Photograph a piece of clothing once. Drape catalogs it, learns how often you wear it, and builds outfits from what is actually in your closet.
Most wardrobe apps are abandoned within a week because nobody wants to fill in a form for every sweater. Drape has no form. You take a photo and a vision pipeline does the cataloging: it separates the garment from the background, classifies it, extracts a color palette and works out which seasons it belongs to.
After that it keeps track of your closet for you. It knows what you wore last Tuesday, which jacket you haven't touched since March, and which trousers go with the new shirt. Outfit suggestions come only from your own items, scored on color harmony, season and how recently each piece was worn.
Drop in a photo and review what was detected: category, dominant colors and seasons, each with a confidence score. Confirming takes one tap.
Each item gets a five-color palette from k-means clustering over the garment's pixels. The palette is then mapped onto named color families.
A top, a bottom, shoes and a layer suited to the season, picked by a scoring model that favors neutral anchors and complementary hues.
Log a wear with one tap. Drape records last-worn dates and wear counts, and flags pieces that haven't been worn in a long time.
Filter by type, color family, season or free text. Sort by recently worn, least worn or newest.
See how your wardrobe is split by color and category, and which pieces you haven't worn in months and could let go of.
A photo from the camera or gallery is downscaled on-device before anything else runs.
Border-sampled background is separated from the garment, so the palette reflects the clothing and not your floor.
The garment classifier assigns one of 14 categories and proposes seasons from silhouette and category.
Palette, category and seasons are saved to your closet, ready to filter and to use in outfits.