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https://github.com/Laurent2916/REVA-QCAV.git
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42 lines
1.1 KiB
Python
42 lines
1.1 KiB
Python
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import torch
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from utils import *
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import torch.nn.functional as F
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from PIL import Image
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from unet_model import UNet
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from torch.autograd import Variable
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import matplotlib.pyplot as plt
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from crf import dense_crf
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def predict_img(net, full_img, gpu=False):
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img = resize_and_crop(full_img)
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left = get_square(img, 0)
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right = get_square(img, 1)
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right = normalize(right)
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left = normalize(left)
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right = np.transpose(right, axes=[2, 0, 1])
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left = np.transpose(left, axes=[2, 0, 1])
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X_l = torch.FloatTensor(left).unsqueeze(0)
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X_r = torch.FloatTensor(right).unsqueeze(0)
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if gpu:
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X_l = Variable(X_l, volatile=True).cuda()
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X_r = Variable(X_r, volatile=True).cuda()
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else:
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X_l = Variable(X_l, volatile=True)
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X_r = Variable(X_r, volatile=True)
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y_l = F.sigmoid(net(X_l))
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y_r = F.sigmoid(net(X_r))
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y_l = F.upsample_bilinear(y_l, scale_factor=2).data[0][0].cpu().numpy()
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y_r = F.upsample_bilinear(y_r, scale_factor=2).data[0][0].cpu().numpy()
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y = merge_masks(y_l, y_r, 1918)
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yy = dense_crf(np.array(full_img).astype(np.uint8), y)
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return yy > 0.5
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