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import numpy as np
import cv2
import torch
import albumentations as albu
from iglovikov_helper_functions.utils.image_utils import load_rgb, pad, unpad
from iglovikov_helper_functions.dl.pytorch.utils import tensor_from_rgb_image
from cloths_segmentation.pre_trained_models import create_model
model = create_model("Unet_2020-10-30")
model.eval()
image = cv2.imread(str(r"test.jpg"))
image_2_extract = image
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
transform = albu.Compose([albu.Normalize(p=1)], p=1)
padded_image, pads = pad(image, factor=32, border=cv2.BORDER_CONSTANT)
x = transform(image=padded_image)["image"]
x = torch.unsqueeze(tensor_from_rgb_image(x), 0)
with torch.no_grad():
prediction = model(x)[0][0]
mask = (prediction > 0).cpu().numpy().astype(np.uint8)
mask = unpad(mask, pads)
rmask = (cv2.cvtColor(mask, cv2.COLOR_BGR2RGB) * 255).astype(np.uint8)
mask2 = np.where((rmask < 255), 0, 1).astype('uint8')
image_2_extract = image_2_extract * mask2[:, :, 1, np.newaxis]
tmp = cv2.cvtColor(image_2_extract, cv2.COLOR_BGR2GRAY)
_, alpha = cv2.threshold(tmp, 0, 255, cv2.THRESH_BINARY)
b, g, r = cv2.split(image_2_extract)
rgba = [b, g, r, alpha]
dst = cv2.merge(rgba, 4)
cv2.imwrite("test.png", dst)
# cv2.waitKey(0)
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