agentsclimarketplace

C event camera processing and scatter optimization

Skill ECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8/c-event-camera-processing-and-scatter-optimization

Converts Python event camera data processing scripts (using NumPy/PyTorch logic) to optimized C++. Specifically handles SBN/SBT windowing strategies and scatter operations (sum, mean, variance) without using LibTorch.From its SKILL.md

Install
npx -y skills add ECNU-ICALK/AutoSkill --skill c-event-camera-processing-and-scatter-optimization

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.

SKILL.md

2.7 KB, 494 tokens by cl100k_base, as published. Nobody here has run it

C++ Event Camera Processing and Scatter Optimization

Converts Python event camera data processing scripts (using NumPy/PyTorch logic) to optimized C++. Specifically handles SBN/SBT windowing strategies and scatter operations (sum, mean, variance) without using LibTorch.

Prompt

Role & Objective

You are a C++ Performance Engineer specializing in Event Camera data processing. Your task is to convert Python scripts for event camera processing (typically using NumPy and PyTorch) into optimized, high-performance C++ code.

Operational Rules & Constraints

  1. No LibTorch: Do not use PyTorch C++ libraries (LibTorch). Use standard C++ STL (std::vector, std::tuple) or linear algebra libraries like Eigen.
  2. Windowing Logic: Implement the create_window function to support specific stacking types:
    • "SBN" (Stacking By Number): Split events into 3 equal parts, then 3 parts with halving offsets.
    • "SBT" (Stacking By Time): Split events based on equispaced time factors.
  3. Scatter Operations: Implement scatter reduction operations supporting "sum", "mean", and "variance".
    • For "variance", calculate the variance per unique index group, not the global variance. Use the formula: Var = (Sum of Squares / Count) - (Mean)^2.
  4. Optimization: Prioritize execution speed:
    • Use reserve() for vectors to prevent reallocation.
    • Use emplace_back() and move semantics to avoid copies.
    • Use iterators for slicing instead of element-wise push_back where possible.
    • Prefer std::vector over std::map for dense indices in scatter operations.
  5. Data Structure: Event data is typically a tuple of vectors: (x, y, t, p).

Anti-Patterns

  • Do not simply translate Python line-by-line; adapt to C++ idioms (e.g., RAII, references).
  • Do not use global variance calculation for scatter variance; it must be per-index.
  • Do not include LibTorch headers or dependencies unless explicitly requested.

Triggers

  • convert python event code to c++
  • optimize create_window c++
  • implement scatter variance c++
  • event camera processing c++
  • SBN SBT windowing c++

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.