mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-11-08 22:42:02 +00:00
115 lines
3.9 KiB
Python
Executable file
115 lines
3.9 KiB
Python
Executable file
import argparse
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import logging
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import os
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import numpy as np
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import torch
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import torch.nn.functional as F
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from PIL import Image
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from torchvision import transforms
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from utils.data_loading import BasicDataset
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from unet import UNet
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from utils.utils import plot_img_and_mask
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def predict_img(net,
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full_img,
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device,
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scale_factor=1,
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out_threshold=0.5):
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net.eval()
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img = torch.from_numpy(BasicDataset.preprocess(full_img, scale_factor, is_mask=False))
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img = img.unsqueeze(0)
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img = img.to(device=device, dtype=torch.float32)
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with torch.no_grad():
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output = net(img)
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if net.n_classes > 1:
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probs = F.softmax(output, dim=1)[0]
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else:
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probs = torch.sigmoid(output)[0]
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tf = transforms.Compose([
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transforms.ToPILImage(),
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transforms.Resize((full_img.size[1], full_img.size[0])),
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transforms.ToTensor()
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])
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full_mask = tf(probs.cpu()).squeeze()
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if net.n_classes == 1:
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return (full_mask > out_threshold).numpy()
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else:
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return F.one_hot(full_mask.argmax(dim=0), net.n_classes).permute(2, 0, 1).numpy()
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def get_args():
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parser = argparse.ArgumentParser(description='Predict masks from input images')
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parser.add_argument('--model', '-m', default='MODEL.pth', metavar='FILE',
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help='Specify the file in which the model is stored')
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parser.add_argument('--input', '-i', metavar='INPUT', nargs='+', help='Filenames of input images', required=True)
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parser.add_argument('--output', '-o', metavar='OUTPUT', nargs='+', help='Filenames of output images')
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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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parser.add_argument('--no-save', '-n', action='store_true', help='Do not save the output masks')
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parser.add_argument('--mask-threshold', '-t', type=float, default=0.5,
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help='Minimum probability value to consider a mask pixel white')
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parser.add_argument('--scale', '-s', type=float, default=0.5,
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help='Scale factor for the input images')
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parser.add_argument('--bilinear', action='store_true', default=False, help='Use bilinear upsampling')
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return parser.parse_args()
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def get_output_filenames(args):
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def _generate_name(fn):
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split = os.path.splitext(fn)
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return f'{split[0]}_OUT{split[1]}'
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return args.output or list(map(_generate_name, args.input))
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def mask_to_image(mask: np.ndarray):
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if mask.ndim == 2:
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return Image.fromarray((mask * 255).astype(np.uint8))
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elif mask.ndim == 3:
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return Image.fromarray((np.argmax(mask, axis=0) * 255 / mask.shape[0]).astype(np.uint8))
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if __name__ == '__main__':
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args = get_args()
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in_files = args.input
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out_files = get_output_filenames(args)
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net = UNet(n_channels=3, n_classes=2, bilinear=args.bilinear)
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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logging.info(f'Loading model {args.model}')
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logging.info(f'Using device {device}')
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net.to(device=device)
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net.load_state_dict(torch.load(args.model, map_location=device))
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logging.info('Model loaded!')
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for i, filename in enumerate(in_files):
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logging.info(f'\nPredicting image {filename} ...')
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img = Image.open(filename)
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mask = predict_img(net=net,
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full_img=img,
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scale_factor=args.scale,
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out_threshold=args.mask_threshold,
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device=device)
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if not args.no_save:
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out_filename = out_files[i]
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result = mask_to_image(mask)
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result.save(out_filename)
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logging.info(f'Mask saved to {out_filename}')
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if args.viz:
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logging.info(f'Visualizing results for image {filename}, close to continue...')
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plot_img_and_mask(img, mask)
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