REVA-QCAV/predict.py

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import argparse
import logging
import os
import numpy as np
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import torch
from PIL import Image
from torchvision import transforms
import torch.nn.functional as F
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from unet import UNet
from utils.data_vis import plot_img_and_mask
from utils.dataset import BasicDataset
from utils.crf import dense_crf
def predict_img(net,
full_img,
device,
scale_factor=1,
out_threshold=0.5,
use_dense_crf=False):
net.eval()
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img = torch.from_numpy(BasicDataset.preprocess(full_img, scale_factor))
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img = img.unsqueeze(0)
img = img.to(device=device, dtype=torch.float32)
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with torch.no_grad():
output = net(img)
if net.n_classes > 1:
probs = F.softmax(output, dim=1)
else:
probs = torch.sigmoid(output)
probs = probs.squeeze(0)
tf = transforms.Compose(
[
transforms.ToPILImage(),
transforms.Resize(full_img.shape[1]),
transforms.ToTensor()
]
)
probs = tf(probs.cpu())
full_mask = probs.squeeze().cpu().numpy()
if use_dense_crf:
full_mask = dense_crf(np.array(full_img).astype(np.uint8), full_mask)
return full_mask > out_threshold
def get_args():
parser = argparse.ArgumentParser(description='Predict masks from input images',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--model', '-m', default='MODEL.pth',
metavar='FILE',
help="Specify the file in which the model is stored")
parser.add_argument('--input', '-i', metavar='INPUT', nargs='+',
help='filenames of input images', required=True)
parser.add_argument('--output', '-o', metavar='INPUT', nargs='+',
help='Filenames of ouput images')
parser.add_argument('--viz', '-v', action='store_true',
help="Visualize the images as they are processed",
default=False)
parser.add_argument('--no-save', '-n', action='store_true',
help="Do not save the output masks",
default=False)
parser.add_argument('--mask-threshold', '-t', type=float,
help="Minimum probability value to consider a mask pixel white",
default=0.5)
parser.add_argument('--scale', '-s', type=float,
help="Scale factor for the input images",
default=0.5)
return parser.parse_args()
def get_output_filenames(args):
in_files = args.input
out_files = []
if not args.output:
for f in in_files:
pathsplit = os.path.splitext(f)
out_files.append("{}_OUT{}".format(pathsplit[0], pathsplit[1]))
elif len(in_files) != len(args.output):
logging.error("Input files and output files are not of the same length")
raise SystemExit()
else:
out_files = args.output
return out_files
def mask_to_image(mask):
return Image.fromarray((mask * 255).astype(np.uint8))
if __name__ == "__main__":
args = get_args()
in_files = args.input
out_files = get_output_filenames(args)
net = UNet(n_channels=3, n_classes=1)
logging.info("Loading model {}".format(args.model))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
logging.info(f'Using device {device}')
net.to(device=device)
net.load_state_dict(torch.load(args.model, map_location=device))
logging.info("Model loaded !")
for i, fn in enumerate(in_files):
logging.info("\nPredicting image {} ...".format(fn))
img = Image.open(fn)
mask = predict_img(net=net,
full_img=img,
scale_factor=args.scale,
out_threshold=args.mask_threshold,
use_dense_crf=False,
device=device)
if not args.no_save:
out_fn = out_files[i]
result = mask_to_image(mask)
result.save(out_files[i])
logging.info("Mask saved to {}".format(out_files[i]))
if args.viz:
logging.info("Visualizing results for image {}, close to continue ...".format(fn))
plot_img_and_mask(img, mask)