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from typing import List | |
import numpy as np | |
from PIL import Image | |
from PIL.Image import Image as PILImage | |
from scipy.special import log_softmax | |
from .session_base import BaseSession | |
pallete1 = [ | |
0, | |
0, | |
0, | |
255, | |
255, | |
255, | |
0, | |
0, | |
0, | |
0, | |
0, | |
0, | |
] | |
pallete2 = [ | |
0, | |
0, | |
0, | |
0, | |
0, | |
0, | |
255, | |
255, | |
255, | |
0, | |
0, | |
0, | |
] | |
pallete3 = [ | |
0, | |
0, | |
0, | |
0, | |
0, | |
0, | |
0, | |
0, | |
0, | |
255, | |
255, | |
255, | |
] | |
class ClothSession(BaseSession): | |
def predict(self, img: PILImage) -> List[PILImage]: | |
ort_outs = self.inner_session.run( | |
None, self.normalize(img, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), (768, 768)) | |
) | |
pred = ort_outs | |
pred = log_softmax(pred[0], 1) | |
pred = np.argmax(pred, axis=1, keepdims=True) | |
pred = np.squeeze(pred, 0) | |
pred = np.squeeze(pred, 0) | |
mask = Image.fromarray(pred.astype("uint8"), mode="L") | |
mask = mask.resize(img.size, Image.LANCZOS) | |
masks = [] | |
mask1 = mask.copy() | |
mask1.putpalette(pallete1) | |
mask1 = mask1.convert("RGB").convert("L") | |
masks.append(mask1) | |
mask2 = mask.copy() | |
mask2.putpalette(pallete2) | |
mask2 = mask2.convert("RGB").convert("L") | |
masks.append(mask2) | |
mask3 = mask.copy() | |
mask3.putpalette(pallete3) | |
mask3 = mask3.convert("RGB").convert("L") | |
masks.append(mask3) | |
return masks | |