acmenra-cv

v0.1.7.7 suspicious
4.0
Medium Risk

Production-ready computer vision utilities for multi-object tracking

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal direct risks but has a moderate metadata risk due to potential inactivity and lack of maintainer information, which raises concerns about its maintenance and support.

  • metadata risk due to potential inactivity
  • lack of maintainer information
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 risks.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
  • Metadata: The package shows signs of potential inactivity and lack of maintainer information, 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: acmenra.ru>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ 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 acmenra-cv 
Your task is to create a real-time multi-object tracking application using the 'acmenra-cv' Python package. This application will allow users to capture video feeds from their webcam or upload video files, and then track multiple objects within those videos. Here are the detailed steps and features for your application:

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with necessary libraries such as 'acmenra-cv', OpenCV, and any other dependencies.
2. **User Interface Design**: Develop a simple yet effective GUI using a library like Tkinter or PyQt. This interface should allow users to select video sources (either webcam or file), start/stop the tracking process, and save the tracked video output.
3. **Video Input Handling**: Implement functionality to handle both live webcam streams and pre-recorded video files. Use OpenCV to capture frames from these sources.
4. **Multi-Object Tracking**: Utilize the 'acmenra-cv' package to perform multi-object tracking on the captured frames. Configure the parameters of the tracking algorithm to suit different scenarios (e.g., indoor vs outdoor environments).
5. **Visualization**: Display the tracked objects in real-time on the user interface. Highlight each object with bounding boxes and labels if possible. Optionally, provide options for customizing colors and labels.
6. **Output Saving**: Allow users to save the processed video with the tracked objects highlighted. Provide options for choosing the output format and quality.
7. **Performance Optimization**: Optimize the application to ensure smooth performance even with high-resolution videos and complex scenes.
8. **Documentation**: Write clear documentation explaining how to install and use the application, including details about the 'acmenra-cv' package and its integration into the project.

Additional Features (Optional):
- Implement a feature to manually adjust tracking parameters during runtime.
- Add support for saving tracking data (e.g., object positions over time) to a file.
- Include a help section in the GUI that provides information about the 'acmenra-cv' package and its capabilities.

Remember to leverage the 'acmenra-cv' package's core functionalities for efficient and accurate multi-object tracking, ensuring your application stands out for its reliability and ease of use.