| | 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_CodeFormer(): |
| | model_codeformer = None |
| |
|
| | plugin_options:dict = None |
| |
|
| | processorname = 'codeformer' |
| | 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_codeformer is None: |
| | |
| | self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu') |
| | model_path = resolve_relative_path('../models/CodeFormer/CodeFormerv0.1.onnx') |
| | self.model_codeformer = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers) |
| | self.model_inputs = self.model_codeformer.get_inputs() |
| | model_outputs = self.model_codeformer.get_outputs() |
| | self.io_binding = self.model_codeformer.io_binding() |
| | self.io_binding.bind_cpu_input(self.model_inputs[1].name, np.array([0.5])) |
| | self.io_binding.bind_output(model_outputs[0].name, self.devicename) |
| |
|
| |
|
| | def Run(self, source_faceset: FaceSet, target_face: Face, temp_frame: Frame) -> Frame: |
| | 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) |
| | |
| | self.io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame.astype(np.float32)) |
| | self.model_codeformer.run_with_iobinding(self.io_binding) |
| | ort_outs = self.io_binding.copy_outputs_to_cpu() |
| | result = ort_outs[0][0] |
| | del ort_outs |
| | |
| | |
| | result = result.transpose((1, 2, 0)) |
| |
|
| | un_min = -1.0 |
| | un_max = 1.0 |
| | result = np.clip(result, un_min, un_max) |
| | result = (result - un_min) / (un_max - un_min) |
| |
|
| | result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR) |
| | result = (result * 255.0).round() |
| | scale_factor = int(result.shape[1] / input_size) |
| | return result.astype(np.uint8), scale_factor |
| |
|
| |
|
| | def Release(self): |
| | del self.model_codeformer |
| | self.model_codeformer = None |
| | del self.io_binding |
| | self.io_binding = None |
| |
|
| |
|
