Context:
I have had several prints that came up fishy with false positives on infill. It would be helpful to see specifically where (when in time) those get flagged instead of blindly scrubbing through the time lapse looking for the meter spikes and highlights/indicators.
Problem:
When viewing a time-lapse of a detected failure (particularly those that don't pause or are successful but smell a bit fishy), there's no easy way to scrub through the video to see where a failure is detected. The focused feedback captures are useful and can give you some hint to where in the print it has detected something, however, those don't highlight what it's detecting. The full screen view of the video does also help (widens the playback/scrub line), but for a long print, you still may not be able to find where something was flagged.
Solution:
Good: A list of timestamp points in the video that can be clicked to "auto-scrub" to that point in the video.
Better: A timeline or some kind of graph of the points in time of failure detection with click points.
Here's a very badly drawn mockup. Imagine more white dots along the red timeline chart to focus on points of detection.
Much interested in this feature. The graph could also give a feedback that the AI process works (I always wonder if the ml container has enough power).
Context: I have had several prints that came up fishy with false positives on infill. It would be helpful to see specifically where (when in time) those get flagged instead of blindly scrubbing through the time lapse looking for the meter spikes and highlights/indicators.
Problem: When viewing a time-lapse of a detected failure (particularly those that don't pause or are successful but smell a bit fishy), there's no easy way to scrub through the video to see where a failure is detected. The focused feedback captures are useful and can give you some hint to where in the print it has detected something, however, those don't highlight what it's detecting. The full screen view of the video does also help (widens the playback/scrub line), but for a long print, you still may not be able to find where something was flagged.
Solution: Good: A list of timestamp points in the video that can be clicked to "auto-scrub" to that point in the video. Better: A timeline or some kind of graph of the points in time of failure detection with click points.
Here's a very badly drawn mockup. Imagine more white dots along the red timeline chart to focus on points of detection.