mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
605 lines
18 KiB
Python
Executable File
605 lines
18 KiB
Python
Executable File
import datetime
|
|
import itertools
|
|
import logging
|
|
import multiprocessing as mp
|
|
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
|
|
|
|
from cv2 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,
|
|
calculate_region,
|
|
clipped,
|
|
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 ffmpeg_process is not 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 not self.stop_event.wait(10):
|
|
now = datetime.datetime.now().timestamp()
|
|
|
|
if not self.capture_thread.is_alive():
|
|
self.logpipe.dump()
|
|
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 is None:
|
|
continue
|
|
p["logpipe"].dump()
|
|
p["process"] = start_or_restart_ffmpeg(
|
|
p["cmd"], self.logger, p["logpipe"], ffmpeg_process=p["process"]
|
|
)
|
|
|
|
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()
|
|
|
|
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,
|
|
labelmap,
|
|
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, 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]
|
|
|
|
|
|
# modified from https://stackoverflow.com/a/40795835
|
|
def intersects_any(box_a, boxes):
|
|
for box in boxes:
|
|
if (
|
|
box_a[2] < box[0]
|
|
or box_a[0] > box[2]
|
|
or box_a[1] > box[3]
|
|
or box_a[3] < box[1]
|
|
):
|
|
continue
|
|
return True
|
|
|
|
|
|
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]) + region[0])
|
|
y_min = int((box[0]) + region[1])
|
|
x_max = int((box[3]) + region[0])
|
|
y_max = int((box[2]) + 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 not stop_event.is_set():
|
|
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)
|
|
|
|
# only get the tracked object boxes that intersect with motion
|
|
tracked_object_boxes = [
|
|
obj["box"]
|
|
for obj in object_tracker.tracked_objects.values()
|
|
if intersects_any(obj["box"], motion_boxes)
|
|
]
|
|
|
|
# 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
|
|
|
|
# Limit to the detections overlapping with motion areas
|
|
# to avoid picking up stationary background objects
|
|
detections_with_motion = [
|
|
d for d in detections if intersects_any(d[2], motion_boxes)
|
|
]
|
|
|
|
# now that we have refined our detections, we need to track objects
|
|
object_tracker.match_and_update(frame_time, detections_with_motion)
|
|
|
|
# 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}")
|