addet test set

halloween/motion.py

@@ -0,0 +1,78 @@
+"""Optischer Bewegungs-Test, ohne Personenmodell oder Besucherzuordnung."""
+
+from io import BytesIO
+
+from PIL import Image
+
+
+class MotionDetector:
+    """Bildbewegung im Bereich entlang der sichtbaren Pfeilnormalen prüfen.
+
+    Feste, konservative Startwerte für den Nahbereichstest, keine Zusage für
+    Außenbetrieb. Nur zwei aufeinanderfolgende gerichtete Beobachtungen lösen
+    aus. Ein ruhiges Bild bedeutet hier keine Personen-/Bereichsfreiheit.
+    """
+
+    def __init__(self):
+        import cv2
+        import numpy as np
+        self.cv, self.np = cv2, np
+        self.previous = None
+        self.previous_at = None
+        self.directed_count = 0
+
+    def detect(self, jpeg, config, captured_at):
+        cv, np = self.cv, self.np
+        image = Image.open(BytesIO(jpeg)).convert('L')
+        scale = min(1., 240 / max(image.size))
+        size = (max(1, round(image.width * scale)), max(1, round(image.height * scale)))
+        gray = np.asarray(image.resize(size, Image.Resampling.BILINEAR))
+        height, width = gray.shape
+        x1, y1, x2, y2 = config['region']
+        region = gray[round(y1*height):round(y2*height), round(x1*width):round(x2*width)]
+        if min(region.shape) < 8:
+            raise ValueError('Bewegungs-Test: Bereich im Kamerabild zu klein')
+        previous, previous_at = self.previous, self.previous_at
+        self.previous = region.copy()
+        self.previous_at = captured_at
+        result = {'ready': False, 'moving': False, 'directed': False,
+                  'trigger': False, 'vector': [0., 0.], 'changed_fraction': 0.,
+                  'confirmed_frames': 0, 'note': 'Neue Bildbasis; kurz ruhig halten'}
+        if (previous is None or previous.shape != region.shape or
+                captured_at <= previous_at or captured_at - previous_at > 0.8):
+            self.directed_count = 0
+            return result
+        result['ready'] = True
+        changed = cv.absdiff(previous, region) > 12
+        changed_fraction = float(changed.mean())
+        result['changed_fraction'] = round(changed_fraction, 5)
+        # Small near-camera motions must not be diluted by a large quiet ROI.
+        result['moving'] = changed_fraction >= 0.001
+        if not result['moving']:
+            result['note'] = 'Bild ruhig / Änderung zu klein'
+            self.directed_count = 0
+            return result
+        flow = cv.calcOpticalFlowFarneback(previous, region, None,
+                                          0.5, 3, 15, 3, 5, 1.2, 0)
+        magnitude = np.linalg.norm(flow, axis=2)
+        # Only changed pixels with meaningful, bounded displacement vote.
+        mask = changed & (magnitude >= 0.35) & (magnitude <= 30)
+        vectors = flow[mask]
+        if len(vectors) < 6:
+            result['note'] = 'Bildänderung erkannt; Richtung noch nicht messbar'
+            self.directed_count = 0
+            return result
+        a, b = config['line']
+        dx, dy = (b[0]-a[0])*width, (b[1]-a[1])*height
+        normal = np.array([-dy, dx], dtype=np.float32)
+        normal *= config['direction'] / np.linalg.norm(normal)
+        projection = vectors @ normal
+        aligned = (projection >= 0.35) & (projection / np.linalg.norm(vectors, axis=1) >= 0.65)
+        result['vector'] = [round(float(v), 2) for v in np.median(vectors, axis=0)]
+        result['directed'] = bool(aligned.mean() >= 0.65)
+        self.directed_count = self.directed_count + 1 if result['directed'] else 0
+        result['confirmed_frames'] = min(2, self.directed_count)
+        result['note'] = ('Bewegung in Pfeilrichtung' if result['directed']
+                          else 'Bewegung nicht in Pfeilrichtung')
+        result['trigger'] = self.directed_count >= 2
+        return result