parser.add_argument('-in_images',
type=str,
default='Contents/Images/',
help='input images directory')
parser.add_argument('-alpha',
type=str,
default='Contents/Dataset/Alpha/',
help='images path')
parser.add_argument('-fx',
type=float,
default=1,
help='output size in x axis')
parser.add_argument('-fy',
type=float,
default=1,
help='output size in y axis')
args = parser.parse_args()
for directory in [args.alpha]:
if not os.path.exists(directory):
os.makedirs(directory)
images_loader = ImagesLoader(args.in_images)
images_loader = ImageResizer(images_loader, args.fx, args.fy)
mog = cv.bgsegm.createBackgroundSubtractorMOG()
index = 0
for image in images_loader:
image = mog.apply(image)
outputname = os.path.join(args.alpha,
str(index).rjust(4, '0') + '.png')
cv.imwrite(outputname, image)
index += 1
print("Processed " + str(index) + " out of " + str(len(images_loader)))
default='Contents/Dataset/weights.npy',
help='weights of each class')
parser.add_argument('-fx',
type=float,
default=1,
help='output size in x axis')
parser.add_argument('-fy',
type=float,
default=1,
help='output size in y axis')
args = parser.parse_args()
for directory in [args.masks, args.labels]:
if not os.path.exists(directory):
os.makedirs(directory)
masks_loader = ImagesLoader(args.in_masks)
masks_loader = ImageResizer(masks_loader, args.fx, args.fy)
classes = getClasses(args.classes)
batch_loader = DataLoader(masks_loader, args.batch, False, num_workers=4)
weights = np.zeros(len(classes))
index = 0
for masks in batch_loader:
masks = torch.ByteTensor(masks).to(args.device)
matrices, _weights = images_to_matrices(masks, classes, args.device)
masks = matrices_to_images(matrices, classes, args.device)
weights += _weights
for i in range(len(masks)):
outputname = os.path.join(args.masks,
str(index).rjust(4, '0') + '.npy')
np.save(outputname, matrices[i].cpu().numpy())
outputname = os.path.join(args.labels,
str(index).rjust(4, '0') + '.png')