ai-vision-tool

v0.5.0 suspicious
4.0
Medium Risk

Composable computer-vision pipeline components for image enhancement, motion analysis, capture, and dataset collection.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential risk, including obfuscation through base64 encoding and the presence of non-HTTPS links in metadata, though no direct evidence of malicious intent was found.

  • Use of base64 decoding potentially for obfuscation
  • Non-HTTPS links in package metadata
Per-check LLM notes
  • Obfuscation: The use of base64 decoding might indicate an attempt to obfuscate code, but it is also commonly used for legitimate purposes such as handling encrypted or compressed data.
  • Credentials: No patterns indicative of credential harvesting were found.
  • Metadata: The maintainer has only one package, and there are non-HTTPS links present, which could indicate potential risk.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (71341 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

  • 67 type-annotated function signatures detected in source
○ 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 score 2.0

Found 1 obfuscation pattern(s)

  • coded else encoded data = base64.b64decode(content) arr = np.frombuffer(data, dtype=np.uint8) i
✓ 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 score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://...
  • Non-HTTPS external link: http://0.0.0.0:7860/
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AI Vision Flow Maintainers" 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 ai-vision-tool 
Create a fully functional mini-app called 'Smart Motion Tracker' using the Python package 'ai-vision-tool'. This app should serve as a user-friendly tool for analyzing video footage to detect and track moving objects. Here’s a detailed breakdown of what your application should achieve:

1. **Video Input**: Allow users to upload a video file from their local device. Ensure the app supports common video formats such as MP4.
2. **Motion Detection**: Utilize 'ai-vision-tool' to analyze the uploaded video frame by frame, identifying areas where motion has occurred. Highlight these areas with bounding boxes.
3. **Object Tracking**: Once motion is detected, track the moving object(s) across frames. The tracking should be accurate enough to follow the object even if it moves out of the initial detection area.
4. **Enhanced Visualization**: Use 'ai-vision-tool' to enhance the visual quality of the video, making the motion detection and tracking more apparent and easier to interpret.
5. **Report Generation**: After processing, generate a report summarizing the motion events. Include timestamps of when motion was detected, duration of each event, and possibly a heat map showing the most active regions.
6. **User Interface**: Develop a simple yet intuitive web interface using Flask or Django. This UI should allow users to upload videos, view the processed results, and download the report.
7. **Dataset Collection**: Incorporate a feature where users can choose to contribute their analyzed videos to a public dataset for further research on motion analysis. Ensure proper consent mechanisms are in place.
8. **Optional Advanced Features**:
   - Implement a feature that allows users to specify certain areas within the video frame to focus the motion detection on.
   - Add real-time processing capabilities for live camera feeds, not just pre-recorded videos.
9. **Documentation**: Provide comprehensive documentation detailing how to install and use the Smart Motion Tracker, including setup instructions and API references.

This project aims to demonstrate the power of 'ai-vision-tool' in practical applications, focusing on usability and effectiveness in motion analysis tasks.