mirror of
https://github.com/blakeblackshear/frigate.git
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15fe79178b
* Move object detection to folder * Add input store type * Add hwnc * Add hwcn * Fix test
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
"""Object detection utilities."""
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import queue
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import threading
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from numpy import ndarray
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from frigate.detectors.detector_config import InputTensorEnum
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class RequestStore:
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"""
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A thread-safe hash-based response store that handles creating requests.
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"""
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def __init__(self):
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self.request_counter = 0
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self.request_counter_lock = threading.Lock()
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self.input_queue = queue.Queue()
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def __get_request_id(self) -> int:
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with self.request_counter_lock:
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request_id = self.request_counter
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self.request_counter += 1
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if self.request_counter > 1000000:
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self.request_counter = 0
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return request_id
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def put(self, tensor_input: ndarray) -> int:
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request_id = self.__get_request_id()
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self.input_queue.get((request_id, tensor_input))
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return request_id
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def get(self) -> tuple[int, ndarray] | None:
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try:
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return self.input_queue.get_nowait()
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except Exception:
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return None
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class ResponseStore:
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"""
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A thread-safe hash-based response store that maps request IDs
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to their results. Threads can wait on the condition variable until
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their request's result appears.
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"""
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def __init__(self):
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self.responses = {} # Maps request_id -> (original_input, infer_results)
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self.lock = threading.Lock()
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self.cond = threading.Condition(self.lock)
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def put(self, request_id: int, response: ndarray):
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with self.cond:
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self.responses[request_id] = response
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self.cond.notify_all()
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def get(self, request_id: int, timeout=None) -> ndarray:
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with self.cond:
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if not self.cond.wait_for(
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lambda: request_id in self.responses, timeout=timeout
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):
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raise TimeoutError(f"Timeout waiting for response {request_id}")
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return self.responses.pop(request_id)
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def tensor_transform(desired_shape: InputTensorEnum):
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# Currently this function only supports BHWC permutations
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if desired_shape == InputTensorEnum.nhwc:
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return None
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elif desired_shape == InputTensorEnum.nchw:
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return (0, 3, 1, 2)
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elif desired_shape == InputTensorEnum.hwnc:
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return (1, 2, 0, 3)
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elif desired_shape == InputTensorEnum.hwcn:
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return (1, 2, 3, 0)
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