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config.py
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config.py
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"""
Forked: https://github.com/qinnzou/Robust-Lane-Detection
"""
import argparse
# Global parameters
DEFAULT_COUNTDOWN = 3
KEY_EVENTS = True
# Dataset setting
IMG_WIDTH = 256
IMG_HEIGHT = 128
IMG_CHANNEL = 3
LABEL_WIDTH = 256
LABEL_HEIGHT = 128
LABEL_CHANNEL = 1
CLASS_NUM = 2
WAIT_FOR_NEXT_FRAME = 0.05
DATA_LOADER_NUMWORKERS = 8
# Screen positioning
SCREEN_PAD_LEFT = 2 # add tolerance padding for the left side
SCREEN_PAD_TOP = 36 # title bar
SCREENSHOT_BOX = (
SCREEN_PAD_LEFT,
SCREEN_PAD_TOP,
1280 + SCREEN_PAD_LEFT,
720 + SCREEN_PAD_TOP,
)
# Paths
PRETRAINED_PATH = "./model/unetlstm.pth"
# Weight
CLASS_WEIGHT = [0.02, 1.02]
# Console colors
CC_HEADER = "\033[95m"
CC_OKBLUE = "\033[94m"
CC_OKCYAN = "\033[96m"
CC_OKGREEN = "\033[92m"
CC_WARNING = "\033[93m"
CC_ERROR = "\033[91m"
CC_ENDC = "\033[0m"
CC_BOLD = "\033[1m"
CC_UNDERLINE = "\033[4m"
def args_setting():
""" Application arguments """
parser = argparse.ArgumentParser(description="PyTorch UNet-ConvLSTM")
parser.add_argument(
"--model",
type=str,
default="UNet-ConvLSTM",
help="( UNet-ConvLSTM | SegNet-ConvLSTM | UNet | SegNet | ",
)
parser.add_argument(
"--test_path",
type=str,
help="path of road data sequence files e.g. ./data/testset/ets2",
default="./data/testset/ets2",
)
parser.add_argument(
"--save_path",
type=str,
help="path for prediction images e.g. ./data/result",
default="./data/result",
)
parser.add_argument(
"--train_path",
type=str,
help="path for the training e.g. ./data/testset/ets2/train_index.txt",
default="./data/testset/ets2/train_index.txt",
)
parser.add_argument(
"--val_path",
type=str,
help="path for the value index file e.g. ./data/testset/ets2/val_index.txt",
default="./data/testset/ets2/val_index.txt",
)
parser.add_argument(
"--batch-size",
type=int,
default=15,
metavar="N",
help="input batch size for training (default: 10)",
)
parser.add_argument(
"--test-batch-size",
type=int,
default=1,
metavar="N",
help="input batch size for testing (default: 100)",
)
parser.add_argument(
"--epochs",
type=int,
default=30,
metavar="N",
help="number of epochs to train (default: 30)",
)
parser.add_argument(
"--lr",
type=float,
default=0.01,
metavar="LR",
help="learning rate (default: 0.01)",
)
parser.add_argument(
"--momentum",
type=float,
default=0.5,
metavar="M",
help="SGD momentum (default: 0.5)",
)
parser.add_argument(
"--cuda", action="store_true", default=True, help="use CUDA training"
)
parser.add_argument(
"--seed", type=int, default=1, metavar="S", help="random seed (default: 1)"
)
parser.add_argument(
"--log-interval",
type=int,
default=10,
metavar="N",
help="how many batches to wait before logging training status",
)
parser.add_argument(
"--mode",
type=int,
default=1,
help="(0: HoughLinesP, 1: Matrix)",
)
parser.add_argument(
"--continuous",
type=bool,
default=True,
help="Single shot or continuous capturing",
)
parser.add_argument(
"--out",
type=bool,
default=False,
help="Writes original and prediction images",
)
parser.add_argument(
"--wff",
type=float,
default=1.0,
help="Wait for frame in seconds (float)",
)
parser.add_argument(
"--nokeys",
type=bool,
default=False,
help="Do not send key events",
)
args = parser.parse_args()
# overwrite parameters
if args.wff is not None:
global WAIT_FOR_NEXT_FRAME
WAIT_FOR_NEXT_FRAME = args.wff
if args.nokeys:
global KEY_EVENTS
KEY_EVENTS = False
return args