func main() {
hd, err := filepath.Abs(filepath.Dir(os.Args[0]))
if err != nil {
panic(err)
}
cp := path.Join(hd, "haarcascade_frontalface_alt.xml")
if _, err := os.Stat(cp); err != nil {
panic(err)
}
cm := opencv.NewCameraCapture(0)
cs := opencv.LoadHaarClassifierCascade(cp)
p := &pair.Checker{Camera: cm, Cascade: cs}
t, err := p.Present()
if err != nil {
panic(err)
}
if !t {
os.Stderr.WriteString("Your pair must be playing ping pong\n")
os.Exit(1)
}
}
func faceDetect(settings CropSettings, i image.Image, o image.Image) error {
cvImage := opencv.FromImage(i)
_, err := os.Stat(settings.FaceDetectionHaarCascadeFilepath)
if err != nil {
return err
}
cascade := opencv.LoadHaarClassifierCascade(settings.FaceDetectionHaarCascadeFilepath)
faces := cascade.DetectObjects(cvImage)
gc := draw2dimg.NewGraphicContext((o).(*image.RGBA))
if settings.DebugMode == true {
log.Println("Faces detected:", len(faces))
}
for _, face := range faces {
if settings.DebugMode == true {
log.Printf("Face: x: %d y: %d w: %d h: %d\n", face.X(), face.Y(), face.Width(), face.Height())
}
draw2dkit.Ellipse(
gc,
float64(face.X()+(face.Width()/2)),
float64(face.Y()+(face.Height()/2)),
float64(face.Width()/2),
float64(face.Height())/2)
gc.SetFillColor(color.RGBA{255, 0, 0, 255})
gc.Fill()
}
return nil
}
func detectFace(input *image.Image, multiple bool) (*bytes.Buffer, error) {
daniel := opencv.LoadImage("static/daniel.jpg").ToImage()
hdaniel := int(daniel.Bounds().Dy())
wdaniel := int(daniel.Bounds().Dx())
_image := opencv.FromImage(*input)
cascade := opencv.LoadHaarClassifierCascade("haarcascade_frontalface_alt.xml")
faces := cascade.DetectObjects(_image)
found := false
bounds := (*input).Bounds()
output := image.NewRGBA(bounds)
draw.Draw(output, bounds, *input, image.ZP, draw.Src)
for _, value := range faces {
x := int(value.X())
y := int(value.Y())
w := int(value.Width())
h := int(value.Height())
y2 := y + h
h = hdaniel * w / wdaniel
y = y2 - h
danielTmp := resize.Resize(uint(w), uint(h), daniel, resize.Lanczos3)
if multiple || !found {
draw.Draw(output, image.Rect(x, y, x+w, y+h), danielTmp, image.ZP, draw.Src)
}
found = true
}
if !found {
return nil, errors.New("No faces found")
}
toOut := new(bytes.Buffer)
err := jpeg.Encode(toOut, output, nil)
if err != nil {
return nil, err
}
return toOut, nil
}
func main() {
cap := opencv.NewCameraCapture(0)
if cap == nil {
panic("can not open camera")
}
defer cap.Release()
_, currentfile, _, _ := runtime.Caller(0)
cascade := opencv.LoadHaarClassifierCascade(path.Join(path.Dir(currentfile), "haarcascade_frontalface_alt.xml"))
timeout := time.Second * 10
lastFace := time.Now()
for {
if cap.GrabFrame() {
img := cap.RetrieveFrame(1)
if img == nil {
continue
}
hasFaces, _ := ProcessImage(img, cascade)
if hasFaces {
lastFace = time.Now()
} else {
delta := time.Since(lastFace)
fmt.Println("no faces for %ts", delta)
if delta > timeout {
fmt.Println("lock!")
lock()
time.Sleep(time.Second * 60)
}
}
}
time.Sleep(time.Second)
}
}
func faceDetect(i *image.Image, o *image.Image) {
cvImage := opencv.FromImage(*i)
_, err := os.Stat(faceDetectionHaarCascade)
if err != nil {
fmt.Println(err)
os.Exit(1)
}
cascade := opencv.LoadHaarClassifierCascade(faceDetectionHaarCascade)
faces := cascade.DetectObjects(cvImage)
gc := draw2dimg.NewGraphicContext((*o).(*image.RGBA))
if debug == true {
fmt.Println("Faces detected:", len(faces))
}
for _, face := range faces {
if debug == true {
fmt.Printf("Face: x: %d y: %d w: %d h: %d\n", face.X(), face.Y(), face.Width(), face.Height())
}
draw2dkit.Ellipse(
gc,
float64(face.X()+(face.Width()/2)),
float64(face.Y()+(face.Height()/2)),
float64(face.Width()/2),
float64(face.Height())/2)
gc.SetFillColor(color.RGBA{255, 0, 0, 255})
gc.Fill()
}
}
func init() {
if _, err := os.Stat(haarCascade); os.IsNotExist(err) {
panic(haarCascade + " not found")
}
cascade = opencv.LoadHaarClassifierCascade(haarCascade)
semaphore = make(chan struct{}, runtime.NumCPU())
}
func NewEyeMarker(classifierPath string) *eyeMarker {
//_, currentfile, _, _ := runtime.Caller(0)
var classifierFile = opencv.LoadHaarClassifierCascade(classifierPath)
if classifierFile == nil {
panic("Classifier must not be nil")
}
return &eyeMarker{classifierFile, 0, []int{}, []int{}}
}
func NewFaceDetector() *FaceDetector {
detector := &FaceDetector{
eyeCascade: opencv.LoadHaarClassifierCascade("/home/joakim/opencv-2.4.9/data/haarcascades/haarcascade_eye.xml"),
glassesCascade: opencv.LoadHaarClassifierCascade("/home/joakim/opencv-2.4.9/data/haarcascades/haarcascade_eye_tree_eyeglasses.xml"),
lefteyeCascade: opencv.LoadHaarClassifierCascade("/home/joakim/opencv-2.4.9/data/haarcascades/haarcascade_mcs_lefteye.xml"),
righteyeCascade: opencv.LoadHaarClassifierCascade("/home/joakim/opencv-2.4.9/data/haarcascades/haarcascade_mcs_righteye.xml"),
faceCascade: opencv.LoadHaarClassifierCascade("/home/joakim/Go/src/github.com/lazywei/go-opencv/samples/haarcascade_frontalface_alt.xml"),
mouthCascade: opencv.LoadHaarClassifierCascade("/home/joakim/opencv-2.4.9/data/haarcascades/haarcascade_mcs_mouth.xml"),
noseCascade: opencv.LoadHaarClassifierCascade("/home/joakim/opencv-2.4.9/data/haarcascades/haarcascade_mcs_nose.xml"),
}
return detector
}
func main() {
_, currentfile, _, _ := runtime.Caller(0)
image := opencv.LoadImage(path.Join(path.Dir(currentfile), "../images/lena.jpg"))
cascade := opencv.LoadHaarClassifierCascade(path.Join(path.Dir(currentfile), "haarcascade_frontalface_alt.xml"))
faces := cascade.DetectObjects(image)
for _, value := range faces {
opencv.Rectangle(image,
opencv.Point{value.X() + value.Width(), value.Y()},
opencv.Point{value.X(), value.Y() + value.Height()},
opencv.ScalarAll(255.0), 1, 1, 0)
}
win := opencv.NewWindow("Face Detection")
win.ShowImage(image)
opencv.WaitKey(0)
}
func faceDetect(settings CropSettings, i image.Image, o image.Image, scale float64) ([]Face, error) {
cvImage := opencv.FromImage(i)
_, err := os.Stat(settings.FaceDetectionHaarCascadeFilepath)
if err != nil {
return []Face{}, err
}
cascade := opencv.LoadHaarClassifierCascade(settings.FaceDetectionHaarCascadeFilepath)
faces := cascade.DetectObjects(cvImage)
gc := draw2dimg.NewGraphicContext((o).(*image.RGBA))
if settings.DebugMode == true {
log.Println("Faces detected:", len(faces))
}
if len(faces) == 0 {
return []Face{}, ErrNoFacesFound
}
resultFaces := []Face{}
for _, face := range faces {
if settings.DebugMode == true {
log.Printf("Face: x: %d y: %d w: %d h: %d\n", face.X(), face.Y(), face.Width(), face.Height())
}
draw2dkit.Ellipse(
gc,
float64(face.X()+(face.Width()/2)),
float64(face.Y()+(face.Height()/2)),
float64(face.Width()/2),
float64(face.Height())/2)
gc.SetFillColor(color.RGBA{255, 0, 0, 255})
gc.Fill()
x := int(float64(face.X()) * scale)
y := int(float64(face.Y()) * scale)
width := int(float64(face.Width()) * scale)
height := int(float64(face.Height()) * scale)
resultFaces = append(resultFaces, Face{X: x, Y: y, Width: width, Height: height})
}
return resultFaces, nil
}
func main() {
win := opencv.NewWindow("Go-OpenCV Webcam Face Detection")
defer win.Destroy()
cap := opencv.NewCameraCapture(0)
if cap == nil {
panic("cannot open camera")
}
defer cap.Release()
cwd, err := os.Getwd()
if err != nil {
panic(err)
}
cascade := opencv.LoadHaarClassifierCascade(path.Join(cwd, "haarcascade_frontalface_alt.xml"))
fmt.Println("Press ESC to quit")
for {
if cap.GrabFrame() {
img := cap.RetrieveFrame(1)
if img != nil {
faces := cascade.DetectObjects(img)
for _, value := range faces {
opencv.Circle(img,
opencv.Point{
value.X() + (value.Width() / 2),
value.Y() + (value.Height() / 2),
},
value.Width()/2,
opencv.ScalarAll(255.0), 1, 1, 0)
}
win.ShowImage(img)
} else {
fmt.Println("nil image")
}
}
key := opencv.WaitKey(1)
if key == 27 {
os.Exit(0)
}
}
}
func ProcessbetterImage(img *opencv.IplImage, win *opencv.Window, pos int) error {
_, currentfile, _, _ := runtime.Caller(0)
//image := opencv.LoadImage(path.Join(path.Dir(currentfile), "../images/eye.jpg"))
if counter == 1 {
//find eye, cache them and then mark them
counter = 0
cachedPointsCenterY = []int{}
cachedPointsCenterX = []int{}
if haarClassifier == nil {
//load and cache haarClassifier
haarClassifier = opencv.LoadHaarClassifierCascade(path.Join(path.Dir(currentfile), "/cascades/haarcascade_eye_tree_eyeglasses.xml"))
}
rects := haarClassifier.DetectObjects(img)
for _, value := range rects {
var pointRect1 = opencv.Point{value.X() + value.Width(), value.Y()}
var pointRect2 = opencv.Point{value.X(), value.Y() + value.Height()}
var centerPointX = int((pointRect1.X + pointRect2.X) / 2)
var centerPointY = int((pointRect1.Y + pointRect2.Y) / 2)
var center = opencv.Point{centerPointX, centerPointY}
cachedPointsCenterX = append(cachedPointsCenterX, center.X)
cachedPointsCenterY = append(cachedPointsCenterY, center.Y)
//draw eye marker
drawEyeMarker(img, center)
}
} else {
//if we have a cached value
for i := 0; i < len(cachedPointsCenterY); i++ {
drawEyeMarker(img, opencv.Point{cachedPointsCenterX[i], cachedPointsCenterY[i]})
}
}
win.ShowImage(img)
//counter for cache
counter = counter + 1
return nil
}
//detects faces and crops em out
func cropFaces(inputs []string, dirOut string, harrcascade string) {
err := os.MkdirAll(dirOut, 0777) // makes dir if not exists
if err != nil {
fmt.Printf("Could not create directory %s\n", dirOut)
log.Fatal(err)
}
fmt.Printf("Cropping %d images.\n", len(inputs))
for _, element := range inputs {
outPath := dirOut + "face_" + filepath.Base(element)
image := opencv.LoadImage(element)
if image == nil {
panic("Loading Image " + element + "failed")
}
defer image.Release()
//detect some faces
cascade := opencv.LoadHaarClassifierCascade(harrcascade)
faces := cascade.DetectObjects(image)
if len(faces) == 0 {
fmt.Printf("Found no face in %s\n", element)
}
for _, value := range faces {
//Some invalid stuff filtering
if value.Width() < image.Width() && value.Height() < image.Height() && value.Width() > 0 && value.Height() > 0 {
//crop out the face
crop := opencv.Crop(image, value.X(), value.Y(), value.Width(), value.Height())
//save cropped
opencv.SaveImage(outPath, crop, 0)
crop.Release()
} else {
fmt.Printf("Couldn't save: %s", value)
}
}
}
}
func main() {
cap := opencv.NewCameraCapture(0)
if cap == nil {
panic("cannot open camera")
}
defer cap.Release()
cwd, err := os.Getwd()
if err != nil {
panic(err)
}
cascade := opencv.LoadHaarClassifierCascade(path.Join(cwd, "haarcascade_frontalface_alt.xml"))
fmt.Println("Press ESC to quit")
for {
if cap.GrabFrame() {
img := cap.RetrieveFrame(1)
if img != nil {
faces := cascade.DetectObjects(img)
for _, value := range faces {
leftX := value.X()
rightX := leftX + value.Width()
lowY := value.Y()
highY := lowY + value.Height()
log.Println("Face Seen! LowLeft Corner: (", leftX, ", ", lowY, "), UpRight Corner: (", rightX, ", ", highY, ")")
}
} else {
fmt.Println("nil image")
}
}
key := opencv.WaitKey(1)
if key == 27 {
os.Exit(0)
}
}
}
func faces(r io.Reader) ([]*Face, error) {
cascade := opencv.LoadHaarClassifierCascade(path.Join(currentDir(), "haarcascade_frontalface_alt.xml"))
return detect(cascade, r)
}
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
}
func processResponse(resp *http.Response) error {
var imgExtension string
// skip not modified or other weird headers that dont include payload
if resp.StatusCode != 200 {
return nil
}
// skip gzipped assets that ignored us when told no gzip
if "gzip" == resp.Header.Get("Content-Encoding") {
return nil
}
switch resp.Header.Get("Content-Type") {
case "image/jpeg":
imgExtension = ".jpg"
case "image/png":
imgExtension = ".png"
case "image/gif":
imgExtension = ".gif"
}
if "" != imgExtension {
//log.Println("Processing", resp.Request.Method, resp.Request.URL.String())
// save locally
fileName := path.Join(os.TempDir(), strconv.FormatInt(time.Now().UnixNano(), 10)+imgExtension)
file, _ := os.Create(fileName)
defer os.Remove(fileName)
// dump Body to file
io.Copy(file, resp.Body)
file.Close()
resp.Body.Close()
// detect faces
image := opencv.LoadImage(fileName, -1)
defer image.Release()
if image != nil {
cascade := opencv.LoadHaarClassifierCascade("./deface_files/haarcascade_frontalface_alt.xml")
faces := cascade.DetectObjects(image)
// replace faces
if len(faces) > 0 {
rageGuy := opencv.LoadImage("./deface_files/rage-guy.png", -1)
defer rageGuy.Release()
for _, value := range faces {
faceWidth := value.Width()
faceHeight := value.Height()
faceX := value.X()
faceY := value.Y()
if (faceWidth <= 0) || (faceHeight <= 0) || (faceX > image.Width()) || (faceY > image.Height()) {
//log.Println("Invalid rectangle for", resp.Request.Method, resp.Request.URL.String(), faceWidth, faceHeight, faceX, faceY)
continue
}
resizedRageGuy := opencv.Resize(rageGuy, faceWidth, faceHeight, 0)
rect := &opencv.Rect{}
rect.Init(faceX, faceY, faceWidth, faceHeight)
image.SetROI(*rect)
// blending them horribly inefficiently because cvSplit/cvThreshold are not exposed in go
// and dont want to link to C
for y := 0; y < resizedRageGuy.Height(); y++ {
for x := 0; x < resizedRageGuy.Width(); x++ {
src := image.Get2D(x, y).Val()
overlay := resizedRageGuy.Get2D(x, y).Val()
if overlay[3] > 0 {
image.Set2D(x, y, opencv.NewScalar(overlay[0], overlay[1], overlay[2], src[3]))
}
}
}
image.ResetROI()
resizedRageGuy.Release()
}
}
// save modified image and pass it to Body
opencv.SaveImage(fileName, image, 0)
} else {
//log.Println("Couldnt load image", resp.Request.Method, resp.Request.URL.String())
}
file, _ = os.Open(fileName)
resp.Body = ioutil.NopCloser(file)
// update content length
fileInfo, _ := file.Stat()
resp.ContentLength = fileInfo.Size()
resp.Header.Set("Content-Length", strconv.FormatInt(fileInfo.Size(), 10))
}
return nil
}
// loadHaarClassifierCascade returns open cv HaarCascade loaded
func loadHaarClassifierCascade(haar string) *cv.HaarCascade {
return cv.LoadHaarClassifierCascade(haar)
}
