mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
481 lines
21 KiB
Python
481 lines
21 KiB
Python
import copy
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import datetime
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import hashlib
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import itertools
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import json
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import logging
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import os
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import queue
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import threading
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import time
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from collections import Counter, defaultdict
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from statistics import mean, median
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from typing import Callable, Dict
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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from frigate.config import FrigateConfig, CameraConfig
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from frigate.edgetpu import load_labels
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from frigate.util import SharedMemoryFrameManager, draw_box_with_label
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logger = logging.getLogger(__name__)
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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 on_edge(box, frame_shape):
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if (
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box[0] == 0 or
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box[1] == 0 or
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box[2] == frame_shape[1]-1 or
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box[3] == frame_shape[0]-1
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):
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return True
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def is_better_thumbnail(current_thumb, new_obj, frame_shape) -> bool:
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# larger is better
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# cutoff images are less ideal, but they should also be smaller?
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# better scores are obviously better too
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# if the new_thumb is on an edge, and the current thumb is not
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if on_edge(new_obj['box'], frame_shape) and not on_edge(current_thumb['box'], frame_shape):
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return False
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# if the score is better by more than 5%
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if new_obj['score'] > current_thumb['score']+.05:
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return True
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# if the area is 10% larger
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if new_obj['area'] > current_thumb['area']*1.1:
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return True
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return False
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class TrackedObject():
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def __init__(self, camera, camera_config: CameraConfig, frame_cache, obj_data):
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self.obj_data = obj_data
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self.camera = camera
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self.camera_config = camera_config
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self.frame_cache = frame_cache
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self.current_zones = []
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self.entered_zones = set()
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self.false_positive = True
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self.top_score = self.computed_score = 0.0
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self.thumbnail_data = None
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self.frame = None
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self._snapshot_jpg_time = 0
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ret, jpg = cv2.imencode('.jpg', np.zeros((300,300,3), np.uint8))
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self._snapshot_jpg = jpg.tobytes()
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# start the score history
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self.score_history = [self.obj_data['score']]
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def _is_false_positive(self):
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# once a true positive, always a true positive
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if not self.false_positive:
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return False
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threshold = self.camera_config.objects.filters[self.obj_data['label']].threshold
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if self.computed_score < threshold:
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return True
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return False
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def compute_score(self):
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scores = self.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 update(self, current_frame_time, obj_data):
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self.obj_data.update(obj_data)
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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.obj_data['frame_time'] != current_frame_time:
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self.score_history.append(0.0)
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else:
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self.score_history.append(self.obj_data['score'])
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# only keep the last 10 scores
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if len(self.score_history) > 10:
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self.score_history = self.score_history[-10:]
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# calculate if this is a false positive
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self.computed_score = self.compute_score()
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if self.computed_score > self.top_score:
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self.top_score = self.computed_score
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self.false_positive = self._is_false_positive()
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if not self.false_positive:
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# determine if this frame is a better thumbnail
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if self.thumbnail_data is None or is_better_thumbnail(self.thumbnail_data, self.obj_data, self.camera_config.frame_shape):
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self.thumbnail_data = {
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'frame_time': self.obj_data['frame_time'],
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'box': self.obj_data['box'],
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'area': self.obj_data['area'],
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'region': self.obj_data['region'],
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'score': self.obj_data['score']
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}
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# check zones
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current_zones = []
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bottom_center = (self.obj_data['centroid'][0], self.obj_data['box'][3])
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# check each zone
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for name, zone in self.camera_config.zones.items():
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contour = zone.contour
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# check if the object is in the zone
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if (cv2.pointPolygonTest(contour, bottom_center, False) >= 0):
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# if the object passed the filters once, dont apply again
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if name in self.current_zones or not zone_filtered(self, zone.filters):
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current_zones.append(name)
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self.entered_zones.add(name)
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self.current_zones = current_zones
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def to_dict(self):
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return {
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'id': self.obj_data['id'],
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'camera': self.camera,
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'frame_time': self.obj_data['frame_time'],
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'label': self.obj_data['label'],
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'top_score': self.top_score,
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'false_positive': self.false_positive,
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'start_time': self.obj_data['start_time'],
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'end_time': self.obj_data.get('end_time', None),
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'score': self.obj_data['score'],
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'box': self.obj_data['box'],
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'area': self.obj_data['area'],
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'region': self.obj_data['region'],
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'current_zones': self.current_zones.copy(),
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'entered_zones': list(self.entered_zones).copy()
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}
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def get_jpg_bytes(self):
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if self.thumbnail_data is None or self._snapshot_jpg_time == self.thumbnail_data['frame_time']:
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return self._snapshot_jpg
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if not self.thumbnail_data['frame_time'] in self.frame_cache:
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logger.error(f"Unable to create thumbnail for {self.obj_data['id']}")
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logger.error(f"Looking for frame_time of {self.thumbnail_data['frame_time']}")
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logger.error(f"Thumbnail frames: {','.join([str(k) for k in self.frame_cache.keys()])}")
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return self._snapshot_jpg
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# TODO: crop first to avoid converting the entire frame?
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snapshot_config = self.camera_config.snapshots
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best_frame = cv2.cvtColor(self.frame_cache[self.thumbnail_data['frame_time']], cv2.COLOR_YUV2BGR_I420)
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if snapshot_config.draw_bounding_boxes:
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thickness = 2
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color = COLOR_MAP[self.obj_data['label']]
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box = self.thumbnail_data['box']
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draw_box_with_label(best_frame, box[0], box[1], box[2], box[3], self.obj_data['label'],
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f"{int(self.thumbnail_data['score']*100)}% {int(self.thumbnail_data['area'])}", thickness=thickness, color=color)
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if snapshot_config.crop_to_region:
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region = self.thumbnail_data['region']
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best_frame = best_frame[region[1]:region[3], region[0]:region[2]]
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if snapshot_config.height:
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height = snapshot_config.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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if snapshot_config.show_timestamp:
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time_to_show = datetime.datetime.fromtimestamp(self.thumbnail_data['frame_time']).strftime("%m/%d/%Y %H:%M:%S")
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size = cv2.getTextSize(time_to_show, cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, thickness=2)
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text_width = size[0][0]
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desired_size = max(200, 0.33*best_frame.shape[1])
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font_scale = desired_size/text_width
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cv2.putText(best_frame, time_to_show, (5, best_frame.shape[0]-7), cv2.FONT_HERSHEY_SIMPLEX,
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fontScale=font_scale, color=(255, 255, 255), thickness=2)
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ret, jpg = cv2.imencode('.jpg', best_frame)
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if ret:
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self._snapshot_jpg = jpg.tobytes()
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return self._snapshot_jpg
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def zone_filtered(obj: TrackedObject, object_config):
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object_name = obj.obj_data['label']
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if object_name in object_config:
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obj_settings = object_config[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.min_area > obj.obj_data['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.max_area < obj.obj_data['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.threshold > 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.camera_config = config.cameras[name]
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self.frame_manager = frame_manager
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self.best_objects: Dict[str, TrackedObject] = {}
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self.object_status = defaultdict(lambda: 'OFF')
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self.tracked_objects: Dict[str, TrackedObject] = {}
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self.frame_cache = {}
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self.zone_objects = defaultdict(lambda: [])
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self._current_frame = np.zeros(self.camera_config.frame_shape_yuv, np.uint8)
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self.current_frame_lock = threading.Lock()
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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 get_current_frame(self, draw=False):
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with self.current_frame_lock:
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frame_copy = np.copy(self._current_frame)
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frame_time = self.current_frame_time
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tracked_objects = {k: v.to_dict() for k,v in self.tracked_objects.items()}
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frame_copy = cv2.cvtColor(frame_copy, cv2.COLOR_YUV2BGR_I420)
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# draw on the frame
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if draw:
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# draw the bounding boxes on the frame
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for obj in 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(frame_copy, 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(frame_copy, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)
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if self.camera_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(frame_copy, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
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if self.camera_config.snapshots.draw_zones:
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for name, zone in self.camera_config.zones.items():
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thickness = 8 if any([name in obj['current_zones'] for obj in tracked_objects.values()]) else 2
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cv2.drawContours(frame_copy, [zone.contour], -1, zone.color, thickness)
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return frame_copy
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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, current_detections):
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self.current_frame_time = frame_time
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# get the new frame
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frame_id = f"{self.name}{frame_time}"
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current_frame = self.frame_manager.get(frame_id, self.camera_config.frame_shape_yuv)
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current_ids = current_detections.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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new_obj = self.tracked_objects[id] = TrackedObject(self.name, self.camera_config, self.frame_cache, current_detections[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, new_obj)
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for id in updated_ids:
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updated_obj = self.tracked_objects[id]
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updated_obj.update(frame_time, current_detections[id])
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if (not updated_obj.false_positive
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and updated_obj.thumbnail_data['frame_time'] == frame_time
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and frame_time not in self.frame_cache):
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self.frame_cache[frame_time] = np.copy(current_frame)
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# call event handlers
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for c in self.callbacks['update']:
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c(self.name, updated_obj)
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for id in removed_ids:
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# publish events to mqtt
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removed_obj = self.tracked_objects[id]
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removed_obj.obj_data['end_time'] = frame_time
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for c in self.callbacks['end']:
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c(self.name, removed_obj)
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del self.tracked_objects[id]
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# TODO: can i switch to looking this up and only changing when an event ends?
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# maybe make an api endpoint that pulls the thumbnail from the file system?
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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.obj_data['label']
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# if the object's thumbnail is not from the current frame
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if obj.false_positive or obj.thumbnail_data['frame_time'] != self.current_frame_time:
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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 older than desired, use the new object
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if (is_better_thumbnail(current_best.thumbnail_data, obj.thumbnail_data, self.camera_config.frame_shape)
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or (now - current_best.thumbnail_data['frame_time']) > self.camera_config.best_image_timeout):
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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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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.obj_data['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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# cleanup thumbnail frame cache
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current_thumb_frames = set([obj.thumbnail_data['frame_time'] for obj in self.tracked_objects.values() if not obj.false_positive])
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current_best_frames = set([obj.thumbnail_data['frame_time'] for obj in self.best_objects.values()])
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thumb_frames_to_delete = [t for t in self.frame_cache.keys() if not t in current_thumb_frames and not t in current_best_frames]
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for t in thumb_frames_to_delete:
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del self.frame_cache[t]
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with self.current_frame_lock:
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self._current_frame = current_frame
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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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class TrackedObjectProcessor(threading.Thread):
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def __init__(self, config: FrigateConfig, client, topic_prefix, tracked_objects_queue, event_queue, stop_event):
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threading.Thread.__init__(self)
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self.name = "detected_frames_processor"
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self.config = 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.frame_manager = SharedMemoryFrameManager()
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def start(camera, obj: TrackedObject):
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self.client.publish(f"{self.topic_prefix}/{camera}/events/start", json.dumps(obj.to_dict()), retain=False)
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self.event_queue.put(('start', camera, obj.to_dict()))
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def update(camera, obj: TrackedObject):
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pass
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def end(camera, obj: TrackedObject):
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self.client.publish(f"{self.topic_prefix}/{camera}/events/end", json.dumps(obj.to_dict()), retain=False)
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if self.config.cameras[camera].save_clips.enabled and not obj.false_positive:
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thumbnail_file_name = f"{camera}-{obj.obj_data['id']}.jpg"
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with open(os.path.join(self.config.save_clips.clips_dir, thumbnail_file_name), 'wb') as f:
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f.write(obj.get_jpg_bytes())
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self.event_queue.put(('end', camera, obj.to_dict()))
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def snapshot(camera, obj: TrackedObject):
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self.client.publish(f"{self.topic_prefix}/{camera}/{obj.obj_data['label']}/snapshot", obj.get_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.config.cameras.keys():
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camera_state = CameraState(camera, self.config, self.frame_manager)
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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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# {
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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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def get_best(self, camera, label):
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# TODO: need a lock here
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camera_state = self.camera_states[camera]
|
|
if label in camera_state.best_objects:
|
|
best_obj = camera_state.best_objects[label]
|
|
best = best_obj.to_dict()
|
|
best['frame'] = camera_state.frame_cache[best_obj.thumbnail_data['frame_time']]
|
|
return best
|
|
else:
|
|
return {}
|
|
|
|
def get_current_frame(self, camera, draw=False):
|
|
return self.camera_states[camera].get_current_frame(draw)
|
|
|
|
def run(self):
|
|
while True:
|
|
if self.stop_event.is_set():
|
|
logger.info(f"Exiting object processor...")
|
|
break
|
|
|
|
try:
|
|
camera, frame_time, current_tracked_objects = self.tracked_objects_queue.get(True, 10)
|
|
except queue.Empty:
|
|
continue
|
|
|
|
camera_state = self.camera_states[camera]
|
|
|
|
camera_state.update(frame_time, current_tracked_objects)
|
|
|
|
# update zone status for each label
|
|
for zone in self.config.cameras[camera].zones.keys():
|
|
# get labels for current camera and all labels in current zone
|
|
labels_for_camera = set([obj.obj_data['label'] for obj in camera_state.tracked_objects.values() if zone in obj.current_zones and not obj.false_positive])
|
|
labels_to_check = labels_for_camera | set(self.zone_data[zone].keys())
|
|
# for each label in zone
|
|
for label in labels_to_check:
|
|
camera_list = self.zone_data[zone][label]
|
|
# remove or add the camera to the list for the current label
|
|
previous_state = len(camera_list) > 0
|
|
if label in labels_for_camera:
|
|
camera_list.add(camera_state.name)
|
|
elif camera_state.name in camera_list:
|
|
camera_list.remove(camera_state.name)
|
|
new_state = len(camera_list) > 0
|
|
# if the value is changing, send over MQTT
|
|
if previous_state == False and new_state == True:
|
|
self.client.publish(f"{self.topic_prefix}/{zone}/{label}", 'ON', retain=False)
|
|
elif previous_state == True and new_state == False:
|
|
self.client.publish(f"{self.topic_prefix}/{zone}/{label}", 'OFF', retain=False)
|