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import cv2
import numpy as np
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet
def init_sr_model(model_path):
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32)
model.load_state_dict(torch.load(model_path)['params'], strict=True)
model.eval()
model = model.cuda()
return model
def enhance(model, image):
img = image.astype(np.float32) / 255.
img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
img = img.unsqueeze(0).cuda()
with torch.no_grad():
output = model(img)
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
output = (output * 255.0).round().astype(np.uint8)
return output
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