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import cv2
import torch
import onnx
import onnxruntime
import numpy as np
import time
# codeformer converted to onnx
# using https://github.com/redthing1/CodeFormer
class CodeFormerEnhancer:
def __init__(self, model_path="codeformer.onnx", device='cpu'):
model = onnx.load(model_path)
session_options = onnxruntime.SessionOptions()
session_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
providers = ["CPUExecutionProvider"]
if device == 'cuda':
providers = [("CUDAExecutionProvider", {"cudnn_conv_algo_search": "DEFAULT"}),"CPUExecutionProvider"]
self.session = onnxruntime.InferenceSession(model_path, sess_options=session_options, providers=providers)
def enhance(self, img, w=0.9):
img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)
img = img.astype(np.float32)[:,:,::-1] / 255.0
img = img.transpose((2, 0, 1))
nrm_mean = np.array([0.5, 0.5, 0.5]).reshape((-1, 1, 1))
nrm_std = np.array([0.5, 0.5, 0.5]).reshape((-1, 1, 1))
img = (img - nrm_mean) / nrm_std
img = np.expand_dims(img, axis=0)
out = self.session.run(None, {'x':img.astype(np.float32), 'w':np.array([w], dtype=np.double)})[0]
out = (out[0].transpose(1,2,0).clip(-1,1) + 1) * 0.5
out = (out * 255)[:,:,::-1]
return out.astype('uint8')
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