mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-11-08 22:42:02 +00:00
142 lines
4.3 KiB
Python
Executable file
142 lines
4.3 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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from PIL import Image
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from torchvision import transforms
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import torch.nn.functional as F
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from unet import UNet
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from utils import plot_img_and_mask
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from utils.dataset import BasicDataset
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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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use_dense_crf=False):
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net.eval()
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ds = BasicDataset('', '', scale=scale_factor)
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img = ds.preprocess(full_img)
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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)
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else:
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probs = torch.sigmoid(output)
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probs = probs.squeeze(0)
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tf = transforms.Compose(
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[
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transforms.ToPILImage(),
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transforms.Resize(full_img.shape[1]),
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transforms.ToTensor()
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]
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)
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probs = tf(probs.cpu())
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full_mask = probs.squeeze().cpu().numpy()
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if use_dense_crf:
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full_mask = dense_crf(np.array(full_img).astype(np.uint8), full_mask)
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return full_mask > out_threshold
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def get_args():
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parser = argparse.ArgumentParser(description='Predict masks from input images',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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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 the model is stored")
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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('--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_true',
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help="Do not save the output masks",
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default=False)
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parser.add_argument('--mask-threshold', '-t', type=float,
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help="Minimum probability value to consider a mask pixel white",
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default=0.5)
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parser.add_argument('--scale', '-s', type=float,
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help="Scale factor for the input images",
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default=0.5)
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return parser.parse_args()
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def get_output_filenames(args):
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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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logging.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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return out_files
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def mask_to_image(mask):
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return Image.fromarray((mask * 255).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=1)
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logging.info("Loading model {}".format(args.model))
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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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, fn in enumerate(in_files):
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logging.info("\nPredicting image {} ...".format(fn))
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img = Image.open(fn)
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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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use_dense_crf=False,
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device=device)
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if not args.no_save:
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out_fn = out_files[i]
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result = mask_to_image(mask)
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result.save(out_files[i])
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logging.info("Mask saved to {}".format(out_files[i]))
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if args.viz:
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logging.info("Visualizing results for image {}, close to continue ...".format(fn))
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plot_img_and_mask(img, mask)
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