MiracleVision visual model

MiracleVision is a visual task solving model trained based on ChinaAi's GPT model. It can be used in many fields such as image classification, object detection, and image generation. Its functions are very powerful.

MiracleVision is a visual task solving model trained based on ChinaAi's GPT model. It can be used in many fields such as image classification, object detection, image generation, etc., and its functions are very powerful. The following are tutorials and detailed examples for using the MiracleVision visual model.

1. Install MiracleVision

First, you need to install MiracleVision and its dependencies. You can install MiracleVision in Python using pip:

```
pip install miracle-vision
```

In addition, you also need to download the API key of ChinaAi API and set it as an environment variable. You can register and get the API key at https://beta.ChinaAi.com/signup/waitlist=platform.

2. Using MiracleVision

Once you have completed the installation and API key settings, you can start using MiracleVision.

1. Python code example using MiracleVision for object detection:

```python
import miraclevision
# 创建MiracleVision对象
mv = miraclevision.MiracleVision()
# 加载物体检测模型
mv.load_model("object_detection")
# 加载图像
image_path = "/path/to/image.jpg"
image = miraclevision.Image(image_path)
# 进行物体检测
results = mv.object_detection(image)
# 输出检测结果
for result in results:
    print(result["label"], result["confidence"], result["box"])
```

In this example, we first create a MiracleVision object and then use `load_model() `Method loads a model named "object_detection". Next, we load an image and pass it to the `object_detection()` method of the MiracleVision object for object detection. Finally, we iterate over the detection results and output the label, confidence, and bounding box for each detected object.

Please note that MiracleVision requires the correct dependencies to be installed and configured on your computer in order to run. If you have problems using MiracleVision, check out the MiracleVision documentation for more information.

2. Sample code for image classification using MiracleVision:

```python
import miraclevision as mv
# 加载ImageNet数据集标签
classnames = mv.get_imagenet_labelname()
# 加载模型（这里使用VGG16模型）
model = mv.load('vgg16')
# 读取要分类的图片
img = mv.imread('test.jpg')
# 对图片进行预处理
img = mv.resize(img, (224, 224))
img = mv.preprocess_input(img)
# 进行图像分类
pred = model.predict(img)
# 输出结果
print(classnames[pred.argmax()])
```

This code first loads the label name of the ImageNet data set, and then loads a pre-trained VGG16 model. Then read the images to be classified into memory by calling the `mv.imread` function, and preprocess them using the `mv.resize` function and the `mv.preprocess_input` function. Finally, the processed image is input into the model for inference, and the predicted result is returned. The output result is the predicted object category name.

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