移除cv，请使用@yizhi/cv包

.npmignore

@@ -0,0 +1,12 @@
+/build
+/cache
+/cxx
+/models
+/node_modules
+/src
+/testdata
+/thirdpart
+/tool
+/.clang-format
+/CMakeLists.txt
+/tsconfig.json

CMakeLists.txt

@@ -10,7 +10,7 @@ if(NOT DEFINED CMAKE_BUILD_TYPE)
 	set(CMAKE_BUILD_TYPE Release)
 endif()
 
-
+set(NODE_COMMON_SOURCES)
 set(NODE_ADDON_FOUND OFF)
 
 include_directories(cxx)
@@ -74,26 +74,14 @@ if(EXISTS ${MNN_CMAKE_FILE})
 	message(STATUS "MNN_LIB_DIR: ${MNN_LIB_DIR}")
 	message(STATUS "MNN_INCLUDE_DIR: ${MNN_INCLUDE_DIR}")
 	message(STATUS "MNN_LIBS: ${MNN_LIBS}")
-	include_directories(${MNN_INCLUDE_DIRS})
+	include_directories(${MNN_INCLUDE_DIR})
 	link_directories(${MNN_LIB_DIR})
-	add_compile_definitions(USE_MNN)
-	set(USE_MNN ON)
-endif()
-
-# OpenCV
-set(OpenCV_CMAKE_FILE ${CMAKE_SOURCE_DIR}/thirdpart/OpenCV/${CMAKE_BUILD_TYPE}/config.cmake)
-if(EXISTS ${OpenCV_CMAKE_FILE})
-	include(${OpenCV_CMAKE_FILE})
-	message(STATUS "OpenCV_LIB_DIR: ${OpenCV_LIB_DIR}")
-	message(STATUS "OpenCV_INCLUDE_DIR: ${OpenCV_INCLUDE_DIR}")
-	message(STATUS "OpenCV_LIBS: ${OpenCV_LIBS}")
-	include_directories(${OpenCV_INCLUDE_DIRS})
-	link_directories(${OpenCV_LIB_DIR})
 
 	if(NODE_ADDON_FOUND)
-		add_node_targert(cv cxx/cv/node.cc)
+		add_node_targert(mnn cxx/mnn/node.cc)
-		target_link_libraries(cv ${OpenCV_LIBS})
+		target_link_libraries(mnn ${MNN_LIBS})
-		target_compile_definitions(cv PUBLIC USE_OPENCV)
+		target_compile_definitions(mnn PUBLIC USE_MNN)
+		list(APPEND NODE_COMMON_SOURCES cxx/mnn/node.cc)
 	endif()
 endif()
 
@@ -111,12 +99,29 @@ if(EXISTS ${ONNXRuntime_CMAKE_FILE})
 		add_node_targert(ort cxx/ort/node.cc)
 		target_link_libraries(ort ${ONNXRuntime_LIBS})
 		target_compile_definitions(ort PUBLIC USE_ONNXRUNTIME)
+		list(APPEND NODE_COMMON_SOURCES cxx/ort/node.cc)
 	endif()
 
 endif()
 
+# 统一的NodeJS插件
+if(NODE_ADDON_FOUND)
+	add_node_targert(addon cxx/node.cc)
+	target_sources(addon PRIVATE ${NODE_COMMON_SOURCES})
+	target_compile_definitions(addon PUBLIC BUILD_MAIN_WORD)
+	# MNN
+	if(EXISTS ${MNN_CMAKE_FILE})
+		target_link_libraries(addon ${MNN_LIBS})
+		target_compile_definitions(addon PUBLIC USE_MNN)
+	endif()
+	# OnnxRuntime
+	if(EXISTS ${ONNXRuntime_CMAKE_FILE})
+		target_link_libraries(addon ${ONNXRuntime_LIBS})
+		target_compile_definitions(addon PUBLIC USE_ONNXRUNTIME)
+	endif()
+endif()
 
-if(MSVC)
+if(MSVC AND NODE_ADDON_FOUND)
   set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /MT")
   execute_process(COMMAND ${CMAKE_AR} /def:${CMAKE_JS_NODELIB_DEF} /out:${CMAKE_JS_NODELIB_TARGET} ${CMAKE_STATIC_LINKER_FLAGS})
 endif()

README.md

@@ -1 +1,46 @@
-## AI工具箱
+#AI工具箱
+
+## 安装
+
+```
+npm install @yizhi/ai
+```
+
+## 使用
+
+```typescript
+import ai from "@yizhi/ai";
+
+// 配置相应的node插件，插件需自行编译
+ai.config("CV_ADDON_FILE", "/path/to/cv.node");
+ai.config("MNN_ADDON_FILE", "/path/to/mnn.node");
+ai.config("ORT_ADDON_FILE", "/path/to/onnxruntime.node");
+
+//直接推理
+const facedet = await ai.deploy.facedet.Yolov5Face.load("YOLOV5S_ONNX");
+const boxes = await facedet.predict("/path/to/image");
+
+//使用自己的模型
+const session = new ai.backend.ort.Session(modelBuffer);
+const outputs = session.run(inputs);
+
+```
+
+## 插件编译
+
+1. 依赖
+	1. cmake
+	1. ninja
+	1. c++编译器(gcc,clang,Visual Studio ...)
+
+1. 编译第三方库
+```
+node thirdpart/install.js --with-mnn --with-onnx
+```
+1. 编译插件
+```
+cmake -B build -G Ninja . -DCMAKE_BUILD_TYPE=Release
+cmake --build build --config Release
+```
+
+注意：注意：在Windows下编译时，需要打开Visual Studio命令行

cxx/common/node.h

@@ -6,8 +6,7 @@
 #include <napi.h>
 
 #define NODE_INIT_OBJECT(name, function)                 \
-	do                                                   \
+	do {                                                 \
-	{                                                    \
 		auto obj = Napi::Object::New(env);               \
 		function(env, obj);                              \
 		exports.Set(Napi::String::New(env, #name), obj); \
@@ -21,4 +20,13 @@ inline uint64_t __node_ptr_of__(Napi::Value value)
 
 #define NODE_PTR_OF(type, value) (reinterpret_cast<type *>(__node_ptr_of__(value)))
 
+
+inline void *dataFromTypedArray(const Napi::Value &val, size_t &bytes)
+{
+	auto arr = val.As<Napi::TypedArray>();
+	auto data = static_cast<uint8_t *>(arr.ArrayBuffer().Data());
+	bytes = arr.ByteLength();
+	return static_cast<void *>(data + arr.ByteOffset());
+}
+
 #endif

cxx/common/tensor.h

@@ -0,0 +1,18 @@
+#ifndef __COMMON_TENSOR_H__
+#define __COMMON_TENSOR_H__
+
+enum class TensorDataType {
+	Unknown,
+	Float32,
+	Float64,
+	Int32,
+	Uint32,
+	Int16,
+	Uint16,
+	Int8,
+	Uint8,
+	Int64,
+	Uint64,
+};
+
+#endif

cxx/cv/node.cc

@@ -1,145 +0,0 @@
-#include <iostream>
-#include <functional>
-#include <opencv2/opencv.hpp>
-#include "node.h"
-
-using namespace Napi;
-
-#define MAT_INSTANCE_METHOD(method) InstanceMethod<&CVMat::method>(#method, static_cast<napi_property_attributes>(napi_writable | napi_configurable))
-
-static FunctionReference *constructor = nullptr;
-
-class CVMat : public ObjectWrap<CVMat> {
-  public:
-	static Napi::Object Init(Napi::Env env, Napi::Object exports)
-	{
-		Function func = DefineClass(env, "Mat", {
-													MAT_INSTANCE_METHOD(IsEmpty),
-													MAT_INSTANCE_METHOD(GetCols),
-													MAT_INSTANCE_METHOD(GetRows),
-													MAT_INSTANCE_METHOD(GetChannels),
-													MAT_INSTANCE_METHOD(Resize),
-													MAT_INSTANCE_METHOD(Crop),
-													MAT_INSTANCE_METHOD(Rotate),
-													MAT_INSTANCE_METHOD(Clone),
-
-													MAT_INSTANCE_METHOD(DrawCircle),
-
-													MAT_INSTANCE_METHOD(Data),
-													MAT_INSTANCE_METHOD(Encode),
-												});
-		constructor = new FunctionReference();
-		*constructor = Napi::Persistent(func);
-		exports.Set("Mat", func);
-		env.SetInstanceData<FunctionReference>(constructor);
-		return exports;
-	}
-
-	CVMat(const CallbackInfo &info)
-		: ObjectWrap<CVMat>(info)
-	{
-		int mode = cv::IMREAD_COLOR_BGR;
-		if (info.Length() > 1 && info[1].IsObject()) {
-			Object options = info[1].As<Object>();
-			if (options.Has("mode") && options.Get("mode").IsNumber()) mode = options.Get("mode").As<Number>().Int32Value();
-		}
-
-		if (info[0].IsString()) im_ = cv::imread(info[0].As<String>().Utf8Value(), mode);
-		else if (info[0].IsTypedArray()) {
-			auto buffer = info[0].As<TypedArray>().ArrayBuffer();
-			uint8_t *bufferPtr = static_cast<uint8_t *>(buffer.Data());
-			std::vector<uint8_t> data(bufferPtr, bufferPtr + buffer.ByteLength());
-			im_ = cv::imdecode(data, mode);
-		}
-	}
-
-	~CVMat() { im_.release(); }
-
-	Napi::Value IsEmpty(const Napi::CallbackInfo &info) { return Boolean::New(info.Env(), im_.empty()); }
-	Napi::Value GetCols(const Napi::CallbackInfo &info) { return Number::New(info.Env(), im_.cols); }
-	Napi::Value GetRows(const Napi::CallbackInfo &info) { return Number::New(info.Env(), im_.rows); }
-	Napi::Value GetChannels(const Napi::CallbackInfo &info) { return Number::New(info.Env(), im_.channels()); }
-	Napi::Value Resize(const Napi::CallbackInfo &info)
-	{
-		return CreateMat(info.Env(), [this, &info](auto &mat) { cv::resize(im_, mat.im_, cv::Size(info[0].As<Number>().Int32Value(), info[1].As<Number>().Int32Value())); });
-	}
-	Napi::Value Crop(const Napi::CallbackInfo &info)
-	{
-		return CreateMat(info.Env(), [this, &info](auto &mat) {
-			mat.im_ = im_(cv::Rect(
-				info[0].As<Number>().Int32Value(), info[1].As<Number>().Int32Value(),
-				info[2].As<Number>().Int32Value(), info[3].As<Number>().Int32Value()));
-		});
-	}
-	Napi::Value Rotate(const Napi::CallbackInfo &info)
-	{
-		return CreateMat(info.Env(), [this, &info](auto &mat) {
-			auto x = info[0].As<Number>().DoubleValue();
-			auto y = info[1].As<Number>().DoubleValue();
-			auto angle = info[2].As<Number>().DoubleValue();
-			cv::Mat rotation_matix = cv::getRotationMatrix2D(cv::Point2f(x, y), angle, 1.0);
-			cv::warpAffine(im_, mat.im_, rotation_matix, im_.size());
-		});
-	}
-
-	Napi::Value Clone(const Napi::CallbackInfo &info)
-	{
-		return CreateMat(info.Env(), [this, &info](auto &mat) { mat.im_ = im_.clone(); });
-	}
-
-	Napi::Value DrawCircle(const Napi::CallbackInfo &info)
-	{
-		int x = info[0].As<Number>().Int32Value();
-		int y = info[1].As<Number>().Int32Value();
-		int radius = info[2].As<Number>().Int32Value();
-		int b = info[3].As<Number>().Int32Value();
-		int g = info[4].As<Number>().Int32Value();
-		int r = info[5].As<Number>().Int32Value();
-		int thickness = info[6].As<Number>().Int32Value();
-		int lineType = info[7].As<Number>().Int32Value();
-		int shift = info[8].As<Number>().Int32Value();
-
-		cv::circle(im_, cv::Point(x, y), radius, cv::Scalar(b, g, r), thickness, lineType, shift);
-		return info.Env().Undefined();
-	}
-
-	Napi::Value Data(const Napi::CallbackInfo &info) { return ArrayBuffer::New(info.Env(), im_.ptr(), im_.elemSize() * im_.total()); }
-	Napi::Value Encode(const Napi::CallbackInfo &info)
-	{
-		auto options = info[0].As<Object>();
-		auto extname = options.Get("extname").As<String>().Utf8Value();
-		cv::imencode(extname, im_, encoded_);
-		return ArrayBuffer::New(info.Env(), encoded_.data(), encoded_.size());
-	}
-
-
-  private:
-	inline Napi::Object EmptyMat(Napi::Env env) { return constructor->New({}).As<Object>(); }
-	inline CVMat &GetMat(Napi::Object obj) { return *ObjectWrap<CVMat>::Unwrap(obj); }
-	inline Napi::Object CreateMat(Napi::Env env, std::function<void(CVMat &mat)> callback)
-	{
-		auto obj = EmptyMat(env);
-		callback(GetMat(obj));
-		return obj;
-	}
-
-  private:
-	cv::Mat im_;
-	std::vector<uint8_t> encoded_;
-};
-
-
-void InstallOpenCVAPI(Env env, Object exports)
-{
-	CVMat::Init(env, exports);
-}
-
-#ifdef USE_OPENCV
-static Object Init(Env env, Object exports)
-{
-	InstallOpenCVAPI(env, exports);
-	return exports;
-}
-NODE_API_MODULE(addon, Init)
-
-#endif

cxx/cv/node.h

@@ -1,8 +0,0 @@
-#ifndef __CV_NODE_H__
-#define __CV_NODE_H__
-
-#include "common/node.h"
-
-void InstallOpenCVAPI(Napi::Env env, Napi::Object exports);
-
-#endif

cxx/mnn/node.cc

@@ -0,0 +1,244 @@
+#include <iostream>
+#include <vector>
+#include <map>
+#include <cstring>
+#include <MNN/Interpreter.hpp>
+#include <MNN/ImageProcess.hpp>
+#include "common/tensor.h"
+#include "node.h"
+
+using namespace Napi;
+
+#define SESSION_INSTANCE_METHOD(method) InstanceMethod<&MNNSession::method>(#method, static_cast<napi_property_attributes>(napi_writable | napi_configurable))
+
+static const std::map<TensorDataType, halide_type_t> DATA_TYPE_MAP = {
+	{TensorDataType::Float32, halide_type_of<float>()},
+	{TensorDataType::Float64, halide_type_of<double>()},
+	{TensorDataType::Int32, halide_type_of<int32_t>()},
+	{TensorDataType::Uint32, halide_type_of<uint32_t>()},
+	{TensorDataType::Int16, halide_type_of<int16_t>()},
+	{TensorDataType::Uint16, halide_type_of<uint16_t>()},
+	{TensorDataType::Int8, halide_type_of<int8_t>()},
+	{TensorDataType::Uint8, halide_type_of<uint8_t>()},
+	{TensorDataType::Int64, halide_type_of<int64_t>()},
+	{TensorDataType::Uint64, halide_type_of<uint64_t>()},
+};
+
+static size_t getShapeSize(const std::vector<int> &shape)
+{
+	if (!shape.size()) return 0;
+	size_t sum = 1;
+	for (auto i : shape) {
+		if (i > 1) sum *= i;
+	};
+	return sum;
+}
+
+static Napi::Value mnnSizeToJavascript(Napi::Env env, const std::vector<int> &shape)
+{
+	auto result = Napi::Array::New(env, shape.size());
+	for (int i = 0; i < shape.size(); ++i) result.Set(i, Napi::Number::New(env, shape[i]));
+	return result;
+}
+
+class MNNSessionRunWorker : public AsyncWorker {
+  public:
+	MNNSessionRunWorker(const Napi::Function &callback, MNN::Interpreter *interpreter, MNN::Session *session)
+		: AsyncWorker(callback), interpreter_(interpreter), session_(session) {}
+
+	~MNNSessionRunWorker()
+	{
+		interpreter_->releaseSession(session_);
+	}
+
+	void Execute()
+	{
+		if (MNN::ErrorCode::NO_ERROR != interpreter_->runSession(session_)) {
+			SetError(std::string("Run session failed"));
+		}
+	}
+
+	void OnOK()
+	{
+		if (HasError()) {
+			Callback().Call({Error::New(Env(), errorMessage_.c_str()).Value(), Env().Undefined()});
+		}
+		else {
+			auto result = Object::New(Env());
+			for (auto it : interpreter_->getSessionOutputAll(session_)) {
+				auto tensor = it.second;
+				auto item = Object::New(Env());
+				auto buffer = ArrayBuffer::New(Env(), tensor->size());
+				memcpy(buffer.Data(), tensor->host<float>(), tensor->size());
+				item.Set("data", buffer);
+				item.Set("shape", mnnSizeToJavascript(Env(), tensor->shape()));
+				result.Set(it.first, item);
+			}
+			Callback().Call({Env().Undefined(), result});
+		}
+	}
+
+	void SetInput(const std::string &name, TensorDataType dataType, const std::vector<int> &shape, void *data, size_t dataBytes)
+	{
+		auto tensor = interpreter_->getSessionInput(session_, name.c_str());
+		if (!tensor) {
+			SetError(std::string("input name #" + name + " not exists"));
+			return;
+		}
+
+		halide_type_t type = tensor->getType();
+		if (dataType != TensorDataType::Unknown) {
+			auto it = DATA_TYPE_MAP.find(dataType);
+			if (it != DATA_TYPE_MAP.end()) type = it->second;
+		}
+
+		if (shape.size()) {
+			interpreter_->resizeTensor(tensor, shape);
+			interpreter_->resizeSession(session_);
+		}
+		auto tensorBytes = getShapeSize(tensor->shape()) * type.bits / 8;
+		if (tensorBytes != dataBytes) {
+			SetError(std::string("input name #" + name + " data size not matched"));
+			return;
+		}
+
+		auto hostTensor = MNN::Tensor::create(tensor->shape(), type, data, MNN::Tensor::CAFFE);
+		tensor->copyFromHostTensor(hostTensor);
+		delete hostTensor;
+	}
+
+	inline void SetError(const std::string &what) { errorMessage_ = what; }
+	inline bool HasError() { return errorMessage_.size() > 0; }
+
+  private:
+	MNN::Interpreter *interpreter_;
+	MNN::Session *session_;
+	std::string errorMessage_;
+};
+
+class MNNSession : public ObjectWrap<MNNSession> {
+  public:
+	static Napi::Object Init(Napi::Env env, Napi::Object exports)
+	{
+		Function func = DefineClass(env, "MNNSession", {
+														   SESSION_INSTANCE_METHOD(GetInputsInfo),
+														   SESSION_INSTANCE_METHOD(GetOutputsInfo),
+														   SESSION_INSTANCE_METHOD(Run),
+													   });
+		FunctionReference *constructor = new FunctionReference();
+		*constructor = Napi::Persistent(func);
+		exports.Set("MNNSession", func);
+		env.SetInstanceData<FunctionReference>(constructor);
+		return exports;
+	}
+
+	MNNSession(const CallbackInfo &info)
+		: ObjectWrap(info)
+	{
+		try {
+			if (info[0].IsString()) {
+				interpreter_ = MNN::Interpreter::createFromFile(info[0].As<String>().Utf8Value().c_str());
+			}
+			else if (info[0].IsTypedArray()) {
+				size_t bufferBytes;
+				auto buffer = dataFromTypedArray(info[0], bufferBytes);
+				interpreter_ = MNN::Interpreter::createFromBuffer(buffer, bufferBytes);
+			}
+			else interpreter_ = nullptr;
+
+			if (interpreter_) {
+				backendConfig_.precision = MNN::BackendConfig::Precision_High;
+				backendConfig_.power = MNN::BackendConfig::Power_High;
+				scheduleConfig_.type = MNN_FORWARD_CPU;
+				scheduleConfig_.numThread = 1;
+				scheduleConfig_.backendConfig = &backendConfig_;
+				session_ = interpreter_->createSession(scheduleConfig_);
+			}
+			else session_ = nullptr;
+		}
+		catch (std::exception &e) {
+			Error::New(info.Env(), e.what()).ThrowAsJavaScriptException();
+		}
+	}
+
+	~MNNSession() {}
+
+	Napi::Value GetInputsInfo(const Napi::CallbackInfo &info) { return BuildInputOutputInfo(info.Env(), interpreter_->getSessionInputAll(session_)); }
+
+	Napi::Value GetOutputsInfo(const Napi::CallbackInfo &info) { return BuildInputOutputInfo(info.Env(), interpreter_->getSessionOutputAll(session_)); }
+
+	Napi::Value Run(const Napi::CallbackInfo &info)
+	{
+		auto worker = new MNNSessionRunWorker(info[1].As<Function>(), interpreter_, interpreter_->createSession(scheduleConfig_));
+		auto inputArgument = info[0].As<Object>();
+		for (auto it = inputArgument.begin(); it != inputArgument.end(); ++it) {
+			auto name = (*it).first.As<String>().Utf8Value();
+			auto inputOption = static_cast<Napi::Value>((*it).second).As<Object>();
+			auto type = inputOption.Has("type") ? static_cast<TensorDataType>(inputOption.Get("type").As<Number>().Int32Value()) : TensorDataType::Unknown;
+			size_t dataByteLen;
+			void *data = dataFromTypedArray(inputOption.Get("data"), dataByteLen);
+			auto shape = inputOption.Has("shape") ? GetShapeFromJavascript(inputOption.Get("shape").As<Array>()) : std::vector<int>();
+			worker->SetInput(name, type, shape, data, dataByteLen);
+		}
+		worker->Queue();
+		return info.Env().Undefined();
+	}
+
+  private:
+	Napi::Object BuildInputOutputInfo(Napi::Env env, const std::map<std::string, MNN::Tensor *> &tensors)
+	{
+		auto result = Object::New(env);
+		for (auto it : tensors) {
+			auto item = Object::New(env);
+			auto name = it.first;
+			auto shape = it.second->shape();
+			auto type = it.second->getType();
+			TensorDataType dataType = TensorDataType::Unknown;
+			for (auto dt : DATA_TYPE_MAP) {
+				if (dt.second == type) {
+					dataType = dt.first;
+					break;
+				}
+			}
+			auto shapeArr = Array::New(env, shape.size());
+			for (size_t i = 0; i < shape.size(); i++) {
+				shapeArr.Set(i, Number::New(env, shape[i]));
+			}
+			item.Set("name", String::New(env, name));
+			item.Set("shape", shapeArr);
+			item.Set("type", Number::New(env, static_cast<int>(dataType)));
+			result.Set(name, item);
+		}
+		return result;
+	}
+
+	std::vector<int> GetShapeFromJavascript(const Napi::Array &shape)
+	{
+		std::vector<int> result;
+		for (size_t i = 0; i < shape.Length(); i++) {
+			result.push_back(shape.Get(i).As<Number>().Int32Value());
+		}
+		return result;
+	}
+
+  private:
+	MNN::Interpreter *interpreter_;
+	MNN::Session *session_;
+	MNN::BackendConfig backendConfig_;
+	MNN::ScheduleConfig scheduleConfig_;
+};
+
+void InstallMNNAPI(Napi::Env env, Napi::Object exports)
+{
+	MNNSession::Init(env, exports);
+}
+
+
+#if defined(USE_MNN) && !defined(BUILD_MAIN_WORD)
+static Object Init(Env env, Object exports)
+{
+	InstallMNNAPI(env, exports);
+	return exports;
+}
+NODE_API_MODULE(addon, Init)
+#endif

cxx/mnn/node.h

@@ -0,0 +1,8 @@
+#ifndef __MNN_NODE_H__
+#define __MNN_NODE_H__
+
+#include "common/node.h"
+
+void InstallMNNAPI(Napi::Env env, Napi::Object exports);
+
+#endif

cxx/mnn/session.h

@@ -1,19 +0,0 @@
-#ifndef __MNN_SESSION_H__
-#define __MNN_SESSION_H__
-
-#include <MNN/Interpreter.hpp>
-#include <MNN/ImageProcess.hpp>
-
-#include "common/session.h"
-
-namespace ai
-{
-	class MNNSession : public Session
-	{
-	public:
-		MNNSession(const void *modelData, size_t size);
-		~MNNSession();
-	};
-}
-
-#endif

cxx/node.cc

@@ -1,65 +1,22 @@
-// #include <unistd.h>
+#include "common/node.h"
-// #include <napi.h>
+#include "mnn/node.h"
-// #include "cv/node.h"
+#include "ort/node.h"
-// #ifdef USE_ORT
-// #include "ort/node.h"
-// #endif
 
-// using namespace Napi;
+using namespace Napi;
 
-// class TestWork : public AsyncWorker
+#if defined(BUILD_MAIN_WORD)
-// {
+Object Init(Env env, Object exports)
-// public:
+{
-// 	TestWork(const Napi::Function &callback, int value) : Napi::AsyncWorker(callback), val_(value) {}
+// OnnxRuntime
-// 	~TestWork() {}
+#ifdef USE_ONNXRUNTIME
+	InstallOrtAPI(env, exports);
+#endif
+// MNN
+#ifdef USE_MNN
+	InstallMNNAPI(env, exports);
+#endif
 
-// 	void Execute()
+	return exports;
-// 	{
+}
-// 		printf("the worker-thread doing! %d \n", val_);
+NODE_API_MODULE(addon, Init)
-// 		sleep(3);
+#endif
-// 		printf("the worker-thread done! %d \n", val_);
-// 	}
-
-// 	void OnOK()
-// 	{
-// 		Callback().Call({Env().Undefined(), Number::New(Env(), 0)});
-// 	}
-
-// private:
-// 	int val_;
-// };
-
-// Value test(const CallbackInfo &info)
-// {
-// 	// ai::ORTSession(nullptr, 0);
-
-// 	// Function callback = info[1].As<Function>();
-// 	// TestWork *work = new TestWork(callback, info[0].As<Number>().Int32Value());
-// 	// work->Queue();
-// 	return info.Env().Undefined();
-// }
-
-// Object Init(Env env, Object exports)
-// {
-// 	//OpenCV
-// 	NODE_INIT_OBJECT(cv, InstallOpenCVAPI);
-// 	//OnnxRuntime
-// 	#ifdef USE_ORT
-// 		NODE_INIT_OBJECT(ort, InstallOrtAPI);
-// 	#endif
-
-// 	Napi::Number::New(env, 0);
-
-// #define ADD_FUNCTION(name) exports.Set(Napi::String::New(env, #name), Napi::Function::New(env, name))
-// 	// ADD_FUNCTION(facedetPredict);
-// 	// ADD_FUNCTION(facedetRelease);
-
-// 	// ADD_FUNCTION(faceRecognitionCreate);
-// 	// ADD_FUNCTION(faceRecognitionPredict);
-// 	// ADD_FUNCTION(faceRecognitionRelease);
-
-// 	// ADD_FUNCTION(getDistance);
-// #undef ADD_FUNCTION
-// 	return exports;
-// }
-// NODE_API_MODULE(addon, Init)

cxx/ort/node.cc

@@ -2,6 +2,7 @@
 #include <vector>
 #include <onnxruntime_cxx_api.h>
 #include "node.h"
+#include "common/tensor.h"
 
 #ifdef WIN32
 #include <locale>
@@ -13,51 +14,31 @@ using namespace Napi;
 
 #define SESSION_INSTANCE_METHOD(method) InstanceMethod<&OrtSession::method>(#method, static_cast<napi_property_attributes>(napi_writable | napi_configurable))
 
-static ONNXTensorElementDataType getDataTypeFromString(const std::string &name)
+static const std::map<TensorDataType, ONNXTensorElementDataType> DATA_TYPE_MAP = {
-{
+	{TensorDataType::Float32, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT},
-	static const std::map<std::string, ONNXTensorElementDataType> dataTypeNameMap = {
+	{TensorDataType::Float64, ONNX_TENSOR_ELEMENT_DATA_TYPE_DOUBLE},
-		{"float32", ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT},
+	{TensorDataType::Int32, ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32},
-		{"float", ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT},
+	{TensorDataType::Uint32, ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT32},
-		{"float64", ONNX_TENSOR_ELEMENT_DATA_TYPE_DOUBLE},
+	{TensorDataType::Int16, ONNX_TENSOR_ELEMENT_DATA_TYPE_INT16},
-		{"double", ONNX_TENSOR_ELEMENT_DATA_TYPE_DOUBLE},
+	{TensorDataType::Uint16, ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT16},
-		{"int8", ONNX_TENSOR_ELEMENT_DATA_TYPE_INT8},
+	{TensorDataType::Int8, ONNX_TENSOR_ELEMENT_DATA_TYPE_INT8},
-		{"uint8", ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8},
+	{TensorDataType::Uint8, ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8},
-		{"int16", ONNX_TENSOR_ELEMENT_DATA_TYPE_INT16},
+	{TensorDataType::Int64, ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64},
-		{"uint16", ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT16},
+	{TensorDataType::Uint64, ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT64},
-		{"int32", ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32},
+};
-		{"uint32", ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT32},
-		{"int64", ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64},
-		{"uint64", ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT64},
-	};
-	auto it = dataTypeNameMap.find(name);
-	return (it == dataTypeNameMap.end()) ? ONNX_TENSOR_ELEMENT_DATA_TYPE_UNDEFINED : it->second;
-}
 
-static size_t getDataTypeSize(ONNXTensorElementDataType type)
+static const std::map<ONNXTensorElementDataType, size_t> DATA_TYPE_SIZE_MAP = {
-{
+	{ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT, 4},
-	static const std::map<ONNXTensorElementDataType, size_t> dataTypeSizeMap = {
+	{ONNX_TENSOR_ELEMENT_DATA_TYPE_DOUBLE, 8},
-		{ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT, 4},
+	{ONNX_TENSOR_ELEMENT_DATA_TYPE_INT8, 1},
-		{ONNX_TENSOR_ELEMENT_DATA_TYPE_DOUBLE, 8},
+	{ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8, 1},
-		{ONNX_TENSOR_ELEMENT_DATA_TYPE_INT8, 1},
+	{ONNX_TENSOR_ELEMENT_DATA_TYPE_INT16, 2},
-		{ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8, 1},
+	{ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT16, 2},
-		{ONNX_TENSOR_ELEMENT_DATA_TYPE_INT16, 2},
+	{ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32, 4},
-		{ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT16, 2},
+	{ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT32, 4},
-		{ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32, 4},
+	{ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64, 8},
-		{ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT32, 4},
+	{ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT64, 8},
-		{ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64, 8},
+};
-		{ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT64, 8},
-	};
-	auto it = dataTypeSizeMap.find(type);
-	return (it == dataTypeSizeMap.end()) ? 0 : it->second;
-}
-
-static void *dataFromTypedArray(const Napi::Value &val, size_t &bytes)
-{
-	auto arr = val.As<TypedArray>();
-	auto data = static_cast<uint8_t *>(arr.ArrayBuffer().Data());
-	bytes = arr.ByteLength();
-	return static_cast<void *>(data + arr.ByteOffset());
-}
 
 class OrtSessionNodeInfo {
   public:
@@ -67,7 +48,22 @@ class OrtSessionNodeInfo {
 	inline const std::string &GetName() const { return name_; }
 	inline const std::vector<int64_t> &GetShape() const { return shape_; }
 	inline ONNXTensorElementDataType GetType() const { return type_; }
-	inline size_t GetElementSize() const { return getDataTypeSize(type_); }
+	inline size_t GetElementSize() const
+	{
+		auto it = DATA_TYPE_SIZE_MAP.find(type_);
+		return (it == DATA_TYPE_SIZE_MAP.end()) ? 0 : it->second;
+	}
+	TensorDataType GetDataType() const
+	{
+		auto datatype = TensorDataType::Unknown;
+		for (auto it : DATA_TYPE_MAP) {
+			if (it.second == type_) {
+				datatype = it.first;
+				break;
+			}
+		}
+		return datatype;
+	}
 	size_t GetElementCount() const
 	{
 		if (!shape_.size()) return 0;
@@ -115,7 +111,8 @@ class OrtSessionRunWorker : public AsyncWorker {
 			for (int i = 0; i < outputNames_.size(); ++i) {
 				size_t bytes = outputElementBytes_[i];
 				Ort::Value &value = outputValues_[i];
-				auto buffer = ArrayBuffer::New(Env(), value.GetTensorMutableRawData(), bytes);
+				auto buffer = ArrayBuffer::New(Env(), bytes);
+				memcpy(buffer.Data(), value.GetTensorMutableRawData(), bytes);
 				result.Set(String::New(Env(), outputNames_[i]), buffer);
 			}
 			Callback().Call({Env().Undefined(), result});
@@ -236,7 +233,12 @@ class OrtSession : public ObjectWrap<OrtSession> {
 				auto inputOption = static_cast<Napi::Value>((*it).second).As<Object>();
 				if (!inputOption.Has("data") || !inputOption.Get("data").IsTypedArray()) worker->SetError((std::string("data is required in inputs #" + name)));
 				else {
-					auto type = inputOption.Has("type") ? getDataTypeFromString(inputOption.Get("type").As<String>().Utf8Value()) : input->GetType();
+					auto type = input->GetType();
+					if (inputOption.Has("type")) {
+						auto t = static_cast<TensorDataType>(inputOption.Get("type").As<Number>().Int32Value());
+						auto it = DATA_TYPE_MAP.find(t);
+						if (it != DATA_TYPE_MAP.end()) type = it->second;
+					}
 					size_t dataByteLen;
 					void *data = dataFromTypedArray(inputOption.Get("data"), dataByteLen);
 					auto shape = inputOption.Has("shape") ? GetShapeFromJavascript(inputOption.Get("shape").As<Array>()) : input->GetShape();
@@ -279,7 +281,7 @@ class OrtSession : public ObjectWrap<OrtSession> {
 			auto &node = *it.second;
 			auto item = Object::New(env);
 			item.Set(String::New(env, "name"), String::New(env, node.GetName()));
-			item.Set(String::New(env, "type"), Number::New(env, node.GetType()));
+			item.Set(String::New(env, "type"), Number::New(env, static_cast<int>(node.GetDataType())));
 			auto &shapeVec = node.GetShape();
 			auto shape = Array::New(env, shapeVec.size());
 			for (int i = 0; i < shapeVec.size(); ++i) shape.Set(i, Number::New(env, shapeVec[i]));
@@ -303,7 +305,7 @@ void InstallOrtAPI(Napi::Env env, Napi::Object exports)
 	OrtSession::Init(env, exports);
 }
 
-#ifdef USE_ONNXRUNTIME
+#if defined(USE_ONNXRUNTIME) && !defined(BUILD_MAIN_WORD)
 static Object Init(Env env, Object exports)
 {
 	InstallOrtAPI(env, exports);

package.json

@@ -1,8 +1,11 @@
 {
-  "name": "ai-box",
+  "name": "@yizhi/ai",
-  "version": "1.0.0",
+  "version": "1.0.6",
-  "main": "index.js",
+  "releaseVersion": "1.0.6",
+  "main": "dist/index.js",
+  "types": "typing/index.d.ts",
   "scripts": {
+    "build": "rm -rf dist typing && tsc",
     "watch": "tsc -w --inlineSourceMap"
   },
   "keywords": [],
@@ -15,5 +18,8 @@
     "compressing": "^1.10.1",
     "node-addon-api": "^8.3.1",
     "unbzip2-stream": "^1.4.3"
+  },
+  "dependencies": {
+    "@yizhi/cv": "^1.0.2"
   }
-}
+}

src/backend/common/session.ts

@@ -2,22 +2,41 @@
 
 export interface SessionNodeInfo {
 	name: string
-	type: number
+	type: DataType
 	shape: number[]
 }
 
-export type SessionNodeType = "float32" | "float64" | "float" | "double" | "int32" | "uint32" | "int16" | "uint16" | "int8" | "uint8" | "int64" | "uint64"
+export type DataTypeString = "float32" | "float64" | "float" | "double" | "int32" | "uint32" | "int16" | "uint16" | "int8" | "uint8" | "int64" | "uint64"
+
+export enum DataType {
+	Unknown,
+	Float32,
+	Float64,
+	Int32,
+	Uint32,
+	Int16,
+	Uint16,
+	Int8,
+	Uint8,
+	Int64,
+	Uint64,
+}
 
 export type SessionNodeData = Float32Array | Float64Array | Int32Array | Uint32Array | Int16Array | Uint16Array | Int8Array | Uint8Array | BigInt64Array | BigUint64Array
 
 export interface SessionRunInputOption {
-	type?: SessionNodeType
+	type?: DataTypeString
 	data: SessionNodeData
 	shape?: number[]
 }
 
+export interface SessionRunOutput {
+	shape: number[]
+	data: Float32Array
+}
+
 export abstract class CommonSession {
-	public abstract run(inputs: Record<string, SessionNodeData | SessionRunInputOption>): Promise<Record<string, Float32Array>>
+	public abstract run(inputs: Record<string, SessionNodeData | SessionRunInputOption>): Promise<Record<string, SessionRunOutput>>
 
 	public abstract get inputs(): Record<string, SessionNodeInfo>;
 	public abstract get outputs(): Record<string, SessionNodeInfo>;
@@ -26,3 +45,36 @@ export abstract class CommonSession {
 export function isTypedArray(val: any): val is SessionNodeData {
 	return val?.buffer instanceof ArrayBuffer;
 }
+
+export function dataTypeFrom(str: DataTypeString) {
+	return {
+		"float32": DataType.Float32,
+		"float64": DataType.Float64,
+		"float": DataType.Float32,
+		"double": DataType.Float64,
+		"int32": DataType.Int32,
+		"uint32": DataType.Uint32,
+		"int16": DataType.Int16,
+		"uint16": DataType.Uint16,
+		"int8": DataType.Int8,
+		"uint8": DataType.Uint8,
+		"int64": DataType.Int64,
+		"uint64": DataType.Uint64,
+	}[str] ?? DataType.Unknown;
+}
+
+export function dataTypeToString(type: DataType): DataTypeString | null {
+	switch (type) {
+		case DataType.Float32: return "float32";
+		case DataType.Float64: return "float64";
+		case DataType.Int32: return "int32";
+		case DataType.Uint32: return "uint32";
+		case DataType.Int16: return "int16";
+		case DataType.Uint16: return "uint16";
+		case DataType.Int8: return "int8";
+		case DataType.Uint8: return "uint8";
+		case DataType.Int64: return "int64";
+		case DataType.Uint64: return "uint64";
+		default: return null;
+	}
+}

src/backend/index.ts

@@ -1 +1 @@
-export * as backend from "./main";
+export * as backend from "./main";

src/backend/main.ts

@@ -1,2 +1,3 @@
-export * as common from "./common";
+export { SessionNodeInfo, DataTypeString, DataType, SessionNodeData, SessionRunInputOption, SessionRunOutput, CommonSession } from "./common";
 export * as ort from "./ort";
+export * as mnn from "./mnn";

src/backend/mnn/index.ts

@@ -0,0 +1 @@
+export { MNNSession as Session } from "./session";

src/backend/mnn/session.ts

@@ -0,0 +1,34 @@
+import { getConfig } from "../../config";
+import { CommonSession, dataTypeFrom, isTypedArray, SessionNodeData, SessionNodeInfo, SessionRunInputOption, SessionRunOutput } from "../common";
+
+export class MNNSession extends CommonSession {
+	#session: any
+	#inputs: Record<string, SessionNodeInfo> | null = null;
+	#outputs: Record<string, SessionNodeInfo> | null = null;
+
+	public constructor(modelData: Uint8Array) {
+		super();
+		const addon = require(getConfig("MNN_ADDON_FILE"));
+		this.#session = new addon.MNNSession(modelData);
+	}
+
+	public run(inputs: Record<string, SessionNodeData | SessionRunInputOption>): Promise<Record<string, SessionRunOutput>> {
+		const inputArgs: Record<string, any> = {};
+		for (const [name, option] of Object.entries(inputs)) {
+			if (isTypedArray(option)) inputArgs[name] = { data: option }
+			else inputArgs[name] = { ...option, type: option.type ? dataTypeFrom(option.type) : undefined };
+		}
+		return new Promise((resolve, reject) => this.#session.Run(inputArgs, (err: any, res: Record<string, { data: ArrayBuffer, shape: number[] }>) => {
+			if (err) return reject(err);
+			const result: Record<string, SessionRunOutput> = {};
+			for (const [name, val] of Object.entries(res)) result[name] = {
+				shape: val.shape,
+				data: new Float32Array(val.data),
+			}
+			resolve(result);
+		}))
+	}
+	public get inputs(): Record<string, SessionNodeInfo> { return this.#inputs ??= this.#session.GetInputsInfo(); }
+	public get outputs(): Record<string, SessionNodeInfo> { return this.#outputs ??= this.#session.GetOutputsInfo(); }
+
+}

src/backend/ort/session.ts

@@ -1,4 +1,5 @@
-import { CommonSession, isTypedArray, SessionNodeData, SessionNodeInfo, SessionRunInputOption } from "../common";
+import { getConfig } from "../../config";
+import { CommonSession, dataTypeFrom, isTypedArray, SessionNodeData, SessionNodeInfo, SessionRunInputOption, SessionRunOutput } from "../common";
 
 export class OrtSession extends CommonSession {
 	#session: any;
@@ -7,7 +8,7 @@ export class OrtSession extends CommonSession {
 
 	public constructor(modelData: Uint8Array) {
 		super();
-		const addon = require("../../../build/ort.node")
+		const addon = require(getConfig("ORT_ADDON_FILE"));
 		this.#session = new addon.OrtSession(modelData);
 	}
 
@@ -19,13 +20,13 @@ export class OrtSession extends CommonSession {
 		const inputArgs: Record<string, any> = {};
 		for (const [name, option] of Object.entries(inputs)) {
 			if (isTypedArray(option)) inputArgs[name] = { data: option }
-			else inputArgs[name] = option;
+			else inputArgs[name] = { ...option, type: option.type ? dataTypeFrom(option.type) : undefined };
 		}
 
-		return new Promise<Record<string, Float32Array>>((resolve, reject) => this.#session.Run(inputArgs, (err: any, res: any) => {
+		return new Promise<Record<string, SessionRunOutput>>((resolve, reject) => this.#session.Run(inputArgs, (err: any, res: Record<string, ArrayBuffer>) => {
 			if (err) return reject(err);
-			const result: Record<string, Float32Array> = {};
+			const result: Record<string, SessionRunOutput> = {};
-			for (const [name, val] of Object.entries(res)) result[name] = new Float32Array(val as ArrayBuffer);
+			for (const [name, val] of Object.entries(res)) result[name] = { data: new Float32Array(val), shape: this.outputs[name].shape };
 			resolve(result);
 		}));
 	}

src/config.ts

@@ -0,0 +1,12 @@
+import path from "path";
+
+const defaultAddonDir = path.join(__dirname, "../build")
+
+const aiConfig = {
+	"MNN_ADDON_FILE": path.join(defaultAddonDir, "mnn.node"),
+	"ORT_ADDON_FILE": path.join(defaultAddonDir, "ort.node"),
+};
+
+export function setConfig<K extends keyof typeof aiConfig>(key: K, value: typeof aiConfig[K]) { aiConfig[key] = value; }
+
+export function getConfig<K extends keyof typeof aiConfig>(key: K): typeof aiConfig[K] { return aiConfig[key]; }

src/cv/index.ts

@@ -1 +0,0 @@
-export * as cv from "./main";

src/cv/main.ts

@@ -1 +0,0 @@
-export { Mat, ImreadModes } from "./mat";

src/cv/mat.ts

@@ -1,79 +0,0 @@
-
-export enum ImreadModes {
-	IMREAD_UNCHANGED = -1,
-	IMREAD_GRAYSCALE = 0,
-	IMREAD_COLOR_BGR = 1,
-	IMREAD_COLOR = 1,
-	IMREAD_ANYDEPTH = 2,
-	IMREAD_ANYCOLOR = 4,
-	IMREAD_LOAD_GDAL = 8,
-	IMREAD_REDUCED_GRAYSCALE_2 = 16,
-	IMREAD_REDUCED_COLOR_2 = 17,
-	IMREAD_REDUCED_GRAYSCALE_4 = 32,
-	IMREAD_REDUCED_COLOR_4 = 33,
-	IMREAD_REDUCED_GRAYSCALE_8 = 64,
-	IMREAD_REDUCED_COLOR_8 = 65,
-	IMREAD_IGNORE_ORIENTATION = 128,
-	IMREAD_COLOR_RGB = 256,
-};
-
-interface MatConstructorOption {
-	mode?: ImreadModes;
-}
-
-export class Mat {
-	#mat: any
-
-	public static async load(image: string, option?: MatConstructorOption) {
-		let buffer: Uint8Array
-		if (/^https?:\/\//.test(image)) buffer = await fetch(image).then(res => res.arrayBuffer()).then(res => new Uint8Array(res));
-		else buffer = await import("fs").then(fs => fs.promises.readFile(image));
-		return new Mat(buffer, option);
-	}
-
-	public constructor(imageData: Uint8Array, option?: MatConstructorOption) {
-		const addon = require("../../build/cv.node");
-		if ((imageData as any) instanceof addon.Mat) this.#mat = imageData;
-		else this.#mat = new addon.Mat(imageData, option);
-	}
-
-	public get empty(): boolean { return this.#mat.IsEmpty() }
-
-	public get cols(): number { return this.#mat.GetCols(); }
-
-	public get rows(): number { return this.#mat.GetRows(); }
-
-	public get width() { return this.cols; }
-
-	public get height() { return this.rows; }
-
-	public get channels() { return this.#mat.GetChannels(); }
-
-	public resize(width: number, height: number) { return new Mat(this.#mat.Resize.bind(this.#mat)(width, height)); }
-
-	public crop(sx: number, sy: number, sw: number, sh: number) { return new Mat(this.#mat.Crop(sx, sy, sw, sh)); }
-
-	public rotate(sx: number, sy: number, angleDeg: number) { return new Mat(this.#mat.Rotate(sx, sy, angleDeg)); }
-
-	public get data() { return new Uint8Array(this.#mat.Data()); }
-
-	public encode(extname: string) { return new Uint8Array(this.#mat.Encode({ extname })); }
-
-	public clone() { return new Mat(this.#mat.Clone()); }
-
-	public circle(x: number, y: number, radius: number, options?: {
-		color?: { r: number, g: number, b: number },
-		thickness?: number
-		lineType?: number
-	}) {
-		this.#mat.DrawCircle(
-			x, y, radius,
-			options?.color?.b ?? 0,
-			options?.color?.g ?? 0,
-			options?.color?.r ?? 0,
-			options?.thickness ?? 1,
-			options?.lineType ?? 8,
-			0,
-		);
-	}
-}

src/deploy/common/model.ts

@@ -1,13 +1,13 @@
+import { cv } from "@yizhi/cv";
 import { backend } from "../../backend";
-import { cv } from "../../cv";
 
-export type ModelConstructor<T> = new (session: backend.common.CommonSession) => T;
+export type ModelConstructor<T> = new (session: backend.CommonSession) => T;
 
 export type ImageSource = cv.Mat | Uint8Array | string;
 
 export interface ImageCropOption {
 	/** 图片裁剪区域 */
-	crop?: { sx: number, sy: number, sw: number, sh: number }
+	crop?: { x: number, y: number, width: number, height: number }
 }
 
 export type ModelType = "onnx" | "mnn"
@@ -24,15 +24,17 @@ export interface ModelCacheResult<T, Create extends boolean> {
 	model: Create extends true ? T : never
 }
 
+const cacheDownloadTasks: Array<{ url: string, cacheDir: string, modelPath: Promise<string> }> = [];
+
 export abstract class Model {
-	protected session: backend.common.CommonSession;
+	protected session: backend.CommonSession;
 
 	protected static async resolveImage<R>(image: ImageSource, resolver: (image: cv.Mat) => R | Promise<R>): Promise<R> {
 		if (typeof image === "string") {
 			if (/^https?:\/\//.test(image)) image = await fetch(image).then(res => res.arrayBuffer()).then(buffer => new Uint8Array(buffer));
 			else image = await import("fs").then(fs => fs.promises.readFile(image as string));
 		}
-		if (image instanceof Uint8Array) image = new cv.Mat(image, { mode: cv.ImreadModes.IMREAD_COLOR_BGR })
+		if (image instanceof Uint8Array) image = cv.imdecode(image, cv.IMREAD_COLOR_BGR);
 		if (image instanceof cv.Mat) return await resolver(image);
 		else throw new Error("Invalid image");
 	}
@@ -45,78 +47,108 @@ export abstract class Model {
 		return new this(new backend.ort.Session(modelData as Uint8Array));
 	}
 
+	public static async fromMNN<T extends Model>(this: ModelConstructor<T>, modelData: Uint8Array | string) {
+		if (typeof modelData === "string") {
+			if (/^https?:\/\//.test(modelData)) modelData = await fetch(modelData).then(res => res.arrayBuffer()).then(buffer => new Uint8Array(buffer));
+			else modelData = await import("fs").then(fs => fs.promises.readFile(modelData as string));
+		}
+		return new this(new backend.mnn.Session(modelData as Uint8Array));
+	}
+
 	protected static async cacheModel<T extends Model, Create extends boolean = false>(this: ModelConstructor<T>, url: string, option?: ModelCacheOption<Create>): Promise<ModelCacheResult<T, Create>> {
 		//初始化目录
 		const [fs, path, os, crypto] = await Promise.all([import("fs"), import("path"), import("os"), import("crypto")]);
 		const cacheDir = option?.cacheDir ?? path.join(os.homedir(), ".aibox_cache/models");
 		await fs.promises.mkdir(cacheDir, { recursive: true });
-		//加载模型配置
-		const cacheJsonFile = path.join(cacheDir, "config.json");
-		let cacheJsonData: Array<{ url: string, filename: string }> = [];
-		if (fs.existsSync(cacheJsonFile) && await fs.promises.stat(cacheJsonFile).then(s => s.isFile())) {
-			try {
-				cacheJsonData = JSON.parse(await fs.promises.readFile(cacheJsonFile, "utf-8"));
-			} catch (e) { console.error(e); }
-		}
-		//不存在则下载
-		let cache = cacheJsonData.find(c => c.url === url);
-		if (!cache) {
-			let saveType = option?.saveType ?? null;
-			const saveTypeDict: Record<string, ModelType> = {
-				".onnx": "onnx",
-				".mnn": "mnn",
-			};
-			const _url = new URL(url);
-			const res = await fetch(_url).then(res => {
-				const filename = res.headers.get("content-disposition")?.match(/filename="(.+?)"/)?.[1];
-				if (filename) saveType = saveTypeDict[path.extname(filename)] ?? saveType;
-				if (!saveType) saveType = saveTypeDict[path.extname(_url.pathname)] ?? "onnx";
-				if (res.status !== 200) throw new Error(`HTTP ${res.status} ${res.statusText}`);
-				return res.blob();
-			}).then(blob => blob.stream()).then(async stream => {
-				const cacheFilename = path.join(cacheDir, Date.now().toString());
-				let fsStream!: ReturnType<typeof fs.createWriteStream>;
-				let hashStream!: ReturnType<typeof crypto.createHash>;
-				let hash!: string;
-				await stream.pipeTo(new WritableStream({
-					start(controller) {
-						fsStream = fs.createWriteStream(cacheFilename);
-						hashStream = crypto.createHash("md5");
-					},
-					async write(chunk, controller) {
-						await new Promise<void>((resolve, reject) => fsStream.write(chunk, err => err ? reject(err) : resolve()));
-						await new Promise<void>((resolve, reject) => hashStream.write(chunk, err => err ? reject(err) : resolve()));
-					},
-					close() {
-						fsStream.end();
-						hashStream.end();
-						hash = hashStream.digest("hex")
-					},
-					abort() { }
-				}));
-				return { filename: cacheFilename, hash };
-			});
-			//重命名
-			const filename = `${res.hash}.${saveType}`;
-			fs.promises.rename(res.filename, path.join(cacheDir, filename));
-			//保存缓存
-			cache = { url, filename };
-			cacheJsonData.push(cache);
-			fs.promises.writeFile(cacheJsonFile, JSON.stringify(cacheJsonData, null, 4));
-		}
-		//返回模型数据
-		const modelPath = path.join(cacheDir, cache.filename);
 
-		const modelType = path.extname(cache.filename).substring(1) as ModelType;
+		//定义函数用于加载模型信息
+		async function resolveModel() {
+			//加载模型配置
+			const cacheJsonFile = path.join(cacheDir, "config.json");
+			let cacheJsonData: Array<{ url: string, filename: string }> = [];
+			if (fs.existsSync(cacheJsonFile) && await fs.promises.stat(cacheJsonFile).then(s => s.isFile())) {
+				try {
+					cacheJsonData = JSON.parse(await fs.promises.readFile(cacheJsonFile, "utf-8"));
+				} catch (e) { console.error(e); }
+			}
+			//不存在则下载
+			let cache = cacheJsonData.find(c => c.url === url);
+			if (!cache || !fs.existsSync(path.join(cacheDir, cache.filename))) {
+				let saveType = option?.saveType ?? null;
+				const saveTypeDict: Record<string, ModelType> = {
+					".onnx": "onnx",
+					".mnn": "mnn",
+				};
+				const _url = new URL(url);
+				const res = await fetch(_url).then(res => {
+					const filename = res.headers.get("content-disposition")?.match(/filename="(.+?)"/)?.[1];
+					if (filename) saveType = saveTypeDict[path.extname(filename)] ?? saveType;
+					if (!saveType) saveType = saveTypeDict[path.extname(_url.pathname)] ?? "onnx";
+					if (res.status !== 200) throw new Error(`HTTP ${res.status} ${res.statusText}`);
+					return res.blob();
+				}).then(blob => blob.stream()).then(async stream => {
+					const cacheFilename = path.join(cacheDir, Date.now().toString());
+					const hash = await new Promise<string>((resolve, reject) => {
+						let fsStream!: ReturnType<typeof fs.createWriteStream>;
+						let hashStream!: ReturnType<typeof crypto.createHash>;
+						stream.pipeTo(new WritableStream({
+							start(controller) {
+								fsStream = fs.createWriteStream(cacheFilename);
+								hashStream = crypto.createHash("md5");
+							},
+							async write(chunk, controller) {
+								await new Promise<void>((resolve, reject) => fsStream.write(chunk, err => err ? reject(err) : resolve()));
+								await new Promise<void>((resolve, reject) => hashStream.write(chunk, err => err ? reject(err) : resolve()));
+							},
+							close() {
+								fsStream.end();
+								hashStream.end();
+								resolve(hashStream.digest("hex"));
+							},
+							abort() { }
+						})).catch(reject);
+					})
+					return { filename: cacheFilename, hash };
+				});
+				//重命名
+				const filename = `${res.hash}.${saveType}`;
+				fs.promises.rename(res.filename, path.join(cacheDir, filename));
+				//保存缓存
+				if (!cache) {
+					cache = { url, filename };
+					cacheJsonData.push(cache);
+				}
+				else {
+					cache.filename = filename;
+					cache.url = url;
+				}
+				fs.promises.writeFile(cacheJsonFile, JSON.stringify(cacheJsonData, null, 4));
+			}
+			//返回模型数据
+			return path.join(cacheDir, cache.filename);
+		}
+
+		//查找任务
+		let cache = cacheDownloadTasks.find(c => c.url === url && c.cacheDir === cacheDir);
+		if (!cache) {
+			cache = { url, cacheDir, modelPath: resolveModel() }
+			cacheDownloadTasks.push(cache);
+		}
+
+		//获取模型数据
+		const modelPath = await cache.modelPath;
+		const modelType = path.extname(modelPath).substring(1) as ModelType;
 		let model: T | undefined = undefined;
 		if (option?.createModel) {
 			if (modelType === "onnx") model = (this as any).fromOnnx(modelPath);
+			else if (modelType == "mnn") model = (this as any).fromMNN(modelPath);
 		}
 
+		//返回结果
 		return { modelPath, modelType, model: model as any }
 	}
 
-	public constructor(session: backend.common.CommonSession) { this.session = session; }
+	public constructor(session: backend.CommonSession) { this.session = session; }
 
 	public get inputs() { return this.session.inputs; }
 	public get outputs() { return this.session.outputs; }

src/deploy/facealign/common.ts

@@ -5,10 +5,41 @@ export interface FacePoint {
 
 type PointType = "leftEye" | "rightEye" | "leftEyebrow" | "rightEyebrow" | "nose" | "mouth" | "contour"
 
+export function indexFromTo(from: number, to: number) {
+	const indexes: number[] = [];
+	for (let i = from; i <= to; i++) {
+		indexes.push(i);
+	}
+	return indexes;
+}
+
+interface PointGroup {
+	/** 用于判断方向的两个点的索引(建议选取眼球中间的点) */
+	directionPointIndex: [number, number];
+	/** 左眼点的索引 */
+	leftEyePointIndex: number[];
+	/** 右眼点的索引 */
+	rightEyePointIndex: number[];
+	/** 左眉点的索引 */
+	leftEyebrowPointIndex: number[];
+	/** 右眉点的索引 */
+	rightEyebrowPointIndex: number[];
+	/** 嘴巴点的索引 */
+	mouthPointIndex: number[];
+	/** 鼻子的索引 */
+	nosePointIndex: number[];
+	/** 轮廓点的索引 */
+	contourPointIndex: number[];
+}
+
 export abstract class FaceAlignmentResult {
 	#points: FacePoint[]
+	#group: PointGroup
 
-	public constructor(points: FacePoint[]) { this.#points = points; }
+	public constructor(points: FacePoint[], group: PointGroup) {
+		this.#points = points;
+		this.#group = group;
+	}
 
 	/** 关键点 */
 	public get points() { return this.#points; }
@@ -18,7 +49,7 @@ export abstract class FaceAlignmentResult {
 		if (typeof type == "string") type = [type];
 		const result: FacePoint[] = [];
 		for (const t of type) {
-			for (const idx of this[`${t}PointIndex` as const]()) {
+			for (const idx of this.#group[`${t}PointIndex` as const]) {
 				result.push(this.points[idx]);
 			}
 		}
@@ -27,32 +58,7 @@ export abstract class FaceAlignmentResult {
 
 	/** 方向 */
 	public get direction() {
-		const [{ x: x1, y: y1 }, { x: x2, y: y2 }] = this.directionPointIndex().map(idx => this.points[idx]);
+		const [{ x: x1, y: y1 }, { x: x2, y: y2 }] = this.#group.directionPointIndex.map(idx => this.points[idx]);
 		return Math.atan2(y1 - y2, x2 - x1)
 	}
-
-	/** 用于判断方向的两个点的索引(建议选取眼球中间的点) */
-	protected abstract directionPointIndex(): [number, number];
-	/** 左眼点的索引 */
-	protected abstract leftEyePointIndex(): number[];
-	/** 右眼点的索引 */
-	protected abstract rightEyePointIndex(): number[];
-	/** 左眉点的索引 */
-	protected abstract leftEyebrowPointIndex(): number[];
-	/** 右眉点的索引 */
-	protected abstract rightEyebrowPointIndex(): number[];
-	/** 嘴巴点的索引 */
-	protected abstract mouthPointIndex(): number[];
-	/** 鼻子的索引 */
-	protected abstract nosePointIndex(): number[];
-	/** 轮廓点的索引 */
-	protected abstract contourPointIndex(): number[];
-
-	protected indexFromTo(from: number, to: number) {
-		const indexes: number[] = [];
-		for (let i = from; i <= to; i++) {
-			indexes.push(i);
-		}
-		return indexes;
-	}
 }

src/deploy/facealign/landmark1000.ts

@@ -1,25 +1,30 @@
-import { writeFileSync } from "fs";
+import cv from "@yizhi/cv";
-import { cv } from "../../cv";
 import { ImageCropOption, ImageSource, Model } from "../common/model";
 import { convertImage } from "../common/processors";
-import { FaceAlignmentResult, FacePoint } from "./common";
+import { FaceAlignmentResult, FacePoint, indexFromTo } from "./common";
 
 interface FaceLandmark1000PredictOption extends ImageCropOption { }
 
 class FaceLandmark1000Result extends FaceAlignmentResult {
-	protected directionPointIndex(): [number, number] { return [401, 529]; }
+	public constructor(points: FacePoint[]) {
-	protected leftEyePointIndex(): number[] { return this.indexFromTo(401, 528); }
+		super(points, {
-	protected rightEyePointIndex(): number[] { return this.indexFromTo(529, 656); }
+			directionPointIndex: [401, 529],
-	protected leftEyebrowPointIndex(): number[] { return this.indexFromTo(273, 336); }
+			leftEyePointIndex: indexFromTo(401, 528),
-	protected rightEyebrowPointIndex(): number[] { return this.indexFromTo(337, 400); }
+			rightEyePointIndex: indexFromTo(529, 656),
-	protected mouthPointIndex(): number[] { return this.indexFromTo(845, 972); }
+			leftEyebrowPointIndex: indexFromTo(273, 336),
-	protected nosePointIndex(): number[] { return this.indexFromTo(657, 844); }
+			rightEyebrowPointIndex: indexFromTo(337, 400),
-	protected contourPointIndex(): number[] { return this.indexFromTo(0, 272); }
+			mouthPointIndex: indexFromTo(845, 972),
+			nosePointIndex: indexFromTo(657, 844),
+			contourPointIndex: indexFromTo(0, 272),
+		});
+	}
+
 }
 
 
 const MODEL_URL_CONFIG = {
 	FACELANDMARK1000_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/facealign/FaceLandmark1000.onnx`,
+	FACELANDMARK1000_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/facealign/FaceLandmark1000.mnn`,
 };
 
 export class FaceLandmark1000 extends Model {
@@ -32,10 +37,10 @@ export class FaceLandmark1000 extends Model {
 
 	public async doPredict(image: cv.Mat, option?: FaceLandmark1000PredictOption) {
 		const input = this.input;
-		if (option?.crop) image = image.crop(option.crop.sx, option.crop.sy, option.crop.sw, option.crop.sh);
+		if (option?.crop) image = cv.crop(image, option?.crop);
-		const ratioWidth = image.width / input.shape[3];
+		const ratioWidth = image.cols / input.shape[3];
-		const ratioHeight = image.height / input.shape[2];
+		const ratioHeight = image.rows / input.shape[2];
-		image = image.resize(input.shape[3], input.shape[2]);
+		image = cv.resize(image, input.shape[3], input.shape[2]);
 
 		const nchwImageData = convertImage(image.data, { sourceImageFormat: "bgr", targetColorFormat: "gray", targetShapeFormat: "nchw", targetNormalize: { mean: [0], std: [1] } });
 
@@ -45,12 +50,12 @@ export class FaceLandmark1000 extends Model {
 				data: nchwImageData,
 				type: "float32",
 			}
-		}).then(res => res[this.output.name]);
+		}).then(res => res[this.output.name].data);
 
 		const points: FacePoint[] = [];
 		for (let i = 0; i < res.length; i += 2) {
-			const x = res[i] * image.width * ratioWidth;
+			const x = res[i] * image.cols * ratioWidth;
-			const y = res[i + 1] * image.height * ratioHeight;
+			const y = res[i + 1] * image.rows * ratioHeight;
 			points.push({ x, y });
 		}
 

src/deploy/facealign/pfld.ts

@@ -1,5 +1,4 @@
-import { writeFileSync } from "fs";
+import cv from "@yizhi/cv";
-import { cv } from "../../cv";
 import { ImageCropOption, ImageSource, Model } from "../common/model";
 import { convertImage } from "../common/processors";
 import { FaceAlignmentResult, FacePoint } from "./common";
@@ -7,20 +6,27 @@ import { FaceAlignmentResult, FacePoint } from "./common";
 export interface PFLDPredictOption extends ImageCropOption { }
 
 class PFLDResult extends FaceAlignmentResult {
-	protected directionPointIndex(): [number, number] { return [36, 92]; }
+	public constructor(points: FacePoint[]) {
-	protected leftEyePointIndex(): number[] { return [33, 34, 35, 36, 37, 38, 39, 40, 41, 42]; }
+		super(points, {
-	protected rightEyePointIndex(): number[] { return [87, 88, 89, 90, 91, 92, 93, 94, 95, 96]; }
+			directionPointIndex: [36, 92],
-	protected leftEyebrowPointIndex(): number[] { return [43, 44, 45, 46, 47, 48, 49, 50, 51]; }
+			leftEyePointIndex: [33, 34, 35, 36, 37, 38, 39, 40, 41, 42],
-	protected rightEyebrowPointIndex(): number[] { return [97, 98, 99, 100, 101, 102, 103, 104, 105]; }
+			rightEyePointIndex: [87, 88, 89, 90, 91, 92, 93, 94, 95, 96],
-	protected mouthPointIndex(): number[] { return [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71]; }
+			leftEyebrowPointIndex: [43, 44, 45, 46, 47, 48, 49, 50, 51],
-	protected nosePointIndex(): number[] { return [72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86]; }
+			rightEyebrowPointIndex: [97, 98, 99, 100, 101, 102, 103, 104, 105],
-	protected contourPointIndex(): number[] { return [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]; }
+			mouthPointIndex: [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71],
+			nosePointIndex: [72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86],
+			contourPointIndex: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32],
+		});
+	}
 }
 
 const MODEL_URL_CONFIG = {
 	PFLD_106_LITE_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/facealign/pfld-106-lite.onnx`,
 	PFLD_106_V2_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/facealign/pfld-106-v2.onnx`,
 	PFLD_106_V3_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/facealign/pfld-106-v3.onnx`,
+	PFLD_106_LITE_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/facealign/pfld-106-lite.mnn`,
+	PFLD_106_V2_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/facealign/pfld-106-v2.mnn`,
+	PFLD_106_V3_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/facealign/pfld-106-v3.mnn`,
 };
 export class PFLD extends Model {
 
@@ -32,10 +38,10 @@ export class PFLD extends Model {
 
 	private async doPredict(image: cv.Mat, option?: PFLDPredictOption) {
 		const input = this.input;
-		if (option?.crop) image = image.crop(option.crop.sx, option.crop.sy, option.crop.sw, option.crop.sh);
+		if (option?.crop) image = cv.crop(image, option.crop);
-		const ratioWidth = image.width / input.shape[3];
+		const ratioWidth = image.cols / input.shape[3];
-		const ratioHeight = image.height / input.shape[2];
+		const ratioHeight = image.rows / input.shape[2];
-		image = image.resize(input.shape[3], input.shape[2]);
+		image = cv.resize(image, input.shape[3], input.shape[2]);
 
 		const nchwImageData = convertImage(image.data, { sourceImageFormat: "bgr", targetColorFormat: "bgr", targetShapeFormat: "nchw", targetNormalize: { mean: [0], std: [255] } })
 
@@ -48,12 +54,12 @@ export class PFLD extends Model {
 				shape: [1, 3, input.shape[2], input.shape[3]],
 			}
 		});
-		const pointsBuffer = res[pointsOutput.name];
+		const pointsBuffer = res[pointsOutput.name].data;
 
 		const points: FacePoint[] = [];
 		for (let i = 0; i < pointsBuffer.length; i += 2) {
-			const x = pointsBuffer[i] * image.width * ratioWidth;
+			const x = pointsBuffer[i] * image.cols * ratioWidth;
-			const y = pointsBuffer[i + 1] * image.height * ratioHeight;
+			const y = pointsBuffer[i + 1] * image.rows * ratioHeight;
 			points.push({ x, y });
 		}
 

src/deploy/faceattr/gender-age.ts

@@ -1,4 +1,4 @@
-import { cv } from "../../cv";
+import cv from "@yizhi/cv";
 import { ImageCropOption, ImageSource, Model } from "../common/model";
 import { convertImage } from "../common/processors";
 
@@ -12,6 +12,7 @@ export interface GenderAgePredictResult {
 
 const MODEL_URL_CONFIG = {
 	INSIGHT_GENDER_AGE_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceattr/insight_gender_age.onnx`,
+	INSIGHT_GENDER_AGE_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceattr/insight_gender_age.mnn`,
 };
 
 export class GenderAge extends Model {
@@ -24,8 +25,8 @@ export class GenderAge extends Model {
 	private async doPredict(image: cv.Mat, option?: GenderAgePredictOption): Promise<GenderAgePredictResult> {
 		const input = this.input;
 		const output = this.output;
-		if (option?.crop) image = image.crop(option.crop.sx, option.crop.sy, option.crop.sw, option.crop.sh);
+		if (option?.crop) image = cv.crop(image, option.crop);
-		image = image.resize(input.shape[3], input.shape[2]);
+		image = cv.resize(image, input.shape[3], input.shape[2]);
 
 		const nchwImage = convertImage(image.data, { sourceImageFormat: "bgr", targetColorFormat: "rgb", targetShapeFormat: "nchw", targetNormalize: { mean: [0], std: [1] } });
 
@@ -35,7 +36,7 @@ export class GenderAge extends Model {
 				data: nchwImage,
 				type: "float32",
 			}
-		}).then(res => res[output.name]);
+		}).then(res => res[output.name].data);
 
 		return {
 			gender: result[0] > result[1] ? "F" : "M",

src/deploy/facedet/common.ts

@@ -1,4 +1,4 @@
-import { cv } from "../../cv"
+import cv from "@yizhi/cv"
 import { ImageSource, Model } from "../common/model"
 
 interface IFaceBoxConstructorOption {
@@ -49,8 +49,8 @@ export class FaceBox {
 
 		return new FaceBox({
 			...this.#option,
-			x1: this.centerX - size, y1: this.centerY - size,
+			x1: cx - size, y1: cy - size,
-			x2: this.centerX + size, y2: this.centerY + size,
+			x2: cx + size, y2: cy + size,
 		});
 	}
 }

src/deploy/facedet/yolov5.ts

@@ -1,9 +1,10 @@
-import { cv } from "../../cv";
+import cv from "@yizhi/cv";
 import { convertImage } from "../common/processors";
 import { FaceBox, FaceDetectOption, FaceDetector, nms } from "./common";
 
 const MODEL_URL_CONFIG = {
 	YOLOV5S_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/facedet/yolov5s.onnx`,
+	YOLOV5S_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/facedet/yolov5s.mnn`,
 };
 
 export class Yolov5Face extends FaceDetector {
@@ -14,9 +15,9 @@ export class Yolov5Face extends FaceDetector {
 
 	public async doPredict(image: cv.Mat, option?: FaceDetectOption): Promise<FaceBox[]> {
 		const input = this.input;
-		const resizedImage = image.resize(input.shape[2], input.shape[3]);
+		const resizedImage = cv.resize(image, input.shape[2], input.shape[3]);
-		const ratioWidth = image.width / resizedImage.width;
+		const ratioWidth = image.cols / resizedImage.cols;
-		const ratioHeight = image.height / resizedImage.height;
+		const ratioHeight = image.rows / resizedImage.rows;
 		const nchwImageData = convertImage(resizedImage.data, { sourceImageFormat: "bgr", targetColorFormat: "bgr", targetShapeFormat: "nchw", targetNormalize: { mean: [127.5], std: [127.5] } });
 
 		const outputData = await this.session.run({ input: nchwImageData }).then(r => r.output);
@@ -26,7 +27,7 @@ export class Yolov5Face extends FaceDetector {
 		const threshold = option?.threshold ?? 0.5;
 		for (let i = 0; i < outShape[1]; i++) {
 			const beg = i * outShape[2];
-			const rectData = outputData.slice(beg, beg + outShape[2]);
+			const rectData = outputData.data.slice(beg, beg + outShape[2]);
 			const x = parseInt(rectData[0] * ratioWidth as any);
 			const y = parseInt(rectData[1] * ratioHeight as any);
 			const w = parseInt(rectData[2] * ratioWidth as any);
@@ -36,7 +37,7 @@ export class Yolov5Face extends FaceDetector {
 			faces.push(new FaceBox({
 				x1: x - w / 2, y1: y - h / 2,
 				x2: x + w / 2, y2: y + h / 2,
-				score, imw: image.width, imh: image.height,
+				score, imw: image.cols, imh: image.rows,
 			}))
 		}
 		return nms(faces, option?.mnsThreshold ?? 0.3).map(box => box.toInt());

src/deploy/faceid/adaface.ts

@@ -1,9 +1,10 @@
-import { Mat } from "../../cv/mat";
+import cv from "@yizhi/cv";
 import { convertImage } from "../common/processors";
 import { FaceRecognition, FaceRecognitionPredictOption } from "./common";
 
 const MODEL_URL_CONFIG = {
 	MOBILEFACENET_ADAFACE_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/mobilefacenet_adaface.onnx`,
+	MOBILEFACENET_ADAFACE_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/mobilefacenet_adaface.mnn`,
 };
 
 export class AdaFace extends FaceRecognition {
@@ -12,12 +13,12 @@ export class AdaFace extends FaceRecognition {
 		return this.cacheModel(MODEL_URL_CONFIG[type ?? "MOBILEFACENET_ADAFACE_ONNX"], { createModel: true }).then(r => r.model);
 	}
 
-	public async doPredict(image: Mat, option?: FaceRecognitionPredictOption): Promise<number[]> {
+	public async doPredict(image: cv.Mat, option?: FaceRecognitionPredictOption): Promise<number[]> {
 		const input = this.input;
 		const output = this.output;
 
-		if (option?.crop) image = image.crop(option.crop.sx, option.crop.sy, option.crop.sw, option.crop.sh);
+		if (option?.crop) image = cv.crop(image, option.crop);
-		image = image.resize(input.shape[3], input.shape[2]);
+		image = cv.resize(image, input.shape[3], input.shape[2]);
 
 		const nchwImageData = convertImage(image.data, { sourceImageFormat: "bgr", targetColorFormat: "rgb", targetShapeFormat: "nchw", targetNormalize: { mean: [127.5], std: [127.5] } });
 
@@ -27,7 +28,7 @@ export class AdaFace extends FaceRecognition {
 				data: nchwImageData,
 				shape: [1, 3, input.shape[2], input.shape[3]],
 			}
-		}).then(res => res[output.name]);
+		}).then(res => res[output.name].data);
 
 		return new Array(...embedding);
 	}

src/deploy/faceid/common.ts

@@ -1,4 +1,4 @@
-import { cv } from "../../cv";
+import cv from "@yizhi/cv";
 import { ImageCropOption, ImageSource, Model } from "../common/model";
 
 export interface FaceRecognitionPredictOption extends ImageCropOption { }

src/deploy/faceid/insightface.ts

@@ -1,4 +1,4 @@
-import { Mat } from "../../cv/mat";
+import cv from "@yizhi/cv";
 import { convertImage } from "../common/processors";
 import { FaceRecognition, FaceRecognitionPredictOption } from "./common";
 
@@ -7,26 +7,33 @@ const MODEL_URL_CONFIG_ARC_FACE = {
 	INSIGHTFACE_ARCFACE_R50_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/ms1mv3_arcface_r50.onnx`,
 	INSIGHTFACE_ARCFACE_R34_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/ms1mv3_arcface_r34.onnx`,
 	INSIGHTFACE_ARCFACE_R18_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/ms1mv3_arcface_r18.onnx`,
+	INSIGHTFACE_ARCFACE_R50_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/ms1mv3_arcface_r50.mnn`,
+	INSIGHTFACE_ARCFACE_R34_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/ms1mv3_arcface_r34.mnn`,
+	INSIGHTFACE_ARCFACE_R18_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/ms1mv3_arcface_r18.mnn`,
 };
 const MODEL_URL_CONFIG_COS_FACE = {
 	INSIGHTFACE_COSFACE_R100_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/glint360k_cosface_r100.onnx`,
 	INSIGHTFACE_COSFACE_R50_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/glint360k_cosface_r50.onnx`,
 	INSIGHTFACE_COSFACE_R34_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/glint360k_cosface_r34.onnx`,
 	INSIGHTFACE_COSFACE_R18_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/glint360k_cosface_r18.onnx`,
+	INSIGHTFACE_COSFACE_R50_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/glint360k_cosface_r50.mnn`,
+	INSIGHTFACE_COSFACE_R34_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/glint360k_cosface_r34.mnn`,
+	INSIGHTFACE_COSFACE_R18_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/glint360k_cosface_r18.mnn`,
 };
 const MODEL_URL_CONFIG_PARTIAL_FC = {
 	INSIGHTFACE_PARTIALFC_R100_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/partial_fc_glint360k_r100.onnx`,
 	INSIGHTFACE_PARTIALFC_R50_ONNX: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/partial_fc_glint360k_r50.onnx`,
+	INSIGHTFACE_PARTIALFC_R50_MNN: `https://www.modelscope.cn/models/luyizhi/basic_cv/resolve/master/faceid/insightface/partial_fc_glint360k_r50.mnn`,
 };
 
 
 export class Insightface extends FaceRecognition {
 
-	public async doPredict(image: Mat, option?: FaceRecognitionPredictOption): Promise<number[]> {
+	public async doPredict(image: cv.Mat, option?: FaceRecognitionPredictOption): Promise<number[]> {
 		const input = this.input;
 		const output = this.output;
-		if (option?.crop) image = image.crop(option.crop.sx, option.crop.sy, option.crop.sw, option.crop.sh);
+		if (option?.crop) image = cv.crop(image, option.crop);
-		image = image.resize(input.shape[3], input.shape[2]);
+		image = cv.resize(image, input.shape[3], input.shape[2]);
 		const nchwImageData = convertImage(image.data, { sourceImageFormat: "bgr", targetColorFormat: "bgr", targetShapeFormat: "nchw", targetNormalize: { mean: [127.5], std: [127.5] } });
 
 		const embedding = await this.session.run({
@@ -37,7 +44,7 @@ export class Insightface extends FaceRecognition {
 			}
 		}).then(res => res[output.name]);
 
-		return new Array(...embedding);
+		return new Array(...embedding.data);
 	}
 
 }

src/index.ts

@@ -0,0 +1,4 @@
+import * as ai from "./main";
+
+export default ai;
+export { ai };

src/main.ts

@@ -0,0 +1,3 @@
+export { deploy } from "./deploy";
+export { backend } from "./backend";
+export { setConfig as config } from "./config";

src/test.ts

@@ -1,10 +1,21 @@
 import fs from "fs";
+import cv from "@yizhi/cv";
 import { deploy } from "./deploy";
-import { cv } from "./cv";
 import { faceidTestData } from "./test_data/faceid";
 import path from "path";
 import crypto from "crypto";
 
+cv.config("ADDON_PATH", path.join(__dirname, "../build/cv.node"));
+
+function loadImage(url: string) {
+	if (/https?:\/\//.test(url)) return fetch(url).then(res => res.arrayBuffer()).then(data => cv.imdecode(new Uint8Array(data)));
+	else return import("fs").then(fs => fs.promises.readFile(url)).then(buffer => cv.imdecode(buffer));
+}
+
+function rotate(im: cv.Mat, x: number, y: number, angle: number) {
+	return cv.warpAffine(im, cv.getRotationMatrix2D(x, y, angle, 1), im.cols, im.rows);
+}
+
 async function cacheImage(group: string, url: string) {
 	const _url = new URL(url);
 	const cacheDir = path.join(__dirname, "../cache/images", group);
@@ -30,31 +41,33 @@ async function cacheImage(group: string, url: string) {
 }
 
 async function testGenderTest() {
-	const facedet = await deploy.facedet.Yolov5Face.load("YOLOV5S_ONNX");
+	const facedet = await deploy.facedet.Yolov5Face.load("YOLOV5S_MNN");
-	const detector = await deploy.faceattr.GenderAgeDetector.load("INSIGHT_GENDER_AGE_ONNX");
+	const detector = await deploy.faceattr.GenderAgeDetector.load("INSIGHT_GENDER_AGE_MNN");
 
-	const image = await cv.Mat.load("https://b0.bdstatic.com/ugc/iHBWUj0XqytakT1ogBfBJwc7c305331d2cf904b9fb3d8dd3ed84f5.jpg");
+	const image = await loadImage("https://b0.bdstatic.com/ugc/iHBWUj0XqytakT1ogBfBJwc7c305331d2cf904b9fb3d8dd3ed84f5.jpg");
 	const boxes = await facedet.predict(image);
 	if (!boxes.length) return console.error("未检测到人脸");
 	for (const [idx, box] of boxes.entries()) {
-		const res = await detector.predict(image, { crop: { sx: box.left, sy: box.top, sw: box.width, sh: box.height } });
+		const res = await detector.predict(image, { crop: { x: box.left, y: box.top, width: box.width, height: box.height } });
 		console.log(`[${idx + 1}]`, res);
 	}
 
 }
 
 async function testFaceID() {
-	const facedet = await deploy.facedet.Yolov5Face.load("YOLOV5S_ONNX");
+	console.log("初始化模型")
-	const faceid = await deploy.faceid.PartialFC.load();
+	const facedet = await deploy.facedet.Yolov5Face.load("YOLOV5S_MNN");
-	const facealign = await deploy.facealign.PFLD.load("PFLD_106_LITE_ONNX");
+	const faceid = await deploy.faceid.CosFace.load("INSIGHTFACE_COSFACE_R50_MNN");
+	const facealign = await deploy.facealign.PFLD.load("PFLD_106_LITE_MNN");
+	console.log("初始化模型完成")
 
 	const { basic, tests } = faceidTestData.stars;
 
 	console.log("正在加载图片资源");
-	const basicImage = await cv.Mat.load(await cacheImage("faceid", basic.image));
+	const basicImage = await loadImage(await cacheImage("faceid", basic.image));
 	const testsImages: Record<string, cv.Mat[]> = {};
 	for (const [name, imgs] of Object.entries(tests)) {
-		testsImages[name] = await Promise.all(imgs.map(img => cacheImage("faceid", img).then(img => cv.Mat.load(img))));
+		testsImages[name] = await Promise.all(imgs.map(img => cacheImage("faceid", img).then(img => loadImage(img))));
 	}
 
 	console.log("正在检测基本数据");
@@ -66,9 +79,9 @@ async function testFaceID() {
 
 	async function getEmbd(image: cv.Mat, box: deploy.facedet.FaceBox) {
 		box = box.toSquare();
-		const alignResult = await facealign.predict(image, { crop: { sx: box.left, sy: box.top, sw: box.width, sh: box.height } });
+		const alignResult = await facealign.predict(image, { crop: { x: box.left, y: box.top, width: box.width, height: box.height } });
-		let faceImage = image.rotate(box.centerX, box.centerY, -alignResult.direction * 180 / Math.PI);
+		let faceImage = rotate(image, box.centerX, box.centerY, -alignResult.direction * 180 / Math.PI);
-		return faceid.predict(faceImage, { crop: { sx: box.left, sy: box.top, sw: box.width, sh: box.height } });
+		return faceid.predict(faceImage, { crop: { x: box.left, y: box.top, width: box.width, height: box.height } });
 	}
 
 	const basicEmbds: number[][] = [];
@@ -111,24 +124,28 @@ async function testFaceID() {
 
 async function testFaceAlign() {
 	const fd = await deploy.facedet.Yolov5Face.load("YOLOV5S_ONNX");
-	const fa = await deploy.facealign.PFLD.load("PFLD_106_LITE_ONNX");
+	const fa = await deploy.facealign.PFLD.load("PFLD_106_LITE_MNN");
 	// const fa = await deploy.facealign.FaceLandmark1000.load("FACELANDMARK1000_ONNX");
-	let image = await cv.Mat.load("https://bkimg.cdn.bcebos.com/pic/d52a2834349b033b5bb5f183119c21d3d539b6001712");
+	let image = await loadImage("https://i0.hdslb.com/bfs/archive/64e47ec9fdac9e24bc2b49b5aaad5560da1bfe3e.jpg");
-	image = image.rotate(image.width / 2, image.height / 2, 0);
+	image = rotate(image, image.cols / 2, image.rows / 2, 0);
 
-	const face = await fd.predict(image).then(res => res[0].toSquare());
+	const face = await fd.predict(image).then(res => {
-	const points = await fa.predict(image, { crop: { sx: face.left, sy: face.top, sw: face.width, sh: face.height } });
+		console.log(res);
+		return res[0].toSquare()
+	});
+	const points = await fa.predict(image, { crop: { x: face.left, y: face.top, width: face.width, height: face.height } });
 
 	points.points.forEach((point, idx) => {
-		image.circle(face.left + point.x, face.top + point.y, 2);
+		cv.circle(image, face.left + point.x, face.top + point.y, 2, 0, 0, 0);
 	})
 	// const point = points.getPointsOf("rightEye")[1];
 	// image.circle(face.left + point.x, face.top + point.y, 2);
-	fs.writeFileSync("testdata/xx.jpg", image.encode(".jpg"));
+	// fs.writeFileSync("testdata/xx.jpg", image.encode(".jpg"));
+	cv.imwrite("testdata/xx.jpg", image);
 
-	let faceImage = image.rotate(face.centerX, face.centerY, -points.direction * 180 / Math.PI);
+	let faceImage = rotate(image, face.centerX, face.centerY, -points.direction * 180 / Math.PI);
-	faceImage = faceImage.crop(face.left, face.top, face.width, face.height);
+	faceImage = cv.crop(faceImage, { x: face.left, y: face.top, width: face.width, height: face.height });
-	fs.writeFileSync("testdata/face.jpg", faceImage.encode(".jpg"));
+	fs.writeFileSync("testdata/face.jpg", cv.imencode(".jpg", faceImage)!);
 
 	console.log(points);
 	console.log(points.direction * 180 / Math.PI);
@@ -142,5 +159,4 @@ async function test() {
 
 test().catch(err => {
 	console.error(err);
-	debugger
+});
-});

src/utils/utils.ts

@@ -1,33 +0,0 @@
-
-export namespace utils {
-
-	export function rgba2rgb<T extends Uint8Array | Float32Array>(data: T): T {
-		const pixelCount = data.length / 4;
-		const result = new (data.constructor as any)(pixelCount * 3) as T;
-		for (let i = 0; i < pixelCount; i++) {
-			result[i * 3 + 0] = data[i * 4 + 0];
-			result[i * 3 + 1] = data[i * 4 + 1];
-			result[i * 3 + 2] = data[i * 4 + 2];
-		}
-		return result;
-	}
-
-	export function rgb2bgr<T extends Uint8Array | Float32Array>(data: T): T {
-		const pixelCount = data.length / 3;
-		const result = new (data.constructor as any)(pixelCount * 3) as T;
-		for (let i = 0; i < pixelCount; i++) {
-			result[i * 3 + 0] = data[i * 3 + 2];
-			result[i * 3 + 1] = data[i * 3 + 1];
-			result[i * 3 + 2] = data[i * 3 + 0];
-		}
-		return result;
-	}
-
-	export function normalize(data: Uint8Array | Float32Array, mean: number[], std: number[]): Float32Array {
-		const result = new Float32Array(data.length);
-		for (let i = 0; i < data.length; i++) {
-			result[i] = (data[i] - mean[i % mean.length]) / std[i % std.length];
-		}
-		return result;
-	}
-}

thirdpart/install.js

@@ -28,7 +28,6 @@ function assert(cond, message) {
 
 const buildOptions = {
 	withMNN: findArg("with-mnn", true) ?? false,
-	withOpenCV: findArg("with-opencv", true) ?? false,
 	withONNX: findArg("with-onnx", true) ?? false,
 	buildType: findArg("build-type", false) ?? "Release",
 	proxy: findArg("proxy"),
@@ -44,6 +43,8 @@ const spawnOption = {
 	}
 };
 
+function P(path) { return path.replace(/\\/g, "/"); }
+
 function checkFile(...items) {
 	return fs.existsSync(path.resolve(...items));
 }
@@ -105,9 +106,6 @@ async function downloadFromURL(name, url, resolver) {
 
 	if (!checkFile(saveName)) {
 		console.log(`开始下载${name}, 地址:${url}`);
-		await fetch(url).then(res => {
-			console.log(res.status)
-		})
 		const result = spawnSync("curl", ["-o", saveName + ".cache", "-L", url, "-s", "-w", "%{http_code}"], { ...spawnOption, stdio: "pipe" });
 		assert(result.status == 0 && result.stdout.toString() == "200", `下载${name}失败`);
 		fs.renameSync(saveName + ".cache", saveName);
@@ -151,37 +149,16 @@ async function main() {
 		"-DMNN_AVX512=ON",
 		"-DMNN_BUILD_TOOLS=ON",
 		"-DMNN_BUILD_CONVERTER=OFF",
-		"-DMNN_WIN_RUNTIME_MT=OFF",
+		"-DMNN_WIN_RUNTIME_MT=ON",
 	], (root) => [
-		`set(MNN_INCLUDE_DIR ${JSON.stringify(path.join(root, "include"))})`,
+		`set(MNN_INCLUDE_DIR ${JSON.stringify(P(path.join(root, "include")))})`,
-		`set(MNN_LIB_DIR ${JSON.stringify(path.join(root, "lib"))})`,
+		`set(MNN_LIB_DIR ${JSON.stringify(P(path.join(root, "lib")))})`,
-		`set(MNN_LIBS MNN)`,
+		`if(WIN32)`,
+		`	set(MNN_LIBS \${MNN_LIB_DIR}/MNN.lib)`,
+		`else()`,
+		`	set(MNN_LIBS \${MNN_LIB_DIR}/libMNN.a)`,
+		`endif()`,
 	].join("\n"));
-	//OpenCV
-	if (buildOptions.withOpenCV) cmakeBuildFromSource("OpenCV", "https://github.com/opencv/opencv.git", "4.11.0", null, [
-		"-DBUILD_SHARED_LIBS=OFF",
-		"-DBUILD_opencv_apps=OFF",
-		"-DBUILD_opencv_js=OFF",
-		"-DBUILD_opencv_python2=OFF",
-		"-DBUILD_opencv_python3=OFF",
-		"-DBUILD_ANDROID_PROJECTS=OFF",
-		"-DBUILD_ANDROID_EXAMPLES=OFF",
-		"-DBUILD_TESTS=OFF",
-		"-DBUILD_FAT_JAVA_LIB=OFF",
-		"-DBUILD_ANDROID_SERVICE=OFF",
-		"-DBUILD_JAVA=OFF",
-		"-DBUILD_PERF_TESTS=OFF"
-	], (root) => [
-		`set(OpenCV_STATIC ON)`,
-		os.platform() == "win32" ?
-			`include(${JSON.stringify(path.join(root, "OpenCVConfig.cmake"))})` :
-			`include(${JSON.stringify(path.join(root, "lib/cmake/opencv4/OpenCVConfig.cmake"))})`,
-		`set(OpenCV_INCLUDE_DIR \${OpenCV_INCLUDE_DIRS})`,
-		os.platform() == "win32" ?
-			"set(OpenCV_LIB_DIR ${OpenCV_LIB_PATH})" :
-			`set(OpenCV_LIB_DIR ${JSON.stringify(path.join(root, "lib"))})`,
-		// `set(OpenCV_LIBS OpenCV_LIBS)`,
-	].join("\n"))
 	//ONNXRuntime
 	if (buildOptions.withONNX && !checkFile(THIRDPARTY_DIR, "ONNXRuntime/config.cmake")) {
 		let url = "";
@@ -229,9 +206,13 @@ async function main() {
 			fs.cpSync(path.join(savedir, dirname), path.join(THIRDPARTY_DIR, "ONNXRuntime"), { recursive: true });
 		});
 		fs.writeFileSync(path.join(THIRDPARTY_DIR, "ONNXRuntime/config.cmake"), [
-			`set(ONNXRuntime_INCLUDE_DIR ${JSON.stringify(path.join(THIRDPARTY_DIR, "ONNXRuntime/include"))})`,
+			`set(ONNXRuntime_INCLUDE_DIR ${JSON.stringify(P(path.join(THIRDPARTY_DIR, "ONNXRuntime/include")))})`,
-			`set(ONNXRuntime_LIB_DIR ${JSON.stringify(path.join(THIRDPARTY_DIR, "ONNXRuntime/lib"))})`,
+			`set(ONNXRuntime_LIB_DIR ${JSON.stringify(P(path.join(THIRDPARTY_DIR, "ONNXRuntime/lib")))})`,
-			`set(ONNXRuntime_LIBS onnxruntime)`,
+			`if(WIN32)`,
+			`	set(ONNXRuntime_LIBS \${ONNXRuntime_LIB_DIR}/onnxruntime.lib)`,
+			`else()`,
+			`	set(ONNXRuntime_LIBS \${ONNXRuntime_LIB_DIR}/libonnxruntime.a)`,
+			`endif()`,
 		].join("\n"));
 	}
 	// if (buildOptions.withONNX) cmakeBuildFromSource("ONNXRuntime", "https://github.com/csukuangfj/onnxruntime-build.git", "main", (name, repo, branch) => {

tool/convert_mnn.js

@@ -0,0 +1,36 @@
+const fs = require("fs");
+const path = require("path");
+const { spawnSync } = require("child_process");
+
+const CONVERTER_PATH = "C:/Develop/Libs/mnn/bin/MNNConvert.exe";
+const MODEL_PATH = path.join(__dirname, "../models");
+
+function onnx2mnn(model) {
+	const name = path.basename(model, ".onnx");
+	const outputFile = path.join(path.dirname(model), name + ".mnn");
+	if (fs.existsSync(outputFile)) console.log(`Skip ${name}`);
+	else {
+		const result = spawnSync(CONVERTER_PATH, [
+			"-f", "ONNX",
+			"--modelFile", model,
+			"--MNNModel", outputFile,
+			"--bizCode", "biz"
+		]);
+		if (result.status !== 0) {
+			console.error(`Failed to convert ${name}`);
+			console.log(result.stdout.toString());
+		}
+		else console.log(`Convert ${name} success`);
+	}
+}
+
+function resolveDir(dir) {
+	const files = fs.readdirSync(dir);
+	for (const file of files) {
+		const filepath = path.join(dir, file);
+		if (fs.statSync(filepath).isDirectory()) resolveDir(filepath);
+		else if (path.extname(file) == ".onnx") onnx2mnn(filepath);
+	}
+}
+
+resolveDir(MODEL_PATH)