mirror of
https://github.com/blakeblackshear/frigate.git
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a70dd02788
* implement common post_processing * fix formatting * rename yolonas to post_process_yolonas
148 lines
4.8 KiB
Python
148 lines
4.8 KiB
Python
import hashlib
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import json
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import logging
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import os
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from enum import Enum
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from typing import Dict, Optional, Tuple
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import matplotlib.pyplot as plt
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import requests
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from pydantic import BaseModel, ConfigDict, Field
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from pydantic.fields import PrivateAttr
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from frigate.plus import PlusApi
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from frigate.util.builtin import load_labels
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logger = logging.getLogger(__name__)
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class PixelFormatEnum(str, Enum):
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rgb = "rgb"
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bgr = "bgr"
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yuv = "yuv"
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class InputTensorEnum(str, Enum):
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nchw = "nchw"
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nhwc = "nhwc"
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class ModelTypeEnum(str, Enum):
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ssd = "ssd"
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yolox = "yolox"
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yolonas = "yolonas"
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class ModelConfig(BaseModel):
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path: Optional[str] = Field(None, title="Custom Object detection model path.")
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labelmap_path: Optional[str] = Field(
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None, title="Label map for custom object detector."
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)
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width: int = Field(default=320, title="Object detection model input width.")
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height: int = Field(default=320, title="Object detection model input height.")
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labelmap: Dict[int, str] = Field(
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default_factory=dict, title="Labelmap customization."
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)
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input_tensor: InputTensorEnum = Field(
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default=InputTensorEnum.nhwc, title="Model Input Tensor Shape"
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)
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input_pixel_format: PixelFormatEnum = Field(
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default=PixelFormatEnum.rgb, title="Model Input Pixel Color Format"
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)
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model_type: ModelTypeEnum = Field(
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default=ModelTypeEnum.ssd, title="Object Detection Model Type"
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)
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_merged_labelmap: Optional[Dict[int, str]] = PrivateAttr()
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_colormap: Dict[int, Tuple[int, int, int]] = PrivateAttr()
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_model_hash: str = PrivateAttr()
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@property
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def merged_labelmap(self) -> Dict[int, str]:
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return self._merged_labelmap
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@property
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def colormap(self) -> Dict[int, Tuple[int, int, int]]:
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return self._colormap
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@property
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def model_hash(self) -> str:
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return self._model_hash
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def __init__(self, **config):
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super().__init__(**config)
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self._merged_labelmap = {
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**load_labels(config.get("labelmap_path", "/labelmap.txt")),
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**config.get("labelmap", {}),
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}
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self._colormap = {}
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def check_and_load_plus_model(
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self, plus_api: PlusApi, detector: str = None
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) -> None:
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if not self.path or not self.path.startswith("plus://"):
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return
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model_id = self.path[7:]
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self.path = f"/config/model_cache/{model_id}"
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model_info_path = f"{self.path}.json"
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# download the model if it doesn't exist
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if not os.path.isfile(self.path):
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download_url = plus_api.get_model_download_url(model_id)
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r = requests.get(download_url)
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with open(self.path, "wb") as f:
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f.write(r.content)
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# download the model info if it doesn't exist
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if not os.path.isfile(model_info_path):
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model_info = plus_api.get_model_info(model_id)
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with open(model_info_path, "w") as f:
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json.dump(model_info, f)
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else:
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with open(model_info_path, "r") as f:
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model_info = json.load(f)
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if detector and detector not in model_info["supportedDetectors"]:
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raise ValueError(f"Model does not support detector type of {detector}")
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self.width = model_info["width"]
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self.height = model_info["height"]
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self.input_tensor = model_info["inputShape"]
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self.input_pixel_format = model_info["pixelFormat"]
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self.model_type = model_info["type"]
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self._merged_labelmap = {
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**{int(key): val for key, val in model_info["labelMap"].items()},
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**self.labelmap,
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}
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def compute_model_hash(self) -> None:
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if not self.path or not os.path.exists(self.path):
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self._model_hash = hashlib.md5(b"unknown").hexdigest()
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else:
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with open(self.path, "rb") as f:
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file_hash = hashlib.md5()
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while chunk := f.read(8192):
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file_hash.update(chunk)
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self._model_hash = file_hash.hexdigest()
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def create_colormap(self, enabled_labels: set[str]) -> None:
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"""Get a list of colors for enabled labels."""
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cmap = plt.cm.get_cmap("tab10", len(enabled_labels))
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for key, val in enumerate(enabled_labels):
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self._colormap[val] = tuple(int(round(255 * c)) for c in cmap(key)[:3])
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model_config = ConfigDict(extra="forbid", protected_namespaces=())
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class BaseDetectorConfig(BaseModel):
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# the type field must be defined in all subclasses
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type: str = Field(default="cpu", title="Detector Type")
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model: Optional[ModelConfig] = Field(
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default=None, title="Detector specific model configuration."
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)
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model_config = ConfigDict(
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extra="allow", arbitrary_types_allowed=True, protected_namespaces=()
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)
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