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https://github.com/blakeblackshear/frigate.git
synced 2026-02-05 18:55:23 +03:00
Load labels dynamically to include custom events and audio, do not include attribute labels
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@ -12,6 +12,16 @@ PLUS_ENV_VAR = "PLUS_API_KEY"
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PLUS_API_HOST = "https://api.frigate.video"
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BTBN_PATH = "/usr/lib/btbn-ffmpeg"
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# Attributes
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ATTRIBUTE_LABEL_MAP = {
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"person": ["face", "amazon"],
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"car": ["ups", "fedex", "amazon", "license_plate"],
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}
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ALL_ATTRIBUTE_LABELS = [
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item for sublist in ATTRIBUTE_LABEL_MAP.values() for item in sublist
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]
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# Regex Consts
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REGEX_CAMERA_NAME = r"^[a-zA-Z0-9_-]+$"
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@ -29,7 +29,7 @@ from playhouse.shortcuts import model_to_dict
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from tzlocal import get_localzone_name
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from frigate.config import FrigateConfig
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from frigate.const import CLIPS_DIR, MAX_SEGMENT_DURATION, RECORD_DIR
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from frigate.const import ALL_ATTRIBUTE_LABELS, CLIPS_DIR, MAX_SEGMENT_DURATION, RECORD_DIR
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from frigate.events.external import ExternalEventProcessor
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from frigate.models import Event, Recordings, Timeline
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from frigate.object_processing import TrackedObject
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@ -410,6 +410,19 @@ def set_sub_label(id):
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)
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@bp.route("/labels")
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def get_labels():
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try:
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events = Event.select(Event.label).distinct()
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except Exception as e:
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return jsonify(
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{"success": False, "message": f"Failed to get labels: {e}"}, "404"
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)
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labels = sorted([e.label for e in events if e.label not in ALL_ATTRIBUTE_LABELS])
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return jsonify(labels)
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@bp.route("/sub_labels")
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def get_sub_labels():
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split_joined = request.args.get("split_joined", type=int)
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@ -15,7 +15,7 @@ import numpy as np
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from setproctitle import setproctitle
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from frigate.config import CameraConfig, DetectConfig
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from frigate.const import CACHE_DIR
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from frigate.const import ATTRIBUTE_LABEL_MAP, ALL_ATTRIBUTE_LABELS, CACHE_DIR
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from frigate.detectors.detector_config import PixelFormatEnum
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from frigate.log import LogPipe
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from frigate.motion import MotionDetector
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@ -723,14 +723,6 @@ def process_frames(
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stop_event,
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exit_on_empty: bool = False,
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):
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# attribute labels are not tracked and are not assigned regions
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attribute_label_map = {
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"person": ["face", "amazon"],
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"car": ["ups", "fedex", "amazon", "license_plate"],
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}
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all_attribute_labels = [
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item for sublist in attribute_label_map.values() for item in sublist
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]
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fps = process_info["process_fps"]
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detection_fps = process_info["detection_fps"]
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current_frame_time = process_info["detection_frame"]
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@ -906,7 +898,7 @@ def process_frames(
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tracked_detections = [
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d
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for d in consolidated_detections
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if d[0] not in all_attribute_labels
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if d[0] not in ALL_ATTRIBUTE_LABELS
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]
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# now that we have refined our detections, we need to track objects
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object_tracker.match_and_update(frame_time, tracked_detections)
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@ -916,7 +908,7 @@ def process_frames(
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# group the attribute detections based on what label they apply to
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attribute_detections = {}
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for label, attribute_labels in attribute_label_map.items():
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for label, attribute_labels in ATTRIBUTE_LABEL_MAP.items():
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attribute_detections[label] = [
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d for d in consolidated_detections if d[0] in attribute_labels
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]
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@ -106,6 +106,7 @@ export default function Events({ path, ...props }) {
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const { data: config } = useSWR('config');
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const { data: allLabels } = useSWR(['labels']);
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const { data: allSubLabels } = useSWR(['sub_labels', { split_joined: 1 }]);
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const filterValues = useMemo(
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@ -120,15 +121,10 @@ export default function Events({ path, ...props }) {
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.filter((value, i, self) => self.indexOf(value) === i),
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'None',
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],
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labels: Object.values(config?.cameras || {})
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.reduce((memo, camera) => {
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memo = memo.concat(camera?.objects?.track || []);
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return memo;
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}, config?.objects?.track || [])
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.filter((value, i, self) => self.indexOf(value) === i),
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labels: Object.values(allLabels || {}),
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sub_labels: (allSubLabels || []).length > 0 ? [...Object.values(allSubLabels), 'None'] : [],
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}),
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[config, allSubLabels]
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[config, allLabels, allSubLabels]
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);
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const onSave = async (e, eventId, save) => {
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