![]() Or scanned pictures that share the same traits. That the most frequent color marks the background, while the second-most frequent color represents the nodes and the least frequent color is the edge color.Īlthough this sounds like a pretty strong assumption, it works surprisingly well for many programmatically-generated graphs and also for certain hand-drawn Have the same color (which is unique for each category) and with the restriction that we only have three unique colors in the image.Īt first, a 3-color quantization step is applied to the image to reduce errors from anti-aliasing or uneven exposure. This segmentation approach tries to distinguish nodes, edges and background based on their color with the requirement that the elements of each category ![]() This list is not exhaustive, as we had several ideas for other algorithms that could work better for certain inputs. The following sections describe the segmentation algorithms Selection of segmentation algorithms and chose one of them depending on the current input. We've explored several approaches here and found that there doesn't seem to be one single approach that works for arbitrary input. That haven't been segmented properly will be missing or merged in subsequent steps. All subsequent operations are performed based on this initial segmentation which is crucial for the overall result, since elements Given the entire input image, we first need to separate it into node and edge regions, which simplifies the identification of more features in the Of course, the quality of the segmentation step impacts all the subsequent processing on the separate regions. Those regions can then be further processed independently to detect different features (e.g. The first and basic step is the image segmentation where the image is broken down into disjoint regions of the overall image, each of which containsĮither a node, or an edge. We decided on a processing pipeline which should give us the flexibility to simultaneously work on separate parts of the project. Library, though in our case, we will build a yFiles WPF application for rendering the parsed diagram. This output could be easily consumed by any general purpose diagramming In this project, we try to make a first approach towards this general problem and see whether we can acquire new experiences in the field of computer vision.Īfter some research on the topic, we decided to take a shot at it with Python and OpenCV for parsing the image and eventually create JSON outputĭescribing the relevant diagram elements with layout and styling information. Still it would be great if we could create a digital graph model from this image. printed in literature, that have consistent styling acrossĪll items. Slightly easier input images are programmatically generated diagrams, e.g. Usually, we want to digitize them afterwards in order to make small incremental changes and insertions or even applyĪn automatic layout algorithm to clean up the chaotic arrangement. This use case isn't that uncommon, because we often sketch out our ideas in form of diagrams during meetings or plan certain processes Motivationĭo you remember the last time you've created a diagram on paper and found yourself re-creating it in a general diagram editor like yEd? This year, four braveĭevelopers set out to new lands and tried to parse photos and screenshots of diagrams as actual yFiles graphs without ever having ![]() To work on projects that are not necessarily connected to the everyday work or the core products of the company. Small groups of developers gather together ![]() Once a year the yWorks office is alive with a very special atmosphere: The project days. ![]()
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