mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-09-19 11:35:28 +00:00
119 lines
3.8 KiB
Python
119 lines
3.8 KiB
Python
import torch
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import torch.nn.functional as F
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from torch.autograd import Variable
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import matplotlib.pyplot as plt
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import numpy
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from PIL import Image
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import argparse
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import os
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from utils import *
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from crf import dense_crf
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from unet import UNet
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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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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', '-m', default='MODEL.pth',
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metavar='FILE',
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help="Specify the file in which is stored the model"
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" (default : 'MODEL.pth')")
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parser.add_argument('--input', '-i', metavar='INPUT', nargs='+',
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help='filenames of input images', required=True)
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parser.add_argument('--output', '-o', metavar='INPUT', nargs='+',
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help='filenames of ouput images')
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parser.add_argument('--cpu', '-c', action='store_true',
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help="Do not use the cuda version of the net",
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default=False)
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parser.add_argument('--viz', '-v', action='store_true',
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help="Visualize the images as they are processed",
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default=False)
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parser.add_argument('--no-save', '-n', action='store_false',
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help="Do not save the output masks",
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default=False)
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args = parser.parse_args()
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print("Using model file : {}".format(args.model))
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net = UNet(3, 1)
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if not args.cpu:
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print("Using CUDA version of the net, prepare your GPU !")
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net.cuda()
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else:
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net.cpu()
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print("Using CPU version of the net, this may be very slow")
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in_files = args.input
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out_files = []
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if not args.output:
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for f in in_files:
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pathsplit = os.path.splitext(f)
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out_files.append("{}_OUT{}".format(pathsplit[0], pathsplit[1]))
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elif len(in_files) != len(args.output):
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print("Error : Input files and output files are not of the same length")
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raise SystemExit()
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else:
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out_files = args.output
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print("Loading model ...")
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net.load_state_dict(torch.load(args.model))
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print("Model loaded !")
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for i, fn in enumerate(in_files):
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print("\nPredicting image {} ...".format(fn))
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img = Image.open(fn)
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out = predict_img(net, img, not args.cpu)
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if args.viz:
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print("Vizualising results for image {}, close to continue ..."
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.format(fn))
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fig = plt.figure()
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a = fig.add_subplot(1, 2, 1)
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a.set_title('Input image')
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plt.imshow(img)
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b = fig.add_subplot(1, 2, 2)
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b.set_title('Output mask')
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plt.imshow(out)
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plt.show()
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if not args.no_save:
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out_fn = out_files[i]
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result = Image.fromarray((out * 255).astype(numpy.uint8))
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result.save(out_files[i])
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print("Mask saved to {}".format(out_files[i]))
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