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update default motion config
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Blake Blackshear
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a1dce08bfc
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59e5ed6a89
@ -261,33 +261,29 @@ motion:
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# Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
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# Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
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# The value should be between 1 and 255.
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threshold: 25
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threshold: 40
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# Optional: The percentage of the image used to detect lightning or other substantial changes where motion detection
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# needs to recalibrate. (default: shown below)
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# Increasing this value will make motion detection more likely to consider lightning or ir mode changes as valid motion.
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# Decreasing this value will make motion detection more likely to ignore large amounts of motion such as a person approaching
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# a doorbell camera.
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lightning_threshold: 0.8
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# Optional: Minimum size in pixels in the resized motion image that counts as motion (default: 30)
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# Optional: Minimum size in pixels in the resized motion image that counts as motion (default: shown below)
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# Increasing this value will prevent smaller areas of motion from being detected. Decreasing will
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# make motion detection more sensitive to smaller moving objects.
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# As a rule of thumb:
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# - 15 - high sensitivity
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# - 30 - medium sensitivity
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# - 50 - low sensitivity
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contour_area: 30
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# Optional: Alpha value passed to cv2.accumulateWeighted when averaging the motion delta across multiple frames (default: shown below)
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# Higher values mean the current frame impacts the delta a lot, and a single raindrop may register as motion.
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# Too low and a fast moving person wont be detected as motion.
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delta_alpha: 0.2
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contour_area: 15
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# Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
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# Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
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# Low values will cause things like moving shadows to be detected as motion for longer.
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# https://www.geeksforgeeks.org/background-subtraction-in-an-image-using-concept-of-running-average/
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frame_alpha: 0.2
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frame_alpha: 0.02
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# Optional: Height of the resized motion frame (default: 50)
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# This operates as an efficient blur alternative. Higher values will result in more granular motion detection at the expense
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# of higher CPU usage. Lower values result in less CPU, but small changes may not register as motion.
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# Higher values will result in more granular motion detection at the expense of higher CPU usage.
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# Lower values result in less CPU, but small changes may not register as motion.
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frame_height: 50
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# Optional: motion mask
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# NOTE: see docs for more detailed info on creating masks
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@ -295,7 +291,7 @@ motion:
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# Optional: improve contrast (default: shown below)
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# Enables dynamic contrast improvement. This should help improve night detections at the cost of making motion detection more sensitive
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# for daytime.
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improve_contrast: False
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improve_contrast: True
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# Optional: Delay when updating camera motion through MQTT from ON -> OFF (default: shown below).
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mqtt_off_delay: 30
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@ -191,7 +191,7 @@ class RecordConfig(FrigateBaseModel):
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class MotionConfig(FrigateBaseModel):
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threshold: int = Field(
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default=25,
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default=40,
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title="Motion detection threshold (1-255).",
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ge=1,
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le=255,
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@ -199,10 +199,10 @@ class MotionConfig(FrigateBaseModel):
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lightning_threshold: float = Field(
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default=0.8, title="Lightning detection threshold (0.3-1.0).", ge=0.3, le=1.0
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)
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improve_contrast: bool = Field(default=False, title="Improve Contrast")
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contour_area: Optional[int] = Field(default=30, title="Contour Area")
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improve_contrast: bool = Field(default=True, title="Improve Contrast")
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contour_area: Optional[int] = Field(default=15, title="Contour Area")
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delta_alpha: float = Field(default=0.2, title="Delta Alpha")
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frame_alpha: float = Field(default=0.2, title="Frame Alpha")
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frame_alpha: float = Field(default=0.02, title="Frame Alpha")
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frame_height: Optional[int] = Field(default=50, title="Frame Height")
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mask: Union[str, List[str]] = Field(
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default="", title="Coordinates polygon for the motion mask."
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@ -1,120 +0,0 @@
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from typing import Tuple
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import cv2
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import imutils
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import numpy as np
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from frigate.config import MotionConfig
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from frigate.motion import MotionDetector
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class CNTMotionDetector(MotionDetector):
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def __init__(
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self,
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frame_shape: Tuple[int, int, int],
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config: MotionConfig,
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fps: int,
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improve_contrast,
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threshold,
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contour_area,
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):
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self.frame_shape = frame_shape
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self.threshold = threshold
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self.contour_area = contour_area
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self.improve_contrast = improve_contrast
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self.resize_factor = frame_shape[0] / config.frame_height
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self.motion_frame_size = (
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config.frame_height,
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config.frame_height * frame_shape[1] // frame_shape[0],
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)
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resized_mask = cv2.resize(
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config.mask,
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dsize=(self.motion_frame_size[1], self.motion_frame_size[0]),
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interpolation=cv2.INTER_LINEAR,
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)
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self.blur_kernel = (
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3, # int(0.05 * self.motion_frame_size[0]),
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3, # int(0.05 * self.motion_frame_size[0]),
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)
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self.mask = np.where(resized_mask == [0])
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# createBackgroundSubtractorCNT(int minPixelStability = 15,
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# bool useHistory = true,
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# int maxPixelStability = 15*60,
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# bool isParallel = true);
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self.bg_subtractor = cv2.bgsegm.createBackgroundSubtractorCNT(
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fps, True, fps * 60, True
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)
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self.save_images = False
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self.frame_counter = 0
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def detect(self, frame):
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motion_boxes = []
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gray = frame[0 : self.frame_shape[0], 0 : self.frame_shape[1]]
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# resize frame
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resized_frame = cv2.resize(
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gray,
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dsize=(self.motion_frame_size[1], self.motion_frame_size[0]),
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interpolation=cv2.INTER_LINEAR,
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)
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if self.improve_contrast.value:
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resized_frame = cv2.equalizeHist(resized_frame)
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blurred_frame = cv2.GaussianBlur(
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resized_frame, self.blur_kernel, cv2.BORDER_DEFAULT
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)
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# mask frame
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blurred_frame[self.mask] = [255]
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fg = self.bg_subtractor.apply(blurred_frame)
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thresh = cv2.threshold(fg, self.threshold.value, 255, cv2.THRESH_BINARY)[1]
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# dilate the thresholded image to fill in holes, then find contours
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# on thresholded image
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cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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cnts = imutils.grab_contours(cnts)
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# loop over the contours
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for c in cnts:
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# if the contour is big enough, count it as motion
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contour_area = cv2.contourArea(c)
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if contour_area > self.contour_area.value:
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x, y, w, h = cv2.boundingRect(c)
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motion_boxes.append(
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(
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int(x * self.resize_factor),
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int(y * self.resize_factor),
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int((x + w) * self.resize_factor),
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int((y + h) * self.resize_factor),
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)
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)
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if self.save_images:
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self.frame_counter += 1
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# print("--------")
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# print(self.frame_counter)
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thresh_boxes = cv2.cvtColor(fg, cv2.COLOR_GRAY2BGR)
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for c in cnts:
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contour_area = cv2.contourArea(c)
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if contour_area > self.contour_area.value:
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x, y, w, h = cv2.boundingRect(c)
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cv2.rectangle(
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thresh_boxes,
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(x, y),
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(x + w, y + h),
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(0, 0, 255),
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2,
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)
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# print("--------")
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image_row_1 = cv2.hconcat(
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[
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cv2.cvtColor(fg, cv2.COLOR_GRAY2BGR),
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cv2.cvtColor(
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self.bg_subtractor.getBackgroundImage(), cv2.COLOR_GRAY2BGR
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),
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]
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)
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image_row_2 = cv2.hconcat(
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[cv2.cvtColor(thresh, cv2.COLOR_GRAY2BGR), thresh_boxes]
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)
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combined_image = cv2.vconcat([image_row_1, image_row_2])
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cv2.imwrite(f"debug/frames/motion-{self.frame_counter}.jpg", combined_image)
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return motion_boxes
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