| from typing import Dict, List, Any |
| from gfpgan import GFPGANer |
| import cv2 |
| from imageio import imread |
| from basicsr.utils import imwrite |
| import io |
| import os |
| import numpy as np |
| import base64 |
|
|
|
|
| class EndpointHandler(): |
| def __init__(self, path=""): |
| self.restorer = GFPGANer( |
| model_path="./GFPGANv1.4.pth", |
| upscale=2, |
| arch="clean", |
| channel_multiplier=2, |
| bg_upsampler=None) |
| |
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
| """ |
| data args: |
| inputs (:obj: `str`) |
| date (:obj: `str`) |
| Return: |
| A :obj:`list` | `dict`: will be serialized and returned |
| """ |
| |
| inputs = data.pop("inputs",data) |
| img = imread(io.BytesIO(base64.b64decode(inputs))) |
| cropped_faces, restored_faces, restored_img = self.restorer.enhance( |
| img, |
| has_aligned=False, |
| only_center_face=False, |
| paste_back=True, |
| weight=0.5) |
|
|
| for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)): |
| retval, buffer = cv2.imencode('.png', restored_face) |
| jpg_as_text = base64.b64encode(buffer) |
| return jpg_as_text |
|
|
