import os import time import datetime import cv2 import threading import ctypes import multiprocessing as mp import subprocess as sp import numpy as np from collections import defaultdict from . util import tonumpyarray, draw_box_with_label from . object_detection import FramePrepper from . objects import ObjectCleaner, BestFrames from . mqtt import MqttObjectPublisher # Stores 2 seconds worth of frames when motion is detected so they can be used for other threads class FrameTracker(threading.Thread): def __init__(self, shared_frame, frame_time, frame_ready, frame_lock, recent_frames): threading.Thread.__init__(self) self.shared_frame = shared_frame self.frame_time = frame_time self.frame_ready = frame_ready self.frame_lock = frame_lock self.recent_frames = recent_frames def run(self): frame_time = 0.0 while True: now = datetime.datetime.now().timestamp() # wait for a frame with self.frame_ready: # if there isnt a frame ready for processing or it is old, wait for a signal if self.frame_time.value == frame_time or (now - self.frame_time.value) > 0.5: self.frame_ready.wait() # lock and make a copy of the frame with self.frame_lock: frame = self.shared_frame.copy() frame_time = self.frame_time.value # add the frame to recent frames self.recent_frames[frame_time] = frame # delete any old frames stored_frame_times = list(self.recent_frames.keys()) for k in stored_frame_times: if (now - k) > 2: del self.recent_frames[k] def get_frame_shape(source): # capture a single frame and check the frame shape so the correct array # size can be allocated in memory video = cv2.VideoCapture(source) ret, frame = video.read() frame_shape = frame.shape video.release() return frame_shape def get_ffmpeg_input(ffmpeg_input): frigate_vars = {k: v for k, v in os.environ.items() if k.startswith('FRIGATE_')} return ffmpeg_input.format(**frigate_vars) class CameraWatchdog(threading.Thread): def __init__(self, camera): threading.Thread.__init__(self) self.camera = camera def run(self): while True: # wait a bit before checking time.sleep(10) if (datetime.datetime.now().timestamp() - self.camera.frame_time.value) > 300: print("last frame is more than 5 minutes old, restarting camera capture...") self.camera.start_or_restart_capture() time.sleep(5) # Thread to read the stdout of the ffmpeg process and update the current frame class CameraCapture(threading.Thread): def __init__(self, camera): threading.Thread.__init__(self) self.camera = camera def run(self): frame_num = 0 while True: if self.camera.ffmpeg_process.poll() != None: print("ffmpeg process is not running. exiting capture thread...") break raw_image = self.camera.ffmpeg_process.stdout.read(self.camera.frame_size) if len(raw_image) == 0: print("ffmpeg didnt return a frame. something is wrong. exiting capture thread...") break frame_num += 1 if (frame_num % self.camera.take_frame) != 0: continue with self.camera.frame_lock: self.camera.frame_time.value = datetime.datetime.now().timestamp() self.camera.current_frame[:] = ( np .frombuffer(raw_image, np.uint8) .reshape(self.camera.frame_shape) ) # Notify with the condition that a new frame is ready with self.camera.frame_ready: self.camera.frame_ready.notify_all() class Camera: def __init__(self, name, ffmpeg_config, global_objects_config, config, prepped_frame_queue, mqtt_client, mqtt_prefix): self.name = name self.config = config self.detected_objects = [] self.recent_frames = {} self.ffmpeg = config.get('ffmpeg', {}) self.ffmpeg_input = get_ffmpeg_input(self.ffmpeg['input']) self.ffmpeg_global_args = self.ffmpeg.get('global_args', ffmpeg_config['global_args']) self.ffmpeg_hwaccel_args = self.ffmpeg.get('hwaccel_args', ffmpeg_config['hwaccel_args']) self.ffmpeg_input_args = self.ffmpeg.get('input_args', ffmpeg_config['input_args']) self.ffmpeg_output_args = self.ffmpeg.get('output_args', ffmpeg_config['output_args']) camera_objects_config = config.get('objects', {}) self.take_frame = self.config.get('take_frame', 1) self.regions = self.config['regions'] self.frame_shape = get_frame_shape(self.ffmpeg_input) self.frame_size = self.frame_shape[0] * self.frame_shape[1] * self.frame_shape[2] self.mqtt_client = mqtt_client self.mqtt_topic_prefix = '{}/{}'.format(mqtt_prefix, self.name) # create a numpy array for the current frame in initialize to zeros self.current_frame = np.zeros(self.frame_shape, np.uint8) # create shared value for storing the frame_time self.frame_time = mp.Value('d', 0.0) # Lock to control access to the frame self.frame_lock = mp.Lock() # Condition for notifying that a new frame is ready self.frame_ready = mp.Condition() # Condition for notifying that objects were parsed self.objects_parsed = mp.Condition() self.ffmpeg_process = None self.capture_thread = None # for each region, create a separate thread to resize the region and prep for detection self.detection_prep_threads = [] for index, region in enumerate(self.config['regions']): region_objects = region.get('objects', {}) # build objects config for region objects_with_config = set().union(global_objects_config.keys(), camera_objects_config.keys(), region_objects.keys()) merged_objects_config = defaultdict(lambda: {}) for obj in objects_with_config: merged_objects_config[obj] = {**global_objects_config.get(obj,{}), **camera_objects_config.get(obj, {}), **region_objects.get(obj, {})} region['objects'] = merged_objects_config self.detection_prep_threads.append(FramePrepper( self.name, self.current_frame, self.frame_time, self.frame_ready, self.frame_lock, region['size'], region['x_offset'], region['y_offset'], index, prepped_frame_queue )) # start a thread to store recent motion frames for processing self.frame_tracker = FrameTracker(self.current_frame, self.frame_time, self.frame_ready, self.frame_lock, self.recent_frames) self.frame_tracker.start() # start a thread to store the highest scoring recent frames for monitored object types self.best_frames = BestFrames(self.objects_parsed, self.recent_frames, self.detected_objects) self.best_frames.start() # start a thread to expire objects from the detected objects list self.object_cleaner = ObjectCleaner(self.objects_parsed, self.detected_objects) self.object_cleaner.start() # start a thread to publish object scores mqtt_publisher = MqttObjectPublisher(self.mqtt_client, self.mqtt_topic_prefix, self.objects_parsed, self.detected_objects, self.best_frames) mqtt_publisher.start() # create a watchdog thread for capture process self.watchdog = CameraWatchdog(self) # load in the mask for object detection if 'mask' in self.config: self.mask = cv2.imread("/config/{}".format(self.config['mask']), cv2.IMREAD_GRAYSCALE) else: self.mask = None if self.mask is None: self.mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8) self.mask[:] = 255 def start_or_restart_capture(self): if not self.ffmpeg_process is None: print("Terminating the existing ffmpeg process...") self.ffmpeg_process.terminate() try: print("Waiting for ffmpeg to exit gracefully...") self.ffmpeg_process.wait(timeout=30) except sp.TimeoutExpired: print("FFmpeg didnt exit. Force killing...") self.ffmpeg_process.kill() self.ffmpeg_process.wait() print("Waiting for the capture thread to exit...") self.capture_thread.join() self.ffmpeg_process = None self.capture_thread = None # create the process to capture frames from the input stream and store in a shared array print("Creating a new ffmpeg process...") self.start_ffmpeg() print("Creating a new capture thread...") self.capture_thread = CameraCapture(self) print("Starting a new capture thread...") self.capture_thread.start() def start_ffmpeg(self): ffmpeg_cmd = (['ffmpeg'] + self.ffmpeg_global_args + self.ffmpeg_hwaccel_args + self.ffmpeg_input_args + ['-i', self.ffmpeg_input] + self.ffmpeg_output_args + ['pipe:']) print(" ".join(ffmpeg_cmd)) self.ffmpeg_process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, bufsize=self.frame_size) def start(self): self.start_or_restart_capture() # start the object detection prep threads for detection_prep_thread in self.detection_prep_threads: detection_prep_thread.start() self.watchdog.start() def join(self): self.capture_thread.join() def get_capture_pid(self): return self.ffmpeg_process.pid def add_objects(self, objects): if len(objects) == 0: return for obj in objects: # find the matching region region = self.regions[obj['region_id']] # Compute some extra properties obj.update({ 'xmin': int((obj['box'][0] * region['size']) + region['x_offset']), 'ymin': int((obj['box'][1] * region['size']) + region['y_offset']), 'xmax': int((obj['box'][2] * region['size']) + region['x_offset']), 'ymax': int((obj['box'][3] * region['size']) + region['y_offset']) }) # Compute the area obj['area'] = (obj['xmax']-obj['xmin'])*(obj['ymax']-obj['ymin']) object_name = obj['name'] if object_name in region['objects']: obj_settings = region['objects'][object_name] # if the min area is larger than the # detected object, don't add it to detected objects if obj_settings.get('min_area',-1) > obj['area']: continue # if the detected object is larger than the # max area, don't add it to detected objects if obj_settings.get('max_area', region['size']**2) < obj['area']: continue # if the score is lower than the threshold, skip if obj_settings.get('threshold', 0) > obj['score']: continue # compute the coordinates of the object and make sure # the location isnt outside the bounds of the image (can happen from rounding) y_location = min(int(obj['ymax']), len(self.mask)-1) x_location = min(int((obj['xmax']-obj['xmin'])/2.0)+obj['xmin'], len(self.mask[0])-1) # if the object is in a masked location, don't add it to detected objects if self.mask[y_location][x_location] == [0]: continue self.detected_objects.append(obj) with self.objects_parsed: self.objects_parsed.notify_all() def get_best(self, label): return self.best_frames.best_frames.get(label) def get_current_frame_with_objects(self): # make a copy of the current detected objects detected_objects = self.detected_objects.copy() # lock and make a copy of the current frame with self.frame_lock: frame = self.current_frame.copy() frame_time = self.frame_time.value # draw the bounding boxes on the screen for obj in detected_objects: draw_box_with_label(frame, obj['xmin'], obj['ymin'], obj['xmax'], obj['ymax'], obj['name'], obj['score'], obj['area']) for region in self.regions: color = (255,255,255) cv2.rectangle(frame, (region['x_offset'], region['y_offset']), (region['x_offset']+region['size'], region['y_offset']+region['size']), color, 2) # print a timestamp time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S") cv2.putText(frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2) # convert to BGR frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) return frame