修改gitignore,移除无用的注释
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@ -44,7 +44,7 @@ Generated\ Files/
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# MSTest test Results
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[Tt]est[Rr]esult*/
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[Bb]uild[Ll]og.*
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[Bb]uild/
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# NUnit
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*.VisualState.xml
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TestResult.xml
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@ -1,7 +1,7 @@
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// 声明包名，必须与您的项目一致
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package com.facesdk.wrapper;
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// 导入所有必需的 Android 工具
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import android.content.Context;
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import android.content.res.AssetManager;
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import android.graphics.Bitmap;
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@ -13,72 +13,63 @@ import java.io.IOException;
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import java.io.InputStream;
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import java.io.OutputStream;
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/**
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* 封装了所有 JNI 调用的 SDK 包装器。
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* 这是 Android App 唯一应该交互的类。
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*/
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public class FaceSDKWrapper {
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private static final String TAG = "FaceSDKWrapper";
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static {
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try {
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// 【【【关键修正】】】
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// 我们必须在加载我们自己的库之前，
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// 首先加载我们的库所依赖的第三方库。
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System.loadLibrary("c++_shared");
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// 1. 加载 ONNX Runtime (来自 build.gradle.kts)
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System.loadLibrary("onnxruntime");
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// 2. 加载 OpenCV (来自 build.gradle.kts, 库名是 opencv_java4)
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System.loadLibrary("opencv_java4");
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// 3. 最后加载我们自己的库
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System.loadLibrary("face_sdk_jni");
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} catch (UnsatisfiedLinkError e) {
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// 如果这里出错，App 无法运行
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Log.e(TAG, "!!! 致命错误: 无法加载一个或多个 Native 库 !!!", e);
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// 抛出运行时异常，使 App 立即停止，而不是稍后崩溃
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throw new RuntimeException("Failed to load native libraries", e);
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}
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}
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// 2. 声明 C++ JNI 函数 (必须与 face_sdk_jni.cpp 匹配)
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// 这些是 Java 和 C++ 之间的“秘密通道”
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private native long nativeInit(String modelDir);
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private native float[] nativeExtractFeature(Bitmap bitmap);
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private native float nativeCompare(float[] feat1, float[] feat2);
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private native void nativeRelease();
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// -----------------------------------------------------------------
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// 公开的 Java API 接口
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// -----------------------------------------------------------------
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// C++ FaceSDK 实例的指针 (句柄)
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private long nativeHandle = 0;
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/**
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* [API 1] 初始化 SDK
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* 负责复制模型并初始化 C++ 引擎
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*
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* @param context Android 应用上下文
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* @return true 成功, false 失败
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*/
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public boolean init(Context context) {
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if (nativeHandle != 0) {
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Log.w(TAG, "SDK already initialized.");
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return true;
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}
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// 步骤 1: 复制模型
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String modelPath = copyModelsFromAssets(context);
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if (modelPath == null) {
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Log.e(TAG, "Failed to copy models from assets.");
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return false;
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}
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// 步骤 2: 调用 C++ JNI 进行初始化
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try {
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this.nativeHandle = nativeInit(modelPath);
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if (this.nativeHandle == 0) {
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@ -94,12 +85,7 @@ public class FaceSDKWrapper {
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return true;
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}
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/**
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* [API 2] 提取特征
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*
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* @param bitmap 包含人脸的 Bitmap
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* @return 512维特征向量, 或在失败时返回 null
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*/
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public float[] extractFeature(Bitmap bitmap) {
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if (nativeHandle == 0) {
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Log.e(TAG, "SDK not initialized. Call init() first.");
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@ -118,17 +104,11 @@ public class FaceSDKWrapper {
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}
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}
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/**
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* [API 3] 比较特征
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*
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* @param feat1 特征1
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* @param feat2 特征2
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* @return 余弦相似度
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*/
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public float compare(float[] feat1, float[] feat2) {
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if (nativeHandle == 0) {
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Log.e(TAG, "SDK not initialized.");
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return -2.0f; // -2.0 作为错误码
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return -2.0f;
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}
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if (feat1 == null || feat2 == null || feat1.length != 512 || feat2.length != 512) {
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Log.e(TAG, "Invalid feature vectors for comparison.");
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@ -143,10 +123,7 @@ public class FaceSDKWrapper {
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}
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}
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/**
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* [API 4] 释放 SDK
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* 在 App 退出时调用
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*/
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public void release() {
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if (nativeHandle != 0) {
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nativeRelease();
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@ -155,19 +132,13 @@ public class FaceSDKWrapper {
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}
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}
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// -----------------------------------------------------------------
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// 任务 2.6: 模型复制逻辑
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// -----------------------------------------------------------------
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/**
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* 将 assets 中的所有 .onnx 模型复制到应用的内部存储。
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* C++ 只能从内部存储读取，不能直接读取 assets。
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*
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* @param context 上下文
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* @return 模型的存储目录路径, 或在失败时返回 null
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*/
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private String copyModelsFromAssets(Context context) {
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// 这是 C++ 需要的 7 个模型
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final String[] modelFiles = {
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"faceboxesv2-640x640.onnx",
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"face_landmarker_pts5_net1.onnx",
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@ -178,7 +149,7 @@ public class FaceSDKWrapper {
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"fsanet-conv.onnx"
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};
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// 目标目录: /data/data/com.facesdk.wrapper/files/models
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File modelDir = new File(context.getFilesDir(), "models");
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if (!modelDir.exists()) {
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if (!modelDir.mkdirs()) {
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@ -189,11 +160,11 @@ public class FaceSDKWrapper {
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AssetManager assetManager = context.getAssets();
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// 循环复制每一个模型
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for (String filename : modelFiles) {
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File outFile = new File(modelDir, filename);
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// 如果文件已存在，跳过复制 (提高启动速度)
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if (outFile.exists()) {
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Log.i(TAG, "Model exists, skipping: " + filename);
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continue;
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@ -210,15 +181,15 @@ public class FaceSDKWrapper {
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}
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} catch (IOException e) {
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Log.e(TAG, "Failed to copy model: " + filename, e);
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// 如果任何一个文件复制失败，则清理并返回失败
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// (清理是可选的，但更健壮)
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// cleanUpModels(modelDir);
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return null;
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}
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}
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Log.i(TAG, "All models copied successfully to: " + modelDir.getAbsolutePath());
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// 返回包含模型的目录路径
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return modelDir.getAbsolutePath();
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}
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}
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File diff suppressed because it is too large
Load Diff
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@ -1,196 +1,181 @@
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#pragma once
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#include <string>
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#include <vector>
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#include <memory>
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#include <stdexcept>
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#include <array>
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#include <cmath>
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#include <numeric>
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#include <algorithm>
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#include <android/log.h>
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#include <array>
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#include <cmath>
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#include <memory>
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#include <numeric>
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#include <stdexcept>
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#include <string>
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#include <vector>
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#include "onnxruntime_cxx_api.h"
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#include "opencv2/calib3d.hpp"
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#include "opencv2/opencv.hpp"
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#include "opencv2/calib3d.hpp" // for estimateAffinePartial2D
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// --- 日志宏 ---
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#define LOG_TAG "FacePipeline_CPP"
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#define LOGI(...) __android_log_print(ANDROID_LOG_INFO, LOG_TAG, __VA_ARGS__)
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#define LOGE(...) __android_log_print(ANDROID_LOG_ERROR, LOG_TAG, __VA_ARGS__)
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/**
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* @brief 计算两个L2归一化的特征向量的余弦相似度
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*/
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inline float compare_features(const std::vector<float>& v1, const std::vector<float>& v2) {
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if (v1.empty() || v1.size() != v2.size()) {
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return 0.0f;
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}
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double dot_product = 0.0;
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for (size_t i = 0; i < v1.size(); ++i) {
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dot_product += v1[i] * v2[i];
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}
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return std::max(-1.0f, std::min(1.0f, static_cast<float>(dot_product)));
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inline float compare_features(const std::vector<float> &v1,
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const std::vector<float> &v2) {
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if (v1.empty() || v1.size() != v2.size()) {
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return 0.0f;
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}
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double dot_product = 0.0;
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for (size_t i = 0; i < v1.size(); ++i) {
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dot_product += v1[i] * v2[i];
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}
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return std::max(-1.0f, std::min(1.0f, static_cast<float>(dot_product)));
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}
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// --- 辅助结构体 (与 facedetector.py::Box 对应) ---
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struct FaceBox {
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float x1, y1, x2, y2, score;
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float x1, y1, x2, y2, score;
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};
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struct FaceLandmark {
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std::array<cv::Point2f, 5> points;
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std::array<cv::Point2f, 5> points;
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};
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struct FacePose {
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float yaw, pitch, roll;
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float yaw, pitch, roll;
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};
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// --- NMS 辅助函数 (与 facedetector.py::hard_nms 对应) ---
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inline float iou_of(const FaceBox& a, const FaceBox& b) {
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float inter_x1 = std::max(a.x1, b.x1);
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float inter_y1 = std::max(a.y1, b.y1);
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float inter_x2 = std::min(a.x2, b.x2);
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float inter_y2 = std::min(a.y2, b.y2);
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inline float iou_of(const FaceBox &a, const FaceBox &b) {
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float inter_x1 = std::max(a.x1, b.x1);
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float inter_y1 = std::max(a.y1, b.y1);
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float inter_x2 = std::min(a.x2, b.x2);
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float inter_y2 = std::min(a.y2, b.y2);
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if (inter_x1 < inter_x2 && inter_y1 < inter_y2) {
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float inter_area = (inter_x2 - inter_x1 + 1.0f) * (inter_y2 - inter_y1 + 1.0f);
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float a_area = (a.x2 - a.x1 + 1.0f) * (a.y2 - a.y1 + 1.0f);
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float b_area = (b.x2 - b.x1 + 1.0f) * (b.y2 - b.y1 + 1.0f);
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float union_area = a_area + b_area - inter_area;
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return inter_area / union_area;
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}
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return 0.0f;
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if (inter_x1 < inter_x2 && inter_y1 < inter_y2) {
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float inter_area =
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(inter_x2 - inter_x1 + 1.0f) * (inter_y2 - inter_y1 + 1.0f);
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float a_area = (a.x2 - a.x1 + 1.0f) * (a.y2 - a.y1 + 1.0f);
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float b_area = (b.x2 - b.x1 + 1.0f) * (b.y2 - b.y1 + 1.0f);
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float union_area = a_area + b_area - inter_area;
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return inter_area / union_area;
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}
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return 0.0f;
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}
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inline std::vector<FaceBox> hard_nms(std::vector<FaceBox>& boxes, float iou_threshold, int topk) {
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if (boxes.empty()) return {};
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std::sort(boxes.begin(), boxes.end(), [](const FaceBox& a, const FaceBox& b) {
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return a.score > b.score;
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});
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inline std::vector<FaceBox> hard_nms(std::vector<FaceBox> &boxes,
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float iou_threshold, int topk) {
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if (boxes.empty())
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return {};
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std::sort(boxes.begin(), boxes.end(), [](const FaceBox &a, const FaceBox &b) {
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return a.score > b.score;
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});
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std::vector<int> merged(boxes.size(), 0);
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std::vector<FaceBox> output;
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std::vector<int> merged(boxes.size(), 0);
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std::vector<FaceBox> output;
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for (int i = 0; i < boxes.size(); ++i) {
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if (merged[i]) continue;
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output.push_back(boxes[i]);
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merged[i] = 1;
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for (int i = 0; i < boxes.size(); ++i) {
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if (merged[i])
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continue;
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for (int j = i + 1; j < boxes.size(); ++j) {
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if (merged[j]) continue;
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if (iou_of(boxes[i], boxes[j]) > iou_threshold) {
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merged[j] = 1;
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}
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}
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if (output.size() >= topk) break;
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output.push_back(boxes[i]);
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merged[i] = 1;
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for (int j = i + 1; j < boxes.size(); ++j) {
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if (merged[j])
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continue;
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if (iou_of(boxes[i], boxes[j]) > iou_threshold) {
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merged[j] = 1;
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}
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}
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return output;
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if (output.size() >= topk)
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break;
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}
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return output;
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}
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/**
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* @class FacePipeline
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* @brief 管理7个ONNX模型并执行完整的人脸识别管线
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*/
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class FacePipeline {
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public:
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FacePipeline(const std::string& model_dir);
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~FacePipeline();
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bool IsInitialized() const { return m_initialized; }
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bool Extract(const cv::Mat& image, std::vector<float>& feature);
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FacePipeline(const std::string &model_dir);
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~FacePipeline();
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bool IsInitialized() const { return m_initialized; }
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bool Extract(const cv::Mat &image, std::vector<float> &feature);
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private:
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// --- 模型加载与初始化 ---
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bool LoadModels(const std::string& model_dir);
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void InitMemoryAllocators();
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bool LoadModels(const std::string &model_dir);
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void InitMemoryAllocators();
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// --- 核心管线步骤 ---
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void preprocess_rotation(const cv::Mat &image, std::vector<float> &blob_data);
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int RunRotation(const cv::Mat& image); // [模型5]
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bool RunDetection(const cv::Mat& image, std::vector<FaceBox>& boxes); // [模型1]
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bool RunPose(const cv::Mat& face_crop, FacePose& pose); // [模型6, 7]
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bool RunLandmark(const cv::Mat& image, const FaceBox& box, FaceLandmark& landmark); // [模型2, 3]
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cv::Mat RunAlignment(const cv::Mat& image, const FaceLandmark& landmark); //
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bool RunRecognition(const cv::Mat& aligned_face, std::vector<float>& feature); // [模型4]
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// --- 预处理/后处理 辅助函数 ---
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// [模型1] FaceBoxesV2
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struct Anchor { float cx, cy, s_kx, s_ky; };
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std::vector<Anchor> m_anchors;
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void generate_anchors_faceboxes(int target_height, int target_width);
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void preprocess_detection(const cv::Mat& img, std::vector<float>& blob_data);
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void preprocess_rotation(const cv::Mat &image, std::vector<float> &blob_data);
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int RunRotation(const cv::Mat &image);
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bool RunDetection(const cv::Mat &image, std::vector<FaceBox> &boxes);
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bool RunPose(const cv::Mat &face_crop, FacePose &pose);
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bool RunLandmark(const cv::Mat &image, const FaceBox &box,
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FaceLandmark &landmark);
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cv::Mat RunAlignment(const cv::Mat &image, const FaceLandmark &landmark);
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bool RunRecognition(const cv::Mat &aligned_face, std::vector<float> &feature);
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// [模型6, 7] FSANet
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void preprocess_pose(const cv::Mat& img, std::vector<float>& blob_data);
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struct Anchor {
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float cx, cy, s_kx, s_ky;
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};
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std::vector<Anchor> m_anchors;
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void generate_anchors_faceboxes(int target_height, int target_width);
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void preprocess_detection(const cv::Mat &img, std::vector<float> &blob_data);
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// [模型2, 3] Landmark5er
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void preprocess_landmark_net1(const cv::Mat& img, std::vector<float>& blob_data);
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std::vector<float> shape_index_process(const Ort::Value& feat_data, const Ort::Value& pos_data);
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void preprocess_pose(const cv::Mat &img, std::vector<float> &blob_data);
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// [模型4] FaceRecognizer
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void preprocess_recognition(const cv::Mat& img, std::vector<float>& blob_data);
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void normalize_sqrt_l2(std::vector<float>& v); //
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void preprocess_landmark_net1(const cv::Mat &img,
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std::vector<float> &blob_data);
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std::vector<float> shape_index_process(const Ort::Value &feat_data,
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const Ort::Value &pos_data);
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// 通用
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void image_to_blob(const cv::Mat& img, std::vector<float>& blob, const float* mean, const float* std);
|
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Ort::Value create_tensor(const std::vector<float>& blob_data, const std::vector<int64_t>& input_shape);
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void preprocess_recognition(const cv::Mat &img,
|
||||
std::vector<float> &blob_data);
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||||
void normalize_sqrt_l2(std::vector<float> &v);
|
||||
|
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// --- ONNX Runtime 核心组件 ---
|
||||
Ort::Env m_env;
|
||||
Ort::SessionOptions m_session_options;
|
||||
Ort::AllocatorWithDefaultOptions m_allocator;
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||||
Ort::MemoryInfo m_memory_info;
|
||||
bool m_initialized = false;
|
||||
void image_to_blob(const cv::Mat &img, std::vector<float> &blob,
|
||||
const float *mean, const float *std);
|
||||
Ort::Value create_tensor(const std::vector<float> &blob_data,
|
||||
const std::vector<int64_t> &input_shape);
|
||||
|
||||
// --- 7个模型的会话 (Session) ---
|
||||
std::unique_ptr<Ort::Session> m_session_detector;
|
||||
std::unique_ptr<Ort::Session> m_session_landmarker1;
|
||||
std::unique_ptr<Ort::Session> m_session_landmarker2;
|
||||
std::unique_ptr<Ort::Session> m_session_recognizer;
|
||||
std::unique_ptr<Ort::Session> m_session_rotator;
|
||||
std::unique_ptr<Ort::Session> m_session_pose_var;
|
||||
std::unique_ptr<Ort::Session> m_session_pose_conv;
|
||||
Ort::Env m_env;
|
||||
Ort::SessionOptions m_session_options;
|
||||
Ort::AllocatorWithDefaultOptions m_allocator;
|
||||
Ort::MemoryInfo m_memory_info;
|
||||
bool m_initialized = false;
|
||||
|
||||
// --- ONNX模型输入/输出名称 (C-style strings) ---
|
||||
// 我们在加载模型时获取这些
|
||||
std::vector<const char*> m_rot_input_names, m_rot_output_names;
|
||||
std::vector<int64_t> m_rot_input_shape;
|
||||
std::unique_ptr<Ort::Session> m_session_detector;
|
||||
std::unique_ptr<Ort::Session> m_session_landmarker1;
|
||||
std::unique_ptr<Ort::Session> m_session_landmarker2;
|
||||
std::unique_ptr<Ort::Session> m_session_recognizer;
|
||||
std::unique_ptr<Ort::Session> m_session_rotator;
|
||||
std::unique_ptr<Ort::Session> m_session_pose_var;
|
||||
std::unique_ptr<Ort::Session> m_session_pose_conv;
|
||||
|
||||
std::vector<const char*> m_det_input_names, m_det_output_names;
|
||||
std::vector<int64_t> m_det_input_shape;
|
||||
|
||||
std::vector<const char*> m_pose_var_input_names, m_pose_var_output_names;
|
||||
std::vector<int64_t> m_pose_var_input_shape;
|
||||
std::vector<const char *> m_rot_input_names, m_rot_output_names;
|
||||
std::vector<int64_t> m_rot_input_shape;
|
||||
|
||||
std::vector<const char*> m_pose_conv_input_names, m_pose_conv_output_names;
|
||||
std::vector<int64_t> m_pose_conv_input_shape;
|
||||
std::vector<const char *> m_det_input_names, m_det_output_names;
|
||||
std::vector<int64_t> m_det_input_shape;
|
||||
|
||||
std::vector<const char*> m_lm1_input_names, m_lm1_output_names;
|
||||
std::vector<int64_t> m_lm1_input_shape;
|
||||
std::vector<const char *> m_pose_var_input_names, m_pose_var_output_names;
|
||||
std::vector<int64_t> m_pose_var_input_shape;
|
||||
|
||||
std::vector<const char*> m_lm2_input_names, m_lm2_output_names;
|
||||
std::vector<int64_t> m_lm2_input_shape;
|
||||
std::vector<const char *> m_pose_conv_input_names, m_pose_conv_output_names;
|
||||
std::vector<int64_t> m_pose_conv_input_shape;
|
||||
|
||||
std::vector<const char*> m_rec_input_names, m_rec_output_names;
|
||||
std::vector<int64_t> m_rec_input_shape;
|
||||
|
||||
// --- 临时缓冲区 ---
|
||||
std::vector<float> m_blob_buffer;
|
||||
std::vector<const char *> m_lm1_input_names, m_lm1_output_names;
|
||||
std::vector<int64_t> m_lm1_input_shape;
|
||||
|
||||
// --- 常量 (来自 Python) ---
|
||||
const float m_det_threshold = 0.35f;
|
||||
const float m_det_iou_threshold = 0.45f;
|
||||
const int m_det_topk = 300;
|
||||
const float m_pose_threshold = 30.0f; // (来自 face_feature_extractor.py)
|
||||
const cv::Mat m_landmark_template = (cv::Mat_<float>(5, 2) <<
|
||||
89.3095f, 72.9025f, // (来自 facealign.py)
|
||||
169.3095f, 72.9025f, //
|
||||
127.8949f, 127.0441f, //
|
||||
96.8796f, 184.8907f, //
|
||||
159.1065f, 184.7601f); //
|
||||
const cv::Size m_align_output_size = cv::Size(256, 256); //
|
||||
std::vector<const char *> m_lm2_input_names, m_lm2_output_names;
|
||||
std::vector<int64_t> m_lm2_input_shape;
|
||||
|
||||
std::vector<const char *> m_rec_input_names, m_rec_output_names;
|
||||
std::vector<int64_t> m_rec_input_shape;
|
||||
|
||||
std::vector<float> m_blob_buffer;
|
||||
|
||||
const float m_det_threshold = 0.35f;
|
||||
const float m_det_iou_threshold = 0.45f;
|
||||
const int m_det_topk = 300;
|
||||
const float m_pose_threshold = 30.0f;
|
||||
const cv::Mat m_landmark_template =
|
||||
(cv::Mat_<float>(5, 2) << 89.3095f, 72.9025f, 169.3095f, 72.9025f,
|
||||
127.8949f, 127.0441f, 96.8796f, 184.8907f, 159.1065f, 184.7601f);
|
||||
const cv::Size m_align_output_size = cv::Size(256, 256);
|
||||
};
|
||||
Loading…
Reference in New Issue