简单几步教你如何使用OpenCV调用ONNX模型 |21xrx.com

简单几步教你如何使用OpenCV调用ONNX模型

2023-10-12 03:35:35 深夜i -- --

OpenCV ONNX 调用模型教程

OpenCV is a powerful open-source library widely used in computer vision applications. It provides various functions and algorithms for image and video processing. In this article, we will explore how to use OpenCV to call an ONNX model, a format commonly used for deep learning models.

Step 1: Install OpenCV and ONNX

The first step is to ensure that OpenCV and ONNX are properly installed on your machine. OpenCV can be installed using various methods, such as pip or conda, depending on your operating system. Similarly, ONNX can be installed using pip or conda.

Step 2: Convert the deep learning model to ONNX format

Before we can use OpenCV to call the ONNX model, we need to convert our deep learning model to the ONNX format. This can be done using specialized tools like ONNX Runtime or PyTorch's ONNX exporter. Make sure to follow the documentation of the deep learning framework you are using to convert the model successfully.

Step 3: Load the ONNX model using OpenCV

Once we have our ONNX model ready, we can use OpenCV to load the model and perform inference. OpenCV provides a function called `dnn.readNetFromONNX()` that allows us to load the ONNX model. We can pass the path to the ONNX model file as an argument to this function.

Step 4: Perform inference on input data

After loading the ONNX model using OpenCV, we can use it to perform inference on input data. OpenCV provides a convenient function called `dnn.blobFromImage()` to preprocess the input image before passing it to the model. This function takes care of resizing, normalization, and other pre-processing steps required by the model.

Step 5: Retrieve the output and post-process

After performing inference, we can retrieve the output of the model using the `forward()` function. This function returns a numpy array containing the predictions generated by the model. We can then apply any post-processing steps required for our specific application.

Step 6: Visualize or use the output

Finally, we can visualize or use the output of our model in the desired way. This might involve drawing bounding boxes around detected objects, overlaying segmentation masks on the input image, or any other custom visualization or action based on the task at hand.

In conclusion, using OpenCV to call an ONNX model is a straightforward process. By following the steps outlined in this article, you can easily load, perform inference, and post-process the output of your deep learning models in real-world computer vision applications. OpenCV's extensive functionality and compatibility with ONNX make it a valuable tool for researchers and developers in the field of computer vision.

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