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#######################################################################################
#
# MIT License
#
# Copyright (c) [2025] [leonelhs@gmail.com]
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
#######################################################################################
#
#
# This file implements an API endpoint GFPGAN system.
# It provides functionality to enhances an image by upscaling it 4 times its original size .
#
#
# Source code is based on or inspired by several projects.
# For more details and proper attribution, please refer to the following resources:
#
# - [GFPGAN] - [https://github.com/TencentARC/GFPGAN]
import gradio as gr
import numpy as np
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
from tiny_esrgan import TinyESRGAN
from tiny_gfpgan import TinyGFPGAN
face_enhancer = TinyGFPGAN()
background_enhancer = TinyESRGAN()
face_helper = FaceRestoreHelper(upscale_factor=4, use_parse=True, model_rootpath='gfpgan/weights')
def predict(img):
img = np.asarray(img)
face_helper.clean_all()
face_helper.read_image(img)
# get face landmarks for each face
face_helper.get_face_landmarks_5(eye_dist_threshold=5)
face_helper.align_warp_face()
face_helper.restored_faces = face_enhancer.enhance(face_helper.cropped_faces)
bg = background_enhancer.enhance(img)
face_helper.get_inverse_affine(None)
return face_helper.paste_faces_to_input_image(upsample_img=bg)
app = gr.Interface(
predict, [
gr.Image(type="pil", label="Image input"),
], [
gr.Image(type="numpy", label="Image face enhanced")
],
title="Image enhancer",
description="GFPGAN")
app.launch(share=False, debug=True, show_error=True, mcp_server=True)
app.queue() |