Customize Data Transformation

Guideline for Developing Your Transformation

Writing Guideline

1. Each transformation is a class with a callable function. An example is shown below.

2. The input to the transformation function is always a dict, which contain data info like img_path, raw label, etc.

3. Please write comments for the call function to clarify the required/modified/added keys in the data dict.

4. Add kwargs in the class init function for extension, which is used to parse global config, such as is_train.

class ToCHWImage(object):
    """ convert hwc image to chw image
    """

    def __init__(self, channel, **kwargs):
        self.is_train = kwargs.get('is_train', True)

    def __call__(self, data: dict):
        '''
        required keys:
            - image
        modified keys:
            - image
        '''
        img = data['image']
        if isinstance(img, Image.Image):
            img = np.array(img)
        data['image'] = img.transpose((2, 0, 1))
        return data

Add Unit Test and Visualization

Please add unit test in tests/ut/transforms for the written transformation and try to cover different cases (inputs and settings).

Please visually check the correctness of the transformation on image and annotation using the jupyter notebook. See transform_tutorial.ipynb.

Important Notes

1. For spatial transformation operaions that will be used in text detection inference or evaluation (e.g. determinstic resize, scale), please record the space transformation information in shape_list. Otherwise, the postprocessing method won't be able to map the results back to the orignal image space. On how to record shape_list, please refer to DetResize.