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Add a path deviation analytics block to workflows #682
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inference/core/workflows/core_steps/analytics/line_following/v1.py
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inference/core/workflows/core_steps/analytics/line_following/v1.py
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inference/core/workflows/core_steps/analytics/line_following/v1.py
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def euclidean_distance(point1, point2): | ||
return np.sqrt(np.sum((point1 - point2) ** 2)) | ||
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def compute_distance(dist_matrix, i, j, path1, path2): |
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I am not Familiar with Frechet distance - as part of knowledge sharing, could u run a short session describing details
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I would be happy to do that - who would be most helpful to invite to the session?
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just fine if you record loom I would watch and ask follow up questions if I have one, I would be fine if you explain the concept of algorithm and some basic details regarding implementation
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That sounds good I will record a Loom to share tomorrow 👍
…to sb/add-line-follow
…to sb/add-line-follow
I just had a chance to address your comments - can you take another look? Thank you!
Description
This PR includes an analytics block that can be useful for analyzing the path or trajectory of detected objects in an image. It implements an algorithm for computing the Fréchet distance between the path of a detected object and an optimal path.
This can be useful for tasks like counting the number of cars taking a left turn, measuring the precision of a baseball pitch, or any other task where you want to measure the extent to which an object deviates from a desired path.
Type of change
Please delete options that are not relevant.
How has this change been tested, please provide a testcase or example of how you tested the change?
TBD.
Any specific deployment considerations
No specific deployment considerations - it is a workflow block that builds on top of the existing structure.
Docs