mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-10-03 13:26:48 +03:00
Nicolas MowenandGitHub
5e2c59d27d
* Dynamically install and load detector dependencies * Cleanup * Cleanup
80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
import logging
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from abc import ABC, abstractmethod
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from typing import ClassVar
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import numpy as np
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from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
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from frigate.util.runtime_deps import RuntimeManifest, activate, ensure_installed
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logger = logging.getLogger(__name__)
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class DetectionApi(ABC):
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type_key: str
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supported_models: list[ModelTypeEnum]
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# pinned SDK artifacts that are installed at runtime instead of being
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# shipped in the image; None when the runtime is already available
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runtime_manifest: ClassVar[RuntimeManifest | None] = None
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@abstractmethod
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def __init__(self, detector_config: BaseDetectorConfig):
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self.detector_config = detector_config
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self.thresh = 0.4
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self.height = detector_config.model.height
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self.width = detector_config.model.width
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@abstractmethod
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def detect_raw(self, tensor_input):
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pass
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@classmethod
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def ensure_dependencies(cls) -> None:
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"""Download and install this detector's runtime if it is not present.
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Runs once in the main process before detector processes start, so a
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single install serves every process and the user site is on sys.path
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before it is inherited.
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"""
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if cls.runtime_manifest is not None:
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ensure_installed(cls.runtime_manifest)
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@classmethod
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def activate_dependencies(cls) -> None:
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"""Make the installed runtime importable in the current process."""
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if cls.runtime_manifest is not None:
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activate(cls.runtime_manifest)
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def calculate_grids_strides(self, expanded=True) -> None:
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grids = []
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expanded_strides = []
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# decode and orient predictions
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strides = [8, 16, 32]
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hsizes = [self.height // stride for stride in strides]
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wsizes = [self.width // stride for stride in strides]
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for hsize, wsize, stride in zip(hsizes, wsizes, strides):
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xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
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if expanded:
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grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
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grids.append(grid)
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shape = grid.shape[:2]
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expanded_strides.append(np.full((*shape, 1), stride))
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else:
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xv = xv.reshape(1, 1, hsize, wsize)
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yv = yv.reshape(1, 1, hsize, wsize)
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grids.extend(np.concatenate((xv, yv), axis=1).tolist())
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expanded_strides.extend(
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np.array([stride, stride]).reshape(1, 2, 1, 1).tolist()
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)
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if expanded:
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self.grids = np.concatenate(grids, 1)
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self.expanded_strides = np.concatenate(expanded_strides, 1)
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else:
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self.grids = grids
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self.expanded_strides = expanded_strides
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