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import os
import cv2
import gdown
import shutil
import argparse
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
from torchvision.utils import save_image
from inplace_abn import InPlaceABN
from dml_csr import dml_csr
from dml_csr import transforms as dml_transforms
def parse_args():
parser = argparse.ArgumentParser(description="Plot segmentation mask of an image.")
parser.add_argument(
"--image_path",
type=str,
default=None,
help="Path to the image file."
)
parser.add_argument("--size", type=int, default=512)
parser.add_argument(
"--checkpoint_path",
type=str,
default='ckpt/DML_CSR/dml_csr_celebA.pth',
help="Path to the DML-CSR pretrained model."
)
parser.add_argument(
"--output_dir",
type=str,
default="output/masks/",
help="Folder to save segmentation mask."
)
args = parser.parse_args()
return args
def download_checkpoint():
os.makedirs('ckpt', exist_ok=True)
id = "1xttWuAj633-ujp_vcm5DtL98PP0b-sUm"
gdown.download(id=id, output='ckpt/DML_CSR.zip')
shutil.unpack_archive('ckpt/DML_CSR.zip', 'ckpt')
os.remove('ckpt/DML_CSR.zip')
def box2cs(box: list) -> tuple:
x, y, w, h = box[:4]
return xywh2cs(x, y, w, h)
def xywh2cs(x: float, y: float, w: float, h: float) -> tuple:
center = np.zeros((2), dtype=np.float32)
center[0] = x + w * 0.5
center[1] = y + h * 0.5
if w > h:
h = w
elif w < h:
w = h
scale = np.array([w * 1.0, h * 1.0], dtype=np.float32)
return center, scale
def labelcolormap(N):
if N == 19: # CelebAMask-HQ
cmap = np.array([(0, 0, 0), (204, 0, 0), (76, 153, 0),
(204, 204, 0), (204, 0, 204), (204, 0, 204), (255, 204, 204),
(255, 204, 204), (102, 51, 0), (102, 51, 0), (102, 204, 0),
(255, 255, 0), (0, 0, 153), (0, 0, 204), (255, 51, 153),
(0, 204, 204), (0, 51, 0), (255, 153, 51), (0, 204, 0)],
dtype=np.uint8)
else:
def uint82bin(n, count=8):
"""returns the binary of integer n, count refers to amount of bits"""
return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)])
cmap = np.zeros((N, 3), dtype=np.uint8)
for i in range(N):
r, g, b = 0, 0, 0
id = i
for j in range(7):
str_id = uint82bin(id)
r = r ^ (np.uint8(str_id[-1]) << (7-j))
g = g ^ (np.uint8(str_id[-2]) << (7-j))
b = b ^ (np.uint8(str_id[-3]) << (7-j))
id = id >> 3
cmap[i, 0] = r
cmap[i, 1] = g
cmap[i, 2] = b
return cmap
class Colorize(object):
def __init__(self, n=19):
self.cmap = labelcolormap(n)
self.cmap = torch.from_numpy(self.cmap[:n])
def __call__(self, gray_image):
size = gray_image.size()
color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)
for label in range(0, len(self.cmap)):
mask = (label == gray_image[0]).cpu()
color_image[0][mask] = self.cmap[label][0]
color_image[1][mask] = self.cmap[label][1]
color_image[2][mask] = self.cmap[label][2]
return color_image
def tensor2label(label_tensor, n_label):
label_tensor = label_tensor.cpu().float()
if label_tensor.size()[0] > 1:
label_tensor = label_tensor.max(0, keepdim=True)[1]
label_tensor = Colorize(n_label)(label_tensor)
#label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
label_numpy = label_tensor.numpy()
label_numpy = label_numpy / 255.0
return label_numpy
def generate_label(inputs, imsize):
pred_batch = []
for input in inputs:
input = input.view(1, 19, imsize, imsize)
pred = np.squeeze(input.data.max(1)[1].cpu().numpy(), axis=0)
pred_batch.append(pred)
pred_batch = np.array(pred_batch)
pred_batch = torch.from_numpy(pred_batch)
label_batch = []
for p in pred_batch:
p = p.view(1, imsize, imsize)
label_batch.append(tensor2label(p, 19))
label_batch = np.array(label_batch)
label_batch = torch.from_numpy(label_batch)
return label_batch
def get_mask(model, image, input_size):
interp = torch.nn.Upsample(size=input_size, mode='bilinear', align_corners=True)
image = image.unsqueeze(0)
with torch.no_grad():
outputs = model(image.cuda())
labels = generate_label(interp(outputs), input_size[0])
return labels[0]
def save_mask(args):
os.makedirs(args.output_dir, exist_ok=True)
cudnn.benchmark = True
cudnn.enabled = True
model = dml_csr.DML_CSR(19, InPlaceABN, False)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = transforms.Compose([transforms.ToTensor(), normalize])
input_size = (args.size, args.size)
image = cv2.imread(args.image_path, cv2.IMREAD_COLOR)
h, w, _ = image.shape
center, s = box2cs([0, 0, w - 1, h - 1])
r = 0
crop_size = np.asarray(input_size)
trans = dml_transforms.get_affine_transform(center, s, r, crop_size)
image = cv2.warpAffine(image, trans, (int(crop_size[1]), int(crop_size[0])),
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0, 0, 0))
image = transform(image)
if not os.path.exists(args.checkpoint_path):
download_checkpoint()
state_dict = torch.load(args.checkpoint_path, map_location='cuda:0')
model.load_state_dict(state_dict)
model.cuda()
model.eval()
mask = get_mask(model, image, input_size)
filename = os.path.join(args.output_dir, os.path.basename(args.image_path).split('.')[0] + '.png')
save_image(mask, filename)
print(f'Mask saved in {filename}')
if __name__ == '__main__':
args = parse_args()
save_mask(args) |