annoviz

v0.2.2 safe
4.0
Medium Risk

Local YOLO annotation viewer and editor

🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risk based on the analysis, with no indications of malicious activities or high-risk behaviors. However, the low maintainer activity and poor metadata quality warrant further attention.

  • Low maintainer activity
  • Poor metadata quality
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low maintainer activity and poor metadata quality, but there are no direct signs of malicious intent.

📦 Package Quality Overall: Low (3.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 2 test file(s) detected (e.g. test_app_config.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (6423 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
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with annoviz 
Create a user-friendly desktop application called 'YOLOviz' using Python and the 'annoviz' package. This application will serve as a local viewer and editor for annotations related to YOLO object detection models. The app should allow users to easily visualize annotated images, modify bounding box labels, and save changes back to the original dataset files.

Step-by-step guide:
1. Setup the project environment by installing necessary packages including 'annoviz', 'Pillow' for image handling, and 'PyQt5' for the GUI.
2. Integrate 'annoviz' into your project to enable the loading of YOLO annotation files (.txt) and images (.jpg, .png).
3. Design a simple yet intuitive GUI using PyQt5 that includes panels for image display, annotation viewing/editing, and file navigation.
4. Implement functionality within the GUI to load and display images along with their corresponding annotations.
5. Add interactive features to allow users to adjust bounding boxes and labels directly on the displayed images.
6. Ensure that any modifications made to the annotations are saved back to the original dataset files when requested by the user.
7. Include a help section within the application explaining common operations and how to use the tool effectively.

Suggested Features:
- Support for batch processing of multiple images and annotations.
- Ability to zoom in/out on images for better annotation precision.
- Option to export modified datasets in different formats for compatibility with other tools.
- Integration with version control systems like Git for tracking changes in the dataset over time.
- Advanced filtering options for selecting specific types of objects or conditions for review.

💬 Discussion Feed

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