mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-10-27 10:52:11 +01:00
upgrade to python3.8 and switch from plasma store to shared_memory
This commit is contained in:
Blake Blackshear
parent
b063099b2a
commit
ec4d048905
25
Dockerfile
25
Dockerfile
@ -1,4 +1,4 @@
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FROM ubuntu:18.04
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FROM ubuntu:20.04
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LABEL maintainer "blakeb@blakeshome.com"
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ENV DEBIAN_FRONTEND=noninteractive
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@ -11,27 +11,26 @@ RUN apt -qq update && apt -qq install --no-install-recommends -y \
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# libcap-dev \
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&& add-apt-repository ppa:deadsnakes/ppa -y \
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&& apt -qq install --no-install-recommends -y \
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python3.7 \
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python3.7-dev \
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python3.8 \
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python3.8-dev \
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python3-pip \
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ffmpeg \
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# VAAPI drivers for Intel hardware accel
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libva-drm2 libva2 i965-va-driver vainfo \
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&& python3.7 -m pip install -U pip \
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&& python3.7 -m pip install -U wheel setuptools \
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&& python3.7 -m pip install -U \
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&& python3.8 -m pip install -U pip \
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&& python3.8 -m pip install -U wheel setuptools \
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&& python3.8 -m pip install -U \
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opencv-python-headless \
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# python-prctl \
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numpy \
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imutils \
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scipy \
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psutil \
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&& python3.7 -m pip install -U \
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&& python3.8 -m pip install -U \
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Flask \
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paho-mqtt \
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PyYAML \
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matplotlib \
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pyarrow \
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click \
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&& echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" > /etc/apt/sources.list.d/coral-edgetpu.list \
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&& wget -q -O - https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add - \
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@ -39,10 +38,10 @@ RUN apt -qq update && apt -qq install --no-install-recommends -y \
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&& echo "libedgetpu1-max libedgetpu/accepted-eula boolean true" | debconf-set-selections \
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&& apt -qq install --no-install-recommends -y \
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libedgetpu1-max \
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## Tensorflow lite (python 3.7 only)
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&& wget -q https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
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&& python3.7 -m pip install tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
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&& rm tflite_runtime-2.1.0.post1-cp37-cp37m-linux_x86_64.whl \
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## Tensorflow lite
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&& wget -q https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp38-cp38-linux_x86_64.whl \
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&& python3.8 -m pip install tflite_runtime-2.1.0.post1-cp38-cp38-linux_x86_64.whl \
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&& rm tflite_runtime-2.1.0.post1-cp38-cp38-linux_x86_64.whl \
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&& rm -rf /var/lib/apt/lists/* \
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&& (apt-get autoremove -y; apt-get autoclean -y)
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@ -60,4 +59,4 @@ COPY detect_objects.py .
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COPY benchmark.py .
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COPY process_clip.py .
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CMD ["python3.7", "-u", "detect_objects.py"]
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CMD ["python3.8", "-u", "detect_objects.py"]
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@ -63,23 +63,13 @@ WEB_PORT = CONFIG.get('web_port', 5000)
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DEBUG = (CONFIG.get('debug', '0') == '1')
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TENSORFLOW_DEVICE = CONFIG.get('tensorflow_device')
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def start_plasma_store():
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plasma_cmd = ['plasma_store', '-m', '400000000', '-s', '/tmp/plasma']
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plasma_process = sp.Popen(plasma_cmd, stdout=sp.DEVNULL, stderr=sp.DEVNULL)
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time.sleep(1)
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rc = plasma_process.poll()
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if rc is not None:
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return None
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return plasma_process
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class CameraWatchdog(threading.Thread):
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def __init__(self, camera_processes, config, tflite_process, tracked_objects_queue, plasma_process, stop_event):
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def __init__(self, camera_processes, config, tflite_process, tracked_objects_queue, stop_event):
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threading.Thread.__init__(self)
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self.camera_processes = camera_processes
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self.config = config
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self.tflite_process = tflite_process
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self.tracked_objects_queue = tracked_objects_queue
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self.plasma_process = plasma_process
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self.stop_event = stop_event
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def run(self):
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@ -93,12 +83,6 @@ class CameraWatchdog(threading.Thread):
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break
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now = datetime.datetime.now().timestamp()
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# check the plasma process
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rc = self.plasma_process.poll()
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if rc != None:
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print(f"plasma_process exited unexpectedly with {rc}")
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self.plasma_process = start_plasma_store()
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# check the detection process
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detection_start = self.tflite_process.detection_start.value
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@ -172,8 +156,6 @@ def main():
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client.connect(MQTT_HOST, MQTT_PORT, 60)
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client.loop_start()
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plasma_process = start_plasma_store()
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##
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# Setup config defaults for cameras
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##
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@ -189,11 +171,16 @@ def main():
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# Queue for clip processing
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event_queue = mp.Queue()
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# create the detection pipes
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detection_pipes = {}
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for name in CONFIG['cameras'].keys():
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detection_pipes[name] = mp.Pipe(duplex=False)
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# Start the shared tflite process
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tflite_process = EdgeTPUProcess(TENSORFLOW_DEVICE)
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tflite_process = EdgeTPUProcess(result_connections={ key:value[1] for (key,value) in detection_pipes.items() }, tf_device=TENSORFLOW_DEVICE)
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# start the camera processes
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# create the camera processes
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camera_processes = {}
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for name, config in CONFIG['cameras'].items():
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# Merge the ffmpeg config with the global config
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@ -236,6 +223,8 @@ def main():
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frame_shape = (config['height'], config['width'], 3)
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else:
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frame_shape = get_frame_shape(ffmpeg_input)
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config['frame_shape'] = frame_shape
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frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
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take_frame = config.get('take_frame', 1)
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@ -275,12 +264,13 @@ def main():
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}
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camera_process = mp.Process(target=track_camera, args=(name, config, frame_queue, frame_shape,
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tflite_process.detection_queue, tracked_objects_queue, camera_processes[name]['process_fps'],
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tflite_process.detection_queue, detection_pipes[name][0], tracked_objects_queue, camera_processes[name]['process_fps'],
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camera_processes[name]['detection_fps'],
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camera_processes[name]['read_start'], camera_processes[name]['detection_frame'], stop_event))
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camera_process.daemon = True
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camera_processes[name]['process'] = camera_process
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# start the camera_processes
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for name, camera_process in camera_processes.items():
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camera_process['process'].start()
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print(f"Camera_process started for {name}: {camera_process['process'].pid}")
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@ -291,7 +281,7 @@ def main():
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object_processor = TrackedObjectProcessor(CONFIG['cameras'], client, MQTT_TOPIC_PREFIX, tracked_objects_queue, event_queue, stop_event)
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object_processor.start()
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camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], tflite_process, tracked_objects_queue, plasma_process, stop_event)
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camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], tflite_process, tracked_objects_queue, stop_event)
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camera_watchdog.start()
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def receiveSignal(signalNumber, frame):
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@ -300,11 +290,9 @@ def main():
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event_processor.join()
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object_processor.join()
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camera_watchdog.join()
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for name, camera_process in camera_processes.items():
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for camera_process in camera_processes.values():
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camera_process['capture_thread'].join()
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rc = camera_watchdog.plasma_process.poll()
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if rc == None:
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camera_watchdog.plasma_process.terminate()
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tflite_process.stop()
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sys.exit()
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signal.signal(signal.SIGTERM, receiveSignal)
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@ -368,9 +356,6 @@ def main():
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'pid': tflite_process.detect_process.pid
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}
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rc = camera_watchdog.plasma_process.poll()
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stats['plasma_store_rc'] = rc
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return jsonify(stats)
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@app.route('/<camera_name>/<label>/best.jpg')
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@ -448,8 +433,6 @@ def main():
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app.run(host='0.0.0.0', port=WEB_PORT, debug=False)
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object_processor.join()
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plasma_process.terminate()
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if __name__ == '__main__':
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main()
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@ -2,12 +2,14 @@ import os
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import datetime
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import hashlib
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import multiprocessing as mp
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import queue
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from multiprocessing.connection import Connection
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from abc import ABC, abstractmethod
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from typing import Dict
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import numpy as np
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import pyarrow.plasma as plasma
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import tflite_runtime.interpreter as tflite
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from tflite_runtime.interpreter import load_delegate
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from frigate.util import EventsPerSecond, listen
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from frigate.util import EventsPerSecond, listen, SharedMemoryFrameManager
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def load_labels(path, encoding='utf-8'):
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"""Loads labels from file (with or without index numbers).
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@ -100,73 +102,77 @@ class LocalObjectDetector(ObjectDetector):
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return detections
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def run_detector(detection_queue, avg_speed, start, tf_device):
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def run_detector(detection_queue, result_connections: Dict[str, Connection], avg_speed, start, tf_device):
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print(f"Starting detection process: {os.getpid()}")
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listen()
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plasma_client = plasma.connect("/tmp/plasma")
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frame_manager = SharedMemoryFrameManager()
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object_detector = LocalObjectDetector(tf_device=tf_device)
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while True:
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object_id_str = detection_queue.get()
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object_id_hash = hashlib.sha1(str.encode(object_id_str))
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object_id = plasma.ObjectID(object_id_hash.digest())
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object_id_out = plasma.ObjectID(hashlib.sha1(str.encode(f"out-{object_id_str}")).digest())
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input_frame = plasma_client.get(object_id, timeout_ms=0)
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connection_id = detection_queue.get()
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input_frame = frame_manager.get(connection_id, (1,300,300,3))
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if input_frame is plasma.ObjectNotAvailable:
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if input_frame is None:
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continue
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# detect and put the output in the plasma store
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start.value = datetime.datetime.now().timestamp()
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plasma_client.put(object_detector.detect_raw(input_frame), object_id_out)
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# TODO: what is the overhead for pickling this result vs writing back to shared memory?
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# I could try using an Event() and waiting in the other process before looking in memory...
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detections = object_detector.detect_raw(input_frame)
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result_connections[connection_id].send(detections)
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duration = datetime.datetime.now().timestamp()-start.value
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start.value = 0.0
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avg_speed.value = (avg_speed.value*9 + duration)/10
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class EdgeTPUProcess():
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def __init__(self, tf_device=None):
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def __init__(self, result_connections, tf_device=None):
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self.result_connections = result_connections
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self.detection_queue = mp.Queue()
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self.avg_inference_speed = mp.Value('d', 0.01)
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self.detection_start = mp.Value('d', 0.0)
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self.detect_process = None
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self.tf_device = tf_device
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self.start_or_restart()
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def stop(self):
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self.detect_process.terminate()
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print("Waiting for detection process to exit gracefully...")
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self.detect_process.join(timeout=30)
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if self.detect_process.exitcode is None:
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print("Detection process didnt exit. Force killing...")
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self.detect_process.kill()
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self.detect_process.join()
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def start_or_restart(self):
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self.detection_start.value = 0.0
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if (not self.detect_process is None) and self.detect_process.is_alive():
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self.detect_process.terminate()
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print("Waiting for detection process to exit gracefully...")
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self.detect_process.join(timeout=30)
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if self.detect_process.exitcode is None:
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print("Detection process didnt exit. Force killing...")
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self.detect_process.kill()
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self.detect_process.join()
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self.detect_process = mp.Process(target=run_detector, args=(self.detection_queue, self.avg_inference_speed, self.detection_start, self.tf_device))
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self.stop()
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self.detect_process = mp.Process(target=run_detector, args=(self.detection_queue, self.result_connections, self.avg_inference_speed, self.detection_start, self.tf_device))
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self.detect_process.daemon = True
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self.detect_process.start()
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class RemoteObjectDetector():
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def __init__(self, name, labels, detection_queue):
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def __init__(self, name, labels, detection_queue, result_connection: Connection):
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self.labels = load_labels(labels)
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self.name = name
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self.fps = EventsPerSecond()
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self.plasma_client = plasma.connect("/tmp/plasma")
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self.detection_queue = detection_queue
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self.result_connection = result_connection
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self.shm = mp.shared_memory.SharedMemory(name=self.name, create=True, size=300*300*3)
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self.np_shm = np.ndarray((1,300,300,3), dtype=np.uint8, buffer=self.shm.buf)
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def detect(self, tensor_input, threshold=.4):
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detections = []
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now = f"{self.name}-{str(datetime.datetime.now().timestamp())}"
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object_id_frame = plasma.ObjectID(hashlib.sha1(str.encode(now)).digest())
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object_id_detections = plasma.ObjectID(hashlib.sha1(str.encode(f"out-{now}")).digest())
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self.plasma_client.put(tensor_input, object_id_frame)
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self.detection_queue.put(now)
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raw_detections = self.plasma_client.get(object_id_detections, timeout_ms=10000)
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if raw_detections is plasma.ObjectNotAvailable:
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self.plasma_client.delete([object_id_frame])
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# copy input to shared memory
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# TODO: what if I just write it there in the first place?
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self.np_shm[:] = tensor_input[:]
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self.detection_queue.put(self.name)
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if self.result_connection.poll(10):
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raw_detections = self.result_connection.recv()
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else:
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return detections
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for d in raw_detections:
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@ -177,6 +183,5 @@ class RemoteObjectDetector():
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float(d[1]),
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(d[2], d[3], d[4], d[5])
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))
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self.plasma_client.delete([object_id_frame, object_id_detections])
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self.fps.update()
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return detections
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@ -10,9 +10,8 @@ import copy
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import numpy as np
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from collections import Counter, defaultdict
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import itertools
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import pyarrow.plasma as plasma
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import matplotlib.pyplot as plt
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from frigate.util import draw_box_with_label, PlasmaFrameManager
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from frigate.util import draw_box_with_label, SharedMemoryFrameManager
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from frigate.edgetpu import load_labels
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from typing import Callable, Dict
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from statistics import mean, median
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@ -59,7 +58,7 @@ class CameraState():
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self.object_status = defaultdict(lambda: 'OFF')
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self.tracked_objects = {}
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self.zone_objects = defaultdict(lambda: [])
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self.current_frame = np.zeros((720,1280,3), np.uint8)
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self.current_frame = np.zeros(self.config['frame_shape'], np.uint8)
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self.current_frame_time = 0.0
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self.previous_frame_id = None
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self.callbacks = defaultdict(lambda: [])
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@ -88,7 +87,7 @@ class CameraState():
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self.current_frame_time = frame_time
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# get the new frame and delete the old frame
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frame_id = f"{self.name}{frame_time}"
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self.current_frame = self.frame_manager.get(frame_id)
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self.current_frame = self.frame_manager.get(frame_id, self.config['frame_shape'])
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if not self.previous_frame_id is None:
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self.frame_manager.delete(self.previous_frame_id)
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self.previous_frame_id = frame_id
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@ -238,7 +237,7 @@ class TrackedObjectProcessor(threading.Thread):
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self.event_queue = event_queue
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self.stop_event = stop_event
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self.camera_states: Dict[str, CameraState] = {}
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self.plasma_client = PlasmaFrameManager(self.stop_event)
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self.frame_manager = SharedMemoryFrameManager()
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def start(camera, obj):
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# publish events to mqtt
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@ -273,7 +272,7 @@ class TrackedObjectProcessor(threading.Thread):
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self.client.publish(f"{self.topic_prefix}/{camera}/{object_name}", status, retain=False)
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for camera in self.camera_config.keys():
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camera_state = CameraState(camera, self.camera_config[camera], self.plasma_client)
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camera_state = CameraState(camera, self.camera_config[camera], self.frame_manager)
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camera_state.on('start', start)
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camera_state.on('update', update)
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camera_state.on('end', end)
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@ -9,7 +9,8 @@ import cv2
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import threading
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import matplotlib.pyplot as plt
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import hashlib
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import pyarrow.plasma as plasma
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from multiprocessing import shared_memory
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from typing import AnyStr
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def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thickness=2, color=None, position='ul'):
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if color is None:
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@ -148,12 +149,16 @@ def listen():
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signal.signal(signal.SIGUSR1, print_stack)
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class FrameManager(ABC):
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@abstractmethod
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def create(self, name, size) -> AnyStr:
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pass
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@abstractmethod
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def get(self, name, timeout_ms=0):
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pass
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@abstractmethod
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def put(self, name, frame):
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def close(self, name):
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pass
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@abstractmethod
|
||||
@ -164,66 +169,45 @@ class DictFrameManager(FrameManager):
|
||||
def __init__(self):
|
||||
self.frames = {}
|
||||
|
||||
def get(self, name, timeout_ms=0):
|
||||
return self.frames.get(name)
|
||||
def create(self, name, size) -> AnyStr:
|
||||
mem = bytearray(size)
|
||||
self.frames[name] = mem
|
||||
return mem
|
||||
|
||||
def put(self, name, frame):
|
||||
self.frames[name] = frame
|
||||
def get(self, name, shape):
|
||||
mem = self.frames[name]
|
||||
return np.ndarray(shape, dtype=np.uint8, buffer=mem)
|
||||
|
||||
def close(self, name):
|
||||
pass
|
||||
|
||||
def delete(self, name):
|
||||
del self.frames[name]
|
||||
|
||||
class PlasmaFrameManager(FrameManager):
|
||||
def __init__(self, stop_event=None):
|
||||
self.stop_event = stop_event
|
||||
self.connect()
|
||||
class SharedMemoryFrameManager(FrameManager):
|
||||
def __init__(self):
|
||||
self.shm_store = {}
|
||||
|
||||
def connect(self):
|
||||
while True:
|
||||
if self.stop_event != None and self.stop_event.is_set():
|
||||
return
|
||||
try:
|
||||
self.plasma_client = plasma.connect("/tmp/plasma")
|
||||
return
|
||||
except:
|
||||
print(f"TrackedObjectProcessor: unable to connect plasma client")
|
||||
time.sleep(10)
|
||||
def create(self, name, size) -> AnyStr:
|
||||
shm = shared_memory.SharedMemory(name=name, create=True, size=size)
|
||||
self.shm_store[name] = shm
|
||||
return shm.buf
|
||||
|
||||
def get(self, name, timeout_ms=0):
|
||||
object_id = plasma.ObjectID(hashlib.sha1(str.encode(name)).digest())
|
||||
while True:
|
||||
if self.stop_event != None and self.stop_event.is_set():
|
||||
return
|
||||
try:
|
||||
frame = self.plasma_client.get(object_id, timeout_ms=timeout_ms)
|
||||
if frame is plasma.ObjectNotAvailable:
|
||||
return None
|
||||
return frame
|
||||
except:
|
||||
self.connect()
|
||||
time.sleep(1)
|
||||
def get(self, name, shape):
|
||||
if name in self.shm_store:
|
||||
shm = self.shm_store[name]
|
||||
else:
|
||||
shm = shared_memory.SharedMemory(name=name)
|
||||
self.shm_store[name] = shm
|
||||
return np.ndarray(shape, dtype=np.uint8, buffer=shm.buf)
|
||||
|
||||
def put(self, name, frame):
|
||||
object_id = plasma.ObjectID(hashlib.sha1(str.encode(name)).digest())
|
||||
while True:
|
||||
if self.stop_event != None and self.stop_event.is_set():
|
||||
return
|
||||
try:
|
||||
self.plasma_client.put(frame, object_id)
|
||||
return
|
||||
except Exception as e:
|
||||
print(f"Failed to put in plasma: {e}")
|
||||
self.connect()
|
||||
time.sleep(1)
|
||||
def close(self, name):
|
||||
if name in self.shm_store:
|
||||
self.shm_store[name].close()
|
||||
del self.shm_store[name]
|
||||
|
||||
def delete(self, name):
|
||||
object_id = plasma.ObjectID(hashlib.sha1(str.encode(name)).digest())
|
||||
while True:
|
||||
if self.stop_event != None and self.stop_event.is_set():
|
||||
return
|
||||
try:
|
||||
self.plasma_client.delete([object_id])
|
||||
return
|
||||
except:
|
||||
self.connect()
|
||||
time.sleep(1)
|
||||
if name in self.shm_store:
|
||||
self.shm_store[name].close()
|
||||
self.shm_store[name].unlink()
|
||||
del self.shm_store[name]
|
||||
@ -5,7 +5,6 @@ import cv2
|
||||
import queue
|
||||
import threading
|
||||
import ctypes
|
||||
import pyarrow.plasma as plasma
|
||||
import multiprocessing as mp
|
||||
import subprocess as sp
|
||||
import numpy as np
|
||||
@ -15,7 +14,7 @@ import json
|
||||
import base64
|
||||
from typing import Dict, List
|
||||
from collections import defaultdict
|
||||
from frigate.util import draw_box_with_label, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond, listen, FrameManager, PlasmaFrameManager
|
||||
from frigate.util import draw_box_with_label, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond, listen, FrameManager, SharedMemoryFrameManager
|
||||
from frigate.objects import ObjectTracker
|
||||
from frigate.edgetpu import RemoteObjectDetector
|
||||
from frigate.motion import MotionDetector
|
||||
@ -154,11 +153,10 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
|
||||
continue
|
||||
|
||||
# put the frame in the frame manager
|
||||
frame_manager.put(f"{camera_name}{current_frame.value}",
|
||||
np
|
||||
.frombuffer(frame_bytes, np.uint8)
|
||||
.reshape(frame_shape)
|
||||
)
|
||||
frame_buffer = frame_manager.create(f"{camera_name}{current_frame.value}", frame_size)
|
||||
frame_buffer[:] = frame_bytes[:]
|
||||
frame_manager.close(f"{camera_name}{current_frame.value}")
|
||||
|
||||
# add to the queue
|
||||
frame_queue.put(current_frame.value)
|
||||
last_frame = current_frame.value
|
||||
@ -173,7 +171,7 @@ class CameraCapture(threading.Thread):
|
||||
self.take_frame = take_frame
|
||||
self.fps = fps
|
||||
self.skipped_fps = EventsPerSecond()
|
||||
self.plasma_client = PlasmaFrameManager(stop_event)
|
||||
self.frame_manager = SharedMemoryFrameManager()
|
||||
self.ffmpeg_process = ffmpeg_process
|
||||
self.current_frame = mp.Value('d', 0.0)
|
||||
self.last_frame = 0
|
||||
@ -182,10 +180,10 @@ class CameraCapture(threading.Thread):
|
||||
|
||||
def run(self):
|
||||
self.skipped_fps.start()
|
||||
capture_frames(self.ffmpeg_process, self.name, self.frame_shape, self.plasma_client, self.frame_queue, self.take_frame,
|
||||
capture_frames(self.ffmpeg_process, self.name, self.frame_shape, self.frame_manager, self.frame_queue, self.take_frame,
|
||||
self.fps, self.skipped_fps, self.stop_event, self.detection_frame, self.current_frame)
|
||||
|
||||
def track_camera(name, config, frame_queue, frame_shape, detection_queue, detected_objects_queue, fps, detection_fps, read_start, detection_frame, stop_event):
|
||||
def track_camera(name, config, frame_queue, frame_shape, detection_queue, result_connection, detected_objects_queue, fps, detection_fps, read_start, detection_frame, stop_event):
|
||||
print(f"Starting process for {name}: {os.getpid()}")
|
||||
listen()
|
||||
|
||||
@ -218,13 +216,13 @@ def track_camera(name, config, frame_queue, frame_shape, detection_queue, detect
|
||||
mask[:] = 255
|
||||
|
||||
motion_detector = MotionDetector(frame_shape, mask, resize_factor=6)
|
||||
object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue)
|
||||
object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue, result_connection)
|
||||
|
||||
object_tracker = ObjectTracker(10)
|
||||
|
||||
plasma_client = PlasmaFrameManager()
|
||||
frame_manager = SharedMemoryFrameManager()
|
||||
|
||||
process_frames(name, frame_queue, frame_shape, plasma_client, motion_detector, object_detector,
|
||||
process_frames(name, frame_queue, frame_shape, frame_manager, motion_detector, object_detector,
|
||||
object_tracker, detected_objects_queue, fps, detection_fps, detection_frame, objects_to_track, object_filters, mask, stop_event)
|
||||
|
||||
print(f"{name}: exiting subprocess")
|
||||
@ -281,7 +279,7 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape,
|
||||
|
||||
current_frame_time.value = frame_time
|
||||
|
||||
frame = frame_manager.get(f"{camera_name}{frame_time}")
|
||||
frame = frame_manager.get(f"{camera_name}{frame_time}", frame_shape)
|
||||
|
||||
if frame is None:
|
||||
print(f"{camera_name}: frame {frame_time} is not in memory store.")
|
||||
@ -364,3 +362,5 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape,
|
||||
detected_objects_queue.put((camera_name, frame_time, object_tracker.tracked_objects))
|
||||
|
||||
detection_fps.value = object_detector.fps.eps()
|
||||
|
||||
frame_manager.close(f"{camera_name}{frame_time}")
|
||||
|
||||
@ -4,7 +4,7 @@ import os
|
||||
import datetime
|
||||
from unittest import TestCase, main
|
||||
from frigate.video import process_frames, start_or_restart_ffmpeg, capture_frames, get_frame_shape
|
||||
from frigate.util import DictFrameManager, EventsPerSecond, draw_box_with_label
|
||||
from frigate.util import DictFrameManager, SharedMemoryFrameManager, EventsPerSecond, draw_box_with_label
|
||||
from frigate.motion import MotionDetector
|
||||
from frigate.edgetpu import LocalObjectDetector
|
||||
from frigate.objects import ObjectTracker
|
||||
@ -19,6 +19,7 @@ class ProcessClip():
|
||||
self.frame_shape = frame_shape
|
||||
self.camera_name = 'camera'
|
||||
self.frame_manager = DictFrameManager()
|
||||
# self.frame_manager = SharedMemoryFrameManager()
|
||||
self.frame_queue = mp.Queue()
|
||||
self.detected_objects_queue = mp.Queue()
|
||||
self.camera_state = CameraState(self.camera_name, config, self.frame_manager)
|
||||
@ -72,13 +73,15 @@ class ProcessClip():
|
||||
for obj in self.camera_state.tracked_objects.values():
|
||||
print(f"{frame_time}: {obj['id']} - {obj['computed_score']} - {obj['score_history']}")
|
||||
|
||||
self.frame_manager.delete(self.camera_state.previous_frame_id)
|
||||
|
||||
return {
|
||||
'object_detected': obj_detected,
|
||||
'top_score': top_computed_score
|
||||
}
|
||||
|
||||
def save_debug_frame(self, debug_path, frame_time, tracked_objects):
|
||||
current_frame = self.frame_manager.get(f"{self.camera_name}{frame_time}")
|
||||
current_frame = self.frame_manager.get(f"{self.camera_name}{frame_time}", self.frame_shape)
|
||||
# draw the bounding boxes on the frame
|
||||
for obj in tracked_objects:
|
||||
thickness = 2
|
||||
@ -132,6 +135,7 @@ def process(path, label, threshold, debug_path):
|
||||
results = []
|
||||
for c in clips:
|
||||
frame_shape = get_frame_shape(c)
|
||||
config['frame_shape'] = frame_shape
|
||||
process_clip = ProcessClip(c, frame_shape, config)
|
||||
process_clip.load_frames()
|
||||
process_clip.process_frames(objects_to_track=config['objects']['track'])
|
||||
|
||||
Loading…
Reference in New Issue
Block a user