# -*- coding: utf-8 -*- # @Time : 2024/4/20 18:45 # @Author : TG # @File : main.py # @Software: PyCharm import sys import os import cv2 from root_dir import ROOT_DIR from classifer import Spec_predict, Data_processing, ImageClassifier import logging from pipe_utils import Pipe import numpy as np from config import Config import time from detector import Detector_to from to_seg import TOSEG def main(is_debug=False): setting = Config() file_handler = logging.FileHandler(os.path.join(ROOT_DIR, 'tomato-passion_fruit.log'), encoding='utf-8') 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) #模型加载 detector = Spec_predict() detector.load(path=setting.brix_model_path) dp = Data_processing() to = Detector_to() tos = TOSEG() #impf为百香果褶皱判别模型，0为褶皱，1为正常 impf = ImageClassifier(model_path=setting.imgclassifier_model_path, class_indices_path=setting.imgclassifier_class_indices_path) print('系统初始化中...') #模型预热 #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） _ = detector.predict(np.ones((setting.n_spec_rows, setting.n_spec_cols, setting.n_spec_bands), dtype=np.uint16)) #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 _ = to.run(np.ones((800, 613, 3), dtype=np.uint8)) _ = tos.toseg(np.ones((800, 613, 3), dtype=np.uint8)) _ = impf.predict(np.ones((800, 613, 3), dtype=np.uint8)) time.sleep(1) print('系统初始化完成') rgb_receive_name = r'\\.\pipe\rgb_receive' rgb_send_name = r'\\.\pipe\rgb_send' spec_receive_name = r'\\.\pipe\spec_receive' pipe = Pipe(rgb_receive_name, rgb_send_name, spec_receive_name) rgb_receive, rgb_send, spec_receive = pipe.create_pipes(rgb_receive_name, rgb_send_name, spec_receive_name) # 预热循环，只处理cmd为'YR'的数据 # 当接收到的第一个指令预热命令时，结束预热循环 while True: data = pipe.receive_rgb_data(rgb_receive) cmd, _ = pipe.parse_img(data) if cmd == 'YR': break print('预热成功') #主循环 q = 1 while True: # st = time.time() #RGB图像部分 images = [] cmd = None for i in range(3): # start_time = time.time() data = pipe.receive_rgb_data(rgb_receive) # end_time = time.time() # print(f'接收第{q}个果子第{i+1}张图数据时间：{end_time-start_time}') # print(f'接收第{q}个果子第{i+1}张图数据长度：{len(data)}') cmd, img = pipe.parse_img(data) # end_time1 = time.time() # img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) # print(f'解码第{q}个果子第{i + 1}张图数据时间：{end_time1 - end_time}') # print(f'接收第{q}个果子第{i+1}张图：{img.shape}') # cv2.imwrite(f'./{q}_{i}.png', img) # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #默认全为有果 prediction = 1 if prediction == 1: images.append(img) else: response = pipe.send_data(cmd='KO', brix=0, diameter=0, green_percentage=0, weigth=0, defect_num=0, total_defect_area=0, rp=np.zeros((100, 100, 3), dtype=np.uint8)) logging.info("图像中无果，跳过此图像") continue if cmd not in ['TO', 'PF', 'YR', 'KO']: logging.error(f'错误指令，指令为{cmd}') continue #Spec数据部分 spec = None if cmd == 'PF': spec_data = pipe.receive_spec_data(spec_receive) # print(f'接收到第{q}个果子的光谱数据长度：{len(spec_data)}') _, spec = pipe.parse_spec(spec_data) # print(f'接收到第{q}个果子的光谱数据尺寸：{spec.shape}') #数据处理部分 if images: # 确保images不为空 response = dp.process_data(cmd, images, spec, pipe, detector, to, impf, q) if response: logging.info(f'处理成功，响应为: {response}') else: logging.error('处理失败') else: logging.error("没有有效的图像进行处理") print(f'第{q}个果子处理完成') # end_time2 = time.time() # print(f'第{q}个果子全流程时间：{(end_time2-st) * 1000}毫秒') q += 1 if __name__ == '__main__': ''' 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 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 ''' main(is_debug=False)