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| from typing import Any, List, Callable | |
| import cv2 | |
| import numpy as np | |
| import onnxruntime | |
| import roop.globals | |
| from roop.typing import Face, Frame, FaceSet | |
| from roop.utilities import resolve_relative_path | |
| class Enhance_GFPGAN(): | |
| plugin_options:dict = None | |
| model_gfpgan = None | |
| name = None | |
| devicename = None | |
| processorname = 'gfpgan' | |
| type = 'enhance' | |
| def Initialize(self, plugin_options:dict): | |
| if self.plugin_options is not None: | |
| if self.plugin_options["devicename"] != plugin_options["devicename"]: | |
| self.Release() | |
| self.plugin_options = plugin_options | |
| if self.model_gfpgan is None: | |
| model_path = resolve_relative_path('../models/GFPGANv1.4.onnx') | |
| self.model_gfpgan = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers) | |
| # replace Mac mps with cpu for the moment | |
| self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu') | |
| self.name = self.model_gfpgan.get_inputs()[0].name | |
| def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame: | |
| # preprocess | |
| input_size = temp_frame.shape[1] | |
| temp_frame = cv2.resize(temp_frame, (512, 512), cv2.INTER_CUBIC) | |
| temp_frame = cv2.cvtColor(temp_frame, cv2.COLOR_BGR2RGB) | |
| temp_frame = temp_frame.astype('float32') / 255.0 | |
| temp_frame = (temp_frame - 0.5) / 0.5 | |
| temp_frame = np.expand_dims(temp_frame, axis=0).transpose(0, 3, 1, 2) | |
| io_binding = self.model_gfpgan.io_binding() | |
| io_binding.bind_cpu_input("input", temp_frame) | |
| io_binding.bind_output("1288", self.devicename) | |
| self.model_gfpgan.run_with_iobinding(io_binding) | |
| ort_outs = io_binding.copy_outputs_to_cpu() | |
| result = ort_outs[0][0] | |
| # post-process | |
| result = np.clip(result, -1, 1) | |
| result = (result + 1) / 2 | |
| result = result.transpose(1, 2, 0) * 255.0 | |
| result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR) | |
| scale_factor = int(result.shape[1] / input_size) | |
| return result.astype(np.uint8), scale_factor | |
| def Release(self): | |
| self.model_gfpgan = None | |