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This article mainly introduces "how to realize the algorithm dataset based on Python". In the daily operation, I believe that many people have doubts about how to realize the algorithm dataset based on Python. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful for you to answer the doubt of "how to realize the algorithm dataset based on Python". Next, please follow the editor to study!

Before generating a dataset, we first need to prepare a dataset corpus. Store the corpus that needs to generate pictures, store them on a line, and generate a text picture on one line. Second, you need a fonts folder to store the font library. You are now ready to generate text recognition datasets.

Import the required package. And import the configuration file config.py.

two。 Create a new config file, write to the corpus path, the font file path and the output path of the generated file.

3. Load the font library and resize the font. When the fonts in the font library are used to generate lines of text in the corpus, different fonts are randomly generated. And then form a picture.

4. Generate the corresponding annotations for the picture. The mapping_list method is used to store the annotations corresponding to the generated pictures. The text file name corresponds to the picture name, and the content of the file is the text on the picture.

5. In order to improve the quality of the training picture and simulate the effect of the real picture, do some processing to the picture, such as Gaussian blur, left and right blur, up and down blur, stain and so on.

6. The resulting image is shown below.

At this point, the study of "how to implement the dataset of Python-based algorithms" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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