delayed_image.helpers module

delayed_image.helpers.dequantize(quant_data, quantization)

Helper for dequantization

Parameters:

• quant_data (ndarray) – data to dequantize

• quantization (Dict[str, Any]) – quantization information dictionary to undo. Expected keys are: orig_dtype (str) orig_min (float) orig_max (float) quant_min (float) quant_max (float) nodata (None | int)

Returns:

dequantized data

Return type:

ndarray

Example

>>> quant_data = (np.random.rand(4, 4) * 256).astype(np.uint8)
>>> quantization = {
>>>     'orig_dtype': 'float32',
>>>     'orig_min': 0,
>>>     'orig_max': 1,
>>>     'quant_min': 0,
>>>     'quant_max': 255,
>>>     'nodata': None,
>>> }
>>> dequantize(quant_data, quantization)

Example

>>> quant_data = np.ones((4, 4), dtype=np.uint8)
>>> quantization = {
>>>     'orig_dtype': 'float32',
>>>     'orig_min': 0,
>>>     'orig_max': 1,
>>>     'quant_min': 1,
>>>     'quant_max': 1,
>>>     'nodata': None,
>>> }
>>> dequantize(quant_data, quantization)

delayed_image.helpers.quantize_float01(imdata, old_min=0, old_max=1, quantize_dtype=<class 'numpy.int16'>)

Note

Setting old_min / old_max indicates the possible extend of the input data (and it will be clipped to it). It does not mean that the input data has to have those min and max values, but it should be between them.

Example

>>> from delayed_image.helpers import *  # NOQA
>>> # Test error when input is not nicely between 0 and 1
>>> imdata = (np.random.randn(32, 32, 3) - 1.) * 2.5
>>> quant1, quantization1 = quantize_float01(imdata, old_min=0, old_max=1)
>>> recon1 = dequantize(quant1, quantization1)
>>> error1 = np.abs((recon1 - imdata)).sum()
>>> print('error1 = {!r}'.format(error1))
>>> #
>>> for i in range(1, 20):
>>>     print('i = {!r}'.format(i))
>>>     quant2, quantization2 = quantize_float01(imdata, old_min=-i, old_max=i)
>>>     recon2 = dequantize(quant2, quantization2)
>>>     error2 = np.abs((recon2 - imdata)).sum()
>>>     print('error2 = {!r}'.format(error2))

Example

>>> # Test dequantize with uint8
>>> from delayed_image.helpers import dequantize
>>> imdata = np.random.randn(32, 32, 3)
>>> quant1, quantization1 = quantize_float01(imdata, old_min=0, old_max=1, quantize_dtype=np.uint8)
>>> recon1 = dequantize(quant1, quantization1)
>>> error1 = np.abs((recon1 - imdata)).sum()
>>> print('error1 = {!r}'.format(error1))

Example

>>> # Test quantization with different signed / unsigned combos
>>> print(quantize_float01(None, 0, 1, np.int16))
>>> print(quantize_float01(None, 0, 1, np.int8))
>>> print(quantize_float01(None, 0, 1, np.uint8))
>>> print(quantize_float01(None, 0, 1, np.uint16))

class delayed_image.helpers.mkslice_cls

Bases: object

Helper to make slice syntax easier to construct

Example

>>> from delayed_image.helpers import mkslice_cls
>>> m = mkslice_cls()
>>> m[0:3]
slice(0, 3, None)
>>> m[0:3, 0:5]
(slice(0, 3, None), slice(0, 5, None))
>>> m()[0:3, 0:5]
(slice(0, 3, None), slice(0, 5, None))