Revamp Your Wardrobe with AI-Powered Smart Closet

Table of Contents

1. Introduction
2. Building an app for outfit recommendations
3. Cataloging the wardrobe
4. Finding fashionable inspiration
5. Implementing the matching feature
6. Creating a product set
7. Adding clothing items to the product set
8. Teaching the model about clothing items
9. Making outfits with ML
10. Customizing outfit recommendations
11. Building the frontend app
12. Conclusion

Building an App for Trendy Outfit Recommendations

Have You ever wished you could have a personal stylist to help you put together stylish outfits? As a software developer, I've always struggled with my fashion choices. But that's about to change as I unveil an exciting project that combines machine learning (ML) with my love for trendy fashion. In this article, I'm going to take you through the process of building an app that will analyze your closet and fashionable inspiration pictures to provide you with personalized outfit recommendations.

1. Introduction

Let's start by introducing the idea behind this app. As a fashion-challenged developer, I've always admired influencers and their impeccable Sense of style. Inspired by them, I decided to leverage ML techniques to make me trendier. The goal is to build an app that can identify the items in my closet, analyze pictures of fashionable influencers, and then assist me in putting together stylish outfits. It will even recommend products to buy if I'm missing an essential piece. Sounds cool, right? Join me as we Delve into the exciting Journey of ML-driven fashion advice.

2. Building an app for outfit recommendations

To get started, I needed a robust framework for building the app. After considering various options, I decided to wrap the entire project in a Firebase app, allowing me to access outfit recommendations conveniently from my phone. With Firebase in place, I can now focus on the Core functionalities of the app – analyzing clothing items in my wardrobe and comparing them with fashion pictures using ML algorithms. Let's dive deeper into the development process.

3. Cataloging the wardrobe

Before proceeding with ML algorithms, it's crucial to catalog my entire wardrobe. Initially, I thought I could simply take a few pictures of myself wearing different clothing items in my everyday life and use computer vision to identify the items. To accomplish this, I utilized the Object Detection feature provided by the Google Cloud Vision API. It worked reasonably well, but I soon realized that the quality of my pictures had to be exceptional for accurate matching with fashion pictures. Therefore, I accepted my fate and meticulously took pictures of every single item in my closet.

4. Finding fashionable inspiration

To make effective outfit recommendations, I needed inspiration from people who knew the ropes of fashion. Enter Laura Medalia, a stylish developer who frequently shares fashionable outfit picks on her Instagram account (@codergirl_). With her permission, I downloaded a collection of her pictures and uploaded them to Cloud Storage, ready to be compared with my own wardrobe pictures. However, there was a minor challenge of filtering out non-clothing pictures from Laura's collection. To overcome this, I leveraged the image classification feature of the Vision API, which provided tags for various objects in an image. Selecting images that were labeled as "Fashion" ensured that I had a curated dataset of inspiration pictures.

5. Implementing the matching feature

Now that I had both my wardrobe pictures and a curated dataset of fashionable inspiration pictures, it was time to implement the feature that would match them. For this task, I turned to another powerful feature of the Google Vision API – Product Search. By utilizing Product Search, I could find similar items Based on a given image. To begin, I created a product set named "dales_closet" using the pyvisionproductsearch library. This product set would hold all the clothing items I own. Adding a new product to the set involved naming it, specifying its category (e.g., apparel), and adding Relevant labels for filtering purposes.

6. Creating a product set

To effectively utilize the Product Search feature, it was essential to Create a product set that would serve as a repository for all my clothing items. Thanks to the pyvisionproductsearch library and the capabilities of the Google Cloud backend, creating a product set was straightforward. I named my product set "dales_closet" and prepared to populate it with my clothing items.

7. Adding clothing items to the product set

Now that my product set was ready, I had to add each clothing item individually. To achieve this, I organized all my clothing item images into a directory structure. Each image represented a different item, and I created a new product in the code for each item. The product's name, category (e.g., apparel), and additional labels like "jackets" were added to facilitate filtering of clothing items. The key to training the model effectively was to include reference images that captured the item from different angles and lighting conditions. To simplify this process, I developed a Python script that automatically created a product set based on an organized directory structure.

8. Teaching the model about clothing items

With the product set in place and the clothing item images added, it was time to teach the ML model about the specific characteristics of each item. This was achieved by providing paths to the various image files, serving as reference images for the model. Including images from different angles and lighting conditions helped the model generalize better. It's important to note that ML models often require some level of fine-tuning and expert intervention since they may not always provide the exact results we desire. This called for the development of a custom algorithm to generate a quality score for each outfit recommendation, taking into account the confidence scores returned by the API for individual clothing items.

9. Making outfits with ML

With the ML model trained and the outfit-making logic in place, it was time to put everything together and generate outfit recommendations. By calling the Search function of the Product Search API and providing it with an image file, I could analyze the image and identify any clothing items present. The API then searched through my closet (product set) and found the best matches for the clothing items in the image. However, outfit recommendations were more than just finding similar items. An outfit consists of multiple pieces that need to complement each other and follow certain fashion rules. To tailor the recommendations accordingly, I developed an algorithm that consolidated the confidence scores of each item into a single quality score for the entire outfit.

10. Customizing outfit recommendations

One of the fascinating aspects of building ML models is that they can be flexible, allowing developers to customize their behavior. In the case of outfit recommendations, I wanted to refine the output to ensure they made sense from a fashion perspective. By fine-tuning the algorithm and incorporating my fashion expertise, I could define rules for what pieces of clothing could be in an outfit. This ensured that the recommendations were not only visually appealing but also practical and fashion-forward.

11. Building the frontend app

To create an intuitive and user-friendly experience, I built a frontend app that pulled in all the outfit recommendations generated by the ML model. The recommendations were sorted based on their quality score, ensuring that users received the best suggestions first. The frontend app seamlessly integrated with the Firebase backend, allowing users to scroll through outfit recommendations and explore different styles effortlessly. By combining the power of ML with a well-designed app interface, I had successfully bridged the gap between fashion and technology.

12. Conclusion

In conclusion, building an app for trendy outfit recommendations was an exciting and challenging journey that demonstrated the potential of ML in the fashion industry. By leveraging ML algorithms, I was able to analyze my wardrobe and fashion inspiration pictures to curate personalized outfit recommendations. The development process involved cataloging my wardrobe, finding fashionable inspiration, implementing matching features, creating a product set, teaching the model about clothing items, and customizing outfit recommendations. The final result was an app that seamlessly combined fashion and technology, offering users a personal stylist experience at their fingertips.

So, if you're ready to up your fashion game and explore the world of trendsetting outfits, join me in this ML-driven fashion revolution. Remember, being a software developer doesn't mean you can't be stylish too!