insightface/cpp-package/inspireface/README.md

InspireFace

0. Overview

InspireFace is a cross-platform face recognition SDK developed in C/C++, supporting multiple operating systems and various backend types for inference, such as CPU, GPU, and NPU.

1. Preparation

1.1. Downloading 3rdparty Files

You can download the third-party libraries necessary for the compilation process, InspireFace-3rdparty, from Google Drive and extract them to any location on your disk.

1.2. Downloading Pack Files

You can download the pack files containing models and configurations needed for compilation from Google Drive and extract them to any location.

1.3. Installing OpenCV

If you intend to use the SDK locally or on a server, ensure that OpenCV is installed on the host device beforehand to enable successful linking during the compilation process. For cross-compilation targets like Android or ARM embedded boards, you can use the pre-compiled OpenCV libraries provided by InspireFace-3rdparty.

1.4. Installing MNN

InspireFace-3rdparty includes pre-compiled MNN libraries tailored for various platforms. However, due to differences in underlying device libraries, you may need to compile the MNN library yourself if the provided versions do not match your hardware.

1.5. Requirements

• CMake (version 3.10 or higher)
• OpenCV (version 4.20 or higher)
  • Use the specific OpenCV-SDK supported by each target platform such as Android, iOS, and Linux.
• NDK (version 16 or higher, only required for Android)
• MNN (version 1.4.0 or higher)
• C++ Compiler
  • Either GCC or Clang can be used (macOS does not require additional installation as Xcode is included)
    • Recommended GCC version is 4.9 or higher
      • Note that in some distributions, GCC (GNU C Compiler) and G++ (GNU C++ Compiler) are installed separately.
      • For instance, on Ubuntu, you need to install both gcc and g++
    • Recommended Clang version is 3.9 or higher
  • arm-linux-gnueabihf (for RV1109/RV1126)
    • Prepare the cross-compilation toolchain in advance, such as gcc-arm-8.3-2019.03-x86_64-arm-linux-gnueabihf
• CUDA (version 10.1 or higher)
  • GPU-based inference requires installing NVIDIA's CUDA dependencies on the device.
• RKNN
  • Adjust and select versions currently supported for specific requirements.

2. Compilation

CMake option are used to control the various details of the compilation phase. Please select according to your actual requirements. Parameter table.

2.1. Local Compilation

Once InspireFace-3rdparty is prepared and OpenCV is installed, you can begin the compilation process. If you are using macOS or Linux, you can quickly compile using the shell scripts provided in the command/ folder at the project root:

cd InspireFace/
# Default, but you can also modify the shell script's -DTHIRD_PARTY_DIR=....
ln -s YOUR_DIR/InspireFace-3rdparty ./3rdparty
# Execute the local compilation script
bash command/build.sh

After compilation, you can find the local file in the build directory, which contains the compilation results. The install directory structure is as follows:

install
└── InspireFace
   ├── include
   │   ├── herror.h
   │   └── inspireface.h
   └── lib
       └── libInspireFace.so

• libInspireFace.so：Compiled dynamic linking library.
• inspireface.h：Header file definition.
• herror.h：Reference error number definition.

2.2. Cross Compilation

Cross compilation requires you to prepare the target platform's cross-compilation toolchain on the host machine in advance. Here, compiling for Rockchip's embedded devices RV1109/RV1126 is used as an example:

# Set the path for the cross-compilation toolchain
export ARM_CROSS_COMPILE_TOOLCHAIN=YOUR_DIR/gcc-arm-8.3-2019.03-x86_64-arm-linux-gnueabihf
# Execute the cross-compilation script for RV1109/RV1126
bash command/build_cross_rv1109rv1126_armhf.sh

After the compilation is complete, you can find the compiled results in the build/rv1109rv1126_armhf/install directory.

2.3. Supported Platforms and Architectures

We have completed the adaptation and testing of the software across various operating systems and CPU architectures. This includes compatibility verification for platforms such as Linux, macOS, iOS, and Android, as well as testing for specific hardware support to ensure stable operation in diverse environments.

No.	Operating System	CPU Architecture	Special Device Support	Adapted	Passed Tests	Verification Device	Remarks
1	Linux	ARMv7	-	- [x]	- [x]	RV1126
2		ARMv8	-	- [x]	- [x]	RK3399
3		x86/x86_64	-	- [x]	- [x]
4		ARMv7	rv1109rv1126	- [x]	- [x]	RV1126	NPU
5		x86/x86_64	CUDA	- [x]	- [ ] ⚠️	RTX3090	Some issues in inference remain unresolved
6	macOS	Intel x86	-	- [x]	- [x]	MacBook Pro 16
7		Apple Silicon	-	- [ ]	- [ ]
8	iOS	ARM	-	- [ ]	- [ ]
9	Android	ARMv7	-	- [ ]	- [ ]
10		ARMv8	-	- [ ]	- [ ]

• Complete compilation scripts and successful compilation.
• Pass unit tests on physical devices.
• Meet all performance benchmarks in tests.

3. Example

3.1. C/C++ Sample

To integrate InspireFace into a C/C++ project, you simply need to link the InspireFace library and include the appropriate header files. Below is a basic example demonstrating face detection:

HResult ret;
// The resource file must be loaded before it can be used
ret = HFLaunchInspireFace(packPath);
if (ret != HSUCCEED) {
    std::cout << "Load Resource error: " << ret << std::endl;
    return ret;
}

// Enable the functions in the pipeline: mask detection, live detection, and face quality detection
HOption option = HF_ENABLE_QUALITY | HF_ENABLE_MASK_DETECT | HF_ENABLE_LIVENESS;
// Non-video or frame sequence mode uses IMAGE-MODE, which is always face detection without tracking
HFDetectMode detMode = HF_DETECT_MODE_IMAGE;
// Maximum number of faces detected
HInt32 maxDetectNum = 5;
// Handle of the current face SDK algorithm context
HFSession session = {0};
ret = HFCreateInspireFaceSessionOptional(option, detMode, maxDetectNum, &session);
if (ret != HSUCCEED) {
    std::cout << "Create FaceContext error: " << ret << std::endl;
    return ret;
}

// Load a image
cv::Mat image = cv::imread(sourcePath);
if (image.empty()) {
    std::cout << "The source entered is not a picture or read error." << std::endl;
    return 1;
}
// Prepare an image parameter structure for configuration
HFImageData imageParam = {0};
imageParam.data = image.data;       // Data buffer
imageParam.width = image.cols;      // Target view width
imageParam.height = image.rows;      // Target view width
imageParam.rotation = HF_CAMERA_ROTATION_0;      // Data source rotate
imageParam.format = HF_STREAM_BGR;      // Data source format

// Create an image data stream
HFImageStream imageHandle = {0};
ret = HFCreateImageStream(&imageParam, &imageHandle);
if (ret != HSUCCEED) {
    std::cout << "Create ImageStream error: " << ret << std::endl;
    return ret;
}

// Execute HF_FaceContextRunFaceTrack captures face information in an image
HFMultipleFaceData multipleFaceData = {0};
ret = HFExecuteFaceTrack(session, imageHandle, &multipleFaceData);
if (ret != HSUCCEED) {
    std::cout << "Execute HFExecuteFaceTrack error: " << ret << std::endl;
    return ret;
}
// Print the number of faces detected
auto faceNum = multipleFaceData.detectedNum;
std::cout << "Num of face: " << faceNum << std::endl;

ret = HFReleaseImageStream(imageHandle);
if (ret != HSUCCEED) {
    printf("Release image stream error: %lu\n", ret);
}
// The memory must be freed at the end of the program
ret = HFReleaseInspireFaceSession(session);
if (ret != HSUCCEED) {
    printf("Release session error: %lu\n", ret);
    return ret;
}

For more examples, you can refer to the cpp/sample sub-project located in the root directory. You can compile these sample executables by enabling the BUILD_WITH_SAMPLE option during the compilation process.

Note: For each error code feedback, you can click on this link to view detailed explanations.

3.2. Python Native Sample

We provide a Python API that allows for more efficient use of the InspireFace library. After compiling the dynamic link library, you need to either symlink or copy it to the python/inspireface/modules/core directory within the root directory. You can then start testing by navigating to the python/ directory. Your Python environment will need to have some dependencies installed:

• python >= 3.7
• opencv-python
• loguru
• tqdm
• numpy
• ctypes

# Use a symbolic link
ln -s YOUR_BUILD_DIR/install/InspireFace/lib/libInspireFace.so python/inspireface/modules/core
# Navigate to the sub-project directory
cd python

Import inspireface for a quick facial detection example:

import cv2
import inspireface as ifac
from inspireface.param import *

# Step 1: Initialize the SDK and load the algorithm resource files.
resource_path = "pack/Pikachu"
ret = ifac.launch(resource_path)
assert ret, "Launch failure. Please ensure the resource path is correct."

# Optional features, loaded during session creation based on the modules specified.
opt = HF_ENABLE_NONE
session = ifac.InspireFaceSession(opt, HF_DETECT_MODE_IMAGE)

# Load the image using OpenCV.
image = cv2.imread(image_path)
assert image is not None, "Please check that the image path is correct."

# Perform face detection on the image.
faces = session.face_detection(image)
print(f"face detection: {len(faces)} found")

# Copy the image for drawing the bounding boxes.
draw = image.copy()
for idx, face in enumerate(faces):
    print(f"{'==' * 20}")
    print(f"idx: {idx}")
    # Print Euler angles of the face.
    print(f"roll: {face.roll}, yaw: {face.yaw}, pitch: {face.pitch}")
    # Draw bounding box around the detected face.
    x1, y1, x2, y2 = face.location
    cv2.rectangle(draw, (x1, y1), (x2, y2), (0, 0, 255), 2)

In the project, more usage examples are provided:

• sample_face_detection.py: Facial detection example
• sample_face_recognition.py: Facial recognition example
• sample_face_track_from_video.py: Facial tracking from video stream example

4. Test

In the project, there is a subproject called cpp/test. To compile it, you need to enable the BUILD_WITH_TEST switch, which will allow you to compile executable programs for testing.

cmake -DBUILD_WITH_TEST=ON ..

If you need to run test cases, you will need to download the required resource files, which are test_res and pack respectively. Unzip the pack file into the test_res folder. The directory structure of test_res should be prepared as follows before testing:

test_res
├── data
├── images
├── pack		<- unzip pack.zip
├── save
├── valid_lfw_funneled.txt
├── video
└── video_frames

After compilation, you can find the executable program "Test" in install/test. The program accepts two optional parameters:

• test_dir：Path to the test resource files
• pack：Name of the model to be tested

./Test --test_dir PATH/test_res --pack Pikachu

During the process of building the test program using CMake, it will involve selecting CMake parameters. For specific details, you can refer to the parameter configuration table.

Note: If you want to view the benchmark test report, you can click on the link.

5. Function support

The following functionalities and technologies are currently supported.

Index	Function	Adaptation	Note
1	Face Detection	- [x]	SCRFD
2	Facial Landmark Detection	- [x]	HyperLandmark
3	Face Recognition	- [x]	ArcFace
4	Face Tracking	- [x]
5	Mask Detection	- [x]
6	Silent Liveness Detection	- [x]	MiniVision
7	Face Quality Detection	- [x]
8	Face Pose Estimation	- [x]
9	Age Prediction	- [ ]
10	Cooperative Liveness Detection	- [ ]

6. Models Pack List

For different scenarios, we currently provide several Packs, each containing multiple models and configurations.

Name	Supported Devices	Note	Link
Pikachu	CPU	Lightweight edge-side model	GDrive
Megatron	CPU, GPU	Local or server-side model	GDrive
Gundam-RV1109	RKNPU	Supports RK1109 and RK1126	GDrive