import numpy as np
import torch

APP_TITLE = "Laboratoire de segmentation multispectrale"
SEED = 42
DEFAULT_PATCH_SIZE = 128
NUM_CHANNELS = 7
NUM_CLASSES = 4
IGNORE_INDEX = 255
MAX_EXPERIMENTS = 3

BAND_NAMES = ["H_1", "H_2", "H_3", "H_4", "H_5", "H_6", "H_7"]
BAND_DESCRIPTIONS = ["H_1", "H_2", "H_3", "H_4", "H_5", "H_6", "H_7"]
CLASS_NAMES = ["Water", "Urban", "Agriculture", "Forest"]
CLASS_COLORS = np.array(
    [
        [30,  144, 255],   # Water       - blue
        [220,  50,  50],   # Urban       - red
        [255, 215,   0],   # Agriculture - yellow
        [34,  139,  34],   # Forest      - green
    ],
    dtype=np.uint8,
)

# (R-band-index, G-band-index, B-band-index) for composite presets
COMPOSITE_PRESETS = {
    "H4 / H3 / H2":  (3, 2, 1),
    "H5 / H4 / H3":  (4, 3, 2),
    "H7 / H5 / H4":  (6, 4, 3),
}

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"


def set_seed(seed: int = SEED):
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)