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import os
import torch
import cv2
from PIL import Image
import numpy as np
from huggingface_hub import hf_hub_url, cached_download
from .rrdbnet_arch import RRDBNet
from .utils import (
pad_reflect,
split_image_into_overlapping_patches,
stich_together,
unpad_image,
)
HF_MODELS = {
2: dict(
repo_id="sberbank-ai/Real-ESRGAN",
filename="RealESRGAN_x2.pth",
),
4: dict(
repo_id="sberbank-ai/Real-ESRGAN",
filename="RealESRGAN_x4.pth",
),
6: dict(
repo_id="alicangonullu/ESRGAN-Ulti",
filename="RealESRGAN_x4plus.pth",
),
8: dict(
repo_id="sberbank-ai/Real-ESRGAN",
filename="RealESRGAN_x8.pth",
),
}
class RealESRGAN:
def __init__(self, device, anime=False, scale=4):
self.device = device
self.scale = scale
if anime:
self.model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=6,
num_grow_ch=32,
scale=scale,
)
else:
self.model = RRDBNet(
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=23,
num_grow_ch=32,
scale=scale,
)
def load_weights(self, model_path, download=True):
if not os.path.exists(model_path) and download:
assert self.scale in [2, 4, 8], "You can download models only with scales: 2, 4, 8"
config = HF_MODELS[self.scale]
cache_dir = os.path.dirname(model_path)
local_filename = os.path.basename(model_path)
config_file_url = hf_hub_url(repo_id=config["repo_id"], filename=config["filename"])
cached_download(config_file_url, cache_dir=cache_dir, force_filename=local_filename)
print("Weights downloaded to:", os.path.join(cache_dir, local_filename))
loadnet = torch.load(model_path)
if "params" in loadnet:
self.model.load_state_dict(loadnet["params"], strict=True)
elif "params_ema" in loadnet:
self.model.load_state_dict(loadnet["params_ema"], strict=True)
else:
self.model.load_state_dict(loadnet, strict=True)
self.model.eval()
self.model.to(self.device)
@torch.cuda.amp.autocast()
def predict(self, lr_image, batch_size=4, patches_size=192, padding=24, pad_size=15):
scale = self.scale
device = self.device
lr_image = np.array(lr_image)
lr_image = pad_reflect(lr_image, pad_size)
patches, p_shape = split_image_into_overlapping_patches(
lr_image, patch_size=patches_size, padding_size=padding
)
img = torch.FloatTensor(patches / 255).permute((0, 3, 1, 2)).to(device).detach()
with torch.no_grad():
res = self.model(img[0:batch_size])
for i in range(batch_size, img.shape[0], batch_size):
res = torch.cat((res, self.model(img[i : i + batch_size])), 0)
sr_image = res.permute((0, 2, 3, 1)).clamp_(0, 1).cpu()
np_sr_image = sr_image.numpy()
padded_size_scaled = tuple(np.multiply(p_shape[0:2], scale)) + (3,)
scaled_image_shape = tuple(np.multiply(lr_image.shape[0:2], scale)) + (3,)
np_sr_image = stich_together(
np_sr_image,
padded_image_shape=padded_size_scaled,
target_shape=scaled_image_shape,
padding_size=padding * scale,
)
sr_img = (np_sr_image * 255).astype(np.uint8)
sr_img = unpad_image(sr_img, pad_size * scale)
sr_img = Image.fromarray(sr_img)
return sr_img
def face_enhance(self, img, scale=4):
from gfpgan import GFPGANer
face_enhancer = GFPGANer(
model_path=r"C:\Users\Admin\Downloads\Term 3\Big Data Capstone Project\Real-ESRGAN-GFP\Img-Upscale-AI\model\GFPGANv1.3.pth",
upscale=scale,
arch="clean",
channel_multiplier=2,
)
_, _, output = face_enhancer.enhance(
img, has_aligned=False, only_center_face=False, paste_back=True
)
return output
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