OpenVINO 异步推理：图像列表输入解决方案

import openvino.runtime as ov
import cv2
import numpy as np
import os

def async_inference_image_list(image_paths, model_path, device="CPU"):
    """
    使用 OpenVINO 异步推理 API 处理图像列表。

    Args:
        image_paths: 图像文件路径列表。
        model_path: OpenVINO 模型文件路径 (.xml)。
        device: 推理设备 (例如 "CPU", "GPU")。
    """

    # 1. 加载 OpenVINO 模型
    core = ov.Core()
    model = core.read_model(model_path)
    compiled_model = core.compile_model(model=model, device_name=device)

    # 2. 获取输入和输出层
    input_layer = compiled_model.input(0)
    output_layer = compiled_model.output(0)

    # 3. 获取输入尺寸
    input_shape = input_layer.shape
    _, _, h, w = input_shape

    # 4. 创建异步推理请求
    infer_requests = [compiled_model.create_infer_request() for _ in range(2)]  # 使用两个请求实现流水线

    # 5. 预处理图像并提交推理请求
    results = []
    for i, image_path in enumerate(image_paths):
        # 读取图像
        image = cv2.imread(image_path)
        if image is None:
            print(f"Error: Could not read image at {image_path}")
            continue

        # 调整图像尺寸并进行预处理
        resized_image = cv2.resize(image, (w, h))
        input_data = np.expand_dims(resized_image.transpose(2, 0, 1), 0)

        # 获取当前和下一个推理请求
        current_request_id = i % 2
        next_request_id = (i + 1) % 2

        # 设置输入张量并启动异步推理
        infer_requests[current_request_id].set_tensor(input_layer, ov.Tensor(input_data))
        infer_requests[current_request_id].start_async()

        # 如果不是第一张图像，则等待上一个推理请求完成
        if i > 0:
            infer_requests[next_request_id].wait()
            output = infer_requests[next_request_id].get_tensor(output_layer).data
            results.append(output)

    # 6. 等待最后一个推理请求完成
    infer_requests[(len(image_paths) - 1) % 2].wait()
    output = infer_requests[(len(image_paths) - 1) % 2].get_tensor(output_layer).data
    results.append(output)

    return results


# 示例用法
if __name__ == "__main__":
    # 创建一些示例图像文件（或者使用你自己的图像文件路径）
    image_paths = ["image1.jpg", "image2.jpg", "image3.jpg"]  # 替换为实际图像路径
    for path in image_paths:
        if not os.path.exists(path):
            # 创建一个假的图像文件
            blank_image = np.zeros((100, 100, 3), dtype=np.uint8)
            cv2.imwrite(path, blank_image)

    model_path = "your_model.xml"  # 替换为你的 OpenVINO 模型路径

    # 执行异步推理
    results = async_inference_image_list(image_paths, model_path)

    # 打印结果
    for i, result in enumerate(results):
        print(f"Image {i+1} result: {result}")