passed in by the Java layer jlong bufferLen = env->GetDirectBufferCapacity(buffer); char *modelBuffer = CreateLocalModelBuffer(env, buffer);- Create a session.
void **labelEnv = new void *; MSNetWork *labelNet = new MSNetWork; *labelEnv = labelNet; // Create context. lite::Context *context = new lite::Context; context->device_ctx_.type = lite::DT_CPU; context->thread_num_ = numThread; //Specify the number of threads to run inference // Create the mindspore session. labelNet->CreateSessionMS(modelBuffer, bufferLen, "device label", context); delete(context);- Load the model file and build a computational graph for inference.
void MSNetWork::CreateSessionMS(char* modelBuffer, size_t bufferLen, std::string name, mindspore::lite::Context* ctx) { CreateSession(modelBuffer, bufferLen, ctx); session = mindspore::session::LiteSession::CreateSession(ctx); auto model = mindspore::lite::Model::Import(modelBuffer, bufferLen); int ret = session->CompileGraph(model); } -
Pre-Process the imagedata and convert the input image into the Tensor format of the MindSpore model.
// Convert the Bitmap image passed in from the JAVA layer to Mat for OpenCV processing LiteMat lite_mat_bgr,lite_norm_mat_cut; if (!BitmapToLiteMat(env, srcBitmap, lite_mat_bgr)){ MS_PRINT("BitmapToLiteMat error"); return NULL; } int srcImageWidth = lite_mat_bgr.width_; int srcImageHeight = lite_mat_bgr.height_; if(!PreProcessImageData(lite_mat_bgr, lite_norm_mat_cut)){ MS_PRINT("PreProcessImageData error"); return NULL; } ImgDims inputDims; inputDims.channel =lite_norm_mat_cut.channel_; inputDims.width = lite_norm_mat_cut.width_; inputDims.height = lite_norm_mat_cut.height_; // Get the mindsore inference environment which created in loadModel(). void **labelEnv = reinterpret_cast<void **>(netEnv); if (labelEnv == nullptr) { MS_PRINT("MindSpore error, labelEnv is a nullptr."); return NULL; } MSNetWork *labelNet = static_cast<MSNetWork *>(*labelEnv); auto mSession = labelNet->session; if (mSession == nullptr) { MS_PRINT("MindSpore error, Session is a nullptr."); return NULL; } MS_PRINT("MindSpore get session."); auto msInputs = mSession->GetInputs(); auto inTensor = msInputs.front(); float *dataHWC = reinterpret_cast<float *>(lite_norm_mat_cut.data_ptr_); // copy input Tensor memcpy(inTensor->MutableData(), dataHWC, inputDims.channel * inputDims.width * inputDims.height * sizeof(float)); delete[] (dataHWC); -
The input image shall be NHWC(1:300:300:3).
bool PreProcessImageData(const LiteMat &lite_mat_bgr, LiteMat *lite_norm_mat_ptr) { bool ret = false; LiteMat lite_mat_resize; LiteMat &lite_norm_mat_cut = *lite_norm_mat_ptr; ret = ResizeBilinear(lite_mat_bgr, lite_mat_resize, 300, 300); if (!ret) { MS_PRINT("ResizeBilinear error"); return false; } LiteMat lite_mat_convert_float; ret = ConvertTo(lite_mat_resize, lite_mat_convert_float, 1.0 / 255.0); if (!ret) { MS_PRINT("ConvertTo error"); return false; } float means[3] = {0.485, 0.456, 0.406}; float vars[3] = {1.0 / 0.229, 1.0 / 0.224, 1.0 / 0.225}; SubStractMeanNormalize(lite_mat_convert_float, lite_norm_mat_cut, means, vars); return true; } -
Perform inference on the input tensor based on the model, obtain the output tensor, and perform post-processing.
Perform graph execution and on-device inference.
// After the model and image tensor data is loaded, run inference. auto status = mSession->RunGraph();Obtain the output data.
auto names = mSession->GetOutputTensorNames(); typedef std::unordered_map<std::string, std::vector<mindspore::tensor::MSTensor *>> Msout; std::unordered_map<std::string, mindspore::tensor::MSTensor *> msOutputs; for (const auto &name : names) { auto temp_dat =mSession->GetOutputByTensorName(name); msOutputs.insert(std::pair<std::string, mindspore::tensor::MSTensor *> {name, temp_dat}); } std::string retStr = ProcessRunnetResult(msOutputs, ret);The model output the object category scores (1:1917:81) and the object detection location offset (1:1917:4). The location offset can be calcalation the object location in getDefaultBoxes function .
void SSDModelUtil::getDefaultBoxes() { float fk[6] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; std::vector<struct WHBox> all_sizes; struct Product mProductData[19 * 19] = {0}; for (int i = 0; i < 6; i++) { fk[i] = config.model_input_height / config.steps[i]; } float scale_rate = (config.max_scale - config.min_scale) / (sizeof(config.num_default) / sizeof(int) - 1); float scales[7] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0}; for (int i = 0; i < sizeof(config.num_default) / sizeof(int); i++) { scales[i] = config.min_scale + scale_rate * i; } for (int idex = 0; idex < sizeof(config.feature_size) / sizeof(int); idex++) { float sk1 = scales[idex]; float sk2 = scales[idex + 1]; float sk3 = sqrt(sk1 * sk2); struct WHBox tempWHBox; all_sizes.clear(); if (idex == 0) { float w = sk1 * sqrt(2); float h = sk1 / sqrt(2); tempWHBox.boxw = 0.1; tempWHBox.boxh = 0.1; all_sizes.push_back(tempWHBox); tempWHBox.boxw = w; tempWHBox.boxh = h; all_sizes.push_back(tempWHBox); tempWHBox.boxw = h; tempWHBox.boxh = w; all_sizes.push_back(tempWHBox); } else { tempWHBox.boxw = sk1; tempWHBox.boxh = sk1; all_sizes.push_back(tempWHBox); for (int j = 0; j < sizeof(config.aspect_ratios[idex]) / sizeof(int); j++) { float w = sk1 * sqrt(config.aspect_ratios[idex][j]); float h = sk1 / sqrt(config.aspect_ratios[idex][j]); tempWHBox.boxw = w; tempWHBox.boxh = h; all_sizes.push_back(tempWHBox); tempWHBox.boxw = h; tempWHBox.boxh = w; all_sizes.push_back(tempWHBox); } tempWHBox.boxw = sk3; tempWHBox.boxh = sk3; all_sizes.push_back(tempWHBox); } for (int i = 0; i < config.feature_size[idex]; i++) { for (int j = 0; j < config.feature_size[idex]; j++) { mProductData[i * config.feature_size[idex] + j].x = i; mProductData[i * config.feature_size[idex] + j].y = j; } } int productLen = config.feature_size[idex] * config.feature_size[idex]; for (int i = 0; i < productLen; i++) { for (int j = 0; j < all_sizes.size(); j++) { struct NormalBox tempBox; float cx = (mProductData[i].y + 0.5) / fk[idex]; float cy = (mProductData[i].x + 0.5) / fk[idex]; tempBox.y = cy; tempBox.x = cx; tempBox.h = all_sizes[j].boxh; tempBox.w = all_sizes[j].boxw; mDefaultBoxes.push_back(tempBox); } } } }-
The higher scores and location of category can be calcluted by the nonMaximumSuppression function.
void SSDModelUtil::nonMaximumSuppression(const YXBoxes *const decoded_boxes, const float *const scores, const std::vector<int> &in_indexes, std::vector<int> &out_indexes, const float nmsThreshold, const int count, const int max_results) { int nR = 0; //number of results std::vector<bool> del(count, false); for (size_t i = 0; i < in_indexes.size(); i++) { if (!del[in_indexes[i]]) { out_indexes.push_back(in_indexes[i]); if (++nR == max_results) { break; } for (size_t j = i + 1; j < in_indexes.size(); j++) { const auto boxi = decoded_boxes[in_indexes[i]], boxj = decoded_boxes[in_indexes[j]]; float a[4] = {boxi.xmin, boxi.ymin, boxi.xmax, boxi.ymax}; float b[4] = {boxj.xmin, boxj.ymin, boxj.xmax, boxj.ymax}; if (IOU(a, b) > nmsThreshold) { del[in_indexes[j]] = true; } } } } } -
For the targets whose probability is greater than the threshold value, the output rectangle box needs to be restored to the original size after the rectangular box is filtered by NMS algorithm.
std::string SSDModelUtil::getDecodeResult(float *branchScores, float *branchBoxData) { std::string result = ""; NormalBox tmpBox[1917] = {0}; float mScores[1917][81] = {0}; float outBuff[1917][7] = {0}; float scoreWithOneClass[1917] = {0}; int outBoxNum = 0; YXBoxes decodedBoxes[1917] = {0}; // Copy branch outputs box data to tmpBox. for (int i = 0; i < 1917; ++i) { tmpBox[i].y = branchBoxData[i * 4 + 0]; tmpBox[i].x = branchBoxData[i * 4 + 1]; tmpBox[i].h = branchBoxData[i * 4 + 2]; tmpBox[i].w = branchBoxData[i * 4 + 3]; } // Copy branch outputs score to mScores. for (int i = 0; i < 1917; ++i) { for (int j = 0; j < 81; ++j) { mScores[i][j] = branchScores[i * 81 + j]; } } // NMS processing. ssd_boxes_decode(tmpBox, decodedBoxes); // const float nms_threshold = 0.6; const float nms_threshold = 0.3; for (int i = 1; i < 81; i++) { std::vector<int> in_indexes; for (int j = 0; j < 1917; j++) { scoreWithOneClass[j] = mScores[j][i]; // if (mScores[j][i] > 0.1) { if (mScores[j][i] > g_thres_map[i]) { in_indexes.push_back(j); } } if (in_indexes.size() == 0) { continue; } sort(in_indexes.begin(), in_indexes.end(), [&](int a, int b) { return scoreWithOneClass[a] > scoreWithOneClass[b]; }); std::vector<int> out_indexes; nonMaximumSuppression(decodedBoxes, scoreWithOneClass, in_indexes, out_indexes, nms_threshold); for (int k = 0; k < out_indexes.size(); k++) { outBuff[outBoxNum][0] = out_indexes[k]; //image id outBuff[outBoxNum][1] = i; //labelid outBuff[outBoxNum][2] = scoreWithOneClass[out_indexes[k]]; //scores outBuff[outBoxNum][3] = decodedBoxes[out_indexes[k]].xmin * inputImageWidth / 300; outBuff[outBoxNum][4] = decodedBoxes[out_indexes[k]].ymin * inputImageHeight / 300; outBuff[outBoxNum][5] = decodedBoxes[out_indexes[k]].xmax * inputImageWidth / 300; outBuff[outBoxNum][6] = decodedBoxes[out_indexes[k]].ymax * inputImageHeight / 300; outBoxNum++; } } MS_PRINT("outBoxNum %d", outBoxNum); for (int i = 0; i < outBoxNum; ++i) { std::string tmpid_str = std::to_string(outBuff[i][0]); result += tmpid_str; // image ID result += "_"; // tmpid_str = std::to_string(outBuff[i][1]); MS_PRINT("label_classes i %d, outBuff %d",i, (int) outBuff[i][1]); tmpid_str = label_classes[(int) outBuff[i][1]]; result += tmpid_str; // label id result += "_"; tmpid_str = std::to_string(outBuff[i][2]); result += tmpid_str; // scores result += "_"; tmpid_str = std::to_string(outBuff[i][3]); result += tmpid_str; // xmin result += "_"; tmpid_str = std::to_string(outBuff[i][4]); result += tmpid_str; // ymin result += "_"; tmpid_str = std::to_string(outBuff[i][5]); result += tmpid_str; // xmax result += "_"; tmpid_str = std::to_string(outBuff[i][6]); result += tmpid_str; // ymax result += ";"; } return result; } std::string SSDModelUtil::getDecodeResult(float *branchScores, float *branchBoxData) { std::string result = ""; NormalBox tmpBox[1917] = {0}; float mScores[1917][81] = {0}; float outBuff[1917][7] = {0}; float scoreWithOneClass[1917] = {0}; int outBoxNum = 0; YXBoxes decodedBoxes[1917] = {0}; // Copy branch outputs box data to tmpBox. for (int i = 0; i < 1917; ++i) { tmpBox[i].y = branchBoxData[i * 4 + 0]; tmpBox[i].x = branchBoxData[i * 4 + 1]; tmpBox[i].h = branchBoxData[i * 4 + 2]; tmpBox[i].w = branchBoxData[i * 4 + 3]; } // Copy branch outputs score to mScores. for (int i = 0; i < 1917; ++i) { for (int j = 0; j < 81; ++j) { mScores[i][j] = branchScores[i * 81 + j]; } } ssd_boxes_decode(tmpBox, decodedBoxes); const float nms_threshold = 0.3; for (int i = 1; i < 81; i++) { std::vector<int> in_indexes; for (int j = 0; j < 1917; j++) { scoreWithOneClass[j] = mScores[j][i]; if (mScores[j][i] > g_thres_map[i]) { in_indexes.push_back(j); } } if (in_indexes.size() == 0) { continue; } sort(in_indexes.begin(), in_indexes.end(), [&](int a, int b) { return scoreWithOneClass[a] > scoreWithOneClass[b]; }); std::vector<int> out_indexes; nonMaximumSuppression(decodedBoxes, scoreWithOneClass, in_indexes, out_indexes, nms_threshold); for (int k = 0; k < out_indexes.size(); k++) { outBuff[outBoxNum][0] = out_indexes[k]; //image id outBuff[outBoxNum][1] = i; //labelid outBuff[outBoxNum][2] = scoreWithOneClass[out_indexes[k]]; //scores outBuff[outBoxNum][3] = decodedBoxes[out_indexes[k]].xmin * inputImageWidth / 300; outBuff[outBoxNum][4] = decodedBoxes[out_indexes[k]].ymin * inputImageHeight / 300; outBuff[outBoxNum][5] = decodedBoxes[out_indexes[k]].xmax * inputImageWidth / 300; outBuff[outBoxNum][6] = decodedBoxes[out_indexes[k]].ymax * inputImageHeight / 300; outBoxNum++; } } MS_PRINT("outBoxNum %d", outBoxNum); for (int i = 0; i < outBoxNum; ++i) { std::string tmpid_str = std::to_string(outBuff[i][0]); result += tmpid_str; // image ID result += "_"; // tmpid_str = std::to_string(outBuff[i][1]); MS_PRINT("label_classes i %d, outBuff %d",i, (int) outBuff[i][1]); tmpid_str = label_classes[(int) outBuff[i][1]]; result += tmpid_str; // label id result += "_"; tmpid_str = std::to_string(outBuff[i][2]); result += tmpid_str; // scores result += "_"; tmpid_str = std::to_string(outBuff[i][3]); result += tmpid_str; // xmin result += "_"; tmpid_str = std::to_string(outBuff[i][4]); result += tmpid_str; // ymin result += "_"; tmpid_str = std::to_string(outBuff[i][5]); result += tmpid_str; // xmax result += "_"; tmpid_str = std::to_string(outBuff[i][6]); result += tmpid_str; // ymax result += ";"; } return result; } -