images are assumed to be between 0 and 255 irrespective of their dtype
Usually, it is conventional that the dtype of the image determines the range of an image. For example, when
- dtype is
uint8
image is assumed to be 0 and 255 and: -
uint16
-> [0,65535] -
float
-> [0,1]
This is expected in many Python libraries such as matplotlib and tensorflow.
However, in bob.bio.face, it is assumed that images are between 0 and 255 irrespective of their type:
- https://gitlab.idiap.ch/bob/bob.bio.face/-/blob/8c50e1ee70d866b6c39d7c228cdf1df475a63822/bob/bio/face/database/pola_thermal.py#L103
- https://gitlab.idiap.ch/bob/bob.bio.face/-/blob/8c50e1ee70d866b6c39d7c228cdf1df475a63822/bob/bio/face/embeddings/resnet50.py#L138
- https://gitlab.idiap.ch/bob/bob.bio.face/-/blob/8c50e1ee70d866b6c39d7c228cdf1df475a63822/bob/bio/face/embeddings/tf2_inception_resnet.py#L19
- https://gitlab.idiap.ch/bob/bob.bio.face/-/blob/8c50e1ee70d866b6c39d7c228cdf1df475a63822/bob/bio/face/embeddings/mobilenet_v2.py#L68
I think this problem has arisen due to unconventional conversion of the image dtype in: https://gitlab.idiap.ch/bob/bob.bio.face/-/blob/8c50e1ee70d866b6c39d7c228cdf1df475a63822/bob/bio/face/preprocessor/Base.py#L102 where the range of the image is not changed during the conversion.
I think it is worth the effort to refactor the code and respect conventional image value ranges in bob.bio.face; a function similar to https://www.tensorflow.org/api_docs/python/tf/image/convert_image_dtype?hl=en could alleviate this problem.