lightning detection

benchmark_motion.py

@@ -13,7 +13,7 @@ from frigate.motion.improved_motion import ImprovedMotionDetector
 # get info on the video
 # cap = cv2.VideoCapture("debug/front_cam_2023_05_23_08_41__2023_05_23_08_43.mp4")
 # cap = cv2.VideoCapture("debug/motion_test_clips/rain_1.mp4")
-cap = cv2.VideoCapture("debug/motion_test_clips/low_contrast_ir.mp4")
+cap = cv2.VideoCapture("debug/motion_test_clips/ir_off.mp4")
 # cap = cv2.VideoCapture("airport.mp4")
 width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
 height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

docs/docs/configuration/index.md

@@ -262,6 +262,12 @@ motion:
   # Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
   # The value should be between 1 and 255.
   threshold: 25
+  # Optional: The percentage of the image used to detect lightning or other substantial changes where motion detection
+  #           needs to recalibrate. (default: shown below)
+  # Increasing this value will make motion detection more likely to consider lightning or ir mode changes as valid motion.
+  # Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching
+  # a doorbell camera.
+  lightning_threshold: 0.8
   # Optional: Minimum size in pixels in the resized motion image that counts as motion (default: 30)
   # Increasing this value will prevent smaller areas of motion from being detected. Decreasing will
   # make motion detection more sensitive to smaller moving objects.

frigate/config.py

@@ -13,9 +13,11 @@ from pydantic.fields import PrivateAttr
 
 from frigate.const import CACHE_DIR, DEFAULT_DB_PATH, REGEX_CAMERA_NAME, YAML_EXT
 from frigate.detectors import DetectorConfig, ModelConfig
-from frigate.detectors.detector_config import InputTensorEnum  # noqa: F401
+from frigate.detectors.detector_config import (
-from frigate.detectors.detector_config import PixelFormatEnum  # noqa: F401
+    BaseDetectorConfig,
-from frigate.detectors.detector_config import BaseDetectorConfig
+    InputTensorEnum,  # noqa: F401
+    PixelFormatEnum,  # noqa: F401
+)
 from frigate.ffmpeg_presets import (
     parse_preset_hardware_acceleration_decode,
     parse_preset_hardware_acceleration_scale,
@@ -194,6 +196,9 @@ class MotionConfig(FrigateBaseModel):
         ge=1,
         le=255,
     )
+    lightning_threshold: float = Field(
+        default=0.8, title="Lightning detection threshold (0.3-1.0).", ge=0.3, le=1.0
+    )
     improve_contrast: bool = Field(default=False, title="Improve Contrast")
     contour_area: Optional[int] = Field(default=30, title="Contour Area")
     delta_alpha: float = Field(default=0.2, title="Delta Alpha")

frigate/motion/improved_motion.py

@@ -34,6 +34,7 @@ class ImprovedMotionDetector(MotionDetector):
         )
         self.mask = np.where(resized_mask == [0])
         self.save_images = False
+        self.calibrating = True
         self.improve_contrast = improve_contrast
         self.threshold = threshold
         self.contour_area = contour_area
@@ -59,7 +60,7 @@ class ImprovedMotionDetector(MotionDetector):
         # mask frame
         resized_frame[self.mask] = [255]
 
-        if self.save_images:
+        if self.save_images or self.calibrating:
             self.frame_counter += 1
         # compare to average
         frameDelta = cv2.absdiff(resized_frame, cv2.convertScaleAbs(self.avg_frame))
@@ -78,9 +79,11 @@ class ImprovedMotionDetector(MotionDetector):
         cnts = imutils.grab_contours(cnts)
 
         # loop over the contours
+        total_contour_area = 0
         for c in cnts:
             # if the contour is big enough, count it as motion
             contour_area = cv2.contourArea(c)
+            total_contour_area += contour_area
             if contour_area > self.contour_area.value:
                 x, y, w, h = cv2.boundingRect(c)
                 motion_boxes.append(
@@ -92,16 +95,26 @@ class ImprovedMotionDetector(MotionDetector):
                     )
                 )
 
+        pct_motion = total_contour_area / (
+            self.motion_frame_size[0] * self.motion_frame_size[1]
+        )
+
+        # once the motion drops to less than 1% for the first time, assume its calibrated
+        if pct_motion < 0.01:
+            self.calibrating = False
+
+        # if calibrating or the motion contours are > 80% of the image area (lightning, ir, ptz) recalibrate
+        if self.calibrating or pct_motion > self.config.lightning_threshold:
+            motion_boxes = []
+            self.calibrating = True
+
         if self.save_images:
             thresh_dilated = cv2.cvtColor(thresh_dilated, cv2.COLOR_GRAY2BGR)
-            for c in cnts:
+            for b in motion_boxes:
-                contour_area = cv2.contourArea(c)
-                if contour_area > self.contour_area.value:
-                    x, y, w, h = cv2.boundingRect(c)
                 cv2.rectangle(
                     thresh_dilated,
-                        (x, y),
+                    (int(b[0] / self.resize_factor), int(b[1] / self.resize_factor)),
-                        (x + w, y + h),
+                    (int(b[2] / self.resize_factor), int(b[3] / self.resize_factor)),
                     (0, 0, 255),
                     2,
                 )