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https://github.com/NanjingForestryUniversity/supermachine-tobacco.git
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112 lines
3.1 KiB
Python
112 lines
3.1 KiB
Python
import os
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import threading
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from queue import Queue
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import numpy as np
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from config import Config
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class Receiver(object):
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def __init__(self):
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self.output = None
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def set_source(self, *args, **kwargs):
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"""
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用于设置数据的来源，每个receiver仅允许有单个来源
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:param args:
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:param kwargs:
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:return:
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"""
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raise NotImplementedError
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def set_output(self, output: Queue):
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"""
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设置单个输出源
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:param output:
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:return:
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"""
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self.output = output
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def start(self, *args, **kwargs):
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"""
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启动接收线程或进程
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:param args:
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:param kwargs:
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:return:
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"""
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raise NotImplementedError
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def stop(self, *args, **kwargs):
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"""
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停止接收线程或进程
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:param args:
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:param kwargs:
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:return:
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"""
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raise NotImplementedError
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class FifoReceiver(Receiver):
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def __init__(self, fifo_path: str, output: Queue, read_max_num: int):
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super().__init__()
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self._input_fifo_path = None
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self._output_queue = None
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self._max_len = read_max_num
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self.set_source(fifo_path)
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self.set_output(output)
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self._need_stop = threading.Event()
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self._need_stop.clear()
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def set_source(self, fifo_path: str):
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if not os.access(fifo_path, os.F_OK):
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os.mkfifo(fifo_path, 0o777)
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self._input_fifo_path = fifo_path
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def set_output(self, output: Queue):
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self._output_queue = output
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def start(self, post_process_func=None, name='fifo_receiver'):
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x = threading.Thread(target=self._receive_thread_func,
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name=name, args=(post_process_func, ))
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x.start()
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def stop(self):
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self._need_stop.set()
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def _receive_thread_func(self, post_process_func=None):
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"""
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接收线程
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:param post_process_func:
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:return:
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"""
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while not self._need_stop.is_set():
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input_fifo = os.open(self._input_fifo_path, os.O_RDONLY)
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data = os.read(input_fifo, self._max_len)
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if post_process_func is not None:
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data = post_process_func(data)
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self._output_queue.put(data)
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os.close(input_fifo)
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self._need_stop.clear()
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@staticmethod
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def spec_data_post_process(data):
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if len(data) < 3:
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threshold = int(float(data))
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print("[INFO] Get Spec threshold: ", threshold)
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return threshold
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else:
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spec_img = np.frombuffer(data, dtype=np.float32).\
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reshape((Config.nRows, Config.nBands, -1)).transpose(0, 2, 1)
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return spec_img
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@staticmethod
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def rgb_data_post_process(data):
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if len(data) < 3:
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threshold = int(float(data))
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print("[INFO] Get RGB threshold: ", threshold)
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return threshold
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else:
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rgb_img = np.frombuffer(data, dtype=np.uint8).reshape((Config.nRgbRows, Config.nRgbCols, -1))
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return rgb_img
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