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3
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 GuoQing LiuandGitHub
|
e4dcd11969 | ||
|
Josh HawkinsandGitHub
|
fcd05ec7bc | ||
|
ZhaiSoul
|
edca412856 |
@@ -1149,6 +1149,11 @@ rknn:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
rknn:
|
||||
type: rknn
|
||||
num_cores: 0
|
||||
|
||||
model: # required
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
@@ -1187,6 +1192,11 @@ rknn:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `yolonas` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
rknn:
|
||||
type: rknn
|
||||
num_cores: 0
|
||||
|
||||
model: # required
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
@@ -1222,6 +1232,11 @@ rknn:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `yolox` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
rknn:
|
||||
type: rknn
|
||||
num_cores: 0
|
||||
|
||||
model: # required
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
@@ -1297,11 +1312,12 @@ degirumAiServer:
|
||||
| **Zoo** | `degirum/public` |
|
||||
| **Token** | your AI Hub token (optional for the public zoo) |
|
||||
yaml: |
|
||||
degirum_detector:
|
||||
type: degirum
|
||||
location: degirum
|
||||
zoo: degirum/public
|
||||
token: dg_example_token
|
||||
detectors:
|
||||
degirum_detector:
|
||||
type: degirum
|
||||
location: degirum
|
||||
zoo: degirum/public
|
||||
token: dg_example_token
|
||||
degirumLocal:
|
||||
title: DeGirum Local
|
||||
models:
|
||||
@@ -1318,11 +1334,12 @@ degirumLocal:
|
||||
| **Zoo** | `degirum/public` |
|
||||
| **Token** | your AI Hub token (optional for the public zoo) |
|
||||
yaml: |
|
||||
degirum_detector:
|
||||
type: degirum
|
||||
location: @local
|
||||
zoo: degirum/public
|
||||
token: dg_example_token
|
||||
detectors:
|
||||
degirum_detector:
|
||||
type: degirum
|
||||
location: "@local"
|
||||
zoo: degirum/public
|
||||
token: dg_example_token
|
||||
degirumCloud:
|
||||
title: DeGirum AI Hub Cloud
|
||||
models:
|
||||
@@ -1339,8 +1356,9 @@ degirumCloud:
|
||||
| **Zoo** | `degirum/public` |
|
||||
| **Token** | your AI Hub token (optional for the public zoo) |
|
||||
yaml: |
|
||||
degirum_detector:
|
||||
type: degirum
|
||||
location: @cloud
|
||||
zoo: degirum/public
|
||||
token: dg_example_token
|
||||
detectors:
|
||||
degirum_detector:
|
||||
type: degirum
|
||||
location: "@cloud"
|
||||
zoo: degirum/public
|
||||
token: dg_example_token
|
||||
|
||||
@@ -67,6 +67,12 @@ This section can be used to set environment variables for those unable to modify
|
||||
|
||||
Variables prefixed with `FRIGATE_` can be referenced in config fields that support environment variable substitution (such as MQTT host and credentials, camera stream URLs, and ONVIF host and credentials) using the `{FRIGATE_VARIABLE_NAME}` syntax.
|
||||
|
||||
:::note
|
||||
|
||||
The `go2rtc` section is an exception. go2rtc runs as a separate process, so its stream definitions can only be substituted with variables that exist in the container's environment (set via Docker `-e`, the `environment:` section of `docker-compose.yml`, or Docker secrets). Variables defined in the `environment_vars` block above are not available to go2rtc streams. Home Assistant app users, who cannot set container environment variables, must instead put credentials directly in their go2rtc stream URLs.
|
||||
|
||||
:::
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
|
||||
@@ -4,6 +4,7 @@ title: License Plate Recognition (LPR)
|
||||
---
|
||||
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import FrigateConfigMock from "@site/src/components/FrigateConfigMock";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
import FaqItem from "@site/src/components/FaqItem";
|
||||
@@ -50,9 +51,11 @@ License plate recognition is disabled by default and must be enabled before it c
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- Set **Enable LPR** to on
|
||||
<FrigateConfigMock
|
||||
section="lpr"
|
||||
values={{ enabled: true }}
|
||||
targets={["enabled"]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -70,7 +73,17 @@ Like other enrichments in Frigate, LPR **must be enabled globally** to use the f
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" /> for the desired camera and disable the **Enable LPR** toggle.
|
||||
<FrigateConfigMock
|
||||
section="lpr"
|
||||
level="camera"
|
||||
values={{ enabled: false }}
|
||||
targets={[
|
||||
{
|
||||
field: "enabled",
|
||||
hint: "Disable the Enable LPR toggle for this camera.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -99,16 +112,40 @@ Fine-tune the LPR feature using these optional parameters. The only optional par
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- **Detection threshold**: License plate object detection confidence score required before recognition runs. This field only applies to the standalone license plate detection model; `threshold` and `min_score` object filters should be used for models like Frigate+ that have license plate detection built in.
|
||||
- Default: `0.7`
|
||||
- **Minimum plate area**: Minimum area (in pixels) a license plate must be before recognition runs. This is an _area_ measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image. Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates.
|
||||
- Default: `1000` pixels
|
||||
- **Device**: Device to use to run license plate detection _and_ recognition models. Auto-selected by Frigate and can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
|
||||
- Default: `None`
|
||||
- **Model size**: The size of the model used to identify regions of text on plates. The `small` model is fast and identifies groups of Latin and Chinese characters. The `large` model identifies Latin characters only, and uses an enhanced text detector to find characters on multi-line plates. If your country or region does not use multi-line plates, you should use the `small` model.
|
||||
- Default: `small`
|
||||
<FrigateConfigMock
|
||||
showNavigationSteps={false}
|
||||
section="lpr"
|
||||
autoPlay={false}
|
||||
values={{
|
||||
enabled: true,
|
||||
detection_threshold: 0.7,
|
||||
min_area: 1000,
|
||||
device: "CPU",
|
||||
model_size: "small",
|
||||
}}
|
||||
targets={[
|
||||
{
|
||||
field: "detection_threshold",
|
||||
hint:
|
||||
"License plate object detection confidence score required before recognition runs. This field only applies to the standalone license plate detection model; threshold and min_score object filters should be used for models like Frigate+ that have license plate detection built in.",
|
||||
},
|
||||
{
|
||||
field: "min_area",
|
||||
hint:
|
||||
"Minimum area (in pixels) a license plate must be before recognition runs. This is an area measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image. Depending on the resolution of your camera's detect stream, you can increase this value to ignore small or distant plates.",
|
||||
},
|
||||
{
|
||||
field: "device",
|
||||
hint:
|
||||
"Device to use to run license plate detection and recognition models. Auto-selected by Frigate and can be CPU, GPU, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the Hardware Accelerated Enrichments documentation.",
|
||||
},
|
||||
{
|
||||
field: "model_size",
|
||||
hint:
|
||||
"The size of the model used to identify regions of text on plates. The small model is fast and identifies groups of Latin and Chinese characters. The large model identifies Latin characters only, and uses an enhanced text detector to find characters on multi-line plates. If your country or region does not use multi-line plates, you should use the small model.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -130,12 +167,34 @@ lpr:
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- **Recognition threshold**: Recognition confidence score required to add the plate to the object as a `recognized_license_plate` and/or `sub_label`.
|
||||
- Default: `0.9`
|
||||
- **Min plate length**: Minimum number of characters a detected license plate must have to be added as a `recognized_license_plate` and/or `sub_label`. Use this to filter out short, incomplete, or incorrect detections.
|
||||
- **Plate format regex**: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded. Websites like https://regex101.com/ can help test regular expressions for your plates.
|
||||
<FrigateConfigMock
|
||||
showNavigationSteps={false}
|
||||
section="lpr"
|
||||
autoPlay={false}
|
||||
values={{
|
||||
enabled: true,
|
||||
recognition_threshold: 0.9,
|
||||
min_plate_length: 4,
|
||||
format: "^[A-Z]{2}[0-9]{2} [A-Z]{3}$",
|
||||
}}
|
||||
targets={[
|
||||
{
|
||||
field: "recognition_threshold",
|
||||
hint:
|
||||
"Recognition confidence score required to add the plate to the object as a recognized_license_plate and/or sub_label.",
|
||||
},
|
||||
{
|
||||
field: "min_plate_length",
|
||||
hint:
|
||||
"Minimum number of characters a detected license plate must have to be added as a recognized_license_plate and/or sub_label. Use this to filter out short, incomplete, or incorrect detections.",
|
||||
},
|
||||
{
|
||||
field: "format",
|
||||
hint:
|
||||
"A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded. Websites like https://regex101.com/ can help test regular expressions for your plates.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -156,10 +215,31 @@ lpr:
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- **Known plates**: Assign custom `sub_label` values to `car` and `motorcycle` objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
|
||||
- **Match distance**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate. For example, setting to `1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`. This parameter will _not_ operate on known plates that are defined as regular expressions.
|
||||
<FrigateConfigMock
|
||||
showNavigationSteps={false}
|
||||
section="lpr"
|
||||
autoPlay={false}
|
||||
values={{
|
||||
enabled: true,
|
||||
match_distance: 1,
|
||||
known_plates: {
|
||||
"Wife's Car": ["ABC-1234"],
|
||||
Johnny: ["J*N-*234"],
|
||||
},
|
||||
}}
|
||||
targets={[
|
||||
{
|
||||
field: "match_distance",
|
||||
hint:
|
||||
"Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate. For example, setting to 1 allows a plate ABCDE to match ABCBE or ABCD. This parameter will not operate on known plates that are defined as regular expressions.",
|
||||
},
|
||||
{
|
||||
field: "known_plates",
|
||||
hint:
|
||||
"Assign custom sub_label values to car and motorcycle objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the recognized_license_plate field rather than the sub_label.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -183,10 +263,19 @@ lpr:
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- **Enhancement level**: A value between 0 and 10 that adjusts the level of image enhancement applied to captured license plates before they are processed for recognition. Higher values increase contrast, sharpen details, and reduce noise, but excessive enhancement can blur or distort characters. This setting is best adjusted at the camera level if running LPR on multiple cameras.
|
||||
- Default: `0` (no enhancement)
|
||||
<FrigateConfigMock
|
||||
showNavigationSteps={false}
|
||||
section="lpr"
|
||||
autoPlay={false}
|
||||
values={{ enabled: true, enhancement: 1 }}
|
||||
targets={[
|
||||
{
|
||||
field: "enhancement",
|
||||
hint:
|
||||
"A value between 0 and 10 that adjusts the level of image enhancement applied to captured license plates before they are processed for recognition. Higher values increase contrast, sharpen details, and reduce noise, but excessive enhancement can blur or distort characters. This setting is best adjusted at the camera level if running LPR on multiple cameras.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -207,17 +296,27 @@ If Frigate is already recognizing plates correctly, leave enhancement at the def
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
Under **Replacement rules**, add regex rules to normalize detected plate strings before matching. Rules fire in order. For example:
|
||||
|
||||
| Pattern | Replacement | Description |
|
||||
| ---------------- | ----------- | -------------------------------------------------- |
|
||||
| `[%#*?]` | _(empty)_ | Remove noise symbols |
|
||||
| `[= ]` | `-` | Normalize `=` or space to dash |
|
||||
| `O` | `0` | Swap `O` to `0` (common OCR error) |
|
||||
| `I` | `1` | Swap `I` to `1` |
|
||||
| `(\w{3})(\w{3})` | `\1-\2` | Split 6 chars into groups (e.g., ABC123 → ABC-123) |
|
||||
<FrigateConfigMock
|
||||
showNavigationSteps={false}
|
||||
section="lpr"
|
||||
autoPlay={false}
|
||||
values={{
|
||||
replace_rules: [
|
||||
{ pattern: "[%#*?]", replacement: "" },
|
||||
{ pattern: "[= ]", replacement: "-" },
|
||||
{ pattern: "O", replacement: "0" },
|
||||
{ pattern: "I", replacement: "1" },
|
||||
{ pattern: "(\\w{3})(\\w{3})", replacement: "\\1-\\2" },
|
||||
],
|
||||
}}
|
||||
targets={[
|
||||
{
|
||||
field: "replace_rules",
|
||||
hint:
|
||||
"Add regex rules to normalize detected plate strings before matching. Rules fire in order.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -252,9 +351,19 @@ These rules must be defined at the global level of your `lpr` config.
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- **Save debug plates**: Set to on to save captured text on plates for debugging. These images are stored in `/media/frigate/clips/lpr`, organized into subdirectories by `<camera>/<event_id>`, and named based on the capture timestamp.
|
||||
<FrigateConfigMock
|
||||
showNavigationSteps={false}
|
||||
section="lpr"
|
||||
autoPlay={false}
|
||||
values={{ enabled: true, debug_save_plates: true }}
|
||||
targets={[
|
||||
{
|
||||
field: "debug_save_plates",
|
||||
hint:
|
||||
"Set to on to save captured text on plates for debugging. These images are stored in /media/frigate/clips/lpr and named based on the capture timestamp.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -279,17 +388,35 @@ These configuration parameters are available at the global level. The only optio
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
| Field | Description |
|
||||
| ------------------------------ | ----------------------------------------------------------------------------------------------------- |
|
||||
| **Enable LPR** | Set to on |
|
||||
| **Minimum plate area** | Set to `1500` to ignore plates with an area (length x width) smaller than 1500 pixels |
|
||||
| **Min plate length** | Set to `4` to only recognize plates with 4 or more characters |
|
||||
| **Known plates > Wife's Car** | `ABC-1234`, `ABC-I234` (accounts for potential confusion between the number one and capital letter I) |
|
||||
| **Known plates > Johnny** | `J*N-*234` (matches JHN-1234 and JMN-I234; `*` matches any number of characters) |
|
||||
| **Known plates > Sally** | `[S5]LL 1234` (matches both SLL 1234 and 5LL 1234) |
|
||||
| **Known plates > Work Trucks** | `EMP-[0-9]{3}[A-Z]` (matches plates like EMP-123A, EMP-456Z) |
|
||||
<FrigateConfigMock
|
||||
showNavigationSteps={false}
|
||||
section="lpr"
|
||||
autoPlay={false}
|
||||
values={{
|
||||
enabled: true,
|
||||
min_area: 1500,
|
||||
min_plate_length: 4,
|
||||
known_plates: {
|
||||
"Wife's Car": ["ABC-1234", "ABC-I234"],
|
||||
Johnny: ["J*N-*234"],
|
||||
Sally: ["[S5]LL 1234"],
|
||||
"Work Trucks": ["EMP-[0-9]{3}[A-Z]"],
|
||||
},
|
||||
}}
|
||||
targets={[
|
||||
{ field: "enabled", hint: "Set to on." },
|
||||
{
|
||||
field: "min_area",
|
||||
hint:
|
||||
"Set to 1500 to ignore plates with an area (length x width) smaller than 1500 pixels.",
|
||||
},
|
||||
{
|
||||
field: "min_plate_length",
|
||||
hint: "Set to 4 to only recognize plates with 4 or more characters.",
|
||||
},
|
||||
"known_plates",
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -321,7 +448,18 @@ If a camera is configured to detect `car` or `motorcycle` but you don't want Fri
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" /> for the desired camera and disable the **Enable LPR** toggle.
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
section="lpr"
|
||||
level="camera"
|
||||
values={{ enabled: false }}
|
||||
targets={[
|
||||
{
|
||||
field: "enabled",
|
||||
hint: "Disable the Enable LPR toggle for this camera.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -362,48 +500,93 @@ An example configuration for a dedicated LPR camera using a `license_plate`-dete
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" /> and set **Enable LPR** to on. Set **Device** to `CPU` (can also be `GPU` if available).
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and add your camera streams.
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
|
||||
|
||||
| Field | Description |
|
||||
| --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| **Enable object detection** | Set to on |
|
||||
| **Detect FPS** | Set to `5`. Increase to `10` if vehicles move quickly across your frame. Higher than 10 is unnecessary and is not recommended. |
|
||||
| **Minimum initialization frames** | Set to `2` |
|
||||
| **Detect width** | Set to `1920` |
|
||||
| **Detect height** | Set to `1080` |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Objects" />.
|
||||
|
||||
| Field | Description |
|
||||
| ---------------------------------------------- | ------------------- |
|
||||
| **Objects to track** | Add `license_plate` |
|
||||
| **Object filters > License Plate > Threshold** | Set to `0.7` |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | --------------------------------------------------------------------- |
|
||||
| **Motion threshold** | Set to `30` |
|
||||
| **Contour area** | Set to `60`. Use an increased value to tune out small motion changes. |
|
||||
| **Improve contrast** | Set to off |
|
||||
|
||||
Also add a motion mask over your camera's timestamp so it is not incorrectly detected as a license plate.
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Recording" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | -------------------------------------------------------- |
|
||||
| **Enable recording** | Set to on. Disable recording if you only want snapshots. |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Snapshots" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | ----------- |
|
||||
| **Enable snapshots** | Set to on |
|
||||
<FrigateConfigMock
|
||||
section="lpr"
|
||||
autoPlay={false}
|
||||
steps={[
|
||||
{
|
||||
section: "lpr",
|
||||
level: "global",
|
||||
values: { enabled: true, device: "CPU" },
|
||||
targets: ["enabled", "device"],
|
||||
},
|
||||
{
|
||||
section: "detect",
|
||||
level: "camera",
|
||||
values: {
|
||||
enabled: true,
|
||||
fps: 5,
|
||||
min_initialized: 2,
|
||||
width: 1920,
|
||||
height: 1080,
|
||||
},
|
||||
targets: [
|
||||
{ field: "enabled", hint: "Set to on." },
|
||||
{
|
||||
field: "fps",
|
||||
hint:
|
||||
"Set to 5. Increase to 10 if vehicles move quickly across your frame. Higher than 10 is unnecessary and is not recommended.",
|
||||
},
|
||||
{ field: "min_initialized", hint: "Set to 2." },
|
||||
{ field: "width", hint: "Set to 1920." },
|
||||
{ field: "height", hint: "Set to 1080." },
|
||||
],
|
||||
},
|
||||
{
|
||||
section: "objects",
|
||||
level: "camera",
|
||||
values: {
|
||||
track: ["license_plate"],
|
||||
filters: { license_plate: { threshold: 0.7 } },
|
||||
},
|
||||
targets: [
|
||||
{ field: "track", hint: "Add license_plate." },
|
||||
"filters",
|
||||
],
|
||||
},
|
||||
{
|
||||
section: "motion",
|
||||
level: "camera",
|
||||
values: {
|
||||
threshold: 30,
|
||||
contour_area: 60,
|
||||
improve_contrast: false,
|
||||
},
|
||||
targets: [
|
||||
{ field: "threshold", hint: "Set to 30." },
|
||||
{
|
||||
field: "contour_area",
|
||||
hint:
|
||||
"Set to 60. Use an increased value to tune out small motion changes.",
|
||||
},
|
||||
{ field: "improve_contrast", hint: "Set to off." },
|
||||
],
|
||||
},
|
||||
{
|
||||
section: "record",
|
||||
level: "camera",
|
||||
values: { enabled: true },
|
||||
targets: [
|
||||
{
|
||||
field: "enabled",
|
||||
hint: "Set to on. Disable recording if you only want snapshots.",
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
section: "snapshots",
|
||||
level: "camera",
|
||||
values: { enabled: true },
|
||||
targets: [{ field: "enabled", hint: "Set to on." }],
|
||||
},
|
||||
{
|
||||
section: "review",
|
||||
level: "camera",
|
||||
values: { "detections.labels": ["license_plate"] },
|
||||
targets: ["detections.labels"],
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -467,54 +650,104 @@ An example configuration for a dedicated LPR camera using the secondary pipeline
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" /> and set **Enable LPR** to on. Set **Device** to `CPU` (can also be `GPU` if available and the correct Docker image is used). Set **Detection threshold** to `0.7` (change if necessary).
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" /> for your dedicated LPR camera.
|
||||
|
||||
| Field | Description |
|
||||
| --------------------- | -------------------------------------------------------------------------------- |
|
||||
| **Enable LPR** | Set to on |
|
||||
| **Enhancement level** | Set to `3` (optional, enhances the image before trying to recognize characters) |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Streams (FFmpeg)" /> and add your camera streams.
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Object detection" />.
|
||||
|
||||
| Field | Description |
|
||||
| --------------------------- | ---------------------------------------------------------------------------------------------------------------------------- |
|
||||
| **Enable object detection** | Set to off to disable Frigate's standard object detection pipeline |
|
||||
| **Detect FPS** | Set to `5`. Increase if necessary, though high values may slow down Frigate's enrichments pipeline and use considerable CPU. |
|
||||
| **Detect width** | Set to `1920` (recommended value, but depends on your camera) |
|
||||
| **Detect height** | Set to `1080` (recommended value, but depends on your camera) |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Objects" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | -------------------------------------------------------------------------------------- |
|
||||
| **Objects to track** | Set to an empty list, required when not using a Frigate+ model for dedicated LPR mode |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | --------------------------------------------------------------------- |
|
||||
| **Motion threshold** | Set to `30` |
|
||||
| **Contour area** | Set to `60`. Use an increased value to tune out small motion changes. |
|
||||
| **Improve contrast** | Set to off |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and add a motion mask over your camera's timestamp so it is not incorrectly detected as a license plate.
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Recording" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | -------------------------------------------------------- |
|
||||
| **Enable recording** | Set to on. Disable recording if you only want snapshots. |
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Review" />.
|
||||
|
||||
| Field | Description |
|
||||
| ----------------------------------------- | --------------- |
|
||||
| **Detections config > Enable detections** | Set to on |
|
||||
| **Detections config > Retain > Default** | Set to `7` days |
|
||||
<FrigateConfigMock
|
||||
steps={[
|
||||
{
|
||||
section: "lpr",
|
||||
level: "global",
|
||||
values: {
|
||||
enabled: true,
|
||||
device: "CPU",
|
||||
detection_threshold: 0.7,
|
||||
},
|
||||
targets: ["enabled", "device", "detection_threshold"],
|
||||
},
|
||||
{
|
||||
section: "lpr",
|
||||
level: "camera",
|
||||
values: { enabled: true, enhancement: 3 },
|
||||
targets: [
|
||||
{ field: "enabled", hint: "Set to on." },
|
||||
{
|
||||
field: "enhancement",
|
||||
hint:
|
||||
"Set to 3. This optional setting enhances the image before trying to recognize characters.",
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
section: "detect",
|
||||
level: "camera",
|
||||
values: { enabled: false, fps: 5, width: 1920, height: 1080 },
|
||||
targets: [
|
||||
{
|
||||
field: "enabled",
|
||||
hint: "Set to off to disable Frigate's standard object detection pipeline.",
|
||||
},
|
||||
{
|
||||
field: "fps",
|
||||
hint:
|
||||
"Set to 5. Increase if necessary, though high values may slow down Frigate's enrichments pipeline and use considerable CPU.",
|
||||
},
|
||||
{
|
||||
field: "width",
|
||||
hint: "Set to 1920. The appropriate value depends on your camera.",
|
||||
},
|
||||
{
|
||||
field: "height",
|
||||
hint: "Set to 1080. The appropriate value depends on your camera.",
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
section: "objects",
|
||||
level: "camera",
|
||||
values: { track: [] },
|
||||
targets: [
|
||||
{
|
||||
field: "track",
|
||||
hint:
|
||||
"Set to an empty list. This is required when not using a Frigate+ model for dedicated LPR mode.",
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
section: "motion",
|
||||
level: "camera",
|
||||
values: {
|
||||
threshold: 30,
|
||||
contour_area: 60,
|
||||
improve_contrast: false,
|
||||
},
|
||||
targets: [
|
||||
{ field: "threshold", hint: "Set to 30." },
|
||||
{
|
||||
field: "contour_area",
|
||||
hint:
|
||||
"Set to 60. Use an increased value to tune out small motion changes.",
|
||||
},
|
||||
{ field: "improve_contrast", hint: "Set to off." },
|
||||
],
|
||||
},
|
||||
{
|
||||
section: "record",
|
||||
level: "camera",
|
||||
values: { enabled: true },
|
||||
targets: [
|
||||
{
|
||||
field: "enabled",
|
||||
hint: "Set to on. Disable recording if you only want snapshots.",
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
section: "review",
|
||||
level: "camera",
|
||||
values: { "detections.enabled": true },
|
||||
targets: ["detections.enabled"],
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -655,11 +888,13 @@ Start with ["Why isn't my license plate being detected and recognized?"](#why-is
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
|
||||
|
||||
- Set **Enable LPR** to on
|
||||
- Set **Device** to `CPU`
|
||||
- Set **Save debug plates** to on
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
section="lpr"
|
||||
showNavigationSteps={false}
|
||||
values={{ enabled: true, device: "CPU", debug_save_plates: true }}
|
||||
targets={["enabled", "device", "debug_save_plates"]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
+121
-65
@@ -6,6 +6,7 @@ title: Live View
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
import FaqItem from "@site/src/components/FaqItem";
|
||||
|
||||
Frigate intelligently displays your camera streams on the Live view dashboard. By default, Frigate employs "smart streaming" where camera images update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any motion or active objects are detected, cameras seamlessly switch to a live stream.
|
||||
|
||||
@@ -341,100 +342,155 @@ When your browser runs into problems playing back your camera streams, it will l
|
||||
|
||||
## Live view FAQ
|
||||
|
||||
1. **Why don't I have audio in my Live view?**
|
||||
### Getting Live View Working
|
||||
|
||||
You must use go2rtc to hear audio in your live streams. If you have go2rtc already configured, you need to ensure your camera is sending PCMA/PCMU or AAC audio. If you can't change your camera's audio codec, you need to [transcode the audio](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) using go2rtc.
|
||||
<FaqItem id="why-dont-i-have-audio-in-my-live-view" question="Why don't I have audio in my Live view?">
|
||||
|
||||
Note that the low bandwidth mode player is a video-only stream. You should not expect to hear audio when in low bandwidth mode, even if you've set up go2rtc.
|
||||
You must use go2rtc to hear audio in your live streams. If you have go2rtc already configured, you need to ensure your camera is sending PCMA/PCMU or AAC audio. If you can't change your camera's audio codec, you need to [transcode the audio](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) using go2rtc.
|
||||
|
||||
2. **Frigate shows that my live stream is in "low bandwidth mode". What does this mean?**
|
||||
If the audio controls don't appear in the UI at all, verify that the Live view is actually using your go2rtc stream. If your go2rtc stream names don't match your Frigate camera name, you must map them with the `live -> streams` config (see [Setting Streams For Live UI](#setting-streams-for-live-ui) above); otherwise the UI falls back to the video-only jsmpeg player.
|
||||
|
||||
Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible.
|
||||
Note that the low bandwidth mode player is a video-only stream. You should not expect to hear audio when in low bandwidth mode, even if you've set up go2rtc.
|
||||
|
||||
When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. Continuous streaming mode does not have an automatic reset mechanism, but you can use the _Reset_ option to force a reload of your stream.
|
||||
</FaqItem>
|
||||
|
||||
If you are using continuous streaming or you are loading more than a few high resolution streams at once on the dashboard, your browser may struggle to begin playback of your streams before the timeout. Frigate always prioritizes showing a live stream as quickly as possible, even if it is a lower quality jsmpeg stream. You can use the "Reset" link/button to try loading your high resolution stream again.
|
||||
<FaqItem id="i-have-unmuted-some-cameras-on-my-dashboard-but-i-do-not-hear-sound-why" question="I have unmuted some cameras on my dashboard, but I do not hear sound. Why?">
|
||||
|
||||
Errors in stream playback (e.g., connection failures, codec issues, or buffering timeouts) that cause the fallback to low bandwidth mode (jsmpeg) are logged to the browser console for easier debugging. These errors may include:
|
||||
- Network issues (e.g., MSE or WebRTC network connection problems).
|
||||
- Unsupported codecs or stream formats (e.g., H.265 in WebRTC, which is not supported in some browsers).
|
||||
- Buffering timeouts or low bandwidth conditions causing fallback to jsmpeg.
|
||||
- Browser compatibility problems (e.g., iOS Safari limitations with MSE).
|
||||
If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.
|
||||
|
||||
To view browser console logs:
|
||||
1. Open the Frigate Live View in your browser.
|
||||
2. Open the browser's Developer Tools (F12 or right-click > Inspect > Console tab).
|
||||
3. Reproduce the error (e.g., load a problematic stream or simulate network issues).
|
||||
4. Look for messages prefixed with the camera name.
|
||||
</FaqItem>
|
||||
|
||||
These logs help identify if the issue is player-specific (MSE vs. WebRTC) or related to camera configuration (e.g., go2rtc streams, codecs). If you see frequent errors:
|
||||
- Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera-settings-recommendations)).
|
||||
- Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
|
||||
- Test with a different stream via the UI dropdown (if `live -> streams` is configured).
|
||||
- For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see [WebRTC Extra Configuration](#webrtc-extra-configuration)).
|
||||
- If your cameras are streaming at a high resolution, your browser may be struggling to load all of the streams before the buffering timeout occurs. Frigate prioritizes showing a true live view as quickly as possible. If the fallback occurs often, change your live view settings to use a lower bandwidth substream.
|
||||
<FaqItem id="my-live-view-shows-a-black-screen-or-doesnt-load-but-the-debug-view-works-why" question="My live view shows a black screen or doesn't load, but the debug view works. Why?">
|
||||
|
||||
3. **It doesn't seem like my cameras are streaming on the Live dashboard. Why?**
|
||||
The debug view plays the `detect` stream processed by Frigate itself, while the Live view plays your go2rtc stream directly in the browser. If the debug view works but the Live view doesn't, your browser usually can't decode what the camera is sending, most often H.265 video or an incompatible audio track.
|
||||
|
||||
On the default Live dashboard ("All Cameras"), your camera images will update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity is detected, cameras seamlessly switch to a full-resolution live stream. If you want to customize this behavior, use a camera group.
|
||||
Work through the [go2rtc troubleshooting guide](/troubleshooting/go2rtc#live-view-is-black-buffering-or-stuck-in-low-bandwidth-mode) to isolate the problem. Two fixes resolve the majority of cases:
|
||||
|
||||
4. **I see a strange diagonal line on my live view, but my recordings look fine. How can I fix it?**
|
||||
1. Restream through go2rtc's FFmpeg module by prefixing your source with `ffmpeg:`, for example `- ffmpeg:rtsp://user:password@192.168.1.5:554/stream`.
|
||||
2. If that doesn't help, transcode to compatible codecs: `- ffmpeg:rtsp://user:password@192.168.1.5:554/stream#video=h264#audio=aac#hardware`.
|
||||
|
||||
This is caused by incorrect dimensions set in your detect width or height (or incorrectly auto-detected), causing the jsmpeg player's rendering engine to display a slightly distorted image. You should enlarge the width and height of your `detect` resolution up to a standard aspect ratio (example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). If changing the resolution to match a standard (4:3, 16:9, or 32:9, etc) aspect ratio does not solve the issue, you can enable "compatibility mode" in your camera group dashboard's stream settings. Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your `detect` width and height fails to resolve the color artifacts and diagonal line.
|
||||
</FaqItem>
|
||||
|
||||
5. **How does "smart streaming" work?**
|
||||
<FaqItem id="how-do-i-get-the-best-live-view-experience-in-home-assistant" question="How do I get the best live view experience in Home Assistant?">
|
||||
|
||||
Because a static image of a scene looks exactly the same as a live stream with no motion or activity, smart streaming updates your camera images once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity (motion or object/audio detection) occurs, cameras seamlessly switch to a live stream.
|
||||
For a full-resolution, low-latency live view in Home Assistant dashboards, use the [Advanced Camera Card](https://card.camera) with the [go2rtc live provider](https://card.camera/#/configuration/cameras/live-provider?id=go2rtc), which streams directly from Frigate's bundled go2rtc. This also supports audio and [two-way talk](#two-way-talk) on capable cameras. See the [Home Assistant integration docs](/integrations/home-assistant) for setup.
|
||||
|
||||
This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats.
|
||||
</FaqItem>
|
||||
|
||||
Smart streaming depends on having your camera's motion `threshold` and `contour_area` config values dialed in. Use the Motion Tuner in Settings in the UI to tune these values in real-time.
|
||||
### Streaming Behavior
|
||||
|
||||
This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras.
|
||||
<FaqItem id="how-does-smart-streaming-work" question={'How does "smart streaming" work?'}>
|
||||
|
||||
6. **I have unmuted some cameras on my dashboard, but I do not hear sound. Why?**
|
||||
Because a static image of a scene looks exactly the same as a live stream with no motion or activity, smart streaming updates your camera images once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity (motion or object/audio detection) occurs, cameras seamlessly switch to a live stream.
|
||||
|
||||
If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.
|
||||
This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats.
|
||||
|
||||
7. **My camera streams have lots of visual artifacts / distortion.**
|
||||
Smart streaming depends on having your camera's motion `threshold` and `contour_area` config values dialed in. Use the Motion Tuner in Settings in the UI to tune these values in real-time.
|
||||
|
||||
Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
|
||||
This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras.
|
||||
|
||||
8. **Why does my camera stream switch aspect ratios on the Live dashboard?**
|
||||
</FaqItem>
|
||||
|
||||
Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
|
||||
<FaqItem id="it-doesnt-seem-like-my-cameras-are-streaming-on-the-live-dashboard-why" question="It doesn't seem like my cameras are streaming on the Live dashboard. Why?">
|
||||
|
||||
To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
|
||||
On the default Live dashboard ("All Cameras"), your camera images will update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity is detected, cameras seamlessly switch to a full-resolution live stream. If you want to customize this behavior, use a camera group.
|
||||
|
||||
Example: Resolutions from two streams
|
||||
- Mismatched (may cause aspect ratio switching on the dashboard):
|
||||
- Live/go2rtc stream: 1920x1080 (16:9)
|
||||
- Detect stream: 640x352 (~1.82:1, not 16:9)
|
||||
</FaqItem>
|
||||
|
||||
- Matched (prevents switching):
|
||||
- Live/go2rtc stream: 1920x1080 (16:9)
|
||||
- Detect stream: 640x360 (16:9)
|
||||
<FaqItem id="frigate-shows-that-my-live-stream-is-in-low-bandwidth-mode-what-does-this-mean" question={'Frigate shows that my live stream is in "low bandwidth mode". What does this mean?'}>
|
||||
|
||||
You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
|
||||
Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible.
|
||||
|
||||
```yaml
|
||||
cameras:
|
||||
front_door:
|
||||
detect:
|
||||
width: 640
|
||||
height: 360 # set this to 360 instead of 352
|
||||
ffmpeg:
|
||||
inputs:
|
||||
- path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
|
||||
roles:
|
||||
- record
|
||||
- path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
|
||||
roles:
|
||||
- detect
|
||||
```
|
||||
When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. Continuous streaming mode does not have an automatic reset mechanism, but you can use the _Reset_ option to force a reload of your stream.
|
||||
|
||||
The same applies to your `record` stream: if its aspect ratio differs from your `detect` stream, your recordings will appear in a different shape than the live view. For consistent framing across live view and recordings, use the same aspect ratio for all of a camera's streams (the resolution can still differ).
|
||||
If you are using continuous streaming or you are loading more than a few high resolution streams at once on the dashboard, your browser may struggle to begin playback of your streams before the timeout. Frigate always prioritizes showing a live stream as quickly as possible, even if it is a lower quality jsmpeg stream. You can use the "Reset" link/button to try loading your high resolution stream again.
|
||||
|
||||
9. **Why does Frigate prefer MSE over WebRTC for live view?**
|
||||
Errors in stream playback (e.g., connection failures, codec issues, or buffering timeouts) that cause the fallback to low bandwidth mode (jsmpeg) are logged to the browser console for easier debugging. These errors may include:
|
||||
|
||||
Frigate prefers MSE because it delivers a better out-of-the-box experience than WebRTC on nearly every axis that matters for a security camera system. MSE is an open standard optimized and supported by all modern browsers, works without any extra configuration (WebRTC requires port forwarding and candidate setup, and lacks H.265 support in some browsers), and requires no internet access for NAT traversal. More importantly, MSE runs over TCP, so every frame arrives and is decoded in order, so nothing is ever silently skipped. WebRTC optimizes for latency over UDP by discarding late or incomplete frames, which works against you on cellular or spotty Wi-Fi: you can end up with frozen video, visual corruption, or gaps in the feed without ever knowing you missed something. Frigate's enhanced MSE player has adaptive speed playback and has been tuned for latency and connection robustness that meets or exceeds WebRTC, so you get near-real-time playback with a guarantee that when the video plays, every frame is actually there - which, for an NVR whose whole purpose is letting you see what happened, matters more than shaving fractions of a second off a latency number. That's why Frigate defaults to MSE and reserves WebRTC for cases that require it, like two-way talk.
|
||||
- Network issues (e.g., MSE or WebRTC network connection problems).
|
||||
- Unsupported codecs or stream formats (e.g., H.265 in WebRTC, which is not supported in some browsers).
|
||||
- Buffering timeouts or low bandwidth conditions causing fallback to jsmpeg.
|
||||
- Browser compatibility problems (e.g., iOS Safari limitations with MSE).
|
||||
|
||||
To view browser console logs:
|
||||
|
||||
1. Open the Frigate Live View in your browser.
|
||||
2. Open the browser's Developer Tools (F12 or right-click > Inspect > Console tab).
|
||||
3. Reproduce the error (e.g., load a problematic stream or simulate network issues).
|
||||
4. Look for messages prefixed with the camera name.
|
||||
|
||||
These logs help identify if the issue is player-specific (MSE vs. WebRTC) or related to camera configuration (e.g., go2rtc streams, codecs). If you see frequent errors:
|
||||
|
||||
- Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera-settings-recommendations)).
|
||||
- Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
|
||||
- Test with a different stream via the UI dropdown (if `live -> streams` is configured).
|
||||
- For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see [WebRTC Extra Configuration](#webrtc-extra-configuration)).
|
||||
- If your cameras are streaming at a high resolution, your browser may be struggling to load all of the streams before the buffering timeout occurs. Frigate prioritizes showing a true live view as quickly as possible. If the fallback occurs often, change your live view settings to use a lower bandwidth substream.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="why-is-my-live-view-delayed-or-lagging-behind-real-time" question="Why is my live view delayed or lagging behind real time?">
|
||||
|
||||
A delay when a stream first starts is usually caused by your camera's I-frame (keyframe) interval. Playback cannot begin until a keyframe arrives, so an interval set higher than your camera's frame rate makes the stream take longer to start. Set the I-frame interval to match the frame rate (or "1x" on Reolink) per the [camera settings recommendations](#camera-settings-recommendations).
|
||||
|
||||
A stream that starts on time but falls further behind live is buffering, which is usually the browser struggling to decode too many high-resolution streams at once. Select a lower-bandwidth substream for your dashboards (see [Setting Streams For Live UI](#setting-streams-for-live-ui)), reduce the number of streams open at once, or improve the network connection between your browser and Frigate. Frigate's player automatically speeds up playback to catch up to live after buffering, and falls back to low bandwidth mode if it stalls for too long. The _Reset_ option forces a fresh connection at the live edge.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="why-does-frigate-prefer-mse-over-webrtc-for-live-view" question="Why does Frigate prefer MSE over WebRTC for live view?">
|
||||
|
||||
Frigate prefers MSE because it delivers a better out-of-the-box experience than WebRTC on nearly every axis that matters for a security camera system. MSE is an open standard optimized and supported by all modern browsers, works without any extra configuration (WebRTC requires port forwarding and candidate setup, and lacks H.265 support in some browsers), and requires no internet access for NAT traversal. More importantly, MSE runs over TCP, so every frame arrives and is decoded in order, so nothing is ever silently skipped. WebRTC optimizes for latency over UDP by discarding late or incomplete frames, which works against you on cellular or spotty Wi-Fi: you can end up with frozen video, visual corruption, or gaps in the feed without ever knowing you missed something. Frigate's enhanced MSE player has adaptive speed playback and has been tuned for latency and connection robustness that meets or exceeds WebRTC, so you get near-real-time playback with a guarantee that when the video plays, every frame is actually there - which, for an NVR whose whole purpose is letting you see what happened, matters more than shaving fractions of a second off a latency number. That's why Frigate defaults to MSE and reserves WebRTC for cases that require it, like two-way talk.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
### Video Quality Issues
|
||||
|
||||
<FaqItem id="i-see-a-strange-diagonal-line-on-my-live-view-but-my-recordings-look-fine-how-can-i-fix-it" question="I see a strange diagonal line on my live view, but my recordings look fine. How can I fix it?">
|
||||
|
||||
This is caused by incorrect dimensions set in your detect width or height (or incorrectly auto-detected), causing the jsmpeg player's rendering engine to display a slightly distorted image. You should enlarge the width and height of your `detect` resolution up to a standard aspect ratio (example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). If changing the resolution to match a standard (4:3, 16:9, or 32:9, etc) aspect ratio does not solve the issue, you can enable "compatibility mode" in your camera group dashboard's stream settings. Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your `detect` width and height fails to resolve the color artifacts and diagonal line.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="my-camera-streams-have-lots-of-visual-artifacts-or-distortion" question="My camera streams have lots of visual artifacts / distortion.">
|
||||
|
||||
Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="why-does-my-camera-stream-switch-aspect-ratios-on-the-live-dashboard" question="Why does my camera stream switch aspect ratios on the Live dashboard?">
|
||||
|
||||
Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
|
||||
|
||||
To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
|
||||
|
||||
Example: Resolutions from two streams
|
||||
|
||||
- Mismatched (may cause aspect ratio switching on the dashboard):
|
||||
- Live/go2rtc stream: 1920x1080 (16:9)
|
||||
- Detect stream: 640x352 (~1.82:1, not 16:9)
|
||||
|
||||
- Matched (prevents switching):
|
||||
- Live/go2rtc stream: 1920x1080 (16:9)
|
||||
- Detect stream: 640x360 (16:9)
|
||||
|
||||
You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
|
||||
|
||||
```yaml
|
||||
cameras:
|
||||
front_door:
|
||||
detect:
|
||||
width: 640
|
||||
height: 360 # set this to 360 instead of 352
|
||||
ffmpeg:
|
||||
inputs:
|
||||
- path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
|
||||
roles:
|
||||
- record
|
||||
- path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
|
||||
roles:
|
||||
- detect
|
||||
```
|
||||
|
||||
The same applies to your `record` stream: if its aspect ratio differs from your `detect` stream, your recordings will appear in a different shape than the live view. For consistent framing across live view and recordings, use the same aspect ratio for all of a camera's streams (the resolution can still differ).
|
||||
|
||||
</FaqItem>
|
||||
|
||||
@@ -5,7 +5,7 @@ title: Masks
|
||||
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
import FrigateConfigMock from "@site/src/components/FrigateConfigMock";
|
||||
|
||||
Frigate has two kinds of masks: motion masks and object filter masks. Both are narrow tools for fine-tuning, **not for hiding an area from Frigate**. Masks should be used sparingly; in most cases where users reach for one, a [zone](zones.md) with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) is the right tool instead. See [Which tool do I need?](#which-tool-do-i-need) and [Common mistakes](#common-mistakes) below if you're new to Frigate's mask behavior.
|
||||
|
||||
@@ -25,19 +25,64 @@ Object filter masks can be used to filter out stubborn false positives in fixed
|
||||
|
||||
## Which tool do I need?
|
||||
|
||||
| What you're trying to do | Recommended tool | How it works |
|
||||
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| Only get alerts/detections for activity in the areas you care about, ignoring activity elsewhere (e.g., alert when someone enters your yard, but not when they walk past on the sidewalk) | A [zone](zones.md) combined with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) | Frigate keeps detecting and tracking activity everywhere in the frame, but a review item is only created once the bottom-center of an object's bounding box enters a required zone. |
|
||||
| Stop a stubborn false positive at a specific fixed spot (e.g., a tree base that keeps being detected as a person) | An **object filter mask** for that object type | Any detection of that object type whose bounding-box bottom-center lands inside the mask is treated as a false positive and discarded. |
|
||||
| Ignore motion in an area that obviously isn't an object of interest (e.g., the camera timestamp, sky, flags, treetops swaying) | A **motion mask** | Motion inside the mask is ignored when deciding whether to run object detection. Objects can still be detected in a motion masked area if motion elsewhere in the frame triggers detection. |
|
||||
| Stop tracking an object type altogether on this camera (e.g., you never care about cats) | Remove the object from the camera's [`objects.track`](objects.md) list | Frigate skips this object type entirely on this camera, regardless of where it appears. |
|
||||
| What you're trying to do | Recommended tool | How it works |
|
||||
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| Only get alerts/detections for activity in the areas you care about, ignoring activity elsewhere (e.g., alert when someone enters your yard, but not when they walk past on the sidewalk) | A [zone](zones.md) combined with [`required_zones`](zones.md#restricting-alerts-and-detections-to-specific-zones) | Frigate keeps detecting and tracking activity everywhere in the frame, but a review item is only created once the bottom-center of an object's bounding box enters a required zone. |
|
||||
| Stop a stubborn false positive at a specific fixed spot (e.g., a tree base that keeps being detected as a person) | An **object filter mask** for that object type | Any detection of that object type whose bounding-box bottom-center lands inside the mask is treated as a false positive and discarded. |
|
||||
| Ignore motion in an area that obviously isn't an object of interest (e.g., the camera timestamp, sky, flags, treetops swaying) | A **motion mask** | Motion inside the mask is ignored when deciding whether to run object detection. Objects can still be detected in a motion masked area if motion elsewhere in the frame triggers detection. |
|
||||
| Stop tracking an object type altogether on this camera (e.g., you never care about cats) | Remove the object from the camera's [`objects.track`](objects.md) list | Frigate skips this object type entirely on this camera, regardless of where it appears. |
|
||||
|
||||
## Using the mask creator
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select a camera. Use the mask editor to draw motion masks and object filter masks directly on the camera feed. Each mask can be given a friendly name and toggled on or off.
|
||||
<FrigateConfigMock
|
||||
level="camera"
|
||||
section="masksAndZones"
|
||||
steps={[
|
||||
{
|
||||
focus: "motionMasks",
|
||||
label: "Motion Mask",
|
||||
hint: "Motion masks prevent unwanted motion from triggering detection. Use them sparingly so object tracking is not disrupted.",
|
||||
},
|
||||
{
|
||||
focus: "motionMask.add",
|
||||
label: "New Motion Mask",
|
||||
hint: "Select the plus button beside Motion Mask to create a mask.",
|
||||
},
|
||||
{
|
||||
focus: "motionMask.canvas",
|
||||
label: "Draw the motion mask",
|
||||
hint: "Select points on the camera image, then close the polygon by selecting the first point again.",
|
||||
},
|
||||
{
|
||||
focus: "motionMask.options",
|
||||
label: "Motion mask options",
|
||||
hint: "Give the mask a friendly name and choose whether it is enabled.",
|
||||
},
|
||||
{
|
||||
focus: "objectMasks",
|
||||
label: "Object Masks",
|
||||
hint: "Object masks filter false positives according to the bottom center of an object's bounding box.",
|
||||
},
|
||||
{
|
||||
focus: "objectMask.add",
|
||||
label: "New Object Mask",
|
||||
hint: "Select the plus button beside Object Masks to create an object filter mask.",
|
||||
},
|
||||
{
|
||||
focus: "objectMask.canvas",
|
||||
label: "Draw the object mask",
|
||||
hint: "Draw a precise polygon over the fixed location that produces false positives.",
|
||||
},
|
||||
{
|
||||
focus: "objectMask.options",
|
||||
label: "Object mask options",
|
||||
hint: "Name the mask, select the object type it applies to, and save it.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
@@ -4,6 +4,7 @@ title: Motion Detection
|
||||
---
|
||||
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import FrigateConfigMock from "@site/src/components/FrigateConfigMock";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
|
||||
@@ -44,13 +45,13 @@ The threshold value dictates how much of a change in a pixels luminance is requi
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Global configuration > Motion detection" /> to set the threshold globally.
|
||||
|
||||
To override for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Motion detection" /> and select the camera, or use the <NavPath path="Settings > Camera configuration > Motion tuner" /> to adjust it live.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| **Motion threshold** | The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive. The value should be between 1 and 255. (default: 30) |
|
||||
<FrigateConfigMock
|
||||
section="motion"
|
||||
fields={["threshold"]}
|
||||
values={{ threshold: 30 }}
|
||||
focus="threshold"
|
||||
hint="The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive. The value should be between 1 and 255. (default: 30)"
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
@@ -5,6 +5,7 @@ title: Object Detectors
|
||||
|
||||
import CommunityBadge from '@site/src/components/CommunityBadge';
|
||||
import ConfigTabs from '@site/src/components/ConfigTabs';
|
||||
import FrigateConfigMock from '@site/src/components/FrigateConfigMock';
|
||||
import TabItem from '@theme/TabItem';
|
||||
import NavPath from '@site/src/components/NavPath';
|
||||
import ModelConfigDropdown from '@site/src/components/ModelConfigDropdown';
|
||||
@@ -112,7 +113,17 @@ See [common Edge TPU troubleshooting steps](/troubleshooting/edgetpu) if the Edg
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`.
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
section="model"
|
||||
values={{ detectors: { coral: { type: "edgetpu", device: "usb" } } }}
|
||||
targets={[
|
||||
{
|
||||
field: "detectors",
|
||||
hint: "Add an EdgeTPU detector named coral and set its device to usb.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -132,7 +143,24 @@ detectors:
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors, specifying `usb:0` and `usb:1` as the device for each.
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
showNavigationSteps={false}
|
||||
section="model"
|
||||
values={{
|
||||
detectors: {
|
||||
coral1: { type: "edgetpu", device: "usb:0" },
|
||||
coral2: { type: "edgetpu", device: "usb:1" },
|
||||
},
|
||||
}}
|
||||
targets={[
|
||||
{
|
||||
field: "detectors",
|
||||
hint:
|
||||
"Add two EdgeTPU detectors and assign usb:0 and usb:1 as their devices.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -157,7 +185,19 @@ _warning: may have [compatibility issues](https://github.com/blakeblackshear/fri
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then leave the device field empty.
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
showNavigationSteps={false}
|
||||
section="model"
|
||||
values={{ detectors: { coral: { type: "edgetpu", device: "" } } }}
|
||||
targets={[
|
||||
{
|
||||
field: "detectors",
|
||||
hint:
|
||||
"Add an EdgeTPU detector and leave Device empty so Frigate auto-detects the native Coral.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -177,7 +217,18 @@ detectors:
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `pci`.
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
showNavigationSteps={false}
|
||||
section="model"
|
||||
values={{ detectors: { coral: { type: "edgetpu", device: "pci" } } }}
|
||||
targets={[
|
||||
{
|
||||
field: "detectors",
|
||||
hint: "Add an EdgeTPU detector named coral and set its device to pci.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -197,7 +248,24 @@ detectors:
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors, specifying `pci:0` and `pci:1` as the device for each.
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
showNavigationSteps={false}
|
||||
section="model"
|
||||
values={{
|
||||
detectors: {
|
||||
coral1: { type: "edgetpu", device: "pci:0" },
|
||||
coral2: { type: "edgetpu", device: "pci:1" },
|
||||
},
|
||||
}}
|
||||
targets={[
|
||||
{
|
||||
field: "detectors",
|
||||
hint:
|
||||
"Add two EdgeTPU detectors and assign pci:0 and pci:1 as their devices.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -220,7 +288,24 @@ detectors:
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > Detectors and model" /> and select **EdgeTPU** from the detector type dropdown and click **Add** to add multiple detectors with different device types (e.g., `usb` and `pci`).
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
showNavigationSteps={false}
|
||||
section="model"
|
||||
values={{
|
||||
detectors: {
|
||||
coral_usb: { type: "edgetpu", device: "usb" },
|
||||
coral_pci: { type: "edgetpu", device: "pci" },
|
||||
},
|
||||
}}
|
||||
targets={[
|
||||
{
|
||||
field: "detectors",
|
||||
hint:
|
||||
"Add two EdgeTPU detectors and assign usb to one device and pci to the other.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
@@ -5,7 +5,7 @@ title: Zones
|
||||
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import NavPath from "@site/src/components/NavPath";
|
||||
import FrigateConfigMock from "@site/src/components/FrigateConfigMock";
|
||||
|
||||
Zones allow you to define a specific area of the frame and apply additional filters for object types so you can determine whether or not an object is within a particular area. Presence in a zone is evaluated based on the bottom center of the bounding box for the object. It does not matter how much of the bounding box overlaps with the zone.
|
||||
|
||||
@@ -25,11 +25,32 @@ During testing, enable the Zones option for the [Debug view](/usage/live#the-sin
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
|
||||
2. Under the **Zones** section, click the plus icon to add a new zone.
|
||||
3. Click on the camera's latest image to create the points for the zone boundary. Click the first point again to close the polygon.
|
||||
4. Configure zone options such as **Friendly name**, **Objects**, **Loitering time**, and **Inertia** in the zone editor.
|
||||
5. Press **Save** when finished.
|
||||
<FrigateConfigMock
|
||||
level="camera"
|
||||
section="masksAndZones"
|
||||
steps={[
|
||||
{
|
||||
focus: "zone.add",
|
||||
label: "Add Zone",
|
||||
hint: "Select the plus button beside Zones to create a zone.",
|
||||
},
|
||||
{
|
||||
focus: "zone.canvas",
|
||||
label: "Draw the zone",
|
||||
hint: "Select points on the camera image, then close the polygon by selecting the first point again.",
|
||||
},
|
||||
{
|
||||
focus: "zone.options",
|
||||
label: "Zone options",
|
||||
hint: "Configure the friendly name, objects, loitering time, inertia, and optional speed settings.",
|
||||
},
|
||||
{
|
||||
focus: "zone.save",
|
||||
label: "Save",
|
||||
hint: "Save the zone after its boundary and options are complete.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -57,11 +78,14 @@ To create an alert only when an object enters the `entire_yard` zone:
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Review" />.
|
||||
|
||||
| Field | Description |
|
||||
| ---------------------------------- | ----------------------------------------------------------------------------------------- |
|
||||
| **Alerts config > Required zones** | Set to `entire_yard` so an object must enter that zone to be considered an alert; leave empty to allow alerts anywhere in the frame. |
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
level="camera"
|
||||
section="review"
|
||||
focus="alerts.required_zones"
|
||||
values={{ "alerts.required_zones": ["entire_yard"] }}
|
||||
hint="Select entire_yard so an object must enter that zone to be considered an alert."
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -87,12 +111,26 @@ You may also want to filter detections to only be created when an object enters
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > Camera configuration > Review" />.
|
||||
|
||||
| Field | Description |
|
||||
| -------------------------------------- | -------------------------------------------------------------------------------------------- |
|
||||
| **Alerts config > Required zones** | Set to `inner_yard` so an object must enter that zone to be considered an alert; leave empty to allow alerts anywhere in the frame. |
|
||||
| **Detections config > Required zones** | Set to `edge_yard` so an object must enter that zone to be considered a detection; leave empty to allow detections anywhere in the frame. |
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
showNavigationSteps={false}
|
||||
level="camera"
|
||||
section="review"
|
||||
values={{
|
||||
"alerts.required_zones": ["inner_yard"],
|
||||
"detections.required_zones": ["edge_yard"],
|
||||
}}
|
||||
targets={[
|
||||
{
|
||||
field: "alerts.required_zones",
|
||||
hint: "Select inner_yard so an object must enter the inner area to be considered an alert.",
|
||||
},
|
||||
{
|
||||
field: "detections.required_zones",
|
||||
hint: "Select edge_yard so activity in the secondary area can be retained as a detection.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -126,8 +164,15 @@ To only save snapshots when an object enters a specific zone, for example an `en
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Camera configuration > Snapshots" /> and select your camera.
|
||||
- Set **Required zones** to `entire_yard`
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
showNavigationSteps={false}
|
||||
level="camera"
|
||||
section="snapshots"
|
||||
focus="required_zones"
|
||||
values={{ required_zones: ["entire_yard"] }}
|
||||
hint="Select entire_yard to save snapshots only after an object enters that zone."
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -154,11 +199,15 @@ Sometimes you want to limit a zone to specific object types to have more granula
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
|
||||
2. Create a zone named `entire_yard` covering everywhere you want to track a person.
|
||||
- Under **Objects**, add `person`
|
||||
3. Create a second zone named `front_yard_street` covering just the street.
|
||||
- Under **Objects**, add `car`
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
showNavigationSteps={false}
|
||||
level="camera"
|
||||
section="masksAndZones"
|
||||
focus="zone.objects"
|
||||
label="Objects"
|
||||
hint="Choose which object types apply to the zone. For example, use person for the yard and car for the street."
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -198,10 +247,15 @@ When using loitering zones, a review item will behave in the following way:
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
|
||||
2. Edit or create the zone (e.g., `sidewalk`).
|
||||
- Set **Loitering time** to the desired number of seconds (e.g., `4`)
|
||||
- Under **Objects**, add the relevant object types (e.g., `person`)
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
showNavigationSteps={false}
|
||||
level="camera"
|
||||
section="masksAndZones"
|
||||
focus="zone.loitering_time"
|
||||
label="Loitering Time"
|
||||
hint="Set the minimum number of seconds an object must remain in the zone before the zone activates."
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -227,9 +281,15 @@ Sometimes an objects bounding box may be slightly incorrect and the bottom cente
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
|
||||
2. Edit or create the zone (e.g., `front_yard`).
|
||||
- Set **Inertia** to the desired number of consecutive frames (e.g., `3`)
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
showNavigationSteps={false}
|
||||
level="camera"
|
||||
section="masksAndZones"
|
||||
focus="zone.inertia"
|
||||
label="Inertia"
|
||||
hint="Set the number of consecutive frames an object must be inside the zone. The default is 3."
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -253,9 +313,15 @@ There may also be cases where you expect an object to quickly enter and exit a z
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
|
||||
2. Edit or create the zone (e.g., `driveway_entrance`).
|
||||
- Set **Inertia** to `1`
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
showNavigationSteps={false}
|
||||
level="camera"
|
||||
section="masksAndZones"
|
||||
focus="zone.inertia"
|
||||
label="Inertia"
|
||||
hint="Set Inertia to 1 when an object should be considered inside the zone immediately."
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -289,11 +355,23 @@ Accurate real-world distance measurements are required to estimate speeds. These
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
|
||||
2. Create or edit a zone with exactly 4 points aligned to the ground plane.
|
||||
3. In the zone editor, enter the real-world **Distances** between each pair of consecutive points.
|
||||
- For example, if the distance between the first and second points is 10 meters, between the second and third is 12 meters, etc.
|
||||
4. Distances are measured in meters (metric) or feet (imperial), depending on the **Unit system** setting.
|
||||
<FrigateConfigMock
|
||||
level="camera"
|
||||
showNavigationSteps={false}
|
||||
section="masksAndZones"
|
||||
steps={[
|
||||
{
|
||||
focus: "zone.canvas",
|
||||
label: "Draw a four-point zone",
|
||||
hint: "Draw exactly four points aligned to the ground plane where objects will travel.",
|
||||
},
|
||||
{
|
||||
focus: "zone.speed",
|
||||
label: "Speed Estimation",
|
||||
hint: "Enter the real-world distance between each pair of consecutive points. Units follow the UI unit system setting.",
|
||||
},
|
||||
]}
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -317,11 +395,14 @@ The `distance` values are measured in meters (metric) or feet (imperial), depend
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
Navigate to <NavPath path="Settings > System > UI" />.
|
||||
|
||||
| Field | Description |
|
||||
| --------------- | -------------------------------------------------------------------- |
|
||||
| **Unit system** | Set to `metric` (kilometers per hour) or `imperial` (miles per hour) |
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
level="global"
|
||||
section="ui"
|
||||
focus="unit_system"
|
||||
values={{ unit_system: "metric" }}
|
||||
hint="Choose metric for kilometers per hour or imperial for miles per hour."
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
@@ -356,10 +437,15 @@ Zones can be configured with a minimum speed requirement, meaning an object must
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
|
||||
2. Edit or create the zone with distances configured.
|
||||
- Set **Speed threshold** to the desired minimum speed (e.g., `20`)
|
||||
- The unit is kph or mph, depending on the **Unit system** setting
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
showNavigationSteps={false}
|
||||
level="camera"
|
||||
section="masksAndZones"
|
||||
focus="zone.speed_threshold"
|
||||
label="Speed Threshold"
|
||||
hint="Set the minimum speed required for an object to be considered inside the zone. Units follow the UI unit system setting."
|
||||
/>
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
Generated
+12
-1
@@ -19,10 +19,12 @@
|
||||
"docusaurus-plugin-openapi-docs": "^4.5.1",
|
||||
"docusaurus-theme-openapi-docs": "^4.5.1",
|
||||
"js-yaml": "^4.1.1",
|
||||
"marked": "^16.4.2",
|
||||
"prism-react-renderer": "^2.4.1",
|
||||
"raw-loader": "^4.0.2",
|
||||
"react": "^18.3.1",
|
||||
"react-dom": "^18.3.1"
|
||||
"react-dom": "^18.3.1",
|
||||
"react-icons": "^5.7.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@docusaurus/module-type-aliases": "^3.7.0",
|
||||
@@ -18761,6 +18763,15 @@
|
||||
"react": "^16.8.0 || ^17 || ^18 || ^19"
|
||||
}
|
||||
},
|
||||
"node_modules/react-icons": {
|
||||
"version": "5.7.0",
|
||||
"resolved": "https://registry.npmmirror.com/react-icons/-/react-icons-5.7.0.tgz",
|
||||
"integrity": "sha512-LBLy340Rzqy6+/yVhZKT3B/QpP1BZaesGqasf09HPOBzRarcDIFH0WwXlXQfE7q7ipxK4MSiC5DIBWURCny6fw==",
|
||||
"license": "MIT",
|
||||
"peerDependencies": {
|
||||
"react": "*"
|
||||
}
|
||||
},
|
||||
"node_modules/react-is": {
|
||||
"version": "16.13.1",
|
||||
"resolved": "https://registry.npmjs.org/react-is/-/react-is-16.13.1.tgz",
|
||||
|
||||
+6
-3
@@ -4,9 +4,11 @@
|
||||
"private": true,
|
||||
"scripts": {
|
||||
"build:config": "node scripts/build-config.mjs",
|
||||
"build:mock": "node scripts/generate-mock-manifest.mjs",
|
||||
"check:mock": "node scripts/generate-mock-manifest.mjs --check",
|
||||
"docusaurus": "docusaurus",
|
||||
"start": "npm run build:config && npm run regen-docs && docusaurus start --host 0.0.0.0",
|
||||
"build": "npm run build:config && npm run regen-docs && docusaurus build",
|
||||
"start": "npm run build:config && npm run build:mock && npm run regen-docs && docusaurus start --host 0.0.0.0",
|
||||
"build": "npm run build:config && npm run build:mock && npm run regen-docs && docusaurus build",
|
||||
"swizzle": "docusaurus swizzle",
|
||||
"deploy": "docusaurus deploy",
|
||||
"clear": "docusaurus clear",
|
||||
@@ -33,7 +35,8 @@
|
||||
"prism-react-renderer": "^2.4.1",
|
||||
"raw-loader": "^4.0.2",
|
||||
"react": "^18.3.1",
|
||||
"react-dom": "^18.3.1"
|
||||
"react-dom": "^18.3.1",
|
||||
"react-icons": "^5.7.0"
|
||||
},
|
||||
"browserslist": {
|
||||
"production": [
|
||||
|
||||
@@ -0,0 +1,366 @@
|
||||
#!/usr/bin/env node
|
||||
|
||||
/** Build the compact field catalog used by documentation config mocks. */
|
||||
|
||||
import fs from "node:fs";
|
||||
import path from "node:path";
|
||||
import { fileURLToPath } from "node:url";
|
||||
|
||||
const scriptDir = path.dirname(fileURLToPath(import.meta.url));
|
||||
const repoRoot = path.resolve(scriptDir, "../..");
|
||||
const schemaPath = path.join(
|
||||
repoRoot,
|
||||
"web/e2e/fixtures/mock-data/config-schema.json",
|
||||
);
|
||||
const localeRoot = path.join(repoRoot, "web/public/locales/en/config");
|
||||
const sectionConfigRoot = path.join(
|
||||
repoRoot,
|
||||
"web/src/components/config-form/section-configs",
|
||||
);
|
||||
const settingsSourcePath = path.join(repoRoot, "web/src/pages/Settings.tsx");
|
||||
const settingsLocalePath = path.join(
|
||||
repoRoot,
|
||||
"web/public/locales/en/views/settings.json",
|
||||
);
|
||||
const outputPath = path.join(
|
||||
repoRoot,
|
||||
"docs/src/components/FrigateConfigMock/manifest.json",
|
||||
);
|
||||
|
||||
const schema = JSON.parse(fs.readFileSync(schemaPath, "utf8"));
|
||||
const translations = {
|
||||
global: JSON.parse(
|
||||
fs.readFileSync(path.join(localeRoot, "global.json"), "utf8"),
|
||||
),
|
||||
camera: JSON.parse(
|
||||
fs.readFileSync(path.join(localeRoot, "cameras.json"), "utf8"),
|
||||
),
|
||||
groups: JSON.parse(
|
||||
fs.readFileSync(path.join(localeRoot, "groups.json"), "utf8"),
|
||||
),
|
||||
};
|
||||
const settingsTranslations = JSON.parse(
|
||||
fs.readFileSync(settingsLocalePath, "utf8"),
|
||||
);
|
||||
|
||||
function resolveNode(node) {
|
||||
if (!node || typeof node !== "object") return {};
|
||||
|
||||
if (node.$ref) {
|
||||
const refName = node.$ref.split("/").at(-1);
|
||||
return {
|
||||
...resolveNode(schema.$defs?.[refName]),
|
||||
...node,
|
||||
$ref: undefined,
|
||||
};
|
||||
}
|
||||
|
||||
const variants = node.anyOf ?? node.oneOf;
|
||||
if (Array.isArray(variants)) {
|
||||
const concrete = variants.find((variant) => variant.type !== "null");
|
||||
return {
|
||||
...resolveNode(concrete),
|
||||
...node,
|
||||
anyOf: undefined,
|
||||
oneOf: undefined,
|
||||
};
|
||||
}
|
||||
|
||||
return node;
|
||||
}
|
||||
|
||||
function translationAt(level, section, fieldPath) {
|
||||
let current = translations[level]?.[section];
|
||||
for (const segment of fieldPath) {
|
||||
if (!current || typeof current !== "object") return {};
|
||||
current = current[segment];
|
||||
}
|
||||
return current && typeof current === "object" ? current : {};
|
||||
}
|
||||
|
||||
function inferWidget(node) {
|
||||
if (Array.isArray(node.enum)) return "select";
|
||||
if (node.type === "boolean") return "switch";
|
||||
if (
|
||||
["integer", "number"].includes(node.type) &&
|
||||
node.minimum !== undefined &&
|
||||
(node.maximum !== undefined || node.exclusiveMaximum !== undefined)
|
||||
) {
|
||||
return "range";
|
||||
}
|
||||
if (node.type === "integer" || node.type === "number") return "number";
|
||||
if (node.type === "array") return "tags";
|
||||
if (node.type === "object") return "object";
|
||||
return "text";
|
||||
}
|
||||
|
||||
function extractArray(source, key) {
|
||||
const match = source.match(new RegExp(`${key}\\s*:\\s*\\[([\\s\\S]*?)\\]`));
|
||||
return match
|
||||
? [...match[1].matchAll(/["']([^"']+)["']/g)].map((item) => item[1])
|
||||
: [];
|
||||
}
|
||||
|
||||
function extractObjectBlock(source, key) {
|
||||
const match = new RegExp(`\\b${key}\\s*:\\s*\\{`).exec(source);
|
||||
if (!match) return "";
|
||||
const start = source.indexOf("{", match.index);
|
||||
let depth = 0;
|
||||
let quote = null;
|
||||
let escaped = false;
|
||||
for (let index = start; index < source.length; index += 1) {
|
||||
const character = source[index];
|
||||
if (quote) {
|
||||
if (escaped) escaped = false;
|
||||
else if (character === "\\") escaped = true;
|
||||
else if (character === quote) quote = null;
|
||||
continue;
|
||||
}
|
||||
if (['"', "'", "`"].includes(character)) {
|
||||
quote = character;
|
||||
continue;
|
||||
}
|
||||
if (character === "{") depth += 1;
|
||||
if (character === "}") {
|
||||
depth -= 1;
|
||||
if (depth === 0) return source.slice(start + 1, index);
|
||||
}
|
||||
}
|
||||
return "";
|
||||
}
|
||||
|
||||
function extractGroups(source) {
|
||||
const fieldGroups = {};
|
||||
const groupsBlock = extractObjectBlock(source, "fieldGroups");
|
||||
for (const match of groupsBlock.matchAll(/(\w+)\s*:\s*\[([\s\S]*?)\]/g)) {
|
||||
fieldGroups[match[1]] = [...match[2].matchAll(/["']([^"']+)["']/g)].map(
|
||||
(item) => item[1],
|
||||
);
|
||||
}
|
||||
return fieldGroups;
|
||||
}
|
||||
|
||||
function loadSectionHints(section, level) {
|
||||
const configPath = path.join(sectionConfigRoot, `${section}.ts`);
|
||||
if (!fs.existsSync(configPath)) return {};
|
||||
const source = fs.readFileSync(configPath, "utf8");
|
||||
const base = extractObjectBlock(source, "base");
|
||||
const override = extractObjectBlock(source, level);
|
||||
const overrideHas = (key) => new RegExp(`\\b${key}\\s*:`).test(override);
|
||||
return {
|
||||
order: overrideHas("fieldOrder")
|
||||
? extractArray(override, "fieldOrder")
|
||||
: extractArray(base, "fieldOrder"),
|
||||
hidden: [
|
||||
...extractArray(base, "hiddenFields"),
|
||||
...extractArray(override, "hiddenFields"),
|
||||
],
|
||||
advanced: overrideHas("advancedFields")
|
||||
? extractArray(override, "advancedFields")
|
||||
: extractArray(base, "advancedFields"),
|
||||
groups: overrideHas("fieldGroups")
|
||||
? extractGroups(override)
|
||||
: extractGroups(base),
|
||||
docs: base.match(/sectionDocs\s*:\s*["']([^"']+)["']/)?.[1] ?? null,
|
||||
};
|
||||
}
|
||||
|
||||
function groupLabel(level, section, group) {
|
||||
const domain = level === "camera" ? "cameras" : "global";
|
||||
return (
|
||||
translations.groups?.[section]?.[domain]?.[group] ??
|
||||
group.replaceAll("_", " ").replace(/^./, (value) => value.toUpperCase())
|
||||
);
|
||||
}
|
||||
|
||||
function collectFields(level, section, sectionNode, hints) {
|
||||
const fields = {};
|
||||
|
||||
function visit(rawNode, fieldPath = []) {
|
||||
const node = resolveNode(rawNode);
|
||||
const properties = node.properties;
|
||||
if (properties && typeof properties === "object") {
|
||||
for (const [name, child] of Object.entries(properties)) {
|
||||
visit(child, [...fieldPath, name]);
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (fieldPath.length === 0) return;
|
||||
const key = fieldPath.join(".");
|
||||
const localized = translationAt(level, section, fieldPath);
|
||||
fields[key] = {
|
||||
label: localized.label ?? node.title ?? fieldPath.at(-1),
|
||||
description: localized.description ?? node.description ?? "",
|
||||
widget: inferWidget(node),
|
||||
default: node.default ?? null,
|
||||
enum: node.enum ?? null,
|
||||
minimum: node.minimum ?? node.exclusiveMinimum ?? null,
|
||||
maximum: node.maximum ?? node.exclusiveMaximum ?? null,
|
||||
advanced: hints.advanced?.includes(key) ?? false,
|
||||
};
|
||||
}
|
||||
|
||||
visit(sectionNode);
|
||||
return fields;
|
||||
}
|
||||
|
||||
function buildLevel(level) {
|
||||
const rootProperties =
|
||||
level === "camera"
|
||||
? resolveNode(schema.$defs.CameraConfig).properties
|
||||
: schema.properties;
|
||||
const result = {};
|
||||
|
||||
for (const [section, rawNode] of Object.entries(rootProperties ?? {})) {
|
||||
const node = resolveNode(rawNode);
|
||||
if (!node.properties) continue;
|
||||
|
||||
const hints = loadSectionHints(section, level);
|
||||
const hidden = new Set(hints.hidden ?? []);
|
||||
const fields = collectFields(level, section, node, hints);
|
||||
for (const key of hidden) delete fields[key];
|
||||
|
||||
const localized = translations[level]?.[section] ?? {};
|
||||
result[section] = {
|
||||
label: localized.label ?? rawNode.title ?? node.title ?? section,
|
||||
description: localized.description ?? rawNode.description ?? "",
|
||||
order: hints.order ?? [],
|
||||
groups: Object.entries(hints.groups ?? {}).map(([key, groupFields]) => ({
|
||||
key,
|
||||
label: groupLabel(level, section, key),
|
||||
fields: groupFields,
|
||||
})),
|
||||
docs: hints.docs ?? null,
|
||||
fields,
|
||||
};
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
function parseSectionMapping(source, constantName, level) {
|
||||
const match = source.match(
|
||||
new RegExp(`const ${constantName}[^=]*=\\s*\\{([\\s\\S]*?)\\n\\};`),
|
||||
);
|
||||
if (!match) return [];
|
||||
return [...match[1].matchAll(/(\w+)\s*:\s*"([^"]+)"/g)].map(
|
||||
([, section, page]) => ({ section, page, level }),
|
||||
);
|
||||
}
|
||||
|
||||
function buildNavigation() {
|
||||
const source = fs.readFileSync(settingsSourcePath, "utf8");
|
||||
const settingsBlock = source.match(
|
||||
/const settingsGroups\s*=\s*\[([\s\S]*?)\n\];/,
|
||||
)?.[1];
|
||||
if (!settingsBlock) return { groups: [], pages: {} };
|
||||
|
||||
const mappings = [
|
||||
...parseSectionMapping(source, "GLOBAL_SECTION_MAPPING", "global"),
|
||||
...parseSectionMapping(source, "CAMERA_SECTION_MAPPING", "camera"),
|
||||
...parseSectionMapping(source, "ENRICHMENTS_SECTION_MAPPING", "global"),
|
||||
...parseSectionMapping(source, "SYSTEM_SECTION_MAPPING", "global"),
|
||||
];
|
||||
const pages = Object.fromEntries(
|
||||
mappings.map((mapping) => [mapping.page, mapping]),
|
||||
);
|
||||
const groupMatches = [...settingsBlock.matchAll(/\{\s*label:\s*"([^"]+)"/g)];
|
||||
const groups = groupMatches.map((match, index) => {
|
||||
const start = match.index ?? 0;
|
||||
const end = groupMatches[index + 1]?.index ?? settingsBlock.length;
|
||||
const sourceSlice = settingsBlock.slice(start, end);
|
||||
const itemKeys = [...sourceSlice.matchAll(/key:\s*"([^"]+)"/g)].map(
|
||||
(item) => item[1],
|
||||
);
|
||||
return {
|
||||
key: match[1],
|
||||
label: settingsTranslations.menu?.[match[1]] ?? match[1],
|
||||
items: itemKeys.map((key) => ({
|
||||
key,
|
||||
label: settingsTranslations.menu?.[key] ?? key,
|
||||
...(key === "masksAndZones"
|
||||
? { section: key, page: key, level: "camera" }
|
||||
: {}),
|
||||
...(pages[key] ?? {}),
|
||||
})),
|
||||
};
|
||||
});
|
||||
|
||||
return { groups, pages };
|
||||
}
|
||||
|
||||
function buildDetectorTypes() {
|
||||
const detectorTranslations = translations.global?.detectors ?? {};
|
||||
const reserved = new Set([
|
||||
"label",
|
||||
"description",
|
||||
"type",
|
||||
"model",
|
||||
"model_path",
|
||||
]);
|
||||
|
||||
return Object.fromEntries(
|
||||
Object.entries(detectorTranslations)
|
||||
.filter(
|
||||
([key, value]) =>
|
||||
!reserved.has(key) &&
|
||||
value &&
|
||||
typeof value === "object" &&
|
||||
typeof value.label === "string" &&
|
||||
typeof value.description === "string",
|
||||
)
|
||||
.map(([type, value]) => [
|
||||
type,
|
||||
{
|
||||
label: value.label,
|
||||
description: value.description,
|
||||
fields: Object.fromEntries(
|
||||
Object.entries(value)
|
||||
.filter(
|
||||
([key, field]) =>
|
||||
!["label", "description"].includes(key) &&
|
||||
field &&
|
||||
typeof field === "object" &&
|
||||
typeof field.label === "string",
|
||||
)
|
||||
.map(([key, field]) => [
|
||||
key,
|
||||
{
|
||||
label: field.label,
|
||||
description: field.description ?? "",
|
||||
},
|
||||
]),
|
||||
),
|
||||
},
|
||||
]),
|
||||
);
|
||||
}
|
||||
|
||||
const manifest = {
|
||||
generatedFrom: path.relative(repoRoot, schemaPath).replaceAll("\\", "/"),
|
||||
detectorTypes: buildDetectorTypes(),
|
||||
levels: {
|
||||
global: buildLevel("global"),
|
||||
camera: buildLevel("camera"),
|
||||
},
|
||||
navigation: buildNavigation(),
|
||||
};
|
||||
|
||||
const serialized = `${JSON.stringify(manifest, null, 2)}\n`;
|
||||
if (process.argv.includes("--check")) {
|
||||
const current = fs.existsSync(outputPath)
|
||||
? fs.readFileSync(outputPath, "utf8")
|
||||
: "";
|
||||
if (current !== serialized) {
|
||||
console.error(
|
||||
`${path.relative(repoRoot, outputPath)} is stale. Run npm run build:mock.`,
|
||||
);
|
||||
process.exit(1);
|
||||
}
|
||||
console.log(`Checked ${path.relative(repoRoot, outputPath)}`);
|
||||
} else {
|
||||
fs.mkdirSync(path.dirname(outputPath), { recursive: true });
|
||||
fs.writeFileSync(outputPath, serialized);
|
||||
console.log(`Generated ${path.relative(repoRoot, outputPath)}`);
|
||||
}
|
||||
@@ -45,7 +45,7 @@ from lib.i18n_loader import load_i18n
|
||||
from lib.nav_map import ALL_CONFIG_SECTIONS
|
||||
from lib.schema_loader import load_schema
|
||||
from lib.section_config_parser import load_section_configs
|
||||
from lib.ui_generator import generate_ui_content, wrap_with_config_tabs
|
||||
from lib.ui_generator import generate_mock_content, generate_ui_content, wrap_with_config_tabs
|
||||
from lib.yaml_extractor import (
|
||||
extract_config_tabs_blocks,
|
||||
extract_yaml_blocks,
|
||||
@@ -60,6 +60,7 @@ def process_file(
|
||||
inject: bool = False,
|
||||
verbose: bool = False,
|
||||
outpath: Path | None = None,
|
||||
mock: bool = False,
|
||||
) -> dict:
|
||||
"""Process a single markdown file for initial injection of bare YAML blocks.
|
||||
|
||||
@@ -114,7 +115,8 @@ def process_file(
|
||||
continue
|
||||
|
||||
# Generate UI content
|
||||
ui_content = generate_ui_content(
|
||||
generator = generate_mock_content if mock else generate_ui_content
|
||||
ui_content = generator(
|
||||
block, schema, i18n, section_configs
|
||||
)
|
||||
|
||||
@@ -188,6 +190,7 @@ def regenerate_file(
|
||||
dry_run: bool = False,
|
||||
verbose: bool = False,
|
||||
outpath: Path | None = None,
|
||||
mock: bool = False,
|
||||
) -> dict:
|
||||
"""Regenerate UI tabs in existing ConfigTabs blocks.
|
||||
|
||||
@@ -233,7 +236,8 @@ def regenerate_file(
|
||||
continue
|
||||
|
||||
# Generate fresh UI content
|
||||
new_ui = generate_ui_content(
|
||||
generator = generate_mock_content if mock else generate_ui_content
|
||||
new_ui = generator(
|
||||
yaml_block, schema, i18n, section_configs
|
||||
)
|
||||
|
||||
@@ -302,6 +306,7 @@ def check_file(
|
||||
i18n: dict,
|
||||
section_configs: dict,
|
||||
verbose: bool = False,
|
||||
mock: bool = False,
|
||||
) -> dict:
|
||||
"""Check for drift between existing UI tabs and what would be generated.
|
||||
|
||||
@@ -333,7 +338,8 @@ def check_file(
|
||||
stats["skipped"] += 1
|
||||
continue
|
||||
|
||||
new_ui = generate_ui_content(
|
||||
generator = generate_mock_content if mock else generate_ui_content
|
||||
new_ui = generator(
|
||||
yaml_block, schema, i18n, section_configs
|
||||
)
|
||||
|
||||
@@ -406,6 +412,10 @@ def _ensure_imports(content: str) -> str:
|
||||
needed_imports.append(
|
||||
'import NavPath from "@site/src/components/NavPath";'
|
||||
)
|
||||
if "<FrigateConfigMock" in content and 'import FrigateConfigMock' not in content:
|
||||
needed_imports.append(
|
||||
'import FrigateConfigMock from "@site/src/components/FrigateConfigMock";'
|
||||
)
|
||||
|
||||
if not needed_imports:
|
||||
return content
|
||||
@@ -472,6 +482,11 @@ def main():
|
||||
action="store_true",
|
||||
help="Show detailed warnings and diagnostics",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--mock",
|
||||
action="store_true",
|
||||
help="Generate focused Frigate UI mocks instead of text instructions",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
# Collect files and determine base directory for relative path computation
|
||||
@@ -525,23 +540,25 @@ def main():
|
||||
print(f"Processing {len(files)} file(s)...\n", file=sys.stderr)
|
||||
|
||||
if args.check:
|
||||
_run_check(files, schema, i18n, section_configs, args.verbose)
|
||||
_run_check(files, schema, i18n, section_configs, args.verbose, args.mock)
|
||||
elif args.regenerate:
|
||||
_run_regenerate(
|
||||
files, schema, i18n, section_configs,
|
||||
args.dry_run, args.verbose, file_outpaths,
|
||||
args.mock,
|
||||
)
|
||||
else:
|
||||
_run_inject(
|
||||
files, schema, i18n, section_configs,
|
||||
args.inject, args.verbose, file_outpaths,
|
||||
args.mock,
|
||||
)
|
||||
|
||||
if outdir is not None:
|
||||
print(f"\nOutput written to: {outdir}", file=sys.stderr)
|
||||
|
||||
|
||||
def _run_inject(files, schema, i18n, section_configs, inject, verbose, file_outpaths):
|
||||
def _run_inject(files, schema, i18n, section_configs, inject, verbose, file_outpaths, mock):
|
||||
"""Run default mode: preview or inject bare YAML blocks."""
|
||||
total_stats = {
|
||||
"files": 0,
|
||||
@@ -557,6 +574,7 @@ def _run_inject(files, schema, i18n, section_configs, inject, verbose, file_outp
|
||||
filepath, schema, i18n, section_configs,
|
||||
inject=inject, verbose=verbose,
|
||||
outpath=file_outpaths.get(filepath),
|
||||
mock=mock,
|
||||
)
|
||||
|
||||
total_stats["files"] += 1
|
||||
@@ -580,7 +598,7 @@ def _run_inject(files, schema, i18n, section_configs, inject, verbose, file_outp
|
||||
print("=" * 60, file=sys.stderr)
|
||||
|
||||
|
||||
def _run_regenerate(files, schema, i18n, section_configs, dry_run, verbose, file_outpaths):
|
||||
def _run_regenerate(files, schema, i18n, section_configs, dry_run, verbose, file_outpaths, mock):
|
||||
"""Run regenerate mode: update existing ConfigTabs blocks."""
|
||||
total_stats = {
|
||||
"files": 0,
|
||||
@@ -595,6 +613,7 @@ def _run_regenerate(files, schema, i18n, section_configs, dry_run, verbose, file
|
||||
filepath, schema, i18n, section_configs,
|
||||
dry_run=dry_run, verbose=verbose,
|
||||
outpath=file_outpaths.get(filepath),
|
||||
mock=mock,
|
||||
)
|
||||
|
||||
total_stats["files"] += 1
|
||||
@@ -617,7 +636,7 @@ def _run_regenerate(files, schema, i18n, section_configs, dry_run, verbose, file
|
||||
print("=" * 60, file=sys.stderr)
|
||||
|
||||
|
||||
def _run_check(files, schema, i18n, section_configs, verbose):
|
||||
def _run_check(files, schema, i18n, section_configs, verbose, mock):
|
||||
"""Run check mode: detect drift without modifying files."""
|
||||
total_stats = {
|
||||
"files": 0,
|
||||
@@ -630,6 +649,7 @@ def _run_check(files, schema, i18n, section_configs, verbose):
|
||||
for filepath in files:
|
||||
stats = check_file(
|
||||
filepath, schema, i18n, section_configs, verbose=verbose,
|
||||
mock=mock,
|
||||
)
|
||||
|
||||
total_stats["files"] += 1
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
"""Generate UI tab markdown content from parsed YAML blocks."""
|
||||
"""Generate UI tab content from parsed YAML blocks."""
|
||||
|
||||
import json
|
||||
from typing import Any
|
||||
|
||||
from .i18n_loader import get_field_description, get_field_label, get_value_label
|
||||
@@ -9,6 +10,80 @@ from .section_config_parser import get_hidden_fields
|
||||
from .yaml_extractor import YamlBlock, get_leaf_paths
|
||||
|
||||
|
||||
def generate_mock_content(
|
||||
block: YamlBlock,
|
||||
schema: dict[str, Any],
|
||||
i18n: dict[str, Any],
|
||||
section_configs: dict[str, dict[str, Any]],
|
||||
) -> str | None:
|
||||
"""Generate a focused Frigate config mock for a YAML block."""
|
||||
if block.section_key is None:
|
||||
return None
|
||||
|
||||
if block.is_camera_level:
|
||||
cameras = block.parsed.get("cameras", {})
|
||||
camera_name = block.camera_name or next(iter(cameras), None)
|
||||
if not camera_name or not isinstance(cameras.get(camera_name), dict):
|
||||
return None
|
||||
config = cameras[camera_name]
|
||||
level = "camera"
|
||||
else:
|
||||
config = block.parsed
|
||||
level = detect_level(block.section_key)
|
||||
if level not in ("global", "camera"):
|
||||
level = "global"
|
||||
|
||||
steps: list[dict[str, object]] = []
|
||||
for section, section_data in config.items():
|
||||
if section not in ALL_CONFIG_SECTIONS or not isinstance(
|
||||
section_data, dict
|
||||
):
|
||||
continue
|
||||
|
||||
hidden = get_hidden_fields(section_configs, section, level)
|
||||
values: dict[str, object] = {}
|
||||
for path, value in get_leaf_paths(section_data):
|
||||
path_parts = list(path)
|
||||
if not _is_hidden(path_parts[-1], path_parts, hidden):
|
||||
values[".".join(path_parts)] = value
|
||||
|
||||
if values:
|
||||
steps.append(
|
||||
{
|
||||
"section": section,
|
||||
"level": level,
|
||||
"fields": list(values),
|
||||
"values": values,
|
||||
"focus": next(iter(values)),
|
||||
}
|
||||
)
|
||||
|
||||
if not steps:
|
||||
return None
|
||||
|
||||
if len(steps) == 1:
|
||||
step = steps[0]
|
||||
return "\n".join(
|
||||
[
|
||||
"<FrigateConfigMock",
|
||||
f' section="{step["section"]}"',
|
||||
f' level="{step["level"]}"',
|
||||
f" fields={{{json.dumps(step['fields'])}}}",
|
||||
f" values={{{json.dumps(step['values'])}}}",
|
||||
f' focus="{step["focus"]}"',
|
||||
"/>",
|
||||
]
|
||||
)
|
||||
|
||||
return "\n".join(
|
||||
[
|
||||
"<FrigateConfigMock",
|
||||
f" steps={{{json.dumps(steps)}}}",
|
||||
"/>",
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
def _format_value(
|
||||
value: object,
|
||||
field_schema: dict[str, Any] | None,
|
||||
|
||||
@@ -0,0 +1,99 @@
|
||||
"""Tests for focused Frigate configuration mock generation."""
|
||||
|
||||
import sys
|
||||
import types
|
||||
import unittest
|
||||
from pathlib import Path
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
|
||||
|
||||
try:
|
||||
import yaml # noqa: F401
|
||||
except ModuleNotFoundError:
|
||||
yaml_stub = types.ModuleType("yaml")
|
||||
yaml_stub.YAMLError = ValueError
|
||||
yaml_stub.safe_load = lambda _value: {}
|
||||
sys.modules["yaml"] = yaml_stub
|
||||
|
||||
from lib.ui_generator import generate_mock_content
|
||||
from lib.yaml_extractor import YamlBlock
|
||||
|
||||
|
||||
def make_block(parsed: dict, section: str, camera: bool = False) -> YamlBlock:
|
||||
"""Create a parsed YAML block for generator tests."""
|
||||
return YamlBlock(
|
||||
raw="",
|
||||
parsed=parsed,
|
||||
line_start=1,
|
||||
line_end=1,
|
||||
highlight=None,
|
||||
has_comments=False,
|
||||
inside_config_tabs=False,
|
||||
section_key=section,
|
||||
is_camera_level=camera,
|
||||
camera_name="front_door" if camera else None,
|
||||
config_keys=list(parsed),
|
||||
)
|
||||
|
||||
|
||||
class TestGenerateMockContent(unittest.TestCase):
|
||||
def test_generates_focused_global_section(self):
|
||||
content = generate_mock_content(
|
||||
make_block({"motion": {"threshold": 30}}, "motion"),
|
||||
{},
|
||||
{},
|
||||
{},
|
||||
)
|
||||
|
||||
self.assertIn('section="motion"', content)
|
||||
self.assertIn('level="global"', content)
|
||||
self.assertIn('fields={["threshold"]}', content)
|
||||
self.assertIn('values={{"threshold": 30}}', content)
|
||||
self.assertIn('focus="threshold"', content)
|
||||
|
||||
def test_unwraps_camera_and_omits_hidden_fields(self):
|
||||
content = generate_mock_content(
|
||||
make_block(
|
||||
{
|
||||
"cameras": {
|
||||
"front_door": {
|
||||
"motion": {
|
||||
"threshold": 20,
|
||||
"raw_mask": "ignored",
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"motion",
|
||||
camera=True,
|
||||
),
|
||||
{},
|
||||
{},
|
||||
{"motion": {"hiddenFields": ["raw_mask"]}},
|
||||
)
|
||||
|
||||
self.assertIn('level="camera"', content)
|
||||
self.assertIn('fields={["threshold"]}', content)
|
||||
self.assertNotIn("raw_mask", content)
|
||||
|
||||
def test_generates_steps_for_multiple_sections(self):
|
||||
content = generate_mock_content(
|
||||
make_block(
|
||||
{
|
||||
"record": {"enabled": True},
|
||||
"snapshots": {"enabled": True},
|
||||
},
|
||||
"record",
|
||||
),
|
||||
{},
|
||||
{},
|
||||
{},
|
||||
)
|
||||
|
||||
self.assertIn("steps={", content)
|
||||
self.assertIn('"section": "record"', content)
|
||||
self.assertIn('"section": "snapshots"', content)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -1,7 +1,9 @@
|
||||
import React, { useState } from "react";
|
||||
import React, { useMemo, useState } from "react";
|
||||
import CodeBlock from "@theme/CodeBlock";
|
||||
import ConfigTabs from "@site/src/components/ConfigTabs";
|
||||
import FrigateConfigMock from "@site/src/components/FrigateConfigMock";
|
||||
import TabItem from "@theme/TabItem";
|
||||
import { load } from "js-yaml";
|
||||
import { marked } from "marked";
|
||||
import styles from "./styles.module.css";
|
||||
|
||||
@@ -103,6 +105,40 @@ export default function ModelConfigDropdown({ models }) {
|
||||
|
||||
const selectedModel = models[selectedModelIndex];
|
||||
const hasChoices = models.length > 1;
|
||||
const mockConfig = useMemo(() => {
|
||||
try {
|
||||
const parsed = load(selectedModel.yaml) ?? {};
|
||||
const model = parsed.model ?? {};
|
||||
const legacyDetectors = Object.fromEntries(
|
||||
Object.entries(parsed).filter(
|
||||
([key, value]) =>
|
||||
key !== "model" &&
|
||||
value &&
|
||||
typeof value === "object" &&
|
||||
typeof value.type === "string",
|
||||
),
|
||||
);
|
||||
const detectors = parsed.detectors ?? legacyDetectors;
|
||||
const values = {
|
||||
detectors,
|
||||
...model,
|
||||
};
|
||||
const targets = [
|
||||
...(Object.keys(detectors).length ? ["detectors"] : []),
|
||||
...(Object.keys(model).length
|
||||
? [
|
||||
{
|
||||
field: "custom_model",
|
||||
hint: `Configure the custom detection model, input size, and input format for ${selectedModel.label}.`,
|
||||
},
|
||||
]
|
||||
: []),
|
||||
];
|
||||
return { targets, values };
|
||||
} catch {
|
||||
return { targets: ["detectors"], values: { detectors: {} } };
|
||||
}
|
||||
}, [selectedModel.label, selectedModel.yaml]);
|
||||
|
||||
const handleModelSelect = (index) => {
|
||||
setSelectedModelIndex(index);
|
||||
@@ -158,7 +194,13 @@ export default function ModelConfigDropdown({ models }) {
|
||||
<h4 className={styles.stepTitle}>Step 3 — Configure the detector</h4>
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
<Markdown>{selectedModel.ui}</Markdown>
|
||||
<FrigateConfigMock
|
||||
autoPlay={false}
|
||||
key={selectedModel.key}
|
||||
section="model"
|
||||
targets={mockConfig.targets}
|
||||
values={mockConfig.values}
|
||||
/>
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
<CodeBlock language="yaml">{selectedModel.yaml}</CodeBlock>
|
||||
|
||||
@@ -376,20 +376,22 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
logger.info(f"Disabled classification processor for model: {model_name}")
|
||||
return
|
||||
|
||||
# Check if processor already exists
|
||||
for processor in self.realtime_processors:
|
||||
if isinstance(
|
||||
processor,
|
||||
(
|
||||
CustomStateClassificationProcessor,
|
||||
CustomObjectClassificationProcessor,
|
||||
),
|
||||
if (
|
||||
isinstance(
|
||||
processor,
|
||||
(
|
||||
CustomStateClassificationProcessor,
|
||||
CustomObjectClassificationProcessor,
|
||||
),
|
||||
)
|
||||
and processor.model_config.name == model_name
|
||||
):
|
||||
if processor.model_config.name == model_name:
|
||||
logger.debug(
|
||||
f"Classification processor for model {model_name} already exists, skipping"
|
||||
)
|
||||
return
|
||||
processor.model_config = model_config
|
||||
logger.debug(
|
||||
f"Updated config for classification processor: {model_name}"
|
||||
)
|
||||
return
|
||||
|
||||
if model_config.state_config is not None:
|
||||
processor = CustomStateClassificationProcessor(
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
{
|
||||
"documentTitle": "Classification Models - Frigate",
|
||||
"disabled": "Disabled",
|
||||
"details": {
|
||||
"scoreInfo": "Score represents the average classification confidence across all detections of this object.",
|
||||
"none": "None",
|
||||
@@ -64,7 +65,20 @@
|
||||
"title": "Edit Classification Model",
|
||||
"descriptionState": "Edit the classes for this state classification model. Changes will require retraining the model.",
|
||||
"descriptionObject": "Edit the object type and classification type for this object classification model.",
|
||||
"stateClassesInfo": "Note: Changing state classes requires retraining the model with the updated classes."
|
||||
"enabled": "Enabled",
|
||||
"enabledDesc": "Run this model. When disabled, it stops running and no longer classifies.",
|
||||
"saveAttempts": "Save Attempts",
|
||||
"saveAttemptsDesc": "Number of classification attempt images to keep for the recent classifications UI.",
|
||||
"motion": "Run on Motion",
|
||||
"motionDesc": "Run classification when motion is detected within the configured crop.",
|
||||
"interval": "Interval",
|
||||
"intervalDesc": "Seconds between periodic classification runs. Leave empty to run only on motion.",
|
||||
"intervalPlaceholder": "No interval",
|
||||
"stateClassesInfo": "Model updated. Retrain the model for the class changes to take effect.",
|
||||
"errors": {
|
||||
"saveAttemptsInvalid": "Save attempts must be a whole number of 0 or greater",
|
||||
"intervalInvalid": "Interval must be a whole number greater than 0"
|
||||
}
|
||||
},
|
||||
"deleteDatasetImages": {
|
||||
"title": "Delete Dataset Images",
|
||||
|
||||
@@ -9,6 +9,7 @@ import {
|
||||
import {
|
||||
Form,
|
||||
FormControl,
|
||||
FormDescription,
|
||||
FormField,
|
||||
FormItem,
|
||||
FormLabel,
|
||||
@@ -17,6 +18,7 @@ import {
|
||||
import { Input } from "@/components/ui/input";
|
||||
import { Label } from "@/components/ui/label";
|
||||
import { RadioGroup, RadioGroupItem } from "@/components/ui/radio-group";
|
||||
import { Switch } from "@/components/ui/switch";
|
||||
import {
|
||||
Select,
|
||||
SelectContent,
|
||||
@@ -50,14 +52,25 @@ type ClassificationModelEditDialogProps = {
|
||||
type ObjectClassificationType = "sub_label" | "attribute";
|
||||
|
||||
type ObjectFormData = {
|
||||
enabled: boolean;
|
||||
saveAttempts: number;
|
||||
objectLabel: string;
|
||||
objectType: ObjectClassificationType;
|
||||
};
|
||||
|
||||
type StateFormData = {
|
||||
enabled: boolean;
|
||||
saveAttempts: number;
|
||||
motion: boolean;
|
||||
interval?: number;
|
||||
classes: string[];
|
||||
};
|
||||
|
||||
const DEFAULT_SAVE_ATTEMPTS = {
|
||||
object: 200,
|
||||
state: 100,
|
||||
} as const;
|
||||
|
||||
export default function ClassificationModelEditDialog({
|
||||
open,
|
||||
model,
|
||||
@@ -71,6 +84,10 @@ export default function ClassificationModelEditDialog({
|
||||
const isStateModel = model.state_config !== undefined;
|
||||
const isObjectModel = model.object_config !== undefined;
|
||||
|
||||
const defaultSaveAttempts = isObjectModel
|
||||
? DEFAULT_SAVE_ATTEMPTS.object
|
||||
: DEFAULT_SAVE_ATTEMPTS.state;
|
||||
|
||||
const objectLabels = useMemo(() => {
|
||||
if (!config) return [];
|
||||
|
||||
@@ -93,8 +110,17 @@ export default function ClassificationModelEditDialog({
|
||||
|
||||
// Define form schema based on model type
|
||||
const formSchema = useMemo(() => {
|
||||
const sharedFields = {
|
||||
enabled: z.boolean(),
|
||||
saveAttempts: z.coerce
|
||||
.number({ message: t("edit.errors.saveAttemptsInvalid") })
|
||||
.int(t("edit.errors.saveAttemptsInvalid"))
|
||||
.min(0, t("edit.errors.saveAttemptsInvalid")),
|
||||
};
|
||||
|
||||
if (isObjectModel) {
|
||||
return z.object({
|
||||
...sharedFields,
|
||||
objectLabel: z
|
||||
.string()
|
||||
.min(1, t("wizard.step1.errors.objectLabelRequired")),
|
||||
@@ -103,6 +129,17 @@ export default function ClassificationModelEditDialog({
|
||||
} else {
|
||||
// State model
|
||||
return z.object({
|
||||
...sharedFields,
|
||||
motion: z.boolean(),
|
||||
interval: z.preprocess(
|
||||
(val) =>
|
||||
val === "" || val === null || val === undefined ? undefined : val,
|
||||
z.coerce
|
||||
.number({ message: t("edit.errors.intervalInvalid") })
|
||||
.int(t("edit.errors.intervalInvalid"))
|
||||
.positive(t("edit.errors.intervalInvalid"))
|
||||
.optional(),
|
||||
),
|
||||
classes: z
|
||||
.array(z.string())
|
||||
.min(1, t("wizard.step1.errors.classRequired"))
|
||||
@@ -129,12 +166,18 @@ export default function ClassificationModelEditDialog({
|
||||
resolver: zodResolver(formSchema),
|
||||
defaultValues: isObjectModel
|
||||
? ({
|
||||
enabled: model.enabled,
|
||||
saveAttempts: model.save_attempts ?? defaultSaveAttempts,
|
||||
objectLabel: model.object_config?.objects?.[0] || "",
|
||||
objectType:
|
||||
(model.object_config
|
||||
?.classification_type as ObjectClassificationType) || "sub_label",
|
||||
} as ObjectFormData)
|
||||
: ({
|
||||
enabled: model.enabled,
|
||||
saveAttempts: model.save_attempts ?? defaultSaveAttempts,
|
||||
motion: model.state_config?.motion ?? false,
|
||||
interval: model.state_config?.interval,
|
||||
classes: [""], // Will be populated from dataset
|
||||
} as StateFormData),
|
||||
mode: "onChange",
|
||||
@@ -151,6 +194,8 @@ export default function ClassificationModelEditDialog({
|
||||
if (open) {
|
||||
if (isObjectModel) {
|
||||
form.reset({
|
||||
enabled: model.enabled,
|
||||
saveAttempts: model.save_attempts ?? defaultSaveAttempts,
|
||||
objectLabel: model.object_config?.objects?.[0] || "",
|
||||
objectType:
|
||||
(model.object_config
|
||||
@@ -158,6 +203,10 @@ export default function ClassificationModelEditDialog({
|
||||
} as ObjectFormData);
|
||||
} else {
|
||||
form.reset({
|
||||
enabled: model.enabled,
|
||||
saveAttempts: model.save_attempts ?? defaultSaveAttempts,
|
||||
motion: model.state_config?.motion ?? false,
|
||||
interval: model.state_config?.interval,
|
||||
classes: [""],
|
||||
} as StateFormData);
|
||||
}
|
||||
@@ -166,7 +215,15 @@ export default function ClassificationModelEditDialog({
|
||||
mutateDataset();
|
||||
}
|
||||
}
|
||||
}, [open, isObjectModel, isStateModel, model, form, mutateDataset]);
|
||||
}, [
|
||||
open,
|
||||
isObjectModel,
|
||||
isStateModel,
|
||||
model,
|
||||
form,
|
||||
mutateDataset,
|
||||
defaultSaveAttempts,
|
||||
]);
|
||||
|
||||
// Update form with classes from dataset when loaded
|
||||
useEffect(() => {
|
||||
@@ -233,6 +290,7 @@ export default function ClassificationModelEditDialog({
|
||||
setIsSaving(true);
|
||||
try {
|
||||
if (isObjectModel) {
|
||||
// object model save
|
||||
const objectData = data as ObjectFormData;
|
||||
|
||||
// Update the config
|
||||
@@ -243,9 +301,10 @@ export default function ClassificationModelEditDialog({
|
||||
classification: {
|
||||
custom: {
|
||||
[model.name]: {
|
||||
enabled: model.enabled,
|
||||
enabled: objectData.enabled,
|
||||
name: model.name,
|
||||
threshold: model.threshold,
|
||||
save_attempts: objectData.saveAttempts,
|
||||
object_config: {
|
||||
objects: [objectData.objectLabel],
|
||||
classification_type: objectData.objectType,
|
||||
@@ -260,7 +319,34 @@ export default function ClassificationModelEditDialog({
|
||||
position: "top-center",
|
||||
});
|
||||
} else {
|
||||
// state model save
|
||||
const stateData = data as StateFormData;
|
||||
|
||||
const stateConfig: { motion: boolean; interval?: number | null } = {
|
||||
motion: stateData.motion,
|
||||
};
|
||||
if (stateData.interval != null) {
|
||||
stateConfig.interval = stateData.interval;
|
||||
} else if (model.state_config?.interval != null) {
|
||||
stateConfig.interval = null;
|
||||
}
|
||||
|
||||
await axios.put("/config/set", {
|
||||
requires_restart: 0,
|
||||
update_topic: `config/classification/custom/${model.name}`,
|
||||
config_data: {
|
||||
classification: {
|
||||
custom: {
|
||||
[model.name]: {
|
||||
enabled: stateData.enabled,
|
||||
save_attempts: stateData.saveAttempts,
|
||||
state_config: stateConfig,
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
});
|
||||
|
||||
const newClasses = stateData.classes.filter(
|
||||
(c) => c.trim().length > 0,
|
||||
);
|
||||
@@ -307,11 +393,11 @@ export default function ClassificationModelEditDialog({
|
||||
if (renamePromises.length > 0) {
|
||||
await Promise.all(renamePromises);
|
||||
await mutate(`classification/${model.name}/dataset`);
|
||||
toast.success(t("toast.success.updatedModel"), {
|
||||
toast.success(t("edit.stateClassesInfo"), {
|
||||
position: "top-center",
|
||||
});
|
||||
} else {
|
||||
toast.info(t("edit.stateClassesInfo"), {
|
||||
toast.success(t("toast.success.updatedModel"), {
|
||||
position: "top-center",
|
||||
});
|
||||
}
|
||||
@@ -359,6 +445,29 @@ export default function ClassificationModelEditDialog({
|
||||
<div className="space-y-6">
|
||||
<Form {...form}>
|
||||
<form onSubmit={form.handleSubmit(onSubmit)} className="space-y-4">
|
||||
<FormField
|
||||
control={form.control}
|
||||
name="enabled"
|
||||
render={({ field }) => (
|
||||
<FormItem className="flex flex-row items-center justify-between gap-4">
|
||||
<div className="space-y-0.5">
|
||||
<FormLabel className="text-primary-variant">
|
||||
{t("edit.enabled")}
|
||||
</FormLabel>
|
||||
<FormDescription className="text-xs">
|
||||
{t("edit.enabledDesc")}
|
||||
</FormDescription>
|
||||
</div>
|
||||
<FormControl>
|
||||
<Switch
|
||||
checked={field.value}
|
||||
onCheckedChange={field.onChange}
|
||||
/>
|
||||
</FormControl>
|
||||
</FormItem>
|
||||
)}
|
||||
/>
|
||||
|
||||
{isObjectModel && (
|
||||
<>
|
||||
<FormField
|
||||
@@ -520,6 +629,77 @@ export default function ClassificationModelEditDialog({
|
||||
</div>
|
||||
)}
|
||||
|
||||
{isStateModel && (
|
||||
<>
|
||||
<FormField
|
||||
control={form.control}
|
||||
name="motion"
|
||||
render={({ field }) => (
|
||||
<FormItem className="flex flex-row items-center justify-between gap-4">
|
||||
<div className="space-y-0.5">
|
||||
<FormLabel className="text-primary-variant">
|
||||
{t("edit.motion")}
|
||||
</FormLabel>
|
||||
<FormDescription className="text-xs">
|
||||
{t("edit.motionDesc")}
|
||||
</FormDescription>
|
||||
</div>
|
||||
<FormControl>
|
||||
<Switch
|
||||
checked={field.value}
|
||||
onCheckedChange={field.onChange}
|
||||
/>
|
||||
</FormControl>
|
||||
</FormItem>
|
||||
)}
|
||||
/>
|
||||
|
||||
<FormField
|
||||
control={form.control}
|
||||
name="interval"
|
||||
render={({ field }) => (
|
||||
<FormItem>
|
||||
<FormLabel className="text-primary-variant">
|
||||
{t("edit.interval")}
|
||||
</FormLabel>
|
||||
<FormControl>
|
||||
<Input
|
||||
className="h-8"
|
||||
inputMode="numeric"
|
||||
placeholder={t("edit.intervalPlaceholder")}
|
||||
{...field}
|
||||
value={field.value ?? ""}
|
||||
/>
|
||||
</FormControl>
|
||||
<FormDescription className="text-xs">
|
||||
{t("edit.intervalDesc")}
|
||||
</FormDescription>
|
||||
<FormMessage />
|
||||
</FormItem>
|
||||
)}
|
||||
/>
|
||||
</>
|
||||
)}
|
||||
|
||||
<FormField
|
||||
control={form.control}
|
||||
name="saveAttempts"
|
||||
render={({ field }) => (
|
||||
<FormItem>
|
||||
<FormLabel className="text-primary-variant">
|
||||
{t("edit.saveAttempts")}
|
||||
</FormLabel>
|
||||
<FormControl>
|
||||
<Input className="h-8" inputMode="numeric" {...field} />
|
||||
</FormControl>
|
||||
<FormDescription className="text-xs">
|
||||
{t("edit.saveAttemptsDesc")}
|
||||
</FormDescription>
|
||||
<FormMessage />
|
||||
</FormItem>
|
||||
)}
|
||||
/>
|
||||
|
||||
<div className="flex flex-col gap-3 pt-3 sm:flex-row sm:justify-end sm:gap-4">
|
||||
<Button
|
||||
type="button"
|
||||
|
||||
@@ -370,6 +370,7 @@ export type CustomClassificationModelConfig = {
|
||||
};
|
||||
};
|
||||
motion: boolean;
|
||||
interval?: number;
|
||||
};
|
||||
};
|
||||
|
||||
|
||||
@@ -3,6 +3,7 @@ import ClassificationModelWizardDialog from "@/components/classification/Classif
|
||||
import ClassificationModelEditDialog from "@/components/classification/ClassificationModelEditDialog";
|
||||
import ActivityIndicator from "@/components/indicators/activity-indicator";
|
||||
import { ImageShadowOverlay } from "@/components/overlay/ImageShadowOverlay";
|
||||
import { Badge } from "@/components/ui/badge";
|
||||
import { Button, buttonVariants } from "@/components/ui/button";
|
||||
import { ToggleGroup, ToggleGroupItem } from "@/components/ui/toggle-group";
|
||||
import useOptimisticState from "@/hooks/use-optimistic-state";
|
||||
@@ -330,7 +331,10 @@ function ModelCard({ config, onClick, onUpdate, onDelete }: ModelCardProps) {
|
||||
{coverImage ? (
|
||||
<>
|
||||
<img
|
||||
className="size-full"
|
||||
className={cn(
|
||||
"size-full",
|
||||
!config.enabled && "opacity-50 grayscale",
|
||||
)}
|
||||
src={`${baseUrl}clips/${config.name}/dataset/${coverImage.name}/${coverImage.img}`}
|
||||
/>
|
||||
<ImageShadowOverlay lowerClassName="h-[30%] z-0" />
|
||||
@@ -338,6 +342,14 @@ function ModelCard({ config, onClick, onUpdate, onDelete }: ModelCardProps) {
|
||||
) : (
|
||||
<Skeleton className="flex size-full items-center justify-center" />
|
||||
)}
|
||||
{!config.enabled && (
|
||||
<Badge
|
||||
variant="secondary"
|
||||
className="absolute right-2 top-2 z-40 text-primary-variant"
|
||||
>
|
||||
{t("disabled")}
|
||||
</Badge>
|
||||
)}
|
||||
<div className="absolute bottom-2 left-3 text-lg text-white smart-capitalize">
|
||||
{config.name}
|
||||
</div>
|
||||
|
||||
@@ -348,6 +348,19 @@ function MotionPreviewClip({
|
||||
}
|
||||
}, [clipStart, clipEnd, playbackRate, preview]);
|
||||
|
||||
useEffect(() => {
|
||||
if (!videoRef.current || !preview || !videoPlaying) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (isSafari || (isFirefox && isMobile)) {
|
||||
// These browsers step frames manually; rebuild the interval at the new rate
|
||||
resetPlayback();
|
||||
} else {
|
||||
videoRef.current.playbackRate = playbackRate;
|
||||
}
|
||||
}, [playbackRate, preview, videoPlaying, resetPlayback]);
|
||||
|
||||
const drawDimOverlay = useCallback(() => {
|
||||
if (!dimOverlayCanvasRef.current) {
|
||||
return;
|
||||
|
||||
Reference in New Issue
Block a user