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Increase mypy coverage and fixes (#22632)

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Nicolas Mowen 2026-03-25 09:28:48 -06:00 committed by GitHub
parent 04a2f42d11
commit 80c4ce2b5d
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11 changed files with 140 additions and 87 deletions
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12
frigate/genai/__init__.py
View File

@ -5,7 +5,7 @@ import importlib
import logging
import os
import re
from typing import Any, Optional
from typing import Any, Callable, Optional
import numpy as np
from playhouse.shortcuts import model_to_dict
@ -31,10 +31,10 @@ __all__ = [
PROVIDERS = {}
def register_genai_provider(key: GenAIProviderEnum):
def register_genai_provider(key: GenAIProviderEnum) -> Callable:
"""Register a GenAI provider."""
def decorator(cls):
def decorator(cls: type) -> type:
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(
event.label,
str(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):
def _init_provider(self) -> Any:
"""Initialize the client."""
return None
@ -402,7 +402,7 @@ Guidelines:
}
def load_providers():
def load_providers() -> None:
package_dir = os.path.dirname(__file__)
for filename in os.listdir(package_dir):
if filename.endswith(".py") and filename != "__init__.py":

14
frigate/genai/azure-openai.py
View File

@ -3,7 +3,7 @@
import base64
import json
import logging
from typing import Any, Optional
from typing import Any, AsyncGenerator, 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):
def _init_provider(self) -> AzureOpenAI | None:
"""Initialize the client."""
try:
parsed_url = urlparse(self.genai_config.base_url)
parsed_url = urlparse(self.genai_config.base_url or "")
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 result.choices[0].message.content.strip()
return str(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)
result = self.provider.chat.completions.create(**request_params) # type: ignore[call-overload]
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)
stream = self.provider.chat.completions.create(**request_params) # type: ignore[call-overload]
for chunk in stream:
if not chunk or not chunk.choices:

67
frigate/genai/gemini.py
View File

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

16
frigate/genai/llama_cpp.py
View File

@ -4,7 +4,7 @@ import base64
import io
import json
import logging
from typing import Any, Optional
from typing import Any, AsyncGenerator, 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")
img = img.convert("RGB") # type: ignore[assignment]
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 # base_url
provider: str | None # base_url
provider_options: dict[str, Any]
def _init_provider(self):
def _init_provider(self) -> str | None:
"""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": {
"image_url": { # type: ignore[dict-item]
"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 choice["message"]["content"].strip()
return str(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],
"multimodal_data": [encoded], # type: ignore[dict-item]
}
)
@ -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(

12
frigate/genai/ollama.py
View File

@ -2,7 +2,7 @@
import json
import logging
from typing import Any, Optional
from typing import Any, AsyncGenerator, Optional
from httpx import RemoteProtocolError, TimeoutException
from ollama import AsyncClient as OllamaAsyncClient
@ -28,10 +28,10 @@ class OllamaClient(GenAIClient):
},
}
provider: ApiClient
provider: ApiClient | None
provider_options: dict[str, Any]
def _init_provider(self):
def _init_provider(self) -> ApiClient | None:
"""Initialize the client."""
self.provider_options = {
**self.LOCAL_OPTIMIZED_OPTIONS,
@ -73,7 +73,7 @@ class OllamaClient(GenAIClient):
"exclusiveMinimum",
"exclusiveMaximum",
}
result = {}
result: dict[str, Any] = {}
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 result["response"].strip()
return str(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

12
frigate/genai/openai.py
View File

@ -3,7 +3,7 @@
import base64
import json
import logging
from typing import Any, Optional
from typing import Any, AsyncGenerator, 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):
def _init_provider(self) -> OpenAI:
"""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 result.choices[0].message.content.strip()
return str(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)
result = self.provider.chat.completions.create(**request_params) # type: ignore[call-overload]
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)
stream = self.provider.chat.completions.create(**request_params) # type: ignore[call-overload]
for chunk in stream:
if not chunk or not chunk.choices:

8
frigate/jobs/media_sync.py
View File

@ -5,7 +5,7 @@ import os
import threading
from dataclasses import dataclass, field
from datetime import datetime
from typing import Optional
from typing import Optional, cast
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} is already running. Rejecting new request."
f"Media sync job {current.id if current else 'unknown'} 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 get_current_job("media_sync")
return cast(Optional[MediaSyncJob], 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 get_job_by_id("media_sync", job_id)
return cast(Optional[MediaSyncJob], get_job_by_id("media_sync", job_id))

39
frigate/jobs/motion_search.py
View File

@ -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
from typing import Any, Optional, cast
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: dict[str, int], roi_bbox: tuple[float, float, float, float]
heatmap: object, 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 = recording.motion
objects = recording.objects
regions = recording.regions
motion: Any = recording.motion
objects: Any = recording.objects
regions: Any = recording.regions
# Old segments without metadata - pass through (conservative)
if motion is None and objects is None and regions is None:
@ -278,6 +278,9 @@ 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
@ -415,11 +418,13 @@ 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,
recording.path,
recording.start_time,
recording.end_time,
str(recording.path),
rec_start,
rec_end,
self.job.start_time_range,
self.job.end_time_range,
polygon_mask,
@ -524,10 +529,12 @@ 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(
recording.path,
recording.start_time,
recording.end_time,
str(recording.path),
rec_start,
rec_end,
self.job.start_time_range,
self.job.end_time_range,
polygon_mask,
@ -672,7 +679,9 @@ class MotionSearchRunner(threading.Thread):
# Handle frame dimension changes
if gray.shape != polygon_mask.shape:
resized_mask = cv2.resize(
polygon_mask, (gray.shape[1], gray.shape[0]), cv2.INTER_NEAREST
polygon_mask,
(gray.shape[1], gray.shape[0]),
interpolation=cv2.INTER_NEAREST,
)
current_bbox = cv2.boundingRect(resized_mask)
else:
@ -698,7 +707,7 @@ class MotionSearchRunner(threading.Thread):
)
if prev_frame_gray is not None:
diff = cv2.absdiff(prev_frame_gray, masked_gray)
diff = cv2.absdiff(prev_frame_gray, masked_gray) # type: ignore[unreachable]
diff_blurred = cv2.GaussianBlur(diff, (3, 3), 0)
_, thresh = cv2.threshold(
diff_blurred, threshold, 255, cv2.THRESH_BINARY
@ -825,7 +834,7 @@ def get_motion_search_job(job_id: str) -> Optional[MotionSearchJob]:
if job_entry:
return job_entry[0]
# Check completed jobs via manager
return get_job_by_id("motion_search", job_id)
return cast(Optional[MotionSearchJob], get_job_by_id("motion_search", job_id))
def cancel_motion_search_job(job_id: str) -> bool:

17
frigate/jobs/vlm_watch.py
View File

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

12
frigate/motion/__init__.py
View File

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

18
frigate/mypy.ini
View File

@ -41,6 +41,24 @@ 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