mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
430 lines
16 KiB
Python
Executable File
430 lines
16 KiB
Python
Executable File
import os
|
|
import time
|
|
import datetime
|
|
import cv2
|
|
import queue
|
|
import threading
|
|
import ctypes
|
|
import multiprocessing as mp
|
|
import subprocess as sp
|
|
import numpy as np
|
|
import copy
|
|
import itertools
|
|
import json
|
|
import base64
|
|
from typing import Dict, List
|
|
from collections import defaultdict
|
|
from frigate.util import draw_box_with_label, yuv_region_2_rgb, area, calculate_region, clipped, intersection_over_union, intersection, EventsPerSecond, listen, FrameManager, SharedMemoryFrameManager
|
|
from frigate.objects import ObjectTracker
|
|
from frigate.edgetpu import RemoteObjectDetector
|
|
from frigate.motion import MotionDetector
|
|
|
|
def get_frame_shape(source):
|
|
ffprobe_cmd = " ".join([
|
|
'ffprobe',
|
|
'-v',
|
|
'panic',
|
|
'-show_error',
|
|
'-show_streams',
|
|
'-of',
|
|
'json',
|
|
'"'+source+'"'
|
|
])
|
|
print(ffprobe_cmd)
|
|
p = sp.Popen(ffprobe_cmd, stdout=sp.PIPE, shell=True)
|
|
(output, err) = p.communicate()
|
|
p_status = p.wait()
|
|
info = json.loads(output)
|
|
print(info)
|
|
|
|
video_info = [s for s in info['streams'] if s['codec_type'] == 'video'][0]
|
|
|
|
if video_info['height'] != 0 and video_info['width'] != 0:
|
|
return (video_info['height'], video_info['width'], 3)
|
|
|
|
# fallback to using opencv if ffprobe didnt succeed
|
|
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)
|
|
|
|
def filtered(obj, objects_to_track, object_filters, mask=None):
|
|
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.get('min_area',-1) > obj[3]:
|
|
return True
|
|
|
|
# if the detected object is larger than the
|
|
# max area, don't add it to detected objects
|
|
if obj_settings.get('max_area', 24000000) < obj[3]:
|
|
return True
|
|
|
|
# if the score is lower than the min_score, skip
|
|
if obj_settings.get('min_score', 0) > obj[1]:
|
|
return True
|
|
|
|
# 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(mask)-1)
|
|
x_location = min(int((obj[2][2]-obj[2][0])/2.0)+obj[2][0], len(mask[0])-1)
|
|
|
|
# if the object is in a masked location, don't add it to detected objects
|
|
if (not mask is None) and (mask[y_location][x_location] == 0):
|
|
return True
|
|
|
|
return False
|
|
|
|
def create_tensor_input(frame, region):
|
|
cropped_frame = yuv_region_2_rgb(frame, region)
|
|
|
|
# Resize to 300x300 if needed
|
|
if cropped_frame.shape != (300, 300, 3):
|
|
cropped_frame = cv2.resize(cropped_frame, dsize=(300, 300), interpolation=cv2.INTER_LINEAR)
|
|
|
|
# Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
|
|
return np.expand_dims(cropped_frame, axis=0)
|
|
|
|
def start_or_restart_ffmpeg(ffmpeg_cmd, frame_size, ffmpeg_process=None):
|
|
if not ffmpeg_process is None:
|
|
print("Terminating the existing ffmpeg process...")
|
|
ffmpeg_process.terminate()
|
|
try:
|
|
print("Waiting for ffmpeg to exit gracefully...")
|
|
ffmpeg_process.communicate(timeout=30)
|
|
except sp.TimeoutExpired:
|
|
print("FFmpeg didnt exit. Force killing...")
|
|
ffmpeg_process.kill()
|
|
ffmpeg_process.communicate()
|
|
ffmpeg_process = None
|
|
|
|
print("Creating ffmpeg process...")
|
|
print(" ".join(ffmpeg_cmd))
|
|
process = sp.Popen(ffmpeg_cmd, stdout = sp.PIPE, 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, take_frame: int, fps:mp.Value, skipped_fps: mp.Value,
|
|
stop_event: mp.Event, current_frame: mp.Value):
|
|
|
|
frame_num = 0
|
|
frame_size = frame_shape[0] * frame_shape[1] * 3 // 2
|
|
frame_rate = EventsPerSecond()
|
|
frame_rate.start()
|
|
skipped_eps = EventsPerSecond()
|
|
skipped_eps.start()
|
|
while True:
|
|
fps.value = frame_rate.eps()
|
|
skipped_fps = skipped_eps.eps()
|
|
if stop_event.is_set():
|
|
print(f"{camera_name}: stop event set. exiting capture thread...")
|
|
break
|
|
|
|
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:
|
|
print(f"{camera_name}: ffmpeg sent a broken frame. something is wrong.")
|
|
|
|
if ffmpeg_process.poll() != None:
|
|
print(f"{camera_name}: ffmpeg process is not running. exiting capture thread...")
|
|
frame_manager.delete(frame_name)
|
|
break
|
|
|
|
continue
|
|
|
|
frame_rate.update()
|
|
|
|
frame_num += 1
|
|
if (frame_num % take_frame) != 0:
|
|
skipped_eps.update()
|
|
frame_manager.delete(frame_name)
|
|
continue
|
|
|
|
# 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, name, config, frame_queue, camera_fps, ffmpeg_pid, stop_event):
|
|
threading.Thread.__init__(self)
|
|
self.name = name
|
|
self.config = config
|
|
self.capture_thread = None
|
|
self.ffmpeg_process = None
|
|
self.stop_event = stop_event
|
|
self.camera_fps = camera_fps
|
|
self.ffmpeg_pid = ffmpeg_pid
|
|
self.frame_queue = frame_queue
|
|
self.frame_shape = self.config['frame_shape']
|
|
self.frame_size = self.frame_shape[0] * self.frame_shape[1] * 3 // 2
|
|
|
|
def run(self):
|
|
self.start_ffmpeg()
|
|
time.sleep(10)
|
|
while True:
|
|
if self.stop_event.is_set():
|
|
print(f"Exiting watchdog...")
|
|
break
|
|
|
|
now = datetime.datetime.now().timestamp()
|
|
|
|
if not self.capture_thread.is_alive():
|
|
self.start_ffmpeg()
|
|
elif now - self.capture_thread.current_frame.value > 5:
|
|
print(f"No frames received from {self.name} in 5 seconds. Exiting ffmpeg...")
|
|
self.ffmpeg_process.terminate()
|
|
try:
|
|
print("Waiting for ffmpeg to exit gracefully...")
|
|
self.ffmpeg_process.communicate(timeout=30)
|
|
except sp.TimeoutExpired:
|
|
print("FFmpeg didnt exit. Force killing...")
|
|
self.ffmpeg_process.kill()
|
|
self.ffmpeg_process.communicate()
|
|
|
|
# wait a bit before checking again
|
|
time.sleep(10)
|
|
|
|
def start_ffmpeg(self):
|
|
self.ffmpeg_process = start_or_restart_ffmpeg(self.config['ffmpeg_cmd'], self.frame_size)
|
|
self.ffmpeg_pid.value = self.ffmpeg_process.pid
|
|
self.capture_thread = CameraCapture(self.name, self.ffmpeg_process, self.frame_shape, self.frame_queue,
|
|
self.config['take_frame'], self.camera_fps, self.stop_event)
|
|
self.capture_thread.start()
|
|
|
|
class CameraCapture(threading.Thread):
|
|
def __init__(self, name, ffmpeg_process, frame_shape, frame_queue, take_frame, fps, stop_event):
|
|
threading.Thread.__init__(self)
|
|
self.name = name
|
|
self.frame_shape = frame_shape
|
|
self.frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
|
|
self.frame_queue = frame_queue
|
|
self.take_frame = take_frame
|
|
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
|
|
self.stop_event = stop_event
|
|
|
|
def run(self):
|
|
self.skipped_fps.start()
|
|
capture_frames(self.ffmpeg_process, self.name, self.frame_shape, self.frame_manager, self.frame_queue, self.take_frame,
|
|
self.fps, self.skipped_fps, self.stop_event, self.current_frame)
|
|
|
|
def capture_camera(name, config, process_info, stop_event):
|
|
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, detection_queue, result_connection, detected_objects_queue, process_info, stop_event):
|
|
listen()
|
|
|
|
frame_queue = process_info['frame_queue']
|
|
|
|
frame_shape = config['frame_shape']
|
|
|
|
# Merge the tracked object config with the global config
|
|
camera_objects_config = config.get('objects', {})
|
|
objects_to_track = camera_objects_config.get('track', [])
|
|
object_filters = camera_objects_config.get('filters', {})
|
|
|
|
# load in the mask for object detection
|
|
if 'mask' in config:
|
|
if config['mask'].startswith('base64,'):
|
|
img = base64.b64decode(config['mask'][7:])
|
|
npimg = np.fromstring(img, dtype=np.uint8)
|
|
mask = cv2.imdecode(npimg, cv2.IMREAD_GRAYSCALE)
|
|
elif config['mask'].startswith('poly,'):
|
|
points = config['mask'].split(',')[1:]
|
|
contour = np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
|
|
mask = np.zeros((frame_shape[0], frame_shape[1]), np.uint8)
|
|
mask[:] = 255
|
|
cv2.fillPoly(mask, pts=[contour], color=(0))
|
|
else:
|
|
mask = cv2.imread("/config/{}".format(config['mask']), cv2.IMREAD_GRAYSCALE)
|
|
else:
|
|
mask = None
|
|
|
|
if mask is None or mask.size == 0:
|
|
mask = np.zeros((frame_shape[0], frame_shape[1]), np.uint8)
|
|
mask[:] = 255
|
|
|
|
motion_detector = MotionDetector(frame_shape, mask, resize_factor=6)
|
|
object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue, result_connection)
|
|
|
|
object_tracker = ObjectTracker(10)
|
|
|
|
frame_manager = SharedMemoryFrameManager()
|
|
|
|
process_frames(name, frame_queue, frame_shape, frame_manager, motion_detector, object_detector,
|
|
object_tracker, detected_objects_queue, process_info, objects_to_track, object_filters, mask, stop_event)
|
|
|
|
print(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, region, objects_to_track, object_filters, mask):
|
|
tensor_input = create_tensor_input(frame, 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, mask):
|
|
continue
|
|
detections.append(det)
|
|
return detections
|
|
|
|
def process_frames(camera_name: str, frame_queue: mp.Queue, frame_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: Dict, mask, stop_event: mp.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() or (exit_on_empty and frame_queue.empty()):
|
|
print(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:
|
|
print(f"{camera_name}: frame {frame_time} is not in memory store.")
|
|
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, region, objects_to_track, object_filters, mask))
|
|
|
|
#########
|
|
# 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])
|
|
|
|
selected_objects.extend(detect(object_detector, frame, region, objects_to_track, object_filters, mask))
|
|
|
|
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))
|
|
detection_fps.value = object_detector.fps.eps()
|
|
frame_manager.close(f"{camera_name}{frame_time}")
|