cv_rapid_response

Name: cv_rapid_response
Availability: InStock
Author: Duke-Hacks

Process local videos frame-by-frame with Roboflow's Python API for object detection.

Updated Jan 17, 2026

Search Tags:#video processing #computer vision #object detection #inference #roboflow #frame-by-frame

Ready to run

npx skills add https://github.com/Duke-Hacks/nex_hacks --skill cv-rapid-response

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cv_rapid_response
Source: https://github.com/Duke-Hacks/nex_hacks/tree/main/docs
Command: npx skills add https://github.com/Duke-Hacks/nex_hacks --skill cv-rapid-response

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires inference, supervision, opencv-python, python-dotenv, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides efficient and reliable methods for processing video files using Roboflow's inference API, overcoming the limitations of the InferencePipeline for local file processing.

Core Features & Use Cases

Frame-by-Frame Processing: Process videos reliably without hanging issues.
Object Detection: Utilize pre-trained or custom models for real-time detection.
Video Analysis: Generate annotated videos and structured detection reports.
Use Case: Analyze security footage to detect specific objects (e.g., people, vehicles) and generate a report of their occurrences and locations.

Quick Start

Use the cv_rapid_response skill to process the video file 'test_cam_1.MOV' and generate an annotated output video and a detection report.

Frequently Asked Questions about cv_rapid_response

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I process local video files frame-by-frame for object detection without the pipeline hanging?▼

Frame-by-frame video processing avoids InferencePipeline hanging issues by reading local video files directly and passing individual frames to the Roboflow inference API. This ensures reliable object detection and prevents the application from freezing during analysis.

Can I use Roboflow inference with OpenCV to generate annotated videos and detection reports?▼

Yes, Roboflow inference integrates with OpenCV to generate annotated videos and structured detection reports. It processes each frame for object detection, converts results using supervision, and outputs annotated video files alongside occurrence and location data.

Why does the Roboflow InferencePipeline hang when processing local video files?▼

The InferencePipeline can hang during local video file processing due to internal stream handling limitations. Bypassing it with manual frame-by-frame extraction and inference API calls provides a reliable workaround for analyzing local footage.

Do I need the supervision library to convert object detection results for video annotation?▼

Yes, the supervision library is required to convert Roboflow inference outputs into annotated video frames. It translates raw detection coordinates and labels into visual bounding boxes, enabling clear output generation for computer vision tasks.

What's the best way to analyze security footage for specific objects like people or vehicles?▼

Analyzing security footage is best handled by extracting frames with OpenCV, running object detection via the Roboflow API, and generating an annotated output video with a structured report of object occurrences and locations.

Does this frame-by-frame video processing approach support custom trained Roboflow models?▼

Yes, frame-by-frame processing supports both pre-trained and custom Roboflow models. The inference API accepts your model endpoint to process video frames, returning detection results that are then converted and annotated for output.