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SKILL verified MIT Self-run

Pywayne Cv Stereo Tag Matcher

skill-wangyendt-wayne-skills-stereo-tag-matcher · by wangyendt

Stereo vision AprilTag matching for dual-camera systems. Use when working with pywayne.cv.stereo_tag_matcher module to match AprilTags from left/right camera views, find common tags between images, stitch stereo images together, and visualize results with color-coded annotations (all tags green, common tags yellow, red connection lines).

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Install

$ agentstack add skill-wangyendt-wayne-skills-stereo-tag-matcher

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Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access Used
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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Reliability & compatibility

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Declared compatibility

Claude CodeClaude Desktop

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About

Pywayne Stereo Tag Matcher

This module matches AprilTags detected in stereo camera pairs.

Quick Start

from pywayne.cv.stereo_tag_matcher import StereoTagMatcher
from pathlib import Path

# Initialize matcher with custom colors
matcher = StereoTagMatcher(
    target_height=600,
    line_color=(0, 0, 255),  # Red
    all_tag_color=(0, 255, 0),  # Green
    common_tag_color=(0, 255, 255)  # Yellow
)

# Process stereo pair
left_img = Path('left.png')
right_img = Path('right.png')
matched_info, stitched = matcher.process_pair(left_img, right_img, show=True)

# Save result
if stitched is not None:
    import cv2
    cv2.imwrite('stereo_result.png', stitched)

Initialization

matcher = StereoTagMatcher(
    target_height=600,      # Fixed height for resizing
    line_color=(0, 0, 255),   # Custom line color (BGR)
    line_thickness=2,
    box_thickness=2,
    all_tag_color=(0, 255, 0),
    common_tag_color=(0, 255, 255)
)

Input

| Parameter | Type | Description | |-----------|------|-------------| | image1_input | str, Path, or np.ndarray | Left camera image | | image2_input | str, Path, or np.ndarray | Right camera image | | show | bool | Display stitched result with cv2.imshow |

Output

Returned Dictionary

{
    "tag_id": {
        "cam1_center": (x, y),      # Left image center
        "cam1_corners": [(x1, y1), ...], # Left image corners
        "cam2_center": (x, y),      # Right image center
        "cam2_corners": [(x1, y1), ...]  # Right image corners
    },
    ...
}

Only tags found in both images are included in the output.

Visualization

The stitched image displays:

  • All tags - Green boxes (BGR: 0, 255, 0)
  • Common tags - Yellow boxes (BGR: 0, 255, 255)
  • Connection lines - Red lines connecting common tag centers (BGR: 0, 0, 255)

Use Cases

  • Stereo camera calibration - Match common tags to calibrate stereo cameras
  • Robot vision - Identify shared landmarks for navigation
  • Augmented reality - Track common fiducial markers

Requirements

  • cv2 (OpenCV) - Image processing and display
  • numpy - Array operations
  • pywayne.cv.apriltag_detector - AprilTag detection

Notes

  • Images are resized to target_height for consistent annotation
  • Tag coordinates are scaled proportionally based on image dimensions
  • Supports both grayscale and BGR color input images

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.