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Merge branch 'dev' into dev-zone-friendly-name

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1
docker/main/requirements.txt
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@ -1,2 +1 @@
scikit-build == 0.18.* scikit-build == 0.18.*
nvidia-pyindex

39
docs/docs/configuration/camera_specific.md
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@ -164,13 +164,35 @@ According to [this discussion](https://github.com/blakeblackshear/frigate/issues
Cameras connected via a Reolink NVR can be connected with the http stream, use `channel[0..15]` in the stream url for the additional channels. Cameras connected via a Reolink NVR can be connected with the http stream, use `channel[0..15]` in the stream url for the additional channels.
The setup of main stream can be also done via RTSP, but isn't always reliable on all hardware versions. The example configuration is working with the oldest HW version RLN16-410 device with multiple types of cameras. The setup of main stream can be also done via RTSP, but isn't always reliable on all hardware versions. The example configuration is working with the oldest HW version RLN16-410 device with multiple types of cameras.
<details>
<summary>Example Config</summary>
:::tip
Reolink's latest cameras support two way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
NOTE: The RTSP stream can not be prefixed with `ffmpeg:`, as go2rtc needs to handle the stream to support two way audio.
Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
:::
```yaml ```yaml
go2rtc: go2rtc:
streams: streams:
# example for connecting to a standard Reolink camera
your_reolink_camera: your_reolink_camera:
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus" - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
your_reolink_camera_sub: your_reolink_camera_sub:
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password" - "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
# example for connectin to a Reolink camera that supports two way talk
your_reolink_camera_twt:
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
- "rtsp://username:password@reolink_ip/Preview_01_sub
your_reolink_camera_twt_sub:
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
- "rtsp://username:password@reolink_ip/Preview_01_sub
# example for connecting to a Reolink NVR
your_reolink_camera_via_nvr: your_reolink_camera_via_nvr:
- "ffmpeg:http://reolink_nvr_ip/flv?port=1935&app=bcs&stream=channel3_main.bcs&user=username&password=password" # channel numbers are 0-15 - "ffmpeg:http://reolink_nvr_ip/flv?port=1935&app=bcs&stream=channel3_main.bcs&user=username&password=password" # channel numbers are 0-15
- "ffmpeg:your_reolink_camera_via_nvr#audio=aac" - "ffmpeg:your_reolink_camera_via_nvr#audio=aac"
@ -201,22 +223,7 @@ cameras:
roles: roles:
- detect - detect
``` ```
</details>
#### Reolink Doorbell
The reolink doorbell supports two way audio via go2rtc and other applications. It is important that the http-flv stream is still used for stability, a secondary rtsp stream can be added that will be using for the two way audio only.
Ensure HTTP is enabled in the camera's advanced network settings. To use two way talk with Frigate, see the [Live view documentation](/configuration/live#two-way-talk).
```yaml
go2rtc:
streams:
your_reolink_doorbell:
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_main.bcs&user=username&password=password#video=copy#audio=copy#audio=opus"
- rtsp://reolink_ip/Preview_01_sub
your_reolink_doorbell_sub:
- "ffmpeg:http://reolink_ip/flv?port=1935&app=bcs&stream=channel0_ext.bcs&user=username&password=password"
```
### Unifi Protect Cameras ### Unifi Protect Cameras

2
docs/docs/configuration/face_recognition.md
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@ -161,6 +161,8 @@ Start with the [Usage](#usage) section and re-read the [Model Requirements](#mod
Accuracy is definitely a going to be improved with higher quality cameras / streams. It is important to look at the DORI (Detection Observation Recognition Identification) range of your camera, if that specification is posted. This specification explains the distance from the camera that a person can be detected, observed, recognized, and identified. The identification range is the most relevant here, and the distance listed by the camera is the furthest that face recognition will realistically work. Accuracy is definitely a going to be improved with higher quality cameras / streams. It is important to look at the DORI (Detection Observation Recognition Identification) range of your camera, if that specification is posted. This specification explains the distance from the camera that a person can be detected, observed, recognized, and identified. The identification range is the most relevant here, and the distance listed by the camera is the furthest that face recognition will realistically work.
Some users have also noted that setting the stream in camera firmware to a constant bit rate (CBR) leads to better image clarity than with a variable bit rate (VBR).
### Why can't I bulk upload photos? ### Why can't I bulk upload photos?
It is important to methodically add photos to the library, bulk importing photos (especially from a general photo library) will lead to over-fitting in that particular scenario and hurt recognition performance. It is important to methodically add photos to the library, bulk importing photos (especially from a general photo library) will lead to over-fitting in that particular scenario and hurt recognition performance.

36
docs/docs/configuration/genai.md
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@ -17,18 +17,17 @@ To use Generative AI, you must define a single provider at the global level of y
genai: genai:
provider: gemini provider: gemini
api_key: "{FRIGATE_GEMINI_API_KEY}" api_key: "{FRIGATE_GEMINI_API_KEY}"
model: gemini-1.5-flash model: gemini-2.0-flash
cameras: cameras:
front_camera: front_camera:
objects:
genai: genai:
enabled: True # <- enable GenAI for your front camera enabled: True # <- enable GenAI for your front camera
use_snapshot: True use_snapshot: True
objects: objects:
- person - person
required_zones: required_zones:
- steps - steps
indoor_camera: indoor_camera:
objects: objects:
genai: genai:
@ -80,7 +79,7 @@ Google Gemini has a free tier allowing [15 queries per minute](https://ai.google
### Supported Models ### Supported Models
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini). At the time of writing, this includes `gemini-1.5-pro` and `gemini-1.5-flash`. You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://ai.google.dev/gemini-api/docs/models/gemini).
### Get API Key ### Get API Key
@ -97,7 +96,7 @@ To start using Gemini, you must first get an API key from [Google AI Studio](htt
genai: genai:
provider: gemini provider: gemini
api_key: "{FRIGATE_GEMINI_API_KEY}" api_key: "{FRIGATE_GEMINI_API_KEY}"
model: gemini-1.5-flash model: gemini-2.0-flash
``` ```
:::note :::note
@ -112,7 +111,7 @@ OpenAI does not have a free tier for their API. With the release of gpt-4o, pric
### Supported Models ### Supported Models
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`. You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://platform.openai.com/docs/models).
### Get API Key ### Get API Key
@ -139,18 +138,19 @@ Microsoft offers several vision models through Azure OpenAI. A subscription is r
### Supported Models ### Supported Models
You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models). At the time of writing, this includes `gpt-4o` and `gpt-4-turbo`. You must use a vision capable model with Frigate. Current model variants can be found [in their documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).
### Create Resource and Get API Key ### Create Resource and Get API Key
To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key and resource URL, which must include the `api-version` parameter (see the example below). The model field is not required in your configuration as the model is part of the deployment name you chose when deploying the resource. To start using Azure OpenAI, you must first [create a resource](https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource). You'll need your API key, model name, and resource URL, which must include the `api-version` parameter (see the example below).
### Configuration ### Configuration
```yaml ```yaml
genai: genai:
provider: azure_openai provider: azure_openai
base_url: https://example-endpoint.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2023-03-15-preview base_url: https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
model: gpt-5-mini
api_key: "{FRIGATE_OPENAI_API_KEY}" api_key: "{FRIGATE_OPENAI_API_KEY}"
``` ```
@ -196,10 +196,10 @@ genai:
model: llava model: llava
objects: objects:
prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance." prompt: "Analyze the {label} in these images from the {camera} security camera. Focus on the actions, behavior, and potential intent of the {label}, rather than just describing its appearance."
object_prompts: object_prompts:
person: "Examine the main person in these images. What are they doing and what might their actions suggest about their intent (e.g., approaching a door, leaving an area, standing still)? Do not describe the surroundings or static details." person: "Examine the main person in these images. What are they doing and what might their actions suggest about their intent (e.g., approaching a door, leaving an area, standing still)? Do not describe the surroundings or static details."
car: "Observe the primary vehicle in these images. Focus on its movement, direction, or purpose (e.g., parking, approaching, circling). If it's a delivery vehicle, mention the company." car: "Observe the primary vehicle in these images. Focus on its movement, direction, or purpose (e.g., parking, approaching, circling). If it's a delivery vehicle, mention the company."
``` ```
Prompts can also be overridden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire. Prompts can also be overridden at the camera level to provide a more detailed prompt to the model about your specific camera, if you desire.

3
docs/docs/configuration/license_plate_recognition.md
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@ -30,8 +30,7 @@ In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle`
## Minimum System Requirements ## Minimum System Requirements
License plate recognition works by running AI models locally on your system. The models are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required. License plate recognition works by running AI models locally on your system. The YOLOv9 plate detector model and the OCR models ([PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)) are relatively lightweight and can run on your CPU or GPU, depending on your configuration. At least 4GB of RAM is required.
## Configuration ## Configuration
License plate recognition is disabled by default. Enable it in your config file: License plate recognition is disabled by default. Enable it in your config file:

2
docs/docs/configuration/live.md
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@ -174,7 +174,7 @@ For devices that support two way talk, Frigate can be configured to use the feat
- Ensure you access Frigate via https (may require [opening port 8971](/frigate/installation/#ports)). - Ensure you access Frigate via https (may require [opening port 8971](/frigate/installation/#ports)).
- For the Home Assistant Frigate card, [follow the docs](http://card.camera/#/usage/2-way-audio) for the correct source. - For the Home Assistant Frigate card, [follow the docs](http://card.camera/#/usage/2-way-audio) for the correct source.
To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-doorbell) To use the Reolink Doorbell with two way talk, you should use the [recommended Reolink configuration](/configuration/camera_specific#reolink-cameras)
As a starting point to check compatibility for your camera, view the list of cameras supported for two-way talk on the [go2rtc repository](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#two-way-audio). For cameras in the category `ONVIF Profile T`, you can use the [ONVIF Conformant Products Database](https://www.onvif.org/conformant-products/)'s FeatureList to check for the presence of `AudioOutput`. A camera that supports `ONVIF Profile T` _usually_ supports this, but due to inconsistent support, a camera that explicitly lists this feature may still not work. If no entry for your camera exists on the database, it is recommended not to buy it or to consult with the manufacturer's support on the feature availability. As a starting point to check compatibility for your camera, view the list of cameras supported for two-way talk on the [go2rtc repository](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#two-way-audio). For cameras in the category `ONVIF Profile T`, you can use the [ONVIF Conformant Products Database](https://www.onvif.org/conformant-products/)'s FeatureList to check for the presence of `AudioOutput`. A camera that supports `ONVIF Profile T` _usually_ supports this, but due to inconsistent support, a camera that explicitly lists this feature may still not work. If no entry for your camera exists on the database, it is recommended not to buy it or to consult with the manufacturer's support on the feature availability.

8
docs/docs/configuration/object_detectors.md
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@ -1455,7 +1455,7 @@ COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
WORKDIR /dfine WORKDIR /dfine
RUN git clone https://github.com/Peterande/D-FINE.git . RUN git clone https://github.com/Peterande/D-FINE.git .
RUN uv pip install --system -r requirements.txt RUN uv pip install --system -r requirements.txt
RUN uv pip install --system onnx onnxruntime onnxsim RUN uv pip install --system onnx onnxruntime onnxsim onnxscript
# Create output directory and download checkpoint # Create output directory and download checkpoint
RUN mkdir -p output RUN mkdir -p output
ARG MODEL_SIZE ARG MODEL_SIZE
@ -1479,9 +1479,9 @@ FROM python:3.11 AS build
RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/* RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/ COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
WORKDIR /rfdetr WORKDIR /rfdetr
RUN uv pip install --system rfdetr onnx onnxruntime onnxsim onnx-graphsurgeon RUN uv pip install --system rfdetr[onnxexport] torch==2.8.0 onnxscript
ARG MODEL_SIZE ARG MODEL_SIZE
RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export()" RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export(simplify=True)"
FROM scratch FROM scratch
ARG MODEL_SIZE ARG MODEL_SIZE
COPY --from=build /rfdetr/output/inference_model.onnx /rfdetr-${MODEL_SIZE}.onnx COPY --from=build /rfdetr/output/inference_model.onnx /rfdetr-${MODEL_SIZE}.onnx
@ -1529,7 +1529,7 @@ COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
WORKDIR /yolov9 WORKDIR /yolov9
ADD https://github.com/WongKinYiu/yolov9.git . ADD https://github.com/WongKinYiu/yolov9.git .
RUN uv pip install --system -r requirements.txt RUN uv pip install --system -r requirements.txt
RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1 RUN uv pip install --system onnx==1.18.0 onnxruntime onnx-simplifier>=0.4.1 onnxscript
ARG MODEL_SIZE ARG MODEL_SIZE
ARG IMG_SIZE ARG IMG_SIZE
ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt

12
docs/docs/frigate/updating.md
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@ -5,7 +5,7 @@ title: Updating
# Updating Frigate # Updating Frigate
The current stable version of Frigate is **0.16.1**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.16.1). The current stable version of Frigate is **0.16.2**. The release notes and any breaking changes for this version can be found on the [Frigate GitHub releases page](https://github.com/blakeblackshear/frigate/releases/tag/v0.16.2).
Keeping Frigate up to date ensures you benefit from the latest features, performance improvements, and bug fixes. The update process varies slightly depending on your installation method (Docker, Home Assistant Addon, etc.). Below are instructions for the most common setups. Keeping Frigate up to date ensures you benefit from the latest features, performance improvements, and bug fixes. The update process varies slightly depending on your installation method (Docker, Home Assistant Addon, etc.). Below are instructions for the most common setups.
@ -33,21 +33,21 @@ If you’re running Frigate via Docker (recommended method), follow these steps:
2. **Update and Pull the Latest Image**: 2. **Update and Pull the Latest Image**:
- If using Docker Compose: - If using Docker Compose:
- Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.16.1` instead of `0.15.2`). For example: - Edit your `docker-compose.yml` file to specify the desired version tag (e.g., `0.16.2` instead of `0.15.2`). For example:
```yaml ```yaml
services: services:
frigate: frigate:
image: ghcr.io/blakeblackshear/frigate:0.16.1 image: ghcr.io/blakeblackshear/frigate:0.16.2
``` ```
- Then pull the image: - Then pull the image:
```bash ```bash
docker pull ghcr.io/blakeblackshear/frigate:0.16.1 docker pull ghcr.io/blakeblackshear/frigate:0.16.2
``` ```
- **Note for `stable` Tag Users**: If your `docker-compose.yml` uses the `stable` tag (e.g., `ghcr.io/blakeblackshear/frigate:stable`), you don’t need to update the tag manually. The `stable` tag always points to the latest stable release after pulling. - **Note for `stable` Tag Users**: If your `docker-compose.yml` uses the `stable` tag (e.g., `ghcr.io/blakeblackshear/frigate:stable`), you don’t need to update the tag manually. The `stable` tag always points to the latest stable release after pulling.
- If using `docker run`: - If using `docker run`:
- Pull the image with the appropriate tag (e.g., `0.16.1`, `0.16.1-tensorrt`, or `stable`): - Pull the image with the appropriate tag (e.g., `0.16.2`, `0.16.2-tensorrt`, or `stable`):
```bash ```bash
docker pull ghcr.io/blakeblackshear/frigate:0.16.1 docker pull ghcr.io/blakeblackshear/frigate:0.16.2
``` ```
3. **Start the Container**: 3. **Start the Container**:

9
docs/docs/integrations/mqtt.md
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@ -161,7 +161,14 @@ Message published for updates to tracked object metadata, for example:
### `frigate/reviews` ### `frigate/reviews`
Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated. When additional objects are detected or when a zone change occurs, it will publish a, `update` message with the same id. When the review activity has ended a final `end` message is published. Message published for each changed review item. The first message is published when the `detection` or `alert` is initiated.
An `update` with the same ID will be published when:
- The severity changes from `detection` to `alert`
- Additional objects are detected
- An object is recognized via face, lpr, etc.
When the review activity has ended a final `end` message is published.
```json ```json
{ {

1
docs/docs/plus/annotating.md
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@ -42,6 +42,7 @@ Misidentified objects should have a correct label added. For example, if a perso
| `w` | Add box | | `w` | Add box |
| `d` | Toggle difficult | | `d` | Toggle difficult |
| `s` | Switch to the next label | | `s` | Switch to the next label |
| `Shift + s` | Switch to the previous label |
| `tab` | Select next largest box | | `tab` | Select next largest box |
| `del` | Delete current box | | `del` | Delete current box |
| `esc` | Deselect/Cancel | | `esc` | Deselect/Cancel |

6
frigate/api/app.py
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@ -696,7 +696,11 @@ def timeline(camera: str = "all", limit: int = 100, source_id: Optional[str] = N
clauses.append((Timeline.camera == camera)) clauses.append((Timeline.camera == camera))
if source_id: if source_id:
clauses.append((Timeline.source_id == source_id)) source_ids = [sid.strip() for sid in source_id.split(",")]
if len(source_ids) == 1:
clauses.append((Timeline.source_id == source_ids[0]))
else:
clauses.append((Timeline.source_id.in_(source_ids)))
if len(clauses) == 0: if len(clauses) == 0:
clauses.append((True)) clauses.append((True))

12
frigate/api/export.py
View File

@ -9,6 +9,7 @@ from typing import List
import psutil import psutil
from fastapi import APIRouter, Depends, Request from fastapi import APIRouter, Depends, Request
from fastapi.responses import JSONResponse from fastapi.responses import JSONResponse
from pathvalidate import sanitize_filepath
from peewee import DoesNotExist from peewee import DoesNotExist
from playhouse.shortcuts import model_to_dict from playhouse.shortcuts import model_to_dict
@ -26,7 +27,7 @@ from frigate.api.defs.response.export_response import (
) )
from frigate.api.defs.response.generic_response import GenericResponse from frigate.api.defs.response.generic_response import GenericResponse
from frigate.api.defs.tags import Tags from frigate.api.defs.tags import Tags
from frigate.const import EXPORT_DIR from frigate.const import CLIPS_DIR, EXPORT_DIR
from frigate.models import Export, Previews, Recordings from frigate.models import Export, Previews, Recordings
from frigate.record.export import ( from frigate.record.export import (
PlaybackFactorEnum, PlaybackFactorEnum,
@ -88,7 +89,14 @@ def export_recording(
playback_factor = body.playback playback_factor = body.playback
playback_source = body.source playback_source = body.source
friendly_name = body.name friendly_name = body.name
existing_image = body.image_path existing_image = sanitize_filepath(body.image_path) if body.image_path else None
# Ensure that existing_image is a valid path
if existing_image and not existing_image.startswith(CLIPS_DIR):
return JSONResponse(
content=({"success": False, "message": "Invalid image path"}),
status_code=400,
)
if playback_source == "recordings": if playback_source == "recordings":
recordings_count = ( recordings_count = (

165
frigate/data_processing/real_time/custom_classification.py
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@ -53,6 +53,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
self.tensor_output_details: dict[str, Any] | None = None self.tensor_output_details: dict[str, Any] | None = None
self.labelmap: dict[int, str] = {} self.labelmap: dict[int, str] = {}
self.classifications_per_second = EventsPerSecond() self.classifications_per_second = EventsPerSecond()
self.state_history: dict[str, dict[str, Any]] = {}
if ( if (
self.metrics self.metrics
@ -94,6 +95,42 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
if self.inference_speed: if self.inference_speed:
self.inference_speed.update(duration) self.inference_speed.update(duration)
def verify_state_change(self, camera: str, detected_state: str) -> str | None:
"""
Verify state change requires 3 consecutive identical states before publishing.
Returns state to publish or None if verification not complete.
"""
if camera not in self.state_history:
self.state_history[camera] = {
"current_state": None,
"pending_state": None,
"consecutive_count": 0,
}
verification = self.state_history[camera]
if detected_state == verification["current_state"]:
verification["pending_state"] = None
verification["consecutive_count"] = 0
return None
if detected_state == verification["pending_state"]:
verification["consecutive_count"] += 1
if verification["consecutive_count"] >= 3:
verification["current_state"] = detected_state
verification["pending_state"] = None
verification["consecutive_count"] = 0
return detected_state
else:
verification["pending_state"] = detected_state
verification["consecutive_count"] = 1
logger.debug(
f"New state '{detected_state}' detected for {camera}, need {3 - verification['consecutive_count']} more consecutive detections"
)
return None
def process_frame(self, frame_data: dict[str, Any], frame: np.ndarray): def process_frame(self, frame_data: dict[str, Any], frame: np.ndarray):
if self.metrics and self.model_config.name in self.metrics.classification_cps: if self.metrics and self.model_config.name in self.metrics.classification_cps:
self.metrics.classification_cps[ self.metrics.classification_cps[
@ -131,6 +168,19 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
self.last_run = now self.last_run = now
should_run = True should_run = True
# Shortcut: always run if we have a pending state verification to complete
if (
not should_run
and camera in self.state_history
and self.state_history[camera]["pending_state"] is not None
and now > self.last_run + 0.5
):
self.last_run = now
should_run = True
logger.debug(
f"Running verification check for pending state: {self.state_history[camera]['pending_state']} ({self.state_history[camera]['consecutive_count']}/3)"
)
if not should_run: if not should_run:
return return
@ -188,10 +238,19 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
score, score,
) )
if score >= self.model_config.threshold: if score < self.model_config.threshold:
logger.debug(
f"Score {score} below threshold {self.model_config.threshold}, skipping verification"
)
return
detected_state = self.labelmap[best_id]
verified_state = self.verify_state_change(camera, detected_state)
if verified_state is not None:
self.requestor.send_data( self.requestor.send_data(
f"{camera}/classification/{self.model_config.name}", f"{camera}/classification/{self.model_config.name}",
self.labelmap[best_id], verified_state,
) )
def handle_request(self, topic, request_data): def handle_request(self, topic, request_data):
@ -230,7 +289,7 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
self.sub_label_publisher = sub_label_publisher self.sub_label_publisher = sub_label_publisher
self.tensor_input_details: dict[str, Any] | None = None self.tensor_input_details: dict[str, Any] | None = None
self.tensor_output_details: dict[str, Any] | None = None self.tensor_output_details: dict[str, Any] | None = None
self.detected_objects: dict[str, float] = {} self.classification_history: dict[str, list[tuple[str, float, float]]] = {}
self.labelmap: dict[int, str] = {} self.labelmap: dict[int, str] = {}
self.classifications_per_second = EventsPerSecond() self.classifications_per_second = EventsPerSecond()
@ -272,6 +331,56 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
if self.inference_speed: if self.inference_speed:
self.inference_speed.update(duration) self.inference_speed.update(duration)
def get_weighted_score(
self,
object_id: str,
current_label: str,
current_score: float,
current_time: float,
) -> tuple[str | None, float]:
"""
Determine weighted score based on history to prevent false positives/negatives.
Requires 60% of attempts to agree on a label before publishing.
Returns (weighted_label, weighted_score) or (None, 0.0) if no weighted score.
"""
if object_id not in self.classification_history:
self.classification_history[object_id] = []
self.classification_history[object_id].append(
(current_label, current_score, current_time)
)
history = self.classification_history[object_id]
if len(history) < 3:
return None, 0.0
label_counts = {}
label_scores = {}
total_attempts = len(history)
for label, score, timestamp in history:
if label not in label_counts:
label_counts[label] = 0
label_scores[label] = []
label_counts[label] += 1
label_scores[label].append(score)
best_label = max(label_counts, key=label_counts.get)
best_count = label_counts[best_label]
consensus_threshold = total_attempts * 0.6
if best_count < consensus_threshold:
return None, 0.0
avg_score = sum(label_scores[best_label]) / len(label_scores[best_label])
if best_label == "none":
return None, 0.0
return best_label, avg_score
def process_frame(self, obj_data, frame): def process_frame(self, obj_data, frame):
if self.metrics and self.model_config.name in self.metrics.classification_cps: if self.metrics and self.model_config.name in self.metrics.classification_cps:
self.metrics.classification_cps[ self.metrics.classification_cps[
@ -284,6 +393,9 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
if obj_data["label"] not in self.model_config.object_config.objects: if obj_data["label"] not in self.model_config.object_config.objects:
return return
if obj_data.get("end_time") is not None:
return
now = datetime.datetime.now().timestamp() now = datetime.datetime.now().timestamp()
x, y, x2, y2 = calculate_region( x, y, x2, y2 = calculate_region(
frame.shape, frame.shape,
@ -331,7 +443,6 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
probs = res / res.sum(axis=0) probs = res / res.sum(axis=0)
best_id = np.argmax(probs) best_id = np.argmax(probs)
score = round(probs[best_id], 2) score = round(probs[best_id], 2)
previous_score = self.detected_objects.get(obj_data["id"], 0.0)
self.__update_metrics(datetime.datetime.now().timestamp() - now) self.__update_metrics(datetime.datetime.now().timestamp() - now)
write_classification_attempt( write_classification_attempt(
@ -347,30 +458,34 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
logger.debug(f"Score {score} is less than threshold.") logger.debug(f"Score {score} is less than threshold.")
return return
if score <= previous_score:
logger.debug(f"Score {score} is worse than previous score {previous_score}")
return
sub_label = self.labelmap[best_id] sub_label = self.labelmap[best_id]
self.detected_objects[obj_data["id"]] = score
if ( consensus_label, consensus_score = self.get_weighted_score(
self.model_config.object_config.classification_type obj_data["id"], sub_label, score, now
== ObjectClassificationType.sub_label )
):
if sub_label != "none": if consensus_label is not None:
if (
self.model_config.object_config.classification_type
== ObjectClassificationType.sub_label
):
self.sub_label_publisher.publish( self.sub_label_publisher.publish(
(obj_data["id"], sub_label, score), (obj_data["id"], consensus_label, consensus_score),
EventMetadataTypeEnum.sub_label, EventMetadataTypeEnum.sub_label,
) )
elif ( elif (
self.model_config.object_config.classification_type self.model_config.object_config.classification_type
== ObjectClassificationType.attribute == ObjectClassificationType.attribute
): ):
self.sub_label_publisher.publish( self.sub_label_publisher.publish(
(obj_data["id"], self.model_config.name, sub_label, score), (
EventMetadataTypeEnum.attribute.value, obj_data["id"],
) self.model_config.name,
consensus_label,
consensus_score,
),
EventMetadataTypeEnum.attribute.value,
)
def handle_request(self, topic, request_data): def handle_request(self, topic, request_data):
if topic == EmbeddingsRequestEnum.reload_classification_model.value: if topic == EmbeddingsRequestEnum.reload_classification_model.value:
@ -388,8 +503,8 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
return None return None
def expire_object(self, object_id, camera): def expire_object(self, object_id, camera):
if object_id in self.detected_objects: if object_id in self.classification_history:
self.detected_objects.pop(object_id) self.classification_history.pop(object_id)
@staticmethod @staticmethod

32
frigate/genai/__init__.py
View File

@ -63,18 +63,24 @@ class GenAIClient:
else: else:
return "" return ""
def get_verified_objects() -> str: def get_verified_object_prompt() -> str:
if review_data["recognized_objects"]: if review_data["recognized_objects"]:
return " - " + "\n - ".join(review_data["recognized_objects"]) object_list = " - " + "\n - ".join(review_data["recognized_objects"])
return f"""## Verified Objects (USE THESE NAMES)
When any of the following verified objects are present in the scene, you MUST use these exact names in your title and scene description:
{object_list}
"""
else: else:
return " None" return ""
context_prompt = f""" context_prompt = f"""
Please analyze the sequence of images ({len(thumbnails)} total) taken in chronological order from the perspective of the {review_data["camera"].replace("_", " ")} security camera. Your task is to analyze the sequence of images ({len(thumbnails)} total) taken in chronological order from the perspective of the {review_data["camera"].replace("_", " ")} security camera.
**Normal activity patterns for this property:** ## Normal Activity Patterns for This Property
{activity_context_prompt} {activity_context_prompt}
## Task Instructions
Your task is to provide a clear, accurate description of the scene that: Your task is to provide a clear, accurate description of the scene that:
1. States exactly what is happening based on observable actions and movements. 1. States exactly what is happening based on observable actions and movements.
2. Evaluates whether the observable evidence suggests normal activity for this property or genuine security concerns. 2. Evaluates whether the observable evidence suggests normal activity for this property or genuine security concerns.
@ -82,6 +88,8 @@ Your task is to provide a clear, accurate description of the scene that:
**IMPORTANT: Start by checking if the activity matches the normal patterns above. If it does, assign Level 0. Only consider higher threat levels if the activity clearly deviates from normal patterns or shows genuine security concerns.** **IMPORTANT: Start by checking if the activity matches the normal patterns above. If it does, assign Level 0. Only consider higher threat levels if the activity clearly deviates from normal patterns or shows genuine security concerns.**
## Analysis Guidelines
When forming your description: When forming your description:
- **CRITICAL: Only describe objects explicitly listed in "Detected objects" below.** Do not infer or mention additional people, vehicles, or objects not present in the detected objects list, even if visual patterns suggest them. If only a car is detected, do not describe a person interacting with it unless "person" is also in the detected objects list. - **CRITICAL: Only describe objects explicitly listed in "Detected objects" below.** Do not infer or mention additional people, vehicles, or objects not present in the detected objects list, even if visual patterns suggest them. If only a car is detected, do not describe a person interacting with it unless "person" is also in the detected objects list.
- **Only describe actions actually visible in the frames.** Do not assume or infer actions that you don't observe happening. If someone walks toward furniture but you never see them sit, do not say they sat. Stick to what you can see across the sequence. - **Only describe actions actually visible in the frames.** Do not assume or infer actions that you don't observe happening. If someone walks toward furniture but you never see them sit, do not say they sat. Stick to what you can see across the sequence.
@ -92,6 +100,8 @@ When forming your description:
- Identify patterns that suggest genuine security concerns: testing doors/windows on vehicles or buildings, accessing unauthorized areas, attempting to conceal actions, extended loitering without apparent purpose, taking items, behavior that clearly doesn't align with the zone context and detected objects. - Identify patterns that suggest genuine security concerns: testing doors/windows on vehicles or buildings, accessing unauthorized areas, attempting to conceal actions, extended loitering without apparent purpose, taking items, behavior that clearly doesn't align with the zone context and detected objects.
- **Weigh all evidence holistically**: Start by checking if the activity matches the normal patterns above. If it does, assign Level 0. Only consider Level 1 if the activity clearly deviates from normal patterns or shows genuine security concerns that warrant attention. - **Weigh all evidence holistically**: Start by checking if the activity matches the normal patterns above. If it does, assign Level 0. Only consider Level 1 if the activity clearly deviates from normal patterns or shows genuine security concerns that warrant attention.
## Response Format
Your response MUST be a flat JSON object with: Your response MUST be a flat JSON object with:
- `title` (string): A concise, one-sentence title that captures the main activity. Include any verified recognized objects (from the "Verified recognized objects" list below) and key detected objects. Examples: "Joe walking dog in backyard", "Unknown person testing car doors at night". - `title` (string): A concise, one-sentence title that captures the main activity. Include any verified recognized objects (from the "Verified recognized objects" list below) and key detected objects. Examples: "Joe walking dog in backyard", "Unknown person testing car doors at night".
- `scene` (string): A narrative description of what happens across the sequence from start to finish. **Only describe actions you can actually observe happening in the frames provided.** Do not infer or assume actions that aren't visible (e.g., if you see someone walking but never see them sit, don't say they sat down). Include setting, detected objects, and their observable actions. Avoid speculation or filling in assumed behaviors. Your description should align with and support the threat level you assign. - `scene` (string): A narrative description of what happens across the sequence from start to finish. **Only describe actions you can actually observe happening in the frames provided.** Do not infer or assume actions that aren't visible (e.g., if you see someone walking but never see them sit, don't say they sat down). Include setting, detected objects, and their observable actions. Avoid speculation or filling in assumed behaviors. Your description should align with and support the threat level you assign.
@ -99,20 +109,22 @@ Your response MUST be a flat JSON object with:
- `potential_threat_level` (integer): 0, 1, or 2 as defined below. Your threat level must be consistent with your scene description and the guidance above. - `potential_threat_level` (integer): 0, 1, or 2 as defined below. Your threat level must be consistent with your scene description and the guidance above.
{get_concern_prompt()} {get_concern_prompt()}
Threat-level definitions: ## Threat Level Definitions
- 0 — **Normal activity (DEFAULT)**: What you observe matches the normal activity patterns above or is consistent with expected activity for this property type. The observable evidence—considering zone context, detected objects, and timing together—supports a benign explanation. **Use this level for routine activities even if minor ambiguous elements exist.** - 0 — **Normal activity (DEFAULT)**: What you observe matches the normal activity patterns above or is consistent with expected activity for this property type. The observable evidence—considering zone context, detected objects, and timing together—supports a benign explanation. **Use this level for routine activities even if minor ambiguous elements exist.**
- 1 — **Potentially suspicious**: Observable behavior raises genuine security concerns that warrant human review. The evidence doesn't support a routine explanation and clearly deviates from the normal patterns above. Examples: testing doors/windows on vehicles or structures, accessing areas that don't align with the activity, taking items that likely don't belong to them, behavior clearly inconsistent with the zone and context, or activity that lacks any visible legitimate indicators. **Only use this level when the activity clearly doesn't match normal patterns.** - 1 — **Potentially suspicious**: Observable behavior raises genuine security concerns that warrant human review. The evidence doesn't support a routine explanation and clearly deviates from the normal patterns above. Examples: testing doors/windows on vehicles or structures, accessing areas that don't align with the activity, taking items that likely don't belong to them, behavior clearly inconsistent with the zone and context, or activity that lacks any visible legitimate indicators. **Only use this level when the activity clearly doesn't match normal patterns.**
- 2 — **Immediate threat**: Clear evidence of forced entry, break-in, vandalism, aggression, weapons, theft in progress, or active property damage. - 2 — **Immediate threat**: Clear evidence of forced entry, break-in, vandalism, aggression, weapons, theft in progress, or active property damage.
Sequence details: ## Sequence Details
- Frame 1 = earliest, Frame {len(thumbnails)} = latest - Frame 1 = earliest, Frame {len(thumbnails)} = latest
- Activity started at {review_data["start"]} and lasted {review_data["duration"]} seconds - Activity started at {review_data["start"]} and lasted {review_data["duration"]} seconds
- Detected objects: {", ".join(review_data["objects"])} - Detected objects: {", ".join(review_data["objects"])}
- Verified recognized objects (use these names when describing these objects):
{get_verified_objects()}
- Zones involved: {", ".join(z.replace("_", " ").title() for z in review_data["zones"]) or "None"} - Zones involved: {", ".join(z.replace("_", " ").title() for z in review_data["zones"]) or "None"}
**IMPORTANT:** {get_verified_object_prompt()}
## Important Notes
- Values must be plain strings, floats, or integers — no nested objects, no extra commentary. - Values must be plain strings, floats, or integers — no nested objects, no extra commentary.
- Only describe objects from the "Detected objects" list above. Do not hallucinate additional objects. - Only describe objects from the "Detected objects" list above. Do not hallucinate additional objects.
{get_language_prompt()} {get_language_prompt()}

508
web/src/components/overlay/ObjectTrackOverlay.tsx
View File

@ -12,38 +12,80 @@ import { TooltipPortal } from "@radix-ui/react-tooltip";
import { cn } from "@/lib/utils"; import { cn } from "@/lib/utils";
import { useTranslation } from "react-i18next"; import { useTranslation } from "react-i18next";
import { resolveZoneName } from "@/hooks/use-zone-friendly-name"; import { resolveZoneName } from "@/hooks/use-zone-friendly-name";
import { Event } from "@/types/event";
type ObjectTrackOverlayProps = { type ObjectTrackOverlayProps = {
camera: string; camera: string;
selectedObjectId: string;
showBoundingBoxes?: boolean; showBoundingBoxes?: boolean;
currentTime: number; currentTime: number;
videoWidth: number; videoWidth: number;
videoHeight: number; videoHeight: number;
className?: string; className?: string;
onSeekToTime?: (timestamp: number, play?: boolean) => void; onSeekToTime?: (timestamp: number, play?: boolean) => void;
objectTimeline?: ObjectLifecycleSequence[]; };
type PathPoint = {
x: number;
y: number;
timestamp: number;
lifecycle_item?: ObjectLifecycleSequence;
objectId: string;
};
type ObjectData = {
objectId: string;
label: string;
color: string;
pathPoints: PathPoint[];
currentZones: string[];
currentBox?: number[];
}; };
export default function ObjectTrackOverlay({ export default function ObjectTrackOverlay({
camera, camera,
selectedObjectId,
showBoundingBoxes = false, showBoundingBoxes = false,
currentTime, currentTime,
videoWidth, videoWidth,
videoHeight, videoHeight,
className, className,
onSeekToTime, onSeekToTime,
objectTimeline,
}: ObjectTrackOverlayProps) { }: ObjectTrackOverlayProps) {
const { t } = useTranslation("views/events"); const { t } = useTranslation("views/events");
const { data: config } = useSWR<FrigateConfig>("config"); const { data: config } = useSWR<FrigateConfig>("config");
const { annotationOffset } = useDetailStream(); const { annotationOffset, selectedObjectIds } = useDetailStream();
const effectiveCurrentTime = currentTime - annotationOffset / 1000; const effectiveCurrentTime = currentTime - annotationOffset / 1000;
// Fetch the full event data to get saved path points // Fetch all event data in a single request (CSV ids)
const { data: eventData } = useSWR(["event_ids", { ids: selectedObjectId }]); const { data: eventsData } = useSWR<Event[]>(
selectedObjectIds.length > 0
? ["event_ids", { ids: selectedObjectIds.join(",") }]
: null,
);
// Fetch timeline data for each object ID using fixed number of hooks
const { data: timelineData } = useSWR<ObjectLifecycleSequence[]>(
selectedObjectIds.length > 0
? `timeline?source_id=${selectedObjectIds.join(",")}&limit=1000`
: null,
{ revalidateOnFocus: false },
);
const timelineResults = useMemo(() => {
// Group timeline entries by source_id
if (!timelineData) return selectedObjectIds.map(() => []);
const grouped: Record<string, ObjectLifecycleSequence[]> = {};
for (const entry of timelineData) {
if (!grouped[entry.source_id]) {
grouped[entry.source_id] = [];
}
grouped[entry.source_id].push(entry);
}
// Return timeline arrays in the same order as selectedObjectIds
return selectedObjectIds.map((id) => grouped[id] || []);
}, [selectedObjectIds, timelineData]);
const typeColorMap = useMemo( const typeColorMap = useMemo(
() => ({ () => ({
@ -59,16 +101,18 @@ export default function ObjectTrackOverlay({
[], [],
); );
const getObjectColor = useMemo(() => { const getObjectColor = useCallback(
return (label: string) => { (label: string, objectId: string) => {
const objectColor = config?.model?.colormap[label]; const objectColor = config?.model?.colormap[label];
if (objectColor) { if (objectColor) {
const reversed = [...objectColor].reverse(); const reversed = [...objectColor].reverse();
return `rgb(${reversed.join(",")})`; return `rgb(${reversed.join(",")})`;
} }
return "rgb(255, 0, 0)"; // fallback red // Fallback to deterministic color based on object ID
}; return generateColorFromId(objectId);
}, [config]); },
[config],
);
const getZoneColor = useCallback( const getZoneColor = useCallback(
(zoneName: string) => { (zoneName: string) => {
@ -82,128 +126,125 @@ export default function ObjectTrackOverlay({
[config, camera], [config, camera],
); );
const currentObjectZones = useMemo(() => { // Build per-object data structures
if (!objectTimeline) return []; const objectsData = useMemo<ObjectData[]>(() => {
if (!eventsData || !Array.isArray(eventsData)) return [];
// Find the most recent timeline event at or before effective current time if (config?.cameras[camera]?.onvif.autotracking.enabled_in_config)
const relevantEvents = objectTimeline
.filter((event) => event.timestamp <= effectiveCurrentTime)
.sort((a, b) => b.timestamp - a.timestamp); // Most recent first
// Get zones from the most recent event
return relevantEvents[0]?.data?.zones || [];
}, [objectTimeline, effectiveCurrentTime]);
const zones = useMemo(() => {
if (!config?.cameras?.[camera]?.zones || !currentObjectZones.length)
return []; return [];
return selectedObjectIds
.map((objectId, index) => {
const eventData = eventsData.find((e) => e.id === objectId);
const timelineData = timelineResults[index];
// get saved path points from event
const savedPathPoints: PathPoint[] =
eventData?.data?.path_data?.map(
([coords, timestamp]: [number[], number]) => ({
x: coords[0],
y: coords[1],
timestamp,
lifecycle_item: undefined,
objectId,
}),
) || [];
// timeline points for this object
const eventSequencePoints: PathPoint[] =
timelineData
?.filter(
(event: ObjectLifecycleSequence) => event.data.box !== undefined,
)
.map((event: ObjectLifecycleSequence) => {
const [left, top, width, height] = event.data.box!;
event.data.zones_friendly_names = event?.data?.zones?.map((zone) => {
return resolveZoneName(config, zone);
});
return {
x: left + width / 2, // Center x
y: top + height, // Bottom y
timestamp: event.timestamp,
lifecycle_item: event,
objectId,
};
}) || [];
// show full path once current time has reached the object's start time
const combinedPoints = [...savedPathPoints, ...eventSequencePoints]
.sort((a, b) => a.timestamp - b.timestamp)
.filter(
(point) =>
currentTime >= (eventData?.start_time ?? 0) &&
point.timestamp >= (eventData?.start_time ?? 0) &&
point.timestamp <= (eventData?.end_time ?? Infinity),
);
// Get color for this object
const label = eventData?.label || "unknown";
const color = getObjectColor(label, objectId);
// Get current zones
const currentZones =
timelineData
?.filter(
(event: ObjectLifecycleSequence) =>
event.timestamp <= effectiveCurrentTime,
)
.sort(
(a: ObjectLifecycleSequence, b: ObjectLifecycleSequence) =>
b.timestamp - a.timestamp,
)[0]?.data?.zones || [];
// Get current bounding box
const currentBox = timelineData
?.filter(
(event: ObjectLifecycleSequence) =>
event.timestamp <= effectiveCurrentTime && event.data.box,
)
.sort(
(a: ObjectLifecycleSequence, b: ObjectLifecycleSequence) =>
b.timestamp - a.timestamp,
)[0]?.data?.box;
return {
objectId,
label,
color,
pathPoints: combinedPoints,
currentZones,
currentBox,
};
})
.filter((obj: ObjectData) => obj.pathPoints.length > 0); // Only include objects with path data
}, [
eventsData,
selectedObjectIds,
timelineResults,
currentTime,
effectiveCurrentTime,
getObjectColor,
config,
camera,
]);
// Collect all zones across all objects
const allZones = useMemo(() => {
if (!config?.cameras?.[camera]?.zones) return [];
const zoneNames = new Set<string>();
objectsData.forEach((obj) => {
obj.currentZones.forEach((zone) => zoneNames.add(zone));
});
return Object.entries(config.cameras[camera].zones) return Object.entries(config.cameras[camera].zones)
.filter(([name]) => currentObjectZones.includes(name)) .filter(([name]) => zoneNames.has(name))
.map(([name, zone]) => ({ .map(([name, zone]) => ({
name, name,
coordinates: zone.coordinates, coordinates: zone.coordinates,
color: getZoneColor(name), color: getZoneColor(name),
})); }));
}, [config, camera, getZoneColor, currentObjectZones]); }, [config, camera, objectsData, getZoneColor]);
// get saved path points from event
const savedPathPoints = useMemo(() => {
return (
eventData?.[0].data?.path_data?.map(
([coords, timestamp]: [number[], number]) => ({
x: coords[0],
y: coords[1],
timestamp,
lifecycle_item: undefined,
}),
) || []
);
}, [eventData]);
// timeline points for selected event
const eventSequencePoints = useMemo(() => {
return (
objectTimeline
?.filter((event) => event.data.box !== undefined)
.map((event) => {
const [left, top, width, height] = event.data.box!;
event.data.zones_friendly_names = event?.data?.zones?.map((zone) => {
return resolveZoneName(config, zone);
});
return {
x: left + width / 2, // Center x
y: top + height, // Bottom y
timestamp: event.timestamp,
lifecycle_item: event,
};
}) || []
);
}, [config, objectTimeline]);
// final object path with timeline points included
const pathPoints = useMemo(() => {
// don't display a path for autotracking cameras
if (config?.cameras[camera]?.onvif.autotracking.enabled_in_config)
return [];
const combinedPoints = [...savedPathPoints, ...eventSequencePoints].sort(
(a, b) => a.timestamp - b.timestamp,
);
// Filter points around current time (within a reasonable window)
const timeWindow = 30; // 30 seconds window
return combinedPoints.filter(
(point) =>
point.timestamp >= currentTime - timeWindow &&
point.timestamp <= currentTime + timeWindow,
);
}, [savedPathPoints, eventSequencePoints, config, camera, currentTime]);
// get absolute positions on the svg canvas for each point
const absolutePositions = useMemo(() => {
if (!pathPoints) return [];
return pathPoints.map((point) => {
// Find the corresponding timeline entry for this point
const timelineEntry = objectTimeline?.find(
(entry) => entry.timestamp == point.timestamp,
);
return {
x: point.x * videoWidth,
y: point.y * videoHeight,
timestamp: point.timestamp,
lifecycle_item:
timelineEntry ||
(point.box // normal path point
? {
timestamp: point.timestamp,
camera: camera,
source: "tracked_object",
source_id: selectedObjectId,
class_type: "visible" as LifecycleClassType,
data: {
camera: camera,
label: point.label,
sub_label: "",
box: point.box,
region: [0, 0, 0, 0], // placeholder
attribute: "",
zones: [],
},
}
: undefined),
};
});
}, [
pathPoints,
videoWidth,
videoHeight,
objectTimeline,
camera,
selectedObjectId,
]);
const generateStraightPath = useCallback( const generateStraightPath = useCallback(
(points: { x: number; y: number }[]) => { (points: { x: number; y: number }[]) => {
@ -218,15 +259,20 @@ export default function ObjectTrackOverlay({
); );
const getPointColor = useCallback( const getPointColor = useCallback(
(baseColor: number[], type?: string) => { (baseColorString: string, type?: string) => {
if (type && typeColorMap[type as keyof typeof typeColorMap]) { if (type && typeColorMap[type as keyof typeof typeColorMap]) {
const typeColor = typeColorMap[type as keyof typeof typeColorMap]; const typeColor = typeColorMap[type as keyof typeof typeColorMap];
if (typeColor) { if (typeColor) {
return `rgb(${typeColor.join(",")})`; return `rgb(${typeColor.join(",")})`;
} }
} }
// normal path point // Parse and darken base color slightly for path points
return `rgb(${baseColor.map((c) => Math.max(0, c - 10)).join(",")})`; const match = baseColorString.match(/\d+/g);
if (match) {
const [r, g, b] = match.map(Number);
return `rgb(${Math.max(0, r - 10)}, ${Math.max(0, g - 10)}, ${Math.max(0, b - 10)})`;
}
return baseColorString;
}, },
[typeColorMap], [typeColorMap],
); );
@ -238,49 +284,8 @@ export default function ObjectTrackOverlay({
[onSeekToTime], [onSeekToTime],
); );
// render bounding box for object at current time if we have a timeline entry
const currentBoundingBox = useMemo(() => {
if (!objectTimeline) return null;
// Find the most recent timeline event at or before effective current time with a bounding box
const relevantEvents = objectTimeline
.filter(
(event) => event.timestamp <= effectiveCurrentTime && event.data.box,
)
.sort((a, b) => b.timestamp - a.timestamp); // Most recent first
const currentEvent = relevantEvents[0];
if (!currentEvent?.data.box) return null;
const [left, top, width, height] = currentEvent.data.box;
return {
left,
top,
width,
height,
centerX: left + width / 2,
centerY: top + height,
};
}, [objectTimeline, effectiveCurrentTime]);
const objectColor = useMemo(() => {
return pathPoints[0]?.label
? getObjectColor(pathPoints[0].label)
: "rgb(255, 0, 0)";
}, [pathPoints, getObjectColor]);
const objectColorArray = useMemo(() => {
return pathPoints[0]?.label
? getObjectColor(pathPoints[0].label).match(/\d+/g)?.map(Number) || [
255, 0, 0,
]
: [255, 0, 0];
}, [pathPoints, getObjectColor]);
// render any zones for object at current time
const zonePolygons = useMemo(() => { const zonePolygons = useMemo(() => {
return zones.map((zone) => { return allZones.map((zone) => {
// Convert zone coordinates from normalized (0-1) to pixel coordinates // Convert zone coordinates from normalized (0-1) to pixel coordinates
const points = zone.coordinates const points = zone.coordinates
.split(",") .split(",")
@ -302,9 +307,9 @@ export default function ObjectTrackOverlay({
stroke: zone.color, stroke: zone.color,
}; };
}); });
}, [zones, videoWidth, videoHeight]); }, [allZones, videoWidth, videoHeight]);
if (!pathPoints.length || !config) { if (objectsData.length === 0 || !config) {
return null; return null;
} }
@ -329,73 +334,102 @@ export default function ObjectTrackOverlay({
/> />
))} ))}
{absolutePositions.length > 1 && ( {objectsData.map((objData) => {
<path const absolutePositions = objData.pathPoints.map((point) => ({
d={generateStraightPath(absolutePositions)} x: point.x * videoWidth,
fill="none" y: point.y * videoHeight,
stroke={objectColor} timestamp: point.timestamp,
strokeWidth="5" lifecycle_item: point.lifecycle_item,
strokeLinecap="round" }));
strokeLinejoin="round"
/>
)}
{absolutePositions.map((pos, index) => ( return (
<Tooltip key={`point-${index}`}> <g key={objData.objectId}>
<TooltipTrigger asChild> {absolutePositions.length > 1 && (
<circle <path
cx={pos.x} d={generateStraightPath(absolutePositions)}
cy={pos.y} fill="none"
r="7" stroke={objData.color}
fill={getPointColor( strokeWidth="5"
objectColorArray, strokeLinecap="round"
pos.lifecycle_item?.class_type, strokeLinejoin="round"
)} />
stroke="white" )}
strokeWidth="3"
style={{ cursor: onSeekToTime ? "pointer" : "default" }}
onClick={() => handlePointClick(pos.timestamp)}
/>
</TooltipTrigger>
<TooltipPortal>
<TooltipContent side="top" className="smart-capitalize">
{pos.lifecycle_item
? `${pos.lifecycle_item.class_type.replace("_", " ")} at ${new Date(pos.timestamp * 1000).toLocaleTimeString()}`
: t("objectTrack.trackedPoint")}
{onSeekToTime && (
<div className="mt-1 text-xs text-muted-foreground">
{t("objectTrack.clickToSeek")}
</div>
)}
</TooltipContent>
</TooltipPortal>
</Tooltip>
))}
{currentBoundingBox && showBoundingBoxes && ( {absolutePositions.map((pos, index) => (
<g> <Tooltip key={`${objData.objectId}-point-${index}`}>
<rect <TooltipTrigger asChild>
x={currentBoundingBox.left * videoWidth} <circle
y={currentBoundingBox.top * videoHeight} cx={pos.x}
width={currentBoundingBox.width * videoWidth} cy={pos.y}
height={currentBoundingBox.height * videoHeight} r="7"
fill="none" fill={getPointColor(
stroke={objectColor} objData.color,
strokeWidth="5" pos.lifecycle_item?.class_type,
opacity="0.9" )}
/> stroke="white"
strokeWidth="3"
style={{ cursor: onSeekToTime ? "pointer" : "default" }}
onClick={() => handlePointClick(pos.timestamp)}
/>
</TooltipTrigger>
<TooltipPortal>
<TooltipContent side="top" className="smart-capitalize">
{pos.lifecycle_item
? `${pos.lifecycle_item.class_type.replace("_", " ")} at ${new Date(pos.timestamp * 1000).toLocaleTimeString()}`
: t("objectTrack.trackedPoint")}
{onSeekToTime && (
<div className="mt-1 text-xs normal-case text-muted-foreground">
{t("objectTrack.clickToSeek")}
</div>
)}
</TooltipContent>
</TooltipPortal>
</Tooltip>
))}
<circle {objData.currentBox && showBoundingBoxes && (
cx={currentBoundingBox.centerX * videoWidth} <g>
cy={currentBoundingBox.centerY * videoHeight} <rect
r="5" x={objData.currentBox[0] * videoWidth}
fill="rgb(255, 255, 0)" // yellow highlight y={objData.currentBox[1] * videoHeight}
stroke={objectColor} width={objData.currentBox[2] * videoWidth}
strokeWidth="5" height={objData.currentBox[3] * videoHeight}
opacity="1" fill="none"
/> stroke={objData.color}
</g> strokeWidth="5"
)} opacity="0.9"
/>
<circle
cx={
(objData.currentBox[0] + objData.currentBox[2] / 2) *
videoWidth
}
cy={
(objData.currentBox[1] + objData.currentBox[3]) *
videoHeight
}
r="5"
fill="rgb(255, 255, 0)" // yellow highlight
stroke={objData.color}
strokeWidth="5"
opacity="1"
/>
</g>
)}
</g>
);
})}
</svg> </svg>
); );
} }
// Generate a deterministic HSL color from a string (object ID)
function generateColorFromId(id: string): string {
let hash = 0;
for (let i = 0; i < id.length; i++) {
hash = id.charCodeAt(i) + ((hash << 5) - hash);
}
// Use golden ratio to distribute hues evenly
const hue = (hash * 137.508) % 360;
return `hsl(${hue}, 70%, 50%)`;
}

26
web/src/components/overlay/detail/ObjectLifecycle.tsx
View File

@ -100,6 +100,10 @@ export default function ObjectLifecycle({
); );
}, [config, event]); }, [config, event]);
const label = event.sub_label
? event.sub_label
: getTranslatedLabel(event.label);
const getZoneColor = useCallback( const getZoneColor = useCallback(
(zoneName: string) => { (zoneName: string) => {
const zoneColor = const zoneColor =
@ -634,17 +638,29 @@ export default function ObjectLifecycle({
}} }}
role="button" role="button"
> >
<div className={cn("ml-1 rounded-full bg-muted-foreground p-2")}> <div
className={cn(
"relative ml-2 rounded-full bg-muted-foreground p-2",
)}
>
{getIconForLabel( {getIconForLabel(
event.label, event.sub_label ? event.label + "-verified" : event.label,
"size-6 text-primary dark:text-white", "size-4 text-white",
)} )}
</div> </div>
<div className="flex items-end gap-2"> <div className="flex items-center gap-2">
<span>{getTranslatedLabel(event.label)}</span> <span className="capitalize">{label}</span>
<span className="text-secondary-foreground"> <span className="text-secondary-foreground">
{formattedStart ?? ""} - {formattedEnd ?? ""} {formattedStart ?? ""} - {formattedEnd ?? ""}
</span> </span>
{event.data?.recognized_license_plate && (
<>
·{" "}
<span className="text-sm text-secondary-foreground">
{event.data.recognized_license_plate}
</span>
</>
)}
</div> </div>
</div> </div>
</div> </div>

21
web/src/components/player/HlsVideoPlayer.tsx
View File

@ -20,7 +20,6 @@ import { cn } from "@/lib/utils";
import { ASPECT_VERTICAL_LAYOUT, RecordingPlayerError } from "@/types/record"; import { ASPECT_VERTICAL_LAYOUT, RecordingPlayerError } from "@/types/record";
import { useTranslation } from "react-i18next"; import { useTranslation } from "react-i18next";
import ObjectTrackOverlay from "@/components/overlay/ObjectTrackOverlay"; import ObjectTrackOverlay from "@/components/overlay/ObjectTrackOverlay";
import { DetailStreamContextType } from "@/context/detail-stream-context";
// Android native hls does not seek correctly // Android native hls does not seek correctly
const USE_NATIVE_HLS = !isAndroid; const USE_NATIVE_HLS = !isAndroid;
@ -54,8 +53,11 @@ type HlsVideoPlayerProps = {
onUploadFrame?: (playTime: number) => Promise<AxiosResponse> | undefined; onUploadFrame?: (playTime: number) => Promise<AxiosResponse> | undefined;
toggleFullscreen?: () => void; toggleFullscreen?: () => void;
onError?: (error: RecordingPlayerError) => void; onError?: (error: RecordingPlayerError) => void;
detail?: Partial<DetailStreamContextType>; isDetailMode?: boolean;
camera?: string;
currentTimeOverride?: number;
}; };
export default function HlsVideoPlayer({ export default function HlsVideoPlayer({
videoRef, videoRef,
containerRef, containerRef,
@ -75,17 +77,15 @@ export default function HlsVideoPlayer({
onUploadFrame, onUploadFrame,
toggleFullscreen, toggleFullscreen,
onError, onError,
detail, isDetailMode = false,
camera,
currentTimeOverride,
}: HlsVideoPlayerProps) { }: HlsVideoPlayerProps) {
const { t } = useTranslation("components/player"); const { t } = useTranslation("components/player");
const { data: config } = useSWR<FrigateConfig>("config"); const { data: config } = useSWR<FrigateConfig>("config");
// for detail stream context in History // for detail stream context in History
const selectedObjectId = detail?.selectedObjectId; const currentTime = currentTimeOverride;
const selectedObjectTimeline = detail?.selectedObjectTimeline;
const currentTime = detail?.currentTime;
const camera = detail?.camera;
const isDetailMode = detail?.isDetailMode ?? false;
// playback // playback
@ -316,16 +316,14 @@ export default function HlsVideoPlayer({
}} }}
> >
{isDetailMode && {isDetailMode &&
selectedObjectId &&
camera && camera &&
currentTime && currentTime &&
videoDimensions.width > 0 && videoDimensions.width > 0 &&
videoDimensions.height > 0 && ( videoDimensions.height > 0 && (
<div className="absolute z-50 size-full"> <div className="absolute z-50 size-full">
<ObjectTrackOverlay <ObjectTrackOverlay
key={`${selectedObjectId}-${currentTime}`} key={`overlay-${currentTime}`}
camera={camera} camera={camera}
selectedObjectId={selectedObjectId}
showBoundingBoxes={!isPlaying} showBoundingBoxes={!isPlaying}
currentTime={currentTime} currentTime={currentTime}
videoWidth={videoDimensions.width} videoWidth={videoDimensions.width}
@ -336,7 +334,6 @@ export default function HlsVideoPlayer({
onSeekToTime(timestamp, play); onSeekToTime(timestamp, play);
} }
}} }}
objectTimeline={selectedObjectTimeline}
/> />
</div> </div>
)} )}

10
web/src/components/player/dynamic/DynamicVideoPlayer.tsx
View File

@ -61,7 +61,11 @@ export default function DynamicVideoPlayer({
const { data: config } = useSWR<FrigateConfig>("config"); const { data: config } = useSWR<FrigateConfig>("config");
// for detail stream context in History // for detail stream context in History
const detail = useDetailStream(); const {
isDetailMode,
camera: contextCamera,
currentTime,
} = useDetailStream();
// controlling playback // controlling playback
@ -295,7 +299,9 @@ export default function DynamicVideoPlayer({
setIsBuffering(true); setIsBuffering(true);
} }
}} }}
detail={detail} isDetailMode={isDetailMode}
camera={contextCamera || camera}
currentTimeOverride={currentTime}
/> />
<PreviewPlayer <PreviewPlayer
className={cn( className={cn(

91
web/src/components/timeline/DetailStream.tsx
View File

@ -172,7 +172,11 @@ export default function DetailStream({
<FrigatePlusDialog <FrigatePlusDialog
upload={upload} upload={upload}
onClose={() => setUpload(undefined)} onClose={() => setUpload(undefined)}
onEventUploaded={() => setUpload(undefined)} onEventUploaded={() => {
if (upload) {
upload.plus_id = "new_upload";
}
}}
/> />
<div <div
@ -255,7 +259,9 @@ function ReviewGroup({
const rawIconLabels: string[] = [ const rawIconLabels: string[] = [
...(fetchedEvents ...(fetchedEvents
? fetchedEvents.map((e) => e.label) ? fetchedEvents.map((e) =>
e.sub_label ? e.label + "-verified" : e.label,
)
: (review.data?.objects ?? [])), : (review.data?.objects ?? [])),
...(review.data?.audio ?? []), ...(review.data?.audio ?? []),
]; ];
@ -318,7 +324,7 @@ function ReviewGroup({
<div className="ml-1 flex flex-col items-start gap-1.5"> <div className="ml-1 flex flex-col items-start gap-1.5">
<div className="flex flex-row gap-3"> <div className="flex flex-row gap-3">
<div className="text-sm font-medium">{displayTime}</div> <div className="text-sm font-medium">{displayTime}</div>
<div className="flex items-center gap-2"> <div className="relative flex items-center gap-2 text-white">
{iconLabels.slice(0, 5).map((lbl, idx) => ( {iconLabels.slice(0, 5).map((lbl, idx) => (
<div <div
key={`${lbl}-${idx}`} key={`${lbl}-${idx}`}
@ -424,30 +430,34 @@ function EventList({
}: EventListProps) { }: EventListProps) {
const { data: config } = useSWR<FrigateConfig>("config"); const { data: config } = useSWR<FrigateConfig>("config");
const { selectedObjectId, setSelectedObjectId } = useDetailStream(); const { selectedObjectIds, toggleObjectSelection } = useDetailStream();
const isSelected = selectedObjectIds.includes(event.id);
const label = event.sub_label || getTranslatedLabel(event.label);
const handleObjectSelect = (event: Event | undefined) => { const handleObjectSelect = (event: Event | undefined) => {
if (event) { if (event) {
onSeek(event.start_time ?? 0); // onSeek(event.start_time ?? 0);
setSelectedObjectId(event.id); toggleObjectSelection(event.id);
} else { } else {
setSelectedObjectId(undefined); toggleObjectSelection(undefined);
} }
}; };
// Clear selectedObjectId when effectiveTime has passed this event's end_time // Clear selection when effectiveTime has passed this event's end_time
useEffect(() => { useEffect(() => {
if (selectedObjectId === event.id && effectiveTime && event.end_time) { if (isSelected && effectiveTime && event.end_time) {
if (effectiveTime >= event.end_time) { if (effectiveTime >= event.end_time) {
setSelectedObjectId(undefined); toggleObjectSelection(event.id);
} }
} }
}, [ }, [
selectedObjectId, isSelected,
event.id, event.id,
event.end_time, event.end_time,
effectiveTime, effectiveTime,
setSelectedObjectId, toggleObjectSelection,
]); ]);
return ( return (
@ -455,48 +465,59 @@ function EventList({
<div <div
className={cn( className={cn(
"rounded-md bg-secondary p-2", "rounded-md bg-secondary p-2",
event.id == selectedObjectId isSelected
? "bg-secondary-highlight" ? "bg-secondary-highlight"
: "outline-transparent duration-500", : "outline-transparent duration-500",
event.id != selectedObjectId && !isSelected &&
(effectiveTime ?? 0) >= (event.start_time ?? 0) - 0.5 && (effectiveTime ?? 0) >= (event.start_time ?? 0) - 0.5 &&
(effectiveTime ?? 0) <= (effectiveTime ?? 0) <=
(event.end_time ?? event.start_time ?? 0) + 0.5 && (event.end_time ?? event.start_time ?? 0) + 0.5 &&
"bg-secondary-highlight", "bg-secondary-highlight",
)} )}
> >
<div className="ml-1.5 flex w-full items-center justify-between"> <div className="ml-1.5 flex w-full items-end justify-between">
<div <div className="flex flex-1 items-center gap-2 text-sm font-medium">
className="flex items-center gap-2 text-sm font-medium"
onClick={(e) => {
e.stopPropagation();
handleObjectSelect(
event.id == selectedObjectId ? undefined : event,
);
}}
role="button"
>
<div <div
className={cn( className={cn(
"rounded-full p-1", "relative rounded-full p-1 text-white",
event.id == selectedObjectId isSelected ? "bg-selected" : "bg-muted-foreground",
? "bg-selected"
: "bg-muted-foreground",
)} )}
onClick={(e) => {
e.stopPropagation();
handleObjectSelect(isSelected ? undefined : event);
}}
> >
{getIconForLabel(event.label, "size-3 text-white")} {getIconForLabel(
event.sub_label ? event.label + "-verified" : event.label,
"size-3 text-white",
)}
</div> </div>
<div className="flex items-end gap-2"> <div
<span>{getTranslatedLabel(event.label)}</span> className="flex flex-1 items-center gap-2"
onClick={(e) => {
e.stopPropagation();
onSeek(event.start_time ?? 0);
}}
role="button"
>
<span className="capitalize">{label}</span>
{event.data?.recognized_license_plate && (
<>
·{" "}
<span className="text-sm text-secondary-foreground">
{event.data.recognized_license_plate}
</span>
</>
)}
</div> </div>
</div> </div>
<div className="mr-2 flex flex-1 flex-row justify-end"> <div className="mr-2 flex flex-row justify-end">
<EventMenu <EventMenu
event={event} event={event}
config={config} config={config}
onOpenUpload={(e) => onOpenUpload?.(e)} onOpenUpload={(e) => onOpenUpload?.(e)}
selectedObjectId={selectedObjectId} isSelected={isSelected}
setSelectedObjectId={handleObjectSelect} onToggleSelection={handleObjectSelect}
/> />
</div> </div>
</div> </div>

21
web/src/components/timeline/EventMenu.tsx
View File

@ -12,14 +12,15 @@ import { useNavigate } from "react-router-dom";
import { useTranslation } from "react-i18next"; import { useTranslation } from "react-i18next";
import { Event } from "@/types/event"; import { Event } from "@/types/event";
import { FrigateConfig } from "@/types/frigateConfig"; import { FrigateConfig } from "@/types/frigateConfig";
import { useState } from "react";
type EventMenuProps = { type EventMenuProps = {
event: Event; event: Event;
config?: FrigateConfig; config?: FrigateConfig;
onOpenUpload?: (e: Event) => void; onOpenUpload?: (e: Event) => void;
onOpenSimilarity?: (e: Event) => void; onOpenSimilarity?: (e: Event) => void;
selectedObjectId?: string; isSelected?: boolean;
setSelectedObjectId?: (event: Event | undefined) => void; onToggleSelection?: (event: Event | undefined) => void;
}; };
export default function EventMenu({ export default function EventMenu({
@ -27,25 +28,26 @@ export default function EventMenu({
config, config,
onOpenUpload, onOpenUpload,
onOpenSimilarity, onOpenSimilarity,
selectedObjectId, isSelected = false,
setSelectedObjectId, onToggleSelection,
}: EventMenuProps) { }: EventMenuProps) {
const apiHost = useApiHost(); const apiHost = useApiHost();
const navigate = useNavigate(); const navigate = useNavigate();
const { t } = useTranslation("views/explore"); const { t } = useTranslation("views/explore");
const [isOpen, setIsOpen] = useState(false);
const handleObjectSelect = () => { const handleObjectSelect = () => {
if (event.id === selectedObjectId) { if (isSelected) {
setSelectedObjectId?.(undefined); onToggleSelection?.(undefined);
} else { } else {
setSelectedObjectId?.(event); onToggleSelection?.(event);
} }
}; };
return ( return (
<> <>
<span tabIndex={0} className="sr-only" /> <span tabIndex={0} className="sr-only" />
<DropdownMenu> <DropdownMenu open={isOpen} onOpenChange={setIsOpen}>
<DropdownMenuTrigger> <DropdownMenuTrigger>
<div className="rounded p-1 pr-2" role="button"> <div className="rounded p-1 pr-2" role="button">
<HiDotsHorizontal className="size-4 text-muted-foreground" /> <HiDotsHorizontal className="size-4 text-muted-foreground" />
@ -54,7 +56,7 @@ export default function EventMenu({
<DropdownMenuPortal> <DropdownMenuPortal>
<DropdownMenuContent> <DropdownMenuContent>
<DropdownMenuItem onSelect={handleObjectSelect}> <DropdownMenuItem onSelect={handleObjectSelect}>
{event.id === selectedObjectId {isSelected
? t("itemMenu.hideObjectDetails.label") ? t("itemMenu.hideObjectDetails.label")
: t("itemMenu.showObjectDetails.label")} : t("itemMenu.showObjectDetails.label")}
</DropdownMenuItem> </DropdownMenuItem>
@ -85,6 +87,7 @@ export default function EventMenu({
config?.plus?.enabled && ( config?.plus?.enabled && (
<DropdownMenuItem <DropdownMenuItem
onSelect={() => { onSelect={() => {
setIsOpen(false);
onOpenUpload?.(event); onOpenUpload?.(event);
}} }}
> >

31
web/src/context/detail-stream-context.tsx
View File

@ -1,16 +1,14 @@
import React, { createContext, useContext, useState, useEffect } from "react"; import React, { createContext, useContext, useState, useEffect } from "react";
import { FrigateConfig } from "@/types/frigateConfig"; import { FrigateConfig } from "@/types/frigateConfig";
import useSWR from "swr"; import useSWR from "swr";
import { ObjectLifecycleSequence } from "@/types/timeline";
export interface DetailStreamContextType { export interface DetailStreamContextType {
selectedObjectId: string | undefined; selectedObjectIds: string[];
selectedObjectTimeline?: ObjectLifecycleSequence[];
currentTime: number; currentTime: number;
camera: string; camera: string;
annotationOffset: number; // milliseconds annotationOffset: number; // milliseconds
setAnnotationOffset: (ms: number) => void; setAnnotationOffset: (ms: number) => void;
setSelectedObjectId: (id: string | undefined) => void; toggleObjectSelection: (id: string | undefined) => void;
isDetailMode: boolean; isDetailMode: boolean;
} }
@ -31,13 +29,21 @@ export function DetailStreamProvider({
currentTime, currentTime,
camera, camera,
}: DetailStreamProviderProps) { }: DetailStreamProviderProps) {
const [selectedObjectId, setSelectedObjectId] = useState< const [selectedObjectIds, setSelectedObjectIds] = useState<string[]>([]);
string | undefined
>();
const { data: selectedObjectTimeline } = useSWR<ObjectLifecycleSequence[]>( const toggleObjectSelection = (id: string | undefined) => {
selectedObjectId ? ["timeline", { source_id: selectedObjectId }] : null, if (id === undefined) {
); setSelectedObjectIds([]);
} else {
setSelectedObjectIds((prev) => {
if (prev.includes(id)) {
return prev.filter((existingId) => existingId !== id);
} else {
return [...prev, id];
}
});
}
};
const { data: config } = useSWR<FrigateConfig>("config"); const { data: config } = useSWR<FrigateConfig>("config");
@ -53,13 +59,12 @@ export function DetailStreamProvider({
}, [config, camera]); }, [config, camera]);
const value: DetailStreamContextType = { const value: DetailStreamContextType = {
selectedObjectId, selectedObjectIds,
selectedObjectTimeline,
currentTime, currentTime,
camera, camera,
annotationOffset, annotationOffset,
setAnnotationOffset, setAnnotationOffset,
setSelectedObjectId, toggleObjectSelection,
isDetailMode, isDetailMode,
}; };

1
web/src/types/event.ts
View File

@ -22,6 +22,7 @@ export interface Event {
area: number; area: number;
ratio: number; ratio: number;
type: "object" | "audio" | "manual"; type: "object" | "audio" | "manual";
recognized_license_plate?: string;
path_data: [number[], number][]; path_data: [number[], number][];
}; };
} }

3
web/src/utils/lifecycleUtil.ts
View File

@ -1,6 +1,7 @@
import { ObjectLifecycleSequence } from "@/types/timeline"; import { ObjectLifecycleSequence } from "@/types/timeline";
import { t } from "i18next"; import { t } from "i18next";
import i18n, { getTranslatedLabel } from "./i18n"; import i18n, { getTranslatedLabel } from "./i18n";
import { capitalizeFirstLetter } from "./stringUtil";
export function getLifecycleItemDescription( export function getLifecycleItemDescription(
lifecycleItem: ObjectLifecycleSequence, lifecycleItem: ObjectLifecycleSequence,
@ -10,7 +11,7 @@ export function getLifecycleItemDescription(
: lifecycleItem.data.sub_label || lifecycleItem.data.label; : lifecycleItem.data.sub_label || lifecycleItem.data.label;
const label = lifecycleItem.data.sub_label const label = lifecycleItem.data.sub_label
? rawLabel ? capitalizeFirstLetter(rawLabel)
: getTranslatedLabel(rawLabel); : getTranslatedLabel(rawLabel);
let supportedListFormat = false; let supportedListFormat = false;

149
web/src/views/recording/RecordingView.tsx
View File

@ -11,6 +11,7 @@ import DetailStream from "@/components/timeline/DetailStream";
import { Button } from "@/components/ui/button"; import { Button } from "@/components/ui/button";
import { ToggleGroup, ToggleGroupItem } from "@/components/ui/toggle-group"; import { ToggleGroup, ToggleGroupItem } from "@/components/ui/toggle-group";
import { useOverlayState } from "@/hooks/use-overlay-state"; import { useOverlayState } from "@/hooks/use-overlay-state";
import { useResizeObserver } from "@/hooks/resize-observer";
import { ExportMode } from "@/types/filter"; import { ExportMode } from "@/types/filter";
import { FrigateConfig } from "@/types/frigateConfig"; import { FrigateConfig } from "@/types/frigateConfig";
import { Preview } from "@/types/preview"; import { Preview } from "@/types/preview";
@ -31,12 +32,7 @@ import {
useRef, useRef,
useState, useState,
} from "react"; } from "react";
import { import { isDesktop, isMobile } from "react-device-detect";
isDesktop,
isMobile,
isMobileOnly,
isTablet,
} from "react-device-detect";
import { IoMdArrowRoundBack } from "react-icons/io"; import { IoMdArrowRoundBack } from "react-icons/io";
import { useNavigate } from "react-router-dom"; import { useNavigate } from "react-router-dom";
import { Toaster } from "@/components/ui/sonner"; import { Toaster } from "@/components/ui/sonner";
@ -55,7 +51,6 @@ import {
RecordingSegment, RecordingSegment,
RecordingStartingPoint, RecordingStartingPoint,
} from "@/types/record"; } from "@/types/record";
import { useResizeObserver } from "@/hooks/resize-observer";
import { cn } from "@/lib/utils"; import { cn } from "@/lib/utils";
import { useFullscreen } from "@/hooks/use-fullscreen"; import { useFullscreen } from "@/hooks/use-fullscreen";
import { useTimezone } from "@/hooks/use-date-utils"; import { useTimezone } from "@/hooks/use-date-utils";
@ -399,49 +394,47 @@ export function RecordingView({
} }
}, [mainCameraAspect]); }, [mainCameraAspect]);
const [{ width: mainWidth, height: mainHeight }] = // use a resize observer to determine whether to use w-full or h-full based on container aspect ratio
const [{ width: containerWidth, height: containerHeight }] =
useResizeObserver(cameraLayoutRef); useResizeObserver(cameraLayoutRef);
const [{ width: previewRowWidth, height: previewRowHeight }] =
useResizeObserver(previewRowRef);
const mainCameraStyle = useMemo(() => { const useHeightBased = useMemo(() => {
if (isMobile || mainCameraAspect != "normal" || !config) { if (!containerWidth || !containerHeight) {
return undefined; return false;
} }
const camera = config.cameras[mainCamera]; const cameraAspectRatio = getCameraAspect(mainCamera);
if (!cameraAspectRatio) {
if (!camera) { return false;
return undefined;
} }
const aspect = getCameraAspect(mainCamera); // Calculate available space for camera after accounting for preview row
// For tall cameras: preview row is side-by-side (takes width)
// For wide/normal cameras: preview row is stacked (takes height)
const availableWidth =
mainCameraAspect == "tall" && previewRowWidth
? containerWidth - previewRowWidth
: containerWidth;
const availableHeight =
mainCameraAspect != "tall" && previewRowHeight
? containerHeight - previewRowHeight
: containerHeight;
if (!aspect) { const availableAspectRatio = availableWidth / availableHeight;
return undefined;
}
const availableHeight = mainHeight - 112; // If available space is wider than camera aspect, constrain by height (h-full)
// If available space is taller than camera aspect, constrain by width (w-full)
let percent; return availableAspectRatio >= cameraAspectRatio;
if (mainWidth / availableHeight < aspect) {
percent = 100;
} else {
const availableWidth = aspect * availableHeight;
percent =
(mainWidth < availableWidth
? mainWidth / availableWidth
: availableWidth / mainWidth) * 100;
}
return {
width: `${Math.round(percent)}%`,
};
}, [ }, [
config, containerWidth,
mainCameraAspect, containerHeight,
mainWidth, previewRowWidth,
mainHeight, previewRowHeight,
mainCamera,
getCameraAspect, getCameraAspect,
mainCamera,
mainCameraAspect,
]); ]);
const previewRowOverflows = useMemo(() => { const previewRowOverflows = useMemo(() => {
@ -685,19 +678,17 @@ export function RecordingView({
<div <div
ref={mainLayoutRef} ref={mainLayoutRef}
className={cn( className={cn(
"flex h-full justify-center overflow-hidden", "flex flex-1 overflow-hidden",
isDesktop ? "" : "flex-col gap-2 landscape:flex-row", isDesktop ? "flex-row" : "flex-col gap-2 landscape:flex-row",
)} )}
> >
<div <div
ref={cameraLayoutRef} ref={cameraLayoutRef}
className={cn( className={cn(
"flex flex-1 flex-wrap", "flex flex-1 flex-wrap overflow-hidden",
isDesktop isDesktop
? timelineType === "detail" ? "min-w-0 px-4"
? "md:w-[40%] lg:w-[70%] xl:w-full" : "portrait:max-h-[50dvh] portrait:flex-shrink-0 portrait:flex-grow-0 portrait:basis-auto",
: "w-[80%]"
: "",
)} )}
> >
<div <div
@ -711,37 +702,24 @@ export function RecordingView({
<div <div
key={mainCamera} key={mainCamera}
className={cn( className={cn(
"relative", "relative flex max-h-full min-h-0 min-w-0 max-w-full items-center justify-center",
isDesktop isDesktop
? cn( ? // Desktop: dynamically switch between w-full and h-full based on
"flex justify-center px-4", // container vs camera aspect ratio to ensure proper fitting
mainCameraAspect == "tall" useHeightBased
? "h-[50%] md:h-[60%] lg:h-[75%] xl:h-[90%]" ? "h-full"
: mainCameraAspect == "wide" : "w-full"
? "w-full"
: "",
)
: cn( : cn(
"pt-2 portrait:w-full", "flex-shrink-0 portrait:w-full landscape:h-full",
isMobileOnly && mainCameraAspect == "wide"
(mainCameraAspect == "wide" ? "aspect-wide"
? "aspect-wide landscape:w-full" : mainCameraAspect == "tall"
: "aspect-video landscape:h-[94%] landscape:xl:h-[65%]"), ? "aspect-tall portrait:h-full"
isTablet && : "aspect-video",
(mainCameraAspect == "wide"
? "aspect-wide landscape:w-full"
: mainCameraAspect == "normal"
? "landscape:w-full"
: "aspect-video landscape:h-[100%]"),
), ),
)} )}
style={{ style={{
width: mainCameraStyle ? mainCameraStyle.width : undefined, aspectRatio: getCameraAspect(mainCamera),
aspectRatio: isDesktop
? mainCameraAspect == "tall"
? getCameraAspect(mainCamera)
: undefined
: Math.max(1, getCameraAspect(mainCamera) ?? 0),
}} }}
> >
{isDesktop && ( {isDesktop && (
@ -782,10 +760,10 @@ export function RecordingView({
<div <div
ref={previewRowRef} ref={previewRowRef}
className={cn( className={cn(
"scrollbar-container flex gap-2 overflow-auto", "scrollbar-container flex flex-shrink-0 gap-2 overflow-auto",
mainCameraAspect == "tall" mainCameraAspect == "tall"
? "h-full w-72 flex-col" ? "ml-2 h-full w-72 min-w-72 flex-col"
: `h-28 w-full`, : "h-28 min-h-28 w-full",
previewRowOverflows ? "" : "items-center justify-center", previewRowOverflows ? "" : "items-center justify-center",
timelineType == "detail" && isDesktop && "mt-4", timelineType == "detail" && isDesktop && "mt-4",
)} )}
@ -971,10 +949,23 @@ function Timeline({
return ( return (
<div <div
className={cn( className={cn(
"relative", "relative overflow-hidden",
isDesktop isDesktop
? `${timelineType == "timeline" ? "w-[100px]" : timelineType == "detail" ? "w-[30%] min-w-[350px]" : "w-60"} no-scrollbar overflow-y-auto` ? cn(
: `overflow-hidden portrait:flex-grow ${timelineType == "timeline" ? "landscape:w-[100px]" : timelineType == "detail" && isDesktop ? "flex-1" : "landscape:w-[300px]"} `, "no-scrollbar overflow-y-auto",
timelineType == "timeline"
? "w-[100px] flex-shrink-0"
: timelineType == "detail"
? "min-w-[20rem] max-w-[30%] flex-shrink-0 flex-grow-0 basis-[30rem] md:min-w-[20rem] md:max-w-[25%] lg:min-w-[30rem] lg:max-w-[33%]"
: "w-60 flex-shrink-0",
)
: cn(
timelineType == "timeline"
? "portrait:flex-grow landscape:w-[100px] landscape:flex-shrink-0"
: timelineType == "detail"
? "portrait:flex-grow landscape:w-[19rem] landscape:flex-shrink-0"
: "portrait:flex-grow landscape:w-[19rem] landscape:flex-shrink-0",
),
)} )}
> >
{isMobile && ( {isMobile && (