HorizontalFlip with Albumentations about torchlm HOT 3 CLOSED

HorizontalFlip with points_flip parameter is not fully support now, in my todo list.

• https://github.com/DefTruth/torchlm/blob/main/torchlm/transforms/transforms.py#L1275

for now, you might need to let points_flip as constant list or indices array within your custom function and use torchlm.bind to bind it togather into the transform pipeline

# First, defined your custom functions
def callable_array_noop(img: np.ndarray, landmarks: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: 
    # do some transform here 
    points_flips = []  # or array ...
    return img.astype(np.uint32), landmarks.astype(np.float32)

# Then, bind your functions and put it into the transforms pipeline.
transform = torchlm.LandmarksCompose([
        torchlm.bind(callable_array_noop, bind_type=torchlm.BindEnum.Callable_Array),
])

See data-augmentation for more details.

from torchlm.

Is possible to use Albumentations?

from torchlm.

sure, torchlm have add a soft-support for Albumentations, data-augmentation will show you the details of how to bind transforms from Albumentations.

NOTE: Please install albumentations first if you want to bind albumentations's transforms. If you have the conflict problem between different installed version of opencv (opencv-python and opencv-python-headless, ablumentations need opencv-python-headless). Please uninstall the opencv-python and opencv-python-headless first, and then reinstall albumentations. See albumentations#1140 for more details.

# first uninstall conflict opencvs
pip uninstall opencv-python
pip uninstall opencv-python-headless
pip uninstall albumentations  # if you have installed albumentations
pip install albumentations # then reinstall albumentations, will also install deps, e.g opencv

Then, check if albumentations is available.

torchlm.albumentations_is_available()  # True or False

transform = torchlm.LandmarksCompose([
    torchlm.bind(torchvision.transforms.GaussianBlur(kernel_size=(5, 25)), prob=0.5),  
    torchlm.bind(albumentations.ColorJitter(p=0.5))
])

from torchlm.