forked from Github/frigate
cleanup old code
This commit is contained in:
Blake Blackshear
@@ -2,7 +2,6 @@ import time
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import datetime
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import threading
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import cv2
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# import prctl
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import itertools
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import copy
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import numpy as np
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@@ -11,237 +10,6 @@ from collections import defaultdict
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from scipy.spatial import distance as dist
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from frigate.util import draw_box_with_label, calculate_region
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# class ObjectCleaner(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("ObjectCleaner")
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# while True:
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# # wait a bit before checking for expired frames
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# time.sleep(0.2)
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# for frame_time in list(self.camera.detected_objects.keys()).copy():
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# if not frame_time in self.camera.frame_cache:
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# del self.camera.detected_objects[frame_time]
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# objects_deregistered = False
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# with self.camera.object_tracker.tracked_objects_lock:
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# now = datetime.datetime.now().timestamp()
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# for id, obj in list(self.camera.object_tracker.tracked_objects.items()):
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# # if the object is more than 10 seconds old
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# # and not in the most recent frame, deregister
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# if (now - obj['frame_time']) > 10 and self.camera.object_tracker.most_recent_frame_time > obj['frame_time']:
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# self.camera.object_tracker.deregister(id)
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# objects_deregistered = True
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# if objects_deregistered:
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# with self.camera.objects_tracked:
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# self.camera.objects_tracked.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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# for raw_obj in objects:
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# name = str(LABELS[raw_obj.label_id])
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# if not name in self.camera.objects_to_track:
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# continue
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# obj = {
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# 'name': name,
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# 'score': float(raw_obj.score),
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# 'box': {
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# 'xmin': int((raw_obj.bounding_box[0][0] * frame['size']) + frame['x_offset']),
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# 'ymin': int((raw_obj.bounding_box[0][1] * frame['size']) + frame['y_offset']),
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# 'xmax': int((raw_obj.bounding_box[1][0] * frame['size']) + frame['x_offset']),
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# 'ymax': int((raw_obj.bounding_box[1][1] * frame['size']) + frame['y_offset'])
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# },
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# 'region': {
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# 'xmin': frame['x_offset'],
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# 'ymin': frame['y_offset'],
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# 'xmax': frame['x_offset']+frame['size'],
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# 'ymax': frame['y_offset']+frame['size']
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# },
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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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# # if the object is within 5 pixels of the region border, and the region is not on the edge
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# # consider the object to be clipped
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# obj['clipped'] = False
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# if ((obj['region']['xmin'] > 5 and obj['box']['xmin']-obj['region']['xmin'] <= 5) or
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# (obj['region']['ymin'] > 5 and obj['box']['ymin']-obj['region']['ymin'] <= 5) or
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# (self.camera.frame_shape[1]-obj['region']['xmax'] > 5 and obj['region']['xmax']-obj['box']['xmax'] <= 5) or
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# (self.camera.frame_shape[0]-obj['region']['ymax'] > 5 and obj['region']['ymax']-obj['box']['ymax'] <= 5)):
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# obj['clipped'] = True
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# # Compute the area
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# # TODO: +1 right?
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# obj['area'] = (obj['box']['xmax']-obj['box']['xmin'])*(obj['box']['ymax']-obj['box']['ymin'])
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# self.camera.detected_objects[frame['frame_time']].append(obj)
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# # TODO: use in_process and processed counts instead to avoid lock
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# with self.camera.regions_in_process_lock:
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# if frame['frame_time'] in self.camera.regions_in_process:
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# self.camera.regions_in_process[frame['frame_time']] -= 1
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# # print(f"{frame['frame_time']} remaining regions {self.camera.regions_in_process[frame['frame_time']]}")
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# if self.camera.regions_in_process[frame['frame_time']] == 0:
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# del self.camera.regions_in_process[frame['frame_time']]
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# # print(f"{frame['frame_time']} no remaining regions")
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# self.camera.finished_frame_queue.put(frame['frame_time'])
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# else:
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# self.camera.finished_frame_queue.put(frame['frame_time'])
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# # Thread that checks finished frames for clipped objects and sends back
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# # for processing if needed
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# # TODO: evaluate whether or not i really need separate threads/queues for each step
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# # given that only 1 thread will really be able to run at a time. you need a
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# # separate process to actually do things in parallel for when you are CPU bound.
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# # threads are good when you are waiting and could be processing while you wait
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# class RegionRefiner(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_time = self.camera.finished_frame_queue.get()
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# detected_objects = self.camera.detected_objects[frame_time].copy()
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# # print(f"{frame_time} finished")
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# # group by name
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# detected_object_groups = defaultdict(lambda: [])
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# for obj in detected_objects:
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# detected_object_groups[obj['name']].append(obj)
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# look_again = False
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# selected_objects = []
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# for group in detected_object_groups.values():
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# # apply non-maxima suppression to suppress weak, overlapping bounding boxes
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# boxes = [(o['box']['xmin'], o['box']['ymin'], o['box']['xmax']-o['box']['xmin'], o['box']['ymax']-o['box']['ymin'])
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# for o in group]
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# confidences = [o['score'] for o in group]
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# idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
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# for index in idxs:
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# obj = group[index[0]]
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# selected_objects.append(obj)
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# if obj['clipped']:
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# box = obj['box']
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# # calculate a new region that will hopefully get the entire object
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# (size, x_offset, y_offset) = calculate_region(self.camera.frame_shape,
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# box['xmin'], box['ymin'],
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# box['xmax'], box['ymax'])
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# # print(f"{frame_time} new region: {size} {x_offset} {y_offset}")
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# with self.camera.regions_in_process_lock:
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# if not frame_time in self.camera.regions_in_process:
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# self.camera.regions_in_process[frame_time] = 1
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# else:
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# self.camera.regions_in_process[frame_time] += 1
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# # add it to the queue
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# self.camera.resize_queue.put({
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# 'camera_name': self.camera.name,
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# 'frame_time': frame_time,
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# 'region_id': -1,
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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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# self.camera.dynamic_region_fps.update()
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# look_again = True
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# # if we are looking again, then this frame is not ready for processing
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# if look_again:
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# # remove the clipped objects
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# self.camera.detected_objects[frame_time] = [o for o in selected_objects if not o['clipped']]
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# continue
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# # filter objects based on camera settings
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# selected_objects = [o for o in selected_objects if not self.filtered(o)]
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# self.camera.detected_objects[frame_time] = selected_objects
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# # print(f"{frame_time} is actually finished")
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# # keep adding frames to the refined queue as long as they are finished
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# with self.camera.regions_in_process_lock:
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# while self.camera.frame_queue.qsize() > 0 and self.camera.frame_queue.queue[0] not in self.camera.regions_in_process:
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# self.camera.last_processed_frame = self.camera.frame_queue.get()
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# self.camera.refined_frame_queue.put(self.camera.last_processed_frame)
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# def filtered(self, obj):
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# object_name = obj['name']
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# if object_name in self.camera.object_filters:
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# obj_settings = self.camera.object_filters[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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# return True
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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', self.camera.frame_shape[0]*self.camera.frame_shape[1]) < obj['area']:
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# return True
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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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# return True
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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['box']['ymax']), len(self.camera.mask)-1)
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# x_location = min(int((obj['box']['xmax']-obj['box']['xmin'])/2.0)+obj['box']['xmin'], len(self.camera.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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# return True
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# return False
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# def has_overlap(self, new_obj, obj, overlap=.7):
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# # compute intersection rectangle with existing object and new objects region
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# existing_obj_current_region = compute_intersection_rectangle(obj['box'], new_obj['region'])
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# # compute intersection rectangle with new object and existing objects region
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# new_obj_existing_region = compute_intersection_rectangle(new_obj['box'], obj['region'])
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# # compute iou for the two intersection rectangles that were just computed
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# iou = compute_intersection_over_union(existing_obj_current_region, new_obj_existing_region)
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# # if intersection is greater than overlap
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# if iou > overlap:
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# return True
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# else:
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# return False
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# def find_group(self, new_obj, groups):
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# for index, group in enumerate(groups):
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# for obj in group:
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# if self.has_overlap(new_obj, obj):
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# return index
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# return None
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class ObjectTracker():
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def __init__(self, max_disappeared):
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self.tracked_objects = {}
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@@ -385,45 +153,3 @@ class ObjectTracker():
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else:
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for col in unusedCols:
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self.register(col, group[col])
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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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# def __init__(self, camera):
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# threading.Thread.__init__(self)
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# self.camera = camera
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# self.best_objects = {}
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# self.best_frames = {}
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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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# # wait until objects have been tracked
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# with self.camera.objects_tracked:
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# self.camera.objects_tracked.wait()
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# # make a copy of tracked objects
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# tracked_objects = list(self.camera.object_tracker.tracked_objects.values())
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# for obj in tracked_objects:
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# if obj['name'] in self.best_objects:
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# now = datetime.datetime.now().timestamp()
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# # if the object is a higher score than the current best score
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# # or the current object is more than 1 minute old, use the new object
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# if obj['score'] > self.best_objects[obj['name']]['score'] or (now - self.best_objects[obj['name']]['frame_time']) > 60:
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# self.best_objects[obj['name']] = copy.deepcopy(obj)
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# else:
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# self.best_objects[obj['name']] = copy.deepcopy(obj)
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# for name, obj in self.best_objects.items():
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# if obj['frame_time'] in self.camera.frame_cache:
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# best_frame = self.camera.frame_cache[obj['frame_time']]
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# draw_box_with_label(best_frame, obj['box']['xmin'], obj['box']['ymin'],
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# obj['box']['xmax'], obj['box']['ymax'], obj['name'], "{}% {}".format(int(obj['score']*100), obj['area']))
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# # print a timestamp
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# if self.camera.snapshot_config['show_timestamp']:
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# time_to_show = datetime.datetime.fromtimestamp(obj['frame_time']).strftime("%m/%d/%Y %H:%M:%S")
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# cv2.putText(best_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
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# self.best_frames[name] = best_frame
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