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Example code of b7s/fluentvision

Informations about the package fluentvision

FluentVision

A fluent PHP 8.3+ API for YOLO object detection powered by Ultralytics YOLO26 and NanoDet-Plus.

Detect, segment, classify, and annotate images with an elegant chainable interface — same PHP result types regardless of which backend runs inference.

Quick Start

Installation

First, install Python dependencies and download models:

The local user running your system must be the same user who will be using FluentVision.

Try something like this:

This will create the environment in the user's "www" folder: /home/www/.fluentvision.

Check your environment:

Providers

Provider Backend Best For
Ultralytics YOLO26 (n/s/m/l/x), YOLOE-26 (s/m/l + PF) Full-featured, multi-task, open-vocabulary detection
NanoDet NanoDet-Plus (M/T/G) Ultra-lightweight, edge devices, real-time

Both providers return identical PHP result types — switch backends without changing your code.

YOLOE-26 Open-Vocabulary Detection

YOLOE models support text prompts to detect anything you can describe — not just the 80 COCO classes:

Variant Suffix Prompts Best For
Text-prompted yoloe-26*-seg.pt ->prompts([...]) required Targeted attribute/concept detection
Prompt-free yoloe-26*-seg-pf.pt Not supported Auto-detect without specifying prompts

Ultralytics Solutions

Run 12 built-in Ultralytics solutions (counting, heatmaps, speed estimation, etc.) with a single fluent call:

See docs/solutions.md for all 12 solutions and their parameters.

Detection Examples

Modern Workspace

Person + Cup

Street Scene with Segment

Video Detection — Street Traffic

Detection Result Array

The detect() method returns an InferenceResult object. Call toArray() to get a plain array:

Fluent API

Image Detection

Video Detection

For realtime stream, check: Real-Time Streaming

Image Annotation

Detect + Annotate in One Call

Use process() to get both detections and an annotated image in a single inference run:

process() runs inference once — more efficient than calling detect() and annotate() separately.

Working with Results

NanoDet Example

Custom Trained Models

Pass a path to your own trained model — provider is auto-inferred from the file extension:

See Custom Models for full details on supported formats, model resolution, and provider auto-inference.

Configuration

Create fluentvision-config.php in your project root:

Or load from a custom path:

CLI Commands

Requirements

Documentation

License

MIT

Image by freepik

All versions of fluentvision with dependencies

PHP Build Version
Package Version
Requires php Version ^8.3
symfony/console Version ^7.0|^8.0
symfony/process Version ^7.0|^8.0
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