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import os
import cv2
import torch
import gradio as gr
from torchvision.transforms.functional import normalize
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils import imwrite, img2tensor, tensor2img
from basicsr.utils.misc import gpu_is_available, get_device
from basicsr.utils.realesrgan_utils import RealESRGANer
from basicsr.utils.registry import ARCH_REGISTRY
from facelib.utils.face_restoration_helper import FaceRestoreHelper
from facelib.utils.misc import is_gray
def imread(img_path):
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return img
def set_realesrgan():
half = True if gpu_is_available() else False
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
upsampler = set_realesrgan()
device = get_device()
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)
def inference(image, background_enhance, face_upsample, upscale, codeformer_fidelity):
"""Run a single prediction on the model"""
try: # global try
# take the default setting for the demo
has_aligned = False
only_center_face = False
draw_box = False
detection_model = "retinaface_resnet50"
img = cv2.imread(str(image), cv2.IMREAD_COLOR)
upscale = int(upscale)
if upscale > 4:
upscale = 4
if upscale > 2 and max(img.shape[:2]) > 1000:
upscale = 2
if max(img.shape[:2]) > 1500:
upscale = 1
background_enhance = False
face_upsample = False
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:
# the input faces are already cropped and aligned
img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)
face_helper.is_gray = is_gray(img, threshold=5)
if face_helper.is_gray:
print('\tgrayscale input: True')
face_helper.cropped_faces = [img]
else:
face_helper.read_image(img)
# get face landmarks for each face
num_det_faces = face_helper.get_face_landmarks_5(
only_center_face=only_center_face, resize=640, eye_dist_threshold=5
)
print(f'\tdetect {num_det_faces} faces')
# align and warp each face
face_helper.align_warp_face()
# face restoration for each cropped face
for idx, cropped_face in enumerate(face_helper.cropped_faces):
# prepare data
cropped_face_t = img2tensor(
cropped_face / 255.0, 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)
try:
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))
del output
torch.cuda.empty_cache()
except RuntimeError as error:
print(f"Failed inference for CodeFormer: {error}")
restored_face = tensor2img(
cropped_face_t, rgb2bgr=True, min_max=(-1, 1)
)
restored_face = restored_face.astype("uint8")
face_helper.add_restored_face(restored_face)
if not has_aligned:
# upsample the background
if bg_upsampler is not None:
# Now only support RealESRGAN for upsampling background
bg_img = bg_upsampler.enhance(img, outscale=upscale)[0]
else:
bg_img = None
face_helper.get_inverse_affine(None)
# paste each restored face to the input image
if face_upsample and face_upsampler is not None:
restored_img = face_helper.paste_faces_to_input_image(
upsample_img=bg_img,
draw_box=draw_box,
face_upsampler=face_upsampler,
)
else:
restored_img = face_helper.paste_faces_to_input_image(
upsample_img=bg_img, draw_box=draw_box
)
# save restored img
save_path = f'output/out.png'
imwrite(restored_img, str(save_path))
restored_img = cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
return restored_img, save_path
except Exception as error:
print('Global exception', error)
return None, None
title = "CodeFormer: Face Restoration "
demo = gr.Interface(
inference, [
gr.inputs.Image(type="filepath", label="Input"),
gr.inputs.Checkbox(default=True, label="Background_Enhance"),
gr.inputs.Checkbox(default=True, label="Face_Upsample"),
gr.inputs.Number(default=2, label="Rescaling_Factor (up to 4)"),
gr.Slider(0, 1, value=0.5, step=0.01, label='Codeformer_Fidelity (0 for better quality, 1 for better identity)')
], [
gr.outputs.Image(type="numpy", label="Output"),
gr.outputs.File(label="Download the output")
],
title=title,
)
demo.queue(concurrency_count=2)
demo.launch()
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