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import logging
import math
import torch
from pytorch_msssim import ms_ssim
[docs]class EncoderDecoder:
"""NOTE: virtual class that *you* need to subclass
An instance of this class encodes an image, calculates the number of bits and decodes the encoded image, resulting in "transformed" image.
Transformed image is similar to the original image, while the encoding+decoding process might have introduced some distortion.
The instance may (say, H266 video encoder+decoder) or may not (say, jpeg encoder+decoder) have an internal state.
"""
# helpers
[docs] def compute_psnr(self, a, b):
mse = torch.mean((a - b) ** 2).item()
return -10 * math.log10(mse)
[docs] def compute_msssim(self, a, b):
return ms_ssim(a, b, data_range=1.0).item()
def __init__(self):
self.logger = logging.getLogger(self.__class__.__name__)
self.reset()
self.compute_metrics = True
raise (AssertionError("virtual"))
[docs] def computeMetrics(self, state: bool):
self.compute_metrics = state
[docs] def getMetrics(self):
"""returns tuple with (psnr, mssim) from latest encode+decode calculation"""
return None, None
[docs] def reset(self):
"""Reset the internal state of the encoder & decoder, if there is any"""
self.cc = 0
def __call__(self, x) -> tuple:
"""Push images(s) through the encoder+decoder, returns number of bits for each image and encoded+decoded images
:param x: a FloatTensor with dimensions (batch, channels, y, x)
Returns (nbitslist, x_hat), where nbitslist is a list of number of bits and x_hat is the image that has gone throught the encoder/decoder process
"""
self.cc += 1
raise (AssertionError("virtual"))
return None, None
[docs] def BGR(self, bgr_image, tag=None):
"""
:param bgr_image: numpy BGR image (y,x,3)
:param tag: a string that can be used to identify & cache images (optional)
Takes in an BGR image, pushes it through encoder + decoder.
Returns nbits, transformed BGR image.
"""
raise (AssertionError("virtual"))
[docs]class VoidEncoderDecoder(EncoderDecoder):
"""Does no encoding/decoding whatsoever. Use for debugging."""
def __init__(self):
self.logger = logging.getLogger(self.__class__.__name__)
self.reset()
[docs] def reset(self):
"""Reset the internal state of the encoder & decoder, if any"""
self.cc = 0
def __call__(self, x) -> tuple:
"""Push images(s) through the encoder+decoder, returns bbps and encoded+decoded images
:param x: a FloatTensor with dimensions (batch, channels, y, x)
Returns (nbitslist, x_hat), where nbitslist is a list of number of bits and x_hat is the image that has gone throught the encoder/decoder process
"""
self.cc += 1
return [0], x
[docs] def BGR(self, bgr_image, tag=None):
"""
:param bgr_image: numpy BGR image (y,x,3)
:param tag: a string that can be used to identify & cache images (optional)
Returns BGR image that has gone through transformation (the encoding + decoding process)
Returns nbits, transformed BGR image
"""
return 0, bgr_image
