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codeformer.py

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r
  • class
    CodeFormerEnhancer
    • func
      __init__
    • func
      enhance
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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")