mirror of
https://github.com/NanjingForestryUniversity/supermachine-wood.git
synced 2025-11-08 10:13:53 +00:00
116 lines
4.1 KiB
Python
116 lines
4.1 KiB
Python
import socket
|
|
import sys
|
|
|
|
import numpy as np
|
|
import cv2
|
|
|
|
import root_dir
|
|
from classifer import WoodClass
|
|
import time
|
|
import os
|
|
|
|
# from database import Database
|
|
from root_dir import ROOT_DIR
|
|
from utils import PreSocket, receive_sock, parse_protocol, ack_sock, done_sock, DualSock, simple_sock
|
|
import logging
|
|
from config import Config
|
|
|
|
|
|
def process_cmd(cmd: str, data: any, connected_sock: socket.socket, detector: WoodClass, settings: Config) -> tuple:
|
|
"""
|
|
处理指令
|
|
|
|
:param cmd: 指令类型
|
|
:param data: 指令内容
|
|
:param connected_sock: socket
|
|
:param detector: 模型
|
|
:return: 是否处理成功
|
|
"""
|
|
result = ''
|
|
if cmd == 'IM':
|
|
data = np.clip(data, 0, 255).astype(dtype=np.uint8)
|
|
wood_color = detector.predict(data)
|
|
result = {0: 'dark', 1: 'middle', 2: 'light'}[wood_color]
|
|
response = simple_sock(connected_sock, cmd_type=cmd, result=wood_color)
|
|
elif cmd == 'TR':
|
|
detector = WoodClass(w=4096, h=1200, n=8000, p1=0.8, debug_mode=False)
|
|
model_name = None
|
|
if "$" in data:
|
|
data, model_name = data.split("$", 1)
|
|
model_name = model_name + ".p"
|
|
settings.data_path = data
|
|
settings.model_path = ROOT_DIR / 'models' / detector.fit_pictures(data_path=settings.data_path, file_name=model_name)
|
|
response = simple_sock(connected_sock, cmd_type=cmd, result=result)
|
|
elif cmd == 'MD':
|
|
settings.model_path = data
|
|
detector.load(path=settings.model_path)
|
|
response = simple_sock(connected_sock, cmd_type=cmd, result=result)
|
|
elif cmd == 'KM':
|
|
x_data, y_data, labels, img_names = detector.get_kmeans_data(data, plot_2d=False)
|
|
result = detector.data_adjustments(x_data, y_data, labels, img_names)
|
|
result = ','.join([str(x) for x in result])
|
|
response = simple_sock(connected_sock, cmd_type=cmd, result=result)
|
|
|
|
|
|
else:
|
|
logging.error(f'错误指令，指令为{cmd}')
|
|
response = False
|
|
return response, result
|
|
|
|
|
|
def main(is_debug=False):
|
|
settings = Config()
|
|
file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'report.log'))
|
|
file_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING)
|
|
console_handler = logging.StreamHandler(sys.stdout)
|
|
console_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING)
|
|
logging.basicConfig(format='%(asctime)s %(filename)s[line:%(lineno)d] - %(levelname)s - %(message)s',
|
|
handlers=[file_handler, console_handler], level=logging.DEBUG)
|
|
dual_sock = DualSock(connect_ip='127.0.0.1')
|
|
|
|
# database = Database(settings.database_addr)
|
|
|
|
|
|
while not dual_sock.status:
|
|
dual_sock.reconnect()
|
|
detector = WoodClass(w=4096, h=1200, n=8000, p1=0.8, debug_mode=False)
|
|
detector.load(path=settings.model_path)
|
|
_ = detector.predict(np.random.randint(1, 254, (1200, 4096, 3), dtype=np.uint8))
|
|
while True:
|
|
pack, next_pack = receive_sock(dual_sock)
|
|
if pack == b"":
|
|
time.sleep(5)
|
|
dual_sock.reconnect()
|
|
continue
|
|
|
|
cmd, data = parse_protocol(pack)
|
|
# ack_sock(received_sock, cmd_type=cmd)
|
|
response, result = process_cmd(cmd=cmd, data=data, connected_sock=dual_sock, detector=detector, settings=settings)
|
|
|
|
# if result != "":
|
|
# database.add_data(result)
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
# 2个端口
|
|
# 接受端口21122
|
|
# 发送端口21123
|
|
# 接收到图片 n_rows * n_bands * n_cols, float32
|
|
# 发送图片 n_rows * n_cols, uint8
|
|
main(is_debug=False)
|
|
# test(r"D:\build-tobacco-Desktop_Qt_5_9_0_MSVC2015_64bit-Release\calibrated15.raw")
|
|
# main()
|
|
# debug_main()
|
|
# test_run(all_data_dir=r'D:\数据')
|
|
# with open(r'D:\数据\虫子\valid2.raw', 'rb') as f:
|
|
# data = np.frombuffer(f.read(), dtype=np.float32).reshape(600, 29, 1024).transpose(0, 2, 1)
|
|
# plt.matshow(data[:, :, 10])
|
|
# plt.show()
|
|
# detector = SpecDetector('model_spec/model_29.p')
|
|
# result = detector.predict(data)
|
|
#
|
|
# plt.matshow(result)
|
|
# plt.show()
|
|
# result = result.reshape((600, 1024))
|