mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-10-15 20:07:23 +02:00
770 lines
28 KiB
Python
770 lines
28 KiB
Python
import copy
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import base64
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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.const import RECORD_DIR, CLIPS_DIR, CACHE_DIR
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from frigate.edgetpu import load_labels
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from frigate.util import SharedMemoryFrameManager, draw_box_with_label, calculate_region
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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
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or box[1] == 0
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or box[2] == frame_shape[1] - 1
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or 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(
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current_thumb["box"], frame_shape
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):
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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"] + 0.05:
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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.last_updated = 0
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self.last_published = 0
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self.frame = None
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self.previous = self.to_dict()
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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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significant_update = False
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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(
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self.thumbnail_data, self.obj_data, self.camera_config.frame_shape
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):
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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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significant_update = True
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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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# if the zones changed, signal an update
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if not self.false_positive and set(self.current_zones) != set(current_zones):
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significant_update = True
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self.current_zones = current_zones
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return significant_update
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def to_dict(self, include_thumbnail: bool = False):
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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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"thumbnail": base64.b64encode(self.get_thumbnail()).decode("utf-8")
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if include_thumbnail
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else None,
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}
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def get_thumbnail(self):
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if (
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self.thumbnail_data is None
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or not self.thumbnail_data["frame_time"] in self.frame_cache
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):
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ret, jpg = cv2.imencode(".jpg", np.zeros((175, 175, 3), np.uint8))
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jpg_bytes = self.get_jpg_bytes(
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timestamp=False, bounding_box=False, crop=True, height=175
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)
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if jpg_bytes:
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return jpg_bytes
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else:
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ret, jpg = cv2.imencode(".jpg", np.zeros((175, 175, 3), np.uint8))
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return jpg.tobytes()
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def get_jpg_bytes(
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self, timestamp=False, bounding_box=False, crop=False, height=None
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):
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if self.thumbnail_data is None:
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return None
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try:
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best_frame = cv2.cvtColor(
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self.frame_cache[self.thumbnail_data["frame_time"]],
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cv2.COLOR_YUV2BGR_I420,
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)
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except KeyError:
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logger.warning(
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f"Unable to create jpg because frame {self.thumbnail_data['frame_time']} is not in the cache"
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)
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return None
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if bounding_box:
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thickness = 2
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color = COLOR_MAP[self.obj_data["label"]]
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# draw the bounding boxes on the frame
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box = self.thumbnail_data["box"]
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draw_box_with_label(
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best_frame,
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box[0],
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box[1],
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box[2],
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box[3],
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self.obj_data["label"],
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f"{int(self.thumbnail_data['score']*100)}% {int(self.thumbnail_data['area'])}",
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thickness=thickness,
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color=color,
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)
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if crop:
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box = self.thumbnail_data["box"]
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region = calculate_region(
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best_frame.shape, box[0], box[1], box[2], box[3], 1.1
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)
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best_frame = best_frame[region[1] : region[3], region[0] : region[2]]
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if height:
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width = int(height * best_frame.shape[1] / best_frame.shape[0])
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best_frame = cv2.resize(
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best_frame, dsize=(width, height), interpolation=cv2.INTER_AREA
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)
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if timestamp:
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time_to_show = datetime.datetime.fromtimestamp(
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self.thumbnail_data["frame_time"]
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).strftime("%m/%d/%Y %H:%M:%S")
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size = cv2.getTextSize(
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time_to_show, cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, thickness=2
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)
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text_width = size[0][0]
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desired_size = max(150, 0.33 * best_frame.shape[1])
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font_scale = desired_size / text_width
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cv2.putText(
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best_frame,
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time_to_show,
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(5, best_frame.shape[0] - 7),
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cv2.FONT_HERSHEY_SIMPLEX,
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fontScale=font_scale,
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color=(255, 255, 255),
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thickness=2,
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)
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ret, jpg = cv2.imencode(".jpg", best_frame, [int(cv2.IMWRITE_JPEG_QUALITY), 70])
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if ret:
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return jpg.tobytes()
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else:
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return None
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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_counts = defaultdict(lambda: 0)
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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.motion_boxes = []
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self.regions = []
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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_options={}):
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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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motion_boxes = self.motion_boxes.copy()
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regions = self.regions.copy()
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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_options.get("bounding_boxes"):
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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(
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frame_copy,
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box[0],
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box[1],
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box[2],
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box[3],
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obj["label"],
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f"{int(obj['score']*100)}% {int(obj['area'])}",
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thickness=thickness,
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color=color,
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)
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if draw_options.get("regions"):
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for region in regions:
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cv2.rectangle(
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frame_copy,
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(region[0], region[1]),
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(region[2], region[3]),
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(0, 255, 0),
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2,
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)
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if draw_options.get("zones"):
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for name, zone in self.camera_config.zones.items():
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thickness = (
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8
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if any(
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[
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name in obj["current_zones"]
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for obj in tracked_objects.values()
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]
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)
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else 2
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)
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cv2.drawContours(frame_copy, [zone.contour], -1, zone.color, thickness)
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if draw_options.get("mask"):
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mask_overlay = np.where(self.camera_config.motion.mask == [0])
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frame_copy[mask_overlay] = [0, 0, 0]
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if draw_options.get("motion_boxes"):
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for m_box in motion_boxes:
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cv2.rectangle(
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frame_copy,
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(m_box[0], m_box[1]),
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(m_box[2], m_box[3]),
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(0, 0, 255),
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2,
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)
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if draw_options.get("timestamp"):
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time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime(
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"%m/%d/%Y %H:%M:%S"
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)
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cv2.putText(
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frame_copy,
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time_to_show,
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(10, 30),
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cv2.FONT_HERSHEY_SIMPLEX,
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fontScale=0.8,
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color=(255, 255, 255),
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thickness=2,
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)
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return frame_copy
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def finished(self, obj_id):
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del self.tracked_objects[obj_id]
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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, motion_boxes, regions):
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self.current_frame_time = frame_time
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self.motion_boxes = motion_boxes
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self.regions = regions
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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(
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frame_id, self.camera_config.frame_shape_yuv
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)
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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(
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self.name, self.camera_config, self.frame_cache, current_detections[id]
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)
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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, frame_time)
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for id in updated_ids:
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updated_obj = self.tracked_objects[id]
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significant_update = updated_obj.update(frame_time, current_detections[id])
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if significant_update:
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# ensure this frame is stored in the cache
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if (
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updated_obj.thumbnail_data["frame_time"] == frame_time
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and frame_time not in self.frame_cache
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):
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self.frame_cache[frame_time] = np.copy(current_frame)
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updated_obj.last_updated = frame_time
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# if it has been more than 5 seconds since the last publish
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# and the last update is greater than the last publish
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if (
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frame_time - updated_obj.last_published > 5
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and updated_obj.last_updated > updated_obj.last_published
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):
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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, frame_time)
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updated_obj.last_published = frame_time
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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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if not "end_time" in removed_obj.obj_data:
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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, frame_time)
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# TODO: can i switch to looking this up and only changing when an event ends?
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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 (
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obj.false_positive
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or obj.thumbnail_data["frame_time"] != self.current_frame_time
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):
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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 (
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is_better_thumbnail(
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current_best.thumbnail_data,
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obj.thumbnail_data,
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self.camera_config.frame_shape,
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)
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or (now - current_best.thumbnail_data["frame_time"])
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> self.camera_config.best_image_timeout
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):
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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], frame_time)
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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], frame_time)
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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():
|
|
if count != self.object_counts[obj_name]:
|
|
self.object_counts[obj_name] = count
|
|
for c in self.callbacks["object_status"]:
|
|
c(self.name, obj_name, count)
|
|
|
|
# expire any objects that are >0 and no longer detected
|
|
expired_objects = [
|
|
obj_name
|
|
for obj_name, count in self.object_counts.items()
|
|
if count > 0 and not obj_name in obj_counter
|
|
]
|
|
for obj_name in expired_objects:
|
|
self.object_counts[obj_name] = 0
|
|
for c in self.callbacks["object_status"]:
|
|
c(self.name, obj_name, 0)
|
|
for c in self.callbacks["snapshot"]:
|
|
c(self.name, self.best_objects[obj_name], frame_time)
|
|
|
|
# cleanup thumbnail frame cache
|
|
current_thumb_frames = set(
|
|
[
|
|
obj.thumbnail_data["frame_time"]
|
|
for obj in self.tracked_objects.values()
|
|
if not obj.false_positive
|
|
]
|
|
)
|
|
current_best_frames = set(
|
|
[obj.thumbnail_data["frame_time"] for obj in self.best_objects.values()]
|
|
)
|
|
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
|
|
]
|
|
for t in thumb_frames_to_delete:
|
|
del self.frame_cache[t]
|
|
|
|
with self.current_frame_lock:
|
|
self._current_frame = current_frame
|
|
if not self.previous_frame_id is None:
|
|
self.frame_manager.delete(self.previous_frame_id)
|
|
self.previous_frame_id = frame_id
|
|
|
|
|
|
class TrackedObjectProcessor(threading.Thread):
|
|
def __init__(
|
|
self,
|
|
config: FrigateConfig,
|
|
client,
|
|
topic_prefix,
|
|
tracked_objects_queue,
|
|
event_queue,
|
|
event_processed_queue,
|
|
stop_event,
|
|
):
|
|
threading.Thread.__init__(self)
|
|
self.name = "detected_frames_processor"
|
|
self.config = config
|
|
self.client = client
|
|
self.topic_prefix = topic_prefix
|
|
self.tracked_objects_queue = tracked_objects_queue
|
|
self.event_queue = event_queue
|
|
self.event_processed_queue = event_processed_queue
|
|
self.stop_event = stop_event
|
|
self.camera_states: Dict[str, CameraState] = {}
|
|
self.frame_manager = SharedMemoryFrameManager()
|
|
|
|
def start(camera, obj: TrackedObject, current_frame_time):
|
|
self.event_queue.put(("start", camera, obj.to_dict()))
|
|
|
|
def update(camera, obj: TrackedObject, current_frame_time):
|
|
after = obj.to_dict()
|
|
message = {
|
|
"before": obj.previous,
|
|
"after": after,
|
|
"type": "new" if obj.previous["false_positive"] else "update",
|
|
}
|
|
self.client.publish(
|
|
f"{self.topic_prefix}/events", json.dumps(message), retain=False
|
|
)
|
|
obj.previous = after
|
|
|
|
def end(camera, obj: TrackedObject, current_frame_time):
|
|
snapshot_config = self.config.cameras[camera].snapshots
|
|
event_data = obj.to_dict(include_thumbnail=True)
|
|
event_data["has_snapshot"] = False
|
|
if not obj.false_positive:
|
|
message = {
|
|
"before": obj.previous,
|
|
"after": obj.to_dict(),
|
|
"type": "end",
|
|
}
|
|
self.client.publish(
|
|
f"{self.topic_prefix}/events", json.dumps(message), retain=False
|
|
)
|
|
# write snapshot to disk if enabled
|
|
if snapshot_config.enabled and self.should_save_snapshot(camera, obj):
|
|
jpg_bytes = obj.get_jpg_bytes(
|
|
timestamp=snapshot_config.timestamp,
|
|
bounding_box=snapshot_config.bounding_box,
|
|
crop=snapshot_config.crop,
|
|
height=snapshot_config.height,
|
|
)
|
|
if jpg_bytes is None:
|
|
logger.warning(
|
|
f"Unable to save snapshot for {obj.obj_data['id']}."
|
|
)
|
|
else:
|
|
with open(
|
|
os.path.join(
|
|
CLIPS_DIR, f"{camera}-{obj.obj_data['id']}.jpg"
|
|
),
|
|
"wb",
|
|
) as j:
|
|
j.write(jpg_bytes)
|
|
event_data["has_snapshot"] = True
|
|
self.event_queue.put(("end", camera, event_data))
|
|
|
|
def snapshot(camera, obj: TrackedObject, current_frame_time):
|
|
mqtt_config = self.config.cameras[camera].mqtt
|
|
if mqtt_config.enabled and self.should_mqtt_snapshot(camera, obj):
|
|
jpg_bytes = obj.get_jpg_bytes(
|
|
timestamp=mqtt_config.timestamp,
|
|
bounding_box=mqtt_config.bounding_box,
|
|
crop=mqtt_config.crop,
|
|
height=mqtt_config.height,
|
|
)
|
|
|
|
if jpg_bytes is None:
|
|
logger.warning(
|
|
f"Unable to send mqtt snapshot for {obj.obj_data['id']}."
|
|
)
|
|
else:
|
|
self.client.publish(
|
|
f"{self.topic_prefix}/{camera}/{obj.obj_data['label']}/snapshot",
|
|
jpg_bytes,
|
|
retain=True,
|
|
)
|
|
|
|
def object_status(camera, object_name, status):
|
|
self.client.publish(
|
|
f"{self.topic_prefix}/{camera}/{object_name}", status, retain=False
|
|
)
|
|
|
|
for camera in self.config.cameras.keys():
|
|
camera_state = CameraState(camera, self.config, self.frame_manager)
|
|
camera_state.on("start", start)
|
|
camera_state.on("update", update)
|
|
camera_state.on("end", end)
|
|
camera_state.on("snapshot", snapshot)
|
|
camera_state.on("object_status", object_status)
|
|
self.camera_states[camera] = camera_state
|
|
|
|
# {
|
|
# 'zone_name': {
|
|
# 'person': {
|
|
# 'camera_1': 2,
|
|
# 'camera_2': 1
|
|
# }
|
|
# }
|
|
# }
|
|
self.zone_data = defaultdict(lambda: defaultdict(lambda: {}))
|
|
|
|
def should_save_snapshot(self, camera, obj: TrackedObject):
|
|
# if there are required zones and there is no overlap
|
|
required_zones = self.config.cameras[camera].snapshots.required_zones
|
|
if len(required_zones) > 0 and not obj.entered_zones & set(required_zones):
|
|
logger.debug(
|
|
f"Not creating snapshot for {obj.obj_data['id']} because it did not enter required zones"
|
|
)
|
|
return False
|
|
|
|
return True
|
|
|
|
def should_mqtt_snapshot(self, camera, obj: TrackedObject):
|
|
# if there are required zones and there is no overlap
|
|
required_zones = self.config.cameras[camera].mqtt.required_zones
|
|
if len(required_zones) > 0 and not obj.entered_zones & set(required_zones):
|
|
logger.debug(
|
|
f"Not sending mqtt for {obj.obj_data['id']} because it did not enter required zones"
|
|
)
|
|
return False
|
|
|
|
return True
|
|
|
|
def get_best(self, camera, label):
|
|
# TODO: need a lock here
|
|
camera_state = self.camera_states[camera]
|
|
if label in camera_state.best_objects:
|
|
best_obj = camera_state.best_objects[label]
|
|
best = best_obj.thumbnail_data.copy()
|
|
best["frame"] = camera_state.frame_cache.get(
|
|
best_obj.thumbnail_data["frame_time"]
|
|
)
|
|
return best
|
|
else:
|
|
return {}
|
|
|
|
def get_current_frame(self, camera, draw_options={}):
|
|
return self.camera_states[camera].get_current_frame(draw_options)
|
|
|
|
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,
|
|
motion_boxes,
|
|
regions,
|
|
) = 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, motion_boxes, regions
|
|
)
|
|
|
|
# update zone counts for each label
|
|
# for each zone in the current camera
|
|
for zone in self.config.cameras[camera].zones.keys():
|
|
# count labels for the camera in the zone
|
|
obj_counter = Counter()
|
|
for obj in camera_state.tracked_objects.values():
|
|
if zone in obj.current_zones and not obj.false_positive:
|
|
obj_counter[obj.obj_data["label"]] += 1
|
|
|
|
# update counts and publish status
|
|
for label in set(
|
|
list(self.zone_data[zone].keys()) + list(obj_counter.keys())
|
|
):
|
|
# if we have previously published a count for this zone/label
|
|
zone_label = self.zone_data[zone][label]
|
|
if camera in zone_label:
|
|
current_count = sum(zone_label.values())
|
|
zone_label[camera] = (
|
|
obj_counter[label] if label in obj_counter else 0
|
|
)
|
|
new_count = sum(zone_label.values())
|
|
if new_count != current_count:
|
|
self.client.publish(
|
|
f"{self.topic_prefix}/{zone}/{label}",
|
|
new_count,
|
|
retain=False,
|
|
)
|
|
# if this is a new zone/label combo for this camera
|
|
else:
|
|
if label in obj_counter:
|
|
zone_label[camera] = obj_counter[label]
|
|
self.client.publish(
|
|
f"{self.topic_prefix}/{zone}/{label}",
|
|
obj_counter[label],
|
|
retain=False,
|
|
)
|
|
|
|
# cleanup event finished queue
|
|
while not self.event_processed_queue.empty():
|
|
event_id, camera = self.event_processed_queue.get()
|
|
self.camera_states[camera].finished(event_id)
|