工程训练赛-智能垃圾分类【包括界面设计和神经网络识别】

# from src.gcxlThread import videoThread import os import sys import threading from PyQt5 import QtCore from PIL import Image from torchvision.transforms import transforms sys.path.append("/home/pi/Downloads/gcxls/") from Net.model import MobileNetV2 from PyQt5.QtMultimediaWidgets import QVideoWidget # import RPi.GPIO as GPIO from PyQt5.QtCore import QUrl, pyqtSignal from PyQt5.QtWidgets import QMainWindow, QApplication, QVBoxLayout, QFileDialog, QLabel, QWidget, QPushButton, \ QHBoxLayout, QLineEdit from PyQt5.QtMultimedia import QMediaPlayer, QMediaContent import cv2 as cv import torch import json import time import pyttsx3 import inspect import ctypes from src.write_Serial import main_write from src.read_Serial import main_read # from gpiozero import LED, Button, PWMLED # 引入LED类 # from signal import pause from time import sleep import layout.Mainwindow as Mainwindow from omxplayer import OMXPlayer from pathlib import Path from time import sleep import datetime # from Net.predict_local import init_artificial_neural_network, prediction_result_from_img print("sys.path:", sys.path) class FullThread(QtCore.QThread): _Full = pyqtSignal(int) def __init__(self): super(FullThread, self).__init__() def __del__(self): self.wait() def run(self): while True: self.a = main_read() if self.a == "e" or "f" or "g" or "h": self._Full.emit(self.a) class VideoThread(QtCore.QThread): _display = pyqtSignal(int) def __init__(self, camera, data_transform, model): super(VideoThread, self).__init__() self.camera = camera self.data_transform = data_transform self.model = model self.candrop = True self.detected = False def __del__(self): self.wait() def run(self): while True: flag_read = main_read() if flag_read == 4: sleep(2) res, cur_frame = self.camera.read() cv.imwrite("a.jpg", cur_frame) self.check() print("1") def check(self): time.sleep(1) # 调用神经网络去识别 img = Image.open("a.jpg") img = self.data_transform(img) # expand batch dimension img = torch.unsqueeze(img, dim=0) with torch.no_grad(): # predict class output = torch.squeeze(self.model(img)) predict = torch.softmax(output, dim=0) predict_cla = torch.argmax(predict).numpy() predict_cla = int(predict_cla) print("predict_cla", predict_cla) self._display.emit(predict_cla) print("write") main_write(predict_cla) class Mainwin(Mainwindow.Ui_MainWindow, QMainWindow): res = "我要开始投放垃圾了" PATH = "/home/pi/Downloads/gcxls/src/2.mp4" def __init__(self): super(Mainwin, self).__init__() self.setupUi(self) self.initUI() def closeEvent(self, event): super(Mainwin, self).closeEvent() self.camera.release() cv.destroyAllWindows() # 初始化所有程序 ''' 1. 进入一个循环，启动摄像头，检测是否有东西投入。 2. 若检测到有东西投入，进入中断，调用神经网络，识别是何种垃圾。 3. 将识别垃圾代号传入到语音播报，界面展示，串口输出等函数中 4. 垃圾处理完毕后，串口输入，同时使用GPIO判断垃圾桶是否满了。若满了，则该垃圾桶无法投入。 5. 返回步骤1 ''' def initUI(self): # 神经网络初始化 self.ress = "" self.data_transform = transforms.Compose( [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) self.model = MobileNetV2(num_classes=4) model_weight_path = "../Net/mobilenet_v2_1.4_224.pth" self.model.load_state_dict(torch.load(model_weight_path)) self.model.eval() self.video_stop.setEnabled(False) self.camera = cv.VideoCapture(0) print('start') self.btn_play.clicked.connect(self.play) self.btn_pause.clicked.connect(self.pause) self.btn_select_video.clicked.connect(self.select_file) self.video_start.clicked.connect(self.startVideo) self.video_stop.clicked.connect(self.stopVideo) self.btn_play.setEnabled(False) self.btn_pause.setEnabled(False) self.detected = False # 标志位，是否检测到。 # 是否能投放的标志位 self.candrop = True # 播放的标志位 self.canplay = True self.show() # 判断垃圾是否满了 ''' "0": "Harmful_waste", a "1": "Kitchen_waste", b "2": "Other_waste", c "3": "Recyclable_waste" ''' def jud(self, i): if i == 0: self.ress = self.ress +"\n" + "有害垃圾已满" self.textBrowser2.setText(self.ress) self.textBrowser2.repaint() if i == 1: self.ress = self.ress +"\n" + "厨余垃圾已满" self.textBrowser2.setText(self.ress) self.textBrowser2.repaint() if i == 2: self.ress = self.ress +"\n" + "其他垃圾已满" self.textBrowser2.setText(self.ress) self.textBrowser2.repaint() if i == 3: self.ress = self.ress +"\n" + "可回收垃圾已满" self.textBrowser2.setText(self.ress) self.textBrowser2.repaint() def judget(self, i): from PyQt5.QtWidgets import QMessageBox if i == 0: self.jud(i) QMessageBox.critical(self, "错误", "有害垃圾已满") elif i == 1: self.jud(i) QMessageBox.critical(self, "错误", "厨余垃圾已满") elif i == 2: self.jud(i) QMessageBox.critical(self, "错误", "其他垃圾已满") elif i == 3: self.jud(i) QMessageBox.critical(self, "错误", "可回收垃圾已满") self.candrop = False # self.stopVideo() def play(self): self.player = OMXPlayer(self.PATH) sleep(10) self.player.quit() def pause(self): print(1) self.player.quit() def select_file(self): url = QFileDialog.getOpenFileUrl()[0] self.btn_play.setEnabled(True) self.btn_pause.setEnabled(True) def _async_raise(self, tid, exctype): """raises the exception, performs cleanup if needed""" tid = ctypes.c_long(tid) if not inspect.isclass(exctype): exctype = type(exctype) res = ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, ctypes.py_object(exctype)) if res == 0: raise ValueError("invalid thread id") elif res != 1: # """if it returns a number greater than one, you're in trouble, # and you should call it again with exc=NULL to revert the effect""" ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, None) raise SystemError("PyThreadState_SetAsyncExc failed") def stop_thread(self, thread): self._async_raise(thread.ident, SystemExit) def startVideo(self): # self.v = VideoThread(self.camera, self.data_transform, self.model) # self.v._display.connect(self.display) self.f = FullThread() self.f._Full.connect(self.judget) print("display") # sel