113 lines
2.8 KiB
Go
113 lines
2.8 KiB
Go
package frame
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import (
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"fmt"
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"log"
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"path/filepath"
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"sync"
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"gocv.io/x/gocv"
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)
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// Classifier pairs a loaded OpenCV cascade with the label under which its
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// detections are stored.
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type Classifier struct {
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Path string
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Label string
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cc gocv.CascadeClassifier
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mu sync.Mutex // DetectMultiScale is not safe for concurrent use
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}
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// Close releases the underlying cascade. Callers must close every classifier
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// built by LoadClassifiers once done with it.
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func (c *Classifier) Close() {
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c.cc.Close()
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}
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func (c *Classifier) label() string {
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if c.Label != "" {
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return c.Label
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}
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return c.Path
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}
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// LoadClassifiers builds a Classifier for every path matched by patterns. Each
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// pattern is a glob; a pattern that matches nothing is an error, so a typo or
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// missing model file fails at startup instead of silently disabling detection.
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func LoadClassifiers(patterns []string) ([]Classifier, error) {
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var classes []Classifier
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for _, pattern := range patterns {
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matches, err := filepath.Glob(pattern)
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if err != nil {
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return nil, fmt.Errorf("bad classifier pattern %q: %w", pattern, err)
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}
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if len(matches) == 0 {
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return nil, fmt.Errorf("classifier pattern %q matched no files", pattern)
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}
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for _, path := range matches {
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cc := gocv.NewCascadeClassifier()
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if !cc.Load(path) {
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cc.Close()
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return nil, fmt.Errorf("cannot load cascade %q", path)
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}
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classes = append(classes, Classifier{Path: path, Label: filepath.Base(path), cc: cc})
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}
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}
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if len(classes) == 0 {
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return nil, fmt.Errorf("no classifiers configured")
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}
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return classes, nil
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}
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// DetectClassifiers runs every classifier against the frame and stores
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// detections under each classifier's label. The frame's Mat is converted once
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// and shared; classifiers serialize on their own detection mutex.
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func (f *Frame) DetectClassifiers(classifiers []Classifier) {
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if len(classifiers) == 0 {
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return
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}
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if f.Detections == nil {
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f.Detections = make(map[string][]Detection)
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}
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mat, err := f.ToMat()
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if err != nil {
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log.Printf("DetectClassifiers: converting frame to Mat: %v", err)
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return
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}
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defer mat.Close()
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var mu sync.Mutex
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var wg sync.WaitGroup
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wg.Add(len(classifiers))
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for i := range classifiers {
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go func(c *Classifier) {
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defer wg.Done()
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c.mu.Lock()
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rects := c.cc.DetectMultiScale(mat)
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c.mu.Unlock()
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if len(rects) == 0 {
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return
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}
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label := c.label()
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results := make([]Detection, 0, len(rects))
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for _, r := range rects {
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results = append(results, Detection{
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DetectionTitle: label,
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DetectionMajorVersion: 1,
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DetectionMinorVersion: 0,
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DetectionPostfix: "",
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DetectionRegion: r,
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DetectionCertainty: 1.0,
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})
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}
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mu.Lock()
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f.Detections[label] = results
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mu.Unlock()
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}(&classifiers[i])
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}
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wg.Wait()
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}
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