app.py

import sys
sys.path.append('CodeFormer')
import os
import cv2
import torch
import torch.nn.functional as F
import gradio as gr

from torchvision.transforms.functional import normalize
from basicsr.utils import imwrite, img2tensor, tensor2img
from basicsr.utils.download_util import load_file_from_url
from facelib.utils.face_restoration_helper import FaceRestoreHelper
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.realesrgan_utils import RealESRGANer
from facelib.utils.misc import is_gray
from basicsr.utils.registry import ARCH_REGISTRY

# Model weight URLs
pretrain_model_url = {
    'codeformer': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth',
    'detection': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/detection_Resnet50_Final.pth',
    'parsing': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth',
    'realesrgan': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/RealESRGAN_x2plus.pth'
}
load_file_from_url(
    url='https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth', 
    model_dir='CodeFormer/weights/CodeFormer', 
    progress=True
)
# Download weights if not already present
for key, url in pretrain_model_url.items():
    file_path = f"CodeFormer/weights/{key}/{url.split('/')[-1]}"
    if not os.path.exists(file_path):
        load_file_from_url(url=url, model_dir=os.path.dirname(file_path), progress=True)

# Helper functions
def imread(img_path):
    img = cv2.imread(img_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    return img

def set_realesrgan():
    half = torch.cuda.is_available()
    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
    upsampler = RealESRGANer(
        scale=2, model_path="CodeFormer/weights/realesrgan/RealESRGAN_x2plus.pth",
        model=model, tile=400, tile_pad=40, pre_pad=0, half=half
    )
    return upsampler

# Model setup
upsampler = set_realesrgan()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
codeformer_net = ARCH_REGISTRY.get("CodeFormer")(
    dim_embd=512, codebook_size=1024, n_head=8, n_layers=9,
    connect_list=["32", "64", "128", "256"]
).to(device)
ckpt_path = "CodeFormer/weights/CodeFormer/codeformer.pth"
checkpoint = torch.load(ckpt_path)["params_ema"]
codeformer_net.load_state_dict(checkpoint)
codeformer_net.eval()

os.makedirs('output', exist_ok=True)

# Inference function
def inference(image, face_align=True, background_enhance=True, face_upsample=True, upscale=2, codeformer_fidelity=0.5):
    try:
        only_center_face = False
        detection_model = "retinaface_resnet50"
        
        # Load image and set parameters
        img = cv2.imread(str(image), cv2.IMREAD_COLOR)
        has_aligned = not face_align
        upscale = min(max(1, int(upscale)), 4)

        face_helper = FaceRestoreHelper(
            upscale, face_size=512, crop_ratio=(1, 1), det_model=detection_model,
            save_ext="png", use_parse=True, device=device
        )
        
        bg_upsampler = upsampler if background_enhance else None
        face_upsampler = upsampler if face_upsample else None

        if has_aligned:
            img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)
            face_helper.is_gray = is_gray(img, threshold=5)
            face_helper.cropped_faces = [img]
        else:
            face_helper.read_image(img)
            num_det_faces = face_helper.get_face_landmarks_5(only_center_face=only_center_face, resize=640, eye_dist_threshold=5)
            face_helper.align_warp_face()

        for cropped_face in face_helper.cropped_faces:
            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
            cropped_face_t = cropped_face_t.unsqueeze(0).to(device)

            with torch.no_grad():
                output = codeformer_net(cropped_face_t, w=codeformer_fidelity, adain=True)[0]
                restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
            face_helper.add_restored_face(restored_face.astype("uint8"), cropped_face)

        restored_img = face_helper.paste_faces_to_input_image(
            upsample_img=bg_upsampler.enhance(img, outscale=upscale)[0] if bg_upsampler else None,
            face_upsampler=face_upsampler
        )

        save_path = 'output/out.png'
        imwrite(restored_img, save_path)
        return cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
    except Exception as error:
        print('Error during inference:', error)
        return None

# Gradio Interface
demo = gr.Interface(
    fn=inference, 
    inputs=[
        gr.Image(type="filepath", label="Input"),
        gr.Checkbox(value=True, label="Pre_Face_Align"),
        gr.Checkbox(value=True, label="Background_Enhance"),
        gr.Checkbox(value=True, label="Face_Upsample"),
        gr.Number(value=2, label="Rescaling_Factor (up to 4)"),
        gr.Slider(0, 1, value=0.5, step=0.01, label='Codeformer_Fidelity')
    ], 
    outputs=gr.Image(type="numpy", label="Output"),
    title="CodeFormer: Robust Face Restoration and Enhancement Network"
)

demo.launch(debug=os.getenv('DEBUG') == '1', share=True)