AI Analysis
Final verdict: SAFE
The package appears to be safe with no indications of malicious activities. It does not engage in risky behaviors such as making network calls, executing shell commands, or using obfuscation techniques.
- No network calls
- No shell executions
- No obfuscation
- No credential harvesting
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell executions detected, which is expected and safe.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package on PyPI, suggesting it may be a new or less active account.
Package Quality Overall: Low (3.8/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Brief PyPI description (434 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
✦ High
Multiple Contributors
10.0
Active multi-contributor project
13 unique contributor(s) across 100 commits in lambdaloop/aniposelibActive community — 5 or more distinct contributors
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: gmail.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository lambdaloop/aniposelib appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Lili Karashchuk" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with aniposelib
Create a mini-application that facilitates camera calibration for a multi-camera setup using the 'aniposelib' package. This application will allow users to input data from multiple cameras, perform intrinsic and extrinsic calibration, and output the calibrated parameters for further use in tracking applications. Here are the steps and features your application should include: 1. **Setup Interface**: Design a user-friendly interface where users can upload calibration images from each camera. These images should contain a checkerboard pattern to assist in calibration. 2. **Camera Calibration**: Utilize 'aniposelib' to automatically detect the checkerboard corners in the uploaded images and perform intrinsic calibration for each camera. Store the results in a structured format. 3. **Extrinsic Calibration**: Allow users to specify common points visible across multiple camera views to perform extrinsic calibration. Use 'aniposelib' to compute the relative positions and orientations of the cameras. 4. **Output Calibration Data**: Provide an option to export the calibration data in a format suitable for further processing or integration into other applications. 5. **Visualization Tool**: Include a feature to visualize the calibration process and results, such as displaying the detected checkerboard patterns and estimated camera poses. 6. **Documentation and Help**: Ensure comprehensive documentation is available within the application to guide users through the calibration process. By following these steps, you'll create a powerful yet accessible tool for anyone working with multi-camera setups in fields like computer vision or robotics.
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