ainvrmqtthomeautomationhome-automationhome-assistantgoogle-coralcameratensorflowrtsprealtimeobject-detection
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118 lines
3.8 KiB
118 lines
3.8 KiB
import datetime
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import multiprocessing as mp
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import os
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import cv2
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import numpy as np
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from frigate.config import MotionConfig
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from frigate.motion.improved_motion import ImprovedMotionDetector
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from frigate.util import create_mask
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# get info on the video
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# cap = cv2.VideoCapture("debug/front_cam_2023_05_23_08_41__2023_05_23_08_43.mp4")
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# cap = cv2.VideoCapture("debug/motion_test_clips/rain_1.mp4")
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cap = cv2.VideoCapture("debug/motion_test_clips/lawn_mower_night_1.mp4")
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# cap = cv2.VideoCapture("airport.mp4")
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fps = cap.get(cv2.CAP_PROP_FPS)
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frame_shape = (height, width, 3)
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# Nick back:
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# "1280,0,1280,316,1170,216,1146,126,1016,127,979,82,839,0",
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# "310,350,300,402,224,405,241,354",
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# "378,0,375,26,0,23,0,0",
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# Front door:
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# "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",
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# "336,833,438,1024,346,1093,103,1052,24,814",
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# Back
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# "1855,0,1851,100,1289,96,1105,161,1045,119,890,121,890,0",
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# "505,95,506,138,388,153,384,114",
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# "689,72,689,122,549,134,547,89",
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# "261,134,264,176,169,195,167,158",
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# "145,159,146,202,70,220,65,183",
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mask = create_mask(
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(height, width),
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[
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"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",
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"336,833,438,1024,346,1093,103,1052,24,814",
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],
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)
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# create the motion config
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motion_config_1 = MotionConfig()
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motion_config_1.mask = np.zeros((height, width), np.uint8)
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motion_config_1.mask[:] = mask
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# motion_config_1.improve_contrast = 1
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motion_config_1.frame_height = 150
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# motion_config_1.frame_alpha = 0.02
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# motion_config_1.threshold = 30
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# motion_config_1.contour_area = 10
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motion_config_2 = MotionConfig()
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motion_config_2.mask = np.zeros((height, width), np.uint8)
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motion_config_2.mask[:] = mask
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# motion_config_2.improve_contrast = 1
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motion_config_2.frame_height = 150
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# motion_config_2.frame_alpha = 0.01
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motion_config_2.threshold = 20
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# motion_config.contour_area = 10
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save_images = True
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improved_motion_detector_1 = ImprovedMotionDetector(
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frame_shape=frame_shape,
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config=motion_config_1,
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fps=fps,
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improve_contrast=mp.Value("i", motion_config_1.improve_contrast),
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threshold=mp.Value("i", motion_config_1.threshold),
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contour_area=mp.Value("i", motion_config_1.contour_area),
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name="default",
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)
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improved_motion_detector_1.save_images = save_images
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improved_motion_detector_2 = ImprovedMotionDetector(
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frame_shape=frame_shape,
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config=motion_config_2,
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fps=fps,
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improve_contrast=mp.Value("i", motion_config_2.improve_contrast),
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threshold=mp.Value("i", motion_config_2.threshold),
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contour_area=mp.Value("i", motion_config_2.contour_area),
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name="compare",
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)
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improved_motion_detector_2.save_images = save_images
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# read and process frames
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ret, frame = cap.read()
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frame_counter = 1
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while ret:
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yuv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2YUV_I420)
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start_frame = datetime.datetime.now().timestamp()
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improved_motion_detector_1.detect(yuv_frame)
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start_frame = datetime.datetime.now().timestamp()
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improved_motion_detector_2.detect(yuv_frame)
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default_frame = f"debug/frames/default-{frame_counter}.jpg"
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compare_frame = f"debug/frames/compare-{frame_counter}.jpg"
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if os.path.exists(default_frame) and os.path.exists(compare_frame):
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images = [
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cv2.imread(default_frame),
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cv2.imread(compare_frame),
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]
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cv2.imwrite(
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f"debug/frames/all-{frame_counter}.jpg",
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cv2.vconcat(images)
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if frame_shape[0] > frame_shape[1]
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else cv2.hconcat(images),
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)
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os.unlink(default_frame)
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os.unlink(compare_frame)
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frame_counter += 1
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ret, frame = cap.read()
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cap.release()
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