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import numpy as np | |
import cv2 | |
import onnxruntime | |
import threading | |
import roop.globals | |
from roop.typing import Frame | |
from roop.utilities import resolve_relative_path | |
THREAD_LOCK_CLIP = threading.Lock() | |
class Mask_XSeg(): | |
plugin_options:dict = None | |
model_xseg = None | |
processorname = 'mask_xseg' | |
type = 'mask' | |
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_xseg is None: | |
model_path = resolve_relative_path('../models/xseg.onnx') | |
onnxruntime.set_default_logger_severity(3) | |
self.model_xseg = onnxruntime.InferenceSession(model_path, None, providers=roop.globals.execution_providers) | |
self.model_inputs = self.model_xseg.get_inputs() | |
self.model_outputs = self.model_xseg.get_outputs() | |
# replace Mac mps with cpu for the moment | |
self.devicename = self.plugin_options["devicename"].replace('mps', 'cpu') | |
def Run(self, img1, keywords:str) -> Frame: | |
temp_frame = cv2.resize(img1, (256, 256), cv2.INTER_CUBIC) | |
temp_frame = temp_frame.astype('float32') / 255.0 | |
temp_frame = temp_frame[None, ...] | |
io_binding = self.model_xseg.io_binding() | |
io_binding.bind_cpu_input(self.model_inputs[0].name, temp_frame) | |
io_binding.bind_output(self.model_outputs[0].name, self.devicename) | |
self.model_xseg.run_with_iobinding(io_binding) | |
ort_outs = io_binding.copy_outputs_to_cpu() | |
result = ort_outs[0][0] | |
result = np.clip(result, 0, 1.0) | |
result[result < 0.1] = 0 | |
# invert values to mask areas to keep | |
result = 1.0 - result | |
return result | |
def Release(self): | |
del self.model_xseg | |
self.model_xseg = None | |