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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,2 +1,3 @@ | ||
| bin | ||
| .env | ||
| .env | ||
| model |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,188 @@ | ||
| package nsfw | ||
|
|
||
| import ( | ||
| "bytes" | ||
| "fmt" | ||
| "image/png" | ||
|
|
||
| tf "github.com/galeone/tensorflow/tensorflow/go" | ||
| "github.com/galeone/tensorflow/tensorflow/go/op" | ||
| tg "github.com/galeone/tfgo" | ||
| "golang.org/x/image/webp" | ||
| ) | ||
|
|
||
| const ( | ||
| ImageDimensions = 224 | ||
| ) | ||
|
|
||
| type Predictor struct { | ||
| model *tg.Model | ||
| } | ||
|
|
||
| type Prediction struct { | ||
| Drawings float32 | ||
| Hentai float32 | ||
| Neutral float32 | ||
| Porn float32 | ||
| Sexy float32 | ||
| } | ||
|
|
||
| func NewPredictor(model *tg.Model) *Predictor { | ||
| return &Predictor{ | ||
| model: model, | ||
| } | ||
| } | ||
|
|
||
| func NewPredictorFromPath(modelPath string) (*Predictor, error) { | ||
| model := tg.LoadModel(modelPath, []string{"serve"}, nil) | ||
| return NewPredictor(model), nil | ||
| } | ||
|
|
||
| func (p *Predictor) Predict(img []byte, imageFormat string) (*Prediction, error) { | ||
| tensor, err := createTensorFromImage(img, imageFormat) | ||
| if err != nil { | ||
| return nil, err | ||
| } | ||
|
|
||
| results := p.model.Exec([]tf.Output{ | ||
| p.model.Op("StatefulPartitionedCall", 0), | ||
| }, map[tf.Output]*tf.Tensor{ | ||
| p.model.Op("serving_default_input", 0): tensor, | ||
| }) | ||
|
|
||
| vals := results[0].Value().([][]float32)[0] | ||
| return &Prediction{ | ||
| Drawings: vals[0], | ||
| Hentai: vals[1], | ||
| Neutral: vals[2], | ||
| Porn: vals[3], | ||
| Sexy: vals[4], | ||
| }, nil | ||
| } | ||
|
|
||
| func (p Prediction) Describe() string { | ||
| return fmt.Sprintf( | ||
| "[Drawing: %.2f%% , Hentai: %.2f%%, Porn: %.2f%%, Sexy: %.2f%%, Neutral: %.2f%%]", | ||
| p.Drawings*100, p.Hentai*100, p.Porn*100, p.Sexy*100, p.Neutral*100) | ||
| } | ||
|
|
||
| func (p Prediction) IsNSFW() bool { | ||
| if p.Porn > 0.3 { | ||
| return true | ||
| } | ||
| if p.Sexy > 0.4 { | ||
| return true | ||
| } | ||
| if p.Hentai > 0.3 { | ||
| return true | ||
| } | ||
|
|
||
| return false | ||
| } | ||
|
|
||
| type Image struct { | ||
| Path *op.Scope | ||
| Input tf.Output | ||
| Output tf.Output | ||
| } | ||
|
|
||
| func (image *Image) Scale(min, max float32) *Image { | ||
| if image.Output.DataType() != tf.Float { | ||
| image.Output = tg.Cast(image.Path, image.Output, tf.Float) | ||
| } | ||
|
|
||
| scaleScope := image.Path.SubScope("scale") | ||
|
|
||
| minVal := op.Min(scaleScope.SubScope("Min"), image.Output, op.Const(scaleScope.SubScope("reductionIndices"), []int32{0, 1, 2}), op.MinKeepDims(false)) | ||
| maxVal := op.Max(scaleScope.SubScope("Max"), image.Output, op.Const(scaleScope.SubScope("reductionIndices"), []int32{0, 1, 2}), op.MaxKeepDims(false)) | ||
| image.Output = op.Div(scaleScope.SubScope("Div"), | ||
| op.Mul(scaleScope.SubScope("Mul"), | ||
| op.Sub(scaleScope.SubScope("Sub"), image.Output, minVal), | ||
| op.Const(scaleScope.SubScope("scaleRange"), max-min)), | ||
| op.Sub(scaleScope.SubScope("Sub"), maxVal, minVal)) | ||
| return image | ||
| } | ||
|
|
||
| func (image *Image) Resize(width, height int32) *Image { | ||
| resizeScope := image.Path.SubScope("resizeArea") | ||
| image.Output = op.ResizeArea(resizeScope.SubScope("ResizeArea"), image.Output, op.Const(resizeScope.SubScope("size"), []int32{width, height})) | ||
| return image | ||
| } | ||
|
|
||
| func NewImage(imageFormat string) *Image { | ||
| s := op.NewScope() | ||
| input := op.Placeholder(s, tf.String) | ||
|
|
||
| var decode tf.Output | ||
| if imageFormat == "png" { | ||
| decode = op.DecodePng(s, input, op.DecodePngChannels(3)) | ||
| } else if imageFormat == "gif" { | ||
| decode = op.DecodeGif(s, input) | ||
| } else if imageFormat == "bmp" { | ||
| decode = op.DecodeBmp(s, input, op.DecodeBmpChannels(3)) | ||
| } else if imageFormat == "jpeg" || imageFormat == "jpg" || imageFormat == "jpe" { | ||
| decode = op.DecodeJpeg(s.SubScope("DecodeJpeg"), input, op.DecodeJpegChannels(3)) | ||
| } else { | ||
| return nil | ||
| } | ||
|
|
||
| output := op.ExpandDims(s.SubScope("ExpandDims"), decode, op.Const(s.SubScope("axis"), []int32{0})) | ||
|
|
||
| img := &Image{Path: s, Output: op.Identity(s.SubScope("Identity"), output), Input: input} | ||
| img.Scale(0, 1) | ||
| return img | ||
| } | ||
|
|
||
| func webpToPng(image []byte) ([]byte, error) { | ||
| img, err := webp.Decode(bytes.NewReader(image)) | ||
| if err != nil { | ||
| return nil, err | ||
| } | ||
| buf := new(bytes.Buffer) | ||
| err = png.Encode(buf, img) | ||
| if err != nil { | ||
| return nil, err | ||
| } | ||
|
|
||
| return buf.Bytes(), nil | ||
| } | ||
|
|
||
| func createTensorFromImage(image []byte, imageFormat string) (*tf.Tensor, error) { | ||
| var err error | ||
| if imageFormat == "webp" { | ||
| image, err = webpToPng(image) | ||
| if err != nil { | ||
| return nil, err | ||
| } | ||
| imageFormat = "png" | ||
| } | ||
| tensor, err := tf.NewTensor(string(image)) | ||
| if err != nil { | ||
| return nil, err | ||
| } | ||
|
|
||
| img := NewImage(imageFormat) | ||
| if img == nil { | ||
| return nil, fmt.Errorf("unknown image format %s", imageFormat) | ||
| } | ||
| img.Resize(ImageDimensions, ImageDimensions) | ||
|
|
||
| graph, err := img.Path.Finalize() | ||
|
|
||
| if err != nil { | ||
| return nil, err | ||
| } | ||
| session, err := tf.NewSession(graph, nil) | ||
| if err != nil { | ||
| return nil, err | ||
| } | ||
| defer session.Close() | ||
| normalized, err := session.Run( | ||
| map[tf.Output]*tf.Tensor{img.Input: tensor}, | ||
| []tf.Output{img.Output}, | ||
| nil) | ||
| if err != nil { | ||
| return nil, err | ||
| } | ||
| return normalized[0], nil | ||
| } |
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