forked from PeterL1n/BackgroundMattingV2
/
inference_images.py
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/
inference_images.py
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"""
Inference images: Extract matting on images.
Example:
python inference_images.py \
--model-type mattingrefine \
--model-backbone resnet50 \
--model-backbone-scale 0.25 \
--model-refine-mode sampling \
--model-refine-sample-pixels 80000 \
--model-checkpoint "PATH_TO_CHECKPOINT" \
--images-src "PATH_TO_IMAGES_SRC_DIR" \
--images-bgr "PATH_TO_IMAGES_BGR_DIR" \
--output-dir "PATH_TO_OUTPUT_DIR" \
--output-type com fgr pha
"""
import argparse
import torch
import os
import shutil
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision import transforms as T
from torchvision.transforms.functional import to_pil_image
from threading import Thread
from tqdm import tqdm
from dataset import ImagesDataset, ZipDataset
from dataset import augmentation as A
from model import MattingBase, MattingRefine
from inference_utils import HomographicAlignment
# --------------- Arguments ---------------
parser = argparse.ArgumentParser(description='Inference images')
parser.add_argument('--model-type', type=str, required=True, choices=['mattingbase', 'mattingrefine'])
parser.add_argument('--model-backbone', type=str, required=True, choices=['resnet101', 'resnet50', 'mobilenetv2'])
parser.add_argument('--model-backbone-scale', type=float, default=0.25)
parser.add_argument('--model-checkpoint', type=str, required=True)
parser.add_argument('--model-refine-mode', type=str, default='sampling', choices=['full', 'sampling', 'thresholding'])
parser.add_argument('--model-refine-sample-pixels', type=int, default=80_000)
parser.add_argument('--model-refine-threshold', type=float, default=0.7)
parser.add_argument('--model-refine-kernel-size', type=int, default=3)
parser.add_argument('--images-src', type=str, required=True)
parser.add_argument('--images-bgr', type=str, required=True)
parser.add_argument('--preprocess-alignment', action='store_true')
parser.add_argument('--output-dir', type=str, required=True)
parser.add_argument('--output-types', type=str, required=True, nargs='+', choices=['com', 'pha', 'fgr', 'err', 'ref'])
parser.add_argument('-y', action='store_true')
args = parser.parse_args()
assert 'err' not in args.output_types or args.model_type in ['mattingbase', 'mattingrefine'], \
'Only mattingbase and mattingrefine support err output'
assert 'ref' not in args.output_types or args.model_type in ['mattingrefine'], \
'Only mattingrefine support ref output'
# --------------- Main ---------------
# Load model
if args.model_type == 'mattingbase':
model = MattingBase(args.model_backbone)
if args.model_type == 'mattingrefine':
model = MattingRefine(
args.model_backbone,
args.model_backbone_scale,
args.model_refine_mode,
args.model_refine_sample_pixels,
args.model_refine_threshold,
args.model_refine_kernel_size)
model = model.cuda().eval()
model.load_state_dict(torch.load(args.model_checkpoint), strict=False)
# Load images
dataset = ZipDataset([
ImagesDataset(args.images_src),
ImagesDataset(args.images_bgr),
], assert_equal_length=True, transforms=A.PairCompose([
HomographicAlignment() if args.preprocess_alignment else A.PairApply(nn.Identity()),
A.PairApply(T.ToTensor)
]))
dataloader = DataLoader(dataset, batch_size=1, num_workers=8, pin_memory=True)
# Create output directory
if os.path.exists(args.output_dir):
if args.y or input(f'Directory {args.output_dir} already exists. Override? [Y/N]: ').lower() == 'y':
shutil.rmtree(args.output_dir)
else:
exit()
for output_type in args.output_types:
os.makedirs(os.path.join(args.output_dir, output_type))
# Worker function
def writer(img, path):
img = to_pil_image(img[0].cpu())
img.save(path)
# Conversion loop
with torch.no_grad():
for i, (src, bgr) in enumerate(tqdm(dataloader)):
filename = dataset.datasets[0].filenames[i]
src = src.cuda(non_blocking=True)
bgr = bgr.cuda(non_blocking=True)
if args.model_type == 'mattingbase':
pha, fgr, err, _ = model(src, bgr)
elif args.model_type == 'mattingrefine':
pha, fgr, _, _, err, ref = model(src, bgr)
elif args.model_type == 'mattingbm':
pha, fgr = model(src, bgr)
if 'com' in args.output_types:
com = torch.cat([fgr * pha.ne(0), pha], dim=1)
Thread(target=writer, args=(com, filename.replace(args.images_src, os.path.join(args.output_dir, 'com')).replace('.jpg', '.png'))).start()
if 'pha' in args.output_types:
Thread(target=writer, args=(pha, filename.replace(args.images_src, os.path.join(args.output_dir, 'pha')).replace('.png', '.jpg'))).start()
if 'fgr' in args.output_types:
Thread(target=writer, args=(fgr, filename.replace(args.images_src, os.path.join(args.output_dir, 'fgr')).replace('.png', '.jpg'))).start()
if 'err' in args.output_types:
err = F.interpolate(err, src.shape[2:], mode='bilinear', align_corners=False)
Thread(target=writer, args=(err, filename.replace(args.images_src, os.path.join(args.output_dir, 'err')).replace('.png', '.jpg'))).start()
if 'ref' in args.output_types:
ref = F.interpolate(ref, src.shape[2:], mode='nearest')
Thread(target=writer, args=(ref, filename.replace(args.images_src, os.path.join(args.output_dir, 'ref')).replace('.png', '.jpg'))).start()