请写出完整的python代码，实现利用ylov5对包含任意数量香蕉的照片进行计数，并且利用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，输出计数结果和结果图像四个功能

以下是实现你所描述的功能的Python代码：

import os
import cv2
import torch
from PyQt5.QtWidgets import QApplication, QMainWindow, QFileDialog, QLabel, QPushButton, QVBoxLayout, QWidget

# 导入yolov5相关库
from models.experimental import attempt_load
from utils.datasets import LoadImages
from utils.general import check_img_size, non_max_suppression, scale_coords
from utils.plots import plot_one_box
from utils.torch_utils import select_device

class BananaCounter(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle("Banana Counter")
        self.setGeometry(100, 100, 800, 600)

        self.image_label = QLabel(self)
        self.image_label.setGeometry(10, 10, 400, 400)

        self.count_label = QLabel(self)
        self.count_label.setGeometry(10, 420, 400, 20)

        self.start_button = QPushButton("开始计数", self)
        self.start_button.setGeometry(10, 450, 100, 30)
        self.start_button.clicked.connect(self.count_bananas)

        self.result_label = QLabel(self)
        self.result_label.setGeometry(10, 490, 400, 20)

    def count_bananas(self):
        # 打开文件对话框选择图像
        file_dialog = QFileDialog()
        file_dialog.setFileMode(QFileDialog.ExistingFiles)
        file_dialog.setNameFilter("Images (*.png *.xpm *.jpg)")
        if file_dialog.exec_():
            file_paths = file_dialog.selectedFiles()
            for file_path in file_paths:
                self.process_image(file_path)

    def process_image(self, file_path):
        # 加载模型
        weights = 'path_to_yolov5_weights'  # 替换为yolov5的权重文件路径
        device = select_device('')
        model = attempt_load(weights, map_location=device)
        imgsz = check_img_size(640, s=model.stride.max())  # 模型输入的图像大小

        # 加载图像
        dataset = LoadImages(file_path, img_size=imgsz)

        # 检测图像中的香蕉
        for path, img, im0s, _ in dataset:
            img = torch.from_numpy(img).to(device)
            img = img.float() / 255.0  # 图像归一化
            if img.ndimension() == 3:
                img = img.unsqueeze(0)

            # 推断
            pred = model(img, augment=False)[0]

            # NMS
            pred = non_max_suppression(pred, 0.4, 0.5)

            # 绘制边界框和计数
            for i, det in enumerate(pred):
                if len(det):
                    det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0s.shape).round()
                    for *xyxy, conf, cls in reversed(det):
                        label = f'{int(cls)}'
                        plot_one_box(xyxy, im0s, label=label, color=(0, 255, 0))

            # 显示图像
            result_image_path = 'result.jpg'  # 结果图像保存路径
            cv2.imwrite(result_image_path, im0s)  # 保存结果图像
            self.display_image(result_image_path)

            # 显示计数结果
            count = len(pred)
            self.display_count(count)

    def display_image(self, image_path):
        pixmap = QPixmap(image_path)
        pixmap = pixmap.scaled(400, 400)
        self.image_label.setPixmap(pixmap)

    def display_count(self, count):
        self.count_label.setText(f"香蕉数量：{count}")

if __name__ == '__main__':
    app = QApplication([])
    window = BananaCounter()
    window.show()
    app.exec_()

请注意，上述代码中涉及到的`'path_to_yolov5_weights'`需要替换为你实际的yolov5权重文件的路径。

此外，你还需要安装以下Python库才能运行代码：
- PyQt5
- OpenCV
- torch

这些库可以通过`pip`命令进行安装。