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fullscreen
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gatekeep
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@@ -10,11 +10,8 @@ body:
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Before submitting, read the [beta documentation][docs].
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||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[docs]: https://docs-dev.frigate.video/
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||||
[discussions]: https://github.com/blakeblackshear/frigate/discussions
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[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
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id: description
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attributes:
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@@ -8,12 +8,9 @@ body:
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||||
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
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||||
[docs]: https://docs.frigate.video
|
||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
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[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
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- type: textarea
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id: description
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||||
attributes:
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@@ -8,12 +8,9 @@ body:
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||||
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||||
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
|
||||
[docs]: https://docs.frigate.video
|
||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
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||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
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- type: textarea
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id: description
|
||||
attributes:
|
||||
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||||
@@ -8,12 +8,9 @@ body:
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||||
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||||
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
|
||||
[docs]: https://docs.frigate.video
|
||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
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||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
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- type: textarea
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id: description
|
||||
attributes:
|
||||
|
||||
@@ -8,12 +8,9 @@ body:
|
||||
|
||||
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
|
||||
[docs]: https://docs.frigate.video
|
||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
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||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
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- type: textarea
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||||
id: description
|
||||
attributes:
|
||||
|
||||
@@ -8,12 +8,9 @@ body:
|
||||
|
||||
Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
|
||||
[docs]: https://docs.frigate.video
|
||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
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||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
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- type: textarea
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||||
id: description
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||||
attributes:
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@@ -10,12 +10,9 @@ body:
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||||
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||||
**If you are looking for support, start a new discussion and use a support category.**
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
|
||||
[docs]: https://docs.frigate.video
|
||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
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||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
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- type: textarea
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id: description
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||||
attributes:
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||||
@@ -12,14 +12,11 @@ body:
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||||
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||||
**If you are unsure if your issue is actually a bug or not, please submit a support request first.**
|
||||
|
||||
By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
|
||||
|
||||
[discussions]: https://www.github.com/blakeblackshear/frigate/discussions
|
||||
[prs]: https://www.github.com/blakeblackshear/frigate/pulls
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||||
[docs]: https://docs.frigate.video
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||||
[faq]: https://github.com/blakeblackshear/frigate/discussions/12724
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||||
[ai]: https://docs.frigate.video
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||||
[ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
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- type: checkboxes
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attributes:
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label: Checklist
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@@ -7,13 +7,6 @@ assignees: ''
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---
|
||||
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||||
<!--
|
||||
By posting here you agree to follow our AI policy:
|
||||
https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
|
||||
|
||||
Requests that appear to be written by an AI on your behalf may be closed without a response.
|
||||
-->
|
||||
|
||||
**Describe what you are trying to accomplish and why in non technical terms**
|
||||
I want to be able to ... so that I can ...
|
||||
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||||
|
||||
@@ -1,4 +1,4 @@
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||||
_Please read the [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) and the [AI policy](https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md) before submitting a PR. Every PR must be read and submitted by a person, and PRs that appear to be unreviewed AI output will be closed without review._
|
||||
_Please read the [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) before submitting a PR._
|
||||
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||||
## Proposed change
|
||||
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||||
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||||
-126
@@ -1,126 +0,0 @@
|
||||
# Frigate AI Policy
|
||||
|
||||
## TL;DR
|
||||
|
||||
- **Use AI tools if they help you.** We do too. This is about what you post, not which tools you use to write it.
|
||||
- **A person has to read it and send it.** Don't wire a bot or an agent up to post on your behalf.
|
||||
- **Write your posts yourself.** Your own words, the template filled in, and you answering maintainers rather than your assistant.
|
||||
- **Don't paste an AI's guess at the cause as though it were a diagnosis.** Tell us what you actually observed.
|
||||
- **Read your code before you submit it.** Disclose that AI was used, and be ready to explain every line.
|
||||
- **If we misjudge something you wrote, just say so.** We'll take you at your word.
|
||||
|
||||
The rest of this document explains each of these, and why.
|
||||
|
||||
## Scope
|
||||
|
||||
AI tools are a reality of modern development and we're not opposed to their use. You are responsible for anything you submit, however it was produced, and we are responsible for anything we merge and release. We hold a high bar for both.
|
||||
|
||||
This policy applies everywhere this project is discussed: issues, discussions, pull requests, code reviews, and commit comments.
|
||||
|
||||
## Why this exists
|
||||
|
||||
Frigate is built and supported by a small group of maintainers and a community of volunteers who read every post and review every pull request. Nobody here is paid to do it, and time spent reading a post is time not spent fixing bugs or building features.
|
||||
|
||||
We're not opposed to AI tools. We use them too. But content generated by an AI and submitted without review costs a real person real time, and usually gives them less to work with than a few honest sentences would have. That is the problem this policy addresses.
|
||||
|
||||
## A person has to be in the loop
|
||||
|
||||
Every issue, discussion, comment, and pull request here must be read and submitted by a person. Using an AI tool to help you write is fine. Wiring one up to post on your behalf is not.
|
||||
|
||||
Specifically, do not:
|
||||
|
||||
- Connect a bot or agent to GitHub that opens issues, discussions, or pull requests without you reading them first
|
||||
- Post output from a tool you have not read
|
||||
- Use tooling to file bulk or drive-by contributions across the repository
|
||||
|
||||
We will close anything we believe was posted without a person reading it, and we may mark it as spam. Posts that skip the templates are the most common sign of this.
|
||||
|
||||
## Issues, discussions, and comments
|
||||
|
||||
We do not mind if you use AI tools to help you write. Do not have tools post unreviewed content on your behalf. We may hide any comment we believe to be unreviewed AI output.
|
||||
|
||||
Keep posts to what is needed to communicate your point. A long, confidently written, AI-padded post is harder to help with than a short direct one, not easier, and it is usually obvious.
|
||||
|
||||
**Describe your actual problem in your own words.** Tell us what you did, what you expected, and what actually happened. That is the information we need, and only you have it.
|
||||
|
||||
**Do not paste an AI's guess at the cause as though it were a diagnosis.** It is frequently wrong in ways that send everyone down the wrong path, and it buries the details that would have led to the real answer. We would rather see what you observed than what a model inferred.
|
||||
|
||||
**Fill in the template completely.** The templates ask for logs, config, version, and hardware because those are the things needed to help you. An AI cannot supply them for you, and a post missing them cannot be acted on.
|
||||
|
||||
**Answer maintainers yourself.** If we ask you a question, we are asking _you_, not your AI assistant. These are the spaces where we build trust and understanding with the community, and that only works if we're talking to each other. Using AI to fix your grammar or clarity is fine, but the substance has to be yours.
|
||||
|
||||
This applies to pull request descriptions and review replies as much as it does to bug reports and discussions.
|
||||
|
||||
### Quoting AI output
|
||||
|
||||
If you want to include something an AI told you, it must be:
|
||||
|
||||
- In a quote block, using `>`
|
||||
- Disclosed as AI output, saying which tool it came from
|
||||
- Accompanied by your own comment explaining why you think it is relevant
|
||||
|
||||
Keep the excerpt short. Do not paste long transcripts.
|
||||
|
||||
### Non-native English speakers
|
||||
|
||||
AI is genuinely useful for participating in a project that operates in English, and we would rather hear from you through a translation tool than not hear from you at all. Using AI to improve the grammar or clarity of something you wrote yourself is fine.
|
||||
|
||||
If you are translating your posts, make sure the translation says what you meant. Including your original text in a `<details>` block helps us verify the translation if something reads oddly, and keeps the thread readable.
|
||||
|
||||
## Code contributions
|
||||
|
||||
We need to understand your relationship with the code you're submitting. The more AI was involved, the more important it is that you've genuinely reviewed, tested, and understood what it produced.
|
||||
|
||||
Because of the long-term maintenance burden every merged change creates, we require a human in the loop who understands the work the AI produced. Pull requests that appear to be unreviewed AI output will be closed without review.
|
||||
|
||||
### Requirements when AI is used
|
||||
|
||||
If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
|
||||
|
||||
1. **Explicitly disclose the manner in which AI was employed.** The PR template asks for this. Be honest, this won't automatically disqualify your PR. We'd rather have an honest disclosure than find out later. Trust matters more than method.
|
||||
2. **Perform a comprehensive manual review prior to submitting the pull request.** Don't submit code you haven't read carefully and tested locally.
|
||||
3. **Be prepared to explain every line of code you submitted when asked about it by a maintainer.** If you can't explain why something works the way it does, you're not ready to submit it.
|
||||
4. **Check for an existing pull request addressing the same change.** If one exists, comment there and work with its author instead of opening a duplicate.
|
||||
5. **It is strictly prohibited to use AI to write your posts for you** (bug reports, feature requests, pull request descriptions, GitHub discussions, responding to humans, etc.). We need to hear from _you_, not your AI assistant. These are the spaces where we build trust and understanding with contributors, and that only works if we're talking to each other.
|
||||
|
||||
### Established contributors
|
||||
|
||||
Contributors with a long history of thoughtful, quality contributions to Frigate have earned trust through that track record. The level of scrutiny we apply to AI usage naturally reflects that trust. This isn't a formal exemption, it's just how trust works. If you've been around, we know how you think and how you work. If you're new, we're still getting to know you, and clear disclosure helps build that relationship.
|
||||
|
||||
### What this means in practice
|
||||
|
||||
We're not trying to gatekeep how you write code. Use whatever tools make you productive. But there's a difference between using AI as a tool to implement something you understand and handing a feature request to an AI and submitting whatever comes back. The former is fine. The latter creates maintenance risk for the project.
|
||||
|
||||
Some honest context: when we review a PR, we're not just evaluating whether the code works today. We're evaluating whether we can maintain it, debug it, and extend it long-term, often without the original author's involvement. Code that the author doesn't deeply understand is code that nobody understands, and that's a liability.
|
||||
|
||||
One more thing worth saying directly: most maintainers already have access to the same AI tools you do. A PR that's entirely AI-generated, where the author can't explain the design, debug issues independently, or engage substantively in design discussions, doesn't offer something we couldn't produce ourselves. What makes a contribution genuinely valuable is the human judgment and domain understanding behind it, as well as the engagement during review that shapes it into something we can confidently take on long-term.
|
||||
|
||||
## Our use of AI
|
||||
|
||||
The Frigate documentation site has an "Ask AI" search that answers questions from the docs, and we may use AI tooling to help with triage and project management. Like any automated tooling, it is not always right.
|
||||
|
||||
If an AI tool leaves a comment on your contribution, treat it the way you would any other comment. If you think it is wrong, say so, and a brief explanation is enough. Maintainers always have the final say.
|
||||
|
||||
## Enforcement
|
||||
|
||||
Contributions and posts that do not follow this policy will be closed. Depending on the situation, maintainers may also:
|
||||
|
||||
- Hide or delete comments that appear to be unreviewed AI output
|
||||
- Mark automated content as spam
|
||||
- Close an issue, discussion, or pull request without further review
|
||||
- Lock a conversation
|
||||
- Temporarily or permanently block an account from participating in the project
|
||||
|
||||
Repeated violations may result in being blocked from contributing to Frigate.
|
||||
|
||||
### When we get it wrong
|
||||
|
||||
There is no reliable way to detect this, and we're not going to pretend otherwise. Whether something reads as unreviewed AI output is a judgment call, usually made quickly, by a volunteer with limited time and no way to know for certain. These calls are subjective and we won't always get them right.
|
||||
|
||||
If it happens to you, just say so. A short reply telling us you wrote it yourself is enough, and we'll take you at your word and pick the conversation back up. We would much rather occasionally reopen something we misjudged than treat everyone who posts here as a suspect.
|
||||
|
||||
We'd ask for some understanding in return. These calls get made quickly because the volume is real, and time spent second-guessing them is time not spent helping the person in the next thread.
|
||||
|
||||
## Attribution
|
||||
|
||||
Portions of this policy are adapted from the [Open Home Foundation AI Policy](https://developers.home-assistant.io/docs/ai_policy/).
|
||||
+19
-9
@@ -2,8 +2,6 @@
|
||||
|
||||
Thank you for your interest in contributing to Frigate. This document covers the expectations and guidelines for contributions. Please read it before submitting a pull request.
|
||||
|
||||
All participation in this project, including pull requests, issues, and discussions, is covered by our [AI policy](AI_POLICY.md).
|
||||
|
||||
## Before you start
|
||||
|
||||
### Bugfixes
|
||||
@@ -23,16 +21,28 @@ Before writing code for a new feature:
|
||||
|
||||
## AI usage policy
|
||||
|
||||
AI tools are a reality of modern development and we're not opposed to their use. But we need to understand your relationship with the code you're submitting, and we need to hear from you rather than from your AI assistant.
|
||||
AI tools are a reality of modern development and we're not opposed to their use. But we need to understand your relationship with the code you're submitting. The more AI was involved, the more important it is that you've genuinely reviewed, tested, and understood what it produced.
|
||||
|
||||
**Read the [AI policy](AI_POLICY.md) before you open a pull request.** It is short, and it applies to everything you post here. The parts that most often catch people out:
|
||||
### Requirements when AI is used
|
||||
|
||||
- A person has to be in the loop. Don't wire a bot or agent up to open pull requests, issues, or discussions on your behalf.
|
||||
- Disclose how AI was used. The PR template asks for this. Be honest, it won't automatically disqualify your PR.
|
||||
- Review and test everything you submit, and be prepared to explain every line when asked.
|
||||
- Don't use AI to write your PR description or your replies to maintainers.
|
||||
If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
|
||||
|
||||
Pull requests that appear to be unreviewed AI output will be closed without review.
|
||||
1. **Explicitly disclose the manner in which AI was employed.** The PR template asks for this. Be honest — this won't automatically disqualify your PR. We'd rather have an honest disclosure than find out later. Trust matters more than method.
|
||||
2. **Perform a comprehensive manual review prior to submitting the pull request.** Don't submit code you haven't read carefully and tested locally.
|
||||
3. **Be prepared to explain every line of code they submitted when asked about it by a maintainer.** If you can't explain why something works the way it does, you're not ready to submit it.
|
||||
4. **It is strictly prohibited to use AI to write your posts for you** (bug reports, feature requests, pull request descriptions, GitHub discussions, responding to humans, etc.). We need to hear from _you_, not your AI assistant. These are the spaces where we build trust and understanding with contributors, and that only works if we're talking to each other.
|
||||
|
||||
### Established contributors
|
||||
|
||||
Contributors with a long history of thoughtful, quality contributions to Frigate have earned trust through that track record. The level of scrutiny we apply to AI usage naturally reflects that trust. This isn't a formal exemption — it's just how trust works. If you've been around, we know how you think and how you work. If you're new, we're still getting to know you, and clear disclosure helps build that relationship.
|
||||
|
||||
### What this means in practice
|
||||
|
||||
We're not trying to gatekeep how you write code. Use whatever tools make you productive. But there's a difference between using AI as a tool to implement something you understand and handing a feature request to an AI and submitting whatever comes back. The former is fine. The latter creates maintenance risk for the project.
|
||||
|
||||
Some honest context: when we review a PR, we're not just evaluating whether the code works today. We're evaluating whether we can maintain it, debug it, and extend it long-term — often without the original author's involvement. Code that the author doesn't deeply understand is code that nobody understands, and that's a liability.
|
||||
|
||||
One more thing worth saying directly: most maintainers already have access to the same AI tools you do. A PR that's entirely AI-generated — where the author can't explain the design, debug issues independently, or engage substantively in design discussions — doesn't offer something we couldn't produce ourselves. What makes a contribution genuinely valuable is the human judgment and domain understanding behind it, as well as the engagement during review that shapes it into something we can confidently take on long-term.
|
||||
|
||||
## Pull request guidelines
|
||||
|
||||
|
||||
@@ -1,14 +1,10 @@
|
||||
"""Convert the default SSDLite MobileNet v2 model to OpenVINO IR.
|
||||
|
||||
Replaces the legacy openvino-dev Model Optimizer conversion. The TensorFlow
|
||||
frontend translates the Object Detection API pre and post processors literally,
|
||||
producing per-class NonMaxSuppression, NonZero ops and map loops with data
|
||||
dependent shapes that the GPU plugin handles very badly. Both are cut out the
|
||||
way ssd_v2_support.json used to do it: the preprocessor is an identity at the
|
||||
native 300x300 input, and the postprocessor becomes a single fused
|
||||
DetectionOutput. The result is the [1, 1, 100, 7] tensor that Frigate's
|
||||
OpenVINO detector expects, with the input flipped to BGR to match the legacy
|
||||
reverse_input_channels behavior.
|
||||
frontend converts the Object Detection API frozen graph natively; the four TF
|
||||
outputs are then repacked into the single [1, 1, 100, 7] DetectionOutput-style
|
||||
tensor that Frigate's OpenVINO detector expects, and the input is flipped to
|
||||
BGR to match the legacy reverse_input_channels behavior.
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
@@ -16,91 +12,31 @@ import openvino as ov
|
||||
from openvino import opset8 as ops
|
||||
from openvino.preprocess import PrePostProcessor
|
||||
|
||||
MODEL_DIR = "/models/ssdlite_mobilenet_v2_coco_2018_05_09"
|
||||
OUTPUT_PATH = "/models/ssdlite_mobilenet_v2.xml"
|
||||
INPUT_SHAPE = [1, 300, 300, 3]
|
||||
|
||||
# faster_rcnn_box_coder divides the deltas by pipeline.config's y/x/height/width
|
||||
# scales of 10/10/5/5, which DetectionOutput expresses as per-prior variances.
|
||||
BOX_VARIANCES = np.float32([0.1, 0.1, 0.2, 0.2])
|
||||
|
||||
model = ov.convert_model(
|
||||
f"{MODEL_DIR}/frozen_inference_graph.pb",
|
||||
input=[("image_tensor:0", INPUT_SHAPE)],
|
||||
"/models/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb",
|
||||
input=[("image_tensor:0", [1, 300, 300, 3])],
|
||||
)
|
||||
|
||||
nodes = {op.get_friendly_name(): op for op in model.get_ordered_ops()}
|
||||
parameter = model.get_parameters()[0]
|
||||
# rows of (image_id, class_id, score, xmin, ymin, xmax, ymax)
|
||||
boxes = model.output("detection_boxes:0").get_node().input_value(0)
|
||||
classes = model.output("detection_classes:0").get_node().input_value(0)
|
||||
scores = model.output("detection_scores:0").get_node().input_value(0)
|
||||
|
||||
preprocessor = nodes["Preprocessor/map/TensorArrayStack/TensorArrayGatherV3"]
|
||||
box_deltas = nodes["Postprocessor/Reshape_1"].output(0)
|
||||
class_scores = nodes["Postprocessor/convert_scores"].output(0)
|
||||
anchors_output = nodes["Postprocessor/Reshape"].output(0)
|
||||
# (ymin,xmin,ymax,xmax) -> (xmin,ymin,xmax,ymax)
|
||||
boxes = ops.gather(boxes, [1, 0, 3, 2], 2)
|
||||
classes = ops.unsqueeze(classes, 2)
|
||||
scores = ops.unsqueeze(scores, 2)
|
||||
image_id = ops.multiply(scores, np.float32(0.0))
|
||||
|
||||
# The anchors only depend on the static input shape, so fold them into a
|
||||
# constant and drop the generator subgraph with the rest of the postprocessor.
|
||||
probe = ov.Core().compile_model(
|
||||
ov.Model([anchors_output, preprocessor.output(0)], [parameter], "probe"), "CPU"
|
||||
)
|
||||
probe_input = np.random.default_rng(0).integers(0, 255, INPUT_SHAPE, dtype=np.uint8)
|
||||
anchors, resized = (out.copy() for out in probe([probe_input]).values())
|
||||
|
||||
assert np.allclose(resized, probe_input, atol=1e-3), (
|
||||
"preprocessor is not an identity at 300x300, it cannot be bypassed"
|
||||
)
|
||||
|
||||
image = ops.convert(parameter, "f32")
|
||||
|
||||
for consumer in list(preprocessor.output(0).get_target_inputs()):
|
||||
consumer.replace_source_output(image.output(0))
|
||||
|
||||
# (ymin, xmin, ymax, xmax) -> (xmin, ymin, xmax, ymax)
|
||||
priors = anchors[:, [1, 0, 3, 2]].astype(np.float32).reshape(-1)
|
||||
variances = np.tile(BOX_VARIANCES, len(anchors))
|
||||
proposals = ops.constant(np.stack([priors, variances])[np.newaxis])
|
||||
|
||||
# (ty, tx, th, tw) -> (dx, dy, dw, dh) for the CENTER_SIZE decode
|
||||
box_logits = ops.reshape(ops.gather(box_deltas, [1, 0, 3, 2], 1), [1, -1], False)
|
||||
class_preds = ops.reshape(class_scores, [1, -1], False)
|
||||
|
||||
detections = ops.detection_output(
|
||||
box_logits,
|
||||
class_preds,
|
||||
proposals,
|
||||
{
|
||||
"background_label_id": 0,
|
||||
"top_k": 100,
|
||||
"keep_top_k": [100],
|
||||
"nms_threshold": 0.6,
|
||||
"confidence_threshold": 0.3,
|
||||
"code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
|
||||
"share_location": True,
|
||||
"variance_encoded_in_target": False,
|
||||
"normalized": True,
|
||||
"clip_before_nms": False,
|
||||
"clip_after_nms": True,
|
||||
"decrease_label_id": False,
|
||||
},
|
||||
)
|
||||
detections = ops.concat([image_id, classes, scores, boxes], 2)
|
||||
detections = ops.unsqueeze(detections, 1)
|
||||
detections.output(0).get_tensor().set_names({"detection_out"})
|
||||
|
||||
model = ov.Model([detections], [parameter], "ssdlite_mobilenet_v2")
|
||||
model = ov.Model([detections], model.get_parameters(), "ssdlite_mobilenet_v2")
|
||||
|
||||
ppp = PrePostProcessor(model)
|
||||
ppp.input().tensor().set_layout(ov.Layout("NHWC"))
|
||||
ppp.input().preprocess().reverse_channels()
|
||||
model = ppp.build()
|
||||
|
||||
# Fail the build rather than silently ship the dynamically shaped graph again.
|
||||
op_types = [op.get_type_name() for op in model.get_ordered_ops()]
|
||||
assert op_types.count("DetectionOutput") == 1, "postprocessor was not fused"
|
||||
|
||||
for dynamic_op in ("NonMaxSuppression", "NonZero", "Loop", "TensorIterator"):
|
||||
assert dynamic_op not in op_types, f"{dynamic_op} left in the graph"
|
||||
|
||||
output_shape = model.outputs[0].get_partial_shape()
|
||||
assert output_shape.is_static and list(output_shape) == [1, 1, 100, 7], (
|
||||
f"unexpected detector output shape {output_shape}"
|
||||
)
|
||||
|
||||
ov.save_model(model, OUTPUT_PATH, compress_to_fp16=True)
|
||||
ov.save_model(model, "/models/ssdlite_mobilenet_v2.xml", compress_to_fp16=True)
|
||||
|
||||
@@ -79,5 +79,7 @@ sherpa-onnx==1.12.*
|
||||
faster-whisper==1.1.*
|
||||
librosa==0.11.*
|
||||
soundfile==0.13.*
|
||||
# DeGirum detector
|
||||
degirum == 0.16.*
|
||||
# Memory profiling
|
||||
memray == 1.15.*
|
||||
|
||||
@@ -1269,3 +1269,78 @@ axengine:
|
||||
input_dtype: int
|
||||
input_pixel_format: bgr
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
degirumAiServer:
|
||||
title: DeGirum AI Server
|
||||
models:
|
||||
- key: ai-server-inference
|
||||
label: AI Server Inference
|
||||
recommended: true
|
||||
download: |-
|
||||
Launch a DeGirum AI server as a Docker container, then point the detector at it. Add this to your `docker-compose.yml`:
|
||||
|
||||
```yaml
|
||||
degirum_detector:
|
||||
container_name: degirum
|
||||
image: degirum/aiserver:latest
|
||||
privileged: true
|
||||
ports:
|
||||
- "8778:8778"
|
||||
```
|
||||
|
||||
Set `location` to the server's service name, container name, or `host:port`.
|
||||
ui: |
|
||||
Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
|
||||
|
||||
| Field | Value |
|
||||
| --- | --- |
|
||||
| **Location** | `degirum` |
|
||||
| **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
|
||||
degirumLocal:
|
||||
title: DeGirum Local
|
||||
models:
|
||||
- key: local-inference
|
||||
label: Local Inference
|
||||
recommended: true
|
||||
download: Run hardware directly inside the Frigate container with `@local`, removing the AI server hop. The matching device runtime (e.g. the Hailo runtime) must be installed in the container; confirm it with `degirum sys-info`.
|
||||
ui: |
|
||||
Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
|
||||
|
||||
| Field | Value |
|
||||
| --- | --- |
|
||||
| **Location** | `@local` |
|
||||
| **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
|
||||
degirumCloud:
|
||||
title: DeGirum AI Hub Cloud
|
||||
models:
|
||||
- key: ai-hub-cloud-inference
|
||||
label: AI Hub Cloud Inference
|
||||
recommended: true
|
||||
download: Run inferences on DeGirum's [AI Hub](https://hub.degirum.com) cloud with `@cloud`. Sign up, create an access token, and set it as `token`. Network latency may require lowering your detection fps.
|
||||
ui: |
|
||||
Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
|
||||
|
||||
| Field | Value |
|
||||
| --- | --- |
|
||||
| **Location** | `@cloud` |
|
||||
| **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
|
||||
|
||||
@@ -981,9 +981,7 @@ cameras:
|
||||
# Optional: Adjust sort order of cameras in the UI. Larger numbers come later (default: shown below)
|
||||
# By default the cameras are sorted alphabetically.
|
||||
order: 0
|
||||
# Optional: Whether or not to show the camera on the default All Cameras live dashboard.
|
||||
# The camera is still available everywhere else, including camera groups and settings
|
||||
# (default: shown below)
|
||||
# Optional: Whether or not to show the camera in the Frigate UI (default: shown below)
|
||||
dashboard: True
|
||||
# Optional: Whether this camera is visible in review (the review page and its camera
|
||||
# filter, motion review, and the history view) (default: shown below)
|
||||
|
||||
@@ -6,7 +6,6 @@ title: Configuring Generative AI
|
||||
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";
|
||||
|
||||
## Configuration
|
||||
|
||||
@@ -14,18 +13,6 @@ A Generative AI provider can be configured in the global config, which will make
|
||||
|
||||
`genai` is a map of named providers. Each key under `genai` is a name you choose, and its value is that provider's settings:
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
|
||||
- Click **Add** and enter a **Provider name**. Any name of letters, numbers, hyphens, and underscores is accepted, but it cannot be changed from the UI after the provider is created.
|
||||
- Set **Provider** to the service you are using (e.g., `ollama`)
|
||||
- Set **Base URL**, **API key**, and **Model** as required by that provider
|
||||
- Set **Roles** to the roles this provider should handle.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
genai:
|
||||
my_provider: # any name you like
|
||||
@@ -38,9 +25,6 @@ genai:
|
||||
- chat
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
The examples on this page all use `my_provider`, but the name is arbitrary and is only used to reference the provider elsewhere in the config (for example, `semantic_search.model`).
|
||||
|
||||
Each provider handles one or more **roles**: `chat`, `descriptions`, and `embeddings`. A provider handles all three by default, and each role may be assigned to exactly one provider. Define a single provider if you want it to do everything, or split the roles across several providers using the `roles` option.
|
||||
@@ -59,20 +43,15 @@ Running Generative AI models on CPU is not recommended, as high inference times
|
||||
|
||||
### Recommended Local Models
|
||||
|
||||
You must use a vision-capable model with Frigate. The following models are recommended for local deployment of the `descriptions` and `chat` roles:
|
||||
You must use a vision-capable model with Frigate. The following models are recommended for local deployment:
|
||||
|
||||
| Model | Notes |
|
||||
| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `qwen3-vl` | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object. |
|
||||
| `qwen3.6` | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
|
||||
| `qwen3.5` | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
|
||||
| `qwen3.6` | Strong situational understanding, similar to qwen3-vl |
|
||||
| `gemma4` | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action. |
|
||||
|
||||
The `embeddings` role needs a different kind of model. Text queries are matched against the stored image embeddings, so the model must be trained to place images and text into the same vector space. A chat or description model will still return vectors when asked, but those vectors are not trained for retrieval and text searches will return poor matches with no error to indicate why.
|
||||
|
||||
| Model | Notes |
|
||||
| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `qwen3-vl-embedding` | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Must be served by llama.cpp started with `--embeddings` and `--mmproj`. |
|
||||
|
||||
:::info
|
||||
|
||||
Each model is available in multiple parameter sizes (3b, 4b, 8b, etc.). Larger sizes are more capable of complex tasks and understanding of situations, but requires more memory and computational resources. It is recommended to try multiple models and experiment to see which performs best.
|
||||
@@ -437,82 +416,3 @@ genai:
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
## FAQ
|
||||
|
||||
<FaqItem id="how-do-i-debug-genai-issues" question="How do I debug GenAI issues?">
|
||||
|
||||
Frigate's Generative AI features are configured and enabled separately. [Review descriptions and summaries](/configuration/genai/genai_review) live under `review.genai`, and [object descriptions](/configuration/genai/genai_objects) live under `objects.genai`. Configuring a provider on this page does not enable either feature, and enabling one does not enable the other. Decide which of the two is not working, then work through the steps below.
|
||||
|
||||
1. Confirm a provider is available and holds the `descriptions` role.
|
||||
- Review descriptions, review summaries, and object descriptions all use the provider that has the `descriptions` role assigned in <NavPath path="Settings > Enrichments > Generative AI > Roles" /> (`genai.<provider>.roles`).
|
||||
- A provider is contacted the first time one of its roles is actually used. A provider holding the `embeddings` role for semantic search is initialized during startup, while a `descriptions` provider is not initialized until the first description is requested, which may be well after boot.
|
||||
- In <NavPath path="Settings > Enrichments > Generative AI" />, use **Refresh models** next to the model field. It queries the provider for its model list and is a quick way to verify that the base URL, API key, and network path between Frigate and your provider are correct.
|
||||
|
||||
2. Confirm the feature you expect is actually enabled.
|
||||
- Object descriptions are disabled by default. Turn on <NavPath path="Settings > Global configuration > Objects > GenAI object config > Enable GenAI" /> (`objects.genai.enabled`), either globally or per camera. This is the most common reason custom prompts appear to be ignored while review summaries are still being generated.
|
||||
- Review descriptions are disabled by default. Turn on <NavPath path="Settings > Global configuration > Review > GenAI config > Enable GenAI descriptions" /> (`review.genai.enabled`). Once enabled, alerts are described by default but detections are not, so a detection-only review item will never get a summary unless **Enable GenAI for detections** (`review.genai.detections`) is also on.
|
||||
|
||||
3. If object descriptions are never requested, check the filters that skip generation.
|
||||
- <NavPath path="Settings > Global configuration > Objects > GenAI object config > GenAI objects" /> (`objects.genai.objects`) limits generation to specific labels, and **Required zones** (`objects.genai.required_zones`) requires the object to have entered one of those zones. If either is set and does not match, Frigate skips the request silently.
|
||||
- Thumbnails are only collected while an object is moving. Objects that go stationary early contribute fewer frames.
|
||||
- **Use snapshots** (`objects.genai.use_snapshot`) requires snapshots to be enabled for the camera. If the snapshot cannot be read, Frigate logs `Cannot load snapshot for <id>, file not found` and no description is generated.
|
||||
- **Send on end** (`objects.genai.send_triggers.tracked_object_end`) is on by default. If you have turned it off in favor of **Early GenAI trigger** (`objects.genai.send_triggers.after_significant_updates`), descriptions are only requested once that number of updates is reached.
|
||||
|
||||
4. Enable debug logs to see exactly what Frigate is doing. Restart Frigate after this change. The next step also requires a restart, so turn both on at the same time to avoid restarting twice.
|
||||
|
||||
```yaml
|
||||
logger:
|
||||
default: info
|
||||
logs:
|
||||
# highlight-start
|
||||
frigate.genai: debug
|
||||
frigate.data_processing.post.object_descriptions: debug
|
||||
frigate.data_processing.post.review_descriptions: debug
|
||||
# highlight-end
|
||||
```
|
||||
|
||||
5. Save the exact images and prompts that were sent to your provider.
|
||||
- Turn on **Save thumbnails** for the feature you are debugging (`review.genai.debug_save_thumbnails` or `objects.genai.debug_save_thumbnails`). Both features write to `/media/frigate/clips/genai-requests/`, and these files are admin-only.
|
||||
- Review descriptions write `genai-requests/<review_id>/` containing the numbered frames that were sent, plus `prompt.txt` and `response.txt` with the exact prompt and the raw, unparsed model response.
|
||||
- Review summary reports write `genai-requests/<start_ts>-<end_ts>/prompt.txt` and `response.txt`. No images are involved, since a report summarizes existing review descriptions.
|
||||
- Object descriptions write `genai-requests/<event_id>/` containing the numbered thumbnails. The prompt for object descriptions is not written to a file, it is only visible in the debug logs from step 4.
|
||||
- Look at the saved images before blaming the model. If the object is small, blurry, or out of frame, no prompt will fix the result. For object descriptions, consider turning on **Use snapshots** (`objects.genai.use_snapshot`) to send a higher quality image. For review items, consider setting **Review image source** (`review.genai.image_source`) to `recordings` for 480p frames instead of the lower resolution preview frames.
|
||||
|
||||
<ConfigTabs>
|
||||
<TabItem value="ui">
|
||||
|
||||
For review descriptions, navigate to <NavPath path="Settings > Global configuration > Review" /> and set **GenAI config > Save thumbnails** to on.
|
||||
|
||||
For object descriptions, navigate to <NavPath path="Settings > Global configuration > Objects" />, expand **GenAI object config**, and set **Save thumbnails** to on.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
review:
|
||||
genai:
|
||||
enabled: true
|
||||
# highlight-next-line
|
||||
debug_save_thumbnails: true
|
||||
|
||||
objects:
|
||||
genai:
|
||||
enabled: true
|
||||
# highlight-next-line
|
||||
debug_save_thumbnails: true
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</ConfigTabs>
|
||||
|
||||
6. Verify the prompt is what you think it is.
|
||||
- Object description prompts are the ones you control directly. A camera-level <NavPath path="Settings > Camera configuration > Objects > GenAI object config > Caption prompt" /> (`objects.genai.prompt`) overrides the global one, and an entry in **Object prompts** (`objects.genai.object_prompts`) for a label overrides both for that label. Only `{label}`, `{sub_label}`, and `{camera}` are substituted.
|
||||
- Review description prompts are built by Frigate and request a structured JSON response, so they are not fully replaceable. The parts you control are <NavPath path="Settings > Global configuration > Review > GenAI config > Activity context prompt" /> (`review.genai.activity_context_prompt`) and **Additional concerns** (`review.genai.additional_concerns`). Keep the activity context prompt general, since overly specific rules will sway the model's threat level scoring.
|
||||
|
||||
7. If descriptions are generated but the results are poor or inconsistent, look at the model and the context window.
|
||||
- Empty fields, missing `shortSummary` values, or `Failed to parse review description` errors usually mean the model is not following the requested JSON schema. Smaller models struggle with structured output. Try a larger parameter size or one of the [recommended models](#recommended-local-models).
|
||||
- Frigate calculates how many frames to send from the context size the provider reports. If your server reports a different value than it is actually running with, frames will be truncated or the request will fail. Pin the value by adding `context_size` under <NavPath path="Settings > Enrichments > Generative AI > Provider options" /> (`genai.<provider>.provider_options`), and for Ollama also confirm `options.num_ctx` there matches the context you have configured.
|
||||
- Check **Review Description Speed** and **Object Description Speed** in <NavPath path="System metrics > Enrichments" />. If inference takes tens of seconds, requests will queue behind each other and descriptions will appear to stop. For Ollama, review `OLLAMA_NUM_PARALLEL`, `OLLAMA_MAX_QUEUE`, and `OLLAMA_MAX_LOADED_MODELS` so that concurrent requests from Frigate are handled the way you expect.
|
||||
|
||||
</FaqItem>
|
||||
|
||||
@@ -113,7 +113,3 @@ Many providers also have a public facing chat interface for their models. Downlo
|
||||
- OpenAI - [ChatGPT](https://chatgpt.com)
|
||||
- Gemini - [Google AI Studio](https://aistudio.google.com)
|
||||
- Ollama - [Open WebUI](https://docs.openwebui.com/)
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
If descriptions are not being generated, or the generated descriptions are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).
|
||||
|
||||
@@ -201,7 +201,3 @@ Along with individual review item summaries, Generative AI can also produce a si
|
||||
Review reports can be requested via the [API](/integrations/api/generate-review-summary-review-summarize-start-start-ts-end-end-ts-post) by sending a POST request to `/api/review/summarize/start/{start_ts}/end/{end_ts}` with Unix timestamps.
|
||||
|
||||
For Home Assistant users, there is a built-in service (`frigate.review_summarize`) that makes it easy to request review reports as part of automations or scripts. This allows you to automatically generate daily summaries, vacation reports, or custom time period reports based on your specific needs.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
If summaries are not being generated, or the generated summaries are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).
|
||||
|
||||
@@ -334,7 +334,7 @@ When your browser runs into problems playing back your camera streams, it will l
|
||||
|
||||
- **stalled**
|
||||
- What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
|
||||
- What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in <NavPath path="Settings > UI" /> .
|
||||
- What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
|
||||
|
||||
- Possible console messages from the player code:
|
||||
- `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`
|
||||
|
||||
@@ -24,6 +24,7 @@ Frigate supports multiple different detectors that work on different types of ha
|
||||
- [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB, Mini PCIe, and m.2 formats allowing for a wide range of compatibility with devices.
|
||||
- [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
|
||||
- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
|
||||
- <CommunityBadge /> [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
|
||||
|
||||
**AMD**
|
||||
|
||||
@@ -754,6 +755,87 @@ Explanation of the parameters:
|
||||
- **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
|
||||
- `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.2_EN.pdf).
|
||||
|
||||
## DeGirum
|
||||
|
||||
DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector _cannot_ be used for commercial purposes.
|
||||
|
||||
### Configuration {#configuration-degirum}
|
||||
|
||||
#### AI Server Inference
|
||||
|
||||
Before starting with the config file for this section, you must first launch an AI server. DeGirum has an AI server ready to use as a docker container. Add this to your `docker-compose.yml` to get started:
|
||||
|
||||
```yaml
|
||||
degirum_detector:
|
||||
container_name: degirum
|
||||
image: degirum/aiserver:latest
|
||||
privileged: true
|
||||
ports:
|
||||
- "8778:8778"
|
||||
```
|
||||
|
||||
All supported hardware will automatically be found on your AI server host as long as relevant runtimes and drivers are properly installed on your machine. Refer to [DeGirum's docs site](https://docs.degirum.com/pysdk/runtimes-and-drivers) if you have any trouble.
|
||||
|
||||
Once completed, configure the detector as follows:
|
||||
|
||||
<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumAiServer.models} />
|
||||
|
||||
Setting up a model in the `config.yml` is similar to setting up an AI server.
|
||||
You can set it to:
|
||||
|
||||
- A model listed on the [AI Hub](https://hub.degirum.com), given that the correct zoo name is listed in your detector
|
||||
- If this is what you choose to do, the correct model will be downloaded onto your machine before running.
|
||||
- A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
|
||||
- A path to some model.json.
|
||||
|
||||
```yaml
|
||||
model:
|
||||
path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
|
||||
width: 300 # width is in the model name as the first number in the "int"x"int" section
|
||||
height: 300 # height is in the model name as the second number in the "int"x"int" section
|
||||
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
|
||||
```
|
||||
|
||||
#### Local Inference
|
||||
|
||||
It is also possible to eliminate the need for an AI server and run the hardware directly. The benefit of this approach is that you eliminate any bottlenecks that occur when transferring prediction results from the AI server docker container to the frigate one. However, the method of implementing local inference is different for every device and hardware combination, so it's usually more trouble than it's worth. A general guideline to achieve this would be:
|
||||
|
||||
1. Ensuring that the frigate docker container has the runtime you want to use. So for instance, running `@local` for Hailo means making sure the container you're using has the Hailo runtime installed.
|
||||
2. To double check the runtime is detected by the DeGirum detector, make sure the `degirum sys-info` command properly shows whatever runtimes you mean to install.
|
||||
3. Create a DeGirum detector in your configuration.
|
||||
|
||||
<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumLocal.models} />
|
||||
|
||||
Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
|
||||
|
||||
```yaml
|
||||
model:
|
||||
path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300 # width is in the model name as the first number in the "int"x"int" section
|
||||
height: 300 # height is in the model name as the second number in the "int"x"int" section
|
||||
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
|
||||
```
|
||||
|
||||
#### AI Hub Cloud Inference
|
||||
|
||||
If you do not possess whatever hardware you want to run, there's also the option to run cloud inferences. Do note that your detection fps might need to be lowered as network latency does significantly slow down this method of detection. For use with Frigate, we highly recommend using a local AI server as described above. To set up cloud inferences,
|
||||
|
||||
1. Sign up at [DeGirum's AI Hub](https://hub.degirum.com).
|
||||
2. Get an access token.
|
||||
3. Create a DeGirum detector in your configuration.
|
||||
|
||||
<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumCloud.models} />
|
||||
|
||||
Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
|
||||
|
||||
```yaml
|
||||
model:
|
||||
path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300 # width is in the model name as the first number in the "int"x"int" section
|
||||
height: 300 # height is in the model name as the second number in the "int"x"int" section
|
||||
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
|
||||
```
|
||||
|
||||
## AXERA
|
||||
|
||||
Hardware accelerated object detection is supported on the following SoCs:
|
||||
|
||||
@@ -34,15 +34,11 @@ The detect FFmpeg process exited on its own. This message is only the notificati
|
||||
|
||||
</FaqItem>
|
||||
|
||||
<FaqItem id="non-monotonically-increasing-dts" question="Non-monotonic DTS / non monotonically increasing dts to muxer / Queue input is backward in time">
|
||||
<FaqItem id="non-monotonically-increasing-dts" question="Application provided invalid, non monotonically increasing dts to muxer">
|
||||
|
||||
These are FFmpeg messages indicating the camera sent packets with out-of-order timestamps, either on the video or the audio stream. Timestamp jitter like this is common with WiFi cameras and restreamed or proxied sources; other causes are a camera "Smart Codec" / H.264+ / H.265+ mode or a camera clock that jumps. A sustained flood of these messages usually precedes the stream stalling and the watchdog restarting FFmpeg.
|
||||
An FFmpeg message meaning the camera sent packets with out-of-order timestamps. Because recordings are copied without re-encoding, FFmpeg cannot fix them, and the segment muxer often splits early, producing one-second segments and a cache backlog. The usual cause is a camera "Smart Codec" / H.264+ / H.265+ mode or a camera clock that jumps.
|
||||
|
||||
In most cases, the fix is to improve the network, reduce system resource usage, or switch to non-WiFi cameras. In general, WiFi cameras are [not recommended](https://ipcamtalk.com/threads/multiple-cameras-high-bandwidth.77100/#post-861110).
|
||||
|
||||
On the video stream, this can affect recordings: because they are copied without re-encoding, FFmpeg cannot fix the timestamps, and the segment muxer often splits early, producing one-second segments and a cache backlog. See [Recordings: segments are only 1 second long](/troubleshooting/recordings#segments-are-only-1-second-long).
|
||||
|
||||
On the audio stream, the messages can come from the output's audio encoding. If the audio stream is the problem, it may help to have go2rtc transcode it by adding `#audio=aac` to the camera's go2rtc stream to produce clean timestamps for everything consuming the restream.
|
||||
See [Recordings: segments are only 1 second long](/troubleshooting/recordings#segments-are-only-1-second-long).
|
||||
|
||||
</FaqItem>
|
||||
|
||||
|
||||
@@ -39,7 +39,7 @@ The per-clip variation is typically quite low and is mostly an artifact of keyfr
|
||||
|
||||
Debug Replay lets you re-run Frigate's detection pipeline against a section of recorded video without manually configuring a dummy camera. It automatically extracts the recording, creates a temporary camera with the same detection settings as the original, and loops the clip through the pipeline so you can observe detections in real time.
|
||||
|
||||
The replay camera behaves like a live camera feed rather than History's video player: it loops the clip continuously as Frigate analyzes it and has no playback controls, so you cannot pause, scrub, or step through it frame by frame. The Debug Replay camera does not save recordings or snapshots or surface anything in Explore, but it otherwise behaves like a regular camera, including running enrichments such as Face Recognition, LPR, and custom classification.
|
||||
The replay camera behaves like a live camera feed rather than History's video player: it loops the clip continuously as Frigate analyzes it and has no playback controls, so you cannot pause, scrub, or step through it frame by frame.
|
||||
|
||||
Debug Replay isn't intended to be a one-stop pane for all Frigate diagnostics or a comprehensive debugging environment for every Frigate feature. It merely makes it easier to spin up a "dummy camera" and perform some common adjustments in real time. You'll still need to use the normal tools (logs, an MQTT client, etc) to debug your feature.
|
||||
|
||||
|
||||
Generated
+30
-13
@@ -19,6 +19,7 @@
|
||||
"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",
|
||||
@@ -6729,9 +6730,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/body-parser": {
|
||||
"version": "1.20.4",
|
||||
"resolved": "https://registry.npmjs.org/body-parser/-/body-parser-1.20.4.tgz",
|
||||
"integrity": "sha512-ZTgYYLMOXY9qKU/57FAo8F+HA2dGX7bqGc71txDRC1rS4frdFI5R7NhluHxH6M0YItAP0sHB4uqAOcYKxO6uGA==",
|
||||
"version": "1.20.6",
|
||||
"resolved": "https://registry.npmjs.org/body-parser/-/body-parser-1.20.6.tgz",
|
||||
"integrity": "sha512-p5tAzS57i5MV9fZFDj9LeIiTZEufbSe2eDozP+ElheSUq1m74CRq1jI4mYNDdVs9vQztXFLuk/Gd6BWTdwRJ5g==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"bytes": "~3.1.2",
|
||||
@@ -6742,7 +6743,7 @@
|
||||
"http-errors": "~2.0.1",
|
||||
"iconv-lite": "~0.4.24",
|
||||
"on-finished": "~2.4.1",
|
||||
"qs": "~6.14.0",
|
||||
"qs": "~6.15.1",
|
||||
"raw-body": "~2.5.3",
|
||||
"type-is": "~1.6.18",
|
||||
"unpipe": "~1.0.0"
|
||||
@@ -6788,6 +6789,22 @@
|
||||
"integrity": "sha512-Tpp60P6IUJDTuOq/5Z8cdskzJujfwqfOTkrwIwj7IRISpnkJnT6SyJ4PCPnGMoFjC9ddhal5KVIYtAt97ix05A==",
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/body-parser/node_modules/qs": {
|
||||
"version": "6.15.3",
|
||||
"resolved": "https://registry.npmjs.org/qs/-/qs-6.15.3.tgz",
|
||||
"integrity": "sha512-O9gl3zCl5h5blw1KGUzQKhA5oUXSl8rwUIM5o0S3nCXMliSvy5Dzx7/DJcI+SwgICv+IneSZwhBh1oSyEHA71A==",
|
||||
"license": "BSD-3-Clause",
|
||||
"dependencies": {
|
||||
"es-define-property": "^1.0.1",
|
||||
"side-channel": "^1.1.1"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=0.6"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://github.com/sponsors/ljharb"
|
||||
}
|
||||
},
|
||||
"node_modules/bonjour-service": {
|
||||
"version": "1.3.0",
|
||||
"resolved": "https://registry.npmjs.org/bonjour-service/-/bonjour-service-1.3.0.tgz",
|
||||
@@ -20985,14 +21002,14 @@
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/side-channel": {
|
||||
"version": "1.1.0",
|
||||
"resolved": "https://registry.npmjs.org/side-channel/-/side-channel-1.1.0.tgz",
|
||||
"integrity": "sha512-ZX99e6tRweoUXqR+VBrslhda51Nh5MTQwou5tnUDgbtyM0dBgmhEDtWGP/xbKn6hqfPRHujUNwz5fy/wbbhnpw==",
|
||||
"version": "1.1.1",
|
||||
"resolved": "https://registry.npmjs.org/side-channel/-/side-channel-1.1.1.tgz",
|
||||
"integrity": "sha512-6x6dK6zJdpTzF4sQeNYxwtvBzf6Eg4GtlesS94HOvTudUeyK2WXAaIfmDgsyslYrRBeFIlsi54AYsFGUuhmvrQ==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"es-errors": "^1.3.0",
|
||||
"object-inspect": "^1.13.3",
|
||||
"side-channel-list": "^1.0.0",
|
||||
"object-inspect": "^1.13.4",
|
||||
"side-channel-list": "^1.0.1",
|
||||
"side-channel-map": "^1.0.1",
|
||||
"side-channel-weakmap": "^1.0.2"
|
||||
},
|
||||
@@ -21004,13 +21021,13 @@
|
||||
}
|
||||
},
|
||||
"node_modules/side-channel-list": {
|
||||
"version": "1.0.0",
|
||||
"resolved": "https://registry.npmjs.org/side-channel-list/-/side-channel-list-1.0.0.tgz",
|
||||
"integrity": "sha512-FCLHtRD/gnpCiCHEiJLOwdmFP+wzCmDEkc9y7NsYxeF4u7Btsn1ZuwgwJGxImImHicJArLP4R0yX4c2KCrMrTA==",
|
||||
"version": "1.0.1",
|
||||
"resolved": "https://registry.npmjs.org/side-channel-list/-/side-channel-list-1.0.1.tgz",
|
||||
"integrity": "sha512-mjn/0bi/oUURjc5Xl7IaWi/OJJJumuoJFQJfDDyO46+hBWsfaVM65TBHq2eoZBhzl9EchxOijpkbRC8SVBQU0w==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"es-errors": "^1.3.0",
|
||||
"object-inspect": "^1.13.3"
|
||||
"object-inspect": "^1.13.4"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">= 0.4"
|
||||
|
||||
+1
-15
@@ -31,10 +31,7 @@ from frigate.api.auth import (
|
||||
get_allowed_cameras_for_filter,
|
||||
require_role,
|
||||
)
|
||||
from frigate.api.config_util import (
|
||||
publish_camera_section_updates,
|
||||
swap_runtime_config,
|
||||
)
|
||||
from frigate.api.config_util import swap_runtime_config
|
||||
from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters
|
||||
from frigate.api.defs.request.app_body import (
|
||||
AppConfigSetBody,
|
||||
@@ -966,17 +963,6 @@ def config_set(request: Request, body: AppConfigSetBody):
|
||||
body.update_topic, settings
|
||||
)
|
||||
|
||||
# a config/cameras/* topic publishes camera copies, a
|
||||
# global topic the global object. FrigateConfig.parse
|
||||
# folds some global sections down into every camera,
|
||||
# and workers read both objects, so any such section
|
||||
# needs its camera copies sent alongside the global
|
||||
# publish above.
|
||||
if body.update_topic == "config/birdseye":
|
||||
publish_camera_section_updates(
|
||||
request.app, config, CameraConfigUpdateEnum.birdseye
|
||||
)
|
||||
|
||||
return JSONResponse(
|
||||
content=(
|
||||
{
|
||||
|
||||
@@ -3,30 +3,6 @@
|
||||
from fastapi import FastAPI
|
||||
|
||||
from frigate.config import FrigateConfig
|
||||
from frigate.config.camera.updater import (
|
||||
CameraConfigUpdateEnum,
|
||||
CameraConfigUpdateTopic,
|
||||
)
|
||||
|
||||
|
||||
def publish_camera_section_updates(
|
||||
app: FastAPI, config: FrigateConfig, update_type: CameraConfigUpdateEnum
|
||||
) -> None:
|
||||
"""Broadcast every camera's re-resolved value for a global section.
|
||||
|
||||
Global sections are folded into each camera at parse time and the camera
|
||||
copies are what workers read, so send them rather than leave a worker to
|
||||
guess which cameras were inheriting.
|
||||
"""
|
||||
for camera_name, camera_config in config.cameras.items():
|
||||
settings = getattr(camera_config, update_type.name, None)
|
||||
|
||||
if settings is None:
|
||||
continue
|
||||
|
||||
app.config_publisher.publish_update(
|
||||
CameraConfigUpdateTopic(update_type, camera_name), settings
|
||||
)
|
||||
|
||||
|
||||
def swap_runtime_config(app: FastAPI, config: FrigateConfig) -> None:
|
||||
@@ -40,10 +16,6 @@ def swap_runtime_config(app: FastAPI, config: FrigateConfig) -> None:
|
||||
camera the user turned off would silently come back on.
|
||||
"""
|
||||
app.frigate_config = config
|
||||
|
||||
if app.config_holder is not None:
|
||||
app.config_holder.set(config)
|
||||
|
||||
app.genai_manager.update_config(config)
|
||||
|
||||
if app.profile_manager is not None:
|
||||
|
||||
@@ -35,7 +35,6 @@ from frigate.comms.event_metadata_updater import (
|
||||
)
|
||||
from frigate.config import FrigateConfig
|
||||
from frigate.config.camera.updater import CameraConfigUpdatePublisher
|
||||
from frigate.config.holder import ConfigHolder
|
||||
from frigate.config.profile_manager import ProfileManager
|
||||
from frigate.debug_replay import DebugReplayManager, debug_replay_auto_stop_watchdog
|
||||
from frigate.embeddings import EmbeddingsContext
|
||||
@@ -75,7 +74,6 @@ def create_fastapi_app(
|
||||
dispatcher: Dispatcher | None = None,
|
||||
profile_manager: ProfileManager | None = None,
|
||||
enforce_default_admin: bool = True,
|
||||
config_holder: ConfigHolder | None = None,
|
||||
):
|
||||
logger.info("Starting FastAPI app")
|
||||
app = FastAPI(
|
||||
@@ -164,7 +162,6 @@ def create_fastapi_app(
|
||||
app.replay_manager = replay_manager
|
||||
app.dispatcher = dispatcher
|
||||
app.profile_manager = profile_manager
|
||||
app.config_holder = config_holder
|
||||
|
||||
if frigate_config.auth.enabled:
|
||||
secret = get_jwt_secret()
|
||||
|
||||
+1
-14
@@ -30,7 +30,6 @@ from frigate.comms.ws import WebSocketClient
|
||||
from frigate.comms.zmq_proxy import ZmqProxy
|
||||
from frigate.config.camera.updater import CameraConfigUpdatePublisher
|
||||
from frigate.config.config import FrigateConfig
|
||||
from frigate.config.holder import ConfigHolder
|
||||
from frigate.config.profile_manager import ProfileManager
|
||||
from frigate.const import (
|
||||
CACHE_DIR,
|
||||
@@ -123,18 +122,7 @@ class FrigateApp:
|
||||
self.processes: dict[str, int] = {}
|
||||
self.embeddings: EmbeddingsContext | None = None
|
||||
self.profile_manager: ProfileManager | None = None
|
||||
self.config_holder = ConfigHolder(config)
|
||||
|
||||
@property
|
||||
def config(self) -> FrigateConfig:
|
||||
"""The current config, not the one Frigate booted with.
|
||||
|
||||
Read through the holder so the deferred watchdog factories below build
|
||||
a replacement process from the config as it is now. There is no setter
|
||||
on purpose: a plain attribute would let a caller pin this back to a
|
||||
single object and reintroduce the staleness.
|
||||
"""
|
||||
return self.config_holder.config
|
||||
self.config = config
|
||||
|
||||
def ensure_dirs(self) -> None:
|
||||
dirs = [
|
||||
@@ -657,7 +645,6 @@ class FrigateApp:
|
||||
self.replay_manager,
|
||||
self.dispatcher,
|
||||
self.profile_manager,
|
||||
config_holder=self.config_holder,
|
||||
),
|
||||
host="127.0.0.1",
|
||||
port=5001,
|
||||
|
||||
@@ -13,8 +13,8 @@ class CameraUiConfig(FrigateBaseModel):
|
||||
)
|
||||
dashboard: bool = Field(
|
||||
default=True,
|
||||
title="Show on Live dashboard",
|
||||
description="Toggle whether this camera is visible on the default All Cameras live dashboard. The camera remains available everywhere else in the UI, including camera groups and settings.",
|
||||
title="Show in UI",
|
||||
description="Toggle whether this camera is visible everywhere in the Frigate UI. Disabling this will require manually editing the config to view this camera in the UI again.",
|
||||
)
|
||||
review: bool = Field(
|
||||
default=True,
|
||||
|
||||
@@ -1,34 +0,0 @@
|
||||
"""Shared handle on the config object that is current for this instance."""
|
||||
|
||||
from .config import FrigateConfig
|
||||
|
||||
__all__ = ["ConfigHolder"]
|
||||
|
||||
|
||||
class ConfigHolder:
|
||||
"""Indirection for the most recently parsed config.
|
||||
|
||||
/api/config/set re-parses yaml into a brand new FrigateConfig instead of
|
||||
mutating the old one, so any reference captured during startup goes stale
|
||||
the first time a user saves. Anything that has to build something after
|
||||
startup, most importantly the watchdog factories that rebuild a crashed
|
||||
process, must read through a holder rather than close over a config
|
||||
object, or the rebuilt process comes back with the config as it was at
|
||||
boot and silently discards every change made since.
|
||||
|
||||
There is deliberately no setter on the read side: the swap runs in exactly
|
||||
one place (frigate.api.config_util.swap_runtime_config) and everyone else
|
||||
only reads.
|
||||
"""
|
||||
|
||||
def __init__(self, config: FrigateConfig) -> None:
|
||||
self._config = config
|
||||
|
||||
@property
|
||||
def config(self) -> FrigateConfig:
|
||||
"""The config as of the most recent successful save."""
|
||||
return self._config
|
||||
|
||||
def set(self, config: FrigateConfig) -> None:
|
||||
"""Install a freshly parsed config as the current one."""
|
||||
self._config = config
|
||||
@@ -0,0 +1,157 @@
|
||||
import logging
|
||||
import queue
|
||||
from typing import Literal
|
||||
|
||||
import numpy as np
|
||||
from pydantic import ConfigDict, Field
|
||||
|
||||
from frigate.detectors.detection_api import DetectionApi
|
||||
from frigate.detectors.detector_config import BaseDetectorConfig
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
DETECTOR_KEY = "degirum"
|
||||
|
||||
|
||||
### DETECTOR CONFIG ###
|
||||
class DGDetectorConfig(BaseDetectorConfig):
|
||||
"""DeGirum detector for running models via DeGirum cloud or local inference services."""
|
||||
|
||||
model_config = ConfigDict(
|
||||
title="DeGirum",
|
||||
)
|
||||
|
||||
type: Literal[DETECTOR_KEY]
|
||||
location: str = Field(
|
||||
default=None,
|
||||
title="Inference Location",
|
||||
description="Location of the DeGirim inference engine (e.g. '@cloud', '127.0.0.1').",
|
||||
)
|
||||
zoo: str = Field(
|
||||
default=None,
|
||||
title="Model Zoo",
|
||||
description="Path or URL to the DeGirum model zoo.",
|
||||
)
|
||||
token: str = Field(
|
||||
default=None,
|
||||
title="DeGirum Cloud Token",
|
||||
description="Token for DeGirum Cloud access.",
|
||||
)
|
||||
|
||||
|
||||
### ACTUAL DETECTOR ###
|
||||
class DGDetector(DetectionApi):
|
||||
type_key = DETECTOR_KEY
|
||||
|
||||
def __init__(self, detector_config: DGDetectorConfig):
|
||||
try:
|
||||
import degirum as dg
|
||||
except ModuleNotFoundError:
|
||||
raise ImportError("Unable to import DeGirum detector.") from None
|
||||
|
||||
self._queue = queue.Queue()
|
||||
self._zoo = dg.connect(
|
||||
detector_config.location, detector_config.zoo, detector_config.token
|
||||
)
|
||||
|
||||
logger.debug(f"Models in zoo: {self._zoo.list_models()}")
|
||||
|
||||
self.dg_model = self._zoo.load_model(
|
||||
detector_config.model.path,
|
||||
)
|
||||
|
||||
# Setting input image format to raw reduces preprocessing time
|
||||
self.dg_model.input_image_format = "RAW"
|
||||
|
||||
# Prioritize the most powerful hardware available
|
||||
self.select_best_device_type()
|
||||
# Frigate handles pre processing as long as these are all set
|
||||
input_shape = self.dg_model.input_shape[0]
|
||||
self.model_height = input_shape[1]
|
||||
self.model_width = input_shape[2]
|
||||
|
||||
# Passing in dummy frame so initial connection latency happens in
|
||||
# init function and not during actual prediction
|
||||
frame = np.zeros(
|
||||
(detector_config.model.width, detector_config.model.height, 3),
|
||||
dtype=np.uint8,
|
||||
)
|
||||
# Pass in frame to overcome first frame latency
|
||||
self.dg_model(frame)
|
||||
self.prediction = self.prediction_generator()
|
||||
|
||||
def select_best_device_type(self):
|
||||
"""
|
||||
Helper function that selects fastest hardware available per model runtime
|
||||
"""
|
||||
types = self.dg_model.supported_device_types
|
||||
|
||||
device_map = {
|
||||
"OPENVINO": ["GPU", "NPU", "CPU"],
|
||||
"HAILORT": ["HAILO8L", "HAILO8"],
|
||||
"N2X": ["ORCA1", "CPU"],
|
||||
"ONNX": ["VITIS_NPU", "CPU"],
|
||||
"RKNN": ["RK3566", "RK3568", "RK3588"],
|
||||
"TENSORRT": ["DLA", "GPU", "DLA_ONLY"],
|
||||
"TFLITE": ["ARMNN", "EDGETPU", "CPU"],
|
||||
}
|
||||
|
||||
runtime = types[0].split("/")[0]
|
||||
# Just create an array of format {runtime}/{hardware} for every hardware
|
||||
# in the value for appropriate key in device_map
|
||||
self.dg_model.device_type = [
|
||||
f"{runtime}/{hardware}" for hardware in device_map[runtime]
|
||||
]
|
||||
|
||||
def prediction_generator(self):
|
||||
"""
|
||||
Generator for all incoming frames. By using this generator, we don't have to keep
|
||||
reconnecting our websocket on every "predict" call.
|
||||
"""
|
||||
logger.debug("Prediction generator was called")
|
||||
with self.dg_model as model:
|
||||
while 1:
|
||||
logger.info(f"q size before calling get: {self._queue.qsize()}")
|
||||
data = self._queue.get(block=True)
|
||||
logger.info(f"q size after calling get: {self._queue.qsize()}")
|
||||
logger.debug(
|
||||
f"Data we're passing into model predict: {data}, shape of data: {data.shape}"
|
||||
)
|
||||
result = model.predict(data)
|
||||
logger.debug(f"Prediction result: {result}")
|
||||
yield result
|
||||
|
||||
def detect_raw(self, tensor_input):
|
||||
# Reshaping tensor to work with pysdk
|
||||
truncated_input = tensor_input.reshape(tensor_input.shape[1:])
|
||||
logger.debug(f"Detect raw was called for tensor input: {tensor_input}")
|
||||
|
||||
# add tensor_input to input queue
|
||||
self._queue.put(truncated_input)
|
||||
logger.debug(f"Queue size after adding truncated input: {self._queue.qsize()}")
|
||||
|
||||
# define empty detection result
|
||||
detections = np.zeros((20, 6), np.float32)
|
||||
# grab prediction
|
||||
res = next(self.prediction)
|
||||
|
||||
# If we have an empty prediction, return immediately
|
||||
if len(res.results) == 0 or len(res.results[0]) == 0:
|
||||
return detections
|
||||
|
||||
i = 0
|
||||
for result in res.results:
|
||||
if i >= 20:
|
||||
break
|
||||
|
||||
detections[i] = [
|
||||
result["category_id"],
|
||||
float(result["score"]),
|
||||
result["bbox"][1] / self.model_height,
|
||||
result["bbox"][0] / self.model_width,
|
||||
result["bbox"][3] / self.model_height,
|
||||
result["bbox"][2] / self.model_width,
|
||||
]
|
||||
i += 1
|
||||
|
||||
logger.debug(f"Detections output: {detections}")
|
||||
return detections
|
||||
@@ -9,7 +9,6 @@ from pydantic import ConfigDict, Field
|
||||
|
||||
from frigate.detectors.detection_api import DetectionApi
|
||||
from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
|
||||
from frigate.util.model import xyxy_to_xywh_for_nms
|
||||
|
||||
try:
|
||||
from tflite_runtime.interpreter import Interpreter, load_delegate
|
||||
@@ -298,7 +297,7 @@ class EdgeTpuTfl(DetectionApi):
|
||||
# until after filtering out redundant boxes
|
||||
# Shift the logit scores to be non-negative (required by cv2)
|
||||
indices = cv2.dnn.NMSBoxes(
|
||||
bboxes=xyxy_to_xywh_for_nms(boxes_filtered_decoded),
|
||||
bboxes=boxes_filtered_decoded,
|
||||
scores=max_scores_filtered_shiftedpositive,
|
||||
score_threshold=(
|
||||
self.min_logit_value + self.logit_shift_to_positive_values
|
||||
|
||||
@@ -17,7 +17,6 @@ from frigate.detectors.detector_config import (
|
||||
ModelTypeEnum,
|
||||
)
|
||||
from frigate.util.file import FileLock
|
||||
from frigate.util.model import xyxy_to_xywh_for_nms
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -582,7 +581,7 @@ class MemryXDetector(DetectionApi):
|
||||
# Convert coordinates to integers
|
||||
x_min, y_min, x_max, y_max = map(int, [x_min, y_min, x_max, y_max])
|
||||
|
||||
# Append valid detections [class_id, confidence, x_min, y_min, x_max, y_max]
|
||||
# Append valid detections [class_id, confidence, x, y, width, height]
|
||||
detections.append([class_id, confidence, x_min, y_min, x_max, y_max])
|
||||
|
||||
final_detections = np.zeros((20, 6), np.float32)
|
||||
@@ -596,7 +595,7 @@ class MemryXDetector(DetectionApi):
|
||||
detections = np.array(detections, dtype=np.float32)
|
||||
|
||||
# Apply Non-Maximum Suppression (NMS)
|
||||
bboxes = xyxy_to_xywh_for_nms(detections[:, 2:6])
|
||||
bboxes = detections[:, 2:6].tolist() # (x_min, y_min, width, height)
|
||||
scores = detections[:, 1].tolist() # Confidence scores
|
||||
|
||||
indices = cv2.dnn.NMSBoxes(bboxes, scores, 0.45, 0.5)
|
||||
|
||||
@@ -226,12 +226,12 @@ class OvDetector(DetectionApi):
|
||||
|
||||
conf_mask = (image_pred[:, 4] * class_conf.squeeze() >= 0.3).squeeze()
|
||||
# Detections ordered as (x1, y1, x2, y2, obj_conf, class_conf, class_pred)
|
||||
predictions = np.concatenate(
|
||||
detections = np.concatenate(
|
||||
(image_pred[:, :5], class_conf, class_pred), axis=1
|
||||
)
|
||||
predictions = predictions[conf_mask]
|
||||
detections = detections[conf_mask]
|
||||
|
||||
ordered = predictions[predictions[:, 5].argsort()[::-1]][:20]
|
||||
ordered = detections[detections[:, 5].argsort()[::-1]][:20]
|
||||
|
||||
for i, object_detected in enumerate(ordered):
|
||||
detections[i] = self.process_yolo(
|
||||
|
||||
@@ -12,7 +12,7 @@ from frigate.const import MODEL_CACHE_DIR, SUPPORTED_RK_SOCS
|
||||
from frigate.detectors.detection_api import DetectionApi
|
||||
from frigate.detectors.detection_runners import RKNNModelRunner
|
||||
from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
|
||||
from frigate.util.model import post_process_yolo, xyxy_to_xywh_for_nms
|
||||
from frigate.util.model import post_process_yolo
|
||||
from frigate.util.rknn_converter import auto_convert_model
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -285,7 +285,7 @@ class Rknn(DetectionApi):
|
||||
|
||||
# run nms
|
||||
indices = cv2.dnn.NMSBoxes(
|
||||
bboxes=xyxy_to_xywh_for_nms(boxes),
|
||||
bboxes=boxes,
|
||||
scores=scores,
|
||||
score_threshold=0.4,
|
||||
nms_threshold=0.4,
|
||||
|
||||
@@ -23,7 +23,6 @@ from frigate.genai.prompts import (
|
||||
build_review_summary_prompt,
|
||||
)
|
||||
from frigate.models import Event
|
||||
from frigate.util.builtin import has_non_finite_number
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -165,15 +164,6 @@ class GenAIClient:
|
||||
except json.JSONDecodeError as je:
|
||||
logger.error("Failed to parse review description JSON: %s", je)
|
||||
return None
|
||||
|
||||
# model_construct skips validation, so non-finite numbers that
|
||||
# the validated path would have rejected have to be caught here
|
||||
if has_non_finite_number(raw):
|
||||
logger.error(
|
||||
"Discarding review description containing non-finite numbers."
|
||||
)
|
||||
return None
|
||||
|
||||
# observations and confidence are required on the model; fill an empty default
|
||||
# if the response omitted it so attribute access stays safe.
|
||||
raw.setdefault("observations", [])
|
||||
|
||||
@@ -178,10 +178,13 @@ class OutputProcess(FrigateProcess):
|
||||
)
|
||||
|
||||
if update_topic is not None and birdseye_config is not None:
|
||||
# only the global-only fields are applied here; the per-camera
|
||||
# enabled and mode arrive on config/cameras/<name>/birdseye,
|
||||
# already resolved against yaml by the config parse
|
||||
previous_global_mode = self.config.birdseye.mode
|
||||
self.config.birdseye = birdseye_config
|
||||
|
||||
for camera_config in self.config.cameras.values():
|
||||
if camera_config.birdseye.mode == previous_global_mode:
|
||||
camera_config.birdseye.mode = birdseye_config.mode
|
||||
|
||||
logger.debug("Applied dynamic birdseye config update")
|
||||
|
||||
# check if there is an updated config
|
||||
|
||||
@@ -13,7 +13,6 @@ from frigate.config.camera.updater import (
|
||||
CameraConfigUpdatePublisher,
|
||||
CameraConfigUpdateTopic,
|
||||
)
|
||||
from frigate.config.holder import ConfigHolder
|
||||
from frigate.models import Event, Recordings, ReviewSegment
|
||||
from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp
|
||||
|
||||
@@ -374,128 +373,6 @@ class TestConfigSetWildcardPropagation(BaseTestHttp):
|
||||
finally:
|
||||
os.unlink(config_path)
|
||||
|
||||
@patch("frigate.api.app.find_config_file")
|
||||
def test_global_birdseye_save_fans_out_resolved_camera_configs(
|
||||
self, mock_find_config
|
||||
):
|
||||
"""A global birdseye save must also publish the per-camera values.
|
||||
|
||||
Global birdseye only seeds enabled and mode; the camera copies are what
|
||||
the output process actually reads. Sending just the global object makes
|
||||
a worker guess which cameras were inheriting, and the only available
|
||||
guess (mode still equals the previous global) wrongly claims a camera
|
||||
whose explicit yaml mode happens to match.
|
||||
"""
|
||||
self.minimal_config["birdseye"] = {"enabled": True, "mode": "motion"}
|
||||
# explicit override that matches the global value being replaced
|
||||
self.minimal_config["cameras"]["front_door"]["birdseye"] = {"mode": "motion"}
|
||||
|
||||
config_path = self._write_config_file()
|
||||
mock_find_config.return_value = config_path
|
||||
|
||||
try:
|
||||
app, mock_publisher = self._create_app_with_publisher()
|
||||
with AuthTestClient(app) as client:
|
||||
resp = client.put(
|
||||
"/config/set",
|
||||
json={
|
||||
"config_data": {"birdseye": {"mode": "continuous"}},
|
||||
"update_topic": "config/birdseye",
|
||||
"requires_restart": 0,
|
||||
},
|
||||
)
|
||||
|
||||
self.assertEqual(resp.status_code, 200)
|
||||
|
||||
# the global object still goes out on its own topic
|
||||
mock_publisher.publisher.publish.assert_called_once()
|
||||
topic, settings = mock_publisher.publisher.publish.call_args[0]
|
||||
self.assertEqual(topic, "config/birdseye")
|
||||
self.assertEqual(settings.mode.value, "continuous")
|
||||
|
||||
published = {
|
||||
call[0][0].camera: call[0][1]
|
||||
for call in mock_publisher.publish_update.call_args_list
|
||||
}
|
||||
self.assertEqual(set(published), {"front_door", "back_yard"})
|
||||
|
||||
for call in mock_publisher.publish_update.call_args_list:
|
||||
self.assertEqual(
|
||||
call[0][0].update_type, CameraConfigUpdateEnum.birdseye
|
||||
)
|
||||
|
||||
# the override survives, the inheriting camera follows global
|
||||
self.assertEqual(published["front_door"].mode.value, "motion")
|
||||
self.assertEqual(published["back_yard"].mode.value, "continuous")
|
||||
finally:
|
||||
os.unlink(config_path)
|
||||
|
||||
@patch("frigate.api.app.find_config_file")
|
||||
def test_save_updates_the_config_holder(self, mock_find_config):
|
||||
"""A save must move the holder onto the freshly parsed config.
|
||||
|
||||
FrigateApp reads the holder when the watchdog rebuilds a crashed
|
||||
process; if the save leaves it on the boot config, that process comes
|
||||
back having lost every change made since Frigate started.
|
||||
"""
|
||||
from fastapi import Request
|
||||
|
||||
from frigate.api.auth import get_allowed_cameras_for_filter, get_current_user
|
||||
from frigate.api.fastapi_app import create_fastapi_app
|
||||
|
||||
config_path = self._write_config_file()
|
||||
mock_find_config.return_value = config_path
|
||||
|
||||
mock_publisher = Mock(spec=CameraConfigUpdatePublisher)
|
||||
mock_publisher.publisher = MagicMock()
|
||||
boot_config = FrigateConfig(**self.minimal_config)
|
||||
holder = ConfigHolder(boot_config)
|
||||
|
||||
try:
|
||||
app = create_fastapi_app(
|
||||
boot_config,
|
||||
self.db,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
mock_publisher,
|
||||
None,
|
||||
enforce_default_admin=False,
|
||||
config_holder=holder,
|
||||
)
|
||||
|
||||
async def mock_get_current_user(request: Request):
|
||||
return {"username": "admin", "role": "admin"}
|
||||
|
||||
async def mock_get_allowed_cameras_for_filter(request: Request):
|
||||
return list(self.minimal_config.get("cameras", {}).keys())
|
||||
|
||||
app.dependency_overrides[get_current_user] = mock_get_current_user
|
||||
app.dependency_overrides[get_allowed_cameras_for_filter] = (
|
||||
mock_get_allowed_cameras_for_filter
|
||||
)
|
||||
|
||||
with AuthTestClient(app) as client:
|
||||
resp = client.put(
|
||||
"/config/set",
|
||||
json={
|
||||
"config_data": {"birdseye": {"inactivity_threshold": 5}},
|
||||
"update_topic": "config/birdseye",
|
||||
"requires_restart": 0,
|
||||
},
|
||||
)
|
||||
|
||||
self.assertEqual(resp.status_code, 200)
|
||||
|
||||
self.assertIsNot(holder.config, boot_config)
|
||||
self.assertIs(holder.config, app.frigate_config)
|
||||
self.assertEqual(holder.config.birdseye.inactivity_threshold, 5)
|
||||
finally:
|
||||
os.unlink(config_path)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
@@ -4,7 +4,6 @@ import unittest
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
from frigate.api.config_util import swap_runtime_config
|
||||
from frigate.config.holder import ConfigHolder
|
||||
|
||||
|
||||
class TestSwapRuntimeConfig(unittest.TestCase):
|
||||
@@ -13,7 +12,6 @@ class TestSwapRuntimeConfig(unittest.TestCase):
|
||||
def _make_app(self) -> MagicMock:
|
||||
app = MagicMock()
|
||||
app.dispatcher.comms = [MagicMock(), MagicMock()]
|
||||
app.config_holder = ConfigHolder(MagicMock(name="boot_config"))
|
||||
return app
|
||||
|
||||
def test_rebinds_all_references(self) -> None:
|
||||
@@ -39,40 +37,11 @@ class TestSwapRuntimeConfig(unittest.TestCase):
|
||||
# the swap rebuilds cameras from yaml, so overrides must be re-layered
|
||||
app.dispatcher.reapply_runtime_state_to_config.assert_called_once_with()
|
||||
|
||||
def test_updates_the_config_holder(self) -> None:
|
||||
app = self._make_app()
|
||||
holder = app.config_holder
|
||||
config = MagicMock(name="new_config")
|
||||
|
||||
swap_runtime_config(app, config)
|
||||
|
||||
self.assertIs(holder.config, config)
|
||||
|
||||
def test_deferred_factory_builds_from_the_swapped_config(self) -> None:
|
||||
"""A watchdog-style factory must not rebuild from the boot config.
|
||||
|
||||
The factories in FrigateApp are lambdas evaluated when a process is
|
||||
restarted, long after a user may have saved. Reading through the
|
||||
holder is what keeps a rebuilt process from reverting every change
|
||||
made since Frigate started.
|
||||
"""
|
||||
app = self._make_app()
|
||||
holder = app.config_holder
|
||||
boot_config = holder.config
|
||||
factory = lambda: holder.config # noqa: E731
|
||||
self.assertIs(factory(), boot_config)
|
||||
|
||||
config = MagicMock(name="new_config")
|
||||
swap_runtime_config(app, config)
|
||||
|
||||
self.assertIs(factory(), config)
|
||||
|
||||
def test_tolerates_missing_optional_collaborators(self) -> None:
|
||||
app = MagicMock()
|
||||
app.profile_manager = None
|
||||
app.stats_emitter = None
|
||||
app.dispatcher = None
|
||||
app.config_holder = None
|
||||
config = MagicMock(name="new_config")
|
||||
|
||||
# must not raise when the optional collaborators are absent
|
||||
|
||||
@@ -1,226 +0,0 @@
|
||||
"""Tests for detector post-processing NMS box format handling.
|
||||
|
||||
cv2.dnn.NMSBoxes expects boxes as [x, y, width, height]. Passing corner
|
||||
coordinates [x1, y1, x2, y2] makes OpenCV treat x2/y2 as width/height,
|
||||
inflating every box toward the bottom-right by its distance from the origin.
|
||||
Two well separated objects far from the origin then appear to overlap and the
|
||||
lower scoring one is silently suppressed.
|
||||
|
||||
The regression geometry used throughout: two boxes with zero true overlap,
|
||||
A = (393, 499, 484, 620) and B = (527, 499, 618, 620) in a 640x640 input
|
||||
(43 px gap). Misread as [x, y, w, h] their IoU is 0.465, above the 0.4 NMS
|
||||
threshold, so the buggy format drops the lower scoring box while correct
|
||||
conversion keeps both.
|
||||
"""
|
||||
|
||||
import math
|
||||
import unittest
|
||||
from queue import Queue
|
||||
|
||||
import numpy as np
|
||||
|
||||
from frigate.detectors.plugins.memryx import MemryXDetector
|
||||
from frigate.util.model import (
|
||||
post_process_dfine,
|
||||
post_process_rfdetr,
|
||||
post_process_yolo,
|
||||
post_process_yolox,
|
||||
)
|
||||
|
||||
WIDTH = 640
|
||||
HEIGHT = 640
|
||||
|
||||
# box A: xyxy (393, 499, 484, 620) as center format
|
||||
A_CX, A_CY, A_W, A_H = 438.5, 559.5, 91.0, 121.0
|
||||
# box B: xyxy (527, 499, 618, 620) as center format
|
||||
B_CX, B_CY, B_W, B_H = 572.5, 559.5, 91.0, 121.0
|
||||
|
||||
# expected normalized output rows: [class_id, conf, y1, x1, y2, x2]
|
||||
A_ROW = [499 / 640, 393 / 640, 620 / 640, 484 / 640]
|
||||
B_ROW = [499 / 640, 527 / 640, 620 / 640, 618 / 640]
|
||||
|
||||
|
||||
def kept(detections: np.ndarray) -> np.ndarray:
|
||||
"""Rows of the padded (20, 6) output that hold real detections."""
|
||||
return detections[detections[:, 1] > 0]
|
||||
|
||||
|
||||
class TestYoloNmsPostProcess(unittest.TestCase):
|
||||
def _single_output(self, rows: list[list[float]]) -> list[np.ndarray]:
|
||||
"""Build a single-tensor YOLO output (1, attrs, anchors) from
|
||||
[cx, cy, w, h, class scores...] rows, padded with empty anchors."""
|
||||
anchors = np.zeros((10, len(rows[0])), dtype=np.float32)
|
||||
anchors[: len(rows)] = np.array(rows, dtype=np.float32)
|
||||
return [anchors.T[np.newaxis, ...]]
|
||||
|
||||
def test_keeps_separated_objects_far_from_origin(self):
|
||||
output = self._single_output(
|
||||
[
|
||||
[A_CX, A_CY, A_W, A_H, 0.90, 0.0],
|
||||
[B_CX, B_CY, B_W, B_H, 0.0, 0.85],
|
||||
]
|
||||
)
|
||||
|
||||
detections = kept(post_process_yolo(output, WIDTH, HEIGHT))
|
||||
|
||||
self.assertEqual(len(detections), 2)
|
||||
np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
|
||||
np.testing.assert_allclose(detections[1], [1, 0.85, *B_ROW], atol=2e-3)
|
||||
|
||||
def test_still_suppresses_true_duplicates(self):
|
||||
# same object twice, shifted 4 px: true IoU 0.92, must dedupe to one
|
||||
output = self._single_output(
|
||||
[
|
||||
[A_CX, A_CY, A_W, A_H, 0.90, 0.0],
|
||||
[A_CX + 4, A_CY, A_W, A_H, 0.85, 0.0],
|
||||
]
|
||||
)
|
||||
|
||||
detections = kept(post_process_yolo(output, WIDTH, HEIGHT))
|
||||
|
||||
self.assertEqual(len(detections), 1)
|
||||
np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
|
||||
|
||||
|
||||
class TestMultipartYoloPostProcess(unittest.TestCase):
|
||||
def _multipart_output(self) -> list[np.ndarray]:
|
||||
"""Build a 3-scale anchor-based YOLO output containing boxes A and B,
|
||||
both decoded through anchor 0 of the stride-32 scale."""
|
||||
outputs = [
|
||||
np.zeros((1, 255, 80, 80), dtype=np.float32),
|
||||
np.zeros((1, 255, 40, 40), dtype=np.float32),
|
||||
np.zeros((1, 255, 20, 20), dtype=np.float32),
|
||||
]
|
||||
stride, (anchor_w, anchor_h) = 32, (142, 110)
|
||||
|
||||
for cx, cy, w, h, conf, class_channel in [
|
||||
(A_CX, A_CY, A_W, A_H, 0.95, 5), # class 0
|
||||
(B_CX, B_CY, B_W, B_H, 0.90, 6), # class 1
|
||||
]:
|
||||
cell_x, cell_y = int(cx // stride), int(cy // stride)
|
||||
dx = (cx / stride - cell_x + 0.5) / 2
|
||||
dy = (cy / stride - cell_y + 0.5) / 2
|
||||
dw = math.sqrt(w / anchor_w) / 2
|
||||
dh = math.sqrt(h / anchor_h) / 2
|
||||
# anchor 0 occupies channels 0-84 of the 255 channel tensor
|
||||
outputs[2][0, 0:4, cell_y, cell_x] = [dx, dy, dw, dh]
|
||||
outputs[2][0, 4, cell_y, cell_x] = conf
|
||||
outputs[2][0, class_channel, cell_y, cell_x] = 1.0
|
||||
|
||||
return outputs
|
||||
|
||||
def test_keeps_separated_objects_far_from_origin(self):
|
||||
detections = kept(post_process_yolo(self._multipart_output(), WIDTH, HEIGHT))
|
||||
|
||||
self.assertEqual(len(detections), 2)
|
||||
np.testing.assert_allclose(detections[0], [0, 0.95, *A_ROW], atol=2e-3)
|
||||
np.testing.assert_allclose(detections[1], [1, 0.90, *B_ROW], atol=2e-3)
|
||||
|
||||
def test_empty_output_returns_no_detections(self):
|
||||
outputs = [
|
||||
np.zeros((1, 255, 80, 80), dtype=np.float32),
|
||||
np.zeros((1, 255, 40, 40), dtype=np.float32),
|
||||
np.zeros((1, 255, 20, 20), dtype=np.float32),
|
||||
]
|
||||
|
||||
detections = kept(post_process_yolo(outputs, WIDTH, HEIGHT))
|
||||
|
||||
self.assertEqual(len(detections), 0)
|
||||
|
||||
|
||||
class TestYoloxPostProcess(unittest.TestCase):
|
||||
def test_keeps_separated_objects_far_from_origin(self):
|
||||
# with zero grids and unit strides the decode reduces to
|
||||
# cx = raw cx and w = exp(raw w)
|
||||
rows = np.zeros((10, 7), dtype=np.float32)
|
||||
rows[0] = [A_CX, A_CY, math.log(A_W), math.log(A_H), 1.0, 0.90, 0.0]
|
||||
rows[1] = [B_CX, B_CY, math.log(B_W), math.log(B_H), 1.0, 0.0, 0.85]
|
||||
predictions = rows[np.newaxis, ...]
|
||||
grids = np.zeros((1, 10, 2), dtype=np.float32)
|
||||
expanded_strides = np.ones((1, 10, 1), dtype=np.float32)
|
||||
|
||||
detections = kept(
|
||||
post_process_yolox(predictions, WIDTH, HEIGHT, grids, expanded_strides)
|
||||
)
|
||||
|
||||
self.assertEqual(len(detections), 2)
|
||||
np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
|
||||
np.testing.assert_allclose(detections[1], [1, 0.85, *B_ROW], atol=2e-3)
|
||||
|
||||
|
||||
class TestDfinePostProcess(unittest.TestCase):
|
||||
def test_keeps_separated_objects_far_from_origin(self):
|
||||
# D-FINE emits absolute pixel xyxy boxes alongside labels and scores
|
||||
labels = np.zeros((1, 10), dtype=np.int64)
|
||||
labels[0, 1] = 1
|
||||
boxes = np.zeros((1, 10, 4), dtype=np.float32)
|
||||
boxes[0, 0] = [393, 499, 484, 620]
|
||||
boxes[0, 1] = [527, 499, 618, 620]
|
||||
scores = np.zeros((1, 10), dtype=np.float32)
|
||||
scores[0, 0] = 0.90
|
||||
scores[0, 1] = 0.85
|
||||
|
||||
detections = kept(post_process_dfine([labels, boxes, scores], WIDTH, HEIGHT))
|
||||
|
||||
self.assertEqual(len(detections), 2)
|
||||
np.testing.assert_allclose(detections[0], [0, 0.90, *A_ROW], atol=2e-3)
|
||||
np.testing.assert_allclose(detections[1], [1, 0.85, *B_ROW], atol=2e-3)
|
||||
|
||||
|
||||
class TestRfdetrPostProcess(unittest.TestCase):
|
||||
def test_keeps_separated_objects_far_from_origin(self):
|
||||
# RF-DETR emits normalized center format boxes and class logits where
|
||||
# logit index 0 is the background class
|
||||
boxes = np.zeros((1, 10, 4), dtype=np.float32)
|
||||
boxes[0, 0] = [A_CX / WIDTH, A_CY / HEIGHT, A_W / WIDTH, A_H / HEIGHT]
|
||||
boxes[0, 1] = [B_CX / WIDTH, B_CY / HEIGHT, B_W / WIDTH, B_H / HEIGHT]
|
||||
# background heavy logits everywhere, then two confident objects
|
||||
logits = np.tile(np.array([10.0, 0.0, 0.0], dtype=np.float32), (1, 10, 1))
|
||||
logits[0, 0] = [0.0, 4.0, 0.0] # class 0 after background offset
|
||||
logits[0, 1] = [0.0, 0.0, 3.5] # class 1 after background offset
|
||||
|
||||
detections = kept(post_process_rfdetr([boxes, logits]))
|
||||
|
||||
conf_a = math.exp(4.0) / (math.exp(4.0) + 2)
|
||||
conf_b = math.exp(3.5) / (math.exp(3.5) + 2)
|
||||
self.assertEqual(len(detections), 2)
|
||||
np.testing.assert_allclose(detections[0], [0, conf_a, *A_ROW], atol=2e-3)
|
||||
np.testing.assert_allclose(detections[1], [1, conf_b, *B_ROW], atol=2e-3)
|
||||
|
||||
|
||||
class TestMemryxSsdlitePostProcess(unittest.TestCase):
|
||||
def test_keeps_separated_objects_far_from_origin(self):
|
||||
# the NMS math runs on the host CPU, so the real method is testable
|
||||
# without MemryX hardware; it only needs the model dimensions and
|
||||
# the output queue
|
||||
detector = object.__new__(MemryXDetector)
|
||||
detector.memx_model_width = WIDTH
|
||||
detector.memx_model_height = HEIGHT
|
||||
detector.output_queue = Queue()
|
||||
|
||||
# this path uses a 0.5 NMS threshold, so use a tighter pair: zero
|
||||
# true overlap (10 px gap), IoU 0.69 when misread as [x, y, w, h]
|
||||
dets = np.zeros((1, 10, 5), dtype=np.float32)
|
||||
dets[0, 0] = [480, 500, 540, 620, 0.90]
|
||||
dets[0, 1] = [550, 500, 610, 620, 0.85]
|
||||
labels = np.zeros((1, 10), dtype=np.float32)
|
||||
labels[0, 1] = 1
|
||||
|
||||
detector.post_process_ssdlite([dets, labels])
|
||||
detections = kept(detector.output_queue.get())
|
||||
|
||||
self.assertEqual(len(detections), 2)
|
||||
np.testing.assert_allclose(
|
||||
detections[0],
|
||||
[0, 0.90, 500 / 640, 480 / 640, 620 / 640, 540 / 640],
|
||||
atol=2e-3,
|
||||
)
|
||||
np.testing.assert_allclose(
|
||||
detections[1],
|
||||
[1, 0.85, 500 / 640, 550 / 640, 620 / 640, 610 / 640],
|
||||
atol=2e-3,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@@ -472,18 +472,6 @@ def sanitize_float(value):
|
||||
return value
|
||||
|
||||
|
||||
def has_non_finite_number(value: Any) -> bool:
|
||||
"""Return True if any number in a parsed JSON value is NaN or infinite."""
|
||||
if isinstance(value, float):
|
||||
return not math.isfinite(value)
|
||||
if isinstance(value, dict):
|
||||
return any(has_non_finite_number(v) for v in value.values())
|
||||
if isinstance(value, list):
|
||||
return any(has_non_finite_number(v) for v in value)
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
|
||||
return 1 - cosine_distance(a, b)
|
||||
|
||||
|
||||
+5
-37
@@ -16,31 +16,6 @@ logger = logging.getLogger(__name__)
|
||||
### Post Processing
|
||||
|
||||
|
||||
def xyxy_to_xywh_for_nms(boxes: np.ndarray | list) -> np.ndarray:
|
||||
"""Convert [x1, y1, x2, y2] boxes to the [x, y, width, height] format
|
||||
that cv2.dnn.NMSBoxes expects.
|
||||
|
||||
Passing corner coordinates directly makes OpenCV treat x2/y2 as the box
|
||||
size, inflating every box toward the bottom-right by its distance from
|
||||
the origin, which suppresses valid detections near other objects.
|
||||
|
||||
Args:
|
||||
boxes: Array-like of shape (N, 4) in corner format.
|
||||
|
||||
Returns:
|
||||
Float32 array of shape (N, 4) in top-left plus size format.
|
||||
"""
|
||||
boxes = np.asarray(boxes, dtype=np.float32)
|
||||
|
||||
if boxes.size == 0:
|
||||
return np.zeros((0, 4), dtype=np.float32)
|
||||
|
||||
xywh = boxes.copy()
|
||||
xywh[:, 2] -= xywh[:, 0]
|
||||
xywh[:, 3] -= xywh[:, 1]
|
||||
return xywh
|
||||
|
||||
|
||||
def post_process_dfine(
|
||||
tensor_output: np.ndarray, width: int, height: int
|
||||
) -> np.ndarray:
|
||||
@@ -50,9 +25,7 @@ def post_process_dfine(
|
||||
|
||||
input_shape = np.array([height, width, height, width])
|
||||
boxes = np.divide(boxes, input_shape, dtype=np.float32)
|
||||
indices = cv2.dnn.NMSBoxes(
|
||||
xyxy_to_xywh_for_nms(boxes), scores, score_threshold=0.4, nms_threshold=0.4
|
||||
)
|
||||
indices = cv2.dnn.NMSBoxes(boxes, scores, score_threshold=0.4, nms_threshold=0.4)
|
||||
detections = np.zeros((20, 6), np.float32)
|
||||
|
||||
for i, (bbox, confidence, class_id) in enumerate(
|
||||
@@ -105,10 +78,7 @@ def post_process_rfdetr(tensor_output: list[np.ndarray, np.ndarray]) -> np.ndarr
|
||||
|
||||
# apply nms
|
||||
indices = cv2.dnn.NMSBoxes(
|
||||
xyxy_to_xywh_for_nms(filtered_boxes),
|
||||
filtered_scores,
|
||||
score_threshold=0.4,
|
||||
nms_threshold=0.4,
|
||||
filtered_boxes, filtered_scores, score_threshold=0.4, nms_threshold=0.4
|
||||
)
|
||||
detections = np.zeros((20, 6), np.float32)
|
||||
|
||||
@@ -189,7 +159,7 @@ def __post_process_multipart_yolo(
|
||||
all_class_ids.append(class_id)
|
||||
|
||||
indices = cv2.dnn.NMSBoxes(
|
||||
bboxes=xyxy_to_xywh_for_nms(all_boxes),
|
||||
bboxes=all_boxes,
|
||||
scores=all_scores,
|
||||
score_threshold=0.4,
|
||||
nms_threshold=0.4,
|
||||
@@ -236,9 +206,7 @@ def __post_process_nms_yolo(predictions: np.ndarray, width, height) -> np.ndarra
|
||||
boxes = boxes_xyxy
|
||||
|
||||
# run NMS
|
||||
indices = cv2.dnn.NMSBoxes(
|
||||
xyxy_to_xywh_for_nms(boxes), scores, score_threshold=0.4, nms_threshold=0.4
|
||||
)
|
||||
indices = cv2.dnn.NMSBoxes(boxes, scores, score_threshold=0.4, nms_threshold=0.4)
|
||||
detections = np.zeros((20, 6), np.float32)
|
||||
for i, (bbox, confidence, class_id) in enumerate(
|
||||
zip(boxes[indices], scores[indices], class_ids[indices])
|
||||
@@ -290,7 +258,7 @@ def post_process_yolox(
|
||||
scores = scores[np.arange(len(cls_inds)), cls_inds]
|
||||
|
||||
indices = cv2.dnn.NMSBoxes(
|
||||
xyxy_to_xywh_for_nms(boxes_xyxy), scores, score_threshold=0.4, nms_threshold=0.4
|
||||
boxes_xyxy, scores, score_threshold=0.4, nms_threshold=0.4
|
||||
)
|
||||
|
||||
detections = np.zeros((20, 6), np.float32)
|
||||
|
||||
@@ -36,8 +36,8 @@ from __future__ import annotations
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
from collections.abc import Iterable
|
||||
from dataclasses import dataclass
|
||||
from typing import Iterable
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
@@ -53,31 +53,21 @@ ARCFACE_INPUT_SIZE = 112
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _bgr_to_rgb(frame: np.ndarray) -> np.ndarray:
|
||||
"""Mirror BaseEmbedding._bgr_to_rgb."""
|
||||
if isinstance(frame, np.ndarray) and frame.ndim == 3:
|
||||
return np.ascontiguousarray(frame[:, :, ::-1])
|
||||
|
||||
return frame
|
||||
|
||||
|
||||
def _process_image_frigate(image: np.ndarray) -> Image.Image:
|
||||
"""Mirror BaseEmbedding._process_image for an ndarray input.
|
||||
|
||||
`Image.fromarray` does not reorder channels, so whatever order it is
|
||||
handed is what reaches the model. Callers swap to RGB first, exactly as
|
||||
ArcfaceEmbedding._preprocess_inputs does.
|
||||
NOTE: Frigate passes the output of `cv2.imread` (BGR) directly in. PIL's
|
||||
`Image.fromarray` does NOT reorder channels, so the embedder effectively
|
||||
receives a BGR-ordered tensor. We replicate that faithfully here. (Tested
|
||||
— swapping to RGB produces near-identical embeddings; this model is
|
||||
robust to channel order.)
|
||||
"""
|
||||
return Image.fromarray(image)
|
||||
|
||||
|
||||
def arcface_preprocess(image_bgr: np.ndarray) -> np.ndarray:
|
||||
"""Mirror ArcfaceEmbedding._preprocess_inputs.
|
||||
|
||||
Face crops arrive BGR from cv2 and #23712 added the swap to RGB before
|
||||
embedding, so this script has to do it too.
|
||||
"""
|
||||
pil = _process_image_frigate(_bgr_to_rgb(image_bgr))
|
||||
"""Mirror ArcfaceEmbedding._preprocess_inputs."""
|
||||
pil = _process_image_frigate(image_bgr)
|
||||
|
||||
width, height = pil.size
|
||||
if width != ARCFACE_INPUT_SIZE or height != ARCFACE_INPUT_SIZE:
|
||||
@@ -148,7 +138,9 @@ class LandmarkAligner:
|
||||
M[0, 2] += tX - eyesCenter[0]
|
||||
M[1, 2] += tY - eyesCenter[1]
|
||||
|
||||
aligned = cv2.warpAffine(image, M, (out_w, out_h), flags=cv2.INTER_CUBIC)
|
||||
aligned = cv2.warpAffine(
|
||||
image, M, (out_w, out_h), flags=cv2.INTER_CUBIC
|
||||
)
|
||||
info = dict(
|
||||
angle=float(angle),
|
||||
eye_dist_px=dist,
|
||||
@@ -441,7 +433,9 @@ def vector_outlier_test(
|
||||
if neg
|
||||
else np.array([])
|
||||
)
|
||||
baseline_conf_neg = np.array([similarity_to_confidence(c) for c in baseline_neg])
|
||||
baseline_conf_neg = np.array(
|
||||
[similarity_to_confidence(c) for c in baseline_neg]
|
||||
)
|
||||
|
||||
print(
|
||||
f"\nBaseline (trim_mean only, {len(pos)} images):"
|
||||
@@ -471,7 +465,9 @@ def vector_outlier_test(
|
||||
mean, keep = iterative_mean(all_embs, T)
|
||||
pos_sims = np.array([cosine(p.embedding, mean) for p in pos])
|
||||
neg_sims = (
|
||||
np.array([cosine(n.embedding, mean) for n in neg]) if neg else np.array([])
|
||||
np.array([cosine(n.embedding, mean) for n in neg])
|
||||
if neg
|
||||
else np.array([])
|
||||
)
|
||||
neg_conf = np.array([similarity_to_confidence(c) for c in neg_sims])
|
||||
margin = pos_sims.min() - (neg_sims.max() if len(neg_sims) else 0)
|
||||
@@ -487,7 +483,9 @@ def vector_outlier_test(
|
||||
# Show which images get dropped at the shipped threshold + neighbors
|
||||
for T_show in (0.25, 0.30, 0.33):
|
||||
_, keep = iterative_mean(all_embs, T_show)
|
||||
print(f"\nAt T={T_show}, the {int((~keep).sum())} dropped positives are:")
|
||||
print(
|
||||
f"\nAt T={T_show}, the {int((~keep).sum())} dropped positives are:"
|
||||
)
|
||||
final_mean = stats.trim_mean(all_embs[keep], base_trim, axis=0)
|
||||
m_n = final_mean / (np.linalg.norm(final_mean) + 1e-9)
|
||||
for i, (p, k) in enumerate(zip(pos, keep)):
|
||||
@@ -503,7 +501,9 @@ def vector_outlier_test(
|
||||
)
|
||||
|
||||
|
||||
def degenerate_embedding_test(pos: list[FaceSample], neg: list[FaceSample]) -> None:
|
||||
def degenerate_embedding_test(
|
||||
pos: list[FaceSample], neg: list[FaceSample]
|
||||
) -> None:
|
||||
"""Detect whether negatives and low-quality positives share a degenerate
|
||||
'tiny/noisy face' region of the embedding space.
|
||||
|
||||
@@ -533,7 +533,8 @@ def degenerate_embedding_test(pos: list[FaceSample], neg: list[FaceSample]) -> N
|
||||
f"(how tightly negatives cluster together)"
|
||||
)
|
||||
print(
|
||||
f" pos<->pos mean cos : {np.nanmean(pp):.3f} (how tightly positives cluster)"
|
||||
f" pos<->pos mean cos : {np.nanmean(pp):.3f} "
|
||||
f"(how tightly positives cluster)"
|
||||
)
|
||||
print(
|
||||
f" pos<->neg mean cos : {pn.mean():.3f} "
|
||||
@@ -557,7 +558,11 @@ def degenerate_embedding_test(pos: list[FaceSample], neg: list[FaceSample]) -> N
|
||||
neg_scores = np.array([cosine(n.embedding, clean_mean) for n in neg])
|
||||
neg_confs = np.array([similarity_to_confidence(c) for c in neg_scores])
|
||||
pos_scores = np.array(
|
||||
[cosine(pos[i].embedding, clean_mean) for i in range(len(pos)) if keep[i]]
|
||||
[
|
||||
cosine(pos[i].embedding, clean_mean)
|
||||
for i in range(len(pos))
|
||||
if keep[i]
|
||||
]
|
||||
)
|
||||
print(
|
||||
f"\n mean_intra >= {thresh}: keeping {int(keep.sum())}/{len(pos)} positives"
|
||||
@@ -580,7 +585,9 @@ def degenerate_embedding_test(pos: list[FaceSample], neg: list[FaceSample]) -> N
|
||||
)
|
||||
|
||||
|
||||
def contamination_analysis(pos: list[FaceSample], neg: list[FaceSample]) -> None:
|
||||
def contamination_analysis(
|
||||
pos: list[FaceSample], neg: list[FaceSample]
|
||||
) -> None:
|
||||
"""Check whether the positive collection contains a second identity.
|
||||
|
||||
Two signals:
|
||||
@@ -610,7 +617,10 @@ def contamination_analysis(pos: list[FaceSample], neg: list[FaceSample]) -> None
|
||||
"\nPositives closer to a negative than to their own class avg"
|
||||
"\n(these are candidates for mislabeled images):"
|
||||
)
|
||||
print(f"\n{'max_neg':>7} {'mean_neg':>8} {'mean_intra':>10} {'delta':>6} name")
|
||||
print(
|
||||
f"\n{'max_neg':>7} {'mean_neg':>8} {'mean_intra':>10} "
|
||||
f"{'delta':>6} name"
|
||||
)
|
||||
rows = list(zip(pos_names, max_to_neg, mean_to_neg, mean_intra))
|
||||
rows.sort(key=lambda r: -(r[1] - r[3]))
|
||||
for nm, mxn, mnn, mi in rows[:15]:
|
||||
@@ -694,9 +704,7 @@ def main() -> int:
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog=__doc__,
|
||||
)
|
||||
ap.add_argument(
|
||||
"--positive", required=True, help="Training folder for one identity"
|
||||
)
|
||||
ap.add_argument("--positive", required=True, help="Training folder for one identity")
|
||||
ap.add_argument(
|
||||
"--negative",
|
||||
default=None,
|
||||
|
||||
Generated
+4
-4
@@ -73,7 +73,7 @@
|
||||
"react-markdown": "^9.0.1",
|
||||
"react-router-dom": "^6.30.3",
|
||||
"react-swipeable": "^7.0.2",
|
||||
"react-zoom-pan-pinch": "3.4.4",
|
||||
"react-zoom-pan-pinch": "3.6.1",
|
||||
"remark-gfm": "^4.0.0",
|
||||
"scroll-into-view-if-needed": "^3.1.0",
|
||||
"sonner": "^2.0.7",
|
||||
@@ -12354,9 +12354,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/react-zoom-pan-pinch": {
|
||||
"version": "3.4.4",
|
||||
"resolved": "https://registry.npmjs.org/react-zoom-pan-pinch/-/react-zoom-pan-pinch-3.4.4.tgz",
|
||||
"integrity": "sha512-lGTu7D9lQpYEQ6sH+NSlLA7gicgKRW8j+D/4HO1AbSV2POvKRFzdWQ8eI0r3xmOsl4dYQcY+teV6MhULeg1xBw==",
|
||||
"version": "3.6.1",
|
||||
"resolved": "https://registry.npmjs.org/react-zoom-pan-pinch/-/react-zoom-pan-pinch-3.6.1.tgz",
|
||||
"integrity": "sha512-SdPqdk7QDSV7u/WulkFOi+cnza8rEZ0XX4ZpeH7vx3UZEg7DoyuAy3MCmm+BWv/idPQL2Oe73VoC0EhfCN+sZQ==",
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">=8",
|
||||
|
||||
+1
-1
@@ -87,7 +87,7 @@
|
||||
"react-markdown": "^9.0.1",
|
||||
"react-router-dom": "^6.30.3",
|
||||
"react-swipeable": "^7.0.2",
|
||||
"react-zoom-pan-pinch": "3.4.4",
|
||||
"react-zoom-pan-pinch": "3.6.1",
|
||||
"remark-gfm": "^4.0.0",
|
||||
"scroll-into-view-if-needed": "^3.1.0",
|
||||
"sonner": "^2.0.7",
|
||||
|
||||
@@ -61,8 +61,7 @@
|
||||
"error": {
|
||||
"endTimeMustAfterStartTime": "L'hora de finalització ha de ser posterior a l'hora d'inici",
|
||||
"noVaildTimeSelected": "No s'ha seleccionat un rang de temps vàlid",
|
||||
"failed": "No s'ha pogut inciar l'exportació: {{error}}",
|
||||
"noValidTimeSelected": "No s'ha seleccionat cap interval de temps vàlid"
|
||||
"failed": "No s'ha pogut inciar l'exportació: {{error}}"
|
||||
},
|
||||
"view": "Vista",
|
||||
"queued": "Exporta a la cua. Mostra el progrés a la pàgina d'exportacions.",
|
||||
|
||||
@@ -493,9 +493,6 @@
|
||||
"max_concurrent": {
|
||||
"label": "Màxim d'exportacions concurrents",
|
||||
"description": "Nombre màxim de treballs d'exportació a processar al mateix temps."
|
||||
},
|
||||
"chapters": {
|
||||
"label": "Metadades de capítol per incrustar en els enregistraments exportats"
|
||||
}
|
||||
},
|
||||
"preview": {
|
||||
|
||||
@@ -380,9 +380,6 @@
|
||||
"max_concurrent": {
|
||||
"label": "Màxim d'exportacions concurrents",
|
||||
"description": "Nombre màxim de treballs d'exportació a processar al mateix temps."
|
||||
},
|
||||
"chapters": {
|
||||
"label": "Metadades de capítol per incrustar en els enregistraments exportats"
|
||||
}
|
||||
},
|
||||
"preview": {
|
||||
@@ -1920,7 +1917,7 @@
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Tipus de Model de detecció d'objecte",
|
||||
"description": "Tipus d'arquitectura del model de detector (ssd, yolox, yolonas, yolo-generic, rfdetr, dfine) utilitzat per alguns detectors per a l'optimització"
|
||||
"description": "Tipus d'arquitectura del model de detector (ssd, yolox, yolonas) usat per l'optimització d'alguns detectors."
|
||||
}
|
||||
},
|
||||
"model_path": {
|
||||
@@ -1969,7 +1966,7 @@
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Tipus de model de detecció d'objectes",
|
||||
"description": "Tipus d'arquitectura del model de detector (ssd, yolox, yolonas, yolo-generic, rfdetr, dfine) utilitzat per alguns detectors per a l'optimització."
|
||||
"description": "Tipus d'arquitectura del model de detector (ssd, yolox, yolonas) utilitzat per alguns detectors per a l'optimització."
|
||||
}
|
||||
},
|
||||
"genai": {
|
||||
|
||||
@@ -192,20 +192,7 @@
|
||||
"title": "Edita el model de classificació",
|
||||
"descriptionState": "Edita les classes per a aquest model de classificació d'estats. Els canvis requeriran tornar a entrenar el model.",
|
||||
"descriptionObject": "Edita el tipus d'objecte i el tipus de classificació per a aquest model de classificació d'objectes.",
|
||||
"stateClassesInfo": "S'ha actualitzat el model. Restringeix el model perquè els canvis de classe tinguin efecte.",
|
||||
"enabled": "Habilitat",
|
||||
"enabledDesc": "Executa aquest model. Quan està desactivat, deixa d'executar-se i ja no classifica.",
|
||||
"saveAttempts": "Desa els intents",
|
||||
"saveAttemptsDesc": "Nombre d'imatges de classificació que s'intenten mantenir per a les classificacions recents UI.",
|
||||
"motion": "Executa en moviment",
|
||||
"motionDesc": "Executa la classificació quan es detecta el moviment dins de l'escapçat configurat.",
|
||||
"interval": "Interval",
|
||||
"intervalDesc": "Segons entre les classificacions periòdiques. Deixeu-ho buit per a executar-se només en moviment.",
|
||||
"intervalPlaceholder": "Sense interval",
|
||||
"errors": {
|
||||
"saveAttemptsInvalid": "Els intents de desar han de ser un nombre sencer de 0 o més",
|
||||
"intervalInvalid": "L'interval ha de ser un nombre sencer més gran que 0"
|
||||
}
|
||||
"stateClassesInfo": "Nota: Canviar les classes d'estat requereix tornar a entrenar el model amb les classes actualitzades."
|
||||
},
|
||||
"tooltip": {
|
||||
"trainingInProgress": "El model s'està entrenant actualment",
|
||||
@@ -215,6 +202,5 @@
|
||||
},
|
||||
"none": "Cap",
|
||||
"reclassifyImageAs": "Reclassifica la imatge com a:",
|
||||
"reclassifyImage": "Reclassifica la imatge",
|
||||
"disabled": "Desactivat"
|
||||
"reclassifyImage": "Reclassifica la imatge"
|
||||
}
|
||||
|
||||
@@ -303,7 +303,7 @@
|
||||
},
|
||||
"offset": {
|
||||
"label": "Òfset d'Anotació",
|
||||
"desc": "Aquestes dades provenen del canal de detecció de la càmera, però estan sobreposades a les imatges del canal de registre. És poc probable que els dos corrents estiguin perfectament sincronitzats. Com a resultat, la caixa contenidora i les imatges no s'alinearan perfectament. Podeu utilitzar aquest paràmetre per a compensar les anotacions cap endavant o cap enrere en el temps per a alinear-les millor amb el metratge gravat.",
|
||||
"desc": "Aquestes dades provenen del flux de detecció de la càmera, però se superposen a les imatges del flux de gravació. És poc probable que els dos fluxos estiguin perfectament sincronitzats. Com a resultat, el quadre delimitador i les imatges no s'alinearan perfectament. Tanmateix, es pot utilitzar el camp <code>annotation_offset</code> per ajustar-ho.",
|
||||
"millisecondsToOffset": "Millisegons per l'òfset de detecció d'anotacions per. <em>Per defecte: 0</em>",
|
||||
"tips": "Reduïu el valor si la reproducció del vídeo es troba per davant dels quadres i els punts de ruta, i augmenteu-lo si es troba per darrere. Aquest valor pot ser negatiu.",
|
||||
"toast": {
|
||||
|
||||
@@ -698,7 +698,7 @@
|
||||
"title": "Crear un nou usuari",
|
||||
"confirmPassword": "Siusplau, confirma la contrasenya",
|
||||
"usernameOnlyInclude": "El nom d'usuari només pot contenir lletres, números, . o _",
|
||||
"desc": "Afegeix un compte d'usuari nou i especifica un rol per a l'accés a les àrees de la interfície d'usuari de Frigate."
|
||||
"desc": "Afegeix un nou compte d'usuari i especifica un rol per accedir a àrees de la interfície de Frigate."
|
||||
}
|
||||
},
|
||||
"title": "Usuaris",
|
||||
@@ -1323,7 +1323,7 @@
|
||||
"details": {
|
||||
"edit": "Edita els detalls de la càmera",
|
||||
"title": "Edita els detalls de la càmera",
|
||||
"description": "Actualitza el nom de visualització, l'URL extern i la visibilitat utilitzada per a aquesta càmera a tota la interfície d'usuari de Frigate.",
|
||||
"description": "Actualitza el nom de visualització, l'URL extern i la visibilitat utilitzada per a aquesta càmera a tota la interfície d'usuari de la Fragata.",
|
||||
"friendlyNameLabel": "Nom a mostrar",
|
||||
"friendlyNameHelp": "Nom amistós que es mostra per a aquesta càmera a tota la interfície d'usuari de Frigate. Deixeu-ho en blanc per utilitzar l'ID de la càmera.",
|
||||
"webuiUrlLabel": "URL de la interfície web de la càmera",
|
||||
@@ -1484,7 +1484,7 @@
|
||||
"successMulti_other": "Configuració copiada a {{count}} càmeres",
|
||||
"successMultiWithRestart_one": "Configuració copiada a la càmera {{count}}. Reinicia Frigate per aplicar tots els canvis.",
|
||||
"successMultiWithRestart_many": "Configuració copiada a {{count}} càmeres. Reinicia Frigate per aplicar tots els canvis.",
|
||||
"successMultiWithRestart_other": "Configuració copiada a {{count}} càmeres. Reinicia Frigate per aplicar tots els canvis.",
|
||||
"successMultiWithRestart_other": "Configuració copiada a {{count}} càmeres. Reinicia la fragata per aplicar tots els canvis.",
|
||||
"partialFailure": "{{successCount}} seccions aplicades; «{{failedSection}}» ha fallat: {{errorMessage}}",
|
||||
"partialFailureMulti": "S'ha copiat a {{successCount}} càmera(es); ha fallat {{failed}}: {{errorMessage}}",
|
||||
"newCameraPartialFailure": "S'ha creat la càmera {{cameraName}} però no s'han pogut copiar alguns paràmetres: {{errorMessage}}",
|
||||
@@ -1641,14 +1641,7 @@
|
||||
"keyLabel": "Clau",
|
||||
"valueLabel": "Valor",
|
||||
"keyPlaceholder": "Nou valor",
|
||||
"remove": "Elimina",
|
||||
"providerNameLabel": "Nom del proveïdor",
|
||||
"providerNamePlaceholder": "p. ex., openai",
|
||||
"variableNameLabel": "Nom de la variable",
|
||||
"variableNamePlaceholder": ". ex., La_Meva_Variable",
|
||||
"loggerNameLabel": "Nom del registrador",
|
||||
"loggerNamePlaceholder": "p. ex., friagte.registre",
|
||||
"keyPatternError": "Utilitza només lletres, números, guions i guions baixos (sense espais)"
|
||||
"remove": "Elimina"
|
||||
},
|
||||
"timezone": {
|
||||
"defaultOption": "Utilitza la zona horària del navegador"
|
||||
@@ -2103,8 +2096,7 @@
|
||||
"autotrackingNoZones": "Autotraquejar requereix al menys una zona. Defineix una zona per aquesta cámera a Mascares/Zones, després usa'l com a requerit a la part inferior."
|
||||
},
|
||||
"ffmpeg": {
|
||||
"hwaccelManualNotRecommended": "No es recomanen arguments manuals d'acceleració de maquinari. Tret que existeixi un requisit específic, seleccioneu el predefinit que coincideixi amb el vostre maquinari.",
|
||||
"inputsMissingGo2rtcStream": "Una entrada a sota apunta a un restream go2rtc que ja no existeix. Seleccioneu un restream existent o introduïu manualment l'URL de la càmera, en cas contrari aquesta càmera no es connectarà."
|
||||
"hwaccelManualNotRecommended": "No es recomanen arguments manuals d'acceleració de maquinari. Tret que existeixi un requisit específic, seleccioneu el predefinit que coincideixi amb el vostre maquinari."
|
||||
},
|
||||
"model": {
|
||||
"optimizedFor320": "Frigate està optimitzada per a un model 320x320, que és la millor opció per a la majoria de configuracions. Un model 640x640 és més lent i només ajuda en escenaris específics.",
|
||||
|
||||
@@ -425,23 +425,5 @@
|
||||
"chop": "Sekání",
|
||||
"crack": "Prasknutí",
|
||||
"chink": "Cinknutí",
|
||||
"field_recording": "Nahrávka z terénu",
|
||||
"change_ringing": "Změnit vyzvánění",
|
||||
"liquid": "Tekutina",
|
||||
"splash": "Šplouchnutí",
|
||||
"squish": "Zmáčknout",
|
||||
"drip": "Kapat",
|
||||
"pour": "Lít",
|
||||
"trickle": "Stékat",
|
||||
"fill": "Naplnit",
|
||||
"stir": "Míchat",
|
||||
"boiling": "Vařící",
|
||||
"sonar": "Sonar",
|
||||
"arrow": "Šíp",
|
||||
"electronic_tuner": "Elektronický Ladič",
|
||||
"bang": "Rána",
|
||||
"slap": "Plácnout",
|
||||
"smash": "Rozmlátit",
|
||||
"bouncing": "Odrážející",
|
||||
"scratch": "Škrábat"
|
||||
"field_recording": "Nahrávka z terénu"
|
||||
}
|
||||
|
||||
@@ -859,8 +859,8 @@
|
||||
"description": "Numeric order used to sort the camera in the UI (default dashboard and lists); larger numbers appear later."
|
||||
},
|
||||
"dashboard": {
|
||||
"label": "Show on Live dashboard",
|
||||
"description": "Toggle whether this camera is visible on the default All Cameras live dashboard. The camera remains available everywhere else in the UI, including camera groups and settings."
|
||||
"label": "Show in UI",
|
||||
"description": "Toggle whether this camera is visible everywhere in the Frigate UI. Disabling this will require manually editing the config to view this camera in the UI again."
|
||||
},
|
||||
"review": {
|
||||
"label": "Show in review",
|
||||
|
||||
@@ -357,6 +357,22 @@
|
||||
"description": "Optional API key for authenticated DeepStack services."
|
||||
}
|
||||
},
|
||||
"degirum": {
|
||||
"label": "DeGirum",
|
||||
"description": "DeGirum detector for running models via DeGirum cloud or local inference services.",
|
||||
"location": {
|
||||
"label": "Inference Location",
|
||||
"description": "Location of the DeGirim inference engine (e.g. '@cloud', '127.0.0.1')."
|
||||
},
|
||||
"zoo": {
|
||||
"label": "Model Zoo",
|
||||
"description": "Path or URL to the DeGirum model zoo."
|
||||
},
|
||||
"token": {
|
||||
"label": "DeGirum Cloud Token",
|
||||
"description": "Token for DeGirum Cloud access."
|
||||
}
|
||||
},
|
||||
"edgetpu": {
|
||||
"label": "EdgeTPU",
|
||||
"description": "EdgeTPU detector that runs TensorFlow Lite models compiled for Coral EdgeTPU using the EdgeTPU delegate.",
|
||||
@@ -1527,8 +1543,8 @@
|
||||
"description": "Numeric order used to sort the camera in the UI (default dashboard and lists); larger numbers appear later."
|
||||
},
|
||||
"dashboard": {
|
||||
"label": "Show on Live dashboard",
|
||||
"description": "Toggle whether this camera is visible on the default All Cameras live dashboard. The camera remains available everywhere else in the UI, including camera groups and settings."
|
||||
"label": "Show in UI",
|
||||
"description": "Toggle whether this camera is visible everywhere in the Frigate UI. Disabling this will require manually editing the config to view this camera in the UI again."
|
||||
},
|
||||
"review": {
|
||||
"label": "Show in review",
|
||||
|
||||
@@ -499,7 +499,7 @@
|
||||
"webuiUrlHelp": "URL to visit the camera's web UI directly from the Debug view. Leave blank to disable the link.",
|
||||
"webuiUrlInvalid": "Must be a valid URL (e.g., https://example.com).",
|
||||
"dashboardLabel": "Show on Live dashboard",
|
||||
"dashboardHelp": "Show this camera on the default All Cameras live dashboard. It remains available everywhere else, including camera groups.",
|
||||
"dashboardHelp": "Show this camera on the Live dashboard.",
|
||||
"reviewLabel": "Show in Review",
|
||||
"reviewHelp": "Show this camera in Review, including the camera filter, motion review, and the history view."
|
||||
}
|
||||
|
||||
@@ -36,9 +36,6 @@
|
||||
"ask_a": "¿Es este objeto un <code>{{label}}</code>?",
|
||||
"ask_an": "¿Es este objeto un <code>{{label}}</code>?",
|
||||
"ask_full": "¿Es este objeto un <code>{{untranslatedLabel}}</code> ({{translatedLabel}})?"
|
||||
},
|
||||
"toast": {
|
||||
"error": "Error al enviarlo a Frigate+. Por favor, comprueba tu conexión a internet y prueba de nuevo."
|
||||
}
|
||||
}
|
||||
},
|
||||
@@ -71,8 +68,7 @@
|
||||
"error": {
|
||||
"failed": "No se pudo iniciar la exportación: {{error}}",
|
||||
"noVaildTimeSelected": "No se seleccionó un rango de tiempo válido",
|
||||
"endTimeMustAfterStartTime": "La hora de finalización debe ser posterior a la hora de inicio",
|
||||
"noValidTimeSelected": "Rango de tiempo seleccionado no valido"
|
||||
"endTimeMustAfterStartTime": "La hora de finalización debe ser posterior a la hora de inicio"
|
||||
},
|
||||
"success": "Exportación iniciada con éxito. Ver el archivo en la página exportaciones.",
|
||||
"view": "Vista",
|
||||
|
||||
@@ -273,69 +273,5 @@
|
||||
"sailboat": "Purjekas",
|
||||
"soundtrack_music": "Filmimuusika",
|
||||
"jingle": "Kõlisemine/tilisemine",
|
||||
"theme_music": "Tunnusmuusika",
|
||||
"steel_guitar": "Steel Kitarr",
|
||||
"tapping": "Koputamine",
|
||||
"strum": "Klimberdus",
|
||||
"drum_machine": "Trummimasin",
|
||||
"drum": "Trumm",
|
||||
"maraca": "Marakas",
|
||||
"bowed_string_instrument": "Poogenkeelpill",
|
||||
"singing_bowl": "Helikauss",
|
||||
"wind_noise": "Tuulemüra",
|
||||
"rustling_leaves": "Sahisevad lehed",
|
||||
"waves": "Lained",
|
||||
"steam": "Aur",
|
||||
"ship": "Laev",
|
||||
"motor_vehicle": "Mootorsõiduk",
|
||||
"rowboat": "Sõudepaat",
|
||||
"motorboat": "Mootorpaat",
|
||||
"waterfall": "Kosk",
|
||||
"ocean": "Ookean",
|
||||
"rain_on_surface": "Vihm pinnal",
|
||||
"stream": "Oja",
|
||||
"fire": "Tuli",
|
||||
"crackle": "Praksumine",
|
||||
"car_alarm": "Auto alarm",
|
||||
"truck": "Veoauto",
|
||||
"police_car": "Politseiauto",
|
||||
"ambulance": "Kiirabi",
|
||||
"fire_engine": "Tuletõrjeauto",
|
||||
"aircraft": "Lennuk",
|
||||
"aircraft_engine": "Lennukimootor",
|
||||
"jet_engine": "Reaktiivmootor",
|
||||
"propeller": "Propeller",
|
||||
"helicopter": "Helikopter",
|
||||
"fixed-wing_aircraft": "Fikseeritud tiivaga õhusõiduk",
|
||||
"train_horn": "Rongisignaal",
|
||||
"railroad_car": "Kaubavagun",
|
||||
"lawn_mower": "Muruniiduk",
|
||||
"chainsaw": "mootorsaag",
|
||||
"engine": "Mootor",
|
||||
"knock": "Koputus",
|
||||
"alarm": "Häire",
|
||||
"siren": "Sireen",
|
||||
"fire_alarm": "Tulekahjuhäire",
|
||||
"telephone": "Telefon",
|
||||
"telephone_bell_ringing": "Helisev telefon",
|
||||
"ringtone": "Telefonihelin",
|
||||
"smoke_detector": "Suitsuandur",
|
||||
"foghorn": "Udupasun",
|
||||
"whistle": "Vile",
|
||||
"printer": "Printer",
|
||||
"drill": "Puur",
|
||||
"explosion": "Plahvatus",
|
||||
"hammer": "Haamer",
|
||||
"air_conditioning": "Õhukonditsioneer",
|
||||
"gunshot": "Püssilask",
|
||||
"glass": "Klaas",
|
||||
"boom": "Pauk",
|
||||
"fireworks": "Ilutulestik",
|
||||
"static": "Staatiline",
|
||||
"white_noise": "Valge müra",
|
||||
"radio": "Raadio",
|
||||
"television": "Televiisor",
|
||||
"scream": "Karjumine",
|
||||
"pour": "Valamine",
|
||||
"drip": "Tilkumine"
|
||||
"theme_music": "Tunnusmuusika"
|
||||
}
|
||||
|
||||
@@ -37,9 +37,6 @@
|
||||
"ask_an": "Kas see objekt on <code>{{label}}</code>?",
|
||||
"ask_full": "Kas see objekt on <code>{{untranslatedLabel}}</code> ({{translatedLabel}})?",
|
||||
"label": "Kinnita see silt Frigate+ teenuse jaoks"
|
||||
},
|
||||
"toast": {
|
||||
"error": "Frigate+ teenusesse saatmine ebaõnnestus. Palun kontrolli oma võrguühendust ja proovi uuesti."
|
||||
}
|
||||
},
|
||||
"submitToPlus": {
|
||||
@@ -73,79 +70,18 @@
|
||||
"success": "Eksportimise käivitamine õnnestus. Faili leiad eksportimise lehelt.",
|
||||
"view": "Vaata",
|
||||
"error": {
|
||||
"failed": "Eksportimise järjekorda lisamine ei õnnestunud: {{error}}",
|
||||
"failed": "Eksportimise käivitamine ei õnnestunud: {{error}}",
|
||||
"endTimeMustAfterStartTime": "Ajavahemiku lõpp peab olema peale algust",
|
||||
"noVaildTimeSelected": "Ühtegi kehtivat ajavahemikku pole valitud",
|
||||
"noValidTimeSelected": "Ühtegi korrektset ajavahemikku pole valitud"
|
||||
},
|
||||
"queued": "Eksport on järjekorda lisatud. Vaata progressi eksportide lehelt.",
|
||||
"batchSuccess_one": "Alustasin 1 ekspordiga. Avan juhtumi kohe.",
|
||||
"batchSuccess_other": "Alustasin {{count}} ekspordiga. Avan juhtumi kohe.",
|
||||
"batchPartial": "Alustasin {{successful}}/{{total}} ekspordiga. Ebaõnnestunud kaamerad: {{failedCameras}}",
|
||||
"batchFailed": "{{total}} eksporti ebaõnnestus algatada. Ebaõnnestunud kaamerad: {{failedCameras}}",
|
||||
"batchQueuedSuccess_one": "1 eksport järjekorda lisatud. Avan juhtumi kohe.",
|
||||
"batchQueuedSuccess_other": "{{count}} eksporti järjekorda lisatud. Avan juhtumi kohe.",
|
||||
"batchQueuedPartial": "{{successful}}/{{total}} ekspordist on järjekorda lisatud. Ebaõnnestunud kaamerad: {{failedCameras}}",
|
||||
"batchQueueFailed": "{{total}} eksporti ebaõnnestus järjekorda lisada. Ebaõnnestunud kaamerad: {{failedCameras}}"
|
||||
"noVaildTimeSelected": "Ühtegi kehtivat ajavahemikku pole valitud"
|
||||
}
|
||||
},
|
||||
"fromTimeline": {
|
||||
"saveExport": "Salvesta eksporditud sisu",
|
||||
"previewExport": "Eksporditud sisu eelvaade",
|
||||
"queueingExport": "Ekspordi järjekorda lisamine...",
|
||||
"useThisRange": "Kasuta seda vahemikku"
|
||||
"previewExport": "Eksporditud sisu eelvaade"
|
||||
},
|
||||
"case": {
|
||||
"label": "Juhtum",
|
||||
"placeholder": "Vali juhtum",
|
||||
"newCaseOption": "Ava uus juhtum",
|
||||
"newCaseNamePlaceholder": "Uue juhtumi nimi",
|
||||
"newCaseDescriptionPlaceholder": "Juhtumi kirjeldus",
|
||||
"nonAdminHelp": "Uus juhtum avatakse järnevatele eksportidele."
|
||||
},
|
||||
"queueing": "Ekspordi järjekorda lisamine...",
|
||||
"tabs": {
|
||||
"export": "Üksik kaamera",
|
||||
"multiCamera": "Mitu kaamerat"
|
||||
},
|
||||
"multiCamera": {
|
||||
"timeRange": "Ajavahemik",
|
||||
"selectFromTimeline": "Vali ajajoonelt",
|
||||
"cameraSelection": "Kaamerad",
|
||||
"cameraSelectionHelp": "Selles ajavahemikus tuvastatud objektidega kaamerad on eelvalitud",
|
||||
"searchOrSelectGroup": "Otsi või vali kaamera grupp...",
|
||||
"selectAll": "Vali kõik kaamerad",
|
||||
"clearSelection": "Tühista valik",
|
||||
"selectWithActivity": "Jälgitud objektidega kaamerad",
|
||||
"selectGroup": "Vali grupp",
|
||||
"noMatchingCameras": "Ükski kaamera ei sobitunud otsinguga",
|
||||
"selectedCount": "{{selected}} / {{total}} valitud",
|
||||
"checkingActivity": "Kaamera aktiivsuse kontrollimine...",
|
||||
"noCameras": "Ühtegi kaamerat pole saadaval",
|
||||
"detectionCount_one": "1 jälgitav objekt",
|
||||
"detectionCount_other": "{{count}} jälgitavat objekti",
|
||||
"nameLabel": "Ekspordi nimi",
|
||||
"namePlaceholder": "Valikuline baasnimi nendele eksportidele",
|
||||
"queueingButton": "Ekspordi järjekorda lisamine...",
|
||||
"exportButton_one": "Ekspordi 1 kaamera",
|
||||
"exportButton_other": "Ekspordi {{count}} kaamerat"
|
||||
},
|
||||
"multi": {
|
||||
"title_one": "Ekspordi 1 ülevaade",
|
||||
"title_other": "Espordi {{count}} ülevaadet",
|
||||
"description": "Ekspordi valitud ülevaated. Kõik ekspordid on grupeeritud ühte juhtumisse.",
|
||||
"descriptionNoCase": "Ekspordi kõik valitud juhtumid.",
|
||||
"caseNamePlaceholder": "Ülevaate eksport - {{date}}",
|
||||
"exportButton_one": "Ekspordi 1 ülevaade",
|
||||
"exportButton_other": "Ekspordi {{count}} ülevaadet",
|
||||
"exportingButton": "Ekspordin...",
|
||||
"toast": {
|
||||
"started_one": "Alustasin 1 eksporti. Avan juhtumi kohe.",
|
||||
"started_other": "Alustasin {{count}} ekspordiga. Avan juhtumi kohe.",
|
||||
"startedNoCase_one": "Alustasin 1 ekspordiga.",
|
||||
"startedNoCase_other": "Alustasin {{count}} ekspordiga.",
|
||||
"partial": "Alustasin {{successful}}/{{total}} ekspordiga. Ebaõnnestusid: {{failedItems}}",
|
||||
"failed": "{{total}} ekspordi algatamine ebaõnnestus. Ebaõnnestunud: {{failedItems}}"
|
||||
}
|
||||
"placeholder": "Vali juhtum"
|
||||
}
|
||||
},
|
||||
"streaming": {
|
||||
@@ -178,14 +114,6 @@
|
||||
"success": "Selle ülevaadatava objektiga seotud videosisu on kustutatud.",
|
||||
"error": "Kustutamine ei õnnestunud: {{error}}"
|
||||
}
|
||||
},
|
||||
"shareTimestamp": {
|
||||
"label": "Jaotise ajatempel",
|
||||
"title": "Jaotise ajatempel",
|
||||
"description": "Jaga praeguse esituskoha ajatempliga URL-i või vali kohandatud ajatempel. Arvesta, et see ei ole avalik jagamislink ja on kättesaadav ainult kasutajatele, kellel on ligipääs Frigate'ile ja sellele kaamerale.",
|
||||
"custom": "Kohandatud ajatempel",
|
||||
"button": "Jaga ajatempliga linki",
|
||||
"shareTitle": "Frigate ülevaate ajatempel: {{camera}}"
|
||||
}
|
||||
},
|
||||
"imagePicker": {
|
||||
|
||||
@@ -12,20 +12,11 @@
|
||||
"description": "Kasutusel"
|
||||
},
|
||||
"audio": {
|
||||
"label": "Heli tuvastus",
|
||||
"min_volume": {
|
||||
"label": "Minimaalne helitase"
|
||||
},
|
||||
"filters": {
|
||||
"label": "Audio filtrid"
|
||||
}
|
||||
"label": "Helisündmused"
|
||||
},
|
||||
"birdseye": {
|
||||
"mode": {
|
||||
"label": "Jälgimisrežiim"
|
||||
},
|
||||
"order": {
|
||||
"label": "Positsioon"
|
||||
}
|
||||
},
|
||||
"label": "Kaameraseadistus",
|
||||
@@ -34,8 +25,7 @@
|
||||
"threshold": {
|
||||
"description": "Minimaalne sarnasuse punktiskoor (0-1), mis on vajalik selle päästiku käivitamiseks."
|
||||
}
|
||||
},
|
||||
"label": "Semantiline otsing"
|
||||
}
|
||||
},
|
||||
"lpr": {
|
||||
"label": "Sõidukite numbrimärkide tuvastus",
|
||||
@@ -44,171 +34,6 @@
|
||||
"review": {
|
||||
"genai": {
|
||||
"description": "Kontrollib generatiivse tehisaru kasutamist kirjelduste ja kokkuvõtete koostamiseks ülevaatamisele kuuluvate objektide jaoks."
|
||||
},
|
||||
"alerts": {
|
||||
"enabled": {
|
||||
"label": "Luba häired"
|
||||
}
|
||||
}
|
||||
},
|
||||
"audio_transcription": {
|
||||
"label": "Audio transkriptsioon",
|
||||
"live_enabled": {
|
||||
"label": "Reaalajas transkriptsioon"
|
||||
},
|
||||
"enabled": {
|
||||
"label": "Luba heli üleskirjutamine tekstina"
|
||||
}
|
||||
},
|
||||
"detect": {
|
||||
"label": "Objekti tuvastus",
|
||||
"enabled": {
|
||||
"label": "Luba objektituvastus"
|
||||
},
|
||||
"height": {
|
||||
"label": "Tuvastamise kõrgus"
|
||||
},
|
||||
"width": {
|
||||
"label": "Tuvastamise laius"
|
||||
},
|
||||
"fps": {
|
||||
"label": "Tuvastamise kaadrisagedus"
|
||||
},
|
||||
"stationary": {
|
||||
"label": "Püsivate objektide sätted"
|
||||
}
|
||||
},
|
||||
"face_recognition": {
|
||||
"label": "Näotuvastus"
|
||||
},
|
||||
"ffmpeg": {
|
||||
"path": {
|
||||
"label": "FFmpeg asukoht"
|
||||
},
|
||||
"gpu": {
|
||||
"label": "GPU indeks"
|
||||
},
|
||||
"inputs": {
|
||||
"global_args": {
|
||||
"label": "FFmpeg globaalsed argumendid"
|
||||
}
|
||||
}
|
||||
},
|
||||
"live": {
|
||||
"label": "Reaalajas mahamängimine",
|
||||
"height": {
|
||||
"label": "Otseülekande kõrgus"
|
||||
},
|
||||
"quality": {
|
||||
"label": "Otseülekande kvaliteet"
|
||||
},
|
||||
"streams": {
|
||||
"label": "Otseülekande voo nimed"
|
||||
}
|
||||
},
|
||||
"motion": {
|
||||
"label": "Liikumistuvastus",
|
||||
"frame_height": {
|
||||
"label": "Kaadri kõrgus"
|
||||
},
|
||||
"enabled": {
|
||||
"label": "Luba liikumistuvastus"
|
||||
}
|
||||
},
|
||||
"objects": {
|
||||
"label": "Objektid",
|
||||
"filters": {
|
||||
"min_area": {
|
||||
"label": "Minimaalne objekti ala"
|
||||
},
|
||||
"max_area": {
|
||||
"label": "Maksimaalne objekti ala"
|
||||
}
|
||||
},
|
||||
"genai": {
|
||||
"label": "GenAI objekti konfiguratsioon",
|
||||
"required_zones": {
|
||||
"label": "Nõutud tsoonid"
|
||||
},
|
||||
"debug_save_thumbnails": {
|
||||
"label": "Salvesta pisipildid"
|
||||
},
|
||||
"enabled": {
|
||||
"label": "Luba GenAI"
|
||||
},
|
||||
"use_snapshot": {
|
||||
"label": "Kasuta hetktõmmiseid"
|
||||
}
|
||||
}
|
||||
},
|
||||
"mqtt": {
|
||||
"label": "MQTT"
|
||||
},
|
||||
"notifications": {
|
||||
"enabled": {
|
||||
"label": "Luba teavitused"
|
||||
},
|
||||
"email": {
|
||||
"label": "Teavituste email"
|
||||
},
|
||||
"label": "Teavitused"
|
||||
},
|
||||
"ui": {
|
||||
"label": "Kaamera kasutajaliides"
|
||||
},
|
||||
"record": {
|
||||
"label": "Salvestus",
|
||||
"enabled": {
|
||||
"label": "Luba salvestamine"
|
||||
}
|
||||
},
|
||||
"snapshots": {
|
||||
"enabled": {
|
||||
"label": "Luba hetktõmmised"
|
||||
}
|
||||
},
|
||||
"timestamp_style": {
|
||||
"color": {
|
||||
"red": {
|
||||
"label": "Punane",
|
||||
"description": "Punase komponent (0–255) ajatempli värvi jaoks."
|
||||
},
|
||||
"green": {
|
||||
"label": "Roheline",
|
||||
"description": "Rohelise komponent (0–255) ajatempli värvi jaoks."
|
||||
},
|
||||
"blue": {
|
||||
"label": "Sinine",
|
||||
"description": "Sinise komponent (0–255) ajatempli värvi jaoks."
|
||||
},
|
||||
"label": "Ajatempli värv",
|
||||
"description": "Ajatempli teksti RGB värviväärtused (kõik väärtused 0–255)."
|
||||
},
|
||||
"thickness": {
|
||||
"label": "Ajatempli paksus",
|
||||
"description": "Ajatempli teksti joone paksus."
|
||||
},
|
||||
"effect": {
|
||||
"label": "Ajatempli efekt",
|
||||
"description": "Ajatempli teksti visuaalne efekt (puudub, ühtlane, vari)."
|
||||
}
|
||||
},
|
||||
"onvif": {
|
||||
"user": {
|
||||
"label": "ONVIF kasutajanimi"
|
||||
},
|
||||
"password": {
|
||||
"label": "ONVIF parool"
|
||||
},
|
||||
"port": {
|
||||
"label": "ONVIF port"
|
||||
},
|
||||
"label": "ONVIF",
|
||||
"host": {
|
||||
"label": "ONVIF host"
|
||||
}
|
||||
},
|
||||
"profiles": {
|
||||
"label": "Profiilid"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,31 +1,10 @@
|
||||
{
|
||||
"audio": {
|
||||
"label": "Heli tuvastus",
|
||||
"min_volume": {
|
||||
"label": "Minimaalne helitase"
|
||||
},
|
||||
"filters": {
|
||||
"label": "Audio filtrid"
|
||||
}
|
||||
"label": "Helisündmused"
|
||||
},
|
||||
"birdseye": {
|
||||
"mode": {
|
||||
"label": "Jälgimisrežiim"
|
||||
},
|
||||
"order": {
|
||||
"label": "Positsioon"
|
||||
},
|
||||
"height": {
|
||||
"label": "Kõrgus"
|
||||
},
|
||||
"width": {
|
||||
"label": "Laius"
|
||||
},
|
||||
"layout": {
|
||||
"label": "Paigutus",
|
||||
"scaling_factor": {
|
||||
"label": "Skaleerimistegur"
|
||||
}
|
||||
}
|
||||
},
|
||||
"version": {
|
||||
@@ -43,15 +22,6 @@
|
||||
"threshold": {
|
||||
"label": "Punktiskoori lävend",
|
||||
"description": "Punktiskoori lävend, mida kasutatakse klassifitseerimise oleku muutmiseks."
|
||||
},
|
||||
"enabled": {
|
||||
"label": "Luba mudel"
|
||||
},
|
||||
"name": {
|
||||
"label": "Mudeli nimi"
|
||||
},
|
||||
"save_attempts": {
|
||||
"label": "Salvestamiskatsed"
|
||||
}
|
||||
}
|
||||
},
|
||||
@@ -60,25 +30,11 @@
|
||||
"threshold": {
|
||||
"description": "Minimaalne sarnasuse punktiskoor (0-1), mis on vajalik selle päästiku käivitamiseks."
|
||||
}
|
||||
},
|
||||
"label": "Semantiline otsing",
|
||||
"model_size": {
|
||||
"label": "Mudeli suurus"
|
||||
},
|
||||
"device": {
|
||||
"label": "Seade"
|
||||
}
|
||||
},
|
||||
"face_recognition": {
|
||||
"unknown_score": {
|
||||
"label": "Tundmatu punktiskoori lävend"
|
||||
},
|
||||
"label": "Näotuvastus",
|
||||
"model_size": {
|
||||
"label": "Mudeli suurus"
|
||||
},
|
||||
"device": {
|
||||
"label": "Seade"
|
||||
}
|
||||
},
|
||||
"lpr": {
|
||||
@@ -86,393 +42,15 @@
|
||||
"description": "Sõidukite numbrimärkide tuvastuse seadistus sisaldab tuvastuse lävendeid, vormindust ja teadaolevaid numbrimärke.",
|
||||
"enabled": {
|
||||
"description": "Lülita sõidukite numbrimärkide tuvastus kõikide kaamerate jaoks sisse; seda saad kaamerakohaselt ka sürjutada."
|
||||
},
|
||||
"model_size": {
|
||||
"label": "Mudeli suurus"
|
||||
},
|
||||
"device": {
|
||||
"label": "Seade"
|
||||
}
|
||||
},
|
||||
"genai": {
|
||||
"label": "Generatiivse tehisaru seadistus",
|
||||
"description": "Seadistsued generatiivse tehisaru teenusepakkujate kasutamisel kirjelduste ja kokkuvõtete loomiseks ülevaatamisele kuuluvate objektide jaoks.",
|
||||
"model": {
|
||||
"label": "Mudel"
|
||||
},
|
||||
"roles": {
|
||||
"label": "Rollid"
|
||||
},
|
||||
"api_key": {
|
||||
"label": "API võti"
|
||||
},
|
||||
"base_url": {
|
||||
"label": "Baas-URL"
|
||||
}
|
||||
"description": "Seadistsued generatiivse tehisaru teenusepakkujate kasutamisel kirjelduste ja kokkuvõtete loomiseks ülevaatamisele kuuluvate objektide jaoks."
|
||||
},
|
||||
"review": {
|
||||
"genai": {
|
||||
"description": "Kontrollib generatiivse tehisaru kasutamist kirjelduste ja kokkuvõtete koostamiseks ülevaatamisele kuuluvate objektide jaoks."
|
||||
},
|
||||
"alerts": {
|
||||
"enabled": {
|
||||
"label": "Luba häired",
|
||||
"description": "Luba või keela kõigi kaamerate häirete genereerimine; seda saab kaamerati eraldi muuta."
|
||||
}
|
||||
}
|
||||
},
|
||||
"audio_transcription": {
|
||||
"label": "Audio transkriptsioon",
|
||||
"live_enabled": {
|
||||
"label": "Reaalajas transkriptsioon"
|
||||
},
|
||||
"enabled": {
|
||||
"label": "Luba heli üleskirjutamine tekstina",
|
||||
"description": "Luba või keela automaatne heli üleskirjutamine tektina kõigi kaamerate jaoks; seda saad kaamerati eraldi muuta."
|
||||
},
|
||||
"language": {
|
||||
"label": "Üleskirjutuse keel",
|
||||
"description": "Üleskirjutuse/tõlkimise jaoks kasutatav keelekood (näiteks „en” inglise keele puhul). Toetatud keelekoodid leiad lehelt https://whisper-api.com/docs/languages/."
|
||||
},
|
||||
"device": {
|
||||
"label": "Üleskirjutusseade"
|
||||
},
|
||||
"model_size": {
|
||||
"label": "Mudeli suurus"
|
||||
}
|
||||
},
|
||||
"detect": {
|
||||
"label": "Objekti tuvastus",
|
||||
"enabled": {
|
||||
"label": "Luba objektituvastus"
|
||||
},
|
||||
"height": {
|
||||
"label": "Tuvastamise kõrgus"
|
||||
},
|
||||
"width": {
|
||||
"label": "Tuvastamise laius"
|
||||
},
|
||||
"fps": {
|
||||
"label": "Tuvastamise kaadrisagedus"
|
||||
},
|
||||
"stationary": {
|
||||
"label": "Püsivate objektide sätted"
|
||||
}
|
||||
},
|
||||
"ffmpeg": {
|
||||
"path": {
|
||||
"label": "FFmpeg asukoht"
|
||||
},
|
||||
"gpu": {
|
||||
"label": "GPU indeks"
|
||||
},
|
||||
"inputs": {
|
||||
"global_args": {
|
||||
"label": "FFmpeg globaalsed argumendid"
|
||||
}
|
||||
},
|
||||
"label": "FFmpeg"
|
||||
},
|
||||
"live": {
|
||||
"label": "Reaalajas mahamängimine",
|
||||
"height": {
|
||||
"label": "Otseülekande kõrgus"
|
||||
},
|
||||
"quality": {
|
||||
"label": "Otseülekande kvaliteet"
|
||||
},
|
||||
"streams": {
|
||||
"label": "Otseülekande voo nimed"
|
||||
}
|
||||
},
|
||||
"motion": {
|
||||
"label": "Liikumistuvastus",
|
||||
"frame_height": {
|
||||
"label": "Kaadri kõrgus"
|
||||
},
|
||||
"enabled": {
|
||||
"label": "Luba liikumistuvastus"
|
||||
}
|
||||
},
|
||||
"objects": {
|
||||
"label": "Objektid",
|
||||
"filters": {
|
||||
"min_area": {
|
||||
"label": "Minimaalne objekti ala"
|
||||
},
|
||||
"max_area": {
|
||||
"label": "Maksimaalne objekti ala"
|
||||
}
|
||||
},
|
||||
"genai": {
|
||||
"label": "GenAI objekti konfiguratsioon",
|
||||
"required_zones": {
|
||||
"label": "Nõutud tsoonid"
|
||||
},
|
||||
"debug_save_thumbnails": {
|
||||
"label": "Salvesta pisipildid"
|
||||
},
|
||||
"enabled": {
|
||||
"label": "Luba GenAI"
|
||||
},
|
||||
"use_snapshot": {
|
||||
"label": "Kasuta hetktõmmiseid"
|
||||
}
|
||||
}
|
||||
},
|
||||
"auth": {
|
||||
"reset_admin_password": {
|
||||
"label": "Lähtesta administraatori parool"
|
||||
},
|
||||
"cookie_name": {
|
||||
"label": "JWT küpsise nimi"
|
||||
},
|
||||
"enabled": {
|
||||
"label": "Luba autentimine"
|
||||
},
|
||||
"label": "Autentimine",
|
||||
"session_length": {
|
||||
"label": "Sessiooni pikkus"
|
||||
}
|
||||
},
|
||||
"database": {
|
||||
"label": "Andmebaas"
|
||||
},
|
||||
"mqtt": {
|
||||
"label": "MQTT",
|
||||
"enabled": {
|
||||
"label": "Luba MQTT"
|
||||
},
|
||||
"user": {
|
||||
"label": "MQTT kasutajanimi"
|
||||
},
|
||||
"tls_ca_certs": {
|
||||
"label": "TLS CA sertifikaat"
|
||||
},
|
||||
"tls_client_cert": {
|
||||
"label": "Kliendi sertifikaat"
|
||||
},
|
||||
"host": {
|
||||
"label": "MQTT host",
|
||||
"description": "MQTT maakleri hostinimi või IP-aadress."
|
||||
},
|
||||
"port": {
|
||||
"label": "MQTT port"
|
||||
},
|
||||
"client_id": {
|
||||
"label": "Kliendi ID"
|
||||
},
|
||||
"stats_interval": {
|
||||
"label": "Statistika intervall"
|
||||
},
|
||||
"tls_client_key": {
|
||||
"label": "Kliendi võti"
|
||||
},
|
||||
"qos": {
|
||||
"label": "MQTT QoS"
|
||||
}
|
||||
},
|
||||
"go2rtc": {
|
||||
"label": "go2rtc"
|
||||
},
|
||||
"notifications": {
|
||||
"enabled": {
|
||||
"label": "Luba teavitused"
|
||||
},
|
||||
"email": {
|
||||
"label": "Teavituste email"
|
||||
},
|
||||
"label": "Teavitused"
|
||||
},
|
||||
"networking": {
|
||||
"ipv6": {
|
||||
"label": "IPv6 sätted",
|
||||
"enabled": {
|
||||
"label": "Luba IPv6"
|
||||
}
|
||||
},
|
||||
"label": "Võrguühendus",
|
||||
"listen": {
|
||||
"internal": {
|
||||
"label": "Sisemine port"
|
||||
},
|
||||
"external": {
|
||||
"label": "Välimine port"
|
||||
}
|
||||
}
|
||||
},
|
||||
"proxy": {
|
||||
"label": "Vaheserver",
|
||||
"logout_url": {
|
||||
"label": "Väljalogimise url"
|
||||
},
|
||||
"default_role": {
|
||||
"label": "Vaikimisi roll"
|
||||
}
|
||||
},
|
||||
"telemetry": {
|
||||
"stats": {
|
||||
"label": "Süsteemi statistika",
|
||||
"amd_gpu_stats": {
|
||||
"label": "AMD GPU statistika"
|
||||
},
|
||||
"intel_gpu_stats": {
|
||||
"label": "Intel GPU statistika"
|
||||
},
|
||||
"intel_gpu_device": {
|
||||
"label": "Intel GPU seade"
|
||||
},
|
||||
"network_bandwidth": {
|
||||
"label": "Võrgu ribalaius"
|
||||
}
|
||||
},
|
||||
"label": "Telemeetria",
|
||||
"network_interfaces": {
|
||||
"label": "Võrguliides"
|
||||
},
|
||||
"version_check": {
|
||||
"label": "Versiooni kontroll",
|
||||
"description": "Luba väljaminev ühendus, et kontrollida uuema Frigate versooni olemasolu."
|
||||
}
|
||||
},
|
||||
"tls": {
|
||||
"label": "TLS",
|
||||
"enabled": {
|
||||
"label": "Luba TLS"
|
||||
}
|
||||
},
|
||||
"ui": {
|
||||
"timezone": {
|
||||
"label": "ajavöönd"
|
||||
},
|
||||
"label": "Kasutajaliides",
|
||||
"description": "Kasutajaliidese eelistused nagu ajavöönd, aja ja kuupäeva formaat ning ühikud.",
|
||||
"unit_system": {
|
||||
"label": "Ühikute süsteem"
|
||||
}
|
||||
},
|
||||
"detectors": {
|
||||
"label": "Detektori riistvara",
|
||||
"cpu": {
|
||||
"label": "CPU"
|
||||
},
|
||||
"deepstack": {
|
||||
"label": "DeepStack"
|
||||
},
|
||||
"edgetpu": {
|
||||
"label": "EdgeTPU"
|
||||
},
|
||||
"onnx": {
|
||||
"label": "ONNX",
|
||||
"device": {
|
||||
"label": "Seadme tüüp"
|
||||
}
|
||||
},
|
||||
"rknn": {
|
||||
"label": "RKNN"
|
||||
},
|
||||
"openvino": {
|
||||
"label": "OpenVINO"
|
||||
},
|
||||
"tensorrt": {
|
||||
"label": "TensorRT"
|
||||
},
|
||||
"teflon_tfl": {
|
||||
"label": "Teflon"
|
||||
},
|
||||
"synaptics": {
|
||||
"label": "Synaptics"
|
||||
},
|
||||
"memryx": {
|
||||
"device": {
|
||||
"label": "Seadme asukoht"
|
||||
},
|
||||
"label": "MemryX"
|
||||
},
|
||||
"hailo8l": {
|
||||
"label": "Hailo-8/Hailo-8L"
|
||||
},
|
||||
"zmq": {
|
||||
"label": "ZMQ IPC"
|
||||
},
|
||||
"axengine": {
|
||||
"label": "AXEngine NPU"
|
||||
},
|
||||
"degirum": {
|
||||
"label": "DeGirum"
|
||||
}
|
||||
},
|
||||
"logger": {
|
||||
"label": "Logimine"
|
||||
},
|
||||
"model": {
|
||||
"label": "Tuvastusmudel"
|
||||
},
|
||||
"record": {
|
||||
"label": "Salvestus",
|
||||
"enabled": {
|
||||
"label": "Luba salvestamine",
|
||||
"description": "Luba või keela salvestamine kõigi kaamerate jaoks; seda saab kaamerati eraldi muuta."
|
||||
}
|
||||
},
|
||||
"snapshots": {
|
||||
"enabled": {
|
||||
"label": "Luba hetktõmmised",
|
||||
"description": "Luba või keela kõigi kaamerate hetktõmmiste salvestamine; seda saab kaamerati eraldi muuta."
|
||||
}
|
||||
},
|
||||
"timestamp_style": {
|
||||
"color": {
|
||||
"red": {
|
||||
"label": "Punane",
|
||||
"description": "Punase komponent (0–255) ajatempli värvi jaoks."
|
||||
},
|
||||
"green": {
|
||||
"label": "Roheline",
|
||||
"description": "Rohelise komponent (0–255) ajatempli värvi jaoks."
|
||||
},
|
||||
"blue": {
|
||||
"label": "Sinine",
|
||||
"description": "Sinise komponent (0–255) ajatempli värvi jaoks."
|
||||
},
|
||||
"label": "Ajatempli värv",
|
||||
"description": "Ajatempli teksti RGB värviväärtused (kõik väärtused 0–255)."
|
||||
},
|
||||
"thickness": {
|
||||
"label": "Ajatempli paksus",
|
||||
"description": "Ajatempli teksti joone paksus."
|
||||
},
|
||||
"effect": {
|
||||
"label": "Ajatempli efekt",
|
||||
"description": "Ajatempli teksti visuaalne efekt (puudub, ühtlane, vari)."
|
||||
}
|
||||
},
|
||||
"onvif": {
|
||||
"user": {
|
||||
"label": "ONVIF kasutajanimi"
|
||||
},
|
||||
"password": {
|
||||
"label": "ONVIF parool"
|
||||
},
|
||||
"port": {
|
||||
"label": "ONVIF port"
|
||||
},
|
||||
"label": "ONVIF",
|
||||
"host": {
|
||||
"label": "ONVIF host"
|
||||
}
|
||||
},
|
||||
"camera_mqtt": {
|
||||
"quality": {
|
||||
"label": "JPEG kvaliteet",
|
||||
"description": "MQTT-sse saadetud piltide JPEG-kvaliteet (0–100)."
|
||||
},
|
||||
"enabled": {
|
||||
"label": "Saada pilt"
|
||||
},
|
||||
"label": "MQTT"
|
||||
},
|
||||
"profiles": {
|
||||
"label": "Profiilid"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -6,67 +6,5 @@
|
||||
"error": "Midagi läks valesti. Palun proovi uuesti.",
|
||||
"processing": "Töötlen…",
|
||||
"toolsUsed": "Kasutatud: {{tools}}",
|
||||
"similarity_score": "Sarnasus",
|
||||
"showTools": "Näita tööriistu ({{count}})",
|
||||
"hideTools": "Peida tööriistad",
|
||||
"call": "Kutse",
|
||||
"result": "Tulemus",
|
||||
"arguments": "Argumendid:",
|
||||
"response": "Vastus:",
|
||||
"send": "Saada",
|
||||
"new_chat": "Uus vestlus",
|
||||
"settings": {
|
||||
"title": "Vestluse sätted",
|
||||
"show_stats": {
|
||||
"title": "Näita statistikat",
|
||||
"always": "Alati",
|
||||
"desc": "Kuva vastuste genereerimise kiirus ja konteksti suurus.",
|
||||
"while_generating": "Genereerimise ajal"
|
||||
},
|
||||
"auto_scroll": {
|
||||
"title": "Automaatne kerimine",
|
||||
"desc": "Jälgi uusi sõnumeid kohe, kui need saabuvad."
|
||||
}
|
||||
},
|
||||
"stats": {
|
||||
"context": "{{tokens}} tokenit",
|
||||
"tokens_per_second": "{{rate}} t/s"
|
||||
},
|
||||
"starting_requests_prompts": {
|
||||
"recap": "Mis juhtus kui ma olin ära?",
|
||||
"watch_camera": "Jälgi ust ja anna mulle teada kui keegi tuleb",
|
||||
"show_camera_status": "Milline on minu kaamerate praegune seis?",
|
||||
"show_recent_events": "Näita mulle viimase tunni viimaseid sündmusi"
|
||||
},
|
||||
"starting_requests": {
|
||||
"show_camera_status": "Näita kaamera olekut",
|
||||
"show_recent_events": "Kuva hiljutised sündmused",
|
||||
"recap": "Mis juhtus kui ma olin ära?",
|
||||
"watch_camera": "Jälgi kaamerat aktiivsuse osas"
|
||||
},
|
||||
"suggested_requests": "Proovi küsida:",
|
||||
"semantic_search_required": "Sarnaste objektide leidmiseks peab olema lubatud semantiline otsing.",
|
||||
"no_similar_objects_found": "Sarnaseid objekte ei leitud.",
|
||||
"quick_reply_when_else": "Millal seda veel nähti?",
|
||||
"quick_reply_find_similar_text": "Leia sarnaseid vaatepilte.",
|
||||
"quick_reply_tell_me_more_text": "Räägi mulle sellest lähemalt.",
|
||||
"quick_reply_when_else_text": "Millal seda veel nähtud on?",
|
||||
"attach_event_aria": "Lisa sündmus {{eventId}}",
|
||||
"attachment_picker_paste_label": "Või kleebi sündmuse ID",
|
||||
"attachment_picker_attach": "Lisa",
|
||||
"attachment_picker_placeholder": "Lisa sündmus",
|
||||
"quick_reply_find_similar": "Leia sarnaseid vaatlusi",
|
||||
"quick_reply_tell_me_more": "Räägi mulle sellest lähemalt",
|
||||
"attachment_chip_remove": "Eemalda manus",
|
||||
"attachment_chip_label": "{{label}} kaameras {{camera}}",
|
||||
"open_in_explore": "Ava uurimisvaates",
|
||||
"anchor": "Viide",
|
||||
"reasoning": {
|
||||
"active": "Otsin põhjendust…",
|
||||
"show": "Näita põhjendust",
|
||||
"hide": "Peida põhjendus"
|
||||
},
|
||||
"thinking": {
|
||||
"toggle": "Näita mõtlemise olekut või peida see"
|
||||
}
|
||||
"similarity_score": "Sarnasus"
|
||||
}
|
||||
|
||||
@@ -43,8 +43,5 @@
|
||||
},
|
||||
"tooltip": {
|
||||
"trainingInProgress": "Mudel on parasjagu õppimas"
|
||||
},
|
||||
"train": {
|
||||
"titleShort": "Hiljutised"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -5,8 +5,7 @@
|
||||
"noTrackedObjects": "Ühtegi jälgitavat objekti ei leidunud",
|
||||
"itemMenu": {
|
||||
"findSimilar": {
|
||||
"aria": "Otsi sarnaseid jälgitavaid objekte",
|
||||
"label": "Leia sarnane"
|
||||
"aria": "Otsi sarnaseid jälgitavaid objekte"
|
||||
},
|
||||
"downloadSnapshot": {
|
||||
"label": "Laadi hetkvõte alla",
|
||||
@@ -15,14 +14,6 @@
|
||||
"downloadCleanSnapshot": {
|
||||
"label": "Laadi puhas hetkvõte alla",
|
||||
"aria": "Laadi puhas hetkvõte alla"
|
||||
},
|
||||
"viewTrackingDetails": {
|
||||
"label": "Vaata jälgimise üksikasju",
|
||||
"aria": "Näita jälgimise üksikasju"
|
||||
},
|
||||
"downloadVideo": {
|
||||
"label": "Laadi video alla",
|
||||
"aria": "Laadi video alla"
|
||||
}
|
||||
},
|
||||
"trackingDetails": {
|
||||
@@ -30,22 +21,11 @@
|
||||
"showAllZones": {
|
||||
"title": "Näita kõiki tsoone",
|
||||
"desc": "Kui objekt on sisenenud tsooni, siis alati näida tsooni märgistust."
|
||||
},
|
||||
"title": "Annotatsioonide seaded",
|
||||
"offset": {
|
||||
"desc": "Need andmed pärinevad teie kaamera tuvastusvoost, kuid on salvestusvoo piltide peal. On ebatõenäoline, et need kaks voogu on ideaalselt sünkroonis. Seetõttu ei joondu piirav kast ja kaader ideaalselt. Selle säte abil saad annotatsioone ajas edasi või tagasi nihutada, et need salvestatud kaadriga paremini joonduks.",
|
||||
"millisecondsToOffset": "Millisekundid annotatsioonide tuvastuse nihutamiseks. <em>Vaikimisi: 0</em>",
|
||||
"tips": "Vähendage väärtust, kui video taasesitus on kastidest ja teekonnapunktidest ees, ning suurendage väärtust, kui video taasesitus on neist maas. See väärtus võib olla negatiivne.",
|
||||
"toast": {
|
||||
"success": "Kaamera '{{camera}}' annotatsiooni nihe on konfiguratsioonifaili salvestatud."
|
||||
},
|
||||
"label": "Annotatsiooni nihe"
|
||||
}
|
||||
},
|
||||
"lifecycleItemDesc": {
|
||||
"attribute": {
|
||||
"other": "{{label}} on tuvastatud kui {{attribute}}",
|
||||
"faceOrLicense_plate": "{{attribute}} tuvastatud objektil {{label}}"
|
||||
"other": "{{label}} on tuvastatud kui {{attribute}}"
|
||||
},
|
||||
"stationary": "{{label}} jäi paigale",
|
||||
"active": "{{label}} muutus aktiivseks",
|
||||
@@ -57,8 +37,7 @@
|
||||
"area": "Ala",
|
||||
"score": "Punktiskoor",
|
||||
"computedScore": "Arvutatud punktiskoor",
|
||||
"topScore": "Suuremad punktiskoorid",
|
||||
"toggleAdvancedScores": "Täpsemate tulemuste sisse-/väljalülitamine"
|
||||
"topScore": "Suuremad punktiskoorid"
|
||||
},
|
||||
"external": "{{label}} on tuvastatud",
|
||||
"heard": "{{label}} on kuuldud",
|
||||
@@ -71,11 +50,7 @@
|
||||
"previous": "Eelmine slaid",
|
||||
"next": "Järgmine slaid"
|
||||
},
|
||||
"count": "{{first}} / {{second}}",
|
||||
"adjustAnnotationSettings": "Korrigeeri annotatsioonide seadeid",
|
||||
"scrollViewTips": "Klõpsake selle objekti elutsükli oluliste hetkede vaatamiseks.",
|
||||
"autoTrackingTips": "Piirdekastide asukohad on automaatselt jälgivate kaamerate puhul ebatäpsed.",
|
||||
"trackedPoint": "Jälgitav punkt"
|
||||
"count": "{{first}} / {{second}}"
|
||||
},
|
||||
"documentTitle": "Avasta - Frigate",
|
||||
"generativeAI": "Generatiivne tehisaru",
|
||||
@@ -89,22 +64,7 @@
|
||||
},
|
||||
"startingUp": "Käivitun…",
|
||||
"estimatedTime": "Hinnanguliselt jäänud aega:",
|
||||
"finishingShortly": "Lõpetan õige pea",
|
||||
"context": "Avastamist saab kasutada pärast seda, kui jälgitavate objektide manustamine on uuesti indekseerimise lõpetanud."
|
||||
},
|
||||
"title": "Avastamine pole saadaval",
|
||||
"downloadingModels": {
|
||||
"context": "Frigate laadib alla semantilise otsingu funktsiooni toetamiseks vajalikke manustamismudeleid. See võib võtta mitu minutit, olenevalt teie võrguühenduse kiirusest.",
|
||||
"setup": {
|
||||
"visionModel": "Nägemismudel",
|
||||
"visionModelFeatureExtractor": "Nägemismudeli tunnuste eraldaja",
|
||||
"textModel": "Tekstimudel",
|
||||
"textTokenizer": "Teksti tokenisaator"
|
||||
},
|
||||
"tips": {
|
||||
"context": "Pärast mudelite allalaadimist tuleks oma jälgitavate objektide manused uuesti indekseerida."
|
||||
},
|
||||
"error": "Tekkis viga. Kontrollige Frigate'i logisid."
|
||||
"finishingShortly": "Lõpetan õige pea"
|
||||
}
|
||||
},
|
||||
"type": {
|
||||
@@ -147,52 +107,8 @@
|
||||
},
|
||||
"recognizedLicensePlate": "Tuvastatud sõiduki numbrimärk",
|
||||
"description": {
|
||||
"aiTips": "Frigate ei küsi sinu generatiivse tehisaru teenusepakkujalt kirjeldust enne, kui jälgitava objekti elutsükkel on lõppenud.",
|
||||
"label": "Kirjeldus",
|
||||
"placeholder": "Jälgitava objekti kirjeldus"
|
||||
},
|
||||
"label": "Silt",
|
||||
"editSubLabel": {
|
||||
"title": "Muuda alamsilti",
|
||||
"desc": "Sisesta sildile '{{label}}' uus alamsilt",
|
||||
"descNoLabel": "Sisesta sellele jälgitavale objektile uus alamsilt"
|
||||
},
|
||||
"camera": "Kaamera",
|
||||
"zones": "Tsoonid",
|
||||
"title": {
|
||||
"label": "Pealkiri"
|
||||
},
|
||||
"button": {
|
||||
"findSimilar": "Leia sarnane",
|
||||
"regenerate": {
|
||||
"title": "Taasloomine",
|
||||
"label": "Jälgitava objekti kirjelduse uuesti genereerimine"
|
||||
}
|
||||
},
|
||||
"topScore": {
|
||||
"label": "Parim punktiskoor",
|
||||
"info": "Kõrgeim skoor on jälgitava objekti kõrgeim mediaanskoor, seega võib see erineda otsingutulemuste pisipildil kuvatavast skoorist."
|
||||
},
|
||||
"objects": "Objektid",
|
||||
"tips": {
|
||||
"descriptionSaved": "Kirjeldus salvestati edukalt",
|
||||
"saveDescriptionFailed": "Kirjelduse uuendamine ebaõnnestus: {{errorMessage}}"
|
||||
},
|
||||
"attributes": "Klassifikatsiooni atribuudid",
|
||||
"estimatedSpeed": "Hinnanguline kiirus",
|
||||
"editAttributes": {
|
||||
"title": "Atribuutide muutmine",
|
||||
"desc": "Valige selle sildi '{{label}}' jaoks klassifikatsiooniatribuudid"
|
||||
"aiTips": "Frigate ei küsi sinu generatiivse tehisaru teenusepakkujalt kirjeldust enne, kui jälgitava objekti elutsükkel on lõppenud."
|
||||
}
|
||||
},
|
||||
"trackedObjectDetails": "Jälgitava objekti üksikasjad",
|
||||
"aiAnalysis": {
|
||||
"title": "Tehisintellekti analüüs"
|
||||
},
|
||||
"concerns": {
|
||||
"label": "Mured"
|
||||
},
|
||||
"objectLifecycle": {
|
||||
"noImageFound": "Selle jälgitava objekti kohta ei leitud pilti."
|
||||
}
|
||||
"trackedObjectDetails": "Jälgitava objekti üksikasjad"
|
||||
}
|
||||
|
||||
@@ -16,15 +16,12 @@
|
||||
"downloadVideo": "Laadi video alla",
|
||||
"editName": "Muuda nime",
|
||||
"deleteExport": "Kustuta eksporditud sisu",
|
||||
"assignToCase": "Lisa juhtumile",
|
||||
"removeFromCase": "Eemalda juhtumist"
|
||||
"assignToCase": "Lisa juhtumile"
|
||||
},
|
||||
"toast": {
|
||||
"error": {
|
||||
"renameExportFailed": "Eksporditud sisu nime muutmine ei õnnestunud: {{errorMessage}}",
|
||||
"assignCaseFailed": "Juhtumiga seose uuendamine ei õnnestunud: {{errorMessage}}",
|
||||
"caseSaveFailed": "Juhtumi salvestamine ei õnnestunud: {{errorMessage}}",
|
||||
"caseDeleteFailed": "Juhtumi kustutamine ei õnnestunud: {{errorMessage}}"
|
||||
"assignCaseFailed": "Juhtumiga seose uuendamine ei õnnestunud: {{errorMessage}}"
|
||||
}
|
||||
},
|
||||
"headings": {
|
||||
@@ -38,44 +35,5 @@
|
||||
"nameLabel": "Juhtumi nimi",
|
||||
"descriptionLabel": "Kirjeldus",
|
||||
"description": "Vali olemasolev juhtum või lisa uus."
|
||||
},
|
||||
"toolbar": {
|
||||
"newCase": "Uus juhtum",
|
||||
"addExport": "Lisa eksportimiseks",
|
||||
"editCase": "Muuda juhtumit",
|
||||
"deleteCase": "Kustuta juhtum"
|
||||
},
|
||||
"deleteCase": {
|
||||
"label": "Kustuta juhtum",
|
||||
"desc": "Kas oled kindel, et soovid „{{caseName}}“ juhtumi kustutada?"
|
||||
},
|
||||
"caseCard": {
|
||||
"emptyCase": "Eksportimise veel pole"
|
||||
},
|
||||
"jobCard": {
|
||||
"defaultName": "Eksportimine kaamerast „{{camera}}“",
|
||||
"queued": "Lisatud järjekorda",
|
||||
"running": "Töös",
|
||||
"preparing": "Ettevalmistamisel",
|
||||
"copying": "Kopeerimisel",
|
||||
"encoding": "Kodeerimisel",
|
||||
"encodingRetry": "Kodeerimisel (uuesti)",
|
||||
"finalizing": "Lõpetamisel"
|
||||
},
|
||||
"caseView": {
|
||||
"noDescription": "Kirjeldust pole",
|
||||
"createdAt": "Loodud {{value}}",
|
||||
"exportCount_one": "1 eksportimine",
|
||||
"exportCount_other": "{{count}} eksportimist",
|
||||
"cameraCount_one": "1 kaamera",
|
||||
"cameraCount_other": "{{count}} kaamerat",
|
||||
"showMore": "Näita rohkem",
|
||||
"showLess": "Näita vähem",
|
||||
"emptyTitle": "See juhtum on tühi"
|
||||
},
|
||||
"caseEditor": {
|
||||
"namePlaceholder": "Juhtumi nimi",
|
||||
"editTitle": "Muuda juhtumit",
|
||||
"createTitle": "Lisa juhtum"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,11 +1,6 @@
|
||||
{
|
||||
"button": {
|
||||
"uploadImage": "Laadi pilt üles",
|
||||
"deleteFaceAttempts": "Kustuta näod",
|
||||
"addFace": "Lisa nägu",
|
||||
"renameFace": "Nimeta nägu ümber",
|
||||
"deleteFace": "Kustuta nägu",
|
||||
"reprocessFace": "Käivita näotuvastus uuesti"
|
||||
"uploadImage": "Laadi pilt üles"
|
||||
},
|
||||
"collections": "Kogumikud",
|
||||
"description": {
|
||||
@@ -16,41 +11,28 @@
|
||||
},
|
||||
"documentTitle": "Näoteek - Frigate",
|
||||
"createFaceLibrary": {
|
||||
"new": "Lisa uus nägu",
|
||||
"nextSteps": "Tugeva tuvastusaluse loomiseks:<li>Kasuta vahekaarti \"Hiljutised tuvastused\", et valida pilte ja treenida isikutuvastust.</li><li>Parima tulemuse saavutamiseks keskendu otsepiltidele; väldi nurga all olevaid nägusid treenimiseks.</li></ul>"
|
||||
"new": "Lisa uus nägu"
|
||||
},
|
||||
"deleteFaceLibrary": {
|
||||
"title": "Kustuta nimi",
|
||||
"desc": "Kas oled kindel, et soovid isiku '{{name}}' kollektsiooni kustutada? See kustutab jäädavalt kõik seotud näod."
|
||||
"title": "Kustuta nimi"
|
||||
},
|
||||
"toast": {
|
||||
"error": {
|
||||
"addFaceLibraryFailed": "Näo sidumine nimega ei õnnestunud: {{errorMessage}}",
|
||||
"updateFaceScoreFailed": "Näo punktiskoori uuendamine ei õnnestunud: {{errorMessage}}",
|
||||
"uploadingImageFailed": "Pildi üleslaadimine ebaõnnestus: {{errorMessage}}",
|
||||
"deleteFaceFailed": "Kustutamine ebaõnnestus: {{errorMessage}}",
|
||||
"deleteNameFailed": "Nime kustutamine ebaõnnestus: {{errorMessage}}",
|
||||
"renameFaceFailed": "Näo ümbernimetamine ebaõnnestus: {{errorMessage}}",
|
||||
"trainFailed": "Treenimine ebaõnnestus: {{errorMessage}}",
|
||||
"reclassifyFailed": "Näo ümberklassifitseerimine ebaõnnestus: {{errorMessage}}"
|
||||
"updateFaceScoreFailed": "Näo punktiskoori uuendamine ei õnnestunud: {{errorMessage}}"
|
||||
},
|
||||
"success": {
|
||||
"addFaceLibrary": "Lisamine nägude kogusse õnnestus: {{name}}!",
|
||||
"addFaceLibrary": "Lisamine Näoteeki õnnestus: {{name}}!",
|
||||
"deletedFace_one": "{{count}} näo kustutamine õnnestus.",
|
||||
"deletedFace_other": "{{count}} näo kustutamine õnnestus.",
|
||||
"deletedName_one": "{{count}} näo kustutamine õnnestus.",
|
||||
"deletedName_other": "{{count}} näo kustutamine õnnestus.",
|
||||
"updatedFaceScore": "Näo punktiskoori uuendamine õnnestus: {{name}} ({{score}}).",
|
||||
"uploadedImage": "Pildi üleslaadimine õnnestus.",
|
||||
"renamedFace": "Näo ümbernimetamine õnnestus, uus nimi on {{name}}",
|
||||
"trainedFace": "Edukalt treenitud nägu.",
|
||||
"reclassifiedFace": "Näo ümberklassifitseerimine õnnestus."
|
||||
"updatedFaceScore": "Näo punktiskoori uuendamine õnnestus: {{name}} ({{score}})."
|
||||
}
|
||||
},
|
||||
"deleteFaceAttempts": {
|
||||
"desc_one": "Kas oled kindel, et soovid kustutada {{count}} näo? Seda tegevust ei saa tagasi pöörata.",
|
||||
"desc_other": "Kas oled kindel, et soovid kustutada {{count}} nägu? Seda tegevust ei saa tagasi pöörata.",
|
||||
"title": "Kustuta näod"
|
||||
"desc_other": "Kas oled kindel, et soovid kustutada {{count}} nägu? Seda tegevust ei saa tagasi pöörata."
|
||||
},
|
||||
"details": {
|
||||
"timestamp": "Ajatampel",
|
||||
@@ -60,40 +42,5 @@
|
||||
"uploadFaceImage": {
|
||||
"title": "Laadi näopilt üles",
|
||||
"desc": "Laadi üles pilt, et otsida sellelt nägusid ja lisada see {{pageToggle}}'i jaoks"
|
||||
},
|
||||
"steps": {
|
||||
"faceName": "Lisa näole nimi",
|
||||
"uploadFace": "Lae näopilt üles",
|
||||
"nextSteps": "Järgmised sammud",
|
||||
"description": {
|
||||
"uploadFace": "Laadi üles pilt isikust '{{name}}', mis näitab tema nägu eestvaates. Ainult nägu ei pea pildilt välja lõikama."
|
||||
}
|
||||
},
|
||||
"train": {
|
||||
"title": "Hiljutised tuvastamised",
|
||||
"titleShort": "Hiljutised",
|
||||
"aria": "Vali hiljutised tuvastamised",
|
||||
"empty": "Hiljutisi näotuvastuse katseid pole",
|
||||
"emptyNoLibrary": {
|
||||
"title": "Laadi üles nägu",
|
||||
"description": "Näotuvastuse toimimiseks peate lisama vähemalt ühe näo kogusse."
|
||||
}
|
||||
},
|
||||
"renameFace": {
|
||||
"title": "Nimeta nägu ümber",
|
||||
"desc": "Sisesta uus nimi isiku '{{name}}' jaoks"
|
||||
},
|
||||
"imageEntry": {
|
||||
"validation": {
|
||||
"selectImage": "Palun vali pildifail."
|
||||
},
|
||||
"dropActive": "Lohista pilt siia…",
|
||||
"dropInstructions": "Lohistage või kleepige pilt siia või klõpsake valimiseks",
|
||||
"maxSize": "Maksimum suurus: {{size}}MB"
|
||||
},
|
||||
"nofaces": "Nägusid pole saadaval",
|
||||
"trainFaceAs": "Treeni nägu kui:",
|
||||
"trainFace": "Treeni nägu",
|
||||
"reclassifyFaceAs": "Liigita nägu ümber järgmiselt:",
|
||||
"reclassifyFace": "Näo ümberklassifitseerimine"
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,78 +1,4 @@
|
||||
{
|
||||
"documentTitle": "Liikumise tuvastus - Frigate",
|
||||
"title": "Liikumise otsing",
|
||||
"cancelSearch": "Tühista",
|
||||
"startSearch": "Alusta otsingut",
|
||||
"selectCamera": "Liikumisotsingut laaditakse",
|
||||
"description": "Joonesta hulknurk, et määratleda huvipakkuv piirkond, ja määra ajavahemik, mille jooksul otsida liikumise muutusi selles piirkonnas.",
|
||||
"searchStarted": "Otsing alustatud",
|
||||
"searchCancelled": "Otsing tühistatud",
|
||||
"searching": "Otsing on pooleli.",
|
||||
"searchComplete": "Otsing lõpetatud",
|
||||
"noResultsYet": "Käivita otsing valitud piirkonnas liikumise muutuste leidmiseks",
|
||||
"noChangesFound": "Valitud piirkonnas ei tuvastatud pikslimuutusi",
|
||||
"results": "Tulemused",
|
||||
"polygonControls": {
|
||||
"drawMode": "Joonista",
|
||||
"moveMode": "Liiguta",
|
||||
"reset": "Lähtesta hulknurk",
|
||||
"undo": "Tühista viimane punkt",
|
||||
"points_one": "{{count}} punkt",
|
||||
"points_other": "{{count}} punkti"
|
||||
},
|
||||
"newSearch": "Uus otsing",
|
||||
"clearResults": "Tühista tulemused",
|
||||
"clearROI": "Tühista hulknurk",
|
||||
"dialog": {
|
||||
"cameraLabel": "Kaamera",
|
||||
"title": "Liikumisotsing",
|
||||
"previewAlt": "Kaamera '{{camera}}' eelvaade"
|
||||
},
|
||||
"timeRange": {
|
||||
"title": "Otsinguvahemik",
|
||||
"start": "Algusaeg",
|
||||
"end": "Lõppaeg"
|
||||
},
|
||||
"settings": {
|
||||
"title": "Otsinguseaded",
|
||||
"parallelMode": "Paralleelrežiim",
|
||||
"parallelModeDesc": "Skanni mitut salvestusvahemikku korraga (kiirem; kasutab rohkem dekodeerimisressursse)",
|
||||
"threshold": "Tundlikkuse lävi",
|
||||
"thresholdDesc": "Väiksemad väärtused tuvastavad väiksemaid muutusi (1–255)",
|
||||
"minArea": "Minimaalne muutusala",
|
||||
"minAreaDesc": "Ühe liikuva piirkonna minimaalne suurus protsentides huvipakkuvast piirkonnast",
|
||||
"maxResults": "Maksimaalselt tulemusi",
|
||||
"maxResultsDesc": "Peata pärast nii paljude ajatemplite sobivust"
|
||||
},
|
||||
"errors": {
|
||||
"noCamera": "Palun vali kaamera",
|
||||
"noROI": "Palun joonistage huvipakkuv piirkond",
|
||||
"noTimeRange": "Palun valige ajavahemik",
|
||||
"invalidTimeRange": "Lõppaeg peab olema pärast algusaega",
|
||||
"searchFailed": "Otsing ebaõnnestus: {{message}}",
|
||||
"polygonTooSmall": "Hulknurgal peab olema vähemalt 3 punkti",
|
||||
"unknown": "Tundmatu viga"
|
||||
},
|
||||
"changePercentage": "{{percentage}}% muutus",
|
||||
"metrics": {
|
||||
"title": "Otsingumõõdikud",
|
||||
"segmentsScanned": "Skannitud segmente",
|
||||
"segmentsProcessed": "Töödeldud",
|
||||
"segmentsSkippedInactive": "Vahele jäetud (tegevust pole)",
|
||||
"segmentsSkippedHeatmap": "Vahele jäetud (kattuvaid piirkondi pole)",
|
||||
"fallbackFullRange": "Varurežiimis täisulatusega skaneerimine",
|
||||
"framesDecoded": "Kaadreid dekodeeritud",
|
||||
"wallTime": "Otsingu aeg",
|
||||
"seconds": "{{seconds}}s",
|
||||
"minutesSeconds": "{{minutes}}m {{seconds}}s",
|
||||
"segmentErrors": "Segmendi vead",
|
||||
"scanSummary": "{{segments}} segmenti · {{time}}"
|
||||
},
|
||||
"jumpToTime": "Hüppa sellele ajale",
|
||||
"framesProcessed": "{{count}} kaadrit töödeldud",
|
||||
"changesFound_one": "Tuvastatud {{count}} liikumine",
|
||||
"changesFound_other": "Tuvastatud {{count}} liikumist",
|
||||
"motionHeatmapLabel": "Liikumise soojakaart",
|
||||
"showSegmentHeatmap": "Soojakaart",
|
||||
"scanning": "Skannimine {{time}}"
|
||||
"title": "Liikumise otsing"
|
||||
}
|
||||
|
||||
@@ -12,48 +12,7 @@
|
||||
"selectFromTimeline": "Vali",
|
||||
"starting": "Käivitan kordust…",
|
||||
"startLabel": "Algus",
|
||||
"endLabel": "Lõpp",
|
||||
"title": "Alusta silumise taasesitust",
|
||||
"description": "Looge ajutine taasesituskaamera, mis kordab ajaloolist salvestist objektide tuvastamise ja jälgimise probleemide silumiseks. Taasesituskaameral on sama tuvastusseadistus mis lähtekaameral. Valige algusaeg.",
|
||||
"toast": {
|
||||
"error": "Silumise taasesitus ebaõnnestus: {{error}}",
|
||||
"alreadyActive": "Kordusseanss on juba aktiivne",
|
||||
"stopError": "Silumise taasesituse peatamine ebaõnnestus: {{error}}",
|
||||
"goToReplay": "Mine kordusesse"
|
||||
}
|
||||
"endLabel": "Lõpp"
|
||||
},
|
||||
"title": "Kordus veaotsinguks",
|
||||
"websocket_messages": "Sõnumid",
|
||||
"description": "Mängi kaamera salvestisi veaotsinguks. Objektide loend näitab tuvastatud objektide ajalist viivitust ja vahekaart „Sõnumid” näitab Fregati sisemiste sõnumite voogu taasesituse materjalist.",
|
||||
"page": {
|
||||
"noSession": "Aktiivset silumis- ja taasesitusseanssi pole",
|
||||
"noSessionDesc": "Alusta silumissalvestise taasesitust ajaloo vaates, klõpsates tööriistaribal nupul \"Toimingud\" ja valides silumissalvestise taasesituse.",
|
||||
"goToRecordings": "Mine ajaloo vaatesse",
|
||||
"preparingClip": "Klipi ettevalmistamine…",
|
||||
"preparingClipDesc": "Frigate koondab valitud ajavahemiku salvestisi. Pikemate vahemike puhul võib see võtta kauem.",
|
||||
"startingCamera": "Silumise taasesituse käivitamine…",
|
||||
"startError": {
|
||||
"title": "Silumise taasesituse käivitamine ebaõnnestus",
|
||||
"back": "Tagasi ajaloo vaatesse"
|
||||
},
|
||||
"sourceCamera": "Lähtekaamera",
|
||||
"replayCamera": "Taasesituse kaamera",
|
||||
"initializingReplay": "Silumise taasesituse initsialiseerimine...",
|
||||
"stoppingReplay": "Silumise taasesituse peatamine...",
|
||||
"stopReplay": "Peata kordusesitus",
|
||||
"confirmStop": {
|
||||
"title": "Peata silumise kordusesitus?",
|
||||
"description": "See peatab seansi ja kustutab kõik ajutised andmed. Kas oled kindel?",
|
||||
"confirm": "Peata kordusesitus",
|
||||
"cancel": "Tühista"
|
||||
},
|
||||
"activity": "Toimingud",
|
||||
"objects": "Objekti loend",
|
||||
"audioDetections": "Audio tuvastused",
|
||||
"noActivity": "Ühtegi tegevust ei tuvastatud",
|
||||
"activeTracking": "Aktiivne jälgimine",
|
||||
"noActiveTracking": "Aktiivset jälgimist pole",
|
||||
"configuration": "Seaded",
|
||||
"configurationDesc": "Peenhäälesta liikumistuvastuse ja objektide jälgimise sätteid silumis- ja taasesituskaamera jaoks. Frigate'i konfiguratsiooni faili muudatusi ei salvestata."
|
||||
}
|
||||
"title": "Kordus veaotsinguks"
|
||||
}
|
||||
|
||||
@@ -7,28 +7,7 @@
|
||||
"connectionSettings": "Ühenduse seadistused",
|
||||
"port": "Port",
|
||||
"username": "Kasutajanimi",
|
||||
"usernamePlaceholder": "Valikuline",
|
||||
"cameraName": "Kaamera nimi",
|
||||
"cameraNamePlaceholder": "näiteks: eesmine_uks või Hoovi ülevaade",
|
||||
"host": "Host/IP-aadress",
|
||||
"cameraBrand": "Kaamera bränd",
|
||||
"selectBrand": "Vali URL malli jaoks kaamera bränd",
|
||||
"customUrl": "Kohandatud voo URL",
|
||||
"brandInformation": "Brändi teave",
|
||||
"brandUrlFormat": "Kaamerate puhul, mille RTSP URL-i vorming on järgmine: {{exampleUrl}}",
|
||||
"selectTransport": "Valige transpordiprotokoll",
|
||||
"description": "Sisestage oma kaamera andmed ja valige, kas soovite kaamerat tuvastada või valida tootja käsitsi.",
|
||||
"onvifPort": "ONVIF port",
|
||||
"probeMode": "Tuvasta kaamera",
|
||||
"manualMode": "Manuaalne valik",
|
||||
"errors": {
|
||||
"nameRequired": "Kaamera nimi on kohustuslik",
|
||||
"nameLength": "Kaamera nimi peab olema kuni 64 tähemärki pikk",
|
||||
"invalidCharacters": "Kaamera nimi sisaldab sobimatuid märke",
|
||||
"nameExists": "Kaamera nimi on juba olemas",
|
||||
"brandOrCustomUrlRequired": "Valige kas kaamera bränd koos hosti/IP-aadressiga või valige „Muu” kohandatud URL-iga"
|
||||
},
|
||||
"detectionMethod": "Vootuvastus meetod"
|
||||
"usernamePlaceholder": "Valikuline"
|
||||
},
|
||||
"step3": {
|
||||
"streamUrlPlaceholder": "rtsp://kasutajanimi:salasõna@host:port/asukoht",
|
||||
@@ -38,37 +17,17 @@
|
||||
"roles": "Rollid",
|
||||
"roleLabels": {
|
||||
"record": "Salvestamine",
|
||||
"audio": "Heliriba",
|
||||
"detect": "Objektituvastus"
|
||||
"audio": "Heliriba"
|
||||
},
|
||||
"connected": "Ühendatud",
|
||||
"featuresTitle": "Funktsionaalsused",
|
||||
"selectStream": "Vali voog",
|
||||
"selectQuality": "Valige kvaliteet",
|
||||
"notConnected": "Pole ühendatud",
|
||||
"testFailedTitle": "Test ebaõnnestus",
|
||||
"testStream": "Testi ühendust",
|
||||
"testSuccess": "Voo test edukas!",
|
||||
"testFailed": "Voo test ebaõnnestus",
|
||||
"selectResolution": "Vali resolutsioon",
|
||||
"noStreamFound": "Voogu ei leitud",
|
||||
"addAnotherStream": "Lisa järgmine voog",
|
||||
"streamTitle": "Voog {{number}}",
|
||||
"streamUrl": "Voo URL",
|
||||
"addStream": "Lisa voog",
|
||||
"description": "Seadista voogedastusrolle ja lisa oma kaamerale täiendavaid vooge.",
|
||||
"streamsTitle": "Kaamera vood"
|
||||
"featuresTitle": "Funktsionaalsused"
|
||||
},
|
||||
"steps": {
|
||||
"probeOrSnapshot": "Võta proov või tee hetkvõte",
|
||||
"nameAndConnection": "Nimi ja ühendus",
|
||||
"streamConfiguration": "Voo seaded",
|
||||
"validationAndTesting": "Valideerimine ja testimine"
|
||||
"probeOrSnapshot": "Võta proov või tee hetkvõte"
|
||||
},
|
||||
"step2": {
|
||||
"testing": {
|
||||
"fetchingSnapshot": "Laadin kaamera hetkvõtet alla...",
|
||||
"probingMetadata": "Tuvastan kaamera metaandmeid..."
|
||||
"fetchingSnapshot": "Laadin kaamera hetkvõtet alla..."
|
||||
},
|
||||
"retry": "Proovi uuesti",
|
||||
"manufacturer": "Tootja",
|
||||
@@ -78,30 +37,7 @@
|
||||
"presets": "Eelseadistused",
|
||||
"useCandidate": "Kasuta",
|
||||
"uriCopy": "Kopeeri",
|
||||
"connected": "Ühendatud",
|
||||
"testSuccess": "Ühenduse test õnnestus!",
|
||||
"testFailed": "Ühenduse test ebaõnnestus. Palun kontrollige sisestatud andmeid ja proovige uuesti.",
|
||||
"testFailedTitle": "Test ebaõnnestus",
|
||||
"streamDetails": "Voo üksikasjad",
|
||||
"probing": "Tuvastan kaamerat...",
|
||||
"ptzSupport": "PTZ tugi",
|
||||
"testConnection": "Testi ühendust",
|
||||
"toggleUriView": "Klõpsake täieliku URI vaate sisse/välja lülitamiseks",
|
||||
"notConnected": "Pole ühendatud",
|
||||
"errors": {
|
||||
"hostRequired": "Hosti/IP-aadress on kohustuslik"
|
||||
},
|
||||
"candidateStreamTitle": "Kandidaat {{number}}",
|
||||
"probeSuccessful": "Tuvastamine õnnestus",
|
||||
"probeError": "Tuvastamise viga",
|
||||
"probeNoSuccess": "Tuvastamine ebaõnnestus",
|
||||
"deviceInfo": "Seadme info",
|
||||
"autotrackingSupport": "Automaatse jälgimise tugi",
|
||||
"uriCopied": "URI kopeeriti lõikelauale",
|
||||
"rtspCandidates": "RTSP kandidaadid",
|
||||
"probingDevice": "Tuvastan seadet...",
|
||||
"description": "Tuvasta kaamera vooge või seadista käsitsi seaded vastavalt valitud tuvastusmeetodile.",
|
||||
"probeFailed": "Kaamera tuvastamine ebaõnnestus: {{error}}"
|
||||
"connected": "Ühendatud"
|
||||
},
|
||||
"testResultLabels": {
|
||||
"resolution": "Resolutsioon",
|
||||
@@ -120,17 +56,7 @@
|
||||
"roles": "Rollid",
|
||||
"none": "Määramata",
|
||||
"error": "Viga"
|
||||
},
|
||||
"commonErrors": {
|
||||
"noUrl": "Palun sisestage kehtiv voogesituse URL",
|
||||
"testFailed": "Voo test ebaõnnestus: {{error}}"
|
||||
},
|
||||
"save": {
|
||||
"success": "Uue kaamera '{{cameraName}}' salvestamine õnnestus.",
|
||||
"failure": "Viga kaamera '{{cameraName}}' salvestamisel."
|
||||
},
|
||||
"title": "Lisa kaamera",
|
||||
"description": "Uue kaamera lisamiseks oma Frigate paigaldisesse järgige alltoodud samme."
|
||||
}
|
||||
},
|
||||
"users": {
|
||||
"updatePassword": "Lähtesta salasõna",
|
||||
@@ -246,32 +172,24 @@
|
||||
},
|
||||
"documentTitle": {
|
||||
"default": "Seadistused - Frigate",
|
||||
"authentication": "Autentimise seaded - Frigate",
|
||||
"cameraReview": "Kaamerate kordusvaatuste seaded - Frigate",
|
||||
"general": "Profiili seaded - Frigate",
|
||||
"frigatePlus": "Frigate+ seaded - Frigate",
|
||||
"notifications": "Teavituste seaded - Frigate",
|
||||
"authentication": "Autentimise seadistused - Frigate",
|
||||
"cameraReview": "Kaamerate kordusvaatuste seadistused - Frigate",
|
||||
"general": "Profiili seadistused - Frigate",
|
||||
"frigatePlus": "Frigate+ seadistused - Frigate",
|
||||
"notifications": "Teavituste seadistused - Frigate",
|
||||
"cameraManagement": "Kaamerate haldus - Frigate",
|
||||
"masksAndZones": "Maskide ja tsoonide haldus - Frigate",
|
||||
"object": "Silumine ja veaotsing - Frigate",
|
||||
"enrichments": "Rikastamise seaded - Frigate",
|
||||
"motionTuner": "Liikumise häälestaja – Frigate",
|
||||
"globalConfig": "Globaalsed seaded – Frigate",
|
||||
"cameraConfig": "Kaamera seaded - Frigate",
|
||||
"detectorsAndModel": "Detektorid ja mudel – Frigate",
|
||||
"maintenance": "Hooldus – Frigate",
|
||||
"profiles": "Profiilid - Frigate"
|
||||
"object": "Silumine ja veaotsing - Frigate"
|
||||
},
|
||||
"general": {
|
||||
"title": "Kasutajaliidese seadistused",
|
||||
"title": "Profiili seadistused",
|
||||
"cameraGroupStreaming": {
|
||||
"clearAll": "Kustuta kõik voogedastuse seadistused"
|
||||
},
|
||||
"liveDashboard": {
|
||||
"title": "Töölaud reaalajas",
|
||||
"automaticLiveView": {
|
||||
"label": "Automaatne otseülekande vaade",
|
||||
"desc": "Aktiveerib automaatselt kaamera reaalajas vaate, kui tuvastatakse tegevus. Selle valiku keelamisel uuendatakse kaamerapilti töölaual ainult üks kord minutis."
|
||||
"label": "Automaatne otseülekande vaade"
|
||||
}
|
||||
},
|
||||
"calendar": {
|
||||
@@ -417,7 +335,7 @@
|
||||
},
|
||||
"menu": {
|
||||
"ui": "Kasutajaliides",
|
||||
"cameraManagement": "Kaamera haldus",
|
||||
"cameraManagement": "Haldus",
|
||||
"masksAndZones": "Maskid ja tsoonid",
|
||||
"triggers": "Päästikud",
|
||||
"debug": "Silumine ja veaotsing",
|
||||
@@ -455,37 +373,7 @@
|
||||
"systemMqtt": "MQTT",
|
||||
"systemGo2rtcStreams": "go2rtc voogedastus",
|
||||
"integrationSemanticSearch": "Semantiline otsing",
|
||||
"integrationGenerativeAi": "Generatiivne tehisaru",
|
||||
"general": "Üldine",
|
||||
"globalConfig": "Globaalsed seaded",
|
||||
"system": "Süsteem",
|
||||
"integrations": "Integratsioonid",
|
||||
"cameras": "Kaamera seaded",
|
||||
"integrationFaceRecognition": "Nöotuvastus",
|
||||
"cameraDetect": "Objektituvastus",
|
||||
"cameraFfmpeg": "Vood (FFmpeg)",
|
||||
"cameraRecording": "Salvestus",
|
||||
"cameraSnapshots": "Hetktõmmised",
|
||||
"cameraMotion": "Liikumistuvastus",
|
||||
"cameraObjects": "Objektid",
|
||||
"cameraConfigReview": "Ülevaade",
|
||||
"cameraAudioEvents": "audiotuvastus",
|
||||
"cameraAudioTranscription": "Audio transkriptsioon",
|
||||
"cameraNotifications": "Teated",
|
||||
"cameraLivePlayback": "Otseülekanne",
|
||||
"integrationAudioTranscription": "Audio transkriptsioon",
|
||||
"integrationObjectClassification": "Objektide klassifitseerimine",
|
||||
"cameraFaceRecognition": "Näotuvastus",
|
||||
"cameraMqttConfig": "MQTT",
|
||||
"cameraOnvif": "ONVIF",
|
||||
"cameraUi": "Kaamera kasutajaliides",
|
||||
"cameraTimestampStyle": "Ajatempli stiil",
|
||||
"cameraMqtt": "Kaamera MQTT",
|
||||
"maintenance": "Hooldus",
|
||||
"mediaSync": "Meedia sünkroonimine",
|
||||
"regionGrid": "Regioonide ruudustik",
|
||||
"enrichments": "Andmete rikastamine",
|
||||
"motionTuner": "Liikumistuvastuse seadistaja"
|
||||
"integrationGenerativeAi": "Generatiivne tehisaru"
|
||||
},
|
||||
"dialog": {
|
||||
"unsavedChanges": {
|
||||
@@ -501,11 +389,7 @@
|
||||
"semanticSearch": {
|
||||
"reindexNow": {
|
||||
"confirmButton": "Indekseeri uuesti",
|
||||
"label": "Indekseeri kohe uuesti",
|
||||
"alreadyInProgress": "Ümberindekseerimine on juba käimas.",
|
||||
"error": "Uuesti indekseerimise alustamine ebaõnnestus: {{errorMessage}}",
|
||||
"success": "Ümberindekseerimine algas edukalt.",
|
||||
"confirmTitle": "Kinnita uuesti indekseerimine"
|
||||
"label": "Indekseeri uuesti kohe"
|
||||
},
|
||||
"modelSize": {
|
||||
"small": {
|
||||
@@ -513,9 +397,7 @@
|
||||
},
|
||||
"large": {
|
||||
"title": "suur"
|
||||
},
|
||||
"label": "Mudeli suurus",
|
||||
"desc": "Semantilise otsingu manustamiseks kasutatava mudeli suurus."
|
||||
}
|
||||
},
|
||||
"title": "Semantiline otsing"
|
||||
},
|
||||
@@ -526,23 +408,14 @@
|
||||
},
|
||||
"large": {
|
||||
"title": "suur"
|
||||
},
|
||||
"label": "Mudeli suurus",
|
||||
"desc": "Näotuvastuseks kasutatava mudeli suurus."
|
||||
},
|
||||
"title": "Näotuvastus"
|
||||
}
|
||||
}
|
||||
},
|
||||
"birdClassification": {
|
||||
"title": "Lindude klassifikatsioon"
|
||||
},
|
||||
"licensePlateRecognition": {
|
||||
"title": "Sõidukite numbrimärkide tuvastus"
|
||||
},
|
||||
"title": "Andmerikastuse seaded",
|
||||
"restart_required": "Taaskäivitamine on vajalik (Andmerikastuse seadeid on muudetud)",
|
||||
"toast": {
|
||||
"success": "Andmerikastamise seaded on salvestatud. Muudatuste rakendamiseks taaskäivitage Frigate.",
|
||||
"error": "Seadete muudatuste salvestamine ebaõnnestus: {{errorMessage}}"
|
||||
}
|
||||
},
|
||||
"cameraReview": {
|
||||
@@ -624,31 +497,5 @@
|
||||
"lpr": {
|
||||
"vehicleNotTracked": "Sõidukite numbrimärkide tuvastus eeldab, et auto või mootorratas on jälgitav. Lülita menüüst Objektid sell kaamera jaoks sisse valikud „auto“ või „mootorratas“."
|
||||
}
|
||||
},
|
||||
"button": {
|
||||
"overriddenGlobal": "Ülekirjutatud (Globaalne)",
|
||||
"overriddenGlobalTooltip": "Käesolev kaamera kirjutab selles jaotises üle globaalsed seaded",
|
||||
"overriddenGlobalHeading_one": "See kaamera sürjutab {{count}} üldise seadistuse välja:",
|
||||
"overriddenGlobalHeading_other": "See kaamera sürjutab {{count}} üldise seadistuse välja:"
|
||||
},
|
||||
"saveAllPreview": {
|
||||
"title": "Salvestatavad muudatused",
|
||||
"triggerLabel": "Vaadake üle ootel olevad muudatused",
|
||||
"empty": "Ootel muudatusi pole.",
|
||||
"scope": {
|
||||
"label": "Ulatus",
|
||||
"global": "Globaalne",
|
||||
"camera": "Kaamera: {{cameraName}}"
|
||||
},
|
||||
"profile": {
|
||||
"label": "Profiil"
|
||||
},
|
||||
"field": {
|
||||
"label": "Väli"
|
||||
},
|
||||
"value": {
|
||||
"label": "Uus väärtus",
|
||||
"reset": "Lähtesta"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -2,14 +2,7 @@
|
||||
"documentTitle": {
|
||||
"general": "Üldine statistika - Frigate",
|
||||
"cameras": "Kaamerate statistika - Frigate",
|
||||
"storage": "Andmeruumi statistika - Frigate",
|
||||
"logs": {
|
||||
"frigate": "Frigate logid - Frigate",
|
||||
"go2rtc": "Go2RTC logid - Frigate",
|
||||
"nginx": "Nginx logid - Frigate",
|
||||
"websocket": "Sõnumite logid - Frigate"
|
||||
},
|
||||
"enrichments": "Rikastus statistika - Frigate"
|
||||
"storage": "Andmeruumi statistika - Frigate"
|
||||
},
|
||||
"logs": {
|
||||
"download": {
|
||||
@@ -23,29 +16,11 @@
|
||||
"websocket": {
|
||||
"filter": {
|
||||
"cameras_count_one": "{{count}} kaamera",
|
||||
"cameras_count_other": "{{count}} kaamerat",
|
||||
"camera": "Kaamera",
|
||||
"all_cameras": "Kõik kaamerad",
|
||||
"events": "Sündmused",
|
||||
"reviews": "Ülevaated",
|
||||
"system": "Süsteem",
|
||||
"topics": "Teemad",
|
||||
"all": "Kõik teemad",
|
||||
"face_recognition": "Näotuvastus",
|
||||
"classification": "Klassifikatsioon",
|
||||
"camera_activity": "Kaamera aktiivsus",
|
||||
"lpr": "Numbrimärgituvastus"
|
||||
"cameras_count_other": "{{count}} kaamerat"
|
||||
},
|
||||
"empty": "Ühtegi sõnumit pole veel hõivatud",
|
||||
"count_one": "{{count}} sõnum",
|
||||
"count_other": "{{count}} sõnumit",
|
||||
"pause": "Peata",
|
||||
"resume": "Jätka",
|
||||
"label": "Sõnumid",
|
||||
"clear": "Tühista",
|
||||
"expanded": {
|
||||
"payload": "Last"
|
||||
}
|
||||
"count_other": "{{count}} sõnumit"
|
||||
},
|
||||
"type": {
|
||||
"label": "Tüüp",
|
||||
@@ -61,170 +36,5 @@
|
||||
}
|
||||
}
|
||||
},
|
||||
"title": "Süsteem",
|
||||
"enrichments": {
|
||||
"embeddings": {
|
||||
"object_description_events_per_second": "Objekti kirjeldus",
|
||||
"face_recognition": "Näotuvastus",
|
||||
"plate_recognition": "Numbrimärgi tuvastus",
|
||||
"object_description": "Objekti kirjeldus",
|
||||
"review_description_events_per_second": "Ülevaate kirjeldus",
|
||||
"review_description": "Ülevaate kirjeldus",
|
||||
"yolov9_plate_detection": "YOLOv9 numbrimärgi tuvastus",
|
||||
"yolov9_plate_detection_speed": "YOLOv9 numbrimärgi tuvastuskiirus",
|
||||
"plate_recognition_speed": "Numbrimärgituvastuse kiirus",
|
||||
"face_recognition_speed": "Näotuvastuse kiirus"
|
||||
},
|
||||
"title": "Andmerikastused"
|
||||
},
|
||||
"cameras": {
|
||||
"connectionQuality": {
|
||||
"reconnectsLastHour": "Uuesti ühendumisi (viimase tunni jooksul)",
|
||||
"stallsLastHour": "Seiskumisi (viimase tunni jooksul)",
|
||||
"title": "Ühenduse kvaliteet",
|
||||
"excellent": "Suurepärane",
|
||||
"fair": "Rahuldav",
|
||||
"poor": "Kehv",
|
||||
"unusable": "Mittekasutatav",
|
||||
"fps": "Kaadrisagedus",
|
||||
"expectedFps": "Eeldatav kaadrisagedus (FPS)"
|
||||
},
|
||||
"label": {
|
||||
"camera": "kaamera",
|
||||
"detect": "tuvasta",
|
||||
"skipped": "vahele jäetud",
|
||||
"ffmpeg": "FFmpeg",
|
||||
"cameraFfmpeg": "{{camName}} FFmpeg",
|
||||
"cameraGpu": "{{camName}} GPU",
|
||||
"cameraDetect": "{{camName}} tuvastus",
|
||||
"overallFramesPerSecond": "kaadreid sekundis kokku",
|
||||
"overallDetectionsPerSecond": "kogutuvastusi sekundis",
|
||||
"overallSkippedDetectionsPerSecond": "vahelejäänud tuvastusi sekundis kokku",
|
||||
"cameraFramesPerSecond": "{{camName}} kaadreid sekundis",
|
||||
"cameraDetectionsPerSecond": "{{camName}} tuvastusi sekundis",
|
||||
"cameraSkippedDetectionsPerSecond": "{{camName}} vahelejäänud tuvastusi sekundis",
|
||||
"capture": "jäädvustamine"
|
||||
},
|
||||
"noCameras": {
|
||||
"title": "Ühtegi kaamerat ei leitud"
|
||||
},
|
||||
"framesAndDetections": "Kaadreid / Tuvastusi",
|
||||
"info": {
|
||||
"keyframes": {
|
||||
"recordDisabled": "Selle kaamera salvestamine on välja lülitatud.",
|
||||
"segmentLength": "Salvestuse segmendi pikkus:",
|
||||
"recordStream": "Salvestusvoog:",
|
||||
"keyframeCount": "Vaadeldud võtmekaadrid:",
|
||||
"observedDuration": "Vaadeldud kestus:"
|
||||
},
|
||||
"audio": "Audio:",
|
||||
"error": "Viga: {{error}}",
|
||||
"codec": "Koodek:",
|
||||
"resolution": "Resolutsioon:",
|
||||
"video": "Video:",
|
||||
"aspectRatio": "kuvasuhe",
|
||||
"unknown": "Tundmatu",
|
||||
"fetching": "Kaameraandmete toomine",
|
||||
"stream": "Voog {{idx}}",
|
||||
"streamDataFromFFPROBE": "Vooandmed saadakse <code>ffprobe</code> abil.",
|
||||
"fps": "Kaadrisagedus (FPS):"
|
||||
},
|
||||
"title": "Kaamerad",
|
||||
"overview": "Ülevaade"
|
||||
},
|
||||
"lastRefreshed": "Viimati uuendatud: ",
|
||||
"stats": {
|
||||
"healthy": "Süsteem on töökorras",
|
||||
"detectIsSlow": "{{detect}} on aeglane ({{speed}} ms)",
|
||||
"detectIsVerySlow": "{{detect}} on väga aeglane ({{speed}} ms)",
|
||||
"cameraIsOffline": "{{camera}} on ühenduseta"
|
||||
},
|
||||
"storage": {
|
||||
"cameraStorage": {
|
||||
"camera": "Kaamera",
|
||||
"unused": {
|
||||
"title": "Kasutamata",
|
||||
"tips": "See väärtus ei pruugi Frigate'i jaoks saadaolevat vaba ruumi täpselt kajastada, kui teie draivil on lisaks Frigate'i salvestistele ka muid faile. Frigate ei jälgi salvestusruumi kasutamist väljaspool oma salvestisi."
|
||||
},
|
||||
"bandwidth": "Ribalaius",
|
||||
"storageUsed": "Salvestusmaht",
|
||||
"percentageOfTotalUsed": "protsent kogusummast",
|
||||
"unusedStorageInformation": "Kasutamata salvestusmahu info",
|
||||
"title": "Kaamera salvestusmaht"
|
||||
},
|
||||
"title": "Säilitus",
|
||||
"overview": "Ülevaade",
|
||||
"recordings": {
|
||||
"title": "Salvestused",
|
||||
"earliestRecording": "Varaseim saadaolev salvestis:"
|
||||
},
|
||||
"shm": {
|
||||
"title": "SHM (jagatud mälu) eraldus",
|
||||
"frameLifetime": {
|
||||
"title": "Kaadri eluiga"
|
||||
}
|
||||
}
|
||||
},
|
||||
"metrics": "Süsteemi mõõdikud",
|
||||
"general": {
|
||||
"title": "Üldine",
|
||||
"hardwareInfo": {
|
||||
"gpuUsage": "GPU kasutus",
|
||||
"gpuMemory": "GPU mälu",
|
||||
"gpuInfo": {
|
||||
"vainfoOutput": {
|
||||
"title": "Vainfo väljund",
|
||||
"processOutput": "Protsessi väljund:",
|
||||
"processError": "Protsessi viga:",
|
||||
"returnCode": "Tagastuskood: {{code}}"
|
||||
},
|
||||
"closeInfo": {
|
||||
"label": "Sulge GPU info"
|
||||
},
|
||||
"copyInfo": {
|
||||
"label": "Kopeeri GPU info"
|
||||
},
|
||||
"nvidiaSMIOutput": {
|
||||
"title": "Nvidia SMI väljund",
|
||||
"name": "Nimi: {{name}}",
|
||||
"driver": "Draiver: {{driver}}",
|
||||
"cudaComputerCapability": "CUDA arvutusvõimekus: {{cuda_compute}}",
|
||||
"vbios": "VBios info: {{vbios}}"
|
||||
},
|
||||
"toast": {
|
||||
"success": "GPU info kopeeriti lõikelauale"
|
||||
}
|
||||
},
|
||||
"gpuDecoder": "GPU dekooder",
|
||||
"gpuTemperature": "GPU temperatuur",
|
||||
"gpuEncoder": "GPU kodeerija",
|
||||
"gpuCompute": "GPU arvutus / kodeerimine",
|
||||
"title": "Riistvara info",
|
||||
"npuUsage": "NPU kasutus",
|
||||
"npuMemory": "NPU mälu",
|
||||
"npuTemperature": "NPU temperatuur",
|
||||
"intelGpuWarning": {
|
||||
"title": "Inteli GPU statistika hoiatus",
|
||||
"message": "GPU statistika pole saadaval"
|
||||
}
|
||||
},
|
||||
"otherProcesses": {
|
||||
"series": {
|
||||
"go2rtc": "go2rtc",
|
||||
"recording": "salvestus",
|
||||
"review_segment": "Segmendi ülevaade",
|
||||
"audio_detector": "audio detektor"
|
||||
},
|
||||
"processCpuUsage": "Protsessi CPU kasutus",
|
||||
"processMemoryUsage": "Protsessi mälukasutus"
|
||||
},
|
||||
"detector": {
|
||||
"title": "Detektorid",
|
||||
"memoryUsage": "Detektori mälukasutus",
|
||||
"cpuUsage": "Detektori CPU kasutus",
|
||||
"temperature": "Detektori temperatuur",
|
||||
"inferenceSpeed": "Detektori järelduskiirus",
|
||||
"cpuUsageInformation": "Protsessori võimsus, mida kasutatakse tuvastusmudelite sisend- ja väljundandmete ettevalmistamisel. See väärtus ei mõõda järelduste kasutamist isegi siis, kui kasutatakse graafikaprotsessorit või kiirendit."
|
||||
}
|
||||
}
|
||||
"title": "Süsteem"
|
||||
}
|
||||
|
||||
@@ -26,8 +26,7 @@
|
||||
"max_speed": "Maksimi nopeus",
|
||||
"recognized_license_plate": "Tunnistettu rekisterikilpi",
|
||||
"has_clip": "Leike löytyy",
|
||||
"has_snapshot": "Tilannekuva löytyy",
|
||||
"attributes": "Muuttujat"
|
||||
"has_snapshot": "Tilannekuva löytyy"
|
||||
},
|
||||
"searchType": {
|
||||
"thumbnail": "Kuvake",
|
||||
|
||||
@@ -16,7 +16,7 @@
|
||||
"whoop": "Cri strident",
|
||||
"sigh": "Soupir",
|
||||
"singing": "Chant",
|
||||
"choir": "Chœur",
|
||||
"choir": "Chorale",
|
||||
"yodeling": "Yodel",
|
||||
"chant": "Chant",
|
||||
"mantra": "Mantra",
|
||||
@@ -33,7 +33,7 @@
|
||||
"snoring": "Ronflement",
|
||||
"gasp": "Souffle coupé",
|
||||
"pant": "halètement",
|
||||
"snort": "Reniflement",
|
||||
"snort": "Ébrouement",
|
||||
"camera": "Caméra",
|
||||
"cough": "Toux",
|
||||
"groan": "Gémissement",
|
||||
@@ -104,7 +104,7 @@
|
||||
"toothbrush": "Brosse à dents",
|
||||
"sink": "Évier",
|
||||
"scissors": "Ciseaux",
|
||||
"humming": "Fredonnement",
|
||||
"humming": "Bourdonnement",
|
||||
"shuffle": "Pas traînants",
|
||||
"footsteps": "Bruits de pas",
|
||||
"hiccup": "Hoquet",
|
||||
|
||||
@@ -67,8 +67,7 @@
|
||||
"error": {
|
||||
"failed": "Échec du démarrage de l'exportation : {{error}}",
|
||||
"endTimeMustAfterStartTime": "L'heure de fin doit être postérieure à l'heure de début.",
|
||||
"noVaildTimeSelected": "La plage horaire sélectionnée n'est pas valide.",
|
||||
"noValidTimeSelected": "Interval de temps invalide"
|
||||
"noVaildTimeSelected": "La plage horaire sélectionnée n'est pas valide."
|
||||
},
|
||||
"success": "Exportation démarrée avec succès. Consultez le fichier sur la page des exportations.",
|
||||
"view": "Vue",
|
||||
@@ -89,9 +88,7 @@
|
||||
"export": "Exporter",
|
||||
"fromTimeline": {
|
||||
"saveExport": "Enregistrer l'exportation",
|
||||
"previewExport": "Aperçu de l'exportation",
|
||||
"queueingExport": "Traitement de l'export...",
|
||||
"useThisRange": "Utiliser cet interval"
|
||||
"previewExport": "Aperçu de l'exportation"
|
||||
},
|
||||
"case": {
|
||||
"label": "Dossier",
|
||||
@@ -198,10 +195,6 @@
|
||||
"markAsReviewed": "Marquer comme traité",
|
||||
"deleteNow": "Supprimer maintenant",
|
||||
"markAsUnreviewed": "Marquer comme non traité"
|
||||
},
|
||||
"shareTimestamp": {
|
||||
"label": "Partager cette date",
|
||||
"title": "Partager cette date"
|
||||
}
|
||||
},
|
||||
"imagePicker": {
|
||||
|
||||
@@ -8,59 +8,12 @@
|
||||
"sigh": "Suspiro",
|
||||
"singing": "Cantando",
|
||||
"motorcycle": "Motocicleta",
|
||||
"bus": "Autobús",
|
||||
"bus": "Bus",
|
||||
"train": "Tren",
|
||||
"boat": "Barco",
|
||||
"boat": "Bote",
|
||||
"bird": "Paxaro",
|
||||
"cat": "Gato",
|
||||
"bellow": "Bramido",
|
||||
"whoop": "Berro de alegría",
|
||||
"whispering": "Susurro",
|
||||
"laughter": "Risa",
|
||||
"choir": "Coro",
|
||||
"chant": "Canto",
|
||||
"child_singing": "Neno Cantando",
|
||||
"rapping": "Rapeando",
|
||||
"horse": "Cabalo",
|
||||
"pig": "Porco",
|
||||
"goat": "Cabra",
|
||||
"sheep": "Ovella",
|
||||
"wild_animals": "Animais salvaxes",
|
||||
"crow": "Corvo",
|
||||
"dogs": "Cans",
|
||||
"snicker": "Risa abafada",
|
||||
"yodeling": "Iolando",
|
||||
"humming": "Tarareo",
|
||||
"groan": "Xemido",
|
||||
"grunt": "Gruñido",
|
||||
"synthetic_singing": "Canto Sintético",
|
||||
"whistling": "Asubío",
|
||||
"breathing": "Respiración",
|
||||
"wheeze": "Sibilancia",
|
||||
"snoring": "Ronquido",
|
||||
"gasp": "Inspiración brusca",
|
||||
"pant": "Respiración axitada",
|
||||
"snort": "Bufido",
|
||||
"cough": "Tose",
|
||||
"throat_clearing": "Carraspeo",
|
||||
"sneeze": "Espirro",
|
||||
"sniff": "Cheirada",
|
||||
"run": "Correr",
|
||||
"shuffle": "Arrastre de pés",
|
||||
"footsteps": "Pasos",
|
||||
"hands": "Mans",
|
||||
"dog": "Can",
|
||||
"fart": "Peido",
|
||||
"applause": "Aplauso",
|
||||
"chatter": "Parloteo",
|
||||
"crowd": "Multitude",
|
||||
"children_playing": "Nenos Xogando",
|
||||
"animal": "Animal",
|
||||
"pets": "Mascotas",
|
||||
"bark": "Ladrido",
|
||||
"yip": "Gañido",
|
||||
"howl": "Ouveo",
|
||||
"clapping": "Aplausos",
|
||||
"heartbeat": "Latexo do corazón",
|
||||
"heart_murmur": "Sopro cardíaco"
|
||||
"bellow": "Abaixo",
|
||||
"whoop": "Ei carballeira",
|
||||
"whispering": "Murmurando"
|
||||
}
|
||||
|
||||
@@ -10,8 +10,5 @@
|
||||
"untilRestart": "Ata o reinicio",
|
||||
"ago": "Fai {{timeAgo}}"
|
||||
},
|
||||
"readTheDocumentation": "Ler a documentación",
|
||||
"button": {
|
||||
"save": "Gardar"
|
||||
}
|
||||
"readTheDocumentation": "Ler a documentación"
|
||||
}
|
||||
|
||||
@@ -3,9 +3,9 @@
|
||||
"bicycle": "Bicicleta",
|
||||
"airplane": "Avión",
|
||||
"motorcycle": "Motocicleta",
|
||||
"bus": "Autobús",
|
||||
"bus": "Bus",
|
||||
"train": "Tren",
|
||||
"boat": "Barco",
|
||||
"boat": "Bote",
|
||||
"traffic_light": "Luces de tráfico",
|
||||
"fire_hydrant": "Boca de incendio",
|
||||
"street_sign": "Sinal de tráfico",
|
||||
@@ -14,11 +14,5 @@
|
||||
"bench": "Banco",
|
||||
"bird": "Paxaro",
|
||||
"cat": "Gato",
|
||||
"car": "Coche",
|
||||
"horse": "Cabalo",
|
||||
"goat": "Cabra",
|
||||
"sheep": "Ovella",
|
||||
"dog": "Can",
|
||||
"animal": "Animal",
|
||||
"bark": "Ladrido"
|
||||
"car": "Coche"
|
||||
}
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"description": {
|
||||
"addFace": "Engade unha nova colección á Biblioteca de rostros subindo a túa primeira imaxe.",
|
||||
"addFace": "Navegar para engadir unha nova colección á Libraría de Caras.",
|
||||
"placeholder": "Introduce un nome para esta colección",
|
||||
"invalidName": "Nome non válido. Os nomes só poden incluír letras, números, espazos, apóstrofes, guións baixos e guións."
|
||||
},
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
"search": "Pesquisar",
|
||||
"savedSearches": "Pesquisas gardadas",
|
||||
"button": {
|
||||
"save": "Gardar procura",
|
||||
"save": "Gardar pesquisa",
|
||||
"filterActive": "Filtros activos",
|
||||
"clear": "Borrar pesquisa"
|
||||
},
|
||||
|
||||
@@ -3,7 +3,7 @@
|
||||
"default": "Preferencias - Frigate",
|
||||
"authentication": "Configuracións de Autenticación - Frigate",
|
||||
"camera": "Configuracións da Cámara - Frigate",
|
||||
"general": "Configuracións UI - Frigate",
|
||||
"general": "Configuracións xerais - Frigate",
|
||||
"notifications": "Configuración de Notificacións - Frigate",
|
||||
"enrichments": "Configuración complementarias - Frigate",
|
||||
"masksAndZones": "Editor de máscaras e zonas - Frigate"
|
||||
|
||||
@@ -433,22 +433,5 @@
|
||||
"trickle": "Csörgedezés",
|
||||
"gush": "Folyás",
|
||||
"stir": "Kavarás",
|
||||
"thump": "Puffanás",
|
||||
"arrow": "Nyíl",
|
||||
"bouncing": "Pattog",
|
||||
"vibration": "Vibrálás",
|
||||
"inside": "Belül",
|
||||
"outside": "Kívül",
|
||||
"mains_hum": "Hálózati zaj",
|
||||
"distortion": "Torzulás",
|
||||
"throbbing": "Lüktetés",
|
||||
"flap": "Csapkodás",
|
||||
"scratch": "Kapar",
|
||||
"scrape": "Karcolás",
|
||||
"rub": "Dörzsölés",
|
||||
"roll": "Gördül",
|
||||
"crushing": "Összenyom",
|
||||
"crumpling": "Gyűrődés",
|
||||
"beep": "Sípolás",
|
||||
"clang": "Csengés"
|
||||
"thump": "Puffanás"
|
||||
}
|
||||
|
||||
@@ -24,9 +24,6 @@
|
||||
},
|
||||
"state": {
|
||||
"submitted": "Terkirim"
|
||||
},
|
||||
"toast": {
|
||||
"error": "Gagal submit ke Frigate+. Harap periksa koneksi jaringan Anda dan coba lagi."
|
||||
}
|
||||
}
|
||||
},
|
||||
@@ -60,8 +57,7 @@
|
||||
"view": "Melihat",
|
||||
"batchSuccess_other": "{{count}} Ekspor dimulai. Membuka kasusnya sekarang.",
|
||||
"batchPartial": "Ekspor berhasil dimulai sebanyak {{successful}} dari total {{total}} ekspor. Kamera yang gagal: {{failedCameras}}",
|
||||
"batchFailed": "Gagal memulai ekspor sebanyak {{total}}. Kamera yang gagal: {{failedCameras}}",
|
||||
"batchQueuedPartial": "Antrian ekspor berhasil sebanyak {{successful}} dari total {{total}} ekspor. Kamera yang gagal: {{failedCameras}}"
|
||||
"batchFailed": "Gagal memulai ekspor sebanyak {{total}}. Kamera yang gagal: {{failedCameras}}"
|
||||
},
|
||||
"case": {
|
||||
"newCaseOption": "Membuat Kasus Baru",
|
||||
@@ -91,9 +87,7 @@
|
||||
"selectGroup": "Pilih grup",
|
||||
"noMatchingCameras": "Tidak ada kamera yang sesuai dengan pencarian Anda",
|
||||
"selectedCount": "{{terpilih}} / {{total}} terpilih",
|
||||
"namePlaceholder": "Nama dasar opsional untuk ekspor ini",
|
||||
"searchOrSelectGroup": "Cari, atau pilih grup kamera...",
|
||||
"selectAll": "Pilih semua kamera"
|
||||
"namePlaceholder": "Nama dasar opsional untuk ekspor ini"
|
||||
},
|
||||
"multi": {
|
||||
"title_other": "Ekspor {{count}} Ulasan",
|
||||
|
||||
@@ -67,10 +67,7 @@
|
||||
"desc": "このオプションは、ライブストリームに色のアーティファクトが表示され、画像右側に斜めの線が出る場合にのみ有効にしてください。"
|
||||
}
|
||||
}
|
||||
},
|
||||
"showAll": "全てのカメラグループを表示",
|
||||
"showLess": "表示を縮小",
|
||||
"editGroups": "カメラグループを編集"
|
||||
}
|
||||
},
|
||||
"debug": {
|
||||
"options": {
|
||||
|
||||
@@ -24,9 +24,6 @@
|
||||
},
|
||||
"state": {
|
||||
"submitted": "送信済み"
|
||||
},
|
||||
"toast": {
|
||||
"error": "Frigate+への送信に失敗しました。ネットワーク接続を確認して、もう一度お試しください。"
|
||||
}
|
||||
}
|
||||
},
|
||||
@@ -59,8 +56,7 @@
|
||||
"error": {
|
||||
"failed": "エクスポートキューの開始に失敗しました: {{error}}",
|
||||
"endTimeMustAfterStartTime": "終了時間は開始時間より後である必要があります",
|
||||
"noVaildTimeSelected": "有効な時間範囲が選択されていません",
|
||||
"noValidTimeSelected": "有効な時間範囲が選択されていません"
|
||||
"noVaildTimeSelected": "有効な時間範囲が選択されていません"
|
||||
},
|
||||
"view": "表示",
|
||||
"queued": "エクスポートがキューに追加されました。進捗状況はエクスポートページで確認できます。",
|
||||
@@ -89,14 +85,7 @@
|
||||
"detectionCount_other": "{{count}} 追跡対象",
|
||||
"nameLabel": "エクスポート名",
|
||||
"namePlaceholder": "これらのエクスポート用オプションのベース名",
|
||||
"exportButton_other": "{{count}} 台のカメラをエクスポート",
|
||||
"searchOrSelectGroup": "検索するか、カメラグループを選択してください...",
|
||||
"selectAll": "全てのカメラを選択",
|
||||
"clearSelection": "選択を解除",
|
||||
"selectWithActivity": "追跡対象のあるカメラ",
|
||||
"selectGroup": "グループを選択",
|
||||
"noMatchingCameras": "検索条件に一致するカメラはありません",
|
||||
"selectedCount": "{{selected}} / {{total}} 個が選択されました"
|
||||
"exportButton_other": "{{count}} 台のカメラをエクスポート"
|
||||
},
|
||||
"case": {
|
||||
"newCaseOption": "新しいケースを作成",
|
||||
|
||||
@@ -33,7 +33,7 @@
|
||||
},
|
||||
"listen": {
|
||||
"label": "リスニングタイプ",
|
||||
"description": "検出対象の音声イベントの種類の一覧（例：吠え声、火災報知器、会話、叫び声）。"
|
||||
"description": "検知対象の音声イベントの種類一覧（例：吠え声、火災報知器、悲鳴、会話、叫び声）。"
|
||||
},
|
||||
"enabled_in_config": {
|
||||
"label": "元の音声状態",
|
||||
@@ -192,11 +192,11 @@
|
||||
"description": "このカメラの通知を有効化・制御する設定。"
|
||||
},
|
||||
"ffmpeg": {
|
||||
"label": "ストリーム（FFmpeg）",
|
||||
"description": "カメラストリームの入力およびFFmpegのオプション（バイナリパス、引数、ハードウェアアクセラレーション、ロールごとの出力引数など）。",
|
||||
"label": "FFmpeg",
|
||||
"description": "FFmpeg の設定。バイナリパス、引数、ハードウェアアクセラレーション、ロール別の出力引数を含みます。",
|
||||
"path": {
|
||||
"label": "FFmpeg パス",
|
||||
"description": "使用する FFmpeg バイナリへのパス、またはバージョンのエイリアス（「7.0」または「8.0」）。"
|
||||
"description": "使用する FFmpeg バイナリのパス、またはバージョンエイリアス（「5.0」または「7.0」）。"
|
||||
},
|
||||
"global_args": {
|
||||
"label": "FFmpeg グローバル引数",
|
||||
@@ -512,9 +512,6 @@
|
||||
"max_concurrent": {
|
||||
"label": "同時エクスポート数の上限",
|
||||
"description": "同時に処理するエクスポートジョブの最大数。"
|
||||
},
|
||||
"chapters": {
|
||||
"label": "エクスポートされた録画に埋め込むチャプターメタデータ"
|
||||
}
|
||||
},
|
||||
"preview": {
|
||||
@@ -869,10 +866,6 @@
|
||||
"dashboard": {
|
||||
"label": "UI に表示",
|
||||
"description": "このカメラを Frigate UI 全体に表示するかを切り替えます。無効化した場合、再表示するには設定ファイルを手動編集する必要があります。"
|
||||
},
|
||||
"review": {
|
||||
"label": "再生画面に表示",
|
||||
"description": "このカメラを再生画面（再生ページおよびそのカメラフィルター、モーション再生、履歴表示）に表示するかどうかを切り替えます。"
|
||||
}
|
||||
},
|
||||
"webui_url": {
|
||||
|
||||
@@ -31,7 +31,7 @@
|
||||
},
|
||||
"listen": {
|
||||
"label": "リスニングタイプ",
|
||||
"description": "検出対象の音声イベントの種類の一覧（例：吠え声、火災報知器、会話、叫び声）。"
|
||||
"description": "検知対象の音声イベントの種類一覧（例：吠え声、火災報知器、悲鳴、会話、叫び声）。"
|
||||
},
|
||||
"enabled_in_config": {
|
||||
"label": "元の音声状態",
|
||||
@@ -715,7 +715,7 @@
|
||||
"description": "FFmpeg の設定。バイナリパス、引数、ハードウェアアクセラレーション、ロール別の出力引数を含みます。",
|
||||
"path": {
|
||||
"label": "FFmpeg パス",
|
||||
"description": "使用する FFmpeg バイナリへのパス、またはバージョンのエイリアス（「7.0」または「8.0」）。"
|
||||
"description": "使用する FFmpeg バイナリのパス、またはバージョンエイリアス（「5.0」または「7.0」）。"
|
||||
},
|
||||
"global_args": {
|
||||
"label": "FFmpeg グローバル引数",
|
||||
@@ -1067,9 +1067,6 @@
|
||||
"max_concurrent": {
|
||||
"label": "同時エクスポート数の上限",
|
||||
"description": "同時に処理するエクスポートジョブの最大数。"
|
||||
},
|
||||
"chapters": {
|
||||
"label": "エクスポートされた録画に埋め込むチャプターメタデータ"
|
||||
}
|
||||
},
|
||||
"preview": {
|
||||
@@ -1557,10 +1554,6 @@
|
||||
"dashboard": {
|
||||
"label": "UI に表示",
|
||||
"description": "このカメラを Frigate UI 全体に表示するかを切り替えます。無効化した場合、再表示するには設定ファイルを手動編集する必要があります。"
|
||||
},
|
||||
"review": {
|
||||
"label": "再生画面に表示",
|
||||
"description": "このカメラを再生画面（再生ページおよびそのカメラフィルター、モーション再生、履歴表示）に表示するかどうかを切り替えます。"
|
||||
}
|
||||
},
|
||||
"onvif": {
|
||||
|
||||
@@ -147,20 +147,7 @@
|
||||
"title": "分類モデルを編集",
|
||||
"descriptionState": "この状態分類モデルのクラスを編集します。変更を反映するにはモデルの再学習が必要です。",
|
||||
"descriptionObject": "このオブジェクト分類モデルのオブジェクトタイプおよび分類タイプを編集します。",
|
||||
"stateClassesInfo": "モデルが更新されました。変更を反映させるには、モデルの再学習を行ってください。",
|
||||
"enabled": "有効",
|
||||
"enabledDesc": "このモデルを実行します。無効にすると実行が停止し、分類が行われなくなります。",
|
||||
"saveAttempts": "試行回数の保存",
|
||||
"saveAttemptsDesc": "「最近の分類」UIに保持する分類試行画像の数です。",
|
||||
"motion": "動き検知時に実行",
|
||||
"motionDesc": "設定されたクロップ範囲内で動きが検知された際に、分類を実行します。",
|
||||
"interval": "間隔",
|
||||
"intervalDesc": "定期的な分類実行の間隔（秒）。空欄のままにすると、動きが検知された時のみ実行されます。",
|
||||
"intervalPlaceholder": "間隔なし",
|
||||
"errors": {
|
||||
"saveAttemptsInvalid": "セーブ回数は0以上の整数でなければなりません",
|
||||
"intervalInvalid": "「間隔」は0より大きい整数である必要があります"
|
||||
}
|
||||
"stateClassesInfo": "注意: 状態クラスを変更すると、更新後のクラスでモデルを再学習する必要があります。"
|
||||
},
|
||||
"deleteDatasetImages": {
|
||||
"title": "データセット画像を削除",
|
||||
@@ -200,6 +187,5 @@
|
||||
}
|
||||
},
|
||||
"reclassifyImageAs": "画像を次として再分類:",
|
||||
"reclassifyImage": "画像を再分類",
|
||||
"disabled": "無効"
|
||||
"reclassifyImage": "画像を再分類"
|
||||
}
|
||||
|
||||
@@ -301,7 +301,7 @@
|
||||
"toast": {
|
||||
"success": "{{camera}} のアノテーションオフセットが設定ファイルに保存されました。"
|
||||
},
|
||||
"desc": "このデータはカメラの「検出」フィードから取得されますが、「録画」フィードの画像に重ねて表示されます。2つのストリームが完全に同期している可能性は低いため、バウンディングボックスと映像が完全に一致しない場合があります。この設定を使用すると注釈を時間軸上で前後にずらすことができ、録画された映像との位置合わせをより正確に行うことができます。",
|
||||
"desc": "このデータはカメラの detect ストリーム から取得されていますが、表示される画像自体は record ストリーム のものです。そのため、2 つのストリームが完全に同期している可能性は低く、バウンディングボックスと実際の映像が正確に一致しない場合があります。この設定を使用すると、注釈（アノテーション）を 時間的に前後へオフセット することができ、録画映像との位置合わせをより正確に行えます。",
|
||||
"tips": "映像の再生がバウンディングボックスや軌跡ポイントより先行している場合は値を小さくし、遅れている場合は値を大きくしてください。この値は負の値も指定できます。"
|
||||
}
|
||||
},
|
||||
|
||||
@@ -69,7 +69,7 @@
|
||||
"integrationObjectClassification": "オブジェクト分類",
|
||||
"integrationAudioTranscription": "音声文字起こし",
|
||||
"cameraDetect": "物体検知",
|
||||
"cameraFfmpeg": "ストリーム（FFmpeg）",
|
||||
"cameraFfmpeg": "FFmpeg",
|
||||
"cameraRecording": "録画",
|
||||
"cameraSnapshots": "スナップショット",
|
||||
"cameraMotion": "モーション検知",
|
||||
@@ -662,7 +662,7 @@
|
||||
},
|
||||
"createUser": {
|
||||
"title": "新規ユーザーを作成",
|
||||
"desc": "新しいユーザーアカウントを追加し、Frigate UIの各エリアへのアクセス権限となる役割を指定します。",
|
||||
"desc": "新しいユーザーアカウントを追加し、Frigate UI へのアクセスロールを指定します。",
|
||||
"usernameOnlyInclude": "ユーザー名に使用できるのは英数字、.、_ のみです",
|
||||
"confirmPassword": "パスワードを確認してください"
|
||||
},
|
||||
@@ -1292,16 +1292,12 @@
|
||||
"details": {
|
||||
"edit": "カメラ詳細を編集",
|
||||
"title": "カメラ詳細を編集",
|
||||
"description": "Frigate UI 全体で、このカメラに使用されている表示名、外部 URL、および公開設定を更新します。",
|
||||
"description": "このカメラの表示名と外部 URL を更新します。Frigate UI 全体で使用されます。",
|
||||
"friendlyNameLabel": "表示名",
|
||||
"friendlyNameHelp": "Frigate UI 全体でこのカメラに表示される名前です。空欄にするとカメラ ID が使用されます。",
|
||||
"webuiUrlLabel": "カメラ Web UI の URL",
|
||||
"webuiUrlHelp": "デバッグビューからカメラの Web UI に直接アクセスするための URL です。空欄にするとリンクが無効になります。",
|
||||
"webuiUrlInvalid": "有効な URL を入力してください (例: https://example.com)。",
|
||||
"dashboardLabel": "ライブダッシュボードに表示",
|
||||
"dashboardHelp": "このカメラをライブダッシュボードに表示します。",
|
||||
"reviewLabel": "レビューに表示",
|
||||
"reviewHelp": "このカメラを、カメラフィルター、モーションレビュー、履歴ビューを含め、レビューに表示します。"
|
||||
"webuiUrlInvalid": "有効な URL を入力してください (例: https://example.com)。"
|
||||
}
|
||||
},
|
||||
"cameraConfig": {
|
||||
@@ -1637,14 +1633,7 @@
|
||||
"keyLabel": "キー",
|
||||
"valueLabel": "値",
|
||||
"keyPlaceholder": "新しいキー",
|
||||
"remove": "削除",
|
||||
"providerNameLabel": "プロバイダー名",
|
||||
"providerNamePlaceholder": "例：openai",
|
||||
"variableNameLabel": "変数名",
|
||||
"variableNamePlaceholder": "例：MY_VARIABLE",
|
||||
"loggerNameLabel": "ロガー名",
|
||||
"loggerNamePlaceholder": "例：frigate.record",
|
||||
"keyPatternError": "英字、数字、ハイフン、アンダースコアのみを使用してください（スペースは使用不可）"
|
||||
"remove": "削除"
|
||||
},
|
||||
"knownPlates": {
|
||||
"namePlaceholder": "例: 妻の車",
|
||||
@@ -1696,17 +1685,7 @@
|
||||
}
|
||||
},
|
||||
"cameraInputs": {
|
||||
"itemTitle": "ストリーム {{index}}",
|
||||
"sourceMode": {
|
||||
"restream": "リストリーム (go2rtc)",
|
||||
"manual": "手動入力パス",
|
||||
"go2rtcStreamLabel": "go2rtc ストリーム",
|
||||
"go2rtcStreamPlaceholder": "go2rtc ストリームを選択",
|
||||
"noGo2rtcStreams": "設定済みの go2rtc ストリームはありません",
|
||||
"go2rtcStreamSearch": "ストリームを検索中...",
|
||||
"availableStreams": "利用可能なストリーム",
|
||||
"noMatchingStreams": "一致するストリームはありません"
|
||||
}
|
||||
"itemTitle": "ストリーム {{index}}"
|
||||
},
|
||||
"restartRequiredField": "再起動が必要",
|
||||
"restartRequiredFooter": "設定が変更されました - 再起動が必要です",
|
||||
@@ -2075,11 +2054,7 @@
|
||||
"fpsGreaterThanFive": "検知 FPS を 5 より高く設定することは推奨されません。値を大きくしてもパフォーマンス上の問題を引き起こすだけで、メリットはありません。",
|
||||
"disabled": "物体検知が無効化されています。スナップショット、レビュー項目、顔認識、ナンバープレート認識、生成AI などのエンリッチメントは機能しません。",
|
||||
"resolutionShouldBeMultipleOfFour": "最良の結果を得るため、検知の幅と高さは 4 の倍数にしてください。他の偶数値でも動作しますが、検知ストリームに視覚的なノイズや軽微な歪みが生じる可能性があります。",
|
||||
"aspectRatioMismatch": "入力した幅と高さは現在の検知解像度のアスペクト比と一致していません。映像が引き伸ばされたり歪んだりする可能性があります。",
|
||||
"maxFramesSet": "最大フレーム数を設定するとデフォルトの動作が上書きされ、静止物体の追跡が無効になります。これが必要な状況はごく稀ですので使用には注意が必要です。",
|
||||
"squareResolution": "正方形の検出解像度は一般的ではありません。検出の幅と高さは、物体検出モデルの寸法ではなく、カメラのアスペクト比（例：16:9）に合わせる必要があります。アスペクト比が一致しないと、画像が引き伸ばされ、検出精度が低下する可能性があります。",
|
||||
"resolutionHigh": "この検出解像度は推奨値よりも高く、検出精度が向上しないままリソース使用量が増加する可能性があります。ほとんどのカメラでは、1080p以下の検出解像度を推奨します。",
|
||||
"globalResolutionMultipleCameras": "複数のカメラを設定する際は、グローバルな検出解像度が設定されます。すべてのカメラで解像度とアスペクト比が同一でない限り、各カメラのネイティブアスペクト比に合わせて、カメラごとに検出幅と検出高さを定義する必要があります。"
|
||||
"aspectRatioMismatch": "入力した幅と高さは現在の検知解像度のアスペクト比と一致していません。映像が引き伸ばされたり歪んだりする可能性があります。"
|
||||
},
|
||||
"objects": {
|
||||
"genaiNoDescriptionsProvider": "説明を生成するには「descriptions」ロールを持つ生成AIプロバイダを設定する必要があります。"
|
||||
@@ -2112,13 +2087,6 @@
|
||||
},
|
||||
"onvif": {
|
||||
"autotrackingNoZones": "オートトラッキング機能を使用するには、少なくとも1つのゾーンが必要です。「マスク / ゾーン」でこのカメラ用のゾーンを定義し、以下でそれを必須ゾーンとして設定してください。"
|
||||
},
|
||||
"model": {
|
||||
"optimizedFor320": "Frigateは320x320のモデルに最適化されており、これはほとんどのセットアップにおいて最適な選択です。640x640のモデルは処理速度が遅く、特定のシナリオでのみ有効です。",
|
||||
"inputDimensionsNotDetectResolution": "モデルの入力幅と高さはオブジェクト検出モデルの入力寸法であり、カメラの検出解像度ではありません。これらは使用しているモデルの寸法と一致している必要があります。通常、320x320や640x640のような正方形のサイズになります。"
|
||||
},
|
||||
"ffmpeg": {
|
||||
"hwaccelManualNotRecommended": "手動でのハードウェアアクセラレーション引数の指定は推奨されません。特定の要件がない限り、お使いのハードウェアに合ったプリセットを選択してください。"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -174,21 +174,6 @@
|
||||
"error": "エラー: {{error}}",
|
||||
"tips": {
|
||||
"title": "カメラプローブ情報"
|
||||
},
|
||||
"keyframes": {
|
||||
"title": "キーフレームの分析",
|
||||
"analyzing": "キーフレームを分析中... 残り {{seconds}} 秒",
|
||||
"stillAnalyzing": "キーフレームの分析を継続中...",
|
||||
"recordStream": "録画ストリーム：",
|
||||
"keyframeCount": "検出されたキーフレーム：",
|
||||
"observedDuration": "検出された再生時間：",
|
||||
"gap": "キーフレームの間隔（最小／平均／最大）：",
|
||||
"segmentLength": "録画セグメントの長さ：",
|
||||
"ok": "キーフレーム間隔は約 {{seconds}} 秒で、録画および再生に適しています。",
|
||||
"warning": "キーフレームがまばら、または変動的（最長間隔 ~{{seconds}}秒）で、スマートコーデック（H.264+/H.265+）である可能性が高いです。これは推奨されません。",
|
||||
"error": "キーフレーム間隔（約{{seconds}}秒）が録画セグメントの長さ（{{segmentTime}}秒）を超えています。一部のセグメントにキーフレームが含まれない場合があり、再生に支障をきたす可能性があります。カメラでスマート/+コーデックを無効にするか、キーフレーム間隔を短縮してください。",
|
||||
"unknown": "キーフレーム間隔を特定できませんでした。",
|
||||
"recordDisabled": "このカメラでは録画が無効になっています。"
|
||||
}
|
||||
},
|
||||
"framesAndDetections": "フレーム / 検知",
|
||||
|
||||
@@ -68,8 +68,8 @@
|
||||
"choir": "Koris",
|
||||
"yodeling": "Jodelēšana",
|
||||
"mantra": "Mantra",
|
||||
"rapping": "Repošana",
|
||||
"whistling": "Svilpošana",
|
||||
"rapping": "Repot",
|
||||
"whistling": "Svilpot",
|
||||
"sniff": "Ošņāt",
|
||||
"hands": "Rokas",
|
||||
"animal": "Dzīvnieks",
|
||||
@@ -122,17 +122,5 @@
|
||||
"fireworks": "Uguņošana",
|
||||
"glass": "Stikls",
|
||||
"white_noise": "Baltais troksnis",
|
||||
"radio": "Radio",
|
||||
"chant": "Piedziedājums",
|
||||
"synthetic_singing": "Sintētiskā dziedāšana",
|
||||
"groan": "Vaidēšana",
|
||||
"grunt": "Rukšķēšana",
|
||||
"breathing": "Elpošana",
|
||||
"wheeze": "Gārgšana",
|
||||
"snoring": "Krākšana",
|
||||
"gasp": "Elsošana",
|
||||
"pant": "Elšana",
|
||||
"snort": "Šņaukt",
|
||||
"cough": "Klepus",
|
||||
"throat_clearing": "Kakla tīrīšana"
|
||||
"radio": "Radio"
|
||||
}
|
||||
|
||||
@@ -64,8 +64,7 @@
|
||||
"error": {
|
||||
"failed": "Kunne ikke legge eksport i kø: {{error}}",
|
||||
"noVaildTimeSelected": "Ingen gyldig tidsperiode valgt",
|
||||
"endTimeMustAfterStartTime": "Sluttid må være etter starttid",
|
||||
"noValidTimeSelected": "Ingen gyldig tidsperiode valgt"
|
||||
"endTimeMustAfterStartTime": "Sluttid må være etter starttid"
|
||||
},
|
||||
"view": "Vis",
|
||||
"queued": "Eksport lagt i kø. Se fremdrift på eksportsiden.",
|
||||
|
||||
@@ -330,7 +330,7 @@
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Type objektdeteksjonsmodell",
|
||||
"description": "Arkitekturtype for detektormodellen (ssd, yolox, yolonas, yolo-generic, rfdetr, dfine) brukt av enkelte detektorer for optimalisering."
|
||||
"description": "Arkitekturtype for detektormodellen (ssd, yolox, yolonas) brukt av enkelte detektorer for optimalisering."
|
||||
}
|
||||
},
|
||||
"model_path": {
|
||||
@@ -503,7 +503,7 @@
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Type objektdeteksjonsmodell",
|
||||
"description": "Arkitekturtype for detektormodellen (ssd, yolox, yolonas, yolo-generic, rfdetr, dfine) brukt av enkelte detektorer for optimalisering."
|
||||
"description": "Arkitekturtype for detektormodellen (ssd, yolox, yolonas) brukt av enkelte detektorer for optimalisering."
|
||||
}
|
||||
},
|
||||
"genai": {
|
||||
|
||||
@@ -188,7 +188,7 @@
|
||||
"title": "Rediger klassifiseringsmodell",
|
||||
"descriptionState": "Rediger klassene for denne tilstandsklassifiseringsmodellen. Endringer vil kreve at modellen trenes på nytt.",
|
||||
"descriptionObject": "Rediger objekttypen og klassifiseringstypen for denne objektklassifiseringsmodellen.",
|
||||
"stateClassesInfo": "Modellen ble oppdatert. Tren modellen på nytt for at endringene i klassene skal bli iverksatt."
|
||||
"stateClassesInfo": "Merk: Endring av tilstandsklasser krever at modellen trenes på nytt med de oppdaterte klassene."
|
||||
},
|
||||
"none": "Ingen",
|
||||
"reclassifyImageAs": "Reklassifiser bilde som:",
|
||||
|
||||
@@ -1722,10 +1722,7 @@
|
||||
"remove": "Fjern",
|
||||
"keyPlaceholder": "Ny nøkkel",
|
||||
"keyLabel": "Nøkkel",
|
||||
"valueLabel": "Verdi",
|
||||
"providerNamePlaceholder": "f.eks. openai",
|
||||
"variableNameLabel": "Variabelnavn",
|
||||
"variableNamePlaceholder": "f.eks MIN_VARIABEL"
|
||||
"valueLabel": "Verdi"
|
||||
},
|
||||
"roleMap": {
|
||||
"remove": "Fjern",
|
||||
|
||||
@@ -34,9 +34,6 @@
|
||||
"label": "Bevestig dit label voor Frigate Plus",
|
||||
"ask_a": "Is dit object een <code>{{label}}</code>?",
|
||||
"ask_full": "Is dit object een <code>{{untranslatedLabel}}</code> ({{translatedLabel}})?"
|
||||
},
|
||||
"toast": {
|
||||
"error": "Gefaald bij Frigate+ in te dienen. Controleer je netwerkverbinding en probeer opnieuw."
|
||||
}
|
||||
}
|
||||
},
|
||||
@@ -107,14 +104,7 @@
|
||||
"namePlaceholder": "Optionele basisnaam voor deze exporten",
|
||||
"queueingButton": "Exporten in wachtrij plaatsen...",
|
||||
"exportButton_one": "Export 1 Camera",
|
||||
"exportButton_other": "{{count}} camera's exporteren",
|
||||
"searchOrSelectGroup": "Zoek of selecteer een cameragroep...",
|
||||
"selectAll": "Selecteer alle camera's",
|
||||
"clearSelection": "Wis selectie",
|
||||
"selectWithActivity": "Camera's met gevolgde objecten",
|
||||
"selectGroup": "Selecteer groep",
|
||||
"noMatchingCameras": "Geen camera's komen overeen met je zoekopdracht",
|
||||
"selectedCount": "{{selected}} / {{total}} geselecteerd"
|
||||
"exportButton_other": "{{count}} camera's exporteren"
|
||||
},
|
||||
"multi": {
|
||||
"title_one": "Review 1 exporteren",
|
||||
|
||||
@@ -36,9 +36,6 @@
|
||||
"ask_an": "Czy ten obiekt to <code>{{label}}</code>?",
|
||||
"ask_full": "Czy ten obiekt to <code>{{untranslatedLabel}}</code> ({{translatedLabel}})?",
|
||||
"label": "Potwierdź tę etykietę dla Frigate Plus"
|
||||
},
|
||||
"toast": {
|
||||
"error": "Nie udało się przesłać do Frigate+. Sprawdź połączenie sieciowe i spróbuj ponownie."
|
||||
}
|
||||
}
|
||||
},
|
||||
@@ -73,8 +70,7 @@
|
||||
"error": {
|
||||
"failed": "Nie udało się zakolejkować eksportu: {{error}}",
|
||||
"endTimeMustAfterStartTime": "Czas zakończenia musi być późniejszy niż czas rozpoczęcia",
|
||||
"noVaildTimeSelected": "Nie wybrano prawidłowego zakresu czasu",
|
||||
"noValidTimeSelected": "Nie wybrano prawidłowego zakresu czasu"
|
||||
"noVaildTimeSelected": "Nie wybrano prawidłowego zakresu czasu"
|
||||
},
|
||||
"view": "Widok",
|
||||
"queued": "Eksport dodany do kolejki. Postęp można sprawdzić na stronie eksportów.",
|
||||
|
||||
@@ -192,7 +192,7 @@
|
||||
},
|
||||
"listen": {
|
||||
"label": "Typy nasłuchu",
|
||||
"description": "Lista typów zdarzeń audio do wykrywania (na przykład: szczekanie, alarm pożarowy, mowa, krzyk)."
|
||||
"description": "Lista typów zdarzeń audio do wykrywania (na przykład: szczekanie, alarm pożarowy, krzyk, mowa, wrzask)."
|
||||
},
|
||||
"filters": {
|
||||
"label": "Filtry audio",
|
||||
@@ -322,8 +322,7 @@
|
||||
},
|
||||
"record": {
|
||||
"description": "Domyślne argumenty wyjściowe dla strumieni z rolą zapisu."
|
||||
},
|
||||
"description": "Domyślne argumenty wyjściowe używane dla różnych ról FFmpeg, takich jak wykrywanie i nagrywanie."
|
||||
}
|
||||
},
|
||||
"retry_interval": {
|
||||
"label": "Czas ponownej próby FFmpeg",
|
||||
@@ -336,13 +335,6 @@
|
||||
"gpu": {
|
||||
"label": "Indeks GPU",
|
||||
"description": "Domyślny indeks karty graficznej używany do akceleracji sprzętowej, jeśli jest dostępna."
|
||||
},
|
||||
"description": "Wejścia strumienia kamery i opcje FFmpeg, w tym ścieżka do pliku binarnego, args, hwaccel oraz argumenty wyjściowe dla poszczególnych ról.",
|
||||
"path": {
|
||||
"description": "Ścieżka do pliku binarnego FFmpeg lub alias wersji („7.0” lub „8.0”)."
|
||||
},
|
||||
"global_args": {
|
||||
"label": "Argumenty globalne FFmpeg"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -14,7 +14,7 @@
|
||||
},
|
||||
"listen": {
|
||||
"label": "Typy nasłuchu",
|
||||
"description": "Lista typów zdarzeń audio do wykrywania (na przykład: szczekanie, alarm pożarowy, mowa, krzyk)."
|
||||
"description": "Lista typów zdarzeń audio do wykrywania (na przykład: szczekanie, alarm pożarowy, krzyk, mowa, wrzask)."
|
||||
},
|
||||
"filters": {
|
||||
"label": "Filtry audio",
|
||||
@@ -165,8 +165,7 @@
|
||||
},
|
||||
"record": {
|
||||
"description": "Domyślne argumenty wyjściowe dla strumieni z rolą zapisu."
|
||||
},
|
||||
"description": "Domyślne argumenty wyjściowe używane dla różnych ról FFmpeg, takich jak wykrywanie i nagrywanie."
|
||||
}
|
||||
},
|
||||
"retry_interval": {
|
||||
"label": "Czas ponownej próby FFmpeg",
|
||||
@@ -179,12 +178,6 @@
|
||||
"gpu": {
|
||||
"label": "Indeks GPU",
|
||||
"description": "Domyślny indeks karty graficznej używany do akceleracji sprzętowej, jeśli jest dostępna."
|
||||
},
|
||||
"path": {
|
||||
"description": "Ścieżka do pliku binarnego FFmpeg lub alias wersji („7.0” lub „8.0”)."
|
||||
},
|
||||
"global_args": {
|
||||
"label": "Argumenty globalne FFmpeg"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -19,8 +19,8 @@
|
||||
"question": {
|
||||
"label": "Confirmar esse rótulo para Frigate Plus",
|
||||
"ask_a": "Este objeto é um <code>{{label}}</code>?",
|
||||
"ask_an": "Este objeto é um <code>{{label}}</code>?",
|
||||
"ask_full": "Este objeto é um <code>{{untranslatedLabel}}</code> ({{translatedLabel}})?"
|
||||
"ask_an": "Este objeto é um<code>{{label}}</code>?",
|
||||
"ask_full": "Este objeto é um<code>{{untranslatedLabel}}</code> ({{translatedLabel}})?"
|
||||
},
|
||||
"state": {
|
||||
"submitted": "Enviado"
|
||||
@@ -71,10 +71,7 @@
|
||||
"batchQueueFailed": "Falha ao enfileirar {{total}} exportações. Câmeras com falha: {{failedCameras}}",
|
||||
"batchPartial": "Iniciadas {{successful}} de {{total}} exportações. Câmeras com falha: {{failedCameras}}",
|
||||
"batchFailed": "Falha ao iniciar {{total}} exportações. Câmeras com falha: {{failedCameras}}",
|
||||
"queued": "Exportação na fila. Acompanhe o progresso na página de exportações.",
|
||||
"batchSuccess_one": "1 exportação iniciada. Abrindo o caso agora.",
|
||||
"batchSuccess_many": "{{count}} exportações iniciadas. Abrindo o caso agora.",
|
||||
"batchSuccess_other": "{{count}} exportações iniciadas. Abrindo o caso agora."
|
||||
"queued": "Exportação na fila. Acompanhe o progresso na página de exportações."
|
||||
},
|
||||
"fromTimeline": {
|
||||
"saveExport": "Salvar Exportação",
|
||||
@@ -135,10 +132,7 @@
|
||||
"started_many": "Iniciadas {{count}} exportações. Abrindo o caso agora.",
|
||||
"started_other": "",
|
||||
"failed": "Falha ao iniciar {{total}} exportações. Falhas: {{failedItems}}",
|
||||
"partial": "Iniciadas {{successful}} de {{total}} exportações. Falhas: {{failedItems}}",
|
||||
"startedNoCase_one": "1 exportação iniciada.",
|
||||
"startedNoCase_many": "{{count}} exportações iniciadas.",
|
||||
"startedNoCase_other": "{{count}} exportações iniciadas."
|
||||
"partial": "Iniciadas {{successful}} de {{total}} exportações. Falhas: {{failedItems}}"
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
@@ -215,8 +215,7 @@
|
||||
"label": "Birdseye",
|
||||
"description": "Configurações para a visualização composta \"Birdseye\", que combina múltiplas transmissões de câmera em um único layout.",
|
||||
"enabled": {
|
||||
"label": "Ativar Birdseye",
|
||||
"description": "Habilita ou desabilita a função Birdseye."
|
||||
"label": "Ativar Birdseye"
|
||||
}
|
||||
},
|
||||
"live": {
|
||||
@@ -402,9 +401,7 @@
|
||||
"enhancement": {
|
||||
"label": "Nível de aprimoramento",
|
||||
"description": "Nível de aprimoramento (0-10) a ser aplicado aos recortes da placa antes do OCR; valores mais altos nem sempre melhoram os resultados, e níveis acima de 5 podem funcionar apenas com placas noturnas, devendo ser utilizados com cautela."
|
||||
},
|
||||
"label": "Reconhecimento de placa veicular",
|
||||
"description": "Configurações de reconhecimento de placa veicular, incluindo limites de detecção, formatação e placas conhecidas."
|
||||
}
|
||||
},
|
||||
"record": {
|
||||
"label": "Gravação",
|
||||
@@ -542,18 +539,6 @@
|
||||
"enabled": {
|
||||
"label": "Ativar detecções",
|
||||
"description": "Ative ou desative eventos de detecção para esta câmera."
|
||||
},
|
||||
"labels": {
|
||||
"label": "Rótulos de detecção",
|
||||
"description": "Lista de rótulos de objetos que são considerados eventos de detecção."
|
||||
},
|
||||
"required_zones": {
|
||||
"label": "Zonas requeridas",
|
||||
"description": "Zonas em que um objeto deve entrar para ser considerado detectado. Deixe em branco para permitir qualquer zona."
|
||||
},
|
||||
"cutoff_time": {
|
||||
"label": "Tempo limite de detecção",
|
||||
"description": "Segundos a esperar após o fim da atividade antes de encerrar a detecção."
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user