import base64 import copy import ctypes import datetime import itertools import json import logging import multiprocessing as mp import os import queue import subprocess as sp import signal import threading import time from collections import defaultdict from setproctitle import setproctitle from typing import Dict, List import cv2 import numpy as np from frigate.config import CameraConfig from frigate.edgetpu import RemoteObjectDetector from frigate.log import LogPipe from frigate.motion import MotionDetector from frigate.objects import ObjectTracker from frigate.util import (EventsPerSecond, FrameManager, SharedMemoryFrameManager, area, calculate_region, clipped, draw_box_with_label, intersection, intersection_over_union, listen, yuv_region_2_rgb) logger = logging.getLogger(__name__) def filtered(obj, objects_to_track, object_filters): object_name = obj[0] if not object_name in objects_to_track: return True if object_name in object_filters: obj_settings = object_filters[object_name] # if the min area is larger than the # detected object, don't add it to detected objects if obj_settings.min_area > obj[3]: return True # if the detected object is larger than the # max area, don't add it to detected objects if obj_settings.max_area < obj[3]: return True # if the score is lower than the min_score, skip if obj_settings.min_score > obj[1]: return True if not obj_settings.mask is None: # 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[2][3]), len(obj_settings.mask)-1) x_location = min(int((obj[2][2]-obj[2][0])/2.0)+obj[2][0], len(obj_settings.mask[0])-1) # if the object is in a masked location, don't add it to detected objects if obj_settings.mask[y_location][x_location] == 0: return True return False def create_tensor_input(frame, model_shape, region): cropped_frame = yuv_region_2_rgb(frame, region) # Resize to 300x300 if needed if cropped_frame.shape != (model_shape[0], model_shape[1], 3): cropped_frame = cv2.resize(cropped_frame, dsize=model_shape, interpolation=cv2.INTER_LINEAR) # Expand dimensions since the model expects images to have shape: [1, height, width, 3] return np.expand_dims(cropped_frame, axis=0) def stop_ffmpeg(ffmpeg_process, logger): logger.info("Terminating the existing ffmpeg process...") ffmpeg_process.terminate() try: logger.info("Waiting for ffmpeg to exit gracefully...") ffmpeg_process.communicate(timeout=30) except sp.TimeoutExpired: logger.info("FFmpeg didnt exit. Force killing...") ffmpeg_process.kill() ffmpeg_process.communicate() ffmpeg_process = None def start_or_restart_ffmpeg(ffmpeg_cmd, logger, logpipe: LogPipe, frame_size=None, ffmpeg_process=None): if not ffmpeg_process is None: stop_ffmpeg(ffmpeg_process, logger) if frame_size is None: process = sp.Popen(ffmpeg_cmd, stdout = sp.DEVNULL, stderr=logpipe, stdin = sp.DEVNULL, start_new_session=True) else: process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, stderr=logpipe, stdin = sp.DEVNULL, bufsize=frame_size*10, start_new_session=True) return process def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: FrameManager, frame_queue, fps:mp.Value, skipped_fps: mp.Value, current_frame: mp.Value): frame_size = frame_shape[0] * frame_shape[1] frame_rate = EventsPerSecond() frame_rate.start() skipped_eps = EventsPerSecond() skipped_eps.start() while True: fps.value = frame_rate.eps() skipped_fps = skipped_eps.eps() current_frame.value = datetime.datetime.now().timestamp() frame_name = f"{camera_name}{current_frame.value}" frame_buffer = frame_manager.create(frame_name, frame_size) try: frame_buffer[:] = ffmpeg_process.stdout.read(frame_size) except Exception as e: logger.info(f"{camera_name}: ffmpeg sent a broken frame. {e}") if ffmpeg_process.poll() != None: logger.info(f"{camera_name}: ffmpeg process is not running. exiting capture thread...") frame_manager.delete(frame_name) break continue frame_rate.update() # if the queue is full, skip this frame if frame_queue.full(): skipped_eps.update() frame_manager.delete(frame_name) continue # close the frame frame_manager.close(frame_name) # add to the queue frame_queue.put(current_frame.value) class CameraWatchdog(threading.Thread): def __init__(self, camera_name, config, frame_queue, camera_fps, ffmpeg_pid, stop_event): threading.Thread.__init__(self) self.logger = logging.getLogger(f"watchdog.{camera_name}") self.camera_name = camera_name self.config = config self.capture_thread = None self.ffmpeg_detect_process = None self.logpipe = LogPipe(f"ffmpeg.{self.camera_name}.detect", logging.ERROR) self.ffmpeg_other_processes = [] self.camera_fps = camera_fps self.ffmpeg_pid = ffmpeg_pid self.frame_queue = frame_queue self.frame_shape = self.config.frame_shape_yuv self.frame_size = self.frame_shape[0] * self.frame_shape[1] self.stop_event = stop_event def run(self): self.start_ffmpeg_detect() for c in self.config.ffmpeg_cmds: if 'detect' in c['roles']: continue logpipe = LogPipe(f"ffmpeg.{self.camera_name}.{'_'.join(sorted(c['roles']))}", logging.ERROR) self.ffmpeg_other_processes.append({ 'cmd': c['cmd'], 'logpipe': logpipe, 'process': start_or_restart_ffmpeg(c['cmd'], self.logger, logpipe) }) time.sleep(10) while True: if self.stop_event.is_set(): stop_ffmpeg(self.ffmpeg_detect_process, self.logger) for p in self.ffmpeg_other_processes: stop_ffmpeg(p['process'], self.logger) p['logpipe'].close() self.logpipe.close() break now = datetime.datetime.now().timestamp() if not self.capture_thread.is_alive(): self.start_ffmpeg_detect() elif now - self.capture_thread.current_frame.value > 20: self.logger.info(f"No frames received from {self.camera_name} in 20 seconds. Exiting ffmpeg...") self.ffmpeg_detect_process.terminate() try: self.logger.info("Waiting for ffmpeg to exit gracefully...") self.ffmpeg_detect_process.communicate(timeout=30) except sp.TimeoutExpired: self.logger.info("FFmpeg didnt exit. Force killing...") self.ffmpeg_detect_process.kill() self.ffmpeg_detect_process.communicate() for p in self.ffmpeg_other_processes: poll = p['process'].poll() if poll == None: continue p['process'] = start_or_restart_ffmpeg(p['cmd'], self.logger, p['logpipe'], ffmpeg_process=p['process']) # wait a bit before checking again time.sleep(10) def start_ffmpeg_detect(self): ffmpeg_cmd = [c['cmd'] for c in self.config.ffmpeg_cmds if 'detect' in c['roles']][0] self.ffmpeg_detect_process = start_or_restart_ffmpeg(ffmpeg_cmd, self.logger, self.logpipe, self.frame_size) self.ffmpeg_pid.value = self.ffmpeg_detect_process.pid self.capture_thread = CameraCapture(self.camera_name, self.ffmpeg_detect_process, self.frame_shape, self.frame_queue, self.camera_fps) self.capture_thread.start() class CameraCapture(threading.Thread): def __init__(self, camera_name, ffmpeg_process, frame_shape, frame_queue, fps): threading.Thread.__init__(self) self.name = f"capture:{camera_name}" self.camera_name = camera_name self.frame_shape = frame_shape self.frame_queue = frame_queue self.fps = fps self.skipped_fps = EventsPerSecond() self.frame_manager = SharedMemoryFrameManager() self.ffmpeg_process = ffmpeg_process self.current_frame = mp.Value('d', 0.0) self.last_frame = 0 def run(self): self.skipped_fps.start() capture_frames(self.ffmpeg_process, self.camera_name, self.frame_shape, self.frame_manager, self.frame_queue, self.fps, self.skipped_fps, self.current_frame) def capture_camera(name, config: CameraConfig, process_info): stop_event = mp.Event() def receiveSignal(signalNumber, frame): stop_event.set() signal.signal(signal.SIGTERM, receiveSignal) signal.signal(signal.SIGINT, receiveSignal) frame_queue = process_info['frame_queue'] camera_watchdog = CameraWatchdog(name, config, frame_queue, process_info['camera_fps'], process_info['ffmpeg_pid'], stop_event) camera_watchdog.start() camera_watchdog.join() def track_camera(name, config: CameraConfig, model_shape, detection_queue, result_connection, detected_objects_queue, process_info): stop_event = mp.Event() def receiveSignal(signalNumber, frame): stop_event.set() signal.signal(signal.SIGTERM, receiveSignal) signal.signal(signal.SIGINT, receiveSignal) threading.current_thread().name = f"process:{name}" setproctitle(f"frigate.process:{name}") listen() frame_queue = process_info['frame_queue'] detection_enabled = process_info['detection_enabled'] frame_shape = config.frame_shape objects_to_track = config.objects.track object_filters = config.objects.filters motion_detector = MotionDetector(frame_shape, config.motion) object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue, result_connection, model_shape) object_tracker = ObjectTracker(config.detect) frame_manager = SharedMemoryFrameManager() process_frames(name, frame_queue, frame_shape, model_shape, frame_manager, motion_detector, object_detector, object_tracker, detected_objects_queue, process_info, objects_to_track, object_filters, detection_enabled, stop_event) logger.info(f"{name}: exiting subprocess") def reduce_boxes(boxes): if len(boxes) == 0: return [] reduced_boxes = cv2.groupRectangles([list(b) for b in itertools.chain(boxes, boxes)], 1, 0.2)[0] return [tuple(b) for b in reduced_boxes] def detect(object_detector, frame, model_shape, region, objects_to_track, object_filters): tensor_input = create_tensor_input(frame, model_shape, region) detections = [] region_detections = object_detector.detect(tensor_input) for d in region_detections: box = d[2] size = region[2]-region[0] x_min = int((box[1] * size) + region[0]) y_min = int((box[0] * size) + region[1]) x_max = int((box[3] * size) + region[0]) y_max = int((box[2] * size) + region[1]) det = (d[0], d[1], (x_min, y_min, x_max, y_max), (x_max-x_min)*(y_max-y_min), region) # apply object filters if filtered(det, objects_to_track, object_filters): continue detections.append(det) return detections def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_shape, frame_manager: FrameManager, motion_detector: MotionDetector, object_detector: RemoteObjectDetector, object_tracker: ObjectTracker, detected_objects_queue: mp.Queue, process_info: Dict, objects_to_track: List[str], object_filters, detection_enabled: mp.Value, stop_event, exit_on_empty: bool = False): fps = process_info['process_fps'] detection_fps = process_info['detection_fps'] current_frame_time = process_info['detection_frame'] fps_tracker = EventsPerSecond() fps_tracker.start() while True: if stop_event.is_set(): break if exit_on_empty and frame_queue.empty(): logger.info(f"Exiting track_objects...") break try: frame_time = frame_queue.get(True, 10) except queue.Empty: continue current_frame_time.value = frame_time frame = frame_manager.get(f"{camera_name}{frame_time}", (frame_shape[0]*3//2, frame_shape[1])) if frame is None: logger.info(f"{camera_name}: frame {frame_time} is not in memory store.") continue if not detection_enabled.value: fps.value = fps_tracker.eps() object_tracker.match_and_update(frame_time, []) 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}") continue # look for motion motion_boxes = motion_detector.detect(frame) tracked_object_boxes = [obj['box'] for obj in object_tracker.tracked_objects.values()] # combine motion boxes with known locations of existing objects combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes) # compute regions regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.2) for a in combined_boxes] # combine overlapping regions combined_regions = reduce_boxes(regions) # re-compute regions regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.0) for a in combined_regions] # resize regions and detect detections = [] 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 = True 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 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: obj = group[index[0]] 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]) 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 # now that we have refined our detections, we need to track objects object_tracker.match_and_update(frame_time, detections) # 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}")