import albumentations as A
import cv2
from albumentations.pytorch import ToTensorV2

from .config import IMAGE_SIZE, scale

# train_transforms = A.Compose(
#     [
#         A.LongestMaxSize(max_size=int(IMAGE_SIZE * scale)),
#         A.PadIfNeeded(
#             min_height=int(IMAGE_SIZE * scale),
#             min_width=int(IMAGE_SIZE * scale),
#             border_mode=cv2.BORDER_CONSTANT,
#         ),
#         A.Rotate(limit=10, interpolation=1, border_mode=4),
#         A.RandomCrop(width=IMAGE_SIZE, height=IMAGE_SIZE),
#         A.ColorJitter(brightness=0.6, contrast=0.6, saturation=0.6, hue=0.6, p=0.4),
#         A.OneOf(
#             [
#                 A.ShiftScaleRotate(
#                     rotate_limit=20, p=0.5, border_mode=cv2.BORDER_CONSTANT
#                 ),
#                 # A.Affine(shear=15, p=0.5, mode="constant"),
#             ],
#             p=1.0,
#         ),
#         A.HorizontalFlip(p=0.5),
#         A.Blur(p=0.1),
#         A.CLAHE(p=0.1),
#         A.Posterize(p=0.1),
#         A.ToGray(p=0.1),
#         A.ChannelShuffle(p=0.05),
#         A.Normalize(
#             mean=[0, 0, 0],
#             std=[1, 1, 1],
#             max_pixel_value=255,
#         ),
#         ToTensorV2(),
#     ],
#     bbox_params=A.BboxParams(
#         format="yolo",
#         min_visibility=0.4,
#         label_fields=[],
#     ),
# )

test_transforms = A.Compose(
    [
        A.LongestMaxSize(max_size=IMAGE_SIZE),
        A.PadIfNeeded(
            min_height=IMAGE_SIZE, min_width=IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
        ),
        A.Normalize(
            mean=[0, 0, 0],
            std=[1, 1, 1],
            max_pixel_value=255,
        ),
        ToTensorV2(),
    ],
    bbox_params=A.BboxParams(format="yolo", min_visibility=0.4, label_fields=[]),
)

resize_transforms = A.Compose(
    [
        A.LongestMaxSize(max_size=IMAGE_SIZE),
        A.PadIfNeeded(
            min_height=IMAGE_SIZE, min_width=IMAGE_SIZE, border_mode=cv2.BORDER_CONSTANT
        ),
    ]
)