This is a sample code showing object detection using Arm NN public C++ API. The compiled application can take
as input and
with detections shown in bounding boxes, class labels and confidence.
This example utilises OpenCV functions to capture and output video data. Top level inference API is provided by Arm NN library.
Object detection example build system does not trigger Arm NN compilation. Thus, before building the application, please ensure that Arm NN libraries and header files are available on your build platform. The application executable binary dynamically links with the following Arm NN libraries:
The build script searches for available Arm NN libraries in the following order:
Arm NN header files will be searched in parent directory of found libraries files under include directory, i.e. libraries found in /usr/lib or /usr/lib64 and header files in /usr/include (or ${ARMNN_LIB_DIR}/include).
Please see find_armnn.cmake for implementation details.
This application uses OpenCV (Open Source Computer Vision Library) for video stream processing. Your host platform may have OpenCV available through linux package manager. If this is the case, please install it using standard way. If not, our build system has a script to download and cross-compile required OpenCV modules as well as FFMPEG and x264 encoder libraries. The latter will build limited OpenCV functionality and application will support only video file input and video file output way of working. Displaying video frames in a window requires building OpenCV with GTK and OpenGL support.
The application executable binary dynamically links with the following OpenCV libraries:
and transitively depends on:
The application searches for above libraries in the following order:
If no OpenCV libraries were found, the cross-compilation build is extended with x264, ffmpeg and OpenCV compilation steps.
Note: Native build does not add third party libraries to compilation.
Please see find_opencv.cmake for implementation details.
There are two flows for building this application:
cmake/aarch64-toolchain.cmakecmake/arm-linux-gnueabihf-toolchain.cmake1 to build tests. Additionally to the main application, object_detection_example-tests unit tests executable will be created.To build this application on a host platform, firstly ensure that required dependencies are installed: For example, for raspberry PI:
sudo apt-get update sudo apt-get -yq install pkg-config sudo apt-get -yq install libgtk2.0-dev zlib1g-dev libjpeg-dev libpng-dev libxvidcore-dev libx264-dev sudo apt-get -yq install libavcodec-dev libavformat-dev libswscale-dev
To build demo application, create a build directory:
mkdir build
cd build
If you have already installed Arm NN and OpenCV:
Inside build directory, run cmake and make commands:
cmake .. make
This will build the following in bin directory:
If you have custom Arm NN and OpenCV location, use OPENCV_LIB_DIR and ARMNN_LIB_DIR options:
cmake -DARMNN_LIB_DIR=/path/to/armnn -DOPENCV_LIB_DIR=/path/to/opencv .. make
This section will explain how to cross-compile the application and dependencies on a Linux x86 machine for arm host platforms.
You will require working cross-compilation toolchain supported by your host platform. For raspberry Pi 3 and 4 with glibc runtime version 2.28, the following toolchains were successfully used:
Choose aarch64-linux-gnu if lscpu command shows architecture as aarch64 or arm-linux-gnueabihf if detected architecture is armv71.
You can check runtime version on your host platform by running:
ldd --version
On build machine, install C and C++ cross compiler toolchains and add them to the PATH variable.
Install package dependencies:
sudo apt-get update sudo apt-get -yq install pkg-config
Package config is required by OpenCV build to discover FFMPEG libs.
To build demo application, create a build directory:
mkdir build
cd build
Inside build directory, run cmake and make commands:
Arm 32bit
cmake -DARMNN_LIB_DIR=<path-to-armnn-libs> -DCMAKE_TOOLCHAIN_FILE=cmake/arm-linux-gnueabihf-toolchain.cmake .. make
Arm 64bit
cmake -DARMNN_LIB_DIR=<path-to-armnn-libs> -DCMAKE_TOOLCHAIN_FILE=cmake/aarch64-toolchain.cmake .. make
Add -j flag to the make command to run compilation in multiple threads.
From the build directory, copy the following to the host platform:
The full list of libs after cross-compilation to copy on your board:
libarmnn.so libarmnn.so.29 libarmnn.so.29.0 libarmnnTfLiteParser.so libarmnnTfLiteParser.so.24.4 libavcodec.so libavcodec.so.58 libavcodec.so.58.54.100 libavdevice.so libavdevice.so.58 libavdevice.so.58.8.100 libavfilter.so libavfilter.so.7 libavfilter.so.7.57.100 libavformat.so libavformat.so.58 libavformat.so.58.29.100 libavutil.so libavutil.so.56 libavutil.so.56.31.100 libopencv_core.so libopencv_core.so.4.0 libopencv_core.so.4.0.0 libopencv_highgui.so libopencv_highgui.so.4.0 libopencv_highgui.so.4.0.0 libopencv_imgcodecs.so libopencv_imgcodecs.so.4.0 libopencv_imgcodecs.so.4.0.0 libopencv_imgproc.so libopencv_imgproc.so.4.0 libopencv_imgproc.so.4.0.0 libopencv_video.so libopencv_video.so.4.0 libopencv_video.so.4.0.0 libopencv_videoio.so libopencv_videoio.so.4.0 libopencv_videoio.so.4.0.0 libpostproc.so libpostproc.so.55 libpostproc.so.55.5.100 libswresample.a libswresample.so libswresample.so.3 libswresample.so.3.5.100 libswscale.so libswscale.so.5 libswscale.so.5.5.100 libx264.so libx264.so.160
Once the application executable is built, it can be executed with the following options:
To run object detection on a supplied video file and output result to a video file:
LD_LIBRARY_PATH=/path/to/armnn/libs:/path/to/opencv/libs ./object_detection_example --label-path /path/to/labels/file --video-file-path /path/to/video/file --model-file-path /path/to/model/file --model-name [YOLO_V3_TINY | SSD_MOBILE] --output-video-file-path /path/to/output/file
To run object detection on a supplied video file and output result to a window gui:
LD_LIBRARY_PATH=/path/to/armnn/libs:/path/to/opencv/libs ./object_detection_example --label-path /path/to/labels/file --video-file-path /path/to/video/file --model-file-path /path/to/model/file --model-name [YOLO_V3_TINY | SSD_MOBILE]
This application has been verified to work against the MobileNet SSD and the YOLO V3 tiny models, which can be downloaded along with their label sets from the Arm Model Zoo:
This section provides a walkthrough of the application, explaining in detail the steps:
After parsing user arguments, the chosen video file or stream is loaded into an OpenCV cv::VideoCapture object. We use IFrameReader interface and OpenCV specific implementation CvVideoFrameReader in our main function to capture frames from the source using the ReadFrame() function.
The CvVideoFrameReader object also tells us information about the input video. Using this information and application arguments, we create one of the implementations of the IFrameOutput interface: CvVideoFileWriter or CvWindowOutput. This object will be used at the end of every loop to write the processed frame to an output video file or gui window. CvVideoFileWriter uses cv::VideoWriter with ffmpeg backend. CvWindowOutput makes use of cv::imshow() function.
See GetFrameSourceAndSink function in Main.cpp for more details.
In order to interpret the result of running inference on the loaded network, it is required to load the labels associated with the model. In the provided example code, the AssignColourToLabel function creates a vector of pairs label - colour that is ordered according to object class index at the output node of the model. Labels are assigned with a randomly generated RGB color. This ensures that each class has a unique color which will prove helpful when plotting the bounding boxes of various detected objects in a frame.
Depending on the model being used, CreatePipeline function returns specific implementation of the object detection pipeline.
All operations with Arm NN and networks are encapsulated in ArmnnNetworkExecutor class.
The first step with Arm NN SDK is to import a graph from file by using the appropriate parser.
The Arm NN SDK provides parsers for reading graphs from a variety of model formats. In our application we specifically focus on .tflite, .pb, .onnx models.
Based on the extension of the provided model file, the corresponding parser is created and the network file loaded with CreateNetworkFromBinaryFile() method. The parser will handle the creation of the underlying Arm NN graph.
Current example accepts tflite format model files, we use ITfLiteParser:
#include "armnnTfLiteParser/ITfLiteParser.hpp" armnnTfLiteParser::ITfLiteParserPtr parser = armnnTfLiteParser::ITfLiteParser::Create(); armnn::INetworkPtr network = parser->CreateNetworkFromBinaryFile(modelPath.c_str());
Arm NN supports optimized execution on multiple CPU and GPU devices. Prior to executing a graph, we must select the appropriate device context. We do this by creating a runtime context with default options with IRuntime().
For example:
#include "armnn/ArmNN.hpp" auto runtime = armnn::IRuntime::Create(armnn::IRuntime::CreationOptions());
We can optimize the imported graph by specifying a list of backends in order of preference and implement backend-specific optimizations. The backends are identified by a string unique to the backend, for example CpuAcc, GpuAcc, CpuRef.
For example:
std::vector<armnn::BackendId> backends{"CpuAcc", "GpuAcc", "CpuRef"};
Internally and transparently, Arm NN splits the graph into subgraph based on backends, it calls a optimize subgraphs function on each of them and, if possible, substitutes the corresponding subgraph in the original graph with its optimized version.
Using the Optimize() function we optimize the graph for inference and load the optimized network onto the compute device with LoadNetwork(). This function creates the backend-specific workloads for the layers and a backend specific workload factory which is called to create the workloads.
For example:
armnn::IOptimizedNetworkPtr optNet = Optimize(*network, backends, m_Runtime->GetDeviceSpec(), armnn::OptimizerOptions()); std::string errorMessage; runtime->LoadNetwork(0, std::move(optNet), errorMessage)); std::cerr << errorMessage << std::endl;
Parsers can also be used to extract the input information for the network. By calling GetSubgraphInputTensorNames we extract all the input names and, with GetNetworkInputBindingInfo, bind the input points of the graph. For example:
std::vector<std::string> inputNames = parser->GetSubgraphInputTensorNames(0); auto inputBindingInfo = parser->GetNetworkInputBindingInfo(0, inputNames[0]);
The input binding information contains all the essential information about the input. It is a tuple consisting of integer identifiers for bindable layers (inputs, outputs) and the tensor info (data type, quantization information, number of dimensions, total number of elements).
Similarly, we can get the output binding information for an output layer by using the parser to retrieve output tensor names and calling GetNetworkOutputBindingInfo().
Generic object detection pipeline has 3 steps to perform data pre-processing, run inference and decode inference results in the post-processing step.
See ObjDetectionPipeline and implementations for MobileNetSSDv1 and YoloV3Tiny for more details.
Each frame captured from source is read as an cv::Mat in BGR format but channels are swapped to RGB in a frame reader code.
cv::Mat processed; ... objectDetectionPipeline->PreProcessing(frame, processed);
A pre-processing step consists of resizing the frame to the required resolution, padding and doing data type conversion to match the model input layer. For example, SSD MobileNet V1 that is used in our example takes for input a tensor with shape [1, 300, 300, 3] and data type uint8.
Pre-processing step returns cv::Mat object containing data ready for inference.
od::InferenceResults results; ... objectDetectionPipeline->Inference(processed, results);
Inference step will call ArmnnNetworkExecutor::Run method that will prepare input tensors and execute inference. A compute device performs inference for the loaded network using the EnqueueWorkload() function of the runtime context. For example:
//const void* inputData = ...; //outputTensors were pre-allocated before armnn::InputTensors inputTensors = {{ inputBindingInfo.first,armnn::ConstTensor(inputBindingInfo.second, inputData)}}; runtime->EnqueueWorkload(0, inputTensors, outputTensors);
We allocate memory for output data once and map it to output tensor objects. After successful inference, we read data from the pre-allocated output data buffer. See ArmnnNetworkExecutor::ArmnnNetworkExecutor and ArmnnNetworkExecutor::Run for more details.
The output from inference must be decoded to obtain information about detected objects in the frame. In the examples there are implementations for two networks but you may also implement your own network decoding solution here.
For SSD MobileNet V1 models, we decode the results to obtain the bounding box positions, classification index, confidence and number of detections in the input frame. See SSDResultDecoder for more details.
For YOLO V3 Tiny models, we decode the output and perform non-maximum suppression to filter out any weak detections below a confidence threshold and any redudant bounding boxes above an intersection-over-union threshold. See YoloResultDecoder for more details.
It is encouraged to experiment with threshold values for confidence and intersection-over-union (IoU) to achieve the best visual results.
The detection results are always returned as a vector of DetectedObject, with the box positions list containing bounding box coordinates in the form [x_min, y_min, x_max, y_max].
Post-processing step accepts a callback function to be invoked when the decoding is finished. We will use it to draw detections on the initial frame. With the obtained detections and using AddInferenceOutputToFrame function, we are able to draw bounding boxes around detected objects and add the associated label and confidence score.
//results - inference output objectDetectionPipeline->PostProcessing(results, [&frame, &labels](od::DetectedObjects detects) -> void { AddInferenceOutputToFrame(detects, *frame, labels); });
The processed frames are written to a file or displayed in a separate window.