mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 14:54:07 +00:00
183 lines
7.6 KiB
Python
183 lines
7.6 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
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# from classifer import ImageClassifier
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import logging
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from utils import Pipe
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import numpy as np
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import time
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def process_data(cmd: str, images: list, spec: any, dp: Data_processing, pipe: Pipe, detector: Spec_predict) -> bool:
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"""
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处理指令
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:param cmd: 指令类型
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:param images: 图像数据列表
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:param spec: 光谱数据
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:param detector: 模型
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:return: 是否处理成功
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"""
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diameter_axis_list = []
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max_defect_num = 0 # 初始化最大缺陷数量为0
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max_total_defect_area = 0 # 初始化最大总像素数为0
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for i, img in enumerate(images):
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if cmd == 'TO':
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# 番茄
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diameter, green_percentage, number_defects, total_pixels, rp = dp.analyze_tomato(img)
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if i <= 2:
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diameter_axis_list.append(diameter)
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max_defect_num = max(max_defect_num, number_defects)
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max_total_defect_area = max(max_total_defect_area, total_pixels)
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if i == 1:
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rp_result = rp
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gp = round(green_percentage)
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elif cmd == 'PF':
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# 百香果
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diameter, weigth, number_defects, total_pixels, rp = dp.analyze_passion_fruit(img)
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if i <= 2:
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diameter_axis_list.append(diameter)
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max_defect_num = max(max_defect_num, number_defects)
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max_total_defect_area = max(max_total_defect_area, total_pixels)
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if i == 1:
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rp_result = rp
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weigth = weigth
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else:
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logging.error(f'错误指令，指令为{cmd}')
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return False
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diameter = round(sum(diameter_axis_list) / 3)
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if cmd == 'TO':
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brix = 0
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weigth = 0
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response = pipe.send_data(cmd=cmd, brix=brix, diameter=diameter, green_percentage=gp, weigth=weigth,
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defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result)
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return response
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elif cmd == 'PF':
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green_percentage = 0
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brix = detector.predict(spec)
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response = pipe.send_data(cmd=cmd, brix=brix, green_percentage=green_percentage, diameter=diameter, weigth=weigth,
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defect_num=max_defect_num, total_defect_area=max_total_defect_area, rp=rp_result)
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return response
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def main(is_debug=False):
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file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'tomato.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(ROOT_DIR/'models'/'passion_fruit_2.joblib')
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# classifier = ImageClassifier(ROOT_DIR/'models'/'resnet34_0619.pth', ROOT_DIR/'models'/'class_indices.json')
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dp = Data_processing()
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print('系统初始化中...')
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#模型预热
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_ = detector.predict(np.ones((30, 30, 224), dtype=np.uint16))
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# _ = classifier.predict(np.ones((224, 224, 3), dtype=np.uint8))
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# _, _, _, _, _ =dp.analyze_tomato(cv2.imread(r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\tomato_img\bad\71.bmp'))
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# _, _, _, _, _ = dp.analyze_passion_fruit(cv2.imread(r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\data\passion_fruit_img\38.bmp'))
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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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# start_time00 = time.time()
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data = pipe.receive_rgb_data(rgb_receive)
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cmd, _ = pipe.parse_img(data)
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# end_time00 = time.time()
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# print(f'接收预热数据时间：{(end_time00 - start_time00) * 1000}毫秒')
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if cmd == 'YR':
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break
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#主循环
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# q = 1
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while True:
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#RGB图像部分
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# start_time = time.time()
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images = []
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cmd = None
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for _ in range(5):
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# start_time1 = time.time()
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data = pipe.receive_rgb_data(rgb_receive)
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# end_time10 = time.time()
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# print(f'接收第{q}组第{i}份RGB数据时间：{(end_time10 - start_time1) * 1000}毫秒')
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# start_time11 = time.time()
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cmd, img = pipe.parse_img(data)
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# end_time1 = time.time()
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# print(f'解析第{q}组第{i}份RGB数据时间：{(end_time1 - start_time11) * 1000}毫秒')
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# print(f'接收第{q}组第{i}张RGB图时间：{(end_time1 - start_time1) * 1000}毫秒')
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# 使用分类器进行预测
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# prediction = classifier.predict(img)
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# print(f'预测结果：{prediction}')
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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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# start_time2 = time.time()
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spec_data = pipe.receive_spec_data(spec_receive)
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# print(f'接收第{q}组光谱数据长度：{len(spec_data)}')
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_, spec = pipe.parse_spec(spec_data)
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# print(f'处理第{q}组光谱数据长度：{len(spec)}')
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# print(spec.shape)
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# print(f'解析第{q}组光谱数据时间：{(time.time() - start_time2) * 1000}毫秒')
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# end_time2 = time.time()
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# print(f'接收第{q}组光谱数据时间：{(end_time2 - start_time2) * 1000}毫秒')
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#数据处理部分
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# start_time3 = time.time()
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if images: # 确保images不为空
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response = process_data(cmd, images, spec, dp, pipe, detector)
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end_time3 = time.time()
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# print(f'第{q}组处理时间：{(end_time3 - start_time3) * 1000}毫秒')
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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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# end_time = time.time()
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# print(f'第{q}组全流程时间：{(end_time - start_time) * 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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