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37 lines
959 B
Python
37 lines
959 B
Python
import torch
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from argparse import Namespace
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import net
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import cv2
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import os
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import numpy as np
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from deploy import inference_img_whole
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# input file list
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image_path = "boy-1518482_1920_12_img.png"
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trimap_path = "boy-1518482_1920_12.png"
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image = cv2.imread(image_path)
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trimap = cv2.imread(trimap_path)
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# print(trimap.shape)
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trimap = trimap[:, :, 0]
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# init model
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args = Namespace(crop_or_resize='whole', cuda=True, max_size=1600, resume='model/stage1_sad_57.1.pth', stage=1)
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model = net.VGG16(args)
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ckpt = torch.load(args.resume)
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model.load_state_dict(ckpt['state_dict'], strict=True)
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model = model.cuda()
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torch.cuda.empty_cache()
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with torch.no_grad():
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pred_mattes = inference_img_whole(args, model, image, trimap)
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pred_mattes = (pred_mattes * 255).astype(np.uint8)
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pred_mattes[trimap == 255] = 255
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pred_mattes[trimap == 0] = 0
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# print(pred_mattes)
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# cv2.imwrite('out.png', pred_mattes)
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# import matplotlib.pyplot as plt
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# plt.imshow(image)
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# plt.show()
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