From f415a480a6b94115f21887c2c0e65b4df4ae4a9d Mon Sep 17 00:00:00 2001
From: "li.zhenye" <li.zhenye@qq.com>
Date: Tue, 26 Jul 2022 22:35:07 +0800
Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E5=A4=9A=E7=BA=BF=E7=A8=8B?=
 =?UTF-8?q?=E6=8E=A5=E6=94=B6=E7=B1=BB?=
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Content-Type: text/plain; charset=UTF-8
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---
 README.md   |   1 +
 transmit.py | 111 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 112 insertions(+)
 create mode 100644 transmit.py

diff --git a/README.md b/README.md
index 3393cfd..7f8520c 100644
--- a/README.md
+++ b/README.md
@@ -90,6 +90,7 @@
 ![image-20220721164406435](https://raw.githubusercontent.com/Karllzy/imagebed/main/img/image-20220721164406435.png)
 
 在新的镜头下，数据的分布和原本的数据已经发生了巨大的变化，根本就不是同一种东西，这就是为什么分类结果会出错了。
+这种情况的出现也和拍摄矫正不到位有关，具体细节询问周超。
 
 ## 预测过程的后处理（异色问题）
 
diff --git a/transmit.py b/transmit.py
new file mode 100644
index 0000000..b990718
--- /dev/null
+++ b/transmit.py
@@ -0,0 +1,111 @@
+import os
+import threading
+from queue import Queue
+import numpy as np
+from config import Config
+
+
+class Receiver(object):
+    def __init__(self):
+        self.output = None
+
+    def set_source(self, *args, **kwargs):
+        """
+        用于设置数据的来源，每个receiver仅允许有单个来源
+        :param args:
+        :param kwargs:
+        :return:
+        """
+        raise NotImplementedError
+
+    def set_output(self, output: Queue):
+        """
+        设置单个输出源
+        :param output:
+        :return:
+        """
+        self.output = output
+
+    def start(self, *args, **kwargs):
+        """
+        启动接收线程或进程
+        :param args:
+        :param kwargs:
+        :return:
+        """
+        raise NotImplementedError
+
+    def stop(self, *args, **kwargs):
+        """
+        停止接收线程或进程
+        :param args:
+        :param kwargs:
+        :return:
+        """
+        raise NotImplementedError
+
+
+class FifoReceiver(Receiver):
+    def __init__(self, fifo_path: str, output: Queue, read_max_num: int):
+        super().__init__()
+        self._input_fifo_path = None
+        self._output_queue = None
+        self._max_len = read_max_num
+
+        self.set_source(fifo_path)
+        self.set_output(output)
+        self._need_stop = threading.Event()
+        self._need_stop.clear()
+
+    def set_source(self, fifo_path: str):
+        if not os.access(fifo_path, os.F_OK):
+            os.mkfifo(fifo_path, 0o777)
+        self._input_fifo_path = fifo_path
+
+    def set_output(self, output: Queue):
+        self._output_queue = output
+
+    def start(self, post_process_func=None, name='fifo_receiver'):
+        x = threading.Thread(target=self._receive_thread_func,
+                             name=name, args=(post_process_func, ))
+        x.start()
+
+    def stop(self):
+        self._need_stop.set()
+
+    def _receive_thread_func(self, post_process_func=None):
+        """
+        接收线程
+
+        :param post_process_func:
+        :return:
+        """
+        while not self._need_stop.is_set():
+            input_fifo = os.open(self._input_fifo_path, os.O_RDONLY)
+            data = os.read(input_fifo, self._max_len)
+            if post_process_func is not None:
+                data = post_process_func(data)
+            self._output_queue.put(data)
+            os.close(input_fifo)
+        self._need_stop.clear()
+
+    @staticmethod
+    def spec_data_post_process(data):
+        if len(data) < 3:
+            threshold = int(float(data))
+            print("[INFO] Get Spec threshold: ", threshold)
+            return threshold
+        else:
+            spec_img = np.frombuffer(data, dtype=np.float32).\
+                reshape((Config.nRows, Config.nBands, -1)).transpose(0, 2, 1)
+            return spec_img
+
+    @staticmethod
+    def rgb_data_post_process(data):
+        if len(data) < 3:
+            threshold = int(float(data))
+            print("[INFO] Get RGB threshold: ", threshold)
+            return threshold
+        else:
+            rgb_img = np.frombuffer(data, dtype=np.uint8).reshape((Config.nRgbRows, Config.nRgbCols, -1))
+            return rgb_img