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https://github.com/blakeblackshear/frigate.git
synced 2025-07-26 13:47:03 +02:00
process detected objects in a queue
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Blake Blackshear
parent
b6130e77ff
commit
be1673b00a
@ -35,7 +35,7 @@ class PreppedQueueProcessor(threading.Thread):
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self.fps.update()
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self.avg_inference_speed = (self.avg_inference_speed*9 + self.engine.get_inference_time())/10
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self.cameras[frame['camera_name']].add_objects(frame)
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self.cameras[frame['camera_name']].detected_objects_queue.put(frame)
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class RegionRequester(threading.Thread):
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def __init__(self, camera):
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@ -4,7 +4,7 @@ import threading
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import cv2
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import prctl
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import numpy as np
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from . util import draw_box_with_label
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from . util import draw_box_with_label, LABELS
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class ObjectCleaner(threading.Thread):
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def __init__(self, objects_parsed, detected_objects):
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@ -37,6 +37,100 @@ class ObjectCleaner(threading.Thread):
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with self._objects_parsed:
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self._objects_parsed.notify_all()
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class DetectedObjectsProcessor(threading.Thread):
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def __init__(self, camera):
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threading.Thread.__init__(self)
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self.camera = camera
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def run(self):
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prctl.set_name(self.__class__.__name__)
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while True:
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frame = self.camera.detected_objects_queue.get()
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objects = frame['detected_objects']
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if len(objects) == 0:
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return
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for raw_obj in objects:
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obj = {
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'score': float(raw_obj.score),
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'box': raw_obj.bounding_box.flatten().tolist(),
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'name': str(LABELS[raw_obj.label_id]),
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'frame_time': frame['frame_time'],
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'region_id': frame['region_id']
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}
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# find the matching region
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region = self.camera.regions[frame['region_id']]
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# Compute some extra properties
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obj.update({
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'xmin': int((obj['box'][0] * frame['size']) + frame['x_offset']),
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'ymin': int((obj['box'][1] * frame['size']) + frame['y_offset']),
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'xmax': int((obj['box'][2] * frame['size']) + frame['x_offset']),
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'ymax': int((obj['box'][3] * frame['size']) + frame['y_offset'])
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})
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# Compute the area
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obj['area'] = (obj['xmax']-obj['xmin'])*(obj['ymax']-obj['ymin'])
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object_name = obj['name']
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if object_name in region['objects']:
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obj_settings = region['objects'][object_name]
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# if the min area is larger than the
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# detected object, don't add it to detected objects
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if obj_settings.get('min_area',-1) > obj['area']:
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continue
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# if the detected object is larger than the
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# max area, don't add it to detected objects
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if obj_settings.get('max_area', region['size']**2) < obj['area']:
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continue
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# if the score is lower than the threshold, skip
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if obj_settings.get('threshold', 0) > obj['score']:
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continue
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# compute the coordinates of the object and make sure
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# the location isnt outside the bounds of the image (can happen from rounding)
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y_location = min(int(obj['ymax']), len(self.mask)-1)
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x_location = min(int((obj['xmax']-obj['xmin'])/2.0)+obj['xmin'], len(self.mask[0])-1)
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# if the object is in a masked location, don't add it to detected objects
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if self.camera.mask[y_location][x_location] == [0]:
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continue
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# 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
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# if ((frame['x_offset'] > 0 and obj['box'][0] < 0.01) or
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# (frame['y_offset'] > 0 and obj['box'][1] < 0.01) or
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# (frame['x_offset']+frame['size'] < self.frame_shape[1] and obj['box'][2] > 0.99) or
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# (frame['y_offset']+frame['size'] < self.frame_shape[0] and obj['box'][3] > 0.99)):
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# size, x_offset, y_offset = calculate_region(self.frame_shape, obj['xmin'], obj['ymin'], obj['xmax'], obj['ymax'])
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# This triggers WAY too often with stationary objects on the edge of a region.
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# Every frame triggers it and fills the queue...
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# I need to create a new region and add it to the list of regions, but
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# it needs to check for a duplicate region first.
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# self.resize_queue.put({
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# 'camera_name': self.name,
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# 'frame_time': frame['frame_time'],
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# 'region_id': frame['region_id'],
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# 'size': size,
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# 'x_offset': x_offset,
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# 'y_offset': y_offset
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# })
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# print('object too close to region border')
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#continue
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self.camera.detected_objects.append(obj)
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with self.camera.objects_parsed:
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self.camera.objects_parsed.notify_all()
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# Maintains the frame and object with the highest score
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class BestFrames(threading.Thread):
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@ -12,7 +12,7 @@ import prctl
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from collections import defaultdict
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from . util import tonumpyarray, LABELS, draw_box_with_label, calculate_region, EventsPerSecond
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from . object_detection import RegionPrepper, RegionRequester
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from . objects import ObjectCleaner, BestFrames
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from . objects import ObjectCleaner, BestFrames, DetectedObjectsProcessor
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from . mqtt import MqttObjectPublisher
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# Stores 2 seconds worth of frames so they can be used for other threads
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@ -144,6 +144,11 @@ class Camera:
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# Queue for prepped frames, max size set to (number of regions * 5)
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max_queue_size = len(self.config['regions'])*5
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self.resize_queue = queue.Queue(max_queue_size)
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# Queue for raw detected objects
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self.detected_objects_queue = queue.Queue()
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self.detected_objects_processor = DetectedObjectsProcessor(self)
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self.detected_objects_processor.start()
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# initialize the frame cache
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self.cached_frame_with_objects = {
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@ -259,91 +264,6 @@ class Camera:
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def get_capture_pid(self):
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return self.ffmpeg_process.pid
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def add_objects(self, frame):
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objects = frame['detected_objects']
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if len(objects) == 0:
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return
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for raw_obj in objects:
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obj = {
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'score': float(raw_obj.score),
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'box': raw_obj.bounding_box.flatten().tolist(),
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'name': str(LABELS[raw_obj.label_id]),
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'frame_time': frame['frame_time'],
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'region_id': frame['region_id']
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}
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# find the matching region
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region = self.regions[frame['region_id']]
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# Compute some extra properties
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obj.update({
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'xmin': int((obj['box'][0] * frame['size']) + frame['x_offset']),
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'ymin': int((obj['box'][1] * frame['size']) + frame['y_offset']),
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'xmax': int((obj['box'][2] * frame['size']) + frame['x_offset']),
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'ymax': int((obj['box'][3] * frame['size']) + frame['y_offset'])
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})
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# Compute the area
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obj['area'] = (obj['xmax']-obj['xmin'])*(obj['ymax']-obj['ymin'])
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object_name = obj['name']
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if object_name in region['objects']:
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obj_settings = region['objects'][object_name]
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# if the min area is larger than the
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# detected object, don't add it to detected objects
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if obj_settings.get('min_area',-1) > obj['area']:
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continue
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# if the detected object is larger than the
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# max area, don't add it to detected objects
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if obj_settings.get('max_area', region['size']**2) < obj['area']:
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continue
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# if the score is lower than the threshold, skip
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if obj_settings.get('threshold', 0) > obj['score']:
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continue
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# compute the coordinates of the object and make sure
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# the location isnt outside the bounds of the image (can happen from rounding)
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y_location = min(int(obj['ymax']), len(self.mask)-1)
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x_location = min(int((obj['xmax']-obj['xmin'])/2.0)+obj['xmin'], len(self.mask[0])-1)
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# if the object is in a masked location, don't add it to detected objects
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if self.mask[y_location][x_location] == [0]:
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continue
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# 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
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# if ((frame['x_offset'] > 0 and obj['box'][0] < 0.01) or
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# (frame['y_offset'] > 0 and obj['box'][1] < 0.01) or
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# (frame['x_offset']+frame['size'] < self.frame_shape[1] and obj['box'][2] > 0.99) or
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# (frame['y_offset']+frame['size'] < self.frame_shape[0] and obj['box'][3] > 0.99)):
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# size, x_offset, y_offset = calculate_region(self.frame_shape, obj['xmin'], obj['ymin'], obj['xmax'], obj['ymax'])
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# This triggers WAY too often with stationary objects on the edge of a region.
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# Every frame triggers it and fills the queue...
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# I need to create a new region and add it to the list of regions, but
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# it needs to check for a duplicate region first.
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# self.resize_queue.put({
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# 'camera_name': self.name,
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# 'frame_time': frame['frame_time'],
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# 'region_id': frame['region_id'],
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# 'size': size,
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# 'x_offset': x_offset,
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# 'y_offset': y_offset
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# })
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# print('object too close to region border')
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#continue
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self.detected_objects.append(obj)
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with self.objects_parsed:
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self.objects_parsed.notify_all()
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def get_best(self, label):
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return self.best_frames.best_frames.get(label)
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