🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks in terms of network, shell execution, obfuscation, and credential harvesting. However, the metadata risk score is elevated due to the maintainer's new or inactive account and lack of proper author information, suggesting potential suspicion.

Metadata risk due to new/inactive maintainer account
Lack of proper maintainer author information

Per-check LLM notes

Network: No network calls detected, which is normal for a labeling tool unless it requires external resources.
Shell: No shell execution detected, reducing the risk of malicious activity.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
Metadata: The maintainer has a new or inactive account and lacks a proper author name, raising some suspicion but not conclusive evidence of malice.

🔬 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: univ-artois.fr>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository crillab/PyImageLabeling appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 PyImageLabeling

Your task is to develop a user-friendly desktop application called 'LabelMaster' using Python, which leverages the PyImageLabeling package to streamline the process of creating image masks for machine learning datasets. This application will enable users to easily label images for various purposes such as object detection, segmentation, and more.

### Features:
1. **User Interface**: Develop a clean and intuitive GUI where users can load their images and interactively draw masks on them. Use libraries like PyQt5 or Tkinter for the GUI.
2. **Interactive Labeling**: Allow users to select different tools for labeling (e.g., rectangle, polygon, freehand). Each tool should be capable of generating precise masks according to user input.
3. **Customizable Classes**: Users should be able to define multiple classes and assign colors to each class for easy differentiation in the final mask.
4. **Save and Load Sessions**: Implement functionality to save the current state of labeling including all drawn masks and associated data. Also, allow loading previous sessions to continue work from where it was left off.
5. **Export Masks**: Provide options to export the labeled masks in formats compatible with common machine learning frameworks like TensorFlow or PyTorch.
6. **Help and Documentation**: Include a built-in help section within the application explaining how to use the different features effectively.

### Utilizing PyImageLabeling:
- Use PyImageLabeling to handle the underlying processes of creating and managing image masks. This includes initializing the mask creation environment, applying user-drawn shapes as masks, and saving/loading mask data efficiently.
- Integrate PyImageLabeling's functionalities into your application's backend to ensure smooth operation and accurate mask generation.
- Explore PyImageLabeling’s documentation to understand its capabilities better and utilize them fully in your application.

### Additional Considerations:
- Ensure the application is responsive and performs well even with large images.
- Make sure the application is cross-platform compatible (Windows, macOS, Linux).
- Include error handling to manage unexpected issues gracefully.
- Aim to make the application accessible to users of varying technical expertise.