Allow configuration of the "variance threshold" parameter (#163)

* fixes name of variable _learning_rate

* adds variance-threshold setting

docs/docs.md

@@ -366,6 +366,16 @@ The following options control motion detection.  A more comprehensive descriptio
     ```
     </span>
 
+ * <b><pre>variance-threshold</pre></b>
+     Threshold on the distance between the pixel and the model to decide whether a pixel is well described by the background model. This parameter does not affect
+     the background update.<br />
+     The threshold is not used by the CNT subtractor.
+    <span class="dvr-scan-default">
+    ```
+    variance-threshold = 16
+    ```
+    </span>
+
  * <b><pre>kernel-size</pre></b>
     Size (in pixels) of the noise reduction kernel. Size must be an odd number starting from 3, 0 to disable, or -1 to auto-set based on video resolution.
     <span class="dvr-scan-default">

dvr-scan.cfg

@@ -92,6 +92,12 @@
 # will not update the model at all, and 1.0 would re-initialize it on every frame.
 #learning-rate = -1
 
+# Threshold on the distance between the pixel and the model to decide whether
+# a pixel is well described by the background model. This parameter does not affect
+# the background update.
+# The threshold is not used by the CNT subtractor.
+#variance-threshold = 16
+
 # Size (in pixels) of the noise reduction kernel. Can be odd integer starting
 # from 3, 0 to disable, or -1 to auto-set using video resolution.
 #kernel-size = -1

dvr_scan/cli/config.py

@@ -320,6 +320,7 @@ def from_config(config_value: str, default: 'RGBValue') -> 'RGBValue':
     'bg-subtractor': 'MOG2',
     'threshold': 0.15,
     'max-threshold': 255.0,
+    'variance-threshold': 16.0,
     'kernel-size': KernelSizeValue(),
     'downscale-factor': 0,
     'learning-rate': float(-1),

dvr_scan/cli/controller.py

@@ -272,6 +272,7 @@ def run_dvr_scan(settings: ProgramSettings) -> ty.List[ty.Tuple[FrameTimecode, F
         detector_type=DetectorType[settings.get('bg-subtractor').upper()],
         threshold=settings.get('threshold'),
         max_threshold=settings.get('max-threshold'),
+        variance_threshold=settings.get('variance-threshold'),
         kernel_size=settings.get('kernel-size'),
         downscale_factor=settings.get('downscale-factor'),
         learning_rate=settings.get('learning-rate'),

dvr_scan/scanner.py

@@ -235,9 +235,10 @@ def __init__(self,
         # Motion Detection Parameters (set_detection_params)
         self._subtractor_type = DetectorType.MOG2 # -b/--bg-subtractor
         self._threshold = 0.15                    # -t/--threshold
+        self._variance_threshold = 16.0           # variance-threshold
         self._kernel_size = None                  # -k/--kernel-size
         self._downscale_factor = 1                # -df/--downscale-factor
-        self._learningRate = -1                   # learning-rate
+        self._learning_rate = -1                  # learning-rate
         self._max_threshold = 255.0               # max-threshold
 
         # Motion Event Parameters (set_event_params)
@@ -366,6 +367,7 @@ def set_detection_params(
         detector_type: DetectorType = DetectorType.MOG2,
         threshold: float = 0.15,
         max_threshold: float = 255.0,
+        variance_threshold: float = 16.0,
         kernel_size: int = -1,
         downscale_factor: int = 1,
         learning_rate: float = -1,
@@ -379,6 +381,7 @@ def set_detection_params(
         self._downscale_factor = max(downscale_factor, 1)
         assert kernel_size == -1 or kernel_size == 0 or kernel_size >= 3
         self._kernel_size = kernel_size
+        self._variance_threshold = variance_threshold
         # TODO: Also allow ability to customize history size, as this is another factor that
         # influences how quickly the background model is updated. When calculated automatically,
         # OpenCV sets learning rate as:
@@ -574,16 +577,19 @@ def scan(self) -> Optional[DetectionResult]:
         # Create background subtractor and motion detector.
         detector = MotionDetector(
             subtractor=self._subtractor_type.value(
-                kernel_size=kernel_size, learning_rate=self._learning_rate),
+                variance_threshold=self._variance_threshold,
+                kernel_size=kernel_size,
+                learning_rate=self._learning_rate),
             frame_size=self._input.resolution,
             downscale=self._downscale_factor,
             regions=self._regions)
 
         logger.info(
-            'Using subtractor %s with kernel_size = %s%s and learning_rate = %s',
+            'Using subtractor %s with kernel_size = %s%s, variance_threshold = %s and learning_rate = %s',
             self._subtractor_type.name,
             str(kernel_size) if kernel_size else 'off',
             ' (auto)' if self._kernel_size == -1 else '',
+            str(self._variance_threshold) if self._variance_threshold != 16.0 else 'auto',
             str(self._learning_rate) if self._learning_rate != -1 else 'auto',
         )
 

dvr_scan/subtractor.py

@@ -51,7 +51,7 @@ def __init__(
         self,
         kernel_size: int,
         history: int = 500,
-        variance_threshold: int = 16,
+        variance_threshold: float = 16.0,
         detect_shadows: bool = False,
         learning_rate: float = -1,
     ):
@@ -93,6 +93,7 @@ def __init__(
         max_pixel_stability: int = 15 * 60,
         is_parallel: bool = True,
         learning_rate: float = -1,
+        variance_threshold = None,
     ):
         if kernel_size < 0 or (kernel_size > 1 and kernel_size % 2 == 0):
             raise ValueError("kernel_size must be odd integer >= 1 or zero (0)")
@@ -118,7 +119,7 @@ def __init__(
         self,
         kernel_size: int,
         history: int = 500,
-        variance_threshold: int = 16,
+        variance_threshold: float = 16.0,
         detect_shadows: bool = False,
         learning_rate: float = -1,
     ):