docs/docs/configuration/custom_classification/object_classification.md

@@ -102,19 +102,8 @@ If examples for some of your classes do not appear in the grid, you can continue
 
 ### Improving the Model
 
-:::tip Diversity matters far more than volume
-
-Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data — the model learns what *that exact moment* looked like rather than what actually defines the class. **This is why Frigate does not implement bulk training in the UI.**
-
-For more detail, see [Frigate Tip: Best Practices for Training Face and Custom Classification Models](https://github.com/blakeblackshear/frigate/discussions/21374).
-
-:::
-
-- **Start small and iterate**: Begin with a small, representative set of images per class. Models often begin working well with surprisingly few examples and improve naturally over time.
-- **Favor hard examples**: When images appear in the Recent Classifications tab, prioritize images scoring below 90–100% or those captured under new lighting, weather, or distance conditions.
-- **Avoid bulk training similar images**: Training large batches of images that already score 100% (or close) adds little new information and increases the risk of overfitting.
-- **The wizard is just the starting point**: You don’t need to find and label every class upfront. Missing classes will naturally appear in Recent Classifications, and those images tend to be more valuable because they represent new conditions and edge cases.
 - **Problem framing**: Keep classes visually distinct and relevant to the chosen object types.
+- **Data collection**: Use the model’s Recent Classification tab to gather balanced examples across times of day, weather, and distances.
 - **Preprocessing**: Ensure examples reflect object crops similar to Frigate’s boxes; keep the subject centered.
 - **Labels**: Keep label names short and consistent; include a `none` class if you plan to ignore uncertain predictions for sub labels.
 - **Threshold**: Tune `threshold` per model to reduce false assignments. Start at `0.8` and adjust based on validation.

docs/docs/configuration/custom_classification/state_classification.md

@@ -70,21 +70,10 @@ Once some images are assigned, training will begin automatically.
 
 ### Improving the Model
 
-:::tip Diversity matters far more than volume
-
-Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data — the model learns what *that exact moment* looked like rather than what actually defines the state. This often leads to models that work perfectly under the original conditions but become unstable when day turns to night, weather changes, or seasonal lighting shifts. **This is why Frigate does not implement bulk training in the UI.**
-
-For more detail, see [Frigate Tip: Best Practices for Training Face and Custom Classification Models](https://github.com/blakeblackshear/frigate/discussions/21374).
-
-:::
-
-- **Start small and iterate**: Begin with a small, representative set of images per class. Models often begin working well with surprisingly few examples and improve naturally over time.
 - **Problem framing**: Keep classes visually distinct and state-focused (e.g., `open`, `closed`, `unknown`). Avoid combining object identity with state in a single model unless necessary.
 - **Data collection**: Use the model's Recent Classifications tab to gather balanced examples across times of day and weather.
 - **When to train**: Focus on cases where the model is entirely incorrect or flips between states when it should not. There's no need to train additional images when the model is already working consistently.
-- **Favor hard examples**: When images appear in the Recent Classifications tab, prioritize images scoring below 90–100% or those captured under new conditions (e.g., first snow of the year, seasonal changes, objects temporarily in view, insects at night). These represent scenarios different from the default state and help prevent overfitting.
+- **Selecting training images**: Images scoring below 100% due to new conditions (e.g., first snow of the year, seasonal changes) or variations (e.g., objects temporarily in view, insects at night) are good candidates for training, as they represent scenarios different from the default state. Training these lower-scoring images that differ from existing training data helps prevent overfitting. Avoid training large quantities of images that look very similar, especially if they already score 100% as this can lead to overfitting.
-- **Avoid bulk training similar images**: Training large batches of images that already score 100% (or close) adds little new information and increases the risk of overfitting.
-- **The wizard is just the starting point**: You don't need to find and label every state upfront. Missing states will naturally appear in Recent Classifications, and those images tend to be more valuable because they represent new conditions and edge cases.
 
 ## Debugging Classification Models
 

frigate/config/camera/updater.py

@@ -32,7 +32,6 @@ class CameraConfigUpdateEnum(str, Enum):
     face_recognition = "face_recognition"
     lpr = "lpr"
     snapshots = "snapshots"
-    timestamp_style = "timestamp_style"
     zones = "zones"
 
 
@@ -134,8 +133,6 @@ class CameraConfigUpdateSubscriber:
             config.snapshots = updated_config
         elif update_type == CameraConfigUpdateEnum.onvif:
             config.onvif = updated_config
-        elif update_type == CameraConfigUpdateEnum.timestamp_style:
-            config.timestamp_style = updated_config
         elif update_type == CameraConfigUpdateEnum.zones:
             config.zones = updated_config
 

frigate/config/config.py

@@ -25,7 +25,6 @@ from frigate.plus import PlusApi
 from frigate.util.builtin import (
     deep_merge,
     get_ffmpeg_arg_list,
-    load_labels,
 )
 from frigate.util.config import (
     CURRENT_CONFIG_VERSION,
@@ -41,7 +40,7 @@ from frigate.util.services import auto_detect_hwaccel
 from .auth import AuthConfig
 from .base import FrigateBaseModel
 from .camera import CameraConfig, CameraLiveConfig
-from .camera.audio import AudioConfig, AudioFilterConfig
+from .camera.audio import AudioConfig
 from .camera.birdseye import BirdseyeConfig
 from .camera.detect import DetectConfig
 from .camera.ffmpeg import FfmpegConfig
@@ -474,7 +473,7 @@ class FrigateConfig(FrigateBaseModel):
     live: CameraLiveConfig = Field(
         default_factory=CameraLiveConfig,
         title="Live playback",
-        description="Settings to control the jsmpeg live stream resolution and quality. This does not affect restreamed cameras that use go2rtc for live view.",
+        description="Settings used by the Web UI to control live stream resolution and quality.",
     )
     motion: Optional[MotionConfig] = Field(
         default=None,
@@ -672,12 +671,6 @@ class FrigateConfig(FrigateBaseModel):
             detector_config.model = model
             self.detectors[key] = detector_config
 
-        all_audio_labels = {
-            label
-            for label in load_labels("/audio-labelmap.txt", prefill=521).values()
-            if label
-        }
-
         for name, camera in self.cameras.items():
             modified_global_config = global_config.copy()
 
@@ -798,14 +791,6 @@ class FrigateConfig(FrigateBaseModel):
                 camera_config.review.genai.enabled
             )
 
-            if camera_config.audio.filters is None:
-                camera_config.audio.filters = {}
-
-            audio_keys = all_audio_labels
-            audio_keys = audio_keys - camera_config.audio.filters.keys()
-            for key in audio_keys:
-                camera_config.audio.filters[key] = AudioFilterConfig()
-
             # Add default filters
             object_keys = camera_config.objects.track
             if camera_config.objects.filters is None:

frigate/detectors/plugins/memryx.py

@@ -317,7 +317,7 @@ class MemryXDetector(DetectionApi):
                             f"Failed to remove downloaded zip {zip_path}: {e}"
                         )
 
-    def send_input(self, connection_id, tensor_input: np.ndarray) -> None:
+    def send_input(self, connection_id, tensor_input: np.ndarray):
         """Pre-process (if needed) and send frame to MemryX input queue"""
         if tensor_input is None:
             raise ValueError("[send_input] No image data provided for inference")

frigate/genai/__init__.py

@@ -5,7 +5,7 @@ import importlib
 import logging
 import os
 import re
-from typing import Any, Callable, Optional
+from typing import Any, Optional
 
 import numpy as np
 from playhouse.shortcuts import model_to_dict
@@ -31,10 +31,10 @@ __all__ = [
 PROVIDERS = {}
 
 
-def register_genai_provider(key: GenAIProviderEnum) -> Callable:
+def register_genai_provider(key: GenAIProviderEnum):
     """Register a GenAI provider."""
 
-    def decorator(cls: type) -> type:
+    def decorator(cls):
         PROVIDERS[key] = cls
         return cls
 
@@ -297,7 +297,7 @@ Guidelines:
         """Generate a description for the frame."""
         try:
             prompt = camera_config.objects.genai.object_prompts.get(
-                str(event.label),
+                event.label,
                 camera_config.objects.genai.prompt,
             ).format(**model_to_dict(event))
         except KeyError as e:
@@ -307,7 +307,7 @@ Guidelines:
         logger.debug(f"Sending images to genai provider with prompt: {prompt}")
         return self._send(prompt, thumbnails)
 
-    def _init_provider(self) -> Any:
+    def _init_provider(self):
         """Initialize the client."""
         return None
 
@@ -402,7 +402,7 @@ Guidelines:
         }
 
 
-def load_providers() -> None:
+def load_providers():
     package_dir = os.path.dirname(__file__)
     for filename in os.listdir(package_dir):
         if filename.endswith(".py") and filename != "__init__.py":

frigate/genai/azure-openai.py

@@ -3,7 +3,7 @@
 import base64
 import json
 import logging
-from typing import Any, AsyncGenerator, Optional
+from typing import Any, Optional
 from urllib.parse import parse_qs, urlparse
 
 from openai import AzureOpenAI
@@ -20,10 +20,10 @@ class OpenAIClient(GenAIClient):
 
     provider: AzureOpenAI
 
-    def _init_provider(self) -> AzureOpenAI | None:
+    def _init_provider(self):
         """Initialize the client."""
         try:
-            parsed_url = urlparse(self.genai_config.base_url or "")
+            parsed_url = urlparse(self.genai_config.base_url)
             query_params = parse_qs(parsed_url.query)
             api_version = query_params.get("api-version", [None])[0]
             azure_endpoint = f"{parsed_url.scheme}://{parsed_url.netloc}/"
@@ -79,7 +79,7 @@ class OpenAIClient(GenAIClient):
             logger.warning("Azure OpenAI returned an error: %s", str(e))
             return None
         if len(result.choices) > 0:
-            return str(result.choices[0].message.content.strip())
+            return result.choices[0].message.content.strip()
         return None
 
     def get_context_size(self) -> int:
@@ -113,7 +113,7 @@ class OpenAIClient(GenAIClient):
                 if openai_tool_choice is not None:
                     request_params["tool_choice"] = openai_tool_choice
 
-            result = self.provider.chat.completions.create(**request_params)  # type: ignore[call-overload]
+            result = self.provider.chat.completions.create(**request_params)
 
             if (
                 result is None
@@ -181,7 +181,7 @@ class OpenAIClient(GenAIClient):
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
-    ) -> AsyncGenerator[tuple[str, Any], None]:
+    ):
         """
         Stream chat with tools; yields content deltas then final message.
 
@@ -214,7 +214,7 @@ class OpenAIClient(GenAIClient):
             tool_calls_by_index: dict[int, dict[str, Any]] = {}
             finish_reason = "stop"
 
-            stream = self.provider.chat.completions.create(**request_params)  # type: ignore[call-overload]
+            stream = self.provider.chat.completions.create(**request_params)
 
             for chunk in stream:
                 if not chunk or not chunk.choices:

frigate/genai/gemini.py

@@ -2,11 +2,10 @@
 
 import json
 import logging
-from typing import Any, AsyncGenerator, Optional
+from typing import Any, Optional
 
 from google import genai
 from google.genai import errors, types
-from google.genai.types import FunctionCallingConfigMode
 
 from frigate.config import GenAIProviderEnum
 from frigate.genai import GenAIClient, register_genai_provider
@@ -20,10 +19,10 @@ class GeminiClient(GenAIClient):
 
     provider: genai.Client
 
-    def _init_provider(self) -> genai.Client:
+    def _init_provider(self):
         """Initialize the client."""
         # Merge provider_options into HttpOptions
-        http_options_dict: dict[str, Any] = {
+        http_options_dict = {
             "timeout": int(self.timeout * 1000),  # requires milliseconds
             "retry_options": types.HttpRetryOptions(
                 attempts=3,
@@ -55,7 +54,7 @@ class GeminiClient(GenAIClient):
         ] + [prompt]
         try:
             # Merge runtime_options into generation_config if provided
-            generation_config_dict: dict[str, Any] = {"candidate_count": 1}
+            generation_config_dict = {"candidate_count": 1}
             generation_config_dict.update(self.genai_config.runtime_options)
 
             if response_format and response_format.get("type") == "json_schema":
@@ -66,7 +65,7 @@ class GeminiClient(GenAIClient):
 
             response = self.provider.models.generate_content(
                 model=self.genai_config.model,
-                contents=contents,  # type: ignore[arg-type]
+                contents=contents,
                 config=types.GenerateContentConfig(
                     **generation_config_dict,
                 ),
@@ -79,8 +78,6 @@ class GeminiClient(GenAIClient):
             return None
 
         try:
-            if response.text is None:
-                return None
             description = response.text.strip()
         except (ValueError, AttributeError):
             # No description was generated
@@ -105,7 +102,7 @@ class GeminiClient(GenAIClient):
         """
         try:
             # Convert messages to Gemini format
-            gemini_messages: list[types.Content] = []
+            gemini_messages = []
             for msg in messages:
                 role = msg.get("role", "user")
                 content = msg.get("content", "")
@@ -113,11 +110,7 @@ class GeminiClient(GenAIClient):
                 # Map roles to Gemini format
                 if role == "system":
                     # Gemini doesn't have system role, prepend to first user message
-                    if (
+                    if gemini_messages and gemini_messages[0].role == "user":
-                        gemini_messages
-                        and gemini_messages[0].role == "user"
-                        and gemini_messages[0].parts
-                    ):
                         gemini_messages[0].parts[
                             0
                         ].text = f"{content}\n\n{gemini_messages[0].parts[0].text}"
@@ -143,7 +136,7 @@ class GeminiClient(GenAIClient):
                         types.Content(
                             role="function",
                             parts=[
-                                types.Part.from_function_response(function_response)  # type: ignore[misc,call-arg,arg-type]
+                                types.Part.from_function_response(function_response)
                             ],
                         )
                     )
@@ -178,25 +171,19 @@ class GeminiClient(GenAIClient):
             if tool_choice:
                 if tool_choice == "none":
                     tool_config = types.ToolConfig(
-                        function_calling_config=types.FunctionCallingConfig(
+                        function_calling_config=types.FunctionCallingConfig(mode="NONE")
-                            mode=FunctionCallingConfigMode.NONE
-                        )
                     )
                 elif tool_choice == "auto":
                     tool_config = types.ToolConfig(
-                        function_calling_config=types.FunctionCallingConfig(
+                        function_calling_config=types.FunctionCallingConfig(mode="AUTO")
-                            mode=FunctionCallingConfigMode.AUTO
-                        )
                     )
                 elif tool_choice == "required":
                     tool_config = types.ToolConfig(
-                        function_calling_config=types.FunctionCallingConfig(
+                        function_calling_config=types.FunctionCallingConfig(mode="ANY")
-                            mode=FunctionCallingConfigMode.ANY
-                        )
                     )
 
             # Build request config
-            config_params: dict[str, Any] = {"candidate_count": 1}
+            config_params = {"candidate_count": 1}
 
             if gemini_tools:
                 config_params["tools"] = gemini_tools
@@ -210,7 +197,7 @@ class GeminiClient(GenAIClient):
 
             response = self.provider.models.generate_content(
                 model=self.genai_config.model,
-                contents=gemini_messages,  # type: ignore[arg-type]
+                contents=gemini_messages,
                 config=types.GenerateContentConfig(**config_params),
             )
 
@@ -304,7 +291,7 @@ class GeminiClient(GenAIClient):
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
-    ) -> AsyncGenerator[tuple[str, Any], None]:
+    ):
         """
         Stream chat with tools; yields content deltas then final message.
 
@@ -312,7 +299,7 @@ class GeminiClient(GenAIClient):
         """
         try:
             # Convert messages to Gemini format
-            gemini_messages: list[types.Content] = []
+            gemini_messages = []
             for msg in messages:
                 role = msg.get("role", "user")
                 content = msg.get("content", "")
@@ -320,11 +307,7 @@ class GeminiClient(GenAIClient):
                 # Map roles to Gemini format
                 if role == "system":
                     # Gemini doesn't have system role, prepend to first user message
-                    if (
+                    if gemini_messages and gemini_messages[0].role == "user":
-                        gemini_messages
-                        and gemini_messages[0].role == "user"
-                        and gemini_messages[0].parts
-                    ):
                         gemini_messages[0].parts[
                             0
                         ].text = f"{content}\n\n{gemini_messages[0].parts[0].text}"
@@ -350,7 +333,7 @@ class GeminiClient(GenAIClient):
                         types.Content(
                             role="function",
                             parts=[
-                                types.Part.from_function_response(function_response)  # type: ignore[misc,call-arg,arg-type]
+                                types.Part.from_function_response(function_response)
                             ],
                         )
                     )
@@ -385,25 +368,19 @@ class GeminiClient(GenAIClient):
             if tool_choice:
                 if tool_choice == "none":
                     tool_config = types.ToolConfig(
-                        function_calling_config=types.FunctionCallingConfig(
+                        function_calling_config=types.FunctionCallingConfig(mode="NONE")
-                            mode=FunctionCallingConfigMode.NONE
-                        )
                     )
                 elif tool_choice == "auto":
                     tool_config = types.ToolConfig(
-                        function_calling_config=types.FunctionCallingConfig(
+                        function_calling_config=types.FunctionCallingConfig(mode="AUTO")
-                            mode=FunctionCallingConfigMode.AUTO
-                        )
                     )
                 elif tool_choice == "required":
                     tool_config = types.ToolConfig(
-                        function_calling_config=types.FunctionCallingConfig(
+                        function_calling_config=types.FunctionCallingConfig(mode="ANY")
-                            mode=FunctionCallingConfigMode.ANY
-                        )
                     )
 
             # Build request config
-            config_params: dict[str, Any] = {"candidate_count": 1}
+            config_params = {"candidate_count": 1}
 
             if gemini_tools:
                 config_params["tools"] = gemini_tools
@@ -422,7 +399,7 @@ class GeminiClient(GenAIClient):
 
             stream = await self.provider.aio.models.generate_content_stream(
                 model=self.genai_config.model,
-                contents=gemini_messages,  # type: ignore[arg-type]
+                contents=gemini_messages,
                 config=types.GenerateContentConfig(**config_params),
             )
 

frigate/genai/llama_cpp.py

@@ -4,7 +4,7 @@ import base64
 import io
 import json
 import logging
-from typing import Any, AsyncGenerator, Optional
+from typing import Any, Optional
 
 import httpx
 import numpy as np
@@ -23,7 +23,7 @@ def _to_jpeg(img_bytes: bytes) -> bytes | None:
     try:
         img = Image.open(io.BytesIO(img_bytes))
         if img.mode != "RGB":
-            img = img.convert("RGB")  # type: ignore[assignment]
+            img = img.convert("RGB")
         buf = io.BytesIO()
         img.save(buf, format="JPEG", quality=85)
         return buf.getvalue()
@@ -36,10 +36,10 @@ def _to_jpeg(img_bytes: bytes) -> bytes | None:
 class LlamaCppClient(GenAIClient):
     """Generative AI client for Frigate using llama.cpp server."""
 
-    provider: str | None  # base_url
+    provider: str  # base_url
     provider_options: dict[str, Any]
 
-    def _init_provider(self) -> str | None:
+    def _init_provider(self):
         """Initialize the client."""
         self.provider_options = {
             **self.genai_config.provider_options,
@@ -75,7 +75,7 @@ class LlamaCppClient(GenAIClient):
                 content.append(
                     {
                         "type": "image_url",
-                        "image_url": {  # type: ignore[dict-item]
+                        "image_url": {
                             "url": f"data:image/jpeg;base64,{encoded_image}",
                         },
                     }
@@ -111,7 +111,7 @@ class LlamaCppClient(GenAIClient):
             ):
                 choice = result["choices"][0]
                 if "message" in choice and "content" in choice["message"]:
-                    return str(choice["message"]["content"].strip())
+                    return choice["message"]["content"].strip()
             return None
         except Exception as e:
             logger.warning("llama.cpp returned an error: %s", str(e))
@@ -229,7 +229,7 @@ class LlamaCppClient(GenAIClient):
             content.append(
                 {
                     "prompt_string": "<__media__>\n",
-                    "multimodal_data": [encoded],  # type: ignore[dict-item]
+                    "multimodal_data": [encoded],
                 }
             )
 
@@ -367,7 +367,7 @@ class LlamaCppClient(GenAIClient):
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
-    ) -> AsyncGenerator[tuple[str, Any], None]:
+    ):
         """Stream chat with tools via OpenAI-compatible streaming API."""
         if self.provider is None:
             logger.warning(

frigate/genai/ollama.py

@@ -2,7 +2,7 @@
 
 import json
 import logging
-from typing import Any, AsyncGenerator, Optional
+from typing import Any, Optional
 
 from httpx import RemoteProtocolError, TimeoutException
 from ollama import AsyncClient as OllamaAsyncClient
@@ -28,10 +28,10 @@ class OllamaClient(GenAIClient):
         },
     }
 
-    provider: ApiClient | None
+    provider: ApiClient
     provider_options: dict[str, Any]
 
-    def _init_provider(self) -> ApiClient | None:
+    def _init_provider(self):
         """Initialize the client."""
         self.provider_options = {
             **self.LOCAL_OPTIMIZED_OPTIONS,
@@ -73,7 +73,7 @@ class OllamaClient(GenAIClient):
             "exclusiveMinimum",
             "exclusiveMaximum",
         }
-        result: dict[str, Any] = {}
+        result = {}
         for key, value in schema.items():
             if not _is_properties and key in STRIP_KEYS:
                 continue
@@ -122,7 +122,7 @@ class OllamaClient(GenAIClient):
             logger.debug(
                 f"Ollama tokens used: eval_count={result.get('eval_count')}, prompt_eval_count={result.get('prompt_eval_count')}"
             )
-            return str(result["response"]).strip()
+            return result["response"].strip()
         except (
             TimeoutException,
             ResponseError,
@@ -263,7 +263,7 @@ class OllamaClient(GenAIClient):
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
-    ) -> AsyncGenerator[tuple[str, Any], None]:
+    ):
         """Stream chat with tools; yields content deltas then final message.
 
         When tools are provided, Ollama streaming does not include tool_calls

frigate/genai/openai.py

@@ -3,7 +3,7 @@
 import base64
 import json
 import logging
-from typing import Any, AsyncGenerator, Optional
+from typing import Any, Optional
 
 from httpx import TimeoutException
 from openai import OpenAI
@@ -21,7 +21,7 @@ class OpenAIClient(GenAIClient):
     provider: OpenAI
     context_size: Optional[int] = None
 
-    def _init_provider(self) -> OpenAI:
+    def _init_provider(self):
         """Initialize the client."""
         # Extract context_size from provider_options as it's not a valid OpenAI client parameter
         # It will be used in get_context_size() instead
@@ -81,7 +81,7 @@ class OpenAIClient(GenAIClient):
                 and hasattr(result, "choices")
                 and len(result.choices) > 0
             ):
-                return str(result.choices[0].message.content.strip())
+                return result.choices[0].message.content.strip()
             return None
         except (TimeoutException, Exception) as e:
             logger.warning("OpenAI returned an error: %s", str(e))
@@ -171,7 +171,7 @@ class OpenAIClient(GenAIClient):
                 }
                 request_params.update(provider_opts)
 
-            result = self.provider.chat.completions.create(**request_params)  # type: ignore[call-overload]
+            result = self.provider.chat.completions.create(**request_params)
 
             if (
                 result is None
@@ -245,7 +245,7 @@ class OpenAIClient(GenAIClient):
         messages: list[dict[str, Any]],
         tools: Optional[list[dict[str, Any]]] = None,
         tool_choice: Optional[str] = "auto",
-    ) -> AsyncGenerator[tuple[str, Any], None]:
+    ):
         """
         Stream chat with tools; yields content deltas then final message.
 
@@ -287,7 +287,7 @@ class OpenAIClient(GenAIClient):
             tool_calls_by_index: dict[int, dict[str, Any]] = {}
             finish_reason = "stop"
 
-            stream = self.provider.chat.completions.create(**request_params)  # type: ignore[call-overload]
+            stream = self.provider.chat.completions.create(**request_params)
 
             for chunk in stream:
                 if not chunk or not chunk.choices:

frigate/jobs/media_sync.py

@@ -5,7 +5,7 @@ import os
 import threading
 from dataclasses import dataclass, field
 from datetime import datetime
-from typing import Optional, cast
+from typing import Optional
 
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.const import CONFIG_DIR, UPDATE_JOB_STATE
@@ -122,7 +122,7 @@ def start_media_sync_job(
     if job_is_running("media_sync"):
         current = get_current_job("media_sync")
         logger.warning(
-            f"Media sync job {current.id if current else 'unknown'} is already running. Rejecting new request."
+            f"Media sync job {current.id} is already running. Rejecting new request."
         )
         return None
 
@@ -146,9 +146,9 @@ def start_media_sync_job(
 
 def get_current_media_sync_job() -> Optional[MediaSyncJob]:
     """Get the current running/queued media sync job, if any."""
-    return cast(Optional[MediaSyncJob], get_current_job("media_sync"))
+    return get_current_job("media_sync")
 
 
 def get_media_sync_job_by_id(job_id: str) -> Optional[MediaSyncJob]:
     """Get media sync job by ID. Currently only tracks the current job."""
-    return cast(Optional[MediaSyncJob], get_job_by_id("media_sync", job_id))
+    return get_job_by_id("media_sync", job_id)

frigate/jobs/motion_search.py

@@ -6,7 +6,7 @@ import threading
 from concurrent.futures import Future, ThreadPoolExecutor, as_completed
 from dataclasses import asdict, dataclass, field
 from datetime import datetime
-from typing import Any, Optional, cast
+from typing import Any, Optional
 
 import cv2
 import numpy as np
@@ -96,7 +96,7 @@ def create_polygon_mask(
         dtype=np.int32,
     )
     mask = np.zeros((frame_height, frame_width), dtype=np.uint8)
-    cv2.fillPoly(mask, [motion_points], (255,))
+    cv2.fillPoly(mask, [motion_points], 255)
     return mask
 
 
@@ -116,7 +116,7 @@ def compute_roi_bbox_normalized(
 
 
 def heatmap_overlaps_roi(
-    heatmap: object, roi_bbox: tuple[float, float, float, float]
+    heatmap: dict[str, int], roi_bbox: tuple[float, float, float, float]
 ) -> bool:
     """Check if a sparse motion heatmap has any overlap with the ROI bounding box.
 
@@ -155,9 +155,9 @@ def segment_passes_activity_gate(recording: Recordings) -> bool:
     Returns True if any of motion, objects, or regions is non-zero/non-null.
     Returns True if all are null (old segments without data).
     """
-    motion: Any = recording.motion
+    motion = recording.motion
-    objects: Any = recording.objects
+    objects = recording.objects
-    regions: Any = recording.regions
+    regions = recording.regions
 
     # Old segments without metadata - pass through (conservative)
     if motion is None and objects is None and regions is None:
@@ -278,9 +278,6 @@ class MotionSearchRunner(threading.Thread):
         frame_width = camera_config.detect.width
         frame_height = camera_config.detect.height
 
-        if frame_width is None or frame_height is None:
-            raise ValueError(f"Camera {camera_name} detect dimensions not configured")
-
         # Create polygon mask
         polygon_mask = create_polygon_mask(
             self.job.polygon_points, frame_width, frame_height
@@ -418,13 +415,11 @@ class MotionSearchRunner(threading.Thread):
                 if self._should_stop():
                     break
 
-                rec_start: float = recording.start_time  # type: ignore[assignment]
-                rec_end: float = recording.end_time  # type: ignore[assignment]
                 future = executor.submit(
                     self._process_recording_for_motion,
-                    str(recording.path),
+                    recording.path,
-                    rec_start,
+                    recording.start_time,
-                    rec_end,
+                    recording.end_time,
                     self.job.start_time_range,
                     self.job.end_time_range,
                     polygon_mask,
@@ -529,12 +524,10 @@ class MotionSearchRunner(threading.Thread):
                 break
 
             try:
-                rec_start: float = recording.start_time  # type: ignore[assignment]
-                rec_end: float = recording.end_time  # type: ignore[assignment]
                 results, frames = self._process_recording_for_motion(
-                    str(recording.path),
+                    recording.path,
-                    rec_start,
+                    recording.start_time,
-                    rec_end,
+                    recording.end_time,
                     self.job.start_time_range,
                     self.job.end_time_range,
                     polygon_mask,
@@ -679,9 +672,7 @@ class MotionSearchRunner(threading.Thread):
                 # Handle frame dimension changes
                 if gray.shape != polygon_mask.shape:
                     resized_mask = cv2.resize(
-                        polygon_mask,
+                        polygon_mask, (gray.shape[1], gray.shape[0]), cv2.INTER_NEAREST
-                        (gray.shape[1], gray.shape[0]),
-                        interpolation=cv2.INTER_NEAREST,
                     )
                     current_bbox = cv2.boundingRect(resized_mask)
                 else:
@@ -707,7 +698,7 @@ class MotionSearchRunner(threading.Thread):
                 )
 
                 if prev_frame_gray is not None:
-                    diff = cv2.absdiff(prev_frame_gray, masked_gray)  # type: ignore[unreachable]
+                    diff = cv2.absdiff(prev_frame_gray, masked_gray)
                     diff_blurred = cv2.GaussianBlur(diff, (3, 3), 0)
                     _, thresh = cv2.threshold(
                         diff_blurred, threshold, 255, cv2.THRESH_BINARY
@@ -834,7 +825,7 @@ def get_motion_search_job(job_id: str) -> Optional[MotionSearchJob]:
         if job_entry:
             return job_entry[0]
     # Check completed jobs via manager
-    return cast(Optional[MotionSearchJob], get_job_by_id("motion_search", job_id))
+    return get_job_by_id("motion_search", job_id)
 
 
 def cancel_motion_search_job(job_id: str) -> bool:

frigate/jobs/vlm_watch.py

@@ -54,9 +54,9 @@ class VLMWatchRunner(threading.Thread):
         job: VLMWatchJob,
         config: FrigateConfig,
         cancel_event: threading.Event,
-        frame_processor: Any,
+        frame_processor,
-        genai_manager: Any,
+        genai_manager,
-        dispatcher: Any,
+        dispatcher,
     ) -> None:
         super().__init__(daemon=True, name=f"vlm_watch_{job.id}")
         self.job = job
@@ -226,12 +226,9 @@ class VLMWatchRunner(threading.Thread):
             remaining = deadline - time.time()
             if remaining <= 0:
                 break
-            result = self.detection_subscriber.check_for_update(
+            topic, payload = self.detection_subscriber.check_for_update(
                 timeout=min(1.0, remaining)
             )
-            if result is None:
-                continue
-            topic, payload = result
             if topic is None or payload is None:
                 continue
             # payload = (camera, frame_name, frame_time, tracked_objects, motion_boxes, regions)
@@ -331,9 +328,9 @@ def start_vlm_watch_job(
     condition: str,
     max_duration_minutes: int,
     config: FrigateConfig,
-    frame_processor: Any,
+    frame_processor,
-    genai_manager: Any,
+    genai_manager,
-    dispatcher: Any,
+    dispatcher,
     labels: list[str] | None = None,
     zones: list[str] | None = None,
 ) -> str:

frigate/motion/__init__.py

@@ -13,10 +13,10 @@ class MotionDetector(ABC):
         frame_shape: Tuple[int, int, int],
         config: MotionConfig,
         fps: int,
-        improve_contrast: bool,
+        improve_contrast,
-        threshold: int,
+        threshold,
-        contour_area: int | None,
+        contour_area,
-    ) -> None:
+    ):
         pass
 
     @abstractmethod
@@ -25,7 +25,7 @@ class MotionDetector(ABC):
         pass
 
     @abstractmethod
-    def is_calibrating(self) -> bool:
+    def is_calibrating(self):
         """Return if motion is recalibrating."""
         pass
 
@@ -35,6 +35,6 @@ class MotionDetector(ABC):
         pass
 
     @abstractmethod
-    def stop(self) -> None:
+    def stop(self):
         """Stop any ongoing work and processes."""
         pass

frigate/motion/frigate_motion.py

@@ -1,9 +1,7 @@
-from typing import Any
-
 import cv2
 import numpy as np
 
-from frigate.config.config import RuntimeMotionConfig
+from frigate.config import MotionConfig
 from frigate.motion import MotionDetector
 from frigate.util.image import grab_cv2_contours
 
@@ -11,20 +9,19 @@ from frigate.util.image import grab_cv2_contours
 class FrigateMotionDetector(MotionDetector):
     def __init__(
         self,
-        frame_shape: tuple[int, ...],
+        frame_shape,
-        config: RuntimeMotionConfig,
+        config: MotionConfig,
         fps: int,
-        improve_contrast: Any,
+        improve_contrast,
-        threshold: Any,
+        threshold,
-        contour_area: Any,
+        contour_area,
-    ) -> None:
+    ):
         self.config = config
         self.frame_shape = frame_shape
-        frame_height = config.frame_height or frame_shape[0]
+        self.resize_factor = frame_shape[0] / config.frame_height
-        self.resize_factor = frame_shape[0] / frame_height
         self.motion_frame_size = (
-            frame_height,
+            config.frame_height,
-            frame_height * frame_shape[1] // frame_shape[0],
+            config.frame_height * frame_shape[1] // frame_shape[0],
         )
         self.avg_frame = np.zeros(self.motion_frame_size, np.float32)
         self.avg_delta = np.zeros(self.motion_frame_size, np.float32)
@@ -41,10 +38,10 @@ class FrigateMotionDetector(MotionDetector):
         self.threshold = threshold
         self.contour_area = contour_area
 
-    def is_calibrating(self) -> bool:
+    def is_calibrating(self):
         return False
 
-    def detect(self, frame: np.ndarray) -> list:
+    def detect(self, frame):
         motion_boxes = []
 
         gray = frame[0 : self.frame_shape[0], 0 : self.frame_shape[1]]
@@ -102,7 +99,7 @@ class FrigateMotionDetector(MotionDetector):
 
             # dilate the thresholded image to fill in holes, then find contours
             # on thresholded image
-            thresh_dilated = cv2.dilate(thresh, None, iterations=2)  # type: ignore[call-overload]
+            thresh_dilated = cv2.dilate(thresh, None, iterations=2)
             contours = cv2.findContours(
                 thresh_dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
             )

frigate/motion/improved_motion.py

@@ -1,12 +1,11 @@
 import logging
-from typing import Optional
 
 import cv2
 import numpy as np
 from scipy.ndimage import gaussian_filter
 
 from frigate.camera import PTZMetrics
-from frigate.config.config import RuntimeMotionConfig
+from frigate.config import MotionConfig
 from frigate.motion import MotionDetector
 from frigate.util.image import grab_cv2_contours
 
@@ -16,23 +15,22 @@ logger = logging.getLogger(__name__)
 class ImprovedMotionDetector(MotionDetector):
     def __init__(
         self,
-        frame_shape: tuple[int, ...],
+        frame_shape,
-        config: RuntimeMotionConfig,
+        config: MotionConfig,
         fps: int,
-        ptz_metrics: Optional[PTZMetrics] = None,
+        ptz_metrics: PTZMetrics = None,
-        name: str = "improved",
+        name="improved",
-        blur_radius: int = 1,
+        blur_radius=1,
-        interpolation: int = cv2.INTER_NEAREST,
+        interpolation=cv2.INTER_NEAREST,
-        contrast_frame_history: int = 50,
+        contrast_frame_history=50,
-    ) -> None:
+    ):
         self.name = name
         self.config = config
         self.frame_shape = frame_shape
-        frame_height = config.frame_height or frame_shape[0]
+        self.resize_factor = frame_shape[0] / config.frame_height
-        self.resize_factor = frame_shape[0] / frame_height
         self.motion_frame_size = (
-            frame_height,
+            config.frame_height,
-            frame_height * frame_shape[1] // frame_shape[0],
+            config.frame_height * frame_shape[1] // frame_shape[0],
         )
         self.avg_frame = np.zeros(self.motion_frame_size, np.float32)
         self.motion_frame_count = 0
@@ -46,20 +44,20 @@ class ImprovedMotionDetector(MotionDetector):
         self.contrast_values[:, 1:2] = 255
         self.contrast_values_index = 0
         self.ptz_metrics = ptz_metrics
-        self.last_stop_time: float | None = None
+        self.last_stop_time = None
 
-    def is_calibrating(self) -> bool:
+    def is_calibrating(self):
         return self.calibrating
 
-    def detect(self, frame: np.ndarray) -> list[tuple[int, int, int, int]]:
+    def detect(self, frame):
-        motion_boxes: list[tuple[int, int, int, int]] = []
+        motion_boxes = []
 
         if not self.config.enabled:
             return motion_boxes
 
         # if ptz motor is moving from autotracking, quickly return
         # a single box that is 80% of the frame
-        if self.ptz_metrics is not None and (
+        if (
             self.ptz_metrics.autotracker_enabled.value
             and not self.ptz_metrics.motor_stopped.is_set()
         ):
@@ -132,19 +130,19 @@ class ImprovedMotionDetector(MotionDetector):
 
         # dilate the thresholded image to fill in holes, then find contours
         # on thresholded image
-        thresh_dilated = cv2.dilate(thresh, None, iterations=1)  # type: ignore[call-overload]
+        thresh_dilated = cv2.dilate(thresh, None, iterations=1)
         contours = cv2.findContours(
             thresh_dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
         )
         contours = grab_cv2_contours(contours)
 
         # loop over the contours
-        total_contour_area: float = 0
+        total_contour_area = 0
         for c in contours:
             # if the contour is big enough, count it as motion
             contour_area = cv2.contourArea(c)
             total_contour_area += contour_area
-            if contour_area > (self.config.contour_area or 0):
+            if contour_area > self.config.contour_area:
                 x, y, w, h = cv2.boundingRect(c)
                 motion_boxes.append(
                     (
@@ -161,7 +159,7 @@ class ImprovedMotionDetector(MotionDetector):
 
         # check if the motor has just stopped from autotracking
         # if so, reassign the average to the current frame so we begin with a new baseline
-        if self.ptz_metrics is not None and (
+        if (
             # ensure we only do this for cameras with autotracking enabled
             self.ptz_metrics.autotracker_enabled.value
             and self.ptz_metrics.motor_stopped.is_set()

frigate/mypy.ini

@@ -41,24 +41,6 @@ ignore_errors = false
 [mypy-frigate.events]
 ignore_errors = false
 
-[mypy-frigate.genai.*]
-ignore_errors = false
-
-[mypy-frigate.jobs.*]
-ignore_errors = false
-
-[mypy-frigate.motion.*]
-ignore_errors = false
-
-[mypy-frigate.object_detection.*]
-ignore_errors = false
-
-[mypy-frigate.output.*]
-ignore_errors = false
-
-[mypy-frigate.ptz]
-ignore_errors = false
-
 [mypy-frigate.log]
 ignore_errors = false
 

frigate/object_detection/base.py

@@ -7,7 +7,6 @@ from abc import ABC, abstractmethod
 from collections import deque
 from multiprocessing import Queue, Value
 from multiprocessing.synchronize import Event as MpEvent
-from typing import Any, Optional
 
 import numpy as np
 import zmq
@@ -35,25 +34,26 @@ logger = logging.getLogger(__name__)
 
 class ObjectDetector(ABC):
     @abstractmethod
-    def detect(self, tensor_input: np.ndarray, threshold: float = 0.4) -> list:
+    def detect(self, tensor_input, threshold: float = 0.4):
         pass
 
 
 class BaseLocalDetector(ObjectDetector):
     def __init__(
         self,
-        detector_config: Optional[BaseDetectorConfig] = None,
+        detector_config: BaseDetectorConfig = None,
-        labels: Optional[str] = None,
+        labels: str = None,
-        stop_event: Optional[MpEvent] = None,
+        stop_event: MpEvent = None,
-    ) -> None:
+    ):
         self.fps = EventsPerSecond()
         if labels is None:
-            self.labels: dict[int, str] = {}
+            self.labels = {}
         else:
             self.labels = load_labels(labels)
 
-        if detector_config and detector_config.model:
+        if detector_config:
             self.input_transform = tensor_transform(detector_config.model.input_tensor)
+
             self.dtype = detector_config.model.input_dtype
         else:
             self.input_transform = None
@@ -77,10 +77,10 @@ class BaseLocalDetector(ObjectDetector):
 
         return tensor_input
 
-    def detect(self, tensor_input: np.ndarray, threshold: float = 0.4) -> list:
+    def detect(self, tensor_input: np.ndarray, threshold=0.4):
         detections = []
 
-        raw_detections = self.detect_raw(tensor_input)  # type: ignore[attr-defined]
+        raw_detections = self.detect_raw(tensor_input)
 
         for d in raw_detections:
             if int(d[0]) < 0 or int(d[0]) >= len(self.labels):
@@ -96,28 +96,28 @@ class BaseLocalDetector(ObjectDetector):
 
 
 class LocalObjectDetector(BaseLocalDetector):
-    def detect_raw(self, tensor_input: np.ndarray) -> np.ndarray:
+    def detect_raw(self, tensor_input: np.ndarray):
         tensor_input = self._transform_input(tensor_input)
-        return self.detect_api.detect_raw(tensor_input=tensor_input)  # type: ignore[no-any-return]
+        return self.detect_api.detect_raw(tensor_input=tensor_input)
 
 
 class AsyncLocalObjectDetector(BaseLocalDetector):
-    def async_send_input(self, tensor_input: np.ndarray, connection_id: str) -> None:
+    def async_send_input(self, tensor_input: np.ndarray, connection_id: str):
         tensor_input = self._transform_input(tensor_input)
-        self.detect_api.send_input(connection_id, tensor_input)
+        return self.detect_api.send_input(connection_id, tensor_input)
 
-    def async_receive_output(self) -> Any:
+    def async_receive_output(self):
         return self.detect_api.receive_output()
 
 
 class DetectorRunner(FrigateProcess):
     def __init__(
         self,
-        name: str,
+        name,
         detection_queue: Queue,
         cameras: list[str],
-        avg_speed: Any,
+        avg_speed: Value,
-        start_time: Any,
+        start_time: Value,
         config: FrigateConfig,
         detector_config: BaseDetectorConfig,
         stop_event: MpEvent,
@@ -129,11 +129,11 @@ class DetectorRunner(FrigateProcess):
         self.start_time = start_time
         self.config = config
         self.detector_config = detector_config
-        self.outputs: dict[str, Any] = {}
+        self.outputs: dict = {}
 
-    def create_output_shm(self, name: str) -> None:
+    def create_output_shm(self, name: str):
         out_shm = UntrackedSharedMemory(name=f"out-{name}", create=False)
-        out_np: np.ndarray = np.ndarray((20, 6), dtype=np.float32, buffer=out_shm.buf)
+        out_np = np.ndarray((20, 6), dtype=np.float32, buffer=out_shm.buf)
         self.outputs[name] = {"shm": out_shm, "np": out_np}
 
     def run(self) -> None:
@@ -155,8 +155,8 @@ class DetectorRunner(FrigateProcess):
                 connection_id,
                 (
                     1,
-                    self.detector_config.model.height,  # type: ignore[union-attr]
+                    self.detector_config.model.height,
-                    self.detector_config.model.width,  # type: ignore[union-attr]
+                    self.detector_config.model.width,
                     3,
                 ),
             )
@@ -187,11 +187,11 @@ class DetectorRunner(FrigateProcess):
 class AsyncDetectorRunner(FrigateProcess):
     def __init__(
         self,
-        name: str,
+        name,
         detection_queue: Queue,
         cameras: list[str],
-        avg_speed: Any,
+        avg_speed: Value,
-        start_time: Any,
+        start_time: Value,
         config: FrigateConfig,
         detector_config: BaseDetectorConfig,
         stop_event: MpEvent,
@@ -203,15 +203,15 @@ class AsyncDetectorRunner(FrigateProcess):
         self.start_time = start_time
         self.config = config
         self.detector_config = detector_config
-        self.outputs: dict[str, Any] = {}
+        self.outputs: dict = {}
         self._frame_manager: SharedMemoryFrameManager | None = None
         self._publisher: ObjectDetectorPublisher | None = None
         self._detector: AsyncLocalObjectDetector | None = None
-        self.send_times: deque[float] = deque()
+        self.send_times = deque()
 
-    def create_output_shm(self, name: str) -> None:
+    def create_output_shm(self, name: str):
         out_shm = UntrackedSharedMemory(name=f"out-{name}", create=False)
-        out_np: np.ndarray = np.ndarray((20, 6), dtype=np.float32, buffer=out_shm.buf)
+        out_np = np.ndarray((20, 6), dtype=np.float32, buffer=out_shm.buf)
         self.outputs[name] = {"shm": out_shm, "np": out_np}
 
     def _detect_worker(self) -> None:
@@ -222,13 +222,12 @@ class AsyncDetectorRunner(FrigateProcess):
             except queue.Empty:
                 continue
 
-            assert self._frame_manager is not None
             input_frame = self._frame_manager.get(
                 connection_id,
                 (
                     1,
-                    self.detector_config.model.height,  # type: ignore[union-attr]
+                    self.detector_config.model.height,
-                    self.detector_config.model.width,  # type: ignore[union-attr]
+                    self.detector_config.model.width,
                     3,
                 ),
             )
@@ -239,13 +238,11 @@ class AsyncDetectorRunner(FrigateProcess):
 
             # mark start time and send to accelerator
             self.send_times.append(time.perf_counter())
-            assert self._detector is not None
             self._detector.async_send_input(input_frame, connection_id)
 
     def _result_worker(self) -> None:
         logger.info("Starting Result Worker Thread")
         while not self.stop_event.is_set():
-            assert self._detector is not None
             connection_id, detections = self._detector.async_receive_output()
 
             # Handle timeout case (queue.Empty) - just continue
@@ -259,7 +256,6 @@ class AsyncDetectorRunner(FrigateProcess):
             duration = time.perf_counter() - ts
 
             # release input buffer
-            assert self._frame_manager is not None
             self._frame_manager.close(connection_id)
 
             if connection_id not in self.outputs:
@@ -268,7 +264,6 @@ class AsyncDetectorRunner(FrigateProcess):
             # write results and publish
             if detections is not None:
                 self.outputs[connection_id]["np"][:] = detections[:]
-            assert self._publisher is not None
             self._publisher.publish(connection_id)
 
             # update timers
@@ -335,14 +330,11 @@ class ObjectDetectProcess:
         self.stop_event = stop_event
         self.start_or_restart()
 
-    def stop(self) -> None:
+    def stop(self):
         # if the process has already exited on its own, just return
         if self.detect_process and self.detect_process.exitcode:
             return
 
-        if self.detect_process is None:
-            return
-
         logging.info("Waiting for detection process to exit gracefully...")
         self.detect_process.join(timeout=30)
         if self.detect_process.exitcode is None:
@@ -351,8 +343,8 @@ class ObjectDetectProcess:
             self.detect_process.join()
         logging.info("Detection process has exited...")
 
-    def start_or_restart(self) -> None:
+    def start_or_restart(self):
-        self.detection_start.value = 0.0  # type: ignore[attr-defined]
+        self.detection_start.value = 0.0
         if (self.detect_process is not None) and self.detect_process.is_alive():
             self.stop()
 
@@ -397,19 +389,17 @@ class RemoteObjectDetector:
         self.detection_queue = detection_queue
         self.stop_event = stop_event
         self.shm = UntrackedSharedMemory(name=self.name, create=False)
-        self.np_shm: np.ndarray = np.ndarray(
+        self.np_shm = np.ndarray(
             (1, model_config.height, model_config.width, 3),
             dtype=np.uint8,
             buffer=self.shm.buf,
         )
         self.out_shm = UntrackedSharedMemory(name=f"out-{self.name}", create=False)
-        self.out_np_shm: np.ndarray = np.ndarray(
+        self.out_np_shm = np.ndarray((20, 6), dtype=np.float32, buffer=self.out_shm.buf)
-            (20, 6), dtype=np.float32, buffer=self.out_shm.buf
-        )
         self.detector_subscriber = ObjectDetectorSubscriber(name)
 
-    def detect(self, tensor_input: np.ndarray, threshold: float = 0.4) -> list:
+    def detect(self, tensor_input, threshold=0.4):
-        detections: list = []
+        detections = []
 
         if self.stop_event.is_set():
             return detections
@@ -441,7 +431,7 @@ class RemoteObjectDetector:
         self.fps.update()
         return detections
 
-    def cleanup(self) -> None:
+    def cleanup(self):
         self.detector_subscriber.stop()
         self.shm.unlink()
         self.out_shm.unlink()

frigate/object_detection/util.py

@@ -13,10 +13,10 @@ class RequestStore:
     A thread-safe hash-based response store that handles creating requests.
     """
 
-    def __init__(self) -> None:
+    def __init__(self):
         self.request_counter = 0
         self.request_counter_lock = threading.Lock()
-        self.input_queue: queue.Queue[tuple[int, ndarray]] = queue.Queue()
+        self.input_queue = queue.Queue()
 
     def __get_request_id(self) -> int:
         with self.request_counter_lock:
@@ -45,19 +45,17 @@ class ResponseStore:
     their request's result appears.
     """
 
-    def __init__(self) -> None:
+    def __init__(self):
-        self.responses: dict[
+        self.responses = {}  # Maps request_id -> (original_input, infer_results)
-            int, ndarray
-        ] = {}  # Maps request_id -> (original_input, infer_results)
         self.lock = threading.Lock()
         self.cond = threading.Condition(self.lock)
 
-    def put(self, request_id: int, response: ndarray) -> None:
+    def put(self, request_id: int, response: ndarray):
         with self.cond:
             self.responses[request_id] = response
             self.cond.notify_all()
 
-    def get(self, request_id: int, timeout: float | None = None) -> ndarray:
+    def get(self, request_id: int, timeout=None) -> ndarray:
         with self.cond:
             if not self.cond.wait_for(
                 lambda: request_id in self.responses, timeout=timeout
@@ -67,9 +65,7 @@ class ResponseStore:
             return self.responses.pop(request_id)
 
 
-def tensor_transform(
+def tensor_transform(desired_shape: InputTensorEnum):
-    desired_shape: InputTensorEnum,
-) -> tuple[int, int, int, int] | None:
     # Currently this function only supports BHWC permutations
     if desired_shape == InputTensorEnum.nhwc:
         return None

frigate/output/birdseye.py

@@ -4,13 +4,13 @@ import datetime
 import glob
 import logging
 import math
+import multiprocessing as mp
 import os
 import queue
 import subprocess as sp
 import threading
 import time
 import traceback
-from multiprocessing.synchronize import Event as MpEvent
 from typing import Any, Optional
 
 import cv2
@@ -74,25 +74,25 @@ class Canvas:
         self,
         canvas_width: int,
         canvas_height: int,
-        scaling_factor: float,
+        scaling_factor: int,
     ) -> None:
         self.scaling_factor = scaling_factor
         gcd = math.gcd(canvas_width, canvas_height)
         self.aspect = get_standard_aspect_ratio(
-            int(canvas_width / gcd), int(canvas_height / gcd)
+            (canvas_width / gcd), (canvas_height / gcd)
         )
         self.width = canvas_width
-        self.height: float = (self.width * self.aspect[1]) / self.aspect[0]
+        self.height = (self.width * self.aspect[1]) / self.aspect[0]
-        self.coefficient_cache: dict[int, float] = {}
+        self.coefficient_cache: dict[int, int] = {}
         self.aspect_cache: dict[str, tuple[int, int]] = {}
 
-    def get_aspect(self, coefficient: float) -> tuple[float, float]:
+    def get_aspect(self, coefficient: int) -> tuple[int, int]:
         return (self.aspect[0] * coefficient, self.aspect[1] * coefficient)
 
-    def get_coefficient(self, camera_count: int) -> float:
+    def get_coefficient(self, camera_count: int) -> int:
         return self.coefficient_cache.get(camera_count, self.scaling_factor)
 
-    def set_coefficient(self, camera_count: int, coefficient: float) -> None:
+    def set_coefficient(self, camera_count: int, coefficient: int) -> None:
         self.coefficient_cache[camera_count] = coefficient
 
     def get_camera_aspect(
@@ -105,7 +105,7 @@ class Canvas:
 
         gcd = math.gcd(camera_width, camera_height)
         camera_aspect = get_standard_aspect_ratio(
-            int(camera_width / gcd), int(camera_height / gcd)
+            camera_width / gcd, camera_height / gcd
         )
         self.aspect_cache[cam_name] = camera_aspect
         return camera_aspect
@@ -116,7 +116,7 @@ class FFMpegConverter(threading.Thread):
         self,
         ffmpeg: FfmpegConfig,
         input_queue: queue.Queue,
-        stop_event: MpEvent,
+        stop_event: mp.Event,
         in_width: int,
         in_height: int,
         out_width: int,
@@ -128,7 +128,7 @@ class FFMpegConverter(threading.Thread):
         self.camera = "birdseye"
         self.input_queue = input_queue
         self.stop_event = stop_event
-        self.bd_pipe: int | None = None
+        self.bd_pipe = None
 
         if birdseye_rtsp:
             self.recreate_birdseye_pipe()
@@ -181,8 +181,7 @@ class FFMpegConverter(threading.Thread):
         os.close(stdin)
         self.reading_birdseye = False
 
-    def __write(self, b: bytes) -> None:
+    def __write(self, b) -> None:
-        assert self.process.stdin is not None
         self.process.stdin.write(b)
 
         if self.bd_pipe:
@@ -201,13 +200,13 @@ class FFMpegConverter(threading.Thread):
 
                 return
 
-    def read(self, length: int) -> Any:
+    def read(self, length):
         try:
-            return self.process.stdout.read1(length)  # type: ignore[union-attr]
+            return self.process.stdout.read1(length)
         except ValueError:
             return False
 
-    def exit(self) -> None:
+    def exit(self):
         if self.bd_pipe:
             os.close(self.bd_pipe)
 
@@ -234,8 +233,8 @@ class BroadcastThread(threading.Thread):
         self,
         camera: str,
         converter: FFMpegConverter,
-        websocket_server: Any,
+        websocket_server,
-        stop_event: MpEvent,
+        stop_event: mp.Event,
     ):
         super().__init__()
         self.camera = camera
@@ -243,7 +242,7 @@ class BroadcastThread(threading.Thread):
         self.websocket_server = websocket_server
         self.stop_event = stop_event
 
-    def run(self) -> None:
+    def run(self):
         while not self.stop_event.is_set():
             buf = self.converter.read(65536)
             if buf:
@@ -271,16 +270,16 @@ class BirdsEyeFrameManager:
     def __init__(
         self,
         config: FrigateConfig,
-        stop_event: MpEvent,
+        stop_event: mp.Event,
     ):
         self.config = config
         width, height = get_canvas_shape(config.birdseye.width, config.birdseye.height)
         self.frame_shape = (height, width)
         self.yuv_shape = (height * 3 // 2, width)
-        self.frame: np.ndarray = np.ndarray(self.yuv_shape, dtype=np.uint8)
+        self.frame = np.ndarray(self.yuv_shape, dtype=np.uint8)
         self.canvas = Canvas(width, height, config.birdseye.layout.scaling_factor)
         self.stop_event = stop_event
-        self.last_refresh_time: float = 0
+        self.last_refresh_time = 0
 
         # initialize the frame as black and with the Frigate logo
         self.blank_frame = np.zeros(self.yuv_shape, np.uint8)
@@ -324,15 +323,15 @@ class BirdsEyeFrameManager:
 
         self.frame[:] = self.blank_frame
 
-        self.cameras: dict[str, Any] = {}
+        self.cameras = {}
         for camera in self.config.cameras.keys():
             self.add_camera(camera)
 
-        self.camera_layout: list[Any] = []
+        self.camera_layout = []
-        self.active_cameras: set[str] = set()
+        self.active_cameras = set()
         self.last_output_time = 0.0
 
-    def add_camera(self, cam: str) -> None:
+    def add_camera(self, cam: str):
         """Add a camera to self.cameras with the correct structure."""
         settings = self.config.cameras[cam]
         # precalculate the coordinates for all the channels
@@ -362,21 +361,16 @@ class BirdsEyeFrameManager:
             },
         }
 
-    def remove_camera(self, cam: str) -> None:
+    def remove_camera(self, cam: str):
         """Remove a camera from self.cameras."""
         if cam in self.cameras:
             del self.cameras[cam]
 
-    def clear_frame(self) -> None:
+    def clear_frame(self):
         logger.debug("Clearing the birdseye frame")
         self.frame[:] = self.blank_frame
 
-    def copy_to_position(
+    def copy_to_position(self, position, camera=None, frame: np.ndarray = None):
-        self,
-        position: Any,
-        camera: Optional[str] = None,
-        frame: Optional[np.ndarray] = None,
-    ) -> None:
         if camera is None:
             frame = None
             channel_dims = None
@@ -395,9 +389,7 @@ class BirdsEyeFrameManager:
             channel_dims,
         )
 
-    def camera_active(
+    def camera_active(self, mode, object_box_count, motion_box_count):
-        self, mode: Any, object_box_count: int, motion_box_count: int
-    ) -> bool:
         if mode == BirdseyeModeEnum.continuous:
             return True
 
@@ -407,8 +399,6 @@ class BirdsEyeFrameManager:
         if mode == BirdseyeModeEnum.objects and object_box_count > 0:
             return True
 
-        return False
-
     def get_camera_coordinates(self) -> dict[str, dict[str, int]]:
         """Return the coordinates of each camera in the current layout."""
         coordinates = {}
@@ -461,7 +451,7 @@ class BirdsEyeFrameManager:
                         - self.cameras[active_camera]["last_active_frame"]
                     ),
                 )
-                active_cameras = set(limited_active_cameras[:max_cameras])
+                active_cameras = limited_active_cameras[:max_cameras]
                 max_camera_refresh = True
                 self.last_refresh_time = now
 
@@ -520,7 +510,7 @@ class BirdsEyeFrameManager:
 
                     # center camera view in canvas and ensure that it fits
                     if scaled_width < self.canvas.width:
-                        coefficient: float = 1
+                        coefficient = 1
                         x_offset = int((self.canvas.width - scaled_width) / 2)
                     else:
                         coefficient = self.canvas.width / scaled_width
@@ -567,7 +557,7 @@ class BirdsEyeFrameManager:
                         calculating = False
                         self.canvas.set_coefficient(len(active_cameras), coefficient)
 
-                    self.camera_layout = layout_candidate or []
+                    self.camera_layout = layout_candidate
                 frame_changed = True
 
             # Draw the layout
@@ -587,12 +577,10 @@ class BirdsEyeFrameManager:
         self,
         cameras_to_add: list[str],
         coefficient: float,
-    ) -> Optional[list[list[Any]]]:
+    ) -> tuple[Any]:
         """Calculate the optimal layout for 2+ cameras."""
 
-        def map_layout(
+        def map_layout(camera_layout: list[list[Any]], row_height: int):
-            camera_layout: list[list[Any]], row_height: int
-        ) -> tuple[int, int, Optional[list[list[Any]]]]:
             """Map the calculated layout."""
             candidate_layout = []
             starting_x = 0
@@ -789,11 +777,11 @@ class Birdseye:
     def __init__(
         self,
         config: FrigateConfig,
-        stop_event: MpEvent,
+        stop_event: mp.Event,
-        websocket_server: Any,
+        websocket_server,
     ) -> None:
         self.config = config
-        self.input: queue.Queue[bytes] = queue.Queue(maxsize=10)
+        self.input = queue.Queue(maxsize=10)
         self.converter = FFMpegConverter(
             config.ffmpeg,
             self.input,
@@ -818,7 +806,7 @@ class Birdseye:
         )
 
         if config.birdseye.restream:
-            self.birdseye_buffer: Any = self.frame_manager.create(
+            self.birdseye_buffer = self.frame_manager.create(
                 "birdseye",
                 self.birdseye_manager.yuv_shape[0] * self.birdseye_manager.yuv_shape[1],
             )

frigate/output/camera.py

@@ -1,11 +1,10 @@
 """Handle outputting individual cameras via jsmpeg."""
 
 import logging
+import multiprocessing as mp
 import queue
 import subprocess as sp
 import threading
-from multiprocessing.synchronize import Event as MpEvent
-from typing import Any
 
 from frigate.config import CameraConfig, FfmpegConfig
 
@@ -18,7 +17,7 @@ class FFMpegConverter(threading.Thread):
         camera: str,
         ffmpeg: FfmpegConfig,
         input_queue: queue.Queue,
-        stop_event: MpEvent,
+        stop_event: mp.Event,
         in_width: int,
         in_height: int,
         out_width: int,
@@ -65,17 +64,16 @@ class FFMpegConverter(threading.Thread):
             start_new_session=True,
         )
 
-    def __write(self, b: bytes) -> None:
+    def __write(self, b) -> None:
-        assert self.process.stdin is not None
         self.process.stdin.write(b)
 
-    def read(self, length: int) -> Any:
+    def read(self, length):
         try:
-            return self.process.stdout.read1(length)  # type: ignore[union-attr]
+            return self.process.stdout.read1(length)
         except ValueError:
             return False
 
-    def exit(self) -> None:
+    def exit(self):
         self.process.terminate()
 
         try:
@@ -100,8 +98,8 @@ class BroadcastThread(threading.Thread):
         self,
         camera: str,
         converter: FFMpegConverter,
-        websocket_server: Any,
+        websocket_server,
-        stop_event: MpEvent,
+        stop_event: mp.Event,
     ):
         super().__init__()
         self.camera = camera
@@ -109,7 +107,7 @@ class BroadcastThread(threading.Thread):
         self.websocket_server = websocket_server
         self.stop_event = stop_event
 
-    def run(self) -> None:
+    def run(self):
         while not self.stop_event.is_set():
             buf = self.converter.read(65536)
             if buf:
@@ -135,15 +133,15 @@ class BroadcastThread(threading.Thread):
 
 class JsmpegCamera:
     def __init__(
-        self, config: CameraConfig, stop_event: MpEvent, websocket_server: Any
+        self, config: CameraConfig, stop_event: mp.Event, websocket_server
     ) -> None:
         self.config = config
-        self.input: queue.Queue[bytes] = queue.Queue(maxsize=config.detect.fps)
+        self.input = queue.Queue(maxsize=config.detect.fps)
         width = int(
             config.live.height * (config.frame_shape[1] / config.frame_shape[0])
         )
         self.converter = FFMpegConverter(
-            config.name or "",
+            config.name,
             config.ffmpeg,
             self.input,
             stop_event,
@@ -154,13 +152,13 @@ class JsmpegCamera:
             config.live.quality,
         )
         self.broadcaster = BroadcastThread(
-            config.name or "", self.converter, websocket_server, stop_event
+            config.name, self.converter, websocket_server, stop_event
         )
 
         self.converter.start()
         self.broadcaster.start()
 
-    def write_frame(self, frame_bytes: bytes) -> None:
+    def write_frame(self, frame_bytes) -> None:
         try:
             self.input.put_nowait(frame_bytes)
         except queue.Full:

frigate/output/output.py

@@ -61,12 +61,6 @@ def check_disabled_camera_update(
             # last camera update was more than 1 second ago
             # need to send empty data to birdseye because current
             # frame is now out of date
-            cam_width = config.cameras[camera].detect.width
-            cam_height = config.cameras[camera].detect.height
-
-            if cam_width is None or cam_height is None:
-                raise ValueError(f"Camera {camera} detect dimensions not configured")
-
             if birdseye and offline_time < 10:
                 # we only need to send blank frames to birdseye at the beginning of a camera being offline
                 birdseye.write_data(
@@ -74,7 +68,10 @@ def check_disabled_camera_update(
                     [],
                     [],
                     now,
-                    get_blank_yuv_frame(cam_width, cam_height),
+                    get_blank_yuv_frame(
+                        config.cameras[camera].detect.width,
+                        config.cameras[camera].detect.height,
+                    ),
                 )
 
     if not has_enabled_camera and birdseye:
@@ -176,7 +173,7 @@ class OutputProcess(FrigateProcess):
                 birdseye_config_subscriber.check_for_update()
             )
 
-            if update_topic is not None and birdseye_config is not None:
+            if update_topic is not None:
                 previous_global_mode = self.config.birdseye.mode
                 self.config.birdseye = birdseye_config
 
@@ -201,10 +198,7 @@ class OutputProcess(FrigateProcess):
                             birdseye,
                         )
 
-            _result = detection_subscriber.check_for_update(timeout=1)
+            (topic, data) = detection_subscriber.check_for_update(timeout=1)
-            if _result is None:
-                continue
-            (topic, data) = _result
             now = datetime.datetime.now().timestamp()
 
             if now - last_disabled_cam_check > 5:
@@ -214,7 +208,7 @@ class OutputProcess(FrigateProcess):
                     self.config, birdseye, preview_recorders, preview_write_times
                 )
 
-            if not topic or data is None:
+            if not topic:
                 continue
 
             (
@@ -268,16 +262,12 @@ class OutputProcess(FrigateProcess):
                 jsmpeg_cameras[camera].write_frame(frame.tobytes())
 
             # send output data to birdseye if websocket is connected or restreaming
-            if (
+            if self.config.birdseye.enabled and (
-                self.config.birdseye.enabled
-                and birdseye is not None
-                and (
                 self.config.birdseye.restream
                 or any(
                     ws.environ["PATH_INFO"].endswith("birdseye")
                     for ws in websocket_server.manager
                 )
-                )
             ):
                 birdseye.write_data(
                     camera,
@@ -292,12 +282,9 @@ class OutputProcess(FrigateProcess):
         move_preview_frames("clips")
 
         while True:
-            _cleanup_result = detection_subscriber.check_for_update(timeout=0)
+            (topic, data) = detection_subscriber.check_for_update(timeout=0)
-            if _cleanup_result is None:
-                break
-            (topic, data) = _cleanup_result
 
-            if not topic or data is None:
+            if not topic:
                 break
 
             (
@@ -335,7 +322,7 @@ class OutputProcess(FrigateProcess):
         logger.info("exiting output process...")
 
 
-def move_preview_frames(loc: str) -> None:
+def move_preview_frames(loc: str):
     preview_holdover = os.path.join(CLIPS_DIR, "preview_restart_cache")
     preview_cache = os.path.join(CACHE_DIR, "preview_frames")
 

frigate/output/preview.py

@@ -22,6 +22,7 @@ from frigate.ffmpeg_presets import (
     parse_preset_hardware_acceleration_encode,
 )
 from frigate.models import Previews
+from frigate.track.object_processing import TrackedObject
 from frigate.util.image import copy_yuv_to_position, get_blank_yuv_frame, get_yuv_crop
 
 logger = logging.getLogger(__name__)
@@ -65,9 +66,7 @@ def get_cache_image_name(camera: str, frame_time: float) -> str:
     )
 
 
-def get_most_recent_preview_frame(
+def get_most_recent_preview_frame(camera: str, before: float = None) -> str | None:
-    camera: str, before: float | None = None
-) -> str | None:
     """Get the most recent preview frame for a camera."""
     if not os.path.exists(PREVIEW_CACHE_DIR):
         return None
@@ -148,12 +147,12 @@ class FFMpegConverter(threading.Thread):
             if t_idx == item_count - 1:
                 # last frame does not get a duration
                 playlist.append(
-                    f"file '{get_cache_image_name(self.config.name, self.frame_times[t_idx])}'"  # type: ignore[arg-type]
+                    f"file '{get_cache_image_name(self.config.name, self.frame_times[t_idx])}'"
                 )
                 continue
 
             playlist.append(
-                f"file '{get_cache_image_name(self.config.name, self.frame_times[t_idx])}'"  # type: ignore[arg-type]
+                f"file '{get_cache_image_name(self.config.name, self.frame_times[t_idx])}'"
             )
             playlist.append(
                 f"duration {self.frame_times[t_idx + 1] - self.frame_times[t_idx]}"
@@ -200,33 +199,30 @@ class FFMpegConverter(threading.Thread):
         # unlink files from cache
         # don't delete last frame as it will be used as first frame in next segment
         for t in self.frame_times[0:-1]:
-            Path(get_cache_image_name(self.config.name, t)).unlink(missing_ok=True)  # type: ignore[arg-type]
+            Path(get_cache_image_name(self.config.name, t)).unlink(missing_ok=True)
 
 
 class PreviewRecorder:
     def __init__(self, config: CameraConfig) -> None:
         self.config = config
-        self.camera_name: str = config.name or ""
+        self.start_time = 0
-        self.start_time: float = 0
+        self.last_output_time = 0
-        self.last_output_time: float = 0
         self.offline = False
-        self.output_frames: list[float] = []
+        self.output_frames = []
 
-        if config.detect.width is None or config.detect.height is None:
+        if config.detect.width > config.detect.height:
-            raise ValueError("Detect width and height must be set for previews.")
-
-        self.detect_width: int = config.detect.width
-        self.detect_height: int = config.detect.height
-
-        if self.detect_width > self.detect_height:
             self.out_height = PREVIEW_HEIGHT
             self.out_width = (
-                int((self.detect_width / self.detect_height) * self.out_height) // 4 * 4
+                int((config.detect.width / config.detect.height) * self.out_height)
+                // 4
+                * 4
             )
         else:
             self.out_width = PREVIEW_HEIGHT
             self.out_height = (
-                int((self.detect_height / self.detect_width) * self.out_width) // 4 * 4
+                int((config.detect.height / config.detect.width) * self.out_width)
+                // 4
+                * 4
             )
 
         # create communication for finished previews
@@ -306,7 +302,7 @@ class PreviewRecorder:
         )
         self.start_time = frame_time
         self.last_output_time = frame_time
-        self.output_frames = []
+        self.output_frames: list[float] = []
 
     def should_write_frame(
         self,
@@ -346,9 +342,7 @@ class PreviewRecorder:
 
     def write_frame_to_cache(self, frame_time: float, frame: np.ndarray) -> None:
         # resize yuv frame
-        small_frame: np.ndarray = np.zeros(
+        small_frame = np.zeros((self.out_height * 3 // 2, self.out_width), np.uint8)
-            (self.out_height * 3 // 2, self.out_width), np.uint8
-        )
         copy_yuv_to_position(
             small_frame,
             (0, 0),
@@ -362,7 +356,7 @@ class PreviewRecorder:
             cv2.COLOR_YUV2BGR_I420,
         )
         cv2.imwrite(
-            get_cache_image_name(self.camera_name, frame_time),
+            get_cache_image_name(self.config.name, frame_time),
             small_frame,
             [
                 int(cv2.IMWRITE_WEBP_QUALITY),
@@ -402,7 +396,7 @@ class PreviewRecorder:
                 ).start()
             else:
                 logger.debug(
-                    f"Not saving preview for {self.camera_name} because there are no saved frames."
+                    f"Not saving preview for {self.config.name} because there are no saved frames."
                 )
 
             self.reset_frame_cache(frame_time)
@@ -422,7 +416,9 @@ class PreviewRecorder:
         if not self.offline:
             self.write_frame_to_cache(
                 frame_time,
-                get_blank_yuv_frame(self.detect_width, self.detect_height),
+                get_blank_yuv_frame(
+                    self.config.detect.width, self.config.detect.height
+                ),
             )
             self.offline = True
 
@@ -435,9 +431,9 @@ class PreviewRecorder:
                 return
 
             old_frame_path = get_cache_image_name(
-                self.camera_name, self.output_frames[-1]
+                self.config.name, self.output_frames[-1]
             )
-            new_frame_path = get_cache_image_name(self.camera_name, frame_time)
+            new_frame_path = get_cache_image_name(self.config.name, frame_time)
             shutil.copy(old_frame_path, new_frame_path)
 
             # save last frame to ensure consistent duration
@@ -451,12 +447,13 @@ class PreviewRecorder:
             self.reset_frame_cache(frame_time)
 
     def stop(self) -> None:
+        self.config_subscriber.stop()
         self.requestor.stop()
 
 
 def get_active_objects(
-    frame_time: float, camera_config: CameraConfig, all_objects: list[dict[str, Any]]
+    frame_time: float, camera_config: CameraConfig, all_objects: list[TrackedObject]
-) -> list[dict[str, Any]]:
+) -> list[TrackedObject]:
     """get active objects for detection."""
     return [
         o

frigate/test/test_config.py

@@ -10,7 +10,7 @@ from ruamel.yaml.constructor import DuplicateKeyError
 from frigate.config import BirdseyeModeEnum, FrigateConfig
 from frigate.const import MODEL_CACHE_DIR
 from frigate.detectors import DetectorTypeEnum
-from frigate.util.builtin import deep_merge, load_labels
+from frigate.util.builtin import deep_merge
 
 
 class TestConfig(unittest.TestCase):
@@ -288,65 +288,6 @@ class TestConfig(unittest.TestCase):
         frigate_config = FrigateConfig(**config)
         assert "dog" in frigate_config.cameras["back"].objects.filters
 
-    def test_default_audio_filters(self):
-        config = {
-            "mqtt": {"host": "mqtt"},
-            "audio": {"listen": ["speech", "yell"]},
-            "cameras": {
-                "back": {
-                    "ffmpeg": {
-                        "inputs": [
-                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
-                        ]
-                    },
-                    "detect": {
-                        "height": 1080,
-                        "width": 1920,
-                        "fps": 5,
-                    },
-                }
-            },
-        }
-
-        frigate_config = FrigateConfig(**config)
-        all_audio_labels = {
-            label
-            for label in load_labels("/audio-labelmap.txt", prefill=521).values()
-            if label
-        }
-
-        assert all_audio_labels.issubset(
-            set(frigate_config.cameras["back"].audio.filters.keys())
-        )
-
-    def test_override_audio_filters(self):
-        config = {
-            "mqtt": {"host": "mqtt"},
-            "cameras": {
-                "back": {
-                    "ffmpeg": {
-                        "inputs": [
-                            {"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
-                        ]
-                    },
-                    "detect": {
-                        "height": 1080,
-                        "width": 1920,
-                        "fps": 5,
-                    },
-                    "audio": {
-                        "listen": ["speech", "yell"],
-                        "filters": {"speech": {"threshold": 0.9}},
-                    },
-                }
-            },
-        }
-
-        frigate_config = FrigateConfig(**config)
-        assert "speech" in frigate_config.cameras["back"].audio.filters
-        assert frigate_config.cameras["back"].audio.filters["speech"].threshold == 0.9
-        assert "babbling" in frigate_config.cameras["back"].audio.filters
-
     def test_inherit_object_filters(self):
         config = {
             "mqtt": {"host": "mqtt"},

frigate/track/object_processing.py

@@ -81,7 +81,6 @@ class TrackedObjectProcessor(threading.Thread):
                 CameraConfigUpdateEnum.motion,
                 CameraConfigUpdateEnum.objects,
                 CameraConfigUpdateEnum.remove,
-                CameraConfigUpdateEnum.timestamp_style,
                 CameraConfigUpdateEnum.zones,
             ],
         )

web/public/locales/en/config/global.json

@@ -752,7 +752,7 @@
   },
   "live": {
     "label": "Live playback",
-    "description": "Settings to control the jsmpeg live stream resolution and quality. This does not affect restreamed cameras that use go2rtc for live view.",
+    "description": "Settings used by the Web UI to control live stream resolution and quality.",
     "streams": {
       "label": "Live stream names",
       "description": "Mapping of configured stream names to restream/go2rtc names used for live playback."

web/public/locales/en/views/settings.json

@@ -825,12 +825,6 @@
       "area": "Area"
     }
   },
-  "timestampPosition": {
-    "tl": "Top left",
-    "tr": "Top right",
-    "bl": "Bottom left",
-    "br": "Bottom right"
-  },
   "users": {
     "title": "Users",
     "management": {
@@ -1348,22 +1342,7 @@
         "preset-nvidia": "NVIDIA GPU",
         "preset-jetson-h264": "NVIDIA Jetson (H.264)",
         "preset-jetson-h265": "NVIDIA Jetson (H.265)",
-        "preset-rkmpp": "Rockchip RKMPP",
+        "preset-rkmpp": "Rockchip RKMPP"
-        "preset-http-jpeg-generic": "HTTP JPEG (Generic)",
-        "preset-http-mjpeg-generic": "HTTP MJPEG (Generic)",
-        "preset-http-reolink": "HTTP - Reolink Cameras",
-        "preset-rtmp-generic": "RTMP (Generic)",
-        "preset-rtsp-generic": "RTSP (Generic)",
-        "preset-rtsp-restream": "RTSP - Restream from go2rtc",
-        "preset-rtsp-restream-low-latency": "RTSP - Restream from go2rtc (Low Latency)",
-        "preset-rtsp-udp": "RTSP - UDP",
-        "preset-rtsp-blue-iris": "RTSP - Blue Iris",
-        "preset-record-generic": "Record (Generic, no audio)",
-        "preset-record-generic-audio-copy": "Record (Generic + Copy Audio)",
-        "preset-record-generic-audio-aac": "Record (Generic + Audio to AAC)",
-        "preset-record-mjpeg": "Record - MJPEG Cameras",
-        "preset-record-jpeg": "Record - JPEG Cameras",
-        "preset-record-ubiquiti": "Record - Ubiquiti Cameras"
       }
     },
     "cameraInputs": {

web/src/components/config-form/section-configs/audio.ts

@@ -19,16 +19,6 @@ const audio: SectionConfigOverrides = {
     hiddenFields: ["enabled_in_config"],
     advancedFields: ["min_volume", "max_not_heard", "num_threads"],
     uiSchema: {
-      filters: {
-        "ui:options": {
-          expandable: false,
-        },
-      },
-      "filters.*": {
-        "ui:options": {
-          additionalPropertyKeyReadonly: true,
-        },
-      },
       listen: {
         "ui:widget": "audioLabels",
       },

web/src/components/config-form/section-configs/objects.ts

@@ -29,11 +29,6 @@ const objects: SectionConfigOverrides = {
     ],
     advancedFields: ["genai"],
     uiSchema: {
-      filters: {
-        "ui:options": {
-          expandable: false,
-        },
-      },
       "filters.*.min_area": {
         "ui:options": {
           suppressMultiSchema: true,

web/src/components/config-form/section-configs/timestamp_style.ts

@@ -4,13 +4,12 @@ const timestampStyle: SectionConfigOverrides = {
   base: {
     sectionDocs: "/configuration/reference",
     restartRequired: [],
-    fieldOrder: ["position", "format", "thickness", "color"],
+    fieldOrder: ["position", "format", "color", "thickness"],
     hiddenFields: ["effect", "enabled_in_config"],
     advancedFields: [],
     uiSchema: {
       position: {
         "ui:size": "xs",
-        "ui:options": { enumI18nPrefix: "timestampPosition" },
       },
       format: {
         "ui:size": "xs",
@@ -18,7 +17,7 @@ const timestampStyle: SectionConfigOverrides = {
     },
   },
   global: {
-    restartRequired: [],
+    restartRequired: ["position", "format", "color", "thickness", "effect"],
   },
   camera: {
     restartRequired: [],

web/src/components/config-form/theme/widgets/SelectWidget.tsx

@@ -1,6 +1,5 @@
 // Select Widget - maps to shadcn/ui Select
 import type { WidgetProps } from "@rjsf/utils";
-import { useTranslation } from "react-i18next";
 import {
   Select,
   SelectContent,
@@ -22,18 +21,9 @@ export function SelectWidget(props: WidgetProps) {
     schema,
   } = props;
 
-  const { t } = useTranslation(["views/settings"]);
   const { enumOptions = [] } = options;
-  const enumI18nPrefix = options["enumI18nPrefix"] as string | undefined;
   const fieldClassName = getSizedFieldClassName(options, "sm");
 
-  const getLabel = (option: { value: unknown; label: string }) => {
-    if (enumI18nPrefix) {
-      return t(`${enumI18nPrefix}.${option.value}`);
-    }
-    return option.label;
-  };
-
   return (
     <Select
       value={value?.toString() ?? ""}
@@ -52,7 +42,7 @@ export function SelectWidget(props: WidgetProps) {
       <SelectContent>
         {enumOptions.map((option: { value: unknown; label: string }) => (
           <SelectItem key={String(option.value)} value={String(option.value)}>
-            {getLabel(option)}
+            {option.label}
           </SelectItem>
         ))}
       </SelectContent>

web/src/views/live/LiveCameraView.tsx

@@ -707,23 +707,14 @@ export default function LiveCameraView({
               }}
             >
               <div
-                className={cn(
+                className={`relative flex flex-col items-center justify-center ${growClassName}`}
-                  "flex flex-col items-center justify-center",
-                  growClassName,
-                )}
                 ref={clickOverlayRef}
                 style={{
                   aspectRatio: constrainedAspectRatio,
                 }}
               >
                 {clickOverlay && overlaySize.width > 0 && (
-                  <div
+                  <div className="absolute inset-0 z-40 cursor-crosshair">
-                    className="absolute z-40 cursor-crosshair"
-                    style={{
-                      width: overlaySize.width,
-                      height: overlaySize.height,
-                    }}
-                  >
                     <Stage
                       width={overlaySize.width}
                       height={overlaySize.height}