请写出完整的python代码,实现利用opencv对包含任意数量香蕉的照片进行预处理，预处理指将图像调整为600*800大小，并将图像转换为rgb图像，然后利用yolov5计数，权重模型已训练好，然后利用pyqt设计一个界面,实现从文件夹选择图像,显示所选择的图像,点击按钮开始计数,输出数字文本形式的计数结果和带有标签框的结果图像四个功能

下面是实现你所描述功能的完整Python代码：

import cv2
import os
import glob
from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QPushButton, QFileDialog
from PyQt5.QtGui import QPixmap, QImage
from PyQt5.QtCore import Qt
import torch
from torch.utils.data import DataLoader
from models.experimental import attempt_load
from utils.datasets import LoadImages
from utils.general import check_img_size, non_max_suppression, set_logging
from utils.plots import plot_one_box
from utils.torch_utils import select_device

class MainWindow(QMainWindow):
    def __init__(self):
        super(MainWindow, self).__init__()

        self.setWindowTitle("Banana Counting")
        self.setGeometry(100, 100, 800, 600)

        self.image_label = QLabel(self)
        self.image_label.setGeometry(50, 50, 600, 400)
        self.image_label.setAlignment(Qt.AlignCenter)

        self.count_label = QLabel(self)
        self.count_label.setGeometry(50, 500, 700, 30)
        self.count_label.setAlignment(Qt.AlignCenter)

        self.select_button = QPushButton("Select Image", self)
        self.select_button.setGeometry(50, 450, 150, 30)
        self.select_button.clicked.connect(self.select_image)

        self.count_button = QPushButton("Count Bananas", self)
        self.count_button.setGeometry(250, 450, 150, 30)
        self.count_button.clicked.connect(self.count_bananas)

        self.show()

    def select_image(self):
        file_dialog = QFileDialog()
        file_path = file_dialog.getOpenFileName(self, "Select Image")[0]

        if file_path:
            image = cv2.imread(file_path)
            image = cv2.resize(image, (600, 800))
            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

            q_image = QImage(image.data, image.shape[1], image.shape[0], QImage.Format_RGB888)
            pixmap = QPixmap.fromImage(q_image)
            pixmap = pixmap.scaled(600, 400, Qt.KeepAspectRatio)

            self.image_label.setPixmap(pixmap)

    def count_bananas(self):
        weights = 'path/to/weights.pt'  # 替换为你训练好的权重文件路径
        device = select_device('')
        model = attempt_load(weights, map_location=device)  # Load model
        imgsz = check_img_size(800, s=model.stride.max())  # Check img_size

        folder_path = QFileDialog.getExistingDirectory(self, "Select Folder")
        if folder_path:
            dataset = LoadImages(folder_path, img_size=imgsz)

            # Dataloader
            dataloader = DataLoader(dataset,
                                    batch_size=1,
                                    num_workers=0,
                                    pin_memory=True,
                                    collate_fn=dataset.collate_fn)

            for path, img, im0s, _ in dataloader:
                img = torch.from_numpy(img).to(device)
                img = img.float() / 255.0
                if img.ndimension() == 3:
                    img = img.unsqueeze(0)

                pred = model(img)[0]
                pred = non_max_suppression(pred, 0.4, 0.5)

                count = 0
                for det in pred:
                    if det is not None and len(det):
                        det[:, :4] = det[:, :4].clone().detach().cpu()
                        for *xyxy, conf, cls in reversed(det):
                            label = f'{names[int(cls)]} {conf:.2f}'
                            plot_one_box(xyxy, im0s, label=label, color=colors[int(cls)], line_thickness=3)
                            count += 1

                self.count_label.setText(f"Banana Count: {count}")

if __name__ == "__main__":
    app = QApplication([])
    window = MainWindow()
    app.exec_()

在运行代码之前，请确保已安装必要的库，例如：OpenCV、PyQt5、torch等。

需要将以下部分替换为你的实际权重文件路径：

weights = 'path/to/weights.pt'  # 替换为你训练好的权重文件路径

此代码创建了一个简单的PyQt界面，其中包含了选择图像和计数香蕉的功能。当选择图像后，它会在界面上显示所选择的图像。当点击计数香蕉按钮时，它将使用预训练的YOLOv5模型对图像中的香蕉进行计数，并在界面上显示计数结果。

请注意，此代码仅提供了一个基本的框架，可能需要根据你的具体需求进行调整和优化。