cv2.stylization运行不出来

如果你无法运行`cv2.stylization`函数，可能是因为你的OpenCV版本不支持该函数。

该函数是在OpenCV 3.4.1及更高版本中引入的，因此，如果你的OpenCV版本低于3.4.1，你将无法使用该函数。

你可以尝试更新你的OpenCV版本或者使用其他方法来实现图像风格化。例如，你可以使用卷积神经网络来实现图像风格化，或者使用其他第三方库，如PyTorch等。

相关问题

opencv中stylization原理

Stylization是OpenCV中的一个图像风格化算法，它可以将一张普通的图片转换成绘画风格的图片。其原理基于图像卷积和双边滤波。

具体来说，Stylization算法首先将输入图像进行边缘检测，得到一张边缘图。然后，通过双边滤波器对输入图像进行处理，使得图像中的颜色分布更加均匀，同时保留边缘信息。接着，将处理后的图像与边缘图进行卷积，得到最终的风格化图像。

Stylization算法中的双边滤波器是一种非线性的滤波器，它可以在保留边缘信息的同时平滑图像。与传统的高斯滤波器不同，双边滤波器同时考虑了像素之间的空间距离和像素之间的灰度值相似度，从而能够更好地保留图像的细节信息。在Stylization算法中，双边滤波器的参数需要根据实际情况进行调整，以达到最佳的效果。

总之，Stylization算法通过边缘检测和双边滤波器的处理，保留了图像的主要特征，同时增强了图像的艺术感，使得输入图像更加具有绘画风格。

Baseline: On-the-fly stylization

On-the-fly stylization is a technique for applying artistic styles to images in real time. It involves using a pre-trained deep learning model to transfer the style of one image onto another image, creating a new image that combines the content of the original image with the style of the reference image.

The baseline approach for on-the-fly stylization involves using a pre-trained neural network, such as the VGG-19 network, to extract the content and style features from the input and reference images. These features are then used to compute the Gram matrix, which captures the correlations between the different feature maps and is used to represent the style of the reference image.

The content and style features are then combined to generate a new image that preserves the content of the input image while incorporating the style of the reference image. This is achieved by optimizing a loss function that balances the content and style losses.

The baseline approach has been extended in various ways, such as incorporating perceptual loss functions and using attention mechanisms to selectively transfer style to specific regions of the input image. Overall, on-the-fly stylization is a powerful technique for generating artistic images in real time and has applications in fields such as entertainment, advertising, and education.