jetson nano上读取基于CSI摄像头的模型推理 python语言

要在Jetson Nano上读取基于CSI摄像头的模型推理，您需要使用NVIDIA Jetson Nano Developer Kit的JetPack软件包中提供的Deep Learning SDK和相关工具。

以下是一些步骤，可以帮助您开始：

1. 安装JetPack软件包，包括TensorRT和CUDA等必要的组件。

2. 连接CSI摄像头并确保其可用。

3. 使用Python和TensorRT API编写您的推理代码。

以下是一个简单的Python代码示例，可以用于从CSI摄像头读取图像并进行推理：

import argparse
import numpy as np
import cv2
import pycuda.autoinit
import pycuda.driver as cuda
import tensorrt as trt

# Define the input shape of the model
INPUT_SHAPE = (3, 224, 224)

# Load the TensorRT engine
def load_engine(engine_path):
    with open(engine_path, 'rb') as f, trt.Runtime(trt.Logger(trt.Logger.WARNING)) as runtime:
        return runtime.deserialize_cuda_engine(f.read())

# Allocate device memory for inputs and outputs
def allocate_buffers(engine):
    inputs = []
    outputs = []
    bindings = []
    for binding in engine:
        size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
        dtype = trt.nptype(engine.get_binding_dtype(binding))
        host_mem = cuda.pagelocked_empty(size, dtype)
        device_mem = cuda.mem_alloc(host_mem.nbytes)
        bindings.append(int(device_mem))
        if engine.binding_is_input(binding):
            inputs.append({'host': host_mem, 'device': device_mem})
        else:
            outputs.append({'host': host_mem, 'device': device_mem})
    return inputs, outputs, bindings

# Preprocess the input image
def preprocess_image(image):
    image = cv2.resize(image, (INPUT_SHAPE[1], INPUT_SHAPE[2]))
    image = image.transpose((2, 0, 1)).astype(np.float32) / 255.0
    return image

# Run inference on the input image
def run_inference(engine, inputs, outputs, bindings, image):
    # Preprocess the input image
    image = preprocess_image(image)

    # Copy the input image to device memory
    cuda.memcpy_htod_async(inputs[0]['device'], image, cuda.Stream())

    # Run inference
    context = engine.create_execution_context()
    context.execute_async_v2(bindings=bindings, stream_handle=cuda.Stream().handle)

    # Copy the output tensor back to host memory
    cuda.memcpy_dtoh_async(outputs[0]['host'], outputs[0]['device'], cuda.Stream())
    cuda.Stream().synchronize()

    # Return the output tensor
    return outputs[0]['host']

# Main function
def main():
    # Parse command-line arguments
    parser = argparse.ArgumentParser()
    parser.add_argument('--engine', type=str, required=True)
    args = parser.parse_args()

    # Load the TensorRT engine
    engine = load_engine(args.engine)

    # Allocate device memory for inputs and outputs
    inputs, outputs, bindings = allocate_buffers(engine)

    # Open the CSI camera
    cap = cv2.VideoCapture("nvarguscamerasrc ! video/x-raw(memory:NVMM), width=1920, height=1080, format=NV12, framerate=30/1 ! nvvidconv ! video/x-raw, format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink")

    while True:
        # Capture a frame from the camera
        ret, frame = cap.read()

        # Run inference on the frame
        output = run_inference(engine, inputs, outputs, bindings, frame)

        # Display the output
        cv2.imshow("Output", output)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    # Release the camera and destroy the windows
    cap.release()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

在这个示例中，我们首先加载了预先训练好的模型的TensorRT引擎。然后，我们为模型的输入和输出分配了设备内存。接下来，我们使用CSI摄像头捕获帧，并将其用于推理。最后，我们显示了推理输出，并在用户按下“q”键时退出了程序。

请注意，这只是一个简单的示例代码，您可能需要根据您的实际情况进行更改。