CrocoDocs
CrocoDocs

Introduction

What is CrocoDocs?Season Breakdown

Getting Started

Programming in FTCJavaBlocksAndroid Studio

Control Systems

IntroductionJoystick MappingPID ControlMotion ProfilingKalman FilterLow-Pass Filter

Autonomous

IntroductionTime vs Encoder-Based MovementOdometryMotion PlanningPure PursuitSensor Fusion

Codebase Etiquette and Good Practices

IntroductionNaming ConventionsCode OrganizationComments and DocumentationTeam Collaboration

Libraries

LibrariesNextFTCPedro PathingFTC DashboardMercurialPanelsSloth

Sensors and Vision

Vision OverviewVision Basics

Vision Overview

Introduction to computer vision in FTC robotics

What is Computer Vision?

Computer vision allows your robot to "see" and understand its environment using cameras. In FTC, vision processing is crucial for:

  • Autonomous navigation - Detecting and aligning with game elements
  • Object detection - Identifying game pieces, signals, and team props
  • Localization - Using AprilTags to determine robot position
  • Color recognition - Distinguishing between alliance colors and zones

Vision Approaches in FTC

FTC provides two main frameworks for vision processing:

EasyOpenCV (Legacy)

EasyOpenCV is a simplified wrapper around OpenCV, the industry-standard computer vision library. It provides direct access to powerful image processing functions.

Best for:

  • Teams familiar with OpenCV
  • Custom image processing algorithms
  • Complex color detection pipelines
  • Full control over processing

VisionPortal (Modern)

VisionPortal is the newer, structured approach introduced by FTC. It provides a standardized interface with built-in processors.

Best for:

  • Teams new to vision processing
  • Quick implementation of common tasks
  • Built-in AprilTag detection
  • ColorBlobLocatorProcessor for simple detection
  • Managing multiple vision processors

Typical Workflow

  1. Development - Write and test your vision pipeline locally using EOCV-Sim
  2. Integration - Add the pipeline to your robot code
  3. Tuning - Adjust parameters using FTC Dashboard while testing
  4. Competition - Use processed data for autonomous routines

What You'll Learn

This section covers:

  • Setting up EasyOpenCV and writing custom pipelines
  • Using EOCV-Sim to test vision code offline
  • Implementing VisionPortal with built-in processors
  • AprilTag detection for localization
  • Creating custom VisionProcessors
  • Migrating between different approaches
  • Advanced techniques and troubleshooting

Starting Point: If you're new to vision, start with EasyOpenCV Basics to understand core concepts. If you want quick results, jump to VisionPortal for the modern approach.

Sloth

Lightning-fast hot code reload for FTC

Vision Basics

Fundamental concepts of image processing for FTC

On this page

What is Computer Vision?Vision Approaches in FTCEasyOpenCV (Legacy)VisionPortal (Modern)Typical WorkflowWhat You'll Learn