移除cv，请使用@yizhi/cv包

CMakeLists.txt

@@ -85,24 +85,6 @@ if(EXISTS ${MNN_CMAKE_FILE})
 	endif()
 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)
-		target_link_libraries(cv ${OpenCV_LIBS})
-		target_compile_definitions(cv PUBLIC USE_OPENCV)
-		list(APPEND NODE_COMMON_SOURCES cxx/cv/node.cc)
-	endif()
-endif()
-
 # OnnxRuntime
 set(ONNXRuntime_CMAKE_FILE ${CMAKE_SOURCE_DIR}/thirdpart/ONNXRuntime/config.cmake)
 if(EXISTS ${ONNXRuntime_CMAKE_FILE})
@@ -137,11 +119,6 @@ if(NODE_ADDON_FOUND)
 		target_link_libraries(addon ${ONNXRuntime_LIBS})
 		target_compile_definitions(addon PUBLIC USE_ONNXRUNTIME)
 	endif()
-	# OpenCV
-	if(EXISTS ${OpenCV_CMAKE_FILE})
-		target_link_libraries(addon ${OpenCV_LIBS})
-		target_compile_definitions(addon PUBLIC USE_OPENCV)
-	endif()
 endif()
 
 if(MSVC AND NODE_ADDON_FOUND)

README.md

@@ -29,14 +29,13 @@ const outputs = session.run(inputs);
 ## 插件编译
 
 1. 依赖
-	1. python3
 	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 --with-opencv
+node thirdpart/install.js --with-mnn --with-onnx
 ```
 1. 编译插件
 ```

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);
-}
-
-#if defined(USE_OPENCV) && !defined(BUILD_MAIN_WORD)
-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/node.cc

@@ -1,5 +1,4 @@
 #include "common/node.h"
-#include "cv/node.h"
 #include "mnn/node.h"
 #include "ort/node.h"
 
@@ -8,11 +7,6 @@ using namespace Napi;
 #if defined(BUILD_MAIN_WORD)
 Object Init(Env env, Object exports)
 {
-// OpenCV
-#ifdef USE_OPENCV
-	printf("use opencv\n");
-	InstallOpenCVAPI(env, exports);
-#endif
 // OnnxRuntime
 #ifdef USE_ONNXRUNTIME
 	InstallOrtAPI(env, exports);

package.json

@@ -1,10 +1,11 @@
 {
   "name": "@yizhi/ai",
-  "version": "1.0.5",
+  "version": "1.0.6",
+  "releaseVersion": "1.0.6",
   "main": "dist/index.js",
   "types": "typing/index.d.ts",
   "scripts": {
-    "build": "tsc",
+    "build": "rm -rf dist typing && tsc",
     "watch": "tsc -w --inlineSourceMap"
   },
   "keywords": [],
@@ -17,5 +18,8 @@
     "compressing": "^1.10.1",
     "node-addon-api": "^8.3.1",
     "unbzip2-stream": "^1.4.3"
+  },
+  "dependencies": {
+    "@yizhi/cv": "^1.0.2"
   }
 }

src/config.ts

@@ -3,7 +3,6 @@ import path from "path";
 const defaultAddonDir = path.join(__dirname, "../build")
 
 const aiConfig = {
-	"CV_ADDON_FILE": path.join(defaultAddonDir, "cv.node"),
 	"MNN_ADDON_FILE": path.join(defaultAddonDir, "mnn.node"),
 	"ORT_ADDON_FILE": path.join(defaultAddonDir, "ort.node"),
 };

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

@@ -1,80 +0,0 @@
-import { getConfig } from "../config";
-
-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(getConfig("CV_ADDON_FILE"));
-		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

@@ -1,5 +1,5 @@
+import { cv } from "@yizhi/cv";
 import { backend } from "../../backend";
-import { cv } from "../../cv";
 
 export type ModelConstructor<T> = new (session: backend.CommonSession) => T;
 
@@ -7,7 +7,7 @@ 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"
@@ -34,7 +34,7 @@ export abstract class Model {
 			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");
 	}

src/deploy/facealign/landmark1000.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, indexFromTo } from "./common";
@@ -38,10 +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] } });
 
@@ -55,8 +54,8 @@ export class FaceLandmark1000 extends Model {
 
 		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";
@@ -39,10 +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] } })
 
@@ -59,8 +58,8 @@ export class PFLD extends Model {
 
 		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";
 
@@ -25,8 +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] } });
 

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 {

src/deploy/facedet/yolov5.ts

@@ -1,4 +1,4 @@
-import { cv } from "../../cv";
+import cv from "@yizhi/cv";
 import { convertImage } from "../common/processors";
 import { FaceBox, FaceDetectOption, FaceDetector, nms } from "./common";
 
@@ -15,9 +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);
@@ -37,7 +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

@@ -1,4 +1,4 @@
-import { Mat } from "../../cv/mat";
+import cv from "@yizhi/cv";
 import { convertImage } from "../common/processors";
 import { FaceRecognition, FaceRecognitionPredictOption } from "./common";
 
@@ -13,12 +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] } });
 

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";
 
@@ -29,11 +29,11 @@ const MODEL_URL_CONFIG_PARTIAL_FC = {
 
 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({

src/main.ts

@@ -1,4 +1,3 @@
 export { deploy } from "./deploy";
-export { cv } from "./cv";
 export { backend } from "./backend";
 export { setConfig as config } from "./config";

src/test.ts

@@ -1,10 +1,20 @@
 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";
-import ai from ".";
+
+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);
@@ -34,11 +44,11 @@ async function testGenderTest() {
 	const facedet = await deploy.facedet.Yolov5Face.load("YOLOV5S_MNN");
 	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);
 	}
 
@@ -54,10 +64,10 @@ async function testFaceID() {
 	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");
@@ -69,9 +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[][] = [];
@@ -116,33 +126,34 @@ async function testFaceAlign() {
 	const fd = await deploy.facedet.Yolov5Face.load("YOLOV5S_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://i0.hdslb.com/bfs/archive/64e47ec9fdac9e24bc2b49b5aaad5560da1bfe3e.jpg");
+	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 => {
 		console.log(res);
 		return res[0].toSquare()
 	});
-	const points = await fa.predict(image, { crop: { sx: face.left, sy: face.top, sw: face.width, sh: face.height } });
+	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);
 }
 
 async function test() {
-	// await testGenderTest();
+	await testGenderTest();
-	// await testFaceID();
+	await testFaceID();
 	await testFaceAlign();
 }
 

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"),
@@ -107,9 +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${name}失败`);
 		fs.renameSync(saveName + ".cache", saveName);
@@ -163,31 +159,6 @@ async function main() {
 		`	set(MNN_LIBS \${MNN_LIB_DIR}/libMNN.a)`,
 		`endif()`,
 	].join("\n"));
-	//OpenCV
-	if (buildOptions.withOpenCV) cmakeBuildFromSource("OpenCV", "https://github.com/opencv/opencv.git", "4.11.0", null, [
-		"-DBUILD_SHARED_LIBS=OFF",
-		"-DBUILD_opencv_apps=OFF",
-		"-DBUILD_opencv_js=OFF",
-		"-DBUILD_opencv_python2=OFF",
-		"-DBUILD_opencv_python3=OFF",
-		"-DBUILD_ANDROID_PROJECTS=OFF",
-		"-DBUILD_ANDROID_EXAMPLES=OFF",
-		"-DBUILD_TESTS=OFF",
-		"-DBUILD_FAT_JAVA_LIB=OFF",
-		"-DBUILD_ANDROID_SERVICE=OFF",
-		"-DBUILD_JAVA=OFF",
-		"-DBUILD_PERF_TESTS=OFF"
-	], (root) => [
-		`set(OpenCV_STATIC ON)`,
-		os.platform() == "win32" ?
-			`include(${JSON.stringify(P(path.join(root, "OpenCVConfig.cmake")))})` :
-			`include(${JSON.stringify(P(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(P(path.join(root, "lib")))})`,
-		// `set(OpenCV_LIBS OpenCV_LIBS)`,
-	].join("\n"))
 	//ONNXRuntime
 	if (buildOptions.withONNX && !checkFile(THIRDPARTY_DIR, "ONNXRuntime/config.cmake")) {
 		let url = "";