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