mirror of
https://github.com/Laurent2916/REVA-QCAV.git
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152 lines
4 KiB
Python
Executable file
152 lines
4 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, full_img, device, scale_factor=1, 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(), transforms.Resize((full_img.size[1], full_img.size[0])), 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(
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description="Predict masks from input images",
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)
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parser.add_argument(
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"--model",
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"-m",
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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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)
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parser.add_argument(
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"--input",
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"-i",
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metavar="INPUT",
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nargs="+",
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help="Filenames of input images",
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required=True,
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)
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parser.add_argument(
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"--output",
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"-o",
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metavar="OUTPUT",
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nargs="+",
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help="Filenames of output images",
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)
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parser.add_argument(
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"--viz",
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"-v",
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action="store_true",
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help="Visualize the images as they are processed",
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)
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parser.add_argument(
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"--no-save",
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"-n",
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action="store_true",
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help="Do not save the output masks",
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)
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parser.add_argument(
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"--mask-threshold",
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"-t",
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type=float,
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default=0.5,
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help="Minimum probability value to consider a mask pixel white",
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)
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parser.add_argument(
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"--scale",
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"-s",
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type=float,
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default=0.5,
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help="Scale factor for the input images",
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)
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parser.add_argument(
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"--bilinear",
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action="store_true",
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default=False,
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help="Use bilinear upsampling",
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)
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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(
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net=net, full_img=img, scale_factor=args.scale, out_threshold=args.mask_threshold, device=device
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)
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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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