"""
Full-reference Image Quality Assessment (FR-IQA) methods
"""
from skimage.measure import compare_ssim, compare_psnr
from ipfml.exceptions import NumpyShapeComparisonException
import numpy as np
def _prepare_arrays(img_true, img_test):
"""
Prepare image data
Raises:
NumpyShapeComparisonException: if shape of images are not the same
"""
img_true = np.asarray(img_true, dtype='float32')
img_test = np.asarray(img_test, dtype='float32')
if img_true.shape != img_test.shape:
raise NumpyShapeComparisonException
return img_true, img_test
[docs]def mse(img_true, img_test):
"""Returns Mean-Squared Error score between two Numpy arrays
Args:
img_true: Image, numpy array of any dimension
img_test: Image, numpy array of any dimension
Returns:
Computed MSE score
Raises:
NumpyShapeComparisonException: if shape of images are not the same
Example:
>>> from ipfml.iqa import fr
>>> import numpy as np
>>> arr1 = np.arange(10)
>>> arr2 = np.arange(5, 15)
>>> mse_score = fr.mse(arr1, arr2)
>>> mse_score
25.0
"""
img_true, img_test = _prepare_arrays(img_true, img_test)
return np.mean(np.square(img_true - img_test), dtype=np.float64)
[docs]def rmse(img_true, img_test):
"""Returns Root Mean-Squared Error score between two Numpy arrays
Args:
img_true: Image, numpy array of any dimension
img_test: Image, numpy array of any dimension
Returns:
Computed RMSE score
Raises:
NumpyShapeComparisonException: if shape of images are not the same
Example:
>>> from ipfml.iqa import fr
>>> import numpy as np
>>> arr1 = np.arange(10)
>>> arr2 = np.arange(5, 15)
>>> rmse_score = fr.rmse(arr1, arr2)
>>> rmse_score
5.0
"""
return np.sqrt(mse(img_true, img_test))
[docs]def mae(img_true, img_test):
"""Returns Mean Absolute Error between two Numpy arrays
Args:
img_true: Image, numpy array of any dimension
img_test: Image, numpy array of any dimension
Returns:
Computed MAE score
Raises:
NumpyShapeComparisonException: if shape of images are not the same
Example:
>>> from ipfml.iqa import fr
>>> import numpy as np
>>> arr1 = np.arange(10)
>>> arr2 = np.arange(5, 15)
>>> mae_score = fr.mae(arr1, arr2)
>>> mae_score
5.0
"""
img_true, img_test = _prepare_arrays(img_true, img_test)
return np.mean(np.absolute(img_true - img_test), dtype=np.float64)
[docs]def pnsr(img_true, img_test):
"""Returns the computed Peak Signal to Noise Ratio (PSNR) between two images
Args:
img_true: Image, numpy array of any dimension
img_test: Image, numpy array of any dimension
Returns:
Computed PSNR score
Example:
>>> from ipfml.iqa import fr
>>> import numpy as np
>>> arr1 = np.arange(10)
>>> arr2 = np.arange(5, 15)
>>> pnsr_score = fr.pnsr(arr1, arr2)
>>> int(pnsr_score)
365
"""
return compare_psnr(img_true, img_test)
[docs]def ms_ssim(img_true, img_test):
"""
Implemented later..
"""
pass
[docs]def vif(img_true, img_test):
"""
Implemented later..
"""
pass