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103 lines
2.4 KiB
Go
103 lines
2.4 KiB
Go
package main
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import "C"
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import (
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"fmt"
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"io/ioutil"
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"log"
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"runtime"
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"unsafe"
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"github.com/esimov/pigo/core"
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)
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var (
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cascade []byte
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err error
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p *pigo.Pigo
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classifier *pigo.Pigo
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)
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func main() {}
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//export FindFaces
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func FindFaces(pixels []uint8) uintptr {
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pointCh := make(chan uintptr)
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dets := clusterDetection(pixels, 480, 640)
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result := make([][]int, len(dets))
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for i := 0; i < len(dets); i++ {
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if dets[i].Q >= 5.0 {
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result[i] = append(result[i], dets[i].Row, dets[i].Col, dets[i].Scale)
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}
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}
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if len(result) > 0 {
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// Since we cannot transfer a 2d array trough an array pointer
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// we have to transform it into 1d array.
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go func() {
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det := make([]int, 0, len(result))
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for _, v := range result {
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det = append(det, v...)
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}
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fmt.Println(det)
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// Include as a first slice element the number of detected faces.
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// We need to transfer this value in order to define the Python array buffer length.
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det = append([]int{len(result), 0, 0}, det...)
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// Convert the slice into an array pointer.
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s := *(*[]int)(unsafe.Pointer(&det))
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p := uintptr(unsafe.Pointer(&s[0]))
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// Ensure `det` is not freed by GC.
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runtime.KeepAlive(det)
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// return the pointer address
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pointCh <- p
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}()
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return <-pointCh
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}
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return 0
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}
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// clusterDetection runs Pigo face detector core methods
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// and returns a cluster with the detected faces coordinates.
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func clusterDetection(pixels []uint8, rows, cols int) []pigo.Detection {
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cParams := pigo.CascadeParams{
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MinSize: 100,
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MaxSize: 1000,
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ShiftFactor: 0.15,
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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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if len(cascade) == 0 {
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cascade, err = ioutil.ReadFile("../../data/facefinder")
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if err != nil {
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log.Fatalf("Error reading the cascade file: %v", err)
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}
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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 = p.Unpack(cascade)
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if err != nil {
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log.Fatalf("Error reading the cascade file: %s", err)
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}
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}
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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 := classifier.RunCascade(cParams, 0.0)
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// Calculate the intersection over union (IoU) of two clusters.
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dets = classifier.ClusterDetections(dets, 0)
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return dets
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}
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