mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-08 22:34:00 +00:00
128 lines
5.3 KiB
Python
128 lines
5.3 KiB
Python
# -*- coding: utf-8 -*-
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# @Time : 2024/4/20 18:45
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# @Author : TG
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# @File : main.py
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# @Software: PyCharm
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import sys
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import os
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import cv2
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from root_dir import ROOT_DIR
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from classifer import Spec_predict, Data_processing, ImageClassifier
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import logging
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from pipe_utils import Pipe
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import numpy as np
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from config import Config
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import time
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from detector import Detector_to
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from to_seg import TOSEG
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def main(is_debug=False):
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setting = Config()
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file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'tomato-passion_fruit.log'), encoding='utf-8')
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file_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING)
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console_handler = logging.StreamHandler(sys.stdout)
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console_handler.setLevel(logging.DEBUG if is_debug else logging.WARNING)
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logging.basicConfig(format='%(asctime)s %(filename)s[line:%(lineno)d] - %(levelname)s - %(message)s',
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handlers=[file_handler, console_handler],
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level=logging.DEBUG)
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#模型加载
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detector = Spec_predict()
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detector.load(path=setting.brix_model_path)
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dp = Data_processing()
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to = Detector_to()
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tos = TOSEG()
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#impf为百香果褶皱判别模型，0为褶皱，1为正常
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impf = ImageClassifier(model_path=setting.imgclassifier_model_path,
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class_indices_path=setting.imgclassifier_class_indices_path)
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print('系统初始化中...')
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#模型预热
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#与qt_test测试时需要注释掉预热，模型接收尺寸为（25，30，13），qt_test发送的数据为（30，30，224），需要对数据进行切片（classifer.py第379行）
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_ = detector.predict(np.ones((setting.n_spec_rows, setting.n_spec_cols, setting.n_spec_bands), dtype=np.uint16))
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#run函数为番茄破损判别模型，返回0表示无破损，1、2、3即表示1、2、3处破损
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_ = to.run(np.ones((800, 613, 3), dtype=np.uint8))
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_ = tos.toseg(np.ones((800, 613, 3), dtype=np.uint8))
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_ = impf.predict(np.ones((800, 613, 3), dtype=np.uint8))
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time.sleep(1)
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print('系统初始化完成')
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rgb_receive_name = r'\\.\pipe\rgb_receive'
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rgb_send_name = r'\\.\pipe\rgb_send'
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spec_receive_name = r'\\.\pipe\spec_receive'
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pipe = Pipe(rgb_receive_name, rgb_send_name, spec_receive_name)
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rgb_receive, rgb_send, spec_receive = pipe.create_pipes(rgb_receive_name, rgb_send_name, spec_receive_name)
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# 预热循环，只处理cmd为'YR'的数据
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# 当接收到的第一个指令预热命令时，结束预热循环
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while True:
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data = pipe.receive_rgb_data(rgb_receive)
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cmd, _ = pipe.parse_img(data)
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if cmd == 'YR':
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break
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print('预热成功')
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#主循环
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q = 1
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while True:
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# st = time.time()
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#RGB图像部分
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images = []
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cmd = None
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for i in range(3):
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# start_time = time.time()
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data = pipe.receive_rgb_data(rgb_receive)
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# end_time = time.time()
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# print(f'接收第{q}个果子第{i+1}张图数据时间：{end_time-start_time}')
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# print(f'接收第{q}个果子第{i+1}张图数据长度：{len(data)}')
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cmd, img = pipe.parse_img(data)
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# end_time1 = time.time()
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# img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
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# print(f'解码第{q}个果子第{i + 1}张图数据时间：{end_time1 - end_time}')
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# print(f'接收第{q}个果子第{i+1}张图：{img.shape}')
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# cv2.imwrite(f'./{q}_{i}.png', img)
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# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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#默认全为有果
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prediction = 1
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if prediction == 1:
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images.append(img)
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else:
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response = pipe.send_data(cmd='KO', brix=0, diameter=0, green_percentage=0, weigth=0, defect_num=0,
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total_defect_area=0, rp=np.zeros((100, 100, 3), dtype=np.uint8))
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logging.info("图像中无果，跳过此图像")
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continue
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if cmd not in ['TO', 'PF', 'YR', 'KO']:
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logging.error(f'错误指令，指令为{cmd}')
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continue
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#Spec数据部分
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spec = None
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if cmd == 'PF':
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spec_data = pipe.receive_spec_data(spec_receive)
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# print(f'接收到第{q}个果子的光谱数据长度：{len(spec_data)}')
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_, spec = pipe.parse_spec(spec_data)
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# print(f'接收到第{q}个果子的光谱数据尺寸：{spec.shape}')
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#数据处理部分
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if images: # 确保images不为空
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response = dp.process_data(cmd, images, spec, pipe, detector, to, impf)
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if response:
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logging.info(f'处理成功，响应为: {response}')
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else:
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logging.error('处理失败')
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else:
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logging.error("没有有效的图像进行处理")
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print(f'第{q}个果子处理完成')
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# end_time2 = time.time()
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# print(f'第{q}个果子全流程时间：{(end_time2-st) * 1000}毫秒')
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q += 1
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if __name__ == '__main__':
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'''
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python与qt采用windows下的命名管道进行通信，数据流按照约定的通信协议进行
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数据处理逻辑为：连续接收5张RGB图，然后根据解析出的指令部分决定是否接收一张光谱图，然后进行处理，最后将处理得到的指标结果进行编码回传
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'''
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main(is_debug=False)
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