mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-07-26 13:47:03 +02:00
Fixes (#18117)
* face library i18n fixes * face library i18n fixes * add ability to use ctrl/cmd S to save in the config editor * Use datetime as ID * Update metrics inference speed to start with 0 ms * fix android formatted thumbnail * ensure role is comma separated and stripped correctly * improve face library deletion - add a confirmation dialog - add ability to select all / delete faces in collections * Implement lazy loading for video previews * Force GPU for large embedding model * GPU is required * settings i18n fixes * Don't delete train tab * webpush debugging logs * Fix incorrectly copying zones * copy path data * Ensure that cache dir exists for Frigate+ * face docs update * Add description to upload image step to clarify the image * Clean up --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
This commit is contained in:
Josh Hawkins
GitHub
parent
52d94231c7
commit
8094dd4075
@ -34,7 +34,7 @@ All of these features run locally on your system.
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The `small` model is optimized for efficiency and runs on the CPU, most CPUs should run the model efficiently.
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The `large` model is optimized for accuracy, an integrated or discrete GPU is highly recommended. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
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The `large` model is optimized for accuracy, an integrated or discrete GPU is required. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
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## Configuration
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@ -107,17 +107,17 @@ When choosing images to include in the face training set it is recommended to al
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### Step 1 - Building a Strong Foundation
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When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-5 "portrait" photos for each person. It is important that the person's face in the photo is straight-on and not turned which will ensure a good starting point.
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When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-5 photos containing just this person's face. It is important that the person's face in the photo is front-facing and not turned, this will ensure a good starting point.
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Then it is recommended to use the `Face Library` tab in Frigate to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are straight-on. Ignore images from cameras that recognize faces from an angle.
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Then it is recommended to use the `Face Library` tab in Frigate to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are front-facing. Ignore images from cameras that recognize faces from an angle.
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Aim to strike a balance between the quality of images while also having a range of conditions (day / night, different weather conditions, different times of day, etc.) in order to have diversity in the images used for each person and not have over-fitting.
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Once a person starts to be consistently recognized correctly on images that are straight-on, it is time to move on to the next step.
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Once a person starts to be consistently recognized correctly on images that are front-facing, it is time to move on to the next step.
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### Step 2 - Expanding The Dataset
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Once straight-on images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone.
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Once front-facing images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone.
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## FAQ
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@ -156,3 +156,7 @@ Face recognition does not run on the recording stream, this would be suboptimal
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### I get an unknown error when taking a photo directly with my iPhone
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By default iOS devices will use HEIC (High Efficiency Image Container) for images, but this format is not supported for uploads. Choosing `large` as the format instead of `original` will use JPG which will work correctly.
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## How can I delete the face database and start over?
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Frigate does not store anything in its database related to face recognition. You can simply delete all of your faces through the Frigate UI or remove the contents of the `/media/frigate/clips/faces` directory.
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@ -268,7 +268,9 @@ def auth(request: Request):
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# if comma-separated with "admin", use "admin", else use default role
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success_response.headers["remote-role"] = (
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"admin" if role and "admin" in role else proxy_config.default_role
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"admin"
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if role and "admin" in [r.strip() for r in role.split(",")]
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else proxy_config.default_role
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)
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return success_response
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@ -1,10 +1,9 @@
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"""Object classification APIs."""
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import datetime
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import logging
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import os
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import random
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import shutil
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import string
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import cv2
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from fastapi import APIRouter, Depends, Request, UploadFile
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@ -120,8 +119,7 @@ def train_face(request: Request, name: str, body: dict = None):
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)
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sanitized_name = sanitize_filename(name)
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rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
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new_name = f"{sanitized_name}-{rand_id}.webp"
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new_name = f"{sanitized_name}-{datetime.datetime.now().timestamp()}.webp"
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new_file_folder = os.path.join(FACE_DIR, f"{sanitized_name}")
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if not os.path.exists(new_file_folder):
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@ -909,7 +909,7 @@ def event_thumbnail(
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elif extension == "webp":
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quality_params = [int(cv2.IMWRITE_WEBP_QUALITY), 60]
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_, img = cv2.imencode(f".{img}", thumbnail, quality_params)
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_, img = cv2.imencode(f".{extension}", thumbnail, quality_params)
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thumbnail_bytes = img.tobytes()
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return Response(
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@ -303,6 +303,9 @@ class WebPushClient(Communicator): # type: ignore[misc]
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and len(payload["before"]["data"]["zones"])
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== len(payload["after"]["data"]["zones"])
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):
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logger.debug(
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f"Skipping notification for {camera} - message is an update and important fields don't have an update"
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)
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return
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self.last_camera_notification_time[camera] = current_time
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@ -325,6 +328,8 @@ class WebPushClient(Communicator): # type: ignore[misc]
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direct_url = f"/review?id={reviewId}" if state == "end" else f"/#{camera}"
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ttl = 3600 if state == "end" else 0
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logger.debug(f"Sending push notification for {camera}, review ID {reviewId}")
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for user in self.web_pushers:
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self.send_push_notification(
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user=user,
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@ -25,7 +25,7 @@ from frigate.comms.event_metadata_updater import (
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from frigate.const import CLIPS_DIR
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from frigate.embeddings.onnx.lpr_embedding import LPR_EMBEDDING_SIZE
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from frigate.types import TrackedObjectUpdateTypesEnum
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from frigate.util.builtin import EventsPerSecond
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from frigate.util.builtin import EventsPerSecond, InferenceSpeed
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from frigate.util.image import area
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logger = logging.getLogger(__name__)
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@ -36,8 +36,10 @@ WRITE_DEBUG_IMAGES = False
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class LicensePlateProcessingMixin:
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.plate_rec_speed = InferenceSpeed(self.metrics.alpr_speed)
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self.plates_rec_second = EventsPerSecond()
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self.plates_rec_second.start()
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self.plate_det_speed = InferenceSpeed(self.metrics.yolov9_lpr_speed)
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self.plates_det_second = EventsPerSecond()
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self.plates_det_second.start()
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self.event_metadata_publisher = EventMetadataPublisher()
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@ -1157,22 +1159,6 @@ class LicensePlateProcessingMixin:
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# 5. Return True if previous plate scores higher
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return prev_score > curr_score
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def __update_yolov9_metrics(self, duration: float) -> None:
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"""
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Update inference metrics.
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"""
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self.metrics.yolov9_lpr_speed.value = (
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self.metrics.yolov9_lpr_speed.value * 9 + duration
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) / 10
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def __update_lpr_metrics(self, duration: float) -> None:
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"""
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Update inference metrics.
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"""
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self.metrics.alpr_speed.value = (
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self.metrics.alpr_speed.value * 9 + duration
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) / 10
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def _generate_plate_event(self, camera: str, plate: str, plate_score: float) -> str:
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"""Generate a unique ID for a plate event based on camera and text."""
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now = datetime.datetime.now().timestamp()
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@ -1228,7 +1214,7 @@ class LicensePlateProcessingMixin:
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f"{camera}: YOLOv9 LPD inference time: {(datetime.datetime.now().timestamp() - yolov9_start) * 1000:.2f} ms"
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)
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self.plates_det_second.update()
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self.__update_yolov9_metrics(
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self.plate_det_speed.update(
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datetime.datetime.now().timestamp() - yolov9_start
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)
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@ -1319,7 +1305,7 @@ class LicensePlateProcessingMixin:
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f"{camera}: YOLOv9 LPD inference time: {(datetime.datetime.now().timestamp() - yolov9_start) * 1000:.2f} ms"
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)
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self.plates_det_second.update()
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self.__update_yolov9_metrics(
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self.plate_det_speed.update(
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datetime.datetime.now().timestamp() - yolov9_start
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)
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@ -1433,7 +1419,7 @@ class LicensePlateProcessingMixin:
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camera, id, license_plate_frame
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)
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self.plates_rec_second.update()
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self.__update_lpr_metrics(datetime.datetime.now().timestamp() - start)
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self.plate_rec_speed.update(datetime.datetime.now().timestamp() - start)
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if license_plates:
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for plate, confidence, text_area in zip(license_plates, confidences, areas):
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@ -5,9 +5,7 @@ import datetime
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import json
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import logging
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import os
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import random
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import shutil
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import string
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from typing import Optional
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import cv2
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@ -27,7 +25,7 @@ from frigate.data_processing.common.face.model import (
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FaceRecognizer,
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)
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from frigate.types import TrackedObjectUpdateTypesEnum
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from frigate.util.builtin import EventsPerSecond
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from frigate.util.builtin import EventsPerSecond, InferenceSpeed
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from frigate.util.image import area
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from ..types import DataProcessorMetrics
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@ -58,6 +56,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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self.person_face_history: dict[str, list[tuple[str, float, int]]] = {}
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self.recognizer: FaceRecognizer | None = None
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self.faces_per_second = EventsPerSecond()
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self.inference_speed = InferenceSpeed(self.metrics.face_rec_speed)
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download_path = os.path.join(MODEL_CACHE_DIR, "facedet")
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self.model_files = {
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@ -155,9 +154,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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def __update_metrics(self, duration: float) -> None:
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self.faces_per_second.update()
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self.metrics.face_rec_speed.value = (
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self.metrics.face_rec_speed.value * 9 + duration
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) / 10
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self.inference_speed.update(duration)
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def process_frame(self, obj_data: dict[str, any], frame: np.ndarray):
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"""Look for faces in image."""
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@ -343,11 +340,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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return {"success": True, "score": score, "face_name": sub_label}
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elif topic == EmbeddingsRequestEnum.register_face.value:
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rand_id = "".join(
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random.choices(string.ascii_lowercase + string.digits, k=6)
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)
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label = request_data["face_name"]
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id = f"{label}-{rand_id}"
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if request_data.get("cropped"):
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thumbnail = request_data["image"]
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@ -376,7 +369,9 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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# write face to library
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folder = os.path.join(FACE_DIR, label)
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file = os.path.join(folder, f"{id}.webp")
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file = os.path.join(
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folder, f"{label}_{datetime.datetime.now().timestamp()}.webp"
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)
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os.makedirs(folder, exist_ok=True)
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# save face image
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@ -7,7 +7,9 @@ from multiprocessing.sharedctypes import Synchronized
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class DataProcessorMetrics:
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image_embeddings_speed: Synchronized
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image_embeddings_eps: Synchronized
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text_embeddings_speed: Synchronized
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text_embeddings_eps: Synchronized
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face_rec_speed: Synchronized
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face_rec_fps: Synchronized
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alpr_speed: Synchronized
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@ -16,15 +18,15 @@ class DataProcessorMetrics:
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yolov9_lpr_pps: Synchronized
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def __init__(self):
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self.image_embeddings_speed = mp.Value("d", 0.01)
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self.image_embeddings_speed = mp.Value("d", 0.0)
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self.image_embeddings_eps = mp.Value("d", 0.0)
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self.text_embeddings_speed = mp.Value("d", 0.01)
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self.text_embeddings_speed = mp.Value("d", 0.0)
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self.text_embeddings_eps = mp.Value("d", 0.0)
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self.face_rec_speed = mp.Value("d", 0.01)
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self.face_rec_speed = mp.Value("d", 0.0)
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self.face_rec_fps = mp.Value("d", 0.0)
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self.alpr_speed = mp.Value("d", 0.01)
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self.alpr_speed = mp.Value("d", 0.0)
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self.alpr_pps = mp.Value("d", 0.0)
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self.yolov9_lpr_speed = mp.Value("d", 0.01)
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self.yolov9_lpr_speed = mp.Value("d", 0.0)
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self.yolov9_lpr_pps = mp.Value("d", 0.0)
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@ -126,6 +126,9 @@ class ModelConfig(BaseModel):
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if not self.path or not self.path.startswith("plus://"):
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return
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# ensure that model cache dir exists
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os.makedirs(MODEL_CACHE_DIR, exist_ok=True)
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model_id = self.path[7:]
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self.path = os.path.join(MODEL_CACHE_DIR, model_id)
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model_info_path = f"{self.path}.json"
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@ -235,7 +235,7 @@ class EmbeddingsContext:
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if os.path.isfile(file_path):
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os.unlink(file_path)
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if len(os.listdir(folder)) == 0:
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if face != "train" and len(os.listdir(folder)) == 0:
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os.rmdir(folder)
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self.requestor.send_data(
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@ -21,7 +21,7 @@ from frigate.data_processing.types import DataProcessorMetrics
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from frigate.db.sqlitevecq import SqliteVecQueueDatabase
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from frigate.models import Event
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from frigate.types import ModelStatusTypesEnum
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from frigate.util.builtin import EventsPerSecond, serialize
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from frigate.util.builtin import EventsPerSecond, InferenceSpeed, serialize
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from frigate.util.path import get_event_thumbnail_bytes
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from .onnx.jina_v1_embedding import JinaV1ImageEmbedding, JinaV1TextEmbedding
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@ -75,8 +75,10 @@ class Embeddings:
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self.metrics = metrics
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self.requestor = InterProcessRequestor()
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self.image_inference_speed = InferenceSpeed(self.metrics.image_embeddings_speed)
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self.image_eps = EventsPerSecond()
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self.image_eps.start()
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self.text_inference_speed = InferenceSpeed(self.metrics.text_embeddings_speed)
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self.text_eps = EventsPerSecond()
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self.text_eps.start()
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@ -183,10 +185,7 @@ class Embeddings:
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(event_id, serialize(embedding)),
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)
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duration = datetime.datetime.now().timestamp() - start
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self.metrics.image_embeddings_speed.value = (
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self.metrics.image_embeddings_speed.value * 9 + duration
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) / 10
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self.image_inference_speed.update(datetime.datetime.now().timestamp() - start)
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self.image_eps.update()
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return embedding
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@ -220,9 +219,7 @@ class Embeddings:
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)
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duration = datetime.datetime.now().timestamp() - start
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self.metrics.text_embeddings_speed.value = (
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self.metrics.text_embeddings_speed.value * 9 + (duration / len(ids))
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) / 10
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self.text_inference_speed.update(duration / len(ids))
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return embeddings
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@ -241,10 +238,7 @@ class Embeddings:
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(event_id, serialize(embedding)),
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)
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duration = datetime.datetime.now().timestamp() - start
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self.metrics.text_embeddings_speed.value = (
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self.metrics.text_embeddings_speed.value * 9 + duration
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) / 10
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self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
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self.text_eps.update()
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return embedding
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@ -276,10 +270,7 @@ class Embeddings:
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items,
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)
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duration = datetime.datetime.now().timestamp() - start
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self.metrics.text_embeddings_speed.value = (
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self.metrics.text_embeddings_speed.value * 9 + (duration / len(ids))
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) / 10
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self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
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return embeddings
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@ -23,10 +23,7 @@ FACENET_INPUT_SIZE = 160
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class FaceNetEmbedding(BaseEmbedding):
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def __init__(
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self,
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device: str = "AUTO",
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):
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def __init__(self):
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super().__init__(
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model_name="facedet",
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model_file="facenet.tflite",
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@ -34,7 +31,6 @@ class FaceNetEmbedding(BaseEmbedding):
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"facenet.tflite": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/facenet.tflite",
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},
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)
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self.device = device
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self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
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self.tokenizer = None
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self.feature_extractor = None
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@ -113,10 +109,7 @@ class FaceNetEmbedding(BaseEmbedding):
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class ArcfaceEmbedding(BaseEmbedding):
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def __init__(
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self,
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device: str = "AUTO",
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):
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def __init__(self):
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super().__init__(
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model_name="facedet",
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model_file="arcface.onnx",
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@ -124,7 +117,6 @@ class ArcfaceEmbedding(BaseEmbedding):
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"arcface.onnx": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/arcface.onnx",
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},
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)
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self.device = device
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self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
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self.tokenizer = None
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self.feature_extractor = None
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@ -154,7 +146,7 @@ class ArcfaceEmbedding(BaseEmbedding):
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self.runner = ONNXModelRunner(
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os.path.join(self.download_path, self.model_file),
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self.device,
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"GPU",
|
||||
)
|
||||
|
||||
def _preprocess_inputs(self, raw_inputs):
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
"""Maintain review segments in db."""
|
||||
|
||||
import copy
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
@ -119,21 +120,23 @@ class PendingReviewSegment:
|
||||
)
|
||||
|
||||
def get_data(self, ended: bool) -> dict:
|
||||
return {
|
||||
ReviewSegment.id.name: self.id,
|
||||
ReviewSegment.camera.name: self.camera,
|
||||
ReviewSegment.start_time.name: self.start_time,
|
||||
ReviewSegment.end_time.name: self.last_update if ended else None,
|
||||
ReviewSegment.severity.name: self.severity.value,
|
||||
ReviewSegment.thumb_path.name: self.frame_path,
|
||||
ReviewSegment.data.name: {
|
||||
"detections": list(set(self.detections.keys())),
|
||||
"objects": list(set(self.detections.values())),
|
||||
"sub_labels": list(self.sub_labels.values()),
|
||||
"zones": self.zones,
|
||||
"audio": list(self.audio),
|
||||
},
|
||||
}.copy()
|
||||
return copy.deepcopy(
|
||||
{
|
||||
ReviewSegment.id.name: self.id,
|
||||
ReviewSegment.camera.name: self.camera,
|
||||
ReviewSegment.start_time.name: self.start_time,
|
||||
ReviewSegment.end_time.name: self.last_update if ended else None,
|
||||
ReviewSegment.severity.name: self.severity.value,
|
||||
ReviewSegment.thumb_path.name: self.frame_path,
|
||||
ReviewSegment.data.name: {
|
||||
"detections": list(set(self.detections.keys())),
|
||||
"objects": list(set(self.detections.values())),
|
||||
"sub_labels": list(self.sub_labels.values()),
|
||||
"zones": self.zones,
|
||||
"audio": list(self.audio),
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
class ReviewSegmentMaintainer(threading.Thread):
|
||||
|
||||
@ -154,7 +154,7 @@ class TrackedObject:
|
||||
"attributes": obj_data["attributes"],
|
||||
"current_estimated_speed": self.current_estimated_speed,
|
||||
"velocity_angle": self.velocity_angle,
|
||||
"path_data": self.path_data,
|
||||
"path_data": self.path_data.copy(),
|
||||
"recognized_license_plate": obj_data.get(
|
||||
"recognized_license_plate"
|
||||
),
|
||||
@ -378,7 +378,7 @@ class TrackedObject:
|
||||
"current_estimated_speed": self.current_estimated_speed,
|
||||
"average_estimated_speed": self.average_estimated_speed,
|
||||
"velocity_angle": self.velocity_angle,
|
||||
"path_data": self.path_data,
|
||||
"path_data": self.path_data.copy(),
|
||||
"recognized_license_plate": self.obj_data.get("recognized_license_plate"),
|
||||
}
|
||||
|
||||
|
||||
@ -11,6 +11,7 @@ import shlex
|
||||
import struct
|
||||
import urllib.parse
|
||||
from collections.abc import Mapping
|
||||
from multiprocessing.sharedctypes import Synchronized
|
||||
from pathlib import Path
|
||||
from typing import Any, Optional, Tuple, Union
|
||||
from zoneinfo import ZoneInfoNotFoundError
|
||||
@ -26,16 +27,16 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class EventsPerSecond:
|
||||
def __init__(self, max_events=1000, last_n_seconds=10):
|
||||
def __init__(self, max_events=1000, last_n_seconds=10) -> None:
|
||||
self._start = None
|
||||
self._max_events = max_events
|
||||
self._last_n_seconds = last_n_seconds
|
||||
self._timestamps = []
|
||||
|
||||
def start(self):
|
||||
def start(self) -> None:
|
||||
self._start = datetime.datetime.now().timestamp()
|
||||
|
||||
def update(self):
|
||||
def update(self) -> None:
|
||||
now = datetime.datetime.now().timestamp()
|
||||
if self._start is None:
|
||||
self._start = now
|
||||
@ -45,7 +46,7 @@ class EventsPerSecond:
|
||||
self._timestamps = self._timestamps[(1 - self._max_events) :]
|
||||
self.expire_timestamps(now)
|
||||
|
||||
def eps(self):
|
||||
def eps(self) -> float:
|
||||
now = datetime.datetime.now().timestamp()
|
||||
if self._start is None:
|
||||
self._start = now
|
||||
@ -58,12 +59,29 @@ class EventsPerSecond:
|
||||
return len(self._timestamps) / seconds
|
||||
|
||||
# remove aged out timestamps
|
||||
def expire_timestamps(self, now):
|
||||
def expire_timestamps(self, now: float) -> None:
|
||||
threshold = now - self._last_n_seconds
|
||||
while self._timestamps and self._timestamps[0] < threshold:
|
||||
del self._timestamps[0]
|
||||
|
||||
|
||||
class InferenceSpeed:
|
||||
def __init__(self, metric: Synchronized) -> None:
|
||||
self.__metric = metric
|
||||
self.__initialized = False
|
||||
|
||||
def update(self, inference_time: float) -> None:
|
||||
if not self.__initialized:
|
||||
self.__metric.value = inference_time
|
||||
self.__initialized = True
|
||||
return
|
||||
|
||||
self.__metric.value = (self.__metric.value * 9 + inference_time) / 10
|
||||
|
||||
def current(self) -> float:
|
||||
return self.__metric.value
|
||||
|
||||
|
||||
def deep_merge(dct1: dict, dct2: dict, override=False, merge_lists=False) -> dict:
|
||||
"""
|
||||
:param dct1: First dict to merge
|
||||
|
||||
@ -8,14 +8,16 @@
|
||||
"subLabelScore": "Sub Label Score",
|
||||
"scoreInfo": "The sub label score is the weighted score for all of the recognized face confidences, so this may differ from the score shown on the snapshot.",
|
||||
"face": "Face Details",
|
||||
"faceDesc": "Details for the face and associated object",
|
||||
"timestamp": "Timestamp"
|
||||
"faceDesc": "Details of the tracked object that generated this face",
|
||||
"timestamp": "Timestamp",
|
||||
"unknown": "Unknown"
|
||||
},
|
||||
"documentTitle": "Face Library - Frigate",
|
||||
"uploadFaceImage": {
|
||||
"title": "Upload Face Image",
|
||||
"desc": "Upload an image to scan for faces and include for {{pageToggle}}"
|
||||
},
|
||||
"collections": "Collections",
|
||||
"createFaceLibrary": {
|
||||
"title": "Create Collection",
|
||||
"desc": "Create a new collection",
|
||||
@ -25,7 +27,10 @@
|
||||
"steps": {
|
||||
"faceName": "Enter Face Name",
|
||||
"uploadFace": "Upload Face Image",
|
||||
"nextSteps": "Next Steps"
|
||||
"nextSteps": "Next Steps",
|
||||
"description": {
|
||||
"uploadFace": "Upload an image of {{name}} that shows their face from a front-facing angle. The image does not need to be cropped to just their face."
|
||||
}
|
||||
},
|
||||
"train": {
|
||||
"title": "Train",
|
||||
@ -38,12 +43,17 @@
|
||||
"title": "Delete Name",
|
||||
"desc": "Are you sure you want to delete the collection {{name}}? This will permanently delete all associated faces."
|
||||
},
|
||||
"deleteFaceAttempts": {
|
||||
"title": "Delete Faces",
|
||||
"desc_one": "Are you sure you want to delete {{count}} face? This action cannot be undone.",
|
||||
"desc_other": "Are you sure you want to delete {{count}} faces? This action cannot be undone."
|
||||
},
|
||||
"renameFace": {
|
||||
"title": "Rename Face",
|
||||
"desc": "Enter a new name for {{name}}"
|
||||
},
|
||||
"button": {
|
||||
"deleteFaceAttempts": "Delete Face Attempts",
|
||||
"deleteFaceAttempts": "Delete Faces",
|
||||
"addFace": "Add Face",
|
||||
"renameFace": "Rename Face",
|
||||
"deleteFace": "Delete Face",
|
||||
|
||||
@ -84,6 +84,7 @@
|
||||
},
|
||||
"classification": {
|
||||
"title": "Classification Settings",
|
||||
"unsavedChanges": "Unsaved Classification settings changes",
|
||||
"birdClassification": {
|
||||
"title": "Bird Classification",
|
||||
"desc": "Bird classification identifies known birds using a quantized Tensorflow model. When a known bird is recognized, its common name will be added as a sub_label. This information is included in the UI, filters, as well as in notifications."
|
||||
@ -168,11 +169,12 @@
|
||||
"notSelectDetections": "All {{detectionsLabels}} objects detected in {{zone}} on {{cameraName}} not categorized as Alerts will be shown as Detections regardless of which zone they are in.",
|
||||
"regardlessOfZoneObjectDetectionsTips": "All {{detectionsLabels}} objects not categorized on {{cameraName}} will be shown as Detections regardless of which zone they are in."
|
||||
},
|
||||
"unsavedChanges": "Unsaved Review Classification settings for {{camera}}",
|
||||
"selectAlertsZones": "Select zones for Alerts",
|
||||
"selectDetectionsZones": "Select zones for Detections",
|
||||
"limitDetections": "Limit detections to specific zones",
|
||||
"toast": {
|
||||
"success": "Review classification configuration has been saved. Restart Frigate to apply changes."
|
||||
"success": "Review Classification configuration has been saved. Restart Frigate to apply changes."
|
||||
}
|
||||
}
|
||||
},
|
||||
@ -338,6 +340,7 @@
|
||||
},
|
||||
"motionDetectionTuner": {
|
||||
"title": "Motion Detection Tuner",
|
||||
"unsavedChanges": "Unsaved Motion Tuner changes ({{camera}})",
|
||||
"desc": {
|
||||
"title": "Frigate uses motion detection as a first line check to see if there is anything happening in the frame worth checking with object detection.",
|
||||
"documentation": "Read the Motion Tuning Guide"
|
||||
@ -527,6 +530,8 @@
|
||||
"registerDevice": "Register This Device",
|
||||
"unregisterDevice": "Unregister This Device",
|
||||
"sendTestNotification": "Send a test notification",
|
||||
"unsavedRegistrations": "Unsaved Notification registrations",
|
||||
"unsavedChanges": "Unsaved Notification changes",
|
||||
"active": "Notifications Active",
|
||||
"suspended": "Notifications suspended {{time}}",
|
||||
"suspendTime": {
|
||||
@ -587,6 +592,7 @@
|
||||
"loadingAvailableModels": "Loading available models…",
|
||||
"modelSelect": "Your available models on Frigate+ can be selected here. Note that only models compatible with your current detector configuration can be selected."
|
||||
},
|
||||
"unsavedChanges": "Unsaved Frigate+ settings changes",
|
||||
"restart_required": "Restart required (Frigate+ model changed)",
|
||||
"toast": {
|
||||
"success": "Frigate+ settings have been saved. Restart Frigate to apply changes.",
|
||||
|
||||
@ -128,13 +128,18 @@ export default function CreateFaceWizardDialog({
|
||||
</TextEntry>
|
||||
)}
|
||||
{step == 1 && (
|
||||
<ImageEntry onSave={onUploadImage}>
|
||||
<div className="flex justify-end py-2">
|
||||
<Button variant="select" type="submit">
|
||||
{t("button.next", { ns: "common" })}
|
||||
</Button>
|
||||
<>
|
||||
<div className="px-8 py-2 text-center text-sm text-secondary-foreground">
|
||||
{t("steps.description.uploadFace", { name })}
|
||||
</div>
|
||||
</ImageEntry>
|
||||
<ImageEntry onSave={onUploadImage}>
|
||||
<div className="flex justify-end py-2">
|
||||
<Button variant="select" type="submit">
|
||||
{t("button.next", { ns: "common" })}
|
||||
</Button>
|
||||
</div>
|
||||
</ImageEntry>
|
||||
</>
|
||||
)}
|
||||
{step == 2 && (
|
||||
<div className="mt-2">
|
||||
|
||||
@ -23,6 +23,7 @@ import {
|
||||
import { useTranslation } from "react-i18next";
|
||||
|
||||
type PreviewPlayerProps = {
|
||||
previewRef?: (ref: HTMLDivElement | null) => void;
|
||||
className?: string;
|
||||
camera: string;
|
||||
timeRange: TimeRange;
|
||||
@ -30,16 +31,19 @@ type PreviewPlayerProps = {
|
||||
startTime?: number;
|
||||
isScrubbing: boolean;
|
||||
forceAspect?: number;
|
||||
isVisible?: boolean;
|
||||
onControllerReady: (controller: PreviewController) => void;
|
||||
onClick?: () => void;
|
||||
};
|
||||
export default function PreviewPlayer({
|
||||
previewRef,
|
||||
className,
|
||||
camera,
|
||||
timeRange,
|
||||
cameraPreviews,
|
||||
startTime,
|
||||
isScrubbing,
|
||||
isVisible = true,
|
||||
onControllerReady,
|
||||
onClick,
|
||||
}: PreviewPlayerProps) {
|
||||
@ -54,6 +58,7 @@ export default function PreviewPlayer({
|
||||
if (currentPreview) {
|
||||
return (
|
||||
<PreviewVideoPlayer
|
||||
visibilityRef={previewRef}
|
||||
className={className}
|
||||
camera={camera}
|
||||
timeRange={timeRange}
|
||||
@ -61,6 +66,7 @@ export default function PreviewPlayer({
|
||||
initialPreview={currentPreview}
|
||||
startTime={startTime}
|
||||
isScrubbing={isScrubbing}
|
||||
isVisible={isVisible}
|
||||
currentHourFrame={currentHourFrame}
|
||||
onControllerReady={onControllerReady}
|
||||
onClick={onClick}
|
||||
@ -110,6 +116,7 @@ export abstract class PreviewController {
|
||||
}
|
||||
|
||||
type PreviewVideoPlayerProps = {
|
||||
visibilityRef?: (ref: HTMLDivElement | null) => void;
|
||||
className?: string;
|
||||
camera: string;
|
||||
timeRange: TimeRange;
|
||||
@ -117,12 +124,14 @@ type PreviewVideoPlayerProps = {
|
||||
initialPreview?: Preview;
|
||||
startTime?: number;
|
||||
isScrubbing: boolean;
|
||||
isVisible: boolean;
|
||||
currentHourFrame?: string;
|
||||
onControllerReady: (controller: PreviewVideoController) => void;
|
||||
onClick?: () => void;
|
||||
setCurrentHourFrame: (src: string | undefined) => void;
|
||||
};
|
||||
function PreviewVideoPlayer({
|
||||
visibilityRef,
|
||||
className,
|
||||
camera,
|
||||
timeRange,
|
||||
@ -130,6 +139,7 @@ function PreviewVideoPlayer({
|
||||
initialPreview,
|
||||
startTime,
|
||||
isScrubbing,
|
||||
isVisible,
|
||||
currentHourFrame,
|
||||
onControllerReady,
|
||||
onClick,
|
||||
@ -267,11 +277,13 @@ function PreviewVideoPlayer({
|
||||
|
||||
return (
|
||||
<div
|
||||
ref={visibilityRef}
|
||||
className={cn(
|
||||
"relative flex w-full justify-center overflow-hidden rounded-lg bg-black md:rounded-2xl",
|
||||
onClick && "cursor-pointer",
|
||||
className,
|
||||
)}
|
||||
data-camera={camera}
|
||||
onClick={onClick}
|
||||
>
|
||||
<img
|
||||
@ -286,45 +298,48 @@ function PreviewVideoPlayer({
|
||||
previewRef.current?.load();
|
||||
}}
|
||||
/>
|
||||
<video
|
||||
ref={previewRef}
|
||||
className={`absolute size-full ${currentHourFrame ? "invisible" : "visible"}`}
|
||||
preload="auto"
|
||||
autoPlay
|
||||
playsInline
|
||||
muted
|
||||
disableRemotePlayback
|
||||
onSeeked={onPreviewSeeked}
|
||||
onLoadedData={() => {
|
||||
if (firstLoad) {
|
||||
setFirstLoad(false);
|
||||
}
|
||||
|
||||
if (controller) {
|
||||
controller.previewReady();
|
||||
} else {
|
||||
previewRef.current?.pause();
|
||||
}
|
||||
|
||||
if (previewRef.current) {
|
||||
setVideoSize([
|
||||
previewRef.current.videoWidth,
|
||||
previewRef.current.videoHeight,
|
||||
]);
|
||||
|
||||
if (startTime && currentPreview) {
|
||||
previewRef.current.currentTime = startTime - currentPreview.start;
|
||||
{isVisible && (
|
||||
<video
|
||||
ref={previewRef}
|
||||
className={`absolute size-full ${currentHourFrame ? "invisible" : "visible"}`}
|
||||
preload="auto"
|
||||
autoPlay
|
||||
playsInline
|
||||
muted
|
||||
disableRemotePlayback
|
||||
onSeeked={onPreviewSeeked}
|
||||
onLoadedData={() => {
|
||||
if (firstLoad) {
|
||||
setFirstLoad(false);
|
||||
}
|
||||
}
|
||||
}}
|
||||
>
|
||||
{currentPreview != undefined && (
|
||||
<source
|
||||
src={`${baseUrl}${currentPreview.src.substring(1)}`}
|
||||
type={currentPreview.type}
|
||||
/>
|
||||
)}
|
||||
</video>
|
||||
|
||||
if (controller) {
|
||||
controller.previewReady();
|
||||
} else {
|
||||
previewRef.current?.pause();
|
||||
}
|
||||
|
||||
if (previewRef.current) {
|
||||
setVideoSize([
|
||||
previewRef.current.videoWidth,
|
||||
previewRef.current.videoHeight,
|
||||
]);
|
||||
|
||||
if (startTime && currentPreview) {
|
||||
previewRef.current.currentTime =
|
||||
startTime - currentPreview.start;
|
||||
}
|
||||
}
|
||||
}}
|
||||
>
|
||||
{currentPreview != undefined && (
|
||||
<source
|
||||
src={`${baseUrl}${currentPreview.src.substring(1)}`}
|
||||
type={currentPreview.type}
|
||||
/>
|
||||
)}
|
||||
</video>
|
||||
)}
|
||||
{cameraPreviews && !currentPreview && (
|
||||
<div className="absolute inset-0 flex items-center justify-center rounded-lg bg-background_alt text-primary dark:bg-black md:rounded-2xl">
|
||||
{t("noPreviewFoundFor", { camera: camera.replaceAll("_", " ") })}
|
||||
|
||||
@ -143,6 +143,12 @@ function ConfigEditor() {
|
||||
scrollBeyondLastLine: false,
|
||||
theme: (systemTheme || theme) == "dark" ? "vs-dark" : "vs-light",
|
||||
});
|
||||
editorRef.current?.addCommand(
|
||||
monaco.KeyMod.CtrlCmd | monaco.KeyCode.KeyS,
|
||||
() => {
|
||||
onHandleSaveConfig("saveonly");
|
||||
},
|
||||
);
|
||||
} else if (editorRef.current) {
|
||||
editorRef.current.setModel(modelRef.current);
|
||||
}
|
||||
@ -158,7 +164,7 @@ function ConfigEditor() {
|
||||
}
|
||||
schemaConfiguredRef.current = false;
|
||||
};
|
||||
}, [config, apiHost, systemTheme, theme]);
|
||||
}, [config, apiHost, systemTheme, theme, onHandleSaveConfig]);
|
||||
|
||||
// monitoring state
|
||||
|
||||
|
||||
@ -6,7 +6,17 @@ import CreateFaceWizardDialog from "@/components/overlay/detail/FaceCreateWizard
|
||||
import TextEntryDialog from "@/components/overlay/dialog/TextEntryDialog";
|
||||
import UploadImageDialog from "@/components/overlay/dialog/UploadImageDialog";
|
||||
import FaceSelectionDialog from "@/components/overlay/FaceSelectionDialog";
|
||||
import { Button } from "@/components/ui/button";
|
||||
import { Button, buttonVariants } from "@/components/ui/button";
|
||||
import {
|
||||
AlertDialog,
|
||||
AlertDialogAction,
|
||||
AlertDialogCancel,
|
||||
AlertDialogContent,
|
||||
AlertDialogDescription,
|
||||
AlertDialogFooter,
|
||||
AlertDialogHeader,
|
||||
AlertDialogTitle,
|
||||
} from "@/components/ui/alert-dialog";
|
||||
import {
|
||||
Dialog,
|
||||
DialogContent,
|
||||
@ -44,7 +54,7 @@ import { TooltipPortal } from "@radix-ui/react-tooltip";
|
||||
import axios from "axios";
|
||||
import { useCallback, useEffect, useMemo, useRef, useState } from "react";
|
||||
import { isDesktop, isMobile } from "react-device-detect";
|
||||
import { useTranslation } from "react-i18next";
|
||||
import { Trans, useTranslation } from "react-i18next";
|
||||
import {
|
||||
LuFolderCheck,
|
||||
LuImagePlus,
|
||||
@ -165,6 +175,11 @@ export default function FaceLibrary() {
|
||||
[selectedFaces, setSelectedFaces],
|
||||
);
|
||||
|
||||
const [deleteDialogOpen, setDeleteDialogOpen] = useState<{
|
||||
name: string;
|
||||
ids: string[];
|
||||
} | null>(null);
|
||||
|
||||
const onDelete = useCallback(
|
||||
(name: string, ids: string[], isName: boolean = false) => {
|
||||
axios
|
||||
@ -191,7 +206,7 @@ export default function FaceLibrary() {
|
||||
|
||||
if (faceImages.length == 1) {
|
||||
// face has been deleted
|
||||
setPageToggle("");
|
||||
setPageToggle("train");
|
||||
}
|
||||
|
||||
refreshFaces();
|
||||
@ -244,29 +259,32 @@ export default function FaceLibrary() {
|
||||
|
||||
// keyboard
|
||||
|
||||
useKeyboardListener(
|
||||
page === "train" ? ["a", "Escape"] : [],
|
||||
(key, modifiers) => {
|
||||
if (modifiers.repeat || !modifiers.down) {
|
||||
return;
|
||||
}
|
||||
useKeyboardListener(["a", "Escape"], (key, modifiers) => {
|
||||
if (modifiers.repeat || !modifiers.down) {
|
||||
return;
|
||||
}
|
||||
|
||||
switch (key) {
|
||||
case "a":
|
||||
if (modifiers.ctrl) {
|
||||
if (selectedFaces.length) {
|
||||
setSelectedFaces([]);
|
||||
} else {
|
||||
setSelectedFaces([...trainImages]);
|
||||
}
|
||||
switch (key) {
|
||||
case "a":
|
||||
if (modifiers.ctrl) {
|
||||
if (selectedFaces.length) {
|
||||
setSelectedFaces([]);
|
||||
} else {
|
||||
setSelectedFaces([
|
||||
...(pageToggle === "train" ? trainImages : faceImages),
|
||||
]);
|
||||
}
|
||||
break;
|
||||
case "Escape":
|
||||
setSelectedFaces([]);
|
||||
break;
|
||||
}
|
||||
},
|
||||
);
|
||||
}
|
||||
break;
|
||||
case "Escape":
|
||||
setSelectedFaces([]);
|
||||
break;
|
||||
}
|
||||
});
|
||||
|
||||
useEffect(() => {
|
||||
setSelectedFaces([]);
|
||||
}, [pageToggle]);
|
||||
|
||||
if (!config) {
|
||||
return <ActivityIndicator />;
|
||||
@ -276,6 +294,41 @@ export default function FaceLibrary() {
|
||||
<div className="flex size-full flex-col p-2">
|
||||
<Toaster />
|
||||
|
||||
<AlertDialog
|
||||
open={!!deleteDialogOpen}
|
||||
onOpenChange={() => setDeleteDialogOpen(null)}
|
||||
>
|
||||
<AlertDialogContent>
|
||||
<AlertDialogHeader>
|
||||
<AlertDialogTitle>{t("deleteFaceAttempts.title")}</AlertDialogTitle>
|
||||
</AlertDialogHeader>
|
||||
<AlertDialogDescription>
|
||||
<Trans
|
||||
ns="views/faceLibrary"
|
||||
values={{ count: deleteDialogOpen?.ids.length }}
|
||||
>
|
||||
deleteFaceAttempts.desc
|
||||
</Trans>
|
||||
</AlertDialogDescription>
|
||||
<AlertDialogFooter>
|
||||
<AlertDialogCancel>
|
||||
{t("button.cancel", { ns: "common" })}
|
||||
</AlertDialogCancel>
|
||||
<AlertDialogAction
|
||||
className={buttonVariants({ variant: "destructive" })}
|
||||
onClick={() => {
|
||||
if (deleteDialogOpen) {
|
||||
onDelete(deleteDialogOpen.name, deleteDialogOpen.ids);
|
||||
setDeleteDialogOpen(null);
|
||||
}
|
||||
}}
|
||||
>
|
||||
{t("button.delete", { ns: "common" })}
|
||||
</AlertDialogAction>
|
||||
</AlertDialogFooter>
|
||||
</AlertDialogContent>
|
||||
</AlertDialog>
|
||||
|
||||
<UploadImageDialog
|
||||
open={upload}
|
||||
title={t("uploadFaceImage.title")}
|
||||
@ -314,7 +367,9 @@ export default function FaceLibrary() {
|
||||
</div>
|
||||
<Button
|
||||
className="flex gap-2"
|
||||
onClick={() => onDelete("train", selectedFaces)}
|
||||
onClick={() =>
|
||||
setDeleteDialogOpen({ name: pageToggle, ids: selectedFaces })
|
||||
}
|
||||
>
|
||||
<LuTrash2 className="size-7 rounded-md p-1 text-secondary-foreground" />
|
||||
{isDesktop && t("button.deleteFaceAttempts")}
|
||||
@ -335,7 +390,13 @@ export default function FaceLibrary() {
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
{pageToggle &&
|
||||
{pageToggle && faceImages.length === 0 && pageToggle !== "train" ? (
|
||||
<div className="absolute left-1/2 top-1/2 flex -translate-x-1/2 -translate-y-1/2 flex-col items-center justify-center text-center">
|
||||
<LuFolderCheck className="size-16" />
|
||||
No faces available
|
||||
</div>
|
||||
) : (
|
||||
pageToggle &&
|
||||
(pageToggle == "train" ? (
|
||||
<TrainingGrid
|
||||
config={config}
|
||||
@ -349,9 +410,12 @@ export default function FaceLibrary() {
|
||||
<FaceGrid
|
||||
faceImages={faceImages}
|
||||
pageToggle={pageToggle}
|
||||
selectedFaces={selectedFaces}
|
||||
onClickFaces={onClickFaces}
|
||||
onDelete={onDelete}
|
||||
/>
|
||||
))}
|
||||
))
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@ -443,7 +507,7 @@ function LibrarySelector({
|
||||
<DropdownMenu>
|
||||
<DropdownMenuTrigger asChild>
|
||||
<Button className="flex justify-between smart-capitalize">
|
||||
{pageToggle || t("selectFace")}
|
||||
{pageToggle == "train" ? t("train.title") : pageToggle}
|
||||
<span className="ml-2 text-primary-variant">
|
||||
({(pageToggle && faceData?.[pageToggle]?.length) || 0})
|
||||
</span>
|
||||
@ -467,7 +531,7 @@ function LibrarySelector({
|
||||
<>
|
||||
<DropdownMenuSeparator />
|
||||
<div className="mb-1 ml-1.5 text-xs text-secondary-foreground">
|
||||
Collections
|
||||
{t("collections")}
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
@ -644,7 +708,7 @@ function TrainingGrid({
|
||||
<div className="flex flex-col gap-1.5">
|
||||
<div className="text-sm text-primary/40">{t("details.person")}</div>
|
||||
<div className="text-sm smart-capitalize">
|
||||
{selectedEvent?.sub_label ?? "Unknown"}
|
||||
{selectedEvent?.sub_label ?? t("details.unknown")}
|
||||
</div>
|
||||
</div>
|
||||
{selectedEvent?.data.sub_label_score && (
|
||||
@ -793,7 +857,7 @@ function FaceAttemptGroup({
|
||||
Person
|
||||
{event?.sub_label
|
||||
? `: ${event.sub_label} (${Math.round((event.data.sub_label_score || 0) * 100)}%)`
|
||||
: ": Unknown"}
|
||||
: ": " + t("details.unknown")}
|
||||
</div>
|
||||
{event && (
|
||||
<Tooltip>
|
||||
@ -968,7 +1032,9 @@ function FaceAttempt({
|
||||
<div className="select-none p-2">
|
||||
<div className="flex w-full flex-row items-center justify-between gap-2">
|
||||
<div className="flex flex-col items-start text-xs text-primary-variant">
|
||||
<div className="smart-capitalize">{data.name}</div>
|
||||
<div className="smart-capitalize">
|
||||
{data.name == "unknown" ? t("details.unknown") : data.name}
|
||||
</div>
|
||||
<div
|
||||
className={cn(
|
||||
"",
|
||||
@ -1007,16 +1073,36 @@ function FaceAttempt({
|
||||
type FaceGridProps = {
|
||||
faceImages: string[];
|
||||
pageToggle: string;
|
||||
selectedFaces: string[];
|
||||
onClickFaces: (images: string[], ctrl: boolean) => void;
|
||||
onDelete: (name: string, ids: string[]) => void;
|
||||
};
|
||||
function FaceGrid({ faceImages, pageToggle, onDelete }: FaceGridProps) {
|
||||
const sortedFaces = useMemo(() => faceImages.sort().reverse(), [faceImages]);
|
||||
function FaceGrid({
|
||||
faceImages,
|
||||
pageToggle,
|
||||
selectedFaces,
|
||||
onClickFaces,
|
||||
onDelete,
|
||||
}: FaceGridProps) {
|
||||
const sortedFaces = useMemo(
|
||||
() => (faceImages || []).sort().reverse(),
|
||||
[faceImages],
|
||||
);
|
||||
|
||||
if (sortedFaces.length === 0) {
|
||||
return (
|
||||
<div className="absolute left-1/2 top-1/2 flex -translate-x-1/2 -translate-y-1/2 flex-col items-center justify-center text-center">
|
||||
<LuFolderCheck className="size-16" />
|
||||
No faces available
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
return (
|
||||
<div
|
||||
className={cn(
|
||||
"scrollbar-container gap-2 overflow-y-scroll",
|
||||
isDesktop ? "flex flex-wrap" : "grid grid-cols-2",
|
||||
"scrollbar-container gap-2 overflow-y-scroll p-1",
|
||||
isDesktop ? "flex flex-wrap" : "grid grid-cols-2 md:grid-cols-4",
|
||||
)}
|
||||
>
|
||||
{sortedFaces.map((image: string) => (
|
||||
@ -1024,6 +1110,8 @@ function FaceGrid({ faceImages, pageToggle, onDelete }: FaceGridProps) {
|
||||
key={image}
|
||||
name={pageToggle}
|
||||
image={image}
|
||||
selected={selectedFaces.includes(image)}
|
||||
onClickFaces={onClickFaces}
|
||||
onDelete={onDelete}
|
||||
/>
|
||||
))}
|
||||
@ -1034,22 +1122,44 @@ function FaceGrid({ faceImages, pageToggle, onDelete }: FaceGridProps) {
|
||||
type FaceImageProps = {
|
||||
name: string;
|
||||
image: string;
|
||||
selected: boolean;
|
||||
onClickFaces: (images: string[], ctrl: boolean) => void;
|
||||
onDelete: (name: string, ids: string[]) => void;
|
||||
};
|
||||
function FaceImage({ name, image, onDelete }: FaceImageProps) {
|
||||
function FaceImage({
|
||||
name,
|
||||
image,
|
||||
selected,
|
||||
onClickFaces,
|
||||
onDelete,
|
||||
}: FaceImageProps) {
|
||||
const { t } = useTranslation(["views/faceLibrary"]);
|
||||
|
||||
return (
|
||||
<div className="relative flex flex-col rounded-lg">
|
||||
<div
|
||||
className={cn(
|
||||
"flex cursor-pointer flex-col gap-2 rounded-lg bg-card outline outline-[3px]",
|
||||
selected
|
||||
? "shadow-selected outline-selected"
|
||||
: "outline-transparent duration-500",
|
||||
)}
|
||||
onClick={(e) => {
|
||||
e.stopPropagation();
|
||||
onClickFaces([image], e.ctrlKey || e.metaKey);
|
||||
}}
|
||||
>
|
||||
<div
|
||||
className={cn(
|
||||
"w-full overflow-hidden rounded-t-lg *:text-card-foreground",
|
||||
"w-full overflow-hidden p-2 *:text-card-foreground",
|
||||
isMobile && "flex justify-center",
|
||||
)}
|
||||
>
|
||||
<img className="h-40" src={`${baseUrl}clips/faces/${name}/${image}`} />
|
||||
<img
|
||||
className="h-40 rounded-lg"
|
||||
src={`${baseUrl}clips/faces/${name}/${image}`}
|
||||
/>
|
||||
</div>
|
||||
<div className="rounded-b-lg bg-card p-2">
|
||||
<div className="rounded-b-lg bg-card p-3">
|
||||
<div className="flex w-full flex-row items-center justify-between gap-2">
|
||||
<div className="flex flex-col items-start text-xs text-primary-variant">
|
||||
<div className="smart-capitalize">{name}</div>
|
||||
@ -1059,7 +1169,10 @@ function FaceImage({ name, image, onDelete }: FaceImageProps) {
|
||||
<TooltipTrigger>
|
||||
<LuTrash2
|
||||
className="size-5 cursor-pointer text-primary-variant hover:text-primary"
|
||||
onClick={() => onDelete(name, [image])}
|
||||
onClick={(e) => {
|
||||
e.stopPropagation();
|
||||
onDelete(name, [image]);
|
||||
}}
|
||||
/>
|
||||
</TooltipTrigger>
|
||||
<TooltipContent>{t("button.deleteFaceAttempts")}</TooltipContent>
|
||||
|
||||
@ -385,6 +385,55 @@ export function RecordingView({
|
||||
// eslint-disable-next-line react-hooks/exhaustive-deps
|
||||
}, [previewRowRef.current?.scrollWidth, previewRowRef.current?.scrollHeight]);
|
||||
|
||||
// visibility listener for lazy loading
|
||||
|
||||
const [visiblePreviews, setVisiblePreviews] = useState<string[]>([]);
|
||||
const visiblePreviewObserver = useRef<IntersectionObserver | null>(null);
|
||||
useEffect(() => {
|
||||
const visibleCameras = new Set<string>();
|
||||
visiblePreviewObserver.current = new IntersectionObserver(
|
||||
(entries) => {
|
||||
entries.forEach((entry) => {
|
||||
const camera = (entry.target as HTMLElement).dataset.camera;
|
||||
|
||||
if (!camera) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (entry.isIntersecting) {
|
||||
visibleCameras.add(camera);
|
||||
} else {
|
||||
visibleCameras.delete(camera);
|
||||
}
|
||||
|
||||
setVisiblePreviews([...visibleCameras]);
|
||||
});
|
||||
},
|
||||
{ threshold: 0.1 },
|
||||
);
|
||||
|
||||
return () => {
|
||||
visiblePreviewObserver.current?.disconnect();
|
||||
};
|
||||
}, []);
|
||||
|
||||
const previewRef = useCallback(
|
||||
(node: HTMLElement | null) => {
|
||||
if (!visiblePreviewObserver.current) {
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
if (node) visiblePreviewObserver.current.observe(node);
|
||||
} catch (e) {
|
||||
// no op
|
||||
}
|
||||
},
|
||||
// we need to listen on the value of the ref
|
||||
// eslint-disable-next-line react-hooks/exhaustive-deps
|
||||
[visiblePreviewObserver.current],
|
||||
);
|
||||
|
||||
return (
|
||||
<div ref={contentRef} className="flex size-full flex-col pt-2">
|
||||
<Toaster closeButton={true} />
|
||||
@ -631,12 +680,14 @@ export function RecordingView({
|
||||
}}
|
||||
>
|
||||
<PreviewPlayer
|
||||
previewRef={previewRef}
|
||||
className="size-full"
|
||||
camera={cam}
|
||||
timeRange={currentTimeRange}
|
||||
cameraPreviews={allPreviews ?? []}
|
||||
startTime={startTime}
|
||||
isScrubbing={scrubbing}
|
||||
isVisible={visiblePreviews.includes(cam)}
|
||||
onControllerReady={(controller) => {
|
||||
previewRefs.current[cam] = controller;
|
||||
controller.scrubToTimestamp(startTime);
|
||||
|
||||
@ -230,7 +230,9 @@ export default function CameraSettingsView({
|
||||
if (changedValue) {
|
||||
addMessage(
|
||||
"camera_settings",
|
||||
`Unsaved review classification settings for ${capitalizeFirstLetter(selectedCamera)}`,
|
||||
t("camera.reviewClassification.unsavedChanges", {
|
||||
camera: selectedCamera,
|
||||
}),
|
||||
undefined,
|
||||
`review_classification_settings_${selectedCamera}`,
|
||||
);
|
||||
|
||||
@ -220,7 +220,7 @@ export default function ClassificationSettingsView({
|
||||
if (changedValue) {
|
||||
addMessage(
|
||||
"search_settings",
|
||||
`Unsaved Classification settings changes`,
|
||||
t("classification.unsavedChanges"),
|
||||
undefined,
|
||||
"search_settings",
|
||||
);
|
||||
|
||||
@ -176,7 +176,7 @@ export default function FrigatePlusSettingsView({
|
||||
if (changedValue) {
|
||||
addMessage(
|
||||
"plus_settings",
|
||||
`Unsaved Frigate+ settings changes`,
|
||||
t("frigatePlus.unsavedChanges"),
|
||||
undefined,
|
||||
"plus_settings",
|
||||
);
|
||||
|
||||
@ -167,7 +167,7 @@ export default function MotionTunerView({
|
||||
if (changedValue) {
|
||||
addMessage(
|
||||
"motion_tuner",
|
||||
`Unsaved motion tuner changes (${selectedCamera})`,
|
||||
t("motionDetectionTuner.unsavedChanges", { camera: selectedCamera }),
|
||||
undefined,
|
||||
`motion_tuner_${selectedCamera}`,
|
||||
);
|
||||
|
||||
@ -105,7 +105,7 @@ export default function NotificationView({
|
||||
if (changedValue) {
|
||||
addMessage(
|
||||
"notification_settings",
|
||||
`Unsaved notification settings`,
|
||||
t("notification.unsavedChanges"),
|
||||
undefined,
|
||||
`notification_settings`,
|
||||
);
|
||||
@ -128,7 +128,7 @@ export default function NotificationView({
|
||||
if (registration) {
|
||||
addMessage(
|
||||
"notification_settings",
|
||||
"Unsaved Notification Registrations",
|
||||
t("notification.unsavedRegistrations"),
|
||||
undefined,
|
||||
"registration",
|
||||
);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user