mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-10-15 20:07:23 +02:00
220 lines
8.6 KiB
Python
220 lines
8.6 KiB
Python
import os
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import time
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import datetime
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import cv2
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import threading
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import ctypes
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import multiprocessing as mp
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from . util import tonumpyarray
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from . object_detection import FramePrepper
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from . objects import ObjectCleaner, ObjectParser, BestPersonFrame
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from . mqtt import MqttObjectPublisher
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# fetch the frames as fast a possible, only decoding the frames when the
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# detection_process has consumed the current frame
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def fetch_frames(shared_arr, shared_frame_time, frame_lock, frame_ready, frame_shape, rtsp_url):
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# convert shared memory array into numpy and shape into image array
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arr = tonumpyarray(shared_arr).reshape(frame_shape)
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# start the video capture
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video = cv2.VideoCapture()
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video.open(rtsp_url)
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# keep the buffer small so we minimize old data
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video.set(cv2.CAP_PROP_BUFFERSIZE,1)
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bad_frame_counter = 0
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while True:
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# check if the video stream is still open, and reopen if needed
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if not video.isOpened():
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success = video.open(rtsp_url)
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if not success:
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time.sleep(1)
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continue
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# grab the frame, but dont decode it yet
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ret = video.grab()
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# snapshot the time the frame was grabbed
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frame_time = datetime.datetime.now()
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if ret:
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# go ahead and decode the current frame
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ret, frame = video.retrieve()
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if ret:
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# Lock access and update frame
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with frame_lock:
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arr[:] = frame
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shared_frame_time.value = frame_time.timestamp()
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# Notify with the condition that a new frame is ready
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with frame_ready:
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frame_ready.notify_all()
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bad_frame_counter = 0
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else:
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print("Unable to decode frame")
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bad_frame_counter += 1
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else:
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print("Unable to grab a frame")
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bad_frame_counter += 1
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if bad_frame_counter > 100:
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video.release()
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video.release()
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# Stores 2 seconds worth of frames when motion is detected so they can be used for other threads
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class FrameTracker(threading.Thread):
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def __init__(self, shared_frame, frame_time, frame_ready, frame_lock, recent_frames):
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threading.Thread.__init__(self)
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self.shared_frame = shared_frame
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self.frame_time = frame_time
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self.frame_ready = frame_ready
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self.frame_lock = frame_lock
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self.recent_frames = recent_frames
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def run(self):
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frame_time = 0.0
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while True:
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now = datetime.datetime.now().timestamp()
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# wait for a frame
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with self.frame_ready:
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# if there isnt a frame ready for processing or it is old, wait for a signal
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if self.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5:
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self.frame_ready.wait()
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# lock and make a copy of the frame
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with self.frame_lock:
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frame = self.shared_frame.copy()
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frame_time = self.frame_time.value
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# add the frame to recent frames
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self.recent_frames[frame_time] = frame
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# delete any old frames
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stored_frame_times = list(self.recent_frames.keys())
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for k in stored_frame_times:
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if (now - k) > 2:
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del self.recent_frames[k]
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def get_frame_shape(rtsp_url):
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# capture a single frame and check the frame shape so the correct array
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# size can be allocated in memory
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video = cv2.VideoCapture(rtsp_url)
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ret, frame = video.read()
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frame_shape = frame.shape
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video.release()
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return frame_shape
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def get_rtsp_url(rtsp_config):
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if (rtsp_config['password'].startswith('$')):
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rtsp_config['password'] = os.getenv(rtsp_config['password'][1:])
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return 'rtsp://{}:{}@{}:{}{}'.format(rtsp_config['user'],
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rtsp_config['password'], rtsp_config['host'], rtsp_config['port'],
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rtsp_config['path'])
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class Camera:
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def __init__(self, name, config, prepped_frame_queue, mqtt_client, mqtt_prefix):
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self.name = name
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self.config = config
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self.detected_objects = []
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self.recent_frames = {}
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self.rtsp_url = get_rtsp_url(self.config['rtsp'])
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self.regions = self.config['regions']
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self.frame_shape = get_frame_shape(self.rtsp_url)
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self.mqtt_client = mqtt_client
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self.mqtt_topic_prefix = '{}/{}'.format(mqtt_prefix, self.name)
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# compute the flattened array length from the shape of the frame
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flat_array_length = self.frame_shape[0] * self.frame_shape[1] * self.frame_shape[2]
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# create shared array for storing the full frame image data
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self.shared_frame_array = mp.Array(ctypes.c_uint8, flat_array_length)
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# create shared value for storing the frame_time
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self.shared_frame_time = mp.Value('d', 0.0)
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# Lock to control access to the frame
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self.frame_lock = mp.Lock()
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# Condition for notifying that a new frame is ready
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self.frame_ready = mp.Condition()
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# Condition for notifying that objects were parsed
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self.objects_parsed = mp.Condition()
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# shape current frame so it can be treated as a numpy image
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self.shared_frame_np = tonumpyarray(self.shared_frame_array).reshape(self.frame_shape)
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# create the process to capture frames from the RTSP stream and store in a shared array
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self.capture_process = mp.Process(target=fetch_frames, args=(self.shared_frame_array,
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self.shared_frame_time, self.frame_lock, self.frame_ready, self.frame_shape, self.rtsp_url))
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self.capture_process.daemon = True
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# for each region, create a separate thread to resize the region and prep for detection
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self.detection_prep_threads = []
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for region in self.config['regions']:
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self.detection_prep_threads.append(FramePrepper(
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self.name,
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self.shared_frame_np,
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self.shared_frame_time,
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self.frame_ready,
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self.frame_lock,
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region['size'], region['x_offset'], region['y_offset'],
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prepped_frame_queue
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))
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# start a thread to store recent motion frames for processing
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self.frame_tracker = FrameTracker(self.shared_frame_np, self.shared_frame_time,
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self.frame_ready, self.frame_lock, self.recent_frames)
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self.frame_tracker.start()
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# start a thread to store the highest scoring recent person frame
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self.best_person_frame = BestPersonFrame(self.objects_parsed, self.recent_frames, self.detected_objects)
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self.best_person_frame.start()
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# start a thread to expire objects from the detected objects list
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self.object_cleaner = ObjectCleaner(self.objects_parsed, self.detected_objects)
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self.object_cleaner.start()
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# start a thread to publish object scores (currently only person)
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mqtt_publisher = MqttObjectPublisher(self.mqtt_client, self.mqtt_topic_prefix, self.objects_parsed, self.detected_objects)
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mqtt_publisher.start()
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def start(self):
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self.capture_process.start()
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# start the object detection prep threads
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for detection_prep_thread in self.detection_prep_threads:
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detection_prep_thread.start()
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def join(self):
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self.capture_process.join()
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def get_capture_pid(self):
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return self.capture_process.pid
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def add_objects(self, objects):
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if len(objects) == 0:
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return
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for obj in objects:
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if obj['name'] == 'person':
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person_area = (obj['xmax']-obj['xmin'])*(obj['ymax']-obj['ymin'])
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# find the matching region
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region = None
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for r in self.regions:
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if (
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obj['xmin'] >= r['x_offset'] and
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obj['ymin'] >= r['y_offset'] and
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obj['xmax'] <= r['x_offset']+r['size'] and
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obj['ymax'] <= r['y_offset']+r['size']
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):
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region = r
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break
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# if the min person area is larger than the
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# detected person, don't add it to detected objects
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if region and region['min_person_area'] > person_area:
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continue
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self.detected_objects.append(obj)
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with self.objects_parsed:
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self.objects_parsed.notify_all()
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def get_best_person(self):
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return self.best_person_frame.best_frame
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