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use a different method for blur and contrast to reduce CPU (#6940)

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* use a different method for blur and contrast to reduce CPU

* blur with radius instead

* use faster interpolation for motion

* improve contrast based on averages

* increase default threshold to 30

* ensure mask is applied after contrast improvement

* update opencv

* update benchmark script
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Blake Blackshear 2023-06-30 07:27:31 -05:00 committed by GitHub
parent d2a2643cd6
commit d51197eaa2
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5 changed files with 60 additions and 23 deletions
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29
benchmark_motion.py
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@ -12,16 +12,32 @@ from frigate.util import create_mask
# 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/ir_off.mp4")
cap = cv2.VideoCapture("debug/motion_test_clips/lawn_mower_night_1.mp4")
# cap = cv2.VideoCapture("airport.mp4")
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS)
frame_shape = (height, width, 3)
# Nick back:
# "1280,0,1280,316,1170,216,1146,126,1016,127,979,82,839,0",
# "310,350,300,402,224,405,241,354",
# "378,0,375,26,0,23,0,0",
# Front door:
# "1080,0,1080,339,1010,280,1020,169,777,163,452,170,318,299,191,365,186,417,139,470,108,516,40,530,0,514,0,0",
# "336,833,438,1024,346,1093,103,1052,24,814",
# Back
# "1855,0,1851,100,1289,96,1105,161,1045,119,890,121,890,0",
# "505,95,506,138,388,153,384,114",
# "689,72,689,122,549,134,547,89",
# "261,134,264,176,169,195,167,158",
# "145,159,146,202,70,220,65,183",
mask = create_mask(
(height, width),
[],
[
"1080,0,1080,339,1010,280,1020,169,777,163,452,170,318,299,191,365,186,417,139,470,108,516,40,530,0,514,0,0",
"336,833,438,1024,346,1093,103,1052,24,814",
],
)
# create the motion config
@ -29,7 +45,7 @@ motion_config_1 = MotionConfig()
motion_config_1.mask = np.zeros((height, width), np.uint8)
motion_config_1.mask[:] = mask
# motion_config_1.improve_contrast = 1
# motion_config_1.frame_height = 150
motion_config_1.frame_height = 150
# motion_config_1.frame_alpha = 0.02
# motion_config_1.threshold = 30
# motion_config_1.contour_area = 10
@ -38,10 +54,11 @@ motion_config_2 = MotionConfig()
motion_config_2.mask = np.zeros((height, width), np.uint8)
motion_config_2.mask[:] = mask
# motion_config_2.improve_contrast = 1
# motion_config_2.frame_height = 150
motion_config_2.frame_height = 150
# motion_config_2.frame_alpha = 0.01
# motion_config_2.threshold = 20
motion_config_2.threshold = 20
# motion_config.contour_area = 10
save_images = True
improved_motion_detector_1 = ImprovedMotionDetector(
@ -52,8 +69,6 @@ improved_motion_detector_1 = ImprovedMotionDetector(
threshold=mp.Value("i", motion_config_1.threshold),
contour_area=mp.Value("i", motion_config_1.contour_area),
name="default",
clipLimit=2.0,
tileGridSize=(8, 8),
)
improved_motion_detector_1.save_images = save_images

2
docs/docs/configuration/index.md
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@ -280,7 +280,7 @@ motion:
# Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
# 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: 20
threshold: 30
# 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.

2
frigate/config.py
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@ -187,7 +187,7 @@ class RecordConfig(FrigateBaseModel):
class MotionConfig(FrigateBaseModel):
threshold: int = Field(
default=20,
default=30,
title="Motion detection threshold (1-255).",
ge=1,
le=255,

48
frigate/motion/improved_motion.py
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@ -1,6 +1,7 @@
import cv2
import imutils
import numpy as np
from scipy.ndimage import gaussian_filter
from frigate.config import MotionConfig
from frigate.motion import MotionDetector
@ -15,9 +16,10 @@ class ImprovedMotionDetector(MotionDetector):
improve_contrast,
threshold,
contour_area,
clipLimit=2.0,
tileGridSize=(2, 2),
name="improved",
blur_radius=1,
interpolation=cv2.INTER_NEAREST,
contrast_frame_history=50,
):
self.name = name
self.config = config
@ -28,13 +30,12 @@ class ImprovedMotionDetector(MotionDetector):
config.frame_height * frame_shape[1] // frame_shape[0],
)
self.avg_frame = np.zeros(self.motion_frame_size, np.float32)
self.avg_delta = np.zeros(self.motion_frame_size, np.float32)
self.motion_frame_count = 0
self.frame_counter = 0
resized_mask = cv2.resize(
config.mask,
dsize=(self.motion_frame_size[1], self.motion_frame_size[0]),
interpolation=cv2.INTER_LINEAR,
interpolation=cv2.INTER_AREA,
)
self.mask = np.where(resized_mask == [0])
self.save_images = False
@ -42,7 +43,11 @@ class ImprovedMotionDetector(MotionDetector):
self.improve_contrast = improve_contrast
self.threshold = threshold
self.contour_area = contour_area
self.clahe = cv2.createCLAHE(clipLimit=clipLimit, tileGridSize=tileGridSize)
self.blur_radius = blur_radius
self.interpolation = interpolation
self.contrast_values = np.zeros((contrast_frame_history, 2), np.uint8)
self.contrast_values[:, 1:2] = 255
self.contrast_values_index = 0
def detect(self, frame):
motion_boxes = []
@ -53,27 +58,44 @@ class ImprovedMotionDetector(MotionDetector):
resized_frame = cv2.resize(
gray,
dsize=(self.motion_frame_size[1], self.motion_frame_size[0]),
interpolation=cv2.INTER_LINEAR,
interpolation=self.interpolation,
)
if self.save_images:
resized_saved = resized_frame.copy()
resized_frame = cv2.GaussianBlur(resized_frame, (3, 3), cv2.BORDER_DEFAULT)
if self.save_images:
blurred_saved = resized_frame.copy()
# Improve contrast
if self.improve_contrast.value:
resized_frame = self.clahe.apply(resized_frame)
# TODO tracking moving average of min/max to avoid sudden contrast changes
minval = np.percentile(resized_frame, 4).astype(np.uint8)
maxval = np.percentile(resized_frame, 96).astype(np.uint8)
# skip contrast calcs if the image is a single color
if minval < maxval:
# keep track of the last 50 contrast values
self.contrast_values[self.contrast_values_index] = [minval, maxval]
self.contrast_values_index += 1
if self.contrast_values_index == len(self.contrast_values):
self.contrast_values_index = 0
avg_min, avg_max = np.mean(self.contrast_values, axis=0)
resized_frame = np.clip(resized_frame, avg_min, avg_max)
resized_frame = (
((resized_frame - avg_min) / (avg_max - avg_min)) * 255
).astype(np.uint8)
if self.save_images:
contrasted_saved = resized_frame.copy()
# mask frame
# this has to come after contrast improvement
resized_frame[self.mask] = [255]
resized_frame = gaussian_filter(resized_frame, sigma=1, radius=self.blur_radius)
if self.save_images:
blurred_saved = resized_frame.copy()
if self.save_images or self.calibrating:
self.frame_counter += 1
# compare to average
@ -134,8 +156,8 @@ class ImprovedMotionDetector(MotionDetector):
)
frames = [
cv2.cvtColor(resized_saved, cv2.COLOR_GRAY2BGR),
cv2.cvtColor(blurred_saved, cv2.COLOR_GRAY2BGR),
cv2.cvtColor(contrasted_saved, cv2.COLOR_GRAY2BGR),
cv2.cvtColor(blurred_saved, cv2.COLOR_GRAY2BGR),
cv2.cvtColor(frameDelta, cv2.COLOR_GRAY2BGR),
cv2.cvtColor(thresh, cv2.COLOR_GRAY2BGR),
thresh_dilated,

2
requirements-wheels.txt
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@ -6,7 +6,7 @@ matplotlib == 3.7.*
mypy == 0.942
numpy == 1.23.*
onvif_zeep == 0.2.12
opencv-python-headless == 4.5.5.*
opencv-python-headless == 4.7.0.*
paho-mqtt == 1.6.*
peewee == 3.16.*
peewee_migrate == 1.10.*