feat: Implement CursorReplayStrategy with Visual Feedback and Self-Correction

[TanCodeX]

/claim #760

What kind of change does this PR introduce?

Feature: New cursor replay strategy with visual feedback and self-correction

Summary

This PR addresses #760 by introducing a new cursor replay strategy that improves targeting accuracy using visual feedback and AI-powered self-correction.

Key Features:

• Red dot visual feedback system for suggested target points
• AI-powered accuracy analysis via OpenAI models
• Self-correction mechanism based on visual feedback
• Grid-based movement with recursive refinement for higher precision
• Robust testing framework to measure accuracy, actions, and performance

This strategy sets the groundwork for improving OpenAdapt’s cursor control system in complex screen environments.

Checklist

• My code follows OpenAdapt's style guidelines
• Follows PEP 8
• Uses consistent naming conventions
• Maintains existing project structure
• Self-reviewed my code
• Verified edge cases
• Validated parameter types
• Checked error handling
• Added tests
• test_grid.py evaluates grid strategy
• Metrics for accuracy, actions, and time
• Test cases for various screen regions
• Linted code
• Used flake8 for Python linting
• Fixed all issues
• Removed unused imports
• Commented the code
• Explained AI logic
• Documented grid algorithm
• Clarified self-correction behavior
• Updated documentation
• Added docstrings for all methods/classes
• Updated requirements.txt
• Included usage examples in comments
• All new and existing tests pass locally
• Visual feedback tests
• Grid strategy accuracy checks
• OpenAI API integration tests

How can your code be run and tested?

1. Install dependencies:

pip install -r requirements.txt

2. Run the grid evaluation:

python -m experiments.cursor.test_grid

Example Output:

Grid Strategy Evaluation Results:
---------------------------------
Total test cases: 45
Average distance error: 5.2 pixels
Average actions per target: 4.3
Average time per target: 0.82 seconds

Results by grid size:
Grid size: 2x2
  Average error: 8.4 pixels
  Average actions: 3.0
  Average time: 0.65 seconds

Grid size: 4x4
  Average error: 4.2 pixels
  Average actions: 4.5
  Average time: 0.85 seconds

Grid size: 8x8
  Average error: 3.1 pixels
  Average actions: 5.5
  Average time: 0.96 seconds

3. Test specific components:

from openadapt.strategies.cursor import CursorReplayStrategy
from experiments.cursor.grid import GridCursorStrategy

# Visual feedback
strategy = CursorReplayStrategy(recording)
img_with_dot = strategy.paint_dot(screenshot, x=100, y=100)

# Grid approach
grid_strategy = GridCursorStrategy(recording, grid_size=(4, 4))
action = grid_strategy.get_next_action_event(screenshot, window_event)

Dependencies:

• opencv-python for visual processing
• numpy for grid calculations
• openai for visual feedback evaluation

[abrichr]

@TanCodeX thank you for your contribution! Can you please show some example output (e.g. video, screenshot, console text)?