对这段代码也使用多线程：def get_image_files(folder): image_extensions = ['.jpg', '.jpeg', '.png'] # 定义图片扩展名 image_files = [] for root, dirs, files in os.walk(folder): for file in files: if os.path.splitext(file)[1].lower() in image_extensions: img_path = os.path.join(root, file) # 将每条数据保存到txt中 path_all_files = open('./all_files.txt','a') path_all_files.write(img_path+'\n') path_all_files.close() print(img_path) image_files.append(img_path) return image_files # 加载ONNX模型 ort_session = onnxruntime.InferenceSession('./best_weights.onnx') # 预处理输入图像 transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)) ]) # 创建线程池 executor = concurrent.futures.ThreadPoolExecutor() # 获取路径下所有图片 image_files = get_image_files('C:/Users/xyzhuge/Desktop/detect-mamaclouds-yolov5-v2/detect-mamaclouds-yolov5-linux-main/res/')

下面是对第二段代码使用多线程的示例：

import concurrent.futures

def get_image_files(folder):
    image_extensions = ['.jpg', '.jpeg', '.png']  # 定义图片扩展名
    image_files = []

    for root, dirs, files in os.walk(folder):
        for file in files:
            if os.path.splitext(file)[1].lower() in image_extensions:
                img_path = os.path.join(root, file)
                # 将每条数据保存到txt中
                path_all_files = open('./all_files.txt','a')
                path_all_files.write(img_path+'\n')
                path_all_files.close()
                print(img_path)
                image_files.append(img_path)

    return image_files

# 创建线程池
executor = concurrent.futures.ThreadPoolExecutor()

# 获取路径下所有图片
image_files = get_image_files('C:/Users/xyzhuge/Desktop/detect-mamaclouds-yolov5-v2/detect-mamaclouds-yolov5-linux-main/res/')

# 定义处理图片的函数
def process_image(image_file):
    # 加载图片
    image = Image.open(image_file)
    # 预处理
    input_data = transform(image).unsqueeze(0).numpy()
    # 运行推理
    ort_inputs = {ort_session.get_inputs()[0].name: input_data}
    ort_outputs = ort_session.run(None, ort_inputs)
    # 获取预测结果
    predicted_class = np.argmax(ort_outputs[0])
    print(f'Image: {image_file}')
    print(f'Predicted class: {predicted_class}')
    # 当为0时，删除图片
    if predicted_class == 0:
        # 将每条数据保存到txt中
        path_all_files = open('./del_files.txt','a')
        path_all_files.write(image_file+'\n')
        path_all_files.close()
        os.remove(image_file)
        print('Deleted')

# 提交任务给线程池
for image_file in image_files:
    executor.submit(process_image, image_file)

# 关闭线程池，等待所有任务完成
executor.shutdown()

这段代码与之前的示例类似，将每个图片的处理任务提交给线程池并行执行。注意在多线程环境下使用共享资源时需要进行适当的同步操作。同时，也请确保在适当的位置关闭文件和线程池，以避免资源泄漏。