Product recognition demo for supermarket shelf images
afb59b487f
One LiveSession per streaming client. Each frame runs detection and tracking, then classification is scheduled for products that are new, big enough, fully in frame and not already settled; results are cached on the track so detection never waits for classification. Empty-slot gaps are smoothed over frames and quality hints (glare, blur, too far) come from a 320 px downscale. Correctness work that the acceptance run forced out: - Classification submissions happen OUTSIDE the session lock. add_done_callback runs the callback inline when the future already completed, so attaching it while holding the lock deadlocked the session on the second frame. - A label is dropped only when its box has drifted off the product for two consecutive frames (panning slides boxes onto neighbours), and the anchor is the box the crop came from, not wherever the track moved by the time classification finished. - An id that disappears and comes back is a restart, not the same object: the old label is cleared instead of silently labelling a neighbour. relabels and restarts counters are monotonic per id so clients can tell a re-verification from a silent flip. - Track ids are tracked per session (tracking.py) and counters survive pruning. Measured on the synthetic pan (30 frames, 248 label observations): 20 re-verifications, 0 unexplained label changes, detection 20 ms/frame. Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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| recognization | ||
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