ssa

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package goroi
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import (
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	"fmt"
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	"log"
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	"os"
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	pigo "github.com/esimov/pigo/core"
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)
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type Pigs struct {
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	Classifier *pigo.Pigo
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}
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func NewPigs(FileCascade string) (*Pigs, error) {
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	// consumers.StartForwardStreamConsumer()
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	// camtron.StartCam()
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	cascadeFile, err := os.ReadFile(FileCascade) // "cascade/facefinder"
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	if err != nil {
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		return nil, fmt.Errorf("os.ReadFile: Error reading the cascade file: %v", err)
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	}
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	pigo := pigo.NewPigo()
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	// Unpack the binary file. This will return the number of cascade trees,
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	// the tree depth, the threshold and the prediction from tree's leaf nodes.
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	classifier, err := pigo.Unpack(cascadeFile)
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	if err != nil {
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		return nil, fmt.Errorf("pigo.Unpack: Error reading the cascade file: %v", err)
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	}
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	return &Pigs{
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		Classifier: classifier,
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	}, nil
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}
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func (p Pigs) getCoords(filepath string) []pigo.Detection {
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	src, err := pigo.GetImage(filepath)
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	if err != nil {
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		log.Fatalf("Cannot open the image file: %v", err)
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	}
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	pixels := pigo.RgbToGrayscale(src)
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	cols, rows := src.Bounds().Max.X, src.Bounds().Max.Y
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	cParams := pigo.CascadeParams{
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		MinSize:     20,
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		MaxSize:     1000,
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		ShiftFactor: 0.1,
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		ScaleFactor: 1.1,
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		ImageParams: pigo.ImageParams{
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			Pixels: pixels,
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			Rows:   rows,
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			Cols:   cols,
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			Dim:    cols,
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		},
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	}
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	angle := 0.0 // cascade rotation angle. 0.0 is 0 radians and 1.0 is 2*pi radians
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	// Run the classifier over the obtained leaf nodes and return the detection results.
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	// The result contains quadruplets representing the row, column, scale and detection score.
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	dets := p.Classifier.RunCascade(cParams, angle)
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	// fmt.Printf("%+v\n", dets)
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	// Calculate the intersection over union (IoU) of two clusters.
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	dets = p.Classifier.ClusterDetections(dets, 0.1)
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	// fmt.Printf("%+v\n", dets)
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	// fmt.Println()
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	return dets
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}
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