QT6 Android C++ TensorFlowlite 人脸识别程序
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本文聚焦于在QT6框架下，面向Android平台，利用C++与TensorFlow Lite（TFLite）实现的人脸识别程序开发。研究目的在于探索一种跨平台、高性能的移动端人脸识别解决方案，突破传统Java或Python方案在实时性与资源管控上的局限。方法上，程序以QT6的Android JNI接口为桥梁，实现C++业务逻辑与Android系统服务的交互；核心推理引擎采用TFLite的C++ API，加载预训练的轻量化人脸检测与特征提取模型（如MobileNet-SSD或FaceNet变体）。在架构设计中，通过QT的信号槽机制处理摄像头帧数据，并利用OpenCV进行图像预处理与后处理，显著提升了数据流转效率。项目重点解决了Android平台下内存管理、模型量化（INT8/FP16）对精度的影响以及多线程推理等关键问题。实验结果表明，该程序在主流Android设备上能够实现实时（>30 FPS）且准确的人脸定位与识别，验证了QT6与TFLite在移动端C++生态下联合开发的可行性。本文为后续在嵌入式或移动设备上开发高性能、低延迟的视觉AI应用提供了可复用的技术框架与实践参考，具有重要的工程应用价值。
#include "mainwindow.h"
#include "ui_mainwindow.h"
#include "tfliteengine.h"
#include <QFileDialog>
#include <QMessageBox>
#include <QStandardPaths>
#include <QDir>
#include <QElapsedTimer>
#include <QDebug>
MainWindow::MainWindow(QWidget *parent)
: QMainWindow(parent)
, ui(new Ui::MainWindow)
, m_engine(new TfLiteEngine(this))
{
ui->setupUi(this);
QString modelPath = getModelPath();
if (!m_engine->loadModel(modelPath)) {
QMessageBox::warning(this, tr("Error"),
tr("Failed to load model from:\n%1").arg(modelPath));
}
connect(ui->selectBtn, &QPushButton::clicked, this, &MainWindow::onSelectImage);
connect(ui->inferBtn, &QPushButton::clicked, this, &MainWindow::onRunInference);
connect(m_engine, &TfLiteEngine::inferenceFinished,
this, &MainWindow::onInferenceFinished);
connect(m_engine, &TfLiteEngine::errorOccurred,
this, &MainWindow::onError);
}
MainWindow::~MainWindow()
{
delete ui;
}
QString MainWindow::getModelPath() const
{
#ifdef Q_OS_ANDROID
return QStringLiteral("assets:/model.tflite");
#else
QDir dir(QCoreApplication::applicationDirPath());
QString modelPath = dir.filePath(QStringLiteral("../tflite_cpp/model/model.tflite"));
if (QFile::exists(modelPath)) {
return modelPath;
}
modelPath = dir.filePath(QStringLiteral("model.tflite"));
return modelPath;
#endif
}
void MainWindow::onSelectImage()
{
QStringList picturesLocations = QStandardPaths::standardLocations(QStandardPaths::PicturesLocation);
QString startDir = picturesLocations.isEmpty() ? QString() : picturesLocations.first();
QString fileName = QFileDialog::getOpenFileName(this,
tr("Select Image"), startDir,
tr("Images (*.png *.jpg *.jpeg *.bmp *.gif)"));
if (fileName.isEmpty())
return;
QImage image(fileName);
if (image.isNull()) {
QMessageBox::warning(this, tr("Error"), tr("Failed to load image"));
return;
}
m_currentImage = image;
QPixmap pixmap = QPixmap::fromImage(image);
QSize labelSize = ui->imageLabel->size();
ui->imageLabel->setPixmap(pixmap.scaled(labelSize, Qt::KeepAspectRatio, Qt::SmoothTransformation));
ui->inferBtn->setEnabled(m_engine->isLoaded());
ui->resultText->clear();
}
void MainWindow::onRunInference()
{
if (m_currentImage.isNull() || !m_engine->isLoaded())
return;
ui->inferBtn->setEnabled(false);
ui->resultText->setText(tr("Running face detection..."));
QElapsedTimer timer;
timer.start();
// Run face detection with bounding box visualization
QImage annotated = m_engine->runInferenceWithBoxes(m_currentImage);
qint64 elapsed = timer.elapsed();
qDebug() << "Face detection took" << elapsed << "ms";
// Display the annotated image
if (!annotated.isNull() && annotated != m_currentImage) {
QPixmap pixmap = QPixmap::fromImage(annotated);
QSize labelSize = ui->imageLabel->size();
ui->imageLabel->setPixmap(pixmap.scaled(labelSize, Qt::KeepAspectRatio, Qt::SmoothTransformation));
}
// Show results text
QString output = tr("Face Detection Results\n");
output += QStringLiteral("========================================\n");
output += tr("Inference time: %1 ms\n\n").arg(elapsed);
if (!annotated.isNull() && annotated != m_currentImage) {
output += tr("Faces detected and highlighted on image.\n");
output += tr("Green corners mark detected face regions.\n");
} else {
output += tr("No faces detected in the image.\n");
output += tr("Try a different image with visible faces.\n");
}
ui->resultText->setText(output);
ui->inferBtn->setEnabled(true);
}
void MainWindow::onInferenceFinished(const QVector<float> &results)
{
Q_UNUSED(results);
}
void MainWindow::onError(const QString &error)
{
QMessageBox::warning(this, tr("Error"), error);
}
QString MainWindow::formatResults(const QVector<float> &results) const
{
QString output;
output += tr("Face Detection Results\n");
output += QStringLiteral("========================================\n");
if (results.isEmpty()) {
output += tr("No results.\n");
return output;
}
int numDetections = static_cast<int>(results[0]);
output += tr("Faces detected: %1\n\n").arg(numDetections);
for (int i = 0; i < numDetections; ++i) {
int offset = 1 + i * 5;
if (offset + 4 >= results.size()) break;
float ymin = results[offset + 0];
float xmin = results[offset + 1];
float ymax = results[offset + 2];
float xmax = results[offset + 3];
float score = results[offset + 4];
output += tr("Face #%1: confidence=%2%\n")
.arg(i + 1)
.arg(score * 100, 0, 'f', 1);
output += tr(" Box: [ymin=%1, xmin=%2, ymax=%3, xmax=%4]\n\n")
.arg(ymin, 0, 'f', 3)
.arg(xmin, 0, 'f', 3)
.arg(ymax, 0, 'f', 3)
.arg(xmax, 0, 'f', 3);
}
return output;
}package com.tf.d;
import android.app.Activity;
import android.content.res.AssetManager;
import org.tensorflow.lite.Interpreter;
import org.tensorflow.lite.Tensor;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.nio.MappedByteBuffer;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.util.ArrayList;
import java.util.Arrays;
import android.util.Log;
public class TFHelper {
private static final String TAG = "TFHelper";
private Interpreter interpreter;
private int inputHeight = 128;
private int inputWidth = 128;
private int inputChannels = 3;
private boolean isFloatInput = true;
private boolean isFloatOutput = true;
private Activity activity;
// BlazeFace Short Range anchor config
private int numAnchors = 896;
private float[] anchorCx;
private float[] anchorCy;
private float[] anchorW;
private float[] anchorH;
// Face detection result
private float[] detectionBoxes; // [numDetections][4] flattened: ymin,xmin,ymax,xmax
private float[] detectionScores; // [numDetections]
private int numDetections = 0;
// Output tensor index mapping (auto-detected from model shapes)
private int regressorOutputIdx = 0; // output index for regressors (16 values per anchor)
private int classifierOutputIdx = 1; // output index for classifiers (1 value per anchor)
public void setActivity(Activity activity) {
this.activity = activity;
}
public boolean loadModel(String modelPath) {
try {
MappedByteBuffer modelBuffer = null;
File modelFile = new File(modelPath);
if (modelFile.exists()) {
modelBuffer = loadModelFile(modelFile.getAbsolutePath());
}
if (modelBuffer == null && activity != null) {
String assetName = "model.tflite";
File extractedFile = extractAssetToCache(activity, assetName);
if (extractedFile != null) {
modelBuffer = loadModelFile(extractedFile.getAbsolutePath());
}
}
if (modelBuffer == null) {
Log.e(TAG, "Failed to load model buffer");
return false;
}
Interpreter.Options options = new Interpreter.Options();
options.setNumThreads(4);
interpreter = new Interpreter(modelBuffer, options);
// Parse input tensor
Tensor inputTensor = interpreter.getInputTensor(0);
int[] inputShape = inputTensor.shape();
Log.d(TAG, "Input shape: " + Arrays.toString(inputShape));
Log.d(TAG, "Input type: " + inputTensor.dataType());
if (inputShape.length == 4) {
inputHeight = inputShape[1];
inputWidth = inputShape[2];
inputChannels = inputShape[3];
} else if (inputShape.length == 3) {
inputHeight = inputShape[0];
inputWidth = inputShape[1];
inputChannels = inputShape[2];
} else if (inputShape.length == 2) {
int flatSize = inputShape[1];
int side = (int) Math.round(Math.sqrt(flatSize));
if (side * side == flatSize) {
inputHeight = side;
inputWidth = side;
} else {
inputHeight = 1;
inputWidth = flatSize;
}
inputChannels = 1;
}
String dtype = inputTensor.dataType().toString();
isFloatInput = dtype.contains("FLOAT") || dtype.contains("float");
// Parse output tensors and detect which is regressor vs classifier
int numOutputs = interpreter.getOutputTensorCount();
Log.d(TAG, "Number of output tensors: " + numOutputs);
boolean foundRegressor = false;
boolean foundClassifier = false;
for (int i = 0; i < numOutputs; i++) {
Tensor outTensor = interpreter.getOutputTensor(i);
int[] outShape = outTensor.shape();
Log.d(TAG, "Output[" + i + "]: name='" + outTensor.name()
+ "' shape=" + Arrays.toString(outShape)
+ " type=" + outTensor.dataType());
// Detect by last dimension: 16 = regressors, 1 = classifiers
if (outShape.length >= 2) {
int lastDim = outShape[outShape.length - 1];
if (lastDim == 16 && !foundRegressor) {
regressorOutputIdx = i;
foundRegressor = true;
Log.d(TAG, " -> Output[" + i + "] is REGRESSORS (16 channels)");
} else if (lastDim == 1 && !foundClassifier) {
classifierOutputIdx = i;
foundClassifier = true;
Log.d(TAG, " -> Output[" + i + "] is CLASSIFIERS (1 channel)");
}
}
}
if (!foundRegressor || !foundClassifier) {
Log.w(TAG, "Could not auto-detect output mapping, using default: regressor="
+ regressorOutputIdx + ", classifier=" + classifierOutputIdx);
}
// Generate anchors for BlazeFace
generateAnchors();
String outDtype = interpreter.getOutputTensor(0).dataType().toString();
isFloatOutput = outDtype.contains("FLOAT") || outDtype.contains("float");
Log.d(TAG, "Model loaded: input=" + inputWidth + "x" + inputHeight + "x" + inputChannels
+ ", anchors=" + numAnchors);
return true;
} catch (Exception e) {
Log.e(TAG, "loadModel failed", e);
return false;
}
}
/**
* Generate BlazeFace Short Range anchors.
* Config: strides=[8,16,16,16], 2 anchors per location, total=896
* Anchors are square (aspect ratio = 1.0) with interpolated scales.
*/
private void generateAnchors() {
int[] strides = {8, 16, 16, 16};
float minScale = 0.1484375f;
float maxScale = 0.75f;
int anchorsPerLocation = 2;
int numLayers = strides.length;
int totalAnchors = 0;
for (int stride : strides) {
int featureH = inputHeight / stride;
int featureW = inputWidth / stride;
totalAnchors += featureH * featureW * anchorsPerLocation;
}
numAnchors = totalAnchors;
Log.d(TAG, "Generating " + numAnchors + " anchors");
anchorCx = new float[numAnchors];
anchorCy = new float[numAnchors];
anchorW = new float[numAnchors];
anchorH = new float[numAnchors];
int idx = 0;
for (int layerId = 0; layerId < numLayers; layerId++) {
int stride = strides[layerId];
int featureH = inputHeight / stride;
int featureW = inputWidth / stride;
float scale = minScale + (maxScale - minScale) * (2.0f * layerId + 1) / (2.0f * numLayers);
float scaleNext = minScale + (maxScale - minScale) * (2.0f * layerId + 2) / (2.0f * numLayers);
for (int y = 0; y < featureH; y++) {
for (int x = 0; x < featureW; x++) {
float cx = (x + 0.5f) / featureW;
float cy = (y + 0.5f) / featureH;
anchorCx[idx] = cx;
anchorCy[idx] = cy;
anchorW[idx] = scale;
anchorH[idx] = scale;
idx++;
float interpolatedScale = (float) Math.sqrt(scale * scaleNext);
anchorCx[idx] = cx;
anchorCy[idx] = cy;
anchorW[idx] = interpolatedScale;
anchorH[idx] = interpolatedScale;
idx++;
}
}
}
}
private MappedByteBuffer loadModelFile(String modelPath) throws IOException {
File file = new File(modelPath);
FileInputStream inputStream = new FileInputStream(file);
java.nio.channels.FileChannel fileChannel = inputStream.getChannel();
return fileChannel.map(java.nio.channels.FileChannel.MapMode.READ_ONLY, 0, fileChannel.size());
}
private File extractAssetToCache(Activity activity, String assetName) {
AssetManager assetManager = activity.getAssets();
File cacheDir = activity.getCacheDir();
File outFile = new File(cacheDir, assetName);
// Always overwrite to ensure latest model is used
// (remove the .exists() check so new model replaces old cached one)
try {
InputStream in = assetManager.open(assetName);
OutputStream out = new FileOutputStream(outFile);
byte[] buffer = new byte[4096];
int read;
while ((read = in.read(buffer)) != -1) out.write(buffer, 0, read);
in.close();
out.close();
Log.d(TAG, "Model extracted to cache: " + outFile.getAbsolutePath()
+ " (" + outFile.length() + " bytes)");
return outFile;
} catch (IOException e) {
Log.e(TAG, "extractAssetToCache failed", e);
return null;
}
}
public int[] getInputDims() {
return new int[]{inputHeight, inputWidth, inputChannels};
}
public int getOutputSize() { return numAnchors * 5 + 1; } // max possible detections * 5 + count header
/**
* Run face detection on the input image.
* Returns a flat float array:
* [0] = number of detections
* For each detection i (starting at index 1):
* [1 + i*5 + 0] = ymin (normalized 0-1)
* [1 + i*5 + 1] = xmin (normalized 0-1)
* [1 + i*5 + 2] = ymax (normalized 0-1)
* [1 + i*5 + 3] = xmax (normalized 0-1)
* [1 + i*5 + 4] = score (0-1)
*/
public float[] runFaceDetection(byte[] imageData) {
if (interpreter == null) {
Log.e(TAG, "interpreter is null");
return null;
}
try {
// Prepare input buffer
int inputPixels = inputHeight * inputWidth * inputChannels;
int inputBytes = isFloatInput ? inputPixels * 4 : inputPixels;
Log.d(TAG, "Input bytes needed: " + inputBytes + ", received: " + imageData.length);
if (isFloatInput) {
// Model expects float input, convert uint8 RGB888 to float [0, 1]
ByteBuffer floatBuffer = ByteBuffer.allocateDirect(inputBytes);
floatBuffer.order(ByteOrder.nativeOrder());
for (int i = 0; i < imageData.length; i++) {
floatBuffer.putFloat((imageData[i] & 0xFF) / 255.0f);
}
// Pad remaining with zeros
while (floatBuffer.position() < inputPixels) {
floatBuffer.putFloat(0.0f);
}
floatBuffer.rewind();
// Run inference
float[][][] regressorsOut = new float[1][numAnchors][16];
float[][][] classificatorsOut = new float[1][numAnchors][1];
java.util.Map<Integer, Object> outputs = new java.util.HashMap<>();
outputs.put(regressorOutputIdx, regressorsOut);
outputs.put(classifierOutputIdx, classificatorsOut);
interpreter.runForMultipleInputsOutputs(new Object[]{floatBuffer}, outputs);
// Process outputs
processOutputs(regressorsOut, classificatorsOut);
} else {
// Uint8 input: copy bytes directly
ByteBuffer inputBuffer = ByteBuffer.allocateDirect(inputBytes);
inputBuffer.order(ByteOrder.nativeOrder());
int copyLen = Math.min(imageData.length, inputBytes);
inputBuffer.put(imageData, 0, copyLen);
while (inputBuffer.position() < inputBytes) {
inputBuffer.put((byte) 0);
}
inputBuffer.rewind();
// Run inference
float[][][] regressorsOut = new float[1][numAnchors][16];
float[][][] classificatorsOut = new float[1][numAnchors][1];
java.util.Map<Integer, Object> outputs = new java.util.HashMap<>();
outputs.put(regressorOutputIdx, regressorsOut);
outputs.put(classifierOutputIdx, classificatorsOut);
interpreter.runForMultipleInputsOutputs(new Object[]{inputBuffer}, outputs);
// Process outputs
processOutputs(regressorsOut, classificatorsOut);
}
return getLastResult();
} catch (Exception e) {
Log.e(TAG, "runFaceDetection failed", e);
return null;
}
}
// Store last detection result
private float[] lastResult = null;
private void processOutputs(float[][][] regressorsOut, float[][][] classificatorsOut) {
// Flatten 3D output to 1D arrays
float[] regressors = new float[numAnchors * 16];
float[] classificators = new float[numAnchors];
for (int a = 0; a < numAnchors; a++) {
System.arraycopy(regressorsOut[0][a], 0, regressors, a * 16, 16);
classificators[a] = classificatorsOut[0][a][0];
}
Log.d(TAG, "Regressors: " + regressors.length + ", Classificators: " + classificators.length);
// Debug: log classification score distribution
float minLogit = Float.MAX_VALUE, maxLogit = -Float.MAX_VALUE;
for (float c : classificators) {
if (c < minLogit) minLogit = c;
if (c > maxLogit) maxLogit = c;
}
float minScore = 1.0f / (1.0f + (float) Math.exp(-minLogit));
float maxScore = 1.0f / (1.0f + (float) Math.exp(-maxLogit));
Log.d(TAG, "Classification logit range: [" + minLogit + ", " + maxLogit + "]");
Log.d(TAG, "Score range (sigmoid): [" + minScore + ", " + maxScore + "]");
// Debug: log raw regressor values for top-scoring anchors
int debugCount = 0;
for (int i = 0; i < numAnchors && debugCount < 3; i++) {
float s = 1.0f / (1.0f + (float) Math.exp(-classificators[i]));
if (s > 0.3f) {
int base = i * 16;
Log.d(TAG, "Top anchor #" + i + " score=" + s
+ " reg=[y0=" + String.format("%.4f", regressors[base])
+ ", x0=" + String.format("%.4f", regressors[base+1])
+ ", y1=" + String.format("%.4f", regressors[base+2])
+ ", x1=" + String.format("%.4f", regressors[base+3])
+ "] anchor=(" + anchorCx[i] + "," + anchorCy[i] + ")");
debugCount++;
}
}
// Decode detections
lastResult = decodeDetections(regressors, classificators, 0.3f, 0.3f);
}
private float[] getLastResult() {
return lastResult != null ? lastResult : new float[]{0};
}
/**
* Decode BlazeFace detections.
* The 16 regression values per anchor are:
* [0..3] = bounding box: cy, cx, h, w as pixel offsets from anchor center
* normalized by dividing by input dimensions
* [4..15] = 6 face keypoints as (y, x) pairs, same encoding
* All output in normalized [0, 1] coordinate space.
*/
private float[] decodeDetections(float[] regressors, float[] classificators,
float scoreThreshold, float iouThreshold) {
ArrayList<float[]> detections = new ArrayList<>(); // [ymin, xmin, ymax, xmax, score]
float invInputH = 1.0f / inputHeight;
float invInputW = 1.0f / inputWidth;
// Step 1: Decode all anchors
for (int i = 0; i < numAnchors; i++) {
float score = 1.0f / (1.0f + (float) Math.exp(-classificators[i]));
if (score < scoreThreshold) continue;
int base = i * 16;
// Get anchor position
float acx = this.anchorCx[i];
float acy = this.anchorCy[i];
// Decode bounding box from first 4 regression values
// All values are pixel offsets from anchor center, normalized by input size
float boxCy = acy + regressors[base] * invInputH;
float boxCx = acx + regressors[base + 1] * invInputW;
float boxH = regressors[base + 2] * invInputH;
float boxW = regressors[base + 3] * invInputW;
// Convert center+size to corners (normalized [0,1])
float ymin = boxCy - boxH / 2.0f;
float xmin = boxCx - boxW / 2.0f;
float ymax = boxCy + boxH / 2.0f;
float xmax = boxCx + boxW / 2.0f;
// Validate box center is in reasonable range
if (boxCy < -0.2f || boxCy > 1.2f || boxCx < -0.2f || boxCx > 1.2f) continue;
if (boxW <= 0 || boxH <= 0) continue;
// Optionally refine box using keypoints (values 4..15)
// Keypoints use same pixel-offset / inputSize encoding
float kpMinY = ymin, kpMaxY = ymax;
float kpMinX = xmin, kpMaxX = xmax;
int validKps = 0;
for (int k = 0; k < 6; k++) {
float kpY = acy + regressors[base + 4 + k * 2] * invInputH;
float kpX = acx + regressors[base + 4 + k * 2 + 1] * invInputW;
if (kpX >= -0.2f && kpX <= 1.2f && kpY >= -0.2f && kpY <= 1.2f) {
kpMinY = Math.min(kpMinY, kpY);
kpMaxY = Math.max(kpMaxY, kpY);
kpMinX = Math.min(kpMinX, kpX);
kpMaxX = Math.max(kpMaxX, kpX);
validKps++;
}
}
// Use union of box and keypoint bounds if we have enough valid keypoints
float finalYmin, finalXmin, finalYmax, finalXmax;
if (validKps >= 3) {
finalYmin = Math.min(ymin, kpMinY);
finalXmin = Math.min(xmin, kpMinX);
finalYmax = Math.max(ymax, kpMaxY);
finalXmax = Math.max(xmax, kpMaxX);
} else {
finalYmin = ymin;
finalXmin = xmin;
finalYmax = ymax;
finalXmax = xmax;
}
// Add margin (15%)
float bW = finalXmax - finalXmin;
float bH = finalYmax - finalYmin;
float margin = Math.max(bW, bH) * 0.15f;
float outYmin = clamp01(finalYmin - margin);
float outXmin = clamp01(finalXmin - margin);
float outYmax = clamp01(finalYmax + margin);
float outXmax = clamp01(finalXmax + margin);
// Validate box size
float finalW = outXmax - outXmin;
float finalH = outYmax - outYmin;
if (finalW < 0.02f || finalH < 0.02f) continue;
if (finalW > 0.98f || finalH > 0.98f) continue;
detections.add(new float[]{outYmin, outXmin, outYmax, outXmax, score});
// Debug
if (score > 0.5f) {
Log.d(TAG, "Face #" + i + " score=" + score
+ " anchor=(" + String.format("%.3f", acx) + "," + String.format("%.3f", acy) + ")"
+ " kps=[" + validKps + "]"
+ " box=[" + String.format("%.3f", outYmin) + "," + String.format("%.3f", outXmin)
+ "," + String.format("%.3f", outYmax) + "," + String.format("%.3f", outXmax) + "]"
+ " raw=(" + String.format("%.2f", regressors[base])
+ "," + String.format("%.2f", regressors[base+1])
+ "," + String.format("%.2f", regressors[base+2])
+ "," + String.format("%.2f", regressors[base+3]) + ")"
+ " decoded_c=(" + String.format("%.3f", boxCy) + "," + String.format("%.3f", boxCx) + ")");
}
}
Log.d(TAG, "Anchors above threshold (" + scoreThreshold + "): " + detections.size());
if (detections.isEmpty()) {
return new float[]{0};
}
// Step 2: Sort by score descending
int n = detections.size();
Integer[] order = new Integer[n];
for (int i = 0; i < n; i++) order[i] = i;
Arrays.sort(order, (a, b) -> Float.compare(detections.get(b)[4], detections.get(a)[4]));
// Step 3: NMS deduplication
boolean[] suppressed = new boolean[n];
ArrayList<Integer> kept = new ArrayList<>();
for (int i = 0; i < n; i++) {
int ci = order[i];
if (suppressed[ci]) continue;
kept.add(ci);
for (int j = i + 1; j < n; j++) {
int cj = order[j];
if (suppressed[cj]) continue;
float iou = computeIoU(detections.get(ci), detections.get(cj));
if (iou > iouThreshold) {
suppressed[cj] = true;
}
}
}
Log.d(TAG, "After NMS: " + kept.size() + " detections");
if (kept.isEmpty()) {
return new float[]{0};
}
// Step 4: Keep only the detection with the largest area
int bestIdx = 0;
float bestArea = 0;
for (int k = 0; k < kept.size(); k++) {
float[] d = detections.get(kept.get(k));
float area = (d[2] - d[0]) * (d[3] - d[1]);
if (area > bestArea) {
bestArea = area;
bestIdx = k;
}
}
numDetections = 1;
float[] result = new float[6];
result[0] = 1;
float[] det = detections.get(kept.get(bestIdx));
// Shift box: down by 1/6 height, left by 1/6 width
float boxH = det[2] - det[0];
float boxW = det[3] - det[1];
float shiftY = boxH / 6.0f;
float shiftX = boxW / 6.0f;
result[1] = clamp01(det[0] + shiftY); // ymin down
result[2] = clamp01(det[1] - shiftX); // xmin left
result[3] = clamp01(det[2] + shiftY); // ymax down
result[4] = clamp01(det[3] - shiftX); // xmax left
result[5] = det[4];
Log.d(TAG, "Best face (largest area): score=" + det[4]
+ " area=" + String.format("%.4f", bestArea)
+ " box=[" + det[0] + "," + det[1] + "," + det[2] + "," + det[3] + "]");
return result;
}
private float clamp01(float v) {
return Math.max(0, Math.min(1, v));
}
private float computeIoU(float[] boxA, float[] boxB) {
float ymin = Math.max(boxA[0], boxB[0]);
float xmin = Math.max(boxA[1], boxB[1]);
float ymax = Math.min(boxA[2], boxB[2]);
float xmax = Math.min(boxA[3], boxB[3]);
float interW = Math.max(0, xmax - xmin);
float interH = Math.max(0, ymax - ymin);
float interArea = interW * interH;
float areaA = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1]);
float areaB = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1]);
float unionArea = areaA + areaB - interArea;
return unionArea > 0 ? interArea / unionArea : 0;
}
/**
* Legacy method for backward compatibility.
* Returns raw detection results as flat array.
*/
public float[] runInference(byte[] imageData) {
return runFaceDetection(imageData);
}
}
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