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| import cv2 | |
| import numpy as np | |
| import onnxruntime | |
| import roop.globals | |
| from roop.utilities import resolve_relative_path | |
| from roop.typing import Frame | |
| class Frame_Masking(): | |
| plugin_options:dict = None | |
| model_masking = None | |
| devicename = None | |
| name = None | |
| processorname = 'removebg' | |
| type = 'frame_masking' | |
| 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_masking is None: | |
| # replace Mac mps with cpu for the moment | |
| self.devicename = self.plugin_options["devicename"] | |
| self.devicename = self.devicename.replace('mps', 'cpu') | |
| model_path = resolve_relative_path('../models/Frame/isnet-general-use.onnx') | |
| self.model_masking = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers) | |
| self.model_inputs = self.model_masking.get_inputs() | |
| model_outputs = self.model_masking.get_outputs() | |
| self.io_binding = self.model_masking.io_binding() | |
| self.io_binding.bind_output(model_outputs[0].name, self.devicename) | |
| def Run(self, temp_frame: Frame) -> Frame: | |
| # Pre process:Resize, BGR->RGB, float32 cast | |
| input_image = cv2.resize(temp_frame, (1024, 1024)) | |
| input_image = cv2.cvtColor(input_image, cv2.COLOR_BGR2RGB) | |
| mean = [0.5, 0.5, 0.5] | |
| std = [1.0, 1.0, 1.0] | |
| input_image = (input_image / 255.0 - mean) / std | |
| input_image = input_image.transpose(2, 0, 1) | |
| input_image = np.expand_dims(input_image, axis=0) | |
| input_image = input_image.astype('float32') | |
| self.io_binding.bind_cpu_input(self.model_inputs[0].name, input_image) | |
| self.model_masking.run_with_iobinding(self.io_binding) | |
| ort_outs = self.io_binding.copy_outputs_to_cpu() | |
| result = ort_outs[0][0] | |
| del ort_outs | |
| # Post process:squeeze, Sigmoid, Normarize, uint8 cast | |
| mask = np.squeeze(result[0]) | |
| min_value = np.min(mask) | |
| max_value = np.max(mask) | |
| mask = (mask - min_value) / (max_value - min_value) | |
| #mask = np.where(mask < score_th, 0, 1) | |
| #mask *= 255 | |
| mask = cv2.resize(mask, (temp_frame.shape[1], temp_frame.shape[0]), interpolation=cv2.INTER_LINEAR) | |
| mask = np.reshape(mask, [mask.shape[0],mask.shape[1],1]) | |
| result = mask * temp_frame.astype(np.float32) | |
| return result.astype(np.uint8) | |
| def Release(self): | |
| del self.model_masking | |
| self.model_masking = None | |
| del self.io_binding | |
| self.io_binding = None | |