306 lines
8.6 KiB
Go
306 lines
8.6 KiB
Go
package service
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import (
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"bytes"
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_ "embed"
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"errors"
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"fmt"
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"image"
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"image/draw"
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"image/png"
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"math"
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"os"
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"runtime"
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"sort"
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"sync"
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ort "github.com/yalue/onnxruntime_go"
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)
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// YuNet 人脸检测模型（opencv_zoo face_detection_yunet_2023mar，输入尺寸已改为
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// 动态），编译进二进制，运行时只额外依赖 onnxruntime 动态库。
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//
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//go:embed assets/yunet.onnx
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var yunetModel []byte
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const (
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// 检出分数下限与 NMS 的 IoU 阈值
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faceScoreThreshold = 0.35
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faceNMSIoU = 0.3
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// 推理输入的长边上限：更大的图先等比缩小，检出框再映射回原图，
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// 避免超大参考图把内存和耗时拉爆。
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faceMaxInferSide = 2560
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)
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// YuNet 的三个输出分支步长
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var faceStrides = []int{8, 16, 32}
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// ErrNoFaceDetected 表示图中没有检出人脸，调用方按原图处理。
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var ErrNoFaceDetected = errors.New("no face detected")
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var (
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faceOnce sync.Once
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faceSession *ort.DynamicAdvancedSession
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faceInitErr error
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)
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// onnxruntimeLibPath 返回 onnxruntime 动态库路径：ONNXRUNTIME_LIB_PATH 优先，
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// 否则用各平台的默认位置。
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func onnxruntimeLibPath() string {
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if p := os.Getenv("ONNXRUNTIME_LIB_PATH"); p != "" {
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return p
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}
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if runtime.GOOS == "windows" {
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return "onnxruntime.dll"
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}
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return "/usr/local/lib/libonnxruntime.so"
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}
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// faceOutputNames 是 YuNet 需要读取的输出名，顺序与 readOutputs 的下标约定一致：
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// 先 cls_*、再 obj_*、最后 bbox_*（关键点分支用不到）。
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func faceOutputNames() []string {
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names := make([]string, 0, len(faceStrides)*3)
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for _, prefix := range []string{"cls", "obj", "bbox"} {
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for _, s := range faceStrides {
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names = append(names, fmt.Sprintf("%s_%d", prefix, s))
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}
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}
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return names
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}
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func faceDetector() (*ort.DynamicAdvancedSession, error) {
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faceOnce.Do(func() {
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ort.SetSharedLibraryPath(onnxruntimeLibPath())
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if err := ort.InitializeEnvironment(); err != nil {
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faceInitErr = fmt.Errorf("onnxruntime init: %w", err)
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return
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}
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faceSession, faceInitErr = ort.NewDynamicAdvancedSessionWithONNXData(
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yunetModel, []string{"input"}, faceOutputNames(), nil)
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})
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if faceInitErr != nil {
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return nil, faceInitErr
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}
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return faceSession, nil
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}
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type faceDetection struct {
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rect image.Rectangle
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score float32
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}
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// detectFaces 返回图中的人脸矩形框（坐标基于 src 的原始尺寸）。
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func detectFaces(src image.Image) ([]image.Rectangle, error) {
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sess, err := faceDetector()
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if err != nil {
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return nil, err
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}
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bounds := src.Bounds()
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long := bounds.Dx()
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if bounds.Dy() > long {
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long = bounds.Dy()
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}
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scale := 1.0
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if long > faceMaxInferSide {
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scale = float64(faceMaxInferSide) / float64(long)
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}
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inW := int(float64(bounds.Dx()) * scale)
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inH := int(float64(bounds.Dy()) * scale)
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if inW < 1 || inH < 1 {
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return nil, nil
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}
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// 输入补齐到 32 的整数倍，三个步长分支才有整数网格
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padW := (inW + 31) / 32 * 32
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padH := (inH + 31) / 32 * 32
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// YuNet 吃 BGR、NCHW、未归一化的 0~255 像素
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pixels := make([]float32, 3*padW*padH)
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plane := padW * padH
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for y := 0; y < inH; y++ {
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srcY := bounds.Min.Y + int(float64(y)/scale)
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for x := 0; x < inW; x++ {
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r, g, b, _ := src.At(bounds.Min.X+int(float64(x)/scale), srcY).RGBA()
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i := y*padW + x
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pixels[i] = float32(b >> 8)
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pixels[plane+i] = float32(g >> 8)
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pixels[2*plane+i] = float32(r >> 8)
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}
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}
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input, err := ort.NewTensor(ort.NewShape(1, 3, int64(padH), int64(padW)), pixels)
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if err != nil {
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return nil, err
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}
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defer input.Destroy()
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outputs := make([]ort.Value, len(faceStrides)*3)
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if err := sess.Run([]ort.Value{input}, outputs); err != nil {
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return nil, err
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}
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defer func() {
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for _, out := range outputs {
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if out != nil {
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out.Destroy()
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}
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}
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}()
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branch := func(i int) ([]float32, error) {
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t, ok := outputs[i].(*ort.Tensor[float32])
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if !ok {
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return nil, fmt.Errorf("yunet output %d is not a float32 tensor", i)
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}
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return t.GetData(), nil
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}
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inferBounds := image.Rect(0, 0, inW, inH)
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var dets []faceDetection
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for si, stride := range faceStrides {
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cls, err := branch(si)
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if err != nil {
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return nil, err
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}
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obj, err := branch(len(faceStrides) + si)
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if err != nil {
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return nil, err
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}
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box, err := branch(2*len(faceStrides) + si)
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if err != nil {
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return nil, err
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}
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cols, rows := padW/stride, padH/stride
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for row := 0; row < rows; row++ {
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for col := 0; col < cols; col++ {
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idx := row*cols + col
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score := float32(math.Sqrt(float64(clampUnit(cls[idx]) * clampUnit(obj[idx]))))
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if score < faceScoreThreshold {
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continue
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}
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cx := (float32(col) + box[idx*4]) * float32(stride)
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cy := (float32(row) + box[idx*4+1]) * float32(stride)
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w := float32(math.Exp(float64(box[idx*4+2]))) * float32(stride)
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h := float32(math.Exp(float64(box[idx*4+3]))) * float32(stride)
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rect := image.Rect(int(cx-w/2), int(cy-h/2), int(cx+w/2), int(cy+h/2)).Intersect(inferBounds)
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if rect.Dx() > 0 && rect.Dy() > 0 {
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dets = append(dets, faceDetection{rect: rect, score: score})
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}
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}
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}
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}
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boxes := make([]image.Rectangle, 0, len(dets))
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for _, d := range suppressOverlaps(dets, faceNMSIoU) {
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rect := d.rect
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if scale != 1 {
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rect = image.Rect(
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int(float64(rect.Min.X)/scale), int(float64(rect.Min.Y)/scale),
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int(float64(rect.Max.X)/scale), int(float64(rect.Max.Y)/scale),
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).Intersect(image.Rect(0, 0, bounds.Dx(), bounds.Dy()))
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}
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if rect.Dx() > 0 && rect.Dy() > 0 {
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boxes = append(boxes, rect.Add(bounds.Min))
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}
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}
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return boxes, nil
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}
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func clampUnit(v float32) float32 {
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if v < 0 {
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return 0
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}
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if v > 1 {
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return 1
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}
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return v
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}
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// suppressOverlaps 按分数从高到低做 NMS，丢掉与已保留框 IoU 超过阈值的框。
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func suppressOverlaps(dets []faceDetection, iouThreshold float64) []faceDetection {
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sort.SliceStable(dets, func(i, j int) bool { return dets[i].score > dets[j].score })
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kept := make([]faceDetection, 0, len(dets))
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for _, d := range dets {
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overlaps := false
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for _, k := range kept {
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if rectIoU(d.rect, k.rect) > iouThreshold {
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overlaps = true
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break
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}
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}
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if !overlaps {
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kept = append(kept, d)
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}
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}
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return kept
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}
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func rectIoU(a, b image.Rectangle) float64 {
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inter := a.Intersect(b)
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if inter.Empty() {
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return 0
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}
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interArea := float64(inter.Dx() * inter.Dy())
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return interArea / (float64(a.Dx()*a.Dy()+b.Dx()*b.Dy()) - interArea)
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}
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// applyFaceNotice 给图中每张人脸盖一层黑丝网眼，返回 PNG。
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// 没检出人脸（或不是可解码的图片）时返回 ErrNoFaceDetected，调用方应继续用原图；
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// 其它错误说明检测器不可用，调用方不应把未打码的图上传。
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func applyFaceNotice(b []byte) ([]byte, error) {
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src, _, err := image.Decode(bytes.NewReader(b))
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if err != nil {
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return nil, ErrNoFaceDetected
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}
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boxes, err := detectFaces(src)
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if err != nil {
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return nil, err
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}
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if len(boxes) == 0 {
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return nil, ErrNoFaceDetected
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}
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w, h := src.Bounds().Dx(), src.Bounds().Dy()
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dst := image.NewRGBA(image.Rect(0, 0, w, h))
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draw.Draw(dst, dst.Bounds(), src, src.Bounds().Min, draw.Src)
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offset := image.Pt(-src.Bounds().Min.X, -src.Bounds().Min.Y)
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for _, box := range boxes {
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r := box.Add(offset).Intersect(dst.Bounds())
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// 网眼只盖脸中央，留出边缘的发型与轮廓。
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r = image.Rect(r.Min.X+r.Dx()/8, r.Min.Y+r.Dy()/8, r.Max.X-r.Dx()/8, r.Max.Y-r.Dy()/8)
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drawStocking(dst, r)
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}
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var out bytes.Buffer
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if err := png.Encode(&out, dst); err != nil {
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return nil, err
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}
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return out.Bytes(), nil
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}
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// drawStocking 在给定区域上盖一层黑丝网眼：细密的深色网格线，遮住五官细节但保留轮廓。
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func drawStocking(dst *image.RGBA, r image.Rectangle) {
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r = r.Intersect(dst.Bounds())
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step := r.Dx() / 24
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if step < 3 {
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step = 3
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}
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line := step / 2
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if line < 1 {
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line = 1
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}
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for y := r.Min.Y; y < r.Max.Y; y++ {
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for x := r.Min.X; x < r.Max.X; x++ {
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if (x-r.Min.X)%step >= line && (y-r.Min.Y)%step >= line {
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continue
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}
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c := dst.RGBAAt(x, y)
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c.R = uint8(uint32(c.R) * 10 / 100)
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c.G = uint8(uint32(c.G) * 10 / 100)
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c.B = uint8(uint32(c.B) * 10 / 100)
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dst.SetRGBA(x, y, c)
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}
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}
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}
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// faceMaskPromptNote 附加到 Seedance 提示词后：告知模型参考图脸部的网格线只是打码，
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// 需要忽略网格本身并完整还原面部细节。
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const faceMaskPromptNote = "参考图人物脸部覆盖的细密网格线仅为隐私打码，不是人物本身的特征：生成时请完全忽略这些网格线，不要在画面中出现任何网格、方格、纹理或遮挡；请依据参考图的五官轮廓完整还原人物真实面孔，保留妆容、眉眼、发型、发饰、耳饰、头冠等一切面部与头部装饰细节，人物面部必须清晰完整、前后镜头保持一致。"
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