go-nude
A Go library for detecting nudity in images, ported from nude.js.
What is go-nude?
go-nude is a Go library that provides nudity detection capabilities for images. It helps developers implement content filtering and moderation features by identifying potentially inappropriate visual content using skin region identification in JPEG images.
Go developers building applications that require automated content moderation, such as social media platforms, image hosting services, or user-generated content systems.
Developers choose go-nude for its minimalistic, single-purpose design with a straightforward API, and because it's a reliable Go port of the established nude.js algorithm, offering consistency with implementations in other languages like Ruby and Python.
Overview
Nudity detection with Go.
Use Cases
Best For
- Implementing automated nudity detection in Go-based web applications
- Adding content filtering to image upload systems to flag inappropriate content
- Building moderation tools for user-generated image galleries or forums
- Creating safety features in chat applications that handle image sharing
- Integrating basic visual content analysis into Go backend services
- Porting existing nudity detection logic from JavaScript, Ruby, or Python to Go
Not Ideal For
- Applications requiring high-precision nudity detection with low false positive rates, such as legal or medical imaging systems
- Projects that need to process multiple image formats beyond JPEG, like PNG or WebP
- Real-time content moderation systems where processing speed is critical and every millisecond counts
- Teams seeking customizable detection thresholds or integration with modern machine learning pipelines
Pros & Cons
Pros
Offers a simple `IsNude()` function that returns a boolean, making it easy to integrate without extensive configuration, as shown in the example code.
Based on the established nude.js algorithm, allowing developers familiar with Ruby or Python implementations to port logic seamlessly to Go.
Follows a single-purpose approach with a clean interface, reducing complexity for basic nudity detection needs, aligning with its philosophy.
Returns errors for issues like invalid image paths or processing failures, aiding in robust application development, as mentioned in the key features.
Cons
Primarily handles JPEG images, as indicated in the key features, limiting its utility in environments with diverse image formats like PNG or GIF.
Relies on the nude.js algorithm, which may not match the accuracy of contemporary machine learning-based solutions, leading to potential inaccuracies in detection.
Lacks parameters to adjust detection sensitivity or tailor the analysis, which could be necessary for specific moderation use cases beyond basic filtering.
The README is minimal, offering few examples or details on performance benchmarks, making it challenging for thorough evaluation and adoption.
Frequently Asked Questions
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