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632bd54 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | import numpy as np
import cv2
import onnxruntime
import roop.globals
from roop.typing import Frame
from roop.utilities import resolve_relative_path, conditional_thread_semaphore
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
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