annolid

v1.6.7 safe
3.0
Low Risk

An annotation and instance segmentation-based multiple animal tracking and behavior analysis package.

🤖 AI Analysis

Final verdict: SAFE

The package annolid v1.6.7 has been assessed with low risks across all categories. There are no indications of malicious activities or supply-chain attacks.

  • No network calls or shell executions detected.
  • Minimal obfuscation and metadata issues that do not indicate malicious intent.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating the package does not attempt to execute commands on the system.
  • Obfuscation: The observed patterns appear to be part of normal code execution rather than malicious obfuscation.
  • Credentials: No suspicious patterns indicating credential harvesting were found.
  • Metadata: The package has some minor issues but no clear signs of malicious intent.

📦 Package Quality Overall: Low (3.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (26899 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 260 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in healthonrails/annolid
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • return None model.eval() model.to("cpu") _POCKET_MODEL = model
  • .CrossEntropyLoss() model.eval() total_loss = 0.0 all_probs: list[list[float]] = []
✓ 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 score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://img.youtube.com/vi/op3A4_LuVj8/0.jpg
  • Non-HTTPS external link: http://www.youtube.com/watch?v=op3A4_LuVj8
✓ Git Repository History

Repository healthonrails/annolid 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 annolid 
Create a real-time animal behavior monitoring system using the Python package 'annolid'. This system will allow users to track and analyze the behaviors of multiple animals in a video stream. Here's a step-by-step guide on how to develop this application:

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with necessary libraries such as OpenCV, PyTorch, and annolid.

2. **Real-Time Video Capture**: Implement functionality to capture video from a webcam or any other video source in real-time. Use OpenCV for this purpose.

3. **Animal Detection and Tracking**: Utilize annolid's capabilities for instance segmentation and tracking. Integrate annolid into your application to detect and track multiple animals within the video stream.

4. **Behavior Analysis**: Based on the tracked instances, perform basic behavior analysis. For example, identify if an animal is moving, resting, or engaging in specific actions like eating or playing.

5. **Visualization**: Display the video feed with overlaid annotations showing the detected animals and their current state (e.g., color-coded for different behaviors).

6. **Data Logging**: Optionally, log the data of detected behaviors over time to a database or file for further analysis.

7. **User Interface**: Develop a simple user interface where users can control the video feed, view logs of behaviors, and customize settings for detection and tracking.

**Suggested Features**:
- Customizable thresholds for detecting different types of behaviors.
- Support for multiple video sources (webcam, RTSP streams, recorded videos).
- Ability to export logs in CSV format.
- Basic machine learning models for predicting future behaviors based on historical data.

By following these steps, you'll create a versatile tool for monitoring and analyzing animal behaviors in real-world scenarios.

💬 Discussion Feed

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