mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
0a4d658c7f
* Starting working on adding motion toggle * Add all info to mqtt command * Send motion to correct funs * Update mqtt docs * Fixes for contingencies * format * mypy * Tweak behavior * Fix motion breaking frames * Fix bad logic in detect set * Always set value for motion boxes
758 lines
25 KiB
Python
Executable File
758 lines
25 KiB
Python
Executable File
import datetime
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import itertools
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import logging
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import multiprocessing as mp
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import queue
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import random
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import signal
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import subprocess as sp
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import threading
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import time
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from collections import defaultdict
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import numpy as np
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from cv2 import cv2, reduce
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from setproctitle import setproctitle
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from frigate.config import CameraConfig, DetectConfig
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from frigate.edgetpu import RemoteObjectDetector
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from frigate.log import LogPipe
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from frigate.motion import MotionDetector
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from frigate.objects import ObjectTracker
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from frigate.util import (
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EventsPerSecond,
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FrameManager,
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SharedMemoryFrameManager,
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area,
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calculate_region,
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clipped,
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intersection,
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intersection_over_union,
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listen,
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yuv_region_2_rgb,
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)
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logger = logging.getLogger(__name__)
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def filtered(obj, objects_to_track, object_filters):
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object_name = obj[0]
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object_score = obj[1]
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object_box = obj[2]
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object_area = obj[3]
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object_ratio = obj[4]
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if not object_name in objects_to_track:
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return True
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if object_name in object_filters:
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obj_settings = object_filters[object_name]
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# if the min area is larger than the
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# detected object, don't add it to detected objects
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if obj_settings.min_area > object_area:
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return True
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# if the detected object is larger than the
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# max area, don't add it to detected objects
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if obj_settings.max_area < object_area:
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return True
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# if the score is lower than the min_score, skip
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if obj_settings.min_score > object_score:
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return True
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# if the object is not proportionally wide enough
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if obj_settings.min_ratio > object_ratio:
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return True
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# if the object is proportionally too wide
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if obj_settings.max_ratio < object_ratio:
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return True
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if not obj_settings.mask is None:
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# compute the coordinates of the object and make sure
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# the location isn't outside the bounds of the image (can happen from rounding)
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object_xmin = object_box[0]
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object_xmax = object_box[2]
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object_ymax = object_box[3]
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y_location = min(int(object_ymax), len(obj_settings.mask) - 1)
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x_location = min(
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int((object_xmax + object_xmin) / 2.0),
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len(obj_settings.mask[0]) - 1,
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)
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# if the object is in a masked location, don't add it to detected objects
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if obj_settings.mask[y_location][x_location] == 0:
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return True
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return False
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def create_tensor_input(frame, model_shape, region):
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cropped_frame = yuv_region_2_rgb(frame, region)
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# Resize to 300x300 if needed
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if cropped_frame.shape != (model_shape[0], model_shape[1], 3):
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cropped_frame = cv2.resize(
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cropped_frame, dsize=model_shape, interpolation=cv2.INTER_LINEAR
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)
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# Expand dimensions since the model expects images to have shape: [1, height, width, 3]
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return np.expand_dims(cropped_frame, axis=0)
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def stop_ffmpeg(ffmpeg_process, logger):
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logger.info("Terminating the existing ffmpeg process...")
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ffmpeg_process.terminate()
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try:
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logger.info("Waiting for ffmpeg to exit gracefully...")
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ffmpeg_process.communicate(timeout=30)
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except sp.TimeoutExpired:
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logger.info("FFmpeg didnt exit. Force killing...")
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ffmpeg_process.kill()
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ffmpeg_process.communicate()
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ffmpeg_process = None
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def start_or_restart_ffmpeg(
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ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None
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):
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if ffmpeg_process is not None:
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stop_ffmpeg(ffmpeg_process, logger)
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if frame_size is None:
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process = sp.Popen(
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ffmpeg_cmd,
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stdout=sp.DEVNULL,
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stderr=logpipe,
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stdin=sp.DEVNULL,
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start_new_session=True,
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)
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else:
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process = sp.Popen(
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ffmpeg_cmd,
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stdout=sp.PIPE,
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stderr=logpipe,
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stdin=sp.DEVNULL,
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bufsize=frame_size * 10,
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start_new_session=True,
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)
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return process
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def capture_frames(
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ffmpeg_process,
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camera_name,
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frame_shape,
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frame_manager: FrameManager,
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frame_queue,
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fps: mp.Value,
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skipped_fps: mp.Value,
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current_frame: mp.Value,
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):
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frame_size = frame_shape[0] * frame_shape[1]
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frame_rate = EventsPerSecond()
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frame_rate.start()
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skipped_eps = EventsPerSecond()
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skipped_eps.start()
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while True:
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fps.value = frame_rate.eps()
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skipped_fps = skipped_eps.eps()
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current_frame.value = datetime.datetime.now().timestamp()
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frame_name = f"{camera_name}{current_frame.value}"
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frame_buffer = frame_manager.create(frame_name, frame_size)
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try:
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frame_buffer[:] = ffmpeg_process.stdout.read(frame_size)
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except Exception as e:
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logger.error(f"{camera_name}: Unable to read frames from ffmpeg process.")
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if ffmpeg_process.poll() != None:
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logger.error(
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f"{camera_name}: ffmpeg process is not running. exiting capture thread..."
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)
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frame_manager.delete(frame_name)
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break
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continue
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frame_rate.update()
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# if the queue is full, skip this frame
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if frame_queue.full():
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skipped_eps.update()
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frame_manager.delete(frame_name)
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continue
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# close the frame
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frame_manager.close(frame_name)
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# add to the queue
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frame_queue.put(current_frame.value)
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class CameraWatchdog(threading.Thread):
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def __init__(
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self, camera_name, config, frame_queue, camera_fps, ffmpeg_pid, stop_event
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):
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threading.Thread.__init__(self)
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self.logger = logging.getLogger(f"watchdog.{camera_name}")
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self.camera_name = camera_name
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self.config = config
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self.capture_thread = None
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self.ffmpeg_detect_process = None
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self.logpipe = LogPipe(f"ffmpeg.{self.camera_name}.detect")
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self.ffmpeg_other_processes = []
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self.camera_fps = camera_fps
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self.ffmpeg_pid = ffmpeg_pid
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self.frame_queue = frame_queue
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self.frame_shape = self.config.frame_shape_yuv
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self.frame_size = self.frame_shape[0] * self.frame_shape[1]
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self.stop_event = stop_event
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def run(self):
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self.start_ffmpeg_detect()
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for c in self.config.ffmpeg_cmds:
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if "detect" in c["roles"]:
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continue
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logpipe = LogPipe(
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f"ffmpeg.{self.camera_name}.{'_'.join(sorted(c['roles']))}"
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)
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self.ffmpeg_other_processes.append(
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{
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"cmd": c["cmd"],
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"logpipe": logpipe,
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"process": start_or_restart_ffmpeg(c["cmd"], self.logger, logpipe),
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}
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)
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time.sleep(10)
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while not self.stop_event.wait(10):
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now = datetime.datetime.now().timestamp()
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if not self.capture_thread.is_alive():
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self.logger.error(
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f"Ffmpeg process crashed unexpectedly for {self.camera_name}."
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)
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self.logger.error(
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"The following ffmpeg logs include the last 100 lines prior to exit."
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)
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self.logpipe.dump()
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self.start_ffmpeg_detect()
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elif now - self.capture_thread.current_frame.value > 20:
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self.logger.info(
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f"No frames received from {self.camera_name} in 20 seconds. Exiting ffmpeg..."
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)
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self.ffmpeg_detect_process.terminate()
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try:
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self.logger.info("Waiting for ffmpeg to exit gracefully...")
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self.ffmpeg_detect_process.communicate(timeout=30)
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except sp.TimeoutExpired:
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self.logger.info("FFmpeg didnt exit. Force killing...")
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self.ffmpeg_detect_process.kill()
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self.ffmpeg_detect_process.communicate()
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for p in self.ffmpeg_other_processes:
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poll = p["process"].poll()
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if poll is None:
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continue
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p["logpipe"].dump()
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p["process"] = start_or_restart_ffmpeg(
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p["cmd"], self.logger, p["logpipe"], ffmpeg_process=p["process"]
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)
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stop_ffmpeg(self.ffmpeg_detect_process, self.logger)
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for p in self.ffmpeg_other_processes:
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stop_ffmpeg(p["process"], self.logger)
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p["logpipe"].close()
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self.logpipe.close()
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def start_ffmpeg_detect(self):
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ffmpeg_cmd = [
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c["cmd"] for c in self.config.ffmpeg_cmds if "detect" in c["roles"]
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][0]
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self.ffmpeg_detect_process = start_or_restart_ffmpeg(
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ffmpeg_cmd, self.logger, self.logpipe, self.frame_size
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)
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self.ffmpeg_pid.value = self.ffmpeg_detect_process.pid
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self.capture_thread = CameraCapture(
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self.camera_name,
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self.ffmpeg_detect_process,
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self.frame_shape,
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self.frame_queue,
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self.camera_fps,
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)
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self.capture_thread.start()
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class CameraCapture(threading.Thread):
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def __init__(self, camera_name, ffmpeg_process, frame_shape, frame_queue, fps):
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threading.Thread.__init__(self)
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self.name = f"capture:{camera_name}"
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self.camera_name = camera_name
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self.frame_shape = frame_shape
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self.frame_queue = frame_queue
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self.fps = fps
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self.skipped_fps = EventsPerSecond()
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self.frame_manager = SharedMemoryFrameManager()
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self.ffmpeg_process = ffmpeg_process
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self.current_frame = mp.Value("d", 0.0)
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self.last_frame = 0
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def run(self):
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self.skipped_fps.start()
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capture_frames(
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self.ffmpeg_process,
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self.camera_name,
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self.frame_shape,
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self.frame_manager,
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self.frame_queue,
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self.fps,
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self.skipped_fps,
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self.current_frame,
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)
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def capture_camera(name, config: CameraConfig, process_info):
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stop_event = mp.Event()
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def receiveSignal(signalNumber, frame):
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stop_event.set()
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signal.signal(signal.SIGTERM, receiveSignal)
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signal.signal(signal.SIGINT, receiveSignal)
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frame_queue = process_info["frame_queue"]
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camera_watchdog = CameraWatchdog(
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name,
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config,
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frame_queue,
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process_info["camera_fps"],
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process_info["ffmpeg_pid"],
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stop_event,
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)
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camera_watchdog.start()
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camera_watchdog.join()
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def track_camera(
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name,
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config: CameraConfig,
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model_shape,
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labelmap,
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detection_queue,
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result_connection,
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detected_objects_queue,
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process_info,
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):
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stop_event = mp.Event()
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def receiveSignal(signalNumber, frame):
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stop_event.set()
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signal.signal(signal.SIGTERM, receiveSignal)
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signal.signal(signal.SIGINT, receiveSignal)
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threading.current_thread().name = f"process:{name}"
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setproctitle(f"frigate.process:{name}")
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listen()
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frame_queue = process_info["frame_queue"]
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detection_enabled = process_info["detection_enabled"]
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motion_enabled = process_info["motion_enabled"]
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improve_contrast_enabled = process_info["improve_contrast_enabled"]
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frame_shape = config.frame_shape
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objects_to_track = config.objects.track
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object_filters = config.objects.filters
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motion_detector = MotionDetector(
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frame_shape, config.motion, improve_contrast_enabled
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)
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object_detector = RemoteObjectDetector(
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name, labelmap, detection_queue, result_connection, model_shape
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)
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object_tracker = ObjectTracker(config.detect)
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frame_manager = SharedMemoryFrameManager()
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process_frames(
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name,
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frame_queue,
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frame_shape,
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model_shape,
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config.detect,
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frame_manager,
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motion_detector,
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object_detector,
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object_tracker,
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detected_objects_queue,
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process_info,
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objects_to_track,
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object_filters,
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detection_enabled,
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motion_enabled,
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stop_event,
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)
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logger.info(f"{name}: exiting subprocess")
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def box_overlaps(b1, b2):
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if b1[2] < b2[0] or b1[0] > b2[2] or b1[1] > b2[3] or b1[3] < b2[1]:
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return False
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return True
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def reduce_boxes(boxes, iou_threshold=0.0):
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clusters = []
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for box in boxes:
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matched = 0
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for cluster in clusters:
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if intersection_over_union(box, cluster) > iou_threshold:
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matched = 1
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cluster[0] = min(cluster[0], box[0])
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cluster[1] = min(cluster[1], box[1])
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cluster[2] = max(cluster[2], box[2])
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cluster[3] = max(cluster[3], box[3])
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if not matched:
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clusters.append(list(box))
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return [tuple(c) for c in clusters]
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def intersects_any(box_a, boxes):
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for box in boxes:
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if box_overlaps(box_a, box):
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return True
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return False
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def detect(
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object_detector, frame, model_shape, region, objects_to_track, object_filters
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):
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tensor_input = create_tensor_input(frame, model_shape, region)
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detections = []
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region_detections = object_detector.detect(tensor_input)
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for d in region_detections:
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box = d[2]
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size = region[2] - region[0]
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x_min = int((box[1] * size) + region[0])
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y_min = int((box[0] * size) + region[1])
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x_max = int((box[3] * size) + region[0])
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y_max = int((box[2] * size) + region[1])
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width = x_max - x_min
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height = y_max - y_min
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area = width * height
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ratio = width / height
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det = (
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d[0],
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d[1],
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(x_min, y_min, x_max, y_max),
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area,
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ratio,
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region,
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)
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# apply object filters
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if filtered(det, objects_to_track, object_filters):
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continue
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detections.append(det)
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return detections
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def process_frames(
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camera_name: str,
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frame_queue: mp.Queue,
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frame_shape,
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model_shape,
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detect_config: DetectConfig,
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frame_manager: FrameManager,
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motion_detector: MotionDetector,
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object_detector: RemoteObjectDetector,
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object_tracker: ObjectTracker,
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detected_objects_queue: mp.Queue,
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process_info: dict,
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objects_to_track: list[str],
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object_filters,
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detection_enabled: mp.Value,
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motion_enabled: mp.Value,
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stop_event,
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exit_on_empty: bool = False,
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):
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fps = process_info["process_fps"]
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detection_fps = process_info["detection_fps"]
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current_frame_time = process_info["detection_frame"]
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fps_tracker = EventsPerSecond()
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fps_tracker.start()
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startup_scan_counter = 0
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while not stop_event.is_set():
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if exit_on_empty and frame_queue.empty():
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logger.info(f"Exiting track_objects...")
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break
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try:
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frame_time = frame_queue.get(True, 10)
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except queue.Empty:
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continue
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current_frame_time.value = frame_time
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frame = frame_manager.get(
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f"{camera_name}{frame_time}", (frame_shape[0] * 3 // 2, frame_shape[1])
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)
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if frame is None:
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logger.info(f"{camera_name}: frame {frame_time} is not in memory store.")
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continue
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# look for motion if enabled
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motion_boxes = motion_detector.detect(frame) if motion_enabled.value else []
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regions = []
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# if detection is disabled
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if not detection_enabled.value:
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object_tracker.match_and_update(frame_time, [])
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else:
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# get stationary object ids
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# check every Nth frame for stationary objects
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# disappeared objects are not stationary
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# also check for overlapping motion boxes
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stationary_object_ids = [
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obj["id"]
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for obj in object_tracker.tracked_objects.values()
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# if there hasn't been motion for 10 frames
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if obj["motionless_count"] >= 10
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# and it isn't due for a periodic check
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|
and (
|
|
detect_config.stationary.interval == 0
|
|
or obj["motionless_count"] % detect_config.stationary.interval != 0
|
|
)
|
|
# and it hasn't disappeared
|
|
and object_tracker.disappeared[obj["id"]] == 0
|
|
# and it doesn't overlap with any current motion boxes
|
|
and not intersects_any(obj["box"], motion_boxes)
|
|
]
|
|
|
|
# get tracked object boxes that aren't stationary
|
|
tracked_object_boxes = [
|
|
obj["box"]
|
|
for obj in object_tracker.tracked_objects.values()
|
|
if not obj["id"] in stationary_object_ids
|
|
]
|
|
|
|
# combine motion boxes with known locations of existing objects
|
|
combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
|
|
|
|
region_min_size = max(model_shape[0], model_shape[1])
|
|
# compute regions
|
|
regions = [
|
|
calculate_region(
|
|
frame_shape,
|
|
a[0],
|
|
a[1],
|
|
a[2],
|
|
a[3],
|
|
region_min_size,
|
|
multiplier=random.uniform(1.2, 1.5),
|
|
)
|
|
for a in combined_boxes
|
|
]
|
|
|
|
# consolidate regions with heavy overlap
|
|
regions = [
|
|
calculate_region(
|
|
frame_shape, a[0], a[1], a[2], a[3], region_min_size, multiplier=1.0
|
|
)
|
|
for a in reduce_boxes(regions, 0.4)
|
|
]
|
|
|
|
# if starting up, get the next startup scan region
|
|
if startup_scan_counter < 9:
|
|
ymin = int(frame_shape[0] / 3 * startup_scan_counter / 3)
|
|
ymax = int(frame_shape[0] / 3 + ymin)
|
|
xmin = int(frame_shape[1] / 3 * startup_scan_counter / 3)
|
|
xmax = int(frame_shape[1] / 3 + xmin)
|
|
regions.append(
|
|
calculate_region(
|
|
frame_shape,
|
|
xmin,
|
|
ymin,
|
|
xmax,
|
|
ymax,
|
|
region_min_size,
|
|
multiplier=1.2,
|
|
)
|
|
)
|
|
startup_scan_counter += 1
|
|
|
|
# resize regions and detect
|
|
# seed with stationary objects
|
|
detections = [
|
|
(
|
|
obj["label"],
|
|
obj["score"],
|
|
obj["box"],
|
|
obj["area"],
|
|
obj["ratio"],
|
|
obj["region"],
|
|
)
|
|
for obj in object_tracker.tracked_objects.values()
|
|
if obj["id"] in stationary_object_ids
|
|
]
|
|
|
|
for region in regions:
|
|
detections.extend(
|
|
detect(
|
|
object_detector,
|
|
frame,
|
|
model_shape,
|
|
region,
|
|
objects_to_track,
|
|
object_filters,
|
|
)
|
|
)
|
|
|
|
#########
|
|
# merge objects, check for clipped objects and look again up to 4 times
|
|
#########
|
|
refining = len(regions) > 0
|
|
refine_count = 0
|
|
while refining and refine_count < 4:
|
|
refining = False
|
|
|
|
# group by name
|
|
detected_object_groups = defaultdict(lambda: [])
|
|
for detection in detections:
|
|
detected_object_groups[detection[0]].append(detection)
|
|
|
|
selected_objects = []
|
|
for group in detected_object_groups.values():
|
|
|
|
# apply non-maxima suppression to suppress weak, overlapping bounding boxes
|
|
# o[2] is the box of the object: xmin, ymin, xmax, ymax
|
|
boxes = [
|
|
(
|
|
o[2][0],
|
|
o[2][1],
|
|
o[2][2] - o[2][0],
|
|
o[2][3] - o[2][1],
|
|
)
|
|
for o in group
|
|
]
|
|
confidences = [o[1] for o in group]
|
|
idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
|
|
|
|
for index in idxs:
|
|
index = index if isinstance(index, np.int32) else index[0]
|
|
obj = group[index]
|
|
if clipped(obj, frame_shape):
|
|
box = obj[2]
|
|
# calculate a new region that will hopefully get the entire object
|
|
region = calculate_region(
|
|
frame_shape,
|
|
box[0],
|
|
box[1],
|
|
box[2],
|
|
box[3],
|
|
region_min_size,
|
|
)
|
|
|
|
regions.append(region)
|
|
|
|
selected_objects.extend(
|
|
detect(
|
|
object_detector,
|
|
frame,
|
|
model_shape,
|
|
region,
|
|
objects_to_track,
|
|
object_filters,
|
|
)
|
|
)
|
|
|
|
refining = True
|
|
else:
|
|
selected_objects.append(obj)
|
|
# set the detections list to only include top, complete objects
|
|
# and new detections
|
|
detections = selected_objects
|
|
|
|
if refining:
|
|
refine_count += 1
|
|
|
|
## drop detections that overlap too much
|
|
consolidated_detections = []
|
|
|
|
# if detection was run on this frame, consolidate
|
|
if len(regions) > 0:
|
|
# group by name
|
|
detected_object_groups = defaultdict(lambda: [])
|
|
for detection in detections:
|
|
detected_object_groups[detection[0]].append(detection)
|
|
|
|
# loop over detections grouped by label
|
|
for group in detected_object_groups.values():
|
|
# if the group only has 1 item, skip
|
|
if len(group) == 1:
|
|
consolidated_detections.append(group[0])
|
|
continue
|
|
|
|
# sort smallest to largest by area
|
|
sorted_by_area = sorted(group, key=lambda g: g[3])
|
|
|
|
for current_detection_idx in range(0, len(sorted_by_area)):
|
|
current_detection = sorted_by_area[current_detection_idx][2]
|
|
overlap = 0
|
|
for to_check_idx in range(
|
|
min(current_detection_idx + 1, len(sorted_by_area)),
|
|
len(sorted_by_area),
|
|
):
|
|
to_check = sorted_by_area[to_check_idx][2]
|
|
# if 90% of smaller detection is inside of another detection, consolidate
|
|
if (
|
|
area(intersection(current_detection, to_check))
|
|
/ area(current_detection)
|
|
> 0.9
|
|
):
|
|
overlap = 1
|
|
break
|
|
if overlap == 0:
|
|
consolidated_detections.append(
|
|
sorted_by_area[current_detection_idx]
|
|
)
|
|
# now that we have refined our detections, we need to track objects
|
|
object_tracker.match_and_update(frame_time, consolidated_detections)
|
|
# else, just update the frame times for the stationary objects
|
|
else:
|
|
object_tracker.update_frame_times(frame_time)
|
|
|
|
# add to the queue if not full
|
|
if detected_objects_queue.full():
|
|
frame_manager.delete(f"{camera_name}{frame_time}")
|
|
continue
|
|
else:
|
|
fps_tracker.update()
|
|
fps.value = fps_tracker.eps()
|
|
detected_objects_queue.put(
|
|
(
|
|
camera_name,
|
|
frame_time,
|
|
object_tracker.tracked_objects,
|
|
motion_boxes,
|
|
regions,
|
|
)
|
|
)
|
|
detection_fps.value = object_detector.fps.eps()
|
|
frame_manager.close(f"{camera_name}{frame_time}")
|