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import numpy as np |
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import cv2 |
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import onnxruntime |
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import roop.globals |
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from roop.typing import Frame |
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from roop.utilities import resolve_relative_path, conditional_thread_semaphore |
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class Mask_XSeg(): |
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plugin_options:dict = None |
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model_xseg = None |
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processorname = 'mask_xseg' |
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type = 'mask' |
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def Initialize(self, plugin_options:dict): |
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if self.plugin_options is not None: |
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if self.plugin_options["devicename"] != plugin_options["devicename"]: |
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self.Release() |
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self.plugin_options = plugin_options |
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if self.model_xseg is None: |
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model_path = resolve_relative_path('../models/xseg.onnx') |
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onnxruntime.set_default_logger_severity(3) |
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self.model_xseg = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers) |
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self.model_inputs = self.model_xseg.get_inputs() |
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self.model_outputs = self.model_xseg.get_outputs() |
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self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu') |
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def Run(self, img1, keywords:str) -> Frame: |
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temp_frame = cv2.resize(img1, (256, 256), cv2.INTER_CUBIC) |
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temp_frame = temp_frame.astype('float32') / 255.0 |
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temp_frame = temp_frame[None, ...] |
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io_binding = self.model_xseg.io_binding() |
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io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame) |
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io_binding.bind_output(self.model_outputs[0].name, self.devicename) |
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self.model_xseg.run_with_iobinding(io_binding) |
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ort_outs = io_binding.copy_outputs_to_cpu() |
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result = ort_outs[0][0] |
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result = np.clip(result, 0, 1.0) |
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result[result < 0.1] = 0 |
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result = 1.0 - result |
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return result |
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def Release(self): |
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del self.model_xseg |
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self.model_xseg = None |
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