{"id":6610,"date":"2023-07-10T09:30:54","date_gmt":"2023-07-10T17:30:54","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=6610"},"modified":"2025-04-24T17:15:14","modified_gmt":"2025-04-24T17:15:14","slug":"kangas-the-pandas-of-computer-vision","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/kangas-the-pandas-of-computer-vision\/","title":{"rendered":"Kangas: The Pandas of Computer Vision"},"content":{"rendered":"\n\n\n\n\n
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\"A<\/figure>
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Photo by Comet ML<\/a><\/figcaption><\/figure>\n

Introduction<\/h2>\n

In the field of computer vision, Kangas<\/a> is one of the tools becoming increasingly popular for image data processing and analysis. Similar to how Pandas revolutionized the way data analysts work with tabular data, Kangas is doing the same for computer vision tasks.<\/p>\n

Kangas is an open-source tool by Comet ML<\/a> for exploring, analyzing, and visualizing large-scale multimedia dataset like images, videos, and audio<\/a>. Kangas enables ML professionals to visualize, sort, group, query, and interpret their data (structured or unstructured) to obtain meaningful insights and speed up model development.<\/p>\n

Pandas, on the other hand, is a popular open-source Python library for data analysis and manipulation for tabular data. It can also be used to clean and prepare data. It is easy to use, fast, and flexible compared to other libraries, but does not natively support unstructured data types, as Kangas does.<\/p>\n

Kangas is to computer vision data what Pandas is to tabular data. Kangas provides methods for reading, manipulating and analyzing images as we will see in a few examples in this tutorial.<\/p>\n

Benefits of Kangas<\/h2>\n
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  1. Simple to use<\/strong>: The main benefit of Kangas is its ability to simplify the process of working with computer vision data. It has a user-friendly API that is fast and data professionals can load, process, and analyze visual data without writing complex lines of code. This makes it easier for data professionals to focus on the task at hand, rather than the technicalities of data processing.<\/li>\n
  2. Speed and efficiency: <\/strong>Compared to other computer vision tools, Kangas can handle large datasets with ease and process them quickly, allowing for real-time data analysis and decision-making. This makes it ideal for use in time-sensitive applications such as self-driving vehicles, where quick and accurate analysis of visual data is crucial.<\/li>\n
  3. Diverse: <\/strong>Kangas provides a wide range of machine learning algorithms that can be applied to computer vision tasks. These algorithms can be used to perform tasks such as image classification, object detection, and image segmentation.<\/li>\n
  4. Ability to handle large amounts of data: <\/strong>Kangas uses a memory-efficient data structure that allows data professionals to process large amounts of image and video data with great performance. This makes it ideal for working with high-resolution images and video data.<\/li>\n
  5. Flexible: <\/strong>Kangas can be run in multi-platform applications like Jupyter notebook, stand-alone applications, or web apps.<\/li>\n<\/ol>\n

    Reading CSV files with Kangas<\/h2>\n

    Reading data from a csv file is quite similar in Kangas and Pandas. The difference is that Kangas creates a DataGrid and Pandas creates a DataFrame. The code below shows how to read data from a csv file into a DataGrid:<\/p>\n

    import<\/span> kangas as<\/span> kg\n\ndg = kg.read_csv(\"path_to_csv_file\"<\/span>)<\/span><\/pre>\n
    This can be compared to the code used to read csv files in Pandas:<\/p>\n
    import<\/span> pandas as<\/span> pd\ndf = pd.read_csv(\"path_to_csv_file\"<\/span>)<\/span><\/pre>\n
    Next, we\u2019ll visualize the data in the csv file using the code below:<\/p>\n
    dg.show()<\/span><\/pre>\n
    Output:<\/strong><\/p>\n
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    \"Kangas\u2019<\/figure>
    <\/picture><\/div>\n<\/div>\n
    Kangas\u2019 visualization of a csv data file\\<\/figcaption>\n<\/figure>\n
    Compared to Pandas\u2019 syntax below:<\/p>\n
    df.head()<\/span><\/pre>\n
    Note that the Kangas DataGrid is interactive, whereas the Pandas DataFrame is static.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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    Have you tried Comet? Sign up for free and easily track experiments, manage models in production, and visualize your model performance<\/a>.<\/p><\/blockquote>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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    Reading image files<\/h2>\n
    Unlike other computer vision image libraries like OpenCV, reading image files using Kangas uses the simplicity of Pandas to ensure the data scientist puts effort where it is required.<\/p>\n
    To read an image file using Kangas, run the code block below:<\/p>\n
    import<\/span> kangas as<\/span> kg\nimage = kg.Image(\"path_to_images\"<\/span>).to_pil()\n<\/span><\/pre>\n
    Visualize the image file by running the name the variable \u201cimage<\/em>\u201d as shown in the code below:<\/p>\n
    image<\/span><\/pre>\n
    Output:<\/strong><\/p>\n
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    \"Visualization<\/figure>
    <\/picture><\/div>\n
    A potato image output using Kangas.<\/figcaption>\n<\/figure>\n
    From the examples above, you can see how similar Kangas\u2019 syntax is to Pandas.<\/p>\n
    Pandas and Kangas Similarities<\/h2>\n
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    1. Syntax:<\/strong> Kangas and Pandas have a similar syntax which is easy to write and use.<\/li>\n
    2. Data handling:<\/strong> Kangas and Pandas both have data handling functionalities. Both can read data of any format from csv, Json to xlsx (Excel) files. Kangas uses DataGrid while Pandas uses Data Frame and Series to store data.<\/li>\n
    3. Data manipulation:<\/strong> Both Kangas and Pandas enable users to filter, sort, merge, and reshape data, but Kangas does so interactively.<\/li>\n
    4. Indexing:<\/strong> Both libraries allow users to index and select data based on labels or conditions. In Pandas, this is done using loc<\/code> and iloc<\/code>methods, while in Kangas it is done from the DataGrid.<\/li>\n
    5. Data analysis:<\/strong> Both libraries provide methods for basic data analysis, like descriptive statistics, aggregation, and grouping operations.<\/li>\n<\/ol>\n
      Kangas and Pandas Differences<\/h2>\n
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      1. Kangas processes image files while Pandas does not.<\/li>\n
      2. Kangas provides a user interface for manipulating the data in the DataGrid while Pandas only allows for programmatic manipulation.<\/li>\n<\/ol>\n
        Creating Kangas DataGrid<\/h2>\n
        A Kangas DataGrid is an open-source SQLite database that provides the ability to store and display large amounts of data and perform fast complex queries. A DataGrid can also be saved, shared, or even served remotely.<\/p>\n
        Some key features of the Kangas DataGrid include:<\/p>\n