mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-07-30 13:48:07 +02:00
process detected objects in a queue
This commit is contained in:
Blake Blackshear
parent
b6130e77ff
commit
be1673b00a
@ -35,7 +35,7 @@ class PreppedQueueProcessor(threading.Thread):
|
||||
self.fps.update()
|
||||
self.avg_inference_speed = (self.avg_inference_speed*9 + self.engine.get_inference_time())/10
|
||||
|
||||
self.cameras[frame['camera_name']].add_objects(frame)
|
||||
self.cameras[frame['camera_name']].detected_objects_queue.put(frame)
|
||||
|
||||
class RegionRequester(threading.Thread):
|
||||
def __init__(self, camera):
|
||||
|
||||
@ -4,7 +4,7 @@ import threading
|
||||
import cv2
|
||||
import prctl
|
||||
import numpy as np
|
||||
from . util import draw_box_with_label
|
||||
from . util import draw_box_with_label, LABELS
|
||||
|
||||
class ObjectCleaner(threading.Thread):
|
||||
def __init__(self, objects_parsed, detected_objects):
|
||||
@ -37,6 +37,100 @@ class ObjectCleaner(threading.Thread):
|
||||
with self._objects_parsed:
|
||||
self._objects_parsed.notify_all()
|
||||
|
||||
class DetectedObjectsProcessor(threading.Thread):
|
||||
def __init__(self, camera):
|
||||
threading.Thread.__init__(self)
|
||||
self.camera = camera
|
||||
|
||||
def run(self):
|
||||
prctl.set_name(self.__class__.__name__)
|
||||
while True:
|
||||
frame = self.camera.detected_objects_queue.get()
|
||||
|
||||
objects = frame['detected_objects']
|
||||
|
||||
if len(objects) == 0:
|
||||
return
|
||||
|
||||
for raw_obj in objects:
|
||||
obj = {
|
||||
'score': float(raw_obj.score),
|
||||
'box': raw_obj.bounding_box.flatten().tolist(),
|
||||
'name': str(LABELS[raw_obj.label_id]),
|
||||
'frame_time': frame['frame_time'],
|
||||
'region_id': frame['region_id']
|
||||
}
|
||||
|
||||
# find the matching region
|
||||
region = self.camera.regions[frame['region_id']]
|
||||
|
||||
# Compute some extra properties
|
||||
obj.update({
|
||||
'xmin': int((obj['box'][0] * frame['size']) + frame['x_offset']),
|
||||
'ymin': int((obj['box'][1] * frame['size']) + frame['y_offset']),
|
||||
'xmax': int((obj['box'][2] * frame['size']) + frame['x_offset']),
|
||||
'ymax': int((obj['box'][3] * frame['size']) + frame['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.camera.mask[y_location][x_location] == [0]:
|
||||
continue
|
||||
|
||||
# look to see if the bounding box is too close to the region border and the region border is not the edge of the frame
|
||||
# if ((frame['x_offset'] > 0 and obj['box'][0] < 0.01) or
|
||||
# (frame['y_offset'] > 0 and obj['box'][1] < 0.01) or
|
||||
# (frame['x_offset']+frame['size'] < self.frame_shape[1] and obj['box'][2] > 0.99) or
|
||||
# (frame['y_offset']+frame['size'] < self.frame_shape[0] and obj['box'][3] > 0.99)):
|
||||
|
||||
# size, x_offset, y_offset = calculate_region(self.frame_shape, obj['xmin'], obj['ymin'], obj['xmax'], obj['ymax'])
|
||||
# This triggers WAY too often with stationary objects on the edge of a region.
|
||||
# Every frame triggers it and fills the queue...
|
||||
# I need to create a new region and add it to the list of regions, but
|
||||
# it needs to check for a duplicate region first.
|
||||
|
||||
# self.resize_queue.put({
|
||||
# 'camera_name': self.name,
|
||||
# 'frame_time': frame['frame_time'],
|
||||
# 'region_id': frame['region_id'],
|
||||
# 'size': size,
|
||||
# 'x_offset': x_offset,
|
||||
# 'y_offset': y_offset
|
||||
# })
|
||||
# print('object too close to region border')
|
||||
#continue
|
||||
|
||||
self.camera.detected_objects.append(obj)
|
||||
|
||||
with self.camera.objects_parsed:
|
||||
self.camera.objects_parsed.notify_all()
|
||||
|
||||
|
||||
# Maintains the frame and object with the highest score
|
||||
class BestFrames(threading.Thread):
|
||||
|
||||
@ -12,7 +12,7 @@ import prctl
|
||||
from collections import defaultdict
|
||||
from . util import tonumpyarray, LABELS, draw_box_with_label, calculate_region, EventsPerSecond
|
||||
from . object_detection import RegionPrepper, RegionRequester
|
||||
from . objects import ObjectCleaner, BestFrames
|
||||
from . objects import ObjectCleaner, BestFrames, DetectedObjectsProcessor
|
||||
from . mqtt import MqttObjectPublisher
|
||||
|
||||
# Stores 2 seconds worth of frames so they can be used for other threads
|
||||
@ -144,6 +144,11 @@ class Camera:
|
||||
# Queue for prepped frames, max size set to (number of regions * 5)
|
||||
max_queue_size = len(self.config['regions'])*5
|
||||
self.resize_queue = queue.Queue(max_queue_size)
|
||||
|
||||
# Queue for raw detected objects
|
||||
self.detected_objects_queue = queue.Queue()
|
||||
self.detected_objects_processor = DetectedObjectsProcessor(self)
|
||||
self.detected_objects_processor.start()
|
||||
|
||||
# initialize the frame cache
|
||||
self.cached_frame_with_objects = {
|
||||
@ -259,91 +264,6 @@ class Camera:
|
||||
def get_capture_pid(self):
|
||||
return self.ffmpeg_process.pid
|
||||
|
||||
def add_objects(self, frame):
|
||||
objects = frame['detected_objects']
|
||||
|
||||
if len(objects) == 0:
|
||||
return
|
||||
|
||||
for raw_obj in objects:
|
||||
obj = {
|
||||
'score': float(raw_obj.score),
|
||||
'box': raw_obj.bounding_box.flatten().tolist(),
|
||||
'name': str(LABELS[raw_obj.label_id]),
|
||||
'frame_time': frame['frame_time'],
|
||||
'region_id': frame['region_id']
|
||||
}
|
||||
|
||||
# find the matching region
|
||||
region = self.regions[frame['region_id']]
|
||||
|
||||
# Compute some extra properties
|
||||
obj.update({
|
||||
'xmin': int((obj['box'][0] * frame['size']) + frame['x_offset']),
|
||||
'ymin': int((obj['box'][1] * frame['size']) + frame['y_offset']),
|
||||
'xmax': int((obj['box'][2] * frame['size']) + frame['x_offset']),
|
||||
'ymax': int((obj['box'][3] * frame['size']) + frame['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
|
||||
|
||||
# look to see if the bounding box is too close to the region border and the region border is not the edge of the frame
|
||||
# if ((frame['x_offset'] > 0 and obj['box'][0] < 0.01) or
|
||||
# (frame['y_offset'] > 0 and obj['box'][1] < 0.01) or
|
||||
# (frame['x_offset']+frame['size'] < self.frame_shape[1] and obj['box'][2] > 0.99) or
|
||||
# (frame['y_offset']+frame['size'] < self.frame_shape[0] and obj['box'][3] > 0.99)):
|
||||
|
||||
# size, x_offset, y_offset = calculate_region(self.frame_shape, obj['xmin'], obj['ymin'], obj['xmax'], obj['ymax'])
|
||||
# This triggers WAY too often with stationary objects on the edge of a region.
|
||||
# Every frame triggers it and fills the queue...
|
||||
# I need to create a new region and add it to the list of regions, but
|
||||
# it needs to check for a duplicate region first.
|
||||
|
||||
# self.resize_queue.put({
|
||||
# 'camera_name': self.name,
|
||||
# 'frame_time': frame['frame_time'],
|
||||
# 'region_id': frame['region_id'],
|
||||
# 'size': size,
|
||||
# 'x_offset': x_offset,
|
||||
# 'y_offset': y_offset
|
||||
# })
|
||||
# print('object too close to region border')
|
||||
#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)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user