mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-10-15 20:07:23 +02:00
128 lines
4.5 KiB
Python
128 lines
4.5 KiB
Python
import os
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import cv2
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import imutils
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import time
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import datetime
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import ctypes
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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 threading
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import json
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import yaml
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from contextlib import closing
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import numpy as np
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from object_detection.utils import visualization_utils as vis_util
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from flask import Flask, Response, make_response, send_file
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import paho.mqtt.client as mqtt
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from frigate.util import tonumpyarray
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from frigate.mqtt import MqttMotionPublisher, MqttObjectPublisher
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from frigate.objects import ObjectParser, ObjectCleaner, BestPersonFrame
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from frigate.motion import detect_motion
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from frigate.video import fetch_frames, FrameTracker, Camera
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from frigate.object_detection import FramePrepper, PreppedQueueProcessor
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with open('/config/config.yml') as f:
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# use safe_load instead load
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CONFIG = yaml.safe_load(f)
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MQTT_HOST = CONFIG['mqtt']['host']
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MQTT_PORT = CONFIG.get('mqtt', {}).get('port', 1883)
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MQTT_TOPIC_PREFIX = CONFIG.get('mqtt', {}).get('topic_prefix', 'frigate')
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MQTT_USER = CONFIG.get('mqtt', {}).get('user')
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MQTT_PASS = CONFIG.get('mqtt', {}).get('password')
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WEB_PORT = CONFIG.get('web_port', 5000)
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DEBUG = (CONFIG.get('debug', '0') == '1')
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def main():
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# connect to mqtt and setup last will
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def on_connect(client, userdata, flags, rc):
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print("On connect called")
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# publish a message to signal that the service is running
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client.publish(MQTT_TOPIC_PREFIX+'/available', 'online', retain=True)
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client = mqtt.Client()
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client.on_connect = on_connect
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client.will_set(MQTT_TOPIC_PREFIX+'/available', payload='offline', qos=1, retain=True)
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if not MQTT_USER is None:
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client.username_pw_set(MQTT_USER, password=MQTT_PASS)
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client.connect(MQTT_HOST, MQTT_PORT, 60)
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client.loop_start()
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# Queue for prepped frames
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# TODO: set length to 1.5x the number of total regions
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prepped_frame_queue = queue.Queue(6)
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camera = Camera('back', CONFIG['cameras']['back'], prepped_frame_queue, client, MQTT_TOPIC_PREFIX)
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cameras = {
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'back': camera
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}
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prepped_queue_processor = PreppedQueueProcessor(
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cameras,
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prepped_frame_queue
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)
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prepped_queue_processor.start()
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camera.start()
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camera.join()
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# create a flask app that encodes frames a mjpeg on demand
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# app = Flask(__name__)
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# @app.route('/best_person.jpg')
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# def best_person():
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# frame = np.zeros(frame_shape, np.uint8) if camera.get_best_person() is None else camera.get_best_person()
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# ret, jpg = cv2.imencode('.jpg', frame)
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# response = make_response(jpg.tobytes())
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# response.headers['Content-Type'] = 'image/jpg'
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# return response
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# @app.route('/')
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# def index():
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# # return a multipart response
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# return Response(imagestream(),
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# mimetype='multipart/x-mixed-replace; boundary=frame')
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# def imagestream():
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# while True:
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# # max out at 5 FPS
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# time.sleep(0.2)
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# # make a copy of the current detected objects
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# detected_objects = DETECTED_OBJECTS.copy()
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# # lock and make a copy of the current frame
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# with frame_lock:
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# frame = frame_arr.copy()
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# # convert to RGB for drawing
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# frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# # draw the bounding boxes on the screen
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# for obj in detected_objects:
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# vis_util.draw_bounding_box_on_image_array(frame,
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# obj['ymin'],
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# obj['xmin'],
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# obj['ymax'],
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# obj['xmax'],
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# color='red',
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# thickness=2,
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# display_str_list=["{}: {}%".format(obj['name'],int(obj['score']*100))],
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# use_normalized_coordinates=False)
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# for region in regions:
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# color = (255,255,255)
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# cv2.rectangle(frame, (region['x_offset'], region['y_offset']),
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# (region['x_offset']+region['size'], region['y_offset']+region['size']),
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# color, 2)
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# # convert back to BGR
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# frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
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# # encode the image into a jpg
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# ret, jpg = cv2.imencode('.jpg', frame)
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# yield (b'--frame\r\n'
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# b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
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# app.run(host='0.0.0.0', port=WEB_PORT, debug=False)
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if __name__ == '__main__':
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main() |