func main() {
flag.Usage = usage
flag.Parse()
if flag.NArg() != 2 {
flag.Usage()
os.Exit(1)
}
var (
imgFile = flag.Arg(0)
visFile = flag.Arg(1)
)
img, err := loadImage(imgFile)
if err != nil {
log.Fatalln(err)
}
const r = 1.5
seq := imgpyr.Sequence(1, 1/r, 0.2)
pyr := imgpyr.New(img, seq)
vis := pyr.Visualize()
if err := saveImage(visFile, vis); err != nil {
log.Fatalln(err)
}
}
func main() {
flag.Usage = usage
flag.Parse()
// Read other arguments.
if flag.NArg() != 3 {
flag.Usage()
os.Exit(1)
}
var (
imgFile = flag.Arg(0)
weightsFile = flag.Arg(1)
reportFile = flag.Arg(2)
)
if geoStep <= 1 {
log.Fatalln("geometric step must be greater than 1")
}
// Load image from file.
img, err := loadImage(imgFile)
if err != nil {
log.Fatalln("could not load image:", err)
}
log.Println("image bounds:", img.Bounds())
// Load weights from file.
weights, err := loadWeights(weightsFile)
if err != nil {
log.Fatalln("could not load weights:", err)
}
log.Println("transform original image")
// Compute feature transform of original image.
featImg := hogImage(img, binSize)
// Check image and detector sizes.
if weights.Width > featImg.Width || weights.Height > featImg.Height {
log.Fatalln("detector larger than feature image")
}
// Plan pyramid levels.
minXScale := float64(weights.Width) / float64(featImg.Width)
minYScale := float64(weights.Height) / float64(featImg.Height)
minScale := math.Max(minXScale, minYScale)
// Construct pyramid.
seq := imgpyr.Sequence(1, 1/geoStep, minScale)
log.Println("number of zoom levels:", seq.Len)
log.Println("compute pyramid")
pyr := imgpyr.NewInterp(img, seq, resize.NearestNeighbor)
// Transform every level of the pyramid.
log.Println("transform each level of pyramid")
featImgs := make([]cv.RealVectorImage, len(pyr.Levels))
for i, lvl := range pyr.Levels {
featImgs[i] = hogImage(lvl, binSize)
}
// Run detector on every level of the pyramid.
log.Println("search each level of pyramid")
scores := make([]cv.RealImage, len(pyr.Levels))
for i, featImg := range featImgs {
scores[i] = slide.CorrelateVectorImages(featImg, weights)
}
// Get detections.
log.Println("non-maxima suppression")
dets := nonMaxSupp(scores, pyr.Scales, weights.ImageSize(), 0.5)
// Generate report.
report(reportFile, pyr, dets, weights.ImageSize(), scores, binSize)
}