func (s smartcropResizer) smartResize(input image.Image, dstWidth, dstHeight int) (image.Image, error) {
if dstWidth < 0 || dstHeight < 0 {
return nil, fmt.Errorf("Please specify both width and height for your target image")
}
scaledInput, scale, err := normalizeInput(input, 1024)
if err != nil {
return input, err
}
cvImage := opencv.FromImage(scaledInput)
_, err = os.Stat(s.haarcascade)
if err != nil {
return input, err
}
cascade := opencv.LoadHaarClassifierCascade(s.haarcascade)
faces := cascade.DetectObjects(cvImage)
if len(faces) == 0 {
return nil, ErrNoFacesFound
}
var biggestFace *opencv.Rect
for _, f := range faces {
if biggestFace == nil {
biggestFace = f
continue
}
biggestArea := biggestFace.Width() * biggestFace.Height()
currentArea := f.Width() * f.Height()
if biggestArea < currentArea {
biggestFace = f
}
}
log.Printf("Faces found %d\n", len(faces))
if biggestFace == nil {
return nil, ErrNoFacesFound
}
faceArea := biggestFace.Width() * biggestFace.Height()
imagePixels := scaledInput.Bounds().Dx() * scaledInput.Bounds().Dy()
faceAreaPercentage := float64(faceArea) / float64(imagePixels)
if faceAreaPercentage < faceImageTreshold {
return nil, fmt.Errorf("face area too small: %.2f.\n", faceAreaPercentage)
}
if sub, ok := input.(subImager); ok {
x := int(float64(biggestFace.X()) * scale)
y := int(float64(biggestFace.Y()) * scale)
width := int(float64(biggestFace.Width()) * scale)
height := int(float64(biggestFace.Height()) * scale)
facePoint := image.Pt(x, y)
target := image.Rect(0, 0, int(float64(dstWidth)*scale), int(float64(dstHeight)*scale))
r := image.Rect(0, 0, x+width, y+height).Add(facePoint)
for !target.In(r) && r.Min.X > 0 && r.Min.Y > 0 {
r = image.Rect(r.Min.X-1, r.Min.Y-1, r.Max.X+1, r.Max.Y+1)
}
cropImage := sub.SubImage(r)
return imaging.Thumbnail(cropImage, dstWidth, dstHeight, imaging.Lanczos), nil
}
return input, err
}
