U !?h@s\ddlZddlmZddlZddlZddlmZddlmZ ddl m Z Gddde Z dS)N)List)Image) BaseSessionc@s<eZdZdZeeedddZeddZeddZ d S) U2netpSessionz;This class represents a session for using the U2netp model.)imgreturnc Os|jd||ddd}|ddddddddf}t|}t|}||||}t|}tj|d ddd }| |j tj j }|gS) z Predicts the mask for the given image using the U2netp model. Parameters: img (PILImage): The input image. Returns: List[PILImage]: The predicted mask. N)g ףp= ?gv/?gCl?)gZd;O?gy&1?g?)@r rZuint8L)mode)Z inner_sessionrun normalizenpmaxminZsqueezerZ fromarrayZastyperesizesizeZ ResamplingZLANCZOS) selfrargskwargsZort_outspredmamimaskrG/var/www/html/venv/lib/python3.8/site-packages/rembg/sessions/u2netp.pypredicts"     zU2netpSession.predictcOsT|j||d}tjd|j||r(dnd||j||ddtj|j|||S)zr Downloads the U2netp model. Returns: str: The path to the downloaded model. z.onnxzIhttps://github.com/danielgatis/rembg/releases/download/v0.0.0/u2netp.onnxNz$md5:8e83ca70e441ab06c318d82300c84806T)fnamepathZ progressbar)namepoochretrieveZchecksum_disabledZ u2net_homeosrjoin)clsrrrrrrdownload_models-s   zU2netpSession.download_modelscOsdS)zq Returns the name of the U2netp model. Returns: str: The name of the model. Zu2netpr)r%rrrrrr DszU2netpSession.nameN) __name__ __module__ __qualname____doc__PILImagerr classmethodr&r rrrrr s  r) r#typingrnumpyrr!ZPILrZ PIL.Imager+baserrrrrrs