mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
363 lines
16 KiB
Python
363 lines
16 KiB
Python
import json
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import hashlib
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import datetime
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import time
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import copy
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import cv2
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import threading
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import queue
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import numpy as np
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from collections import Counter, defaultdict
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import itertools
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import pyarrow.plasma as plasma
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import matplotlib.pyplot as plt
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from frigate.util import draw_box_with_label, PlasmaFrameManager
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from frigate.edgetpu import load_labels
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from typing import Callable, Dict
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from statistics import mean, median
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PATH_TO_LABELS = '/labelmap.txt'
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LABELS = load_labels(PATH_TO_LABELS)
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cmap = plt.cm.get_cmap('tab10', len(LABELS.keys()))
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COLOR_MAP = {}
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for key, val in LABELS.items():
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COLOR_MAP[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3])
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def zone_filtered(obj, object_config):
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object_name = obj['label']
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object_filters = object_config.get('filters', {})
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if object_name in object_filters:
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obj_settings = 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', 24000000) < 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['computed_score']:
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return True
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return False
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# Maintains the state of a camera
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class CameraState():
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def __init__(self, name, config, frame_manager):
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self.name = name
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self.config = config
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self.frame_manager = frame_manager
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self.best_objects = {}
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self.object_status = defaultdict(lambda: 'OFF')
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self.tracked_objects = {}
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self.zone_objects = defaultdict(lambda: [])
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self.current_frame = np.zeros((720,1280,3), np.uint8)
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self.current_frame_time = 0.0
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self.previous_frame_id = None
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self.callbacks = defaultdict(lambda: [])
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def false_positive(self, obj):
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# once a true positive, always a true positive
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if not obj.get('false_positive', True):
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return False
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threshold = self.config['objects'].get('filters', {}).get(obj['label'], {}).get('threshold', 0.85)
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if obj['computed_score'] < threshold:
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return True
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return False
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def compute_score(self, obj):
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scores = obj['score_history'][:]
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# pad with zeros if you dont have at least 3 scores
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if len(scores) < 3:
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scores += [0.0]*(3 - len(scores))
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return median(scores)
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def on(self, event_type: str, callback: Callable[[Dict], None]):
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self.callbacks[event_type].append(callback)
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def update(self, frame_time, tracked_objects):
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self.current_frame_time = frame_time
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# get the new frame and delete the old frame
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frame_id = f"{self.name}{frame_time}"
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self.current_frame = self.frame_manager.get(frame_id)
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if not self.previous_frame_id is None:
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self.frame_manager.delete(self.previous_frame_id)
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self.previous_frame_id = frame_id
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current_ids = tracked_objects.keys()
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previous_ids = self.tracked_objects.keys()
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removed_ids = list(set(previous_ids).difference(current_ids))
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new_ids = list(set(current_ids).difference(previous_ids))
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updated_ids = list(set(current_ids).intersection(previous_ids))
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for id in new_ids:
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self.tracked_objects[id] = tracked_objects[id]
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self.tracked_objects[id]['zones'] = []
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# start the score history
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self.tracked_objects[id]['score_history'] = [self.tracked_objects[id]['score']]
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# calculate if this is a false positive
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self.tracked_objects[id]['computed_score'] = self.compute_score(self.tracked_objects[id])
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self.tracked_objects[id]['false_positive'] = self.false_positive(self.tracked_objects[id])
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# call event handlers
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for c in self.callbacks['start']:
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c(self.name, tracked_objects[id])
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for id in updated_ids:
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self.tracked_objects[id].update(tracked_objects[id])
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# if the object is not in the current frame, add a 0.0 to the score history
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if self.tracked_objects[id]['frame_time'] != self.current_frame_time:
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self.tracked_objects[id]['score_history'].append(0.0)
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else:
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self.tracked_objects[id]['score_history'].append(self.tracked_objects[id]['score'])
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# only keep the last 10 scores
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if len(self.tracked_objects[id]['score_history']) > 10:
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self.tracked_objects[id]['score_history'] = self.tracked_objects[id]['score_history'][-10:]
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# calculate if this is a false positive
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self.tracked_objects[id]['computed_score'] = self.compute_score(self.tracked_objects[id])
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self.tracked_objects[id]['false_positive'] = self.false_positive(self.tracked_objects[id])
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# call event handlers
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for c in self.callbacks['update']:
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c(self.name, self.tracked_objects[id])
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for id in removed_ids:
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# publish events to mqtt
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self.tracked_objects[id]['end_time'] = frame_time
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for c in self.callbacks['end']:
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c(self.name, self.tracked_objects[id])
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del self.tracked_objects[id]
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# check to see if the objects are in any zones
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for obj in self.tracked_objects.values():
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current_zones = []
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bottom_center = (obj['centroid'][0], obj['box'][3])
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# check each zone
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for name, zone in self.config['zones'].items():
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contour = zone['contour']
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# check if the object is in the zone and not filtered
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if (cv2.pointPolygonTest(contour, bottom_center, False) >= 0
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and not zone_filtered(obj, zone.get('filters', {}))):
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current_zones.append(name)
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obj['zones'] = current_zones
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# draw on the frame
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if not self.current_frame is None:
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# draw the bounding boxes on the frame
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for obj in self.tracked_objects.values():
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thickness = 2
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color = COLOR_MAP[obj['label']]
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if obj['frame_time'] != frame_time:
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thickness = 1
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color = (255,0,0)
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# draw the bounding boxes on the frame
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box = obj['box']
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draw_box_with_label(self.current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
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# draw the regions on the frame
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region = obj['region']
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cv2.rectangle(self.current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
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if self.config['snapshots']['show_timestamp']:
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time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
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cv2.putText(self.current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
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if self.config['snapshots']['draw_zones']:
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for name, zone in self.config['zones'].items():
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thickness = 8 if any([name in obj['zones'] for obj in self.tracked_objects.values()]) else 2
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cv2.drawContours(self.current_frame, [zone['contour']], -1, zone['color'], thickness)
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# maintain best objects
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for obj in self.tracked_objects.values():
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object_type = obj['label']
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# if the object wasn't seen on the current frame, skip it
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if obj['frame_time'] != self.current_frame_time or obj['false_positive']:
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continue
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if object_type in self.best_objects:
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current_best = self.best_objects[object_type]
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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'] > current_best['score'] or (now - current_best['frame_time']) > 60:
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obj['frame'] = np.copy(self.current_frame)
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self.best_objects[object_type] = obj
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for c in self.callbacks['snapshot']:
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c(self.name, self.best_objects[object_type])
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else:
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obj['frame'] = np.copy(self.current_frame)
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self.best_objects[object_type] = obj
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for c in self.callbacks['snapshot']:
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c(self.name, self.best_objects[object_type])
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# update overall camera state for each object type
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obj_counter = Counter()
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for obj in self.tracked_objects.values():
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if not obj['false_positive']:
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obj_counter[obj['label']] += 1
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# report on detected objects
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for obj_name, count in obj_counter.items():
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new_status = 'ON' if count > 0 else 'OFF'
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if new_status != self.object_status[obj_name]:
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self.object_status[obj_name] = new_status
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for c in self.callbacks['object_status']:
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c(self.name, obj_name, new_status)
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# expire any objects that are ON and no longer detected
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expired_objects = [obj_name for obj_name, status in self.object_status.items() if status == 'ON' and not obj_name in obj_counter]
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for obj_name in expired_objects:
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self.object_status[obj_name] = 'OFF'
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for c in self.callbacks['object_status']:
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c(self.name, obj_name, 'OFF')
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for c in self.callbacks['snapshot']:
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c(self.name, self.best_objects[obj_name])
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class TrackedObjectProcessor(threading.Thread):
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def __init__(self, camera_config, zone_config, client, topic_prefix, tracked_objects_queue, event_queue, stop_event):
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threading.Thread.__init__(self)
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self.camera_config = camera_config
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self.zone_config = zone_config
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self.client = client
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self.topic_prefix = topic_prefix
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self.tracked_objects_queue = tracked_objects_queue
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self.event_queue = event_queue
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self.stop_event = stop_event
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self.camera_states: Dict[str, CameraState] = {}
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self.plasma_client = PlasmaFrameManager(self.stop_event)
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def start(camera, obj):
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# publish events to mqtt
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self.client.publish(f"{self.topic_prefix}/{camera}/events/start", json.dumps({x: obj[x] for x in obj if x not in ['frame']}), retain=False)
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self.event_queue.put(('start', camera, obj))
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def update(camera, obj):
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pass
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def end(camera, obj):
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self.client.publish(f"{self.topic_prefix}/{camera}/events/end", json.dumps({x: obj[x] for x in obj if x not in ['frame']}), retain=False)
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self.event_queue.put(('end', camera, obj))
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def snapshot(camera, obj):
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best_frame = cv2.cvtColor(obj['frame'], cv2.COLOR_RGB2BGR)
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mqtt_config = self.camera_config[camera].get('mqtt', {'crop_to_region': False})
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if mqtt_config.get('crop_to_region'):
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region = obj['region']
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best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
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if 'snapshot_height' in mqtt_config:
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height = int(mqtt_config['snapshot_height'])
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width = int(height*best_frame.shape[1]/best_frame.shape[0])
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best_frame = cv2.resize(best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA)
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ret, jpg = cv2.imencode('.jpg', best_frame)
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if ret:
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jpg_bytes = jpg.tobytes()
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self.client.publish(f"{self.topic_prefix}/{camera}/{obj['label']}/snapshot", jpg_bytes, retain=True)
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def object_status(camera, object_name, status):
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self.client.publish(f"{self.topic_prefix}/{camera}/{object_name}", status, retain=False)
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for camera in self.camera_config.keys():
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camera_state = CameraState(camera, self.camera_config[camera], self.plasma_client)
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camera_state.on('start', start)
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camera_state.on('update', update)
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camera_state.on('end', end)
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camera_state.on('snapshot', snapshot)
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camera_state.on('object_status', object_status)
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self.camera_states[camera] = camera_state
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self.camera_data = defaultdict(lambda: {
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'best_objects': {},
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'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')),
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'tracked_objects': {},
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'current_frame': np.zeros((720,1280,3), np.uint8),
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'current_frame_time': 0.0,
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'object_id': None
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})
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# {
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# 'zone_name': {
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# 'person': ['camera_1', 'camera_2']
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# }
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# }
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self.zone_data = defaultdict(lambda: defaultdict(lambda: set()))
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# set colors for zones
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zone_colors = {}
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colors = plt.cm.get_cmap('tab10', len(self.zone_config.keys()))
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for i, zone in enumerate(self.zone_config.keys()):
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zone_colors[zone] = tuple(int(round(255 * c)) for c in colors(i)[:3])
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# create zone contours
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for zone_name, config in zone_config.items():
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for camera, camera_zone_config in config.items():
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camera_zone = {}
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camera_zone['color'] = zone_colors[zone_name]
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coordinates = camera_zone_config['coordinates']
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if isinstance(coordinates, list):
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camera_zone['contour'] = np.array([[int(p.split(',')[0]), int(p.split(',')[1])] for p in coordinates])
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elif isinstance(coordinates, str):
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points = coordinates.split(',')
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camera_zone['contour'] = np.array([[int(points[i]), int(points[i+1])] for i in range(0, len(points), 2)])
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else:
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print(f"Unable to parse zone coordinates for {zone_name} - {camera}")
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self.camera_config[camera]['zones'][zone_name] = camera_zone
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def get_best(self, camera, label):
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best_objects = self.camera_states[camera].best_objects
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if label in best_objects:
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return best_objects[label]
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else:
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return {}
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def get_current_frame(self, camera):
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return self.camera_states[camera].current_frame
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def run(self):
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while True:
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if self.stop_event.is_set():
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print(f"Exiting object processor...")
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break
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try:
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camera, frame_time, current_tracked_objects = self.tracked_objects_queue.get(True, 10)
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except queue.Empty:
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continue
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camera_state = self.camera_states[camera]
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camera_state.update(frame_time, current_tracked_objects)
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# update zone status for each label
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for zone in camera_state.config['zones'].keys():
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# get labels for current camera and all labels in current zone
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labels_for_camera = set([obj['label'] for obj in camera_state.tracked_objects.values() if zone in obj['zones'] and not obj['false_positive']])
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labels_to_check = labels_for_camera | set(self.zone_data[zone].keys())
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# for each label in zone
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for label in labels_to_check:
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camera_list = self.zone_data[zone][label]
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# remove or add the camera to the list for the current label
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previous_state = len(camera_list) > 0
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if label in labels_for_camera:
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camera_list.add(camera_state.name)
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elif camera_state.name in camera_list:
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camera_list.remove(camera_state.name)
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new_state = len(camera_list) > 0
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# if the value is changing, send over MQTT
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if previous_state == False and new_state == True:
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self.client.publish(f"{self.topic_prefix}/{zone}/{label}", 'ON', retain=False)
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elif previous_state == True and new_state == False:
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self.client.publish(f"{self.topic_prefix}/{zone}/{label}", 'OFF', retain=False)
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