🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity such as network calls, shell executions, obfuscation, or credential harvesting. The only slight concern is the maintainer having only one package, which could suggest a less established presence.

No network calls
No shell executions
Single package by maintainer

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interaction for its intended functionality.
Shell: No shell executions detected, indicating no immediate risk of command execution from the package.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but there are no other suspicious flags.

🔬 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 2.0

1 maintainer concern(s) found

Author "T. Schrödter" 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 PedPy

Create a pedestrian flow analysis tool using the Python package 'PedPy'. This tool should allow users to upload video footage of pedestrian movements in a specific location, such as a busy street corner or shopping mall entrance. The application will then analyze the video data to provide insights into pedestrian behavior patterns, including average speed, directionality, density, and potential bottlenecks.

Steps to complete the project:
1. Set up the environment by installing necessary packages including PedPy, OpenCV for video processing, and Matplotlib for visualization.
2. Develop a user interface that allows users to select and upload a video file.
3. Implement a function that processes the uploaded video frame by frame using OpenCV to detect and track pedestrians.
4. Use PedPy to analyze the tracked pedestrian data, calculating metrics like average speed, directionality, and density over time.
5. Visualize the analyzed data using Matplotlib, providing graphs and charts that represent pedestrian flow dynamics.
6. Add a feature to identify areas where pedestrian density exceeds a certain threshold, indicating potential safety concerns or bottlenecks.
7. Allow users to export the analyzed data and visualizations as a report in PDF format.

Suggested features include real-time tracking updates on the user interface, interactive heatmaps showing pedestrian density changes over time, and alerts for high-density zones.

The core functionality of PedPy will be leveraged to process the detected pedestrian trajectories, enabling advanced analytics such as calculating the movement patterns, estimating pedestrian speeds, and identifying trends in pedestrian behavior.