使用OpenVINO异步推理处理图像子集

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

def async_inference(model_path, image_paths):
    """
    使用OpenVINO异步API对图像列表进行推理。

    Args:
        model_path (str): 模型文件路径。
        image_paths (list): 图像文件路径列表。
    """

    # 1. 加载模型
    core = ov.Core()
    model = core.read_model(model_path)
    compiled_model = core.compile_model(model, "CPU") # 根据实际情况选择设备

    # 2. 获取输入/输出信息
    input_layer = compiled_model.input(0)
    output_layer = compiled_model.output(0)
    input_shape = input_layer.shape

    # 3. 创建推理请求
    num_requests = 2  # 可以根据实际情况调整
    infer_requests = [compiled_model.create_infer_request() for _ in range(num_requests)]

    # 4. 准备输入数据
    images = []
    for image_path in image_paths:
        image = cv2.imread(image_path)
        resized_image = cv2.resize(image, (input_shape[3], input_shape[2])) # 调整大小
        input_data = np.expand_dims(resized_image.transpose(2, 0, 1), 0) # 调整维度
        images.append(input_data)

    # 5. 异步推理
    results = []
    for i in range(len(images)):
        request_id = i % num_requests
        infer_requests[request_id].infer({input_layer: images[i]}) # 使用infer
        results.append(infer_requests[request_id].get_output_tensor(output_layer).data)

    # 等待所有请求完成
    for request in infer_requests:
        request.wait()

    return results

if __name__ == "__main__":
    model_path = "path/to/your/model.xml"  # 替换为你的模型文件路径
    image_paths = ["path/to/image1.jpg", "path/to/image2.jpg", "path/to/image3.jpg"]  # 替换为你的图像文件路径列表

    results = async_inference(model_path, image_paths)

    # 处理推理结果
    for i, result in enumerate(results):
        print(f"Image {i+1} result: {result}")