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configurable motion and detect settings

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This commit is contained in:
Blake Blackshear
2020-12-18 23:00:13 -06:00
parent 724d8187c6
commit 3bc7cdaab6
6 changed files with 141 additions and 32 deletions
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28
frigate/motion.py
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@@ -1,13 +1,15 @@
import cv2
import imutils
import numpy as np
from frigate.config import MotionConfig
class MotionDetector():
def __init__(self, frame_shape, mask, resize_factor=4):
def __init__(self, frame_shape, mask, config: MotionConfig):
self.config = config
self.frame_shape = frame_shape
self.resize_factor = resize_factor
self.motion_frame_size = (int(frame_shape[0]/resize_factor), int(frame_shape[1]/resize_factor))
self.resize_factor = frame_shape[0]/config.frame_height
self.motion_frame_size = (config.frame_height, config.frame_height*frame_shape[1]//frame_shape[0])
self.avg_frame = np.zeros(self.motion_frame_size, np.float)
self.avg_delta = np.zeros(self.motion_frame_size, np.float)
self.motion_frame_count = 0
@@ -23,6 +25,8 @@ class MotionDetector():
# resize frame
resized_frame = cv2.resize(gray, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
# TODO: can I improve the contrast of the grayscale image here?
# convert to grayscale
# resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
@@ -38,14 +42,13 @@ class MotionDetector():
frameDelta = cv2.absdiff(resized_frame, cv2.convertScaleAbs(self.avg_frame))
# compute the average delta over the past few frames
# the alpha value can be modified to configure how sensitive the motion detection is.
# higher values mean the current frame impacts the delta a lot, and a single raindrop may
# register as motion, too low and a fast moving person wont be detected as motion
# this also assumes that a person is in the same location across more than a single frame
cv2.accumulateWeighted(frameDelta, self.avg_delta, 0.2)
cv2.accumulateWeighted(frameDelta, self.avg_delta, self.config.delta_alpha)
# compute the threshold image for the current frame
current_thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
# TODO: threshold
current_thresh = cv2.threshold(frameDelta, self.config.threshold, 255, cv2.THRESH_BINARY)[1]
# black out everything in the avg_delta where there isnt motion in the current frame
avg_delta_image = cv2.convertScaleAbs(self.avg_delta)
@@ -53,7 +56,7 @@ class MotionDetector():
# then look for deltas above the threshold, but only in areas where there is a delta
# in the current frame. this prevents deltas from previous frames from being included
thresh = cv2.threshold(avg_delta_image, 25, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.threshold(avg_delta_image, self.config.threshold, 255, cv2.THRESH_BINARY)[1]
# dilate the thresholded image to fill in holes, then find contours
# on thresholded image
@@ -65,19 +68,18 @@ class MotionDetector():
for c in cnts:
# if the contour is big enough, count it as motion
contour_area = cv2.contourArea(c)
if contour_area > 100:
if contour_area > self.config.contour_area:
x, y, w, h = cv2.boundingRect(c)
motion_boxes.append((x*self.resize_factor, y*self.resize_factor, (x+w)*self.resize_factor, (y+h)*self.resize_factor))
if len(motion_boxes) > 0:
self.motion_frame_count += 1
# TODO: this really depends on FPS
if self.motion_frame_count >= 10:
# only average in the current frame if the difference persists for at least 3 frames
cv2.accumulateWeighted(resized_frame, self.avg_frame, 0.2)
# only average in the current frame if the difference persists for a bit
cv2.accumulateWeighted(resized_frame, self.avg_frame, self.config.frame_alpha)
else:
# when no motion, just keep averaging the frames together
cv2.accumulateWeighted(resized_frame, self.avg_frame, 0.2)
cv2.accumulateWeighted(resized_frame, self.avg_frame, self.config.frame_alpha)
self.motion_frame_count = 0
return motion_boxes