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track and report all detected object types

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This commit is contained in:
Blake Blackshear
2019-12-14 15:18:21 -06:00
parent 5c01720567
commit bee99ca6ff
7 changed files with 160 additions and 131 deletions
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99
frigate/video.py
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@@ -7,9 +7,10 @@ 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, BestPersonFrame
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
@@ -70,8 +71,8 @@ class CameraWatchdog(threading.Thread):
# wait a bit before checking
time.sleep(10)
if (datetime.datetime.now().timestamp() - self.camera.frame_time.value) > 10:
print("last frame is more than 10 seconds old, restarting camera capture...")
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)
@@ -111,7 +112,7 @@ class CameraCapture(threading.Thread):
self.camera.frame_ready.notify_all()
class Camera:
def __init__(self, name, ffmpeg_config, config, prepped_frame_queue, mqtt_client, mqtt_prefix):
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 = []
@@ -124,6 +125,8 @@ class Camera:
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)
@@ -147,20 +150,23 @@ class Camera:
# for each region, create a separate thread to resize the region and prep for detection
self.detection_prep_threads = []
for region in self.config['regions']:
# set a default threshold of 0.5 if not defined
if not 'threshold' in region:
region['threshold'] = 0.5
if not isinstance(region['threshold'], float):
print('Threshold is not a float. Setting to 0.5 default.')
region['threshold'] = 0.5
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'], region['threshold'],
region['size'], region['x_offset'], region['y_offset'], index,
prepped_frame_queue
))
@@ -169,22 +175,22 @@ class Camera:
self.frame_ready, self.frame_lock, self.recent_frames)
self.frame_tracker.start()
# start a thread to store the highest scoring recent person frame
self.best_person_frame = BestPersonFrame(self.objects_parsed, self.recent_frames, self.detected_objects)
self.best_person_frame.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 (currently only person)
mqtt_publisher = MqttObjectPublisher(self.mqtt_client, self.mqtt_topic_prefix, self.objects_parsed, self.detected_objects, self.best_person_frame)
# 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 person detection
# 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:
@@ -252,38 +258,45 @@ class Camera:
return
for obj in objects:
# Store object area to use in bounding box labels
# 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'])
if obj['name'] == 'person':
# find the matching region
region = None
for r in self.regions:
if (
obj['xmin'] >= r['x_offset'] and
obj['ymin'] >= r['y_offset'] and
obj['xmax'] <= r['x_offset']+r['size'] and
obj['ymax'] <= r['y_offset']+r['size']
):
region = r
break
# if the min person area is larger than the
# detected person, don't add it to detected objects
if region and 'min_person_area' in region and region['min_person_area'] > obj['area']:
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 person is larger than the
# max person area, don't add it to detected objects
if region and 'max_person_area' in region and region['max_person_area'] < obj['area']:
# 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 person and make sure
# 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 person is in a masked location, continue
# if the object is in a masked location, don't add it to detected objects
if self.mask[y_location][x_location] == [0]:
continue
@@ -291,9 +304,9 @@ class Camera:
with self.objects_parsed:
self.objects_parsed.notify_all()
def get_best_person(self):
return self.best_person_frame.best_frame
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