diff options
Diffstat (limited to 'Wrappers')
| -rwxr-xr-x | Wrappers/Python/ccpi/framework/TestData.py | 278 | ||||
| -rw-r--r-- | Wrappers/Python/test/test_NexusReaderWriter.py | 131 | ||||
| -rwxr-xr-x | Wrappers/Python/test/test_TestData.py | 19 | ||||
| -rwxr-xr-x | Wrappers/Python/test/testclass.py | 41 |
4 files changed, 420 insertions, 49 deletions
diff --git a/Wrappers/Python/ccpi/framework/TestData.py b/Wrappers/Python/ccpi/framework/TestData.py index e7dc908..b512e81 100755 --- a/Wrappers/Python/ccpi/framework/TestData.py +++ b/Wrappers/Python/ccpi/framework/TestData.py @@ -1,15 +1,22 @@ # -*- coding: utf-8 -*-
-from ccpi.framework import ImageData, ImageGeometry
+from ccpi.framework import ImageData, ImageGeometry, DataContainer
import numpy
+import numpy as np
from PIL import Image
import os
import os.path
+import sys
data_dir = os.path.abspath(os.path.join(
os.path.dirname(__file__),
'../data/')
)
+# this is the default location after a conda install
+data_dir = os.path.abspath(
+ os.path.join(sys.prefix, 'share','ccpi')
+)
+
class TestData(object):
BOAT = 'boat.tiff'
CAMERA = 'camera.png'
@@ -69,53 +76,226 @@ class TestData(object): print ("data.geometry", data.geometry)
return data
- def camera(**kwargs):
-
- tmp = Image.open(os.path.join(data_dir, 'camera.png'))
-
- size = kwargs.get('size',(512, 512))
-
- data = numpy.array(tmp.resize(size))
-
- data = data/data.max()
-
- return ImageData(data)
-
-
- def boat(**kwargs):
-
- tmp = Image.open(os.path.join(data_dir, 'boat.tiff'))
-
- size = kwargs.get('size',(512, 512))
-
- data = numpy.array(tmp.resize(size))
-
- data = data/data.max()
-
- return ImageData(data)
-
-
- def peppers(**kwargs):
-
- tmp = Image.open(os.path.join(data_dir, 'peppers.tiff'))
-
- size = kwargs.get('size',(512, 512))
-
- data = numpy.array(tmp.resize(size))
-
- data = data/data.max()
-
- return ImageData(data)
-
- def shapes(**kwargs):
-
- tmp = Image.open(os.path.join(data_dir, 'shapes.png')).convert('LA')
-
- size = kwargs.get('size',(300, 200))
+ @staticmethod
+ def random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs):
+ if issubclass(type(image), DataContainer):
+ arr = TestData.scikit_random_noise(image.as_array(), mode=mode, seed=seed, clip=clip,
+ **kwargs)
+ out = image.copy()
+ out.fill(arr)
+ return out
+ elif issubclass(type(image), numpy.ndarray):
+ return TestData.scikit_random_noise(image, mode=mode, seed=seed, clip=clip,
+ **kwargs)
+
+ @staticmethod
+ def scikit_random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs):
+ """
+ Function to add random noise of various types to a floating-point image.
+ Parameters
+ ----------
+ image : ndarray
+ Input image data. Will be converted to float.
+ mode : str, optional
+ One of the following strings, selecting the type of noise to add:
+ - 'gaussian' Gaussian-distributed additive noise.
+ - 'localvar' Gaussian-distributed additive noise, with specified
+ local variance at each point of `image`.
+ - 'poisson' Poisson-distributed noise generated from the data.
+ - 'salt' Replaces random pixels with 1.
+ - 'pepper' Replaces random pixels with 0 (for unsigned images) or
+ -1 (for signed images).
+ - 's&p' Replaces random pixels with either 1 or `low_val`, where
+ `low_val` is 0 for unsigned images or -1 for signed
+ images.
+ - 'speckle' Multiplicative noise using out = image + n*image, where
+ n is uniform noise with specified mean & variance.
+ seed : int, optional
+ If provided, this will set the random seed before generating noise,
+ for valid pseudo-random comparisons.
+ clip : bool, optional
+ If True (default), the output will be clipped after noise applied
+ for modes `'speckle'`, `'poisson'`, and `'gaussian'`. This is
+ needed to maintain the proper image data range. If False, clipping
+ is not applied, and the output may extend beyond the range [-1, 1].
+ mean : float, optional
+ Mean of random distribution. Used in 'gaussian' and 'speckle'.
+ Default : 0.
+ var : float, optional
+ Variance of random distribution. Used in 'gaussian' and 'speckle'.
+ Note: variance = (standard deviation) ** 2. Default : 0.01
+ local_vars : ndarray, optional
+ Array of positive floats, same shape as `image`, defining the local
+ variance at every image point. Used in 'localvar'.
+ amount : float, optional
+ Proportion of image pixels to replace with noise on range [0, 1].
+ Used in 'salt', 'pepper', and 'salt & pepper'. Default : 0.05
+ salt_vs_pepper : float, optional
+ Proportion of salt vs. pepper noise for 's&p' on range [0, 1].
+ Higher values represent more salt. Default : 0.5 (equal amounts)
+ Returns
+ -------
+ out : ndarray
+ Output floating-point image data on range [0, 1] or [-1, 1] if the
+ input `image` was unsigned or signed, respectively.
+ Notes
+ -----
+ Speckle, Poisson, Localvar, and Gaussian noise may generate noise outside
+ the valid image range. The default is to clip (not alias) these values,
+ but they may be preserved by setting `clip=False`. Note that in this case
+ the output may contain values outside the ranges [0, 1] or [-1, 1].
+ Use this option with care.
+ Because of the prevalence of exclusively positive floating-point images in
+ intermediate calculations, it is not possible to intuit if an input is
+ signed based on dtype alone. Instead, negative values are explicitly
+ searched for. Only if found does this function assume signed input.
+ Unexpected results only occur in rare, poorly exposes cases (e.g. if all
+ values are above 50 percent gray in a signed `image`). In this event,
+ manually scaling the input to the positive domain will solve the problem.
+ The Poisson distribution is only defined for positive integers. To apply
+ this noise type, the number of unique values in the image is found and
+ the next round power of two is used to scale up the floating-point result,
+ after which it is scaled back down to the floating-point image range.
+ To generate Poisson noise against a signed image, the signed image is
+ temporarily converted to an unsigned image in the floating point domain,
+ Poisson noise is generated, then it is returned to the original range.
- data = numpy.array(tmp.resize(size))
-
- data = data/data.max()
+ This function is adapted from scikit-image.
+ https://github.com/scikit-image/scikit-image/blob/master/skimage/util/noise.py
+
+ Copyright (C) 2019, the scikit-image team
+ All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without
+ modification, are permitted provided that the following conditions are
+ met:
+
+ 1. Redistributions of source code must retain the above copyright
+ notice, this list of conditions and the following disclaimer.
+ 2. Redistributions in binary form must reproduce the above copyright
+ notice, this list of conditions and the following disclaimer in
+ the documentation and/or other materials provided with the
+ distribution.
+ 3. Neither the name of skimage nor the names of its contributors may be
+ used to endorse or promote products derived from this software without
+ specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+ INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+ HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+ STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+ IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+ POSSIBILITY OF SUCH DAMAGE.
- return ImageData(data)
-
+ """
+ mode = mode.lower()
+
+ # Detect if a signed image was input
+ if image.min() < 0:
+ low_clip = -1.
+ else:
+ low_clip = 0.
+
+ image = numpy.asarray(image, dtype=(np.float64))
+ if seed is not None:
+ np.random.seed(seed=seed)
+
+ allowedtypes = {
+ 'gaussian': 'gaussian_values',
+ 'localvar': 'localvar_values',
+ 'poisson': 'poisson_values',
+ 'salt': 'sp_values',
+ 'pepper': 'sp_values',
+ 's&p': 's&p_values',
+ 'speckle': 'gaussian_values'}
+
+ kwdefaults = {
+ 'mean': 0.,
+ 'var': 0.01,
+ 'amount': 0.05,
+ 'salt_vs_pepper': 0.5,
+ 'local_vars': np.zeros_like(image) + 0.01}
+
+ allowedkwargs = {
+ 'gaussian_values': ['mean', 'var'],
+ 'localvar_values': ['local_vars'],
+ 'sp_values': ['amount'],
+ 's&p_values': ['amount', 'salt_vs_pepper'],
+ 'poisson_values': []}
+
+ for key in kwargs:
+ if key not in allowedkwargs[allowedtypes[mode]]:
+ raise ValueError('%s keyword not in allowed keywords %s' %
+ (key, allowedkwargs[allowedtypes[mode]]))
+
+ # Set kwarg defaults
+ for kw in allowedkwargs[allowedtypes[mode]]:
+ kwargs.setdefault(kw, kwdefaults[kw])
+
+ if mode == 'gaussian':
+ noise = np.random.normal(kwargs['mean'], kwargs['var'] ** 0.5,
+ image.shape)
+ out = image + noise
+
+ elif mode == 'localvar':
+ # Ensure local variance input is correct
+ if (kwargs['local_vars'] <= 0).any():
+ raise ValueError('All values of `local_vars` must be > 0.')
+
+ # Safe shortcut usage broadcasts kwargs['local_vars'] as a ufunc
+ out = image + np.random.normal(0, kwargs['local_vars'] ** 0.5)
+
+ elif mode == 'poisson':
+ # Determine unique values in image & calculate the next power of two
+ vals = len(np.unique(image))
+ vals = 2 ** np.ceil(np.log2(vals))
+
+ # Ensure image is exclusively positive
+ if low_clip == -1.:
+ old_max = image.max()
+ image = (image + 1.) / (old_max + 1.)
+
+ # Generating noise for each unique value in image.
+ out = np.random.poisson(image * vals) / float(vals)
+
+ # Return image to original range if input was signed
+ if low_clip == -1.:
+ out = out * (old_max + 1.) - 1.
+
+ elif mode == 'salt':
+ # Re-call function with mode='s&p' and p=1 (all salt noise)
+ out = random_noise(image, mode='s&p', seed=seed,
+ amount=kwargs['amount'], salt_vs_pepper=1.)
+
+ elif mode == 'pepper':
+ # Re-call function with mode='s&p' and p=1 (all pepper noise)
+ out = random_noise(image, mode='s&p', seed=seed,
+ amount=kwargs['amount'], salt_vs_pepper=0.)
+
+ elif mode == 's&p':
+ out = image.copy()
+ p = kwargs['amount']
+ q = kwargs['salt_vs_pepper']
+ flipped = np.random.choice([True, False], size=image.shape,
+ p=[p, 1 - p])
+ salted = np.random.choice([True, False], size=image.shape,
+ p=[q, 1 - q])
+ peppered = ~salted
+ out[flipped & salted] = 1
+ out[flipped & peppered] = low_clip
+
+ elif mode == 'speckle':
+ noise = np.random.normal(kwargs['mean'], kwargs['var'] ** 0.5,
+ image.shape)
+ out = image + image * noise
+
+ # Clip back to original range, if necessary
+ if clip:
+ out = np.clip(out, low_clip, 1.0)
+
+ return out
\ No newline at end of file diff --git a/Wrappers/Python/test/test_NexusReaderWriter.py b/Wrappers/Python/test/test_NexusReaderWriter.py new file mode 100644 index 0000000..9add7b7 --- /dev/null +++ b/Wrappers/Python/test/test_NexusReaderWriter.py @@ -0,0 +1,131 @@ +import unittest +import os +from ccpi.io import NEXUSDataReader +from ccpi.io import NEXUSDataWriter +from ccpi.framework import AcquisitionData, AcquisitionGeometry, ImageData, ImageGeometry +import numpy + + +class TestNexusReaderWriter(unittest.TestCase): + + def setUp(self): + pass + + def testwriteImageData(self): + im_size = 5 + ig = ImageGeometry(voxel_num_x = im_size, + voxel_num_y = im_size) + im = ig.allocate() + writer = NEXUSDataWriter() + writer.set_up(file_name = os.path.join(os.getcwd(), 'test_nexus_im.nxs'), + data_container = im) + writer.write_file() + self.stestreadImageData() + + def testwriteAcquisitionData(self): + im_size = 5 + ag2d = AcquisitionGeometry(geom_type = 'parallel', + dimension = '2D', + angles = numpy.array([0, 1]), + pixel_num_h = im_size, + pixel_size_h = 1, + pixel_num_v = im_size, + pixel_size_v = 1) + ad2d = ag2d.allocate() + writer = NEXUSDataWriter() + writer.set_up(file_name = os.path.join(os.getcwd(), 'test_nexus_ad2d.nxs'), + data_container = ad2d) + writer.write_file() + + ag3d = AcquisitionGeometry(geom_type = 'cone', + dimension = '3D', + angles = numpy.array([0, 1]), + pixel_num_h = im_size, + pixel_size_h = 1, + pixel_num_v = im_size, + pixel_size_v = 1, + dist_source_center = 1, + dist_center_detector = 1, + channels = im_size) + ad3d = ag3d.allocate() + writer = NEXUSDataWriter() + writer.set_up(file_name = os.path.join(os.getcwd(), 'test_nexus_ad3d.nxs'), + data_container = ad3d) + writer.write_file() + + self.stestreadAcquisitionData() + + def stestreadImageData(self): + + im_size = 5 + ig_test = ImageGeometry(voxel_num_x = im_size, + voxel_num_y = im_size) + im_test = ig_test.allocate() + + reader = NEXUSDataReader() + reader.set_up(nexus_file = os.path.join(os.getcwd(), 'test_nexus_im.nxs')) + im = reader.load_data() + ig = reader.get_geometry() + numpy.testing.assert_array_equal(im.as_array(), im_test.as_array(), 'Loaded image is not correct') + self.assertEqual(ig.voxel_num_x, ig_test.voxel_num_x, 'ImageGeometry is not correct') + self.assertEqual(ig.voxel_num_y, ig_test.voxel_num_y, 'ImageGeometry is not correct') + + def stestreadAcquisitionData(self): + im_size = 5 + ag2d_test = AcquisitionGeometry(geom_type = 'parallel', + dimension = '2D', + angles = numpy.array([0, 1]), + pixel_num_h = im_size, + pixel_size_h = 1, + pixel_num_v = im_size, + pixel_size_v = 1) + ad2d_test = ag2d_test.allocate() + + reader2d = NEXUSDataReader() + reader2d.set_up(nexus_file = os.path.join(os.getcwd(), 'test_nexus_ad2d.nxs')) + ad2d = reader2d.load_data() + ag2d = reader2d.get_geometry() + numpy.testing.assert_array_equal(ad2d.as_array(), ad2d_test.as_array(), 'Loaded image is not correct') + self.assertEqual(ag2d.geom_type, ag2d_test.geom_type, 'ImageGeometry.geom_type is not correct') + numpy.testing.assert_array_equal(ag2d.angles, ag2d_test.angles, 'ImageGeometry.angles is not correct') + self.assertEqual(ag2d.pixel_num_h, ag2d_test.pixel_num_h, 'ImageGeometry.pixel_num_h is not correct') + self.assertEqual(ag2d.pixel_size_h, ag2d_test.pixel_size_h, 'ImageGeometry.pixel_size_h is not correct') + self.assertEqual(ag2d.pixel_num_v, ag2d_test.pixel_num_v, 'ImageGeometry.pixel_num_v is not correct') + self.assertEqual(ag2d.pixel_size_v, ag2d_test.pixel_size_v, 'ImageGeometry.pixel_size_v is not correct') + + ag3d_test = AcquisitionGeometry(geom_type = 'cone', + dimension = '3D', + angles = numpy.array([0, 1]), + pixel_num_h = im_size, + pixel_size_h = 1, + pixel_num_v = im_size, + pixel_size_v = 1, + dist_source_center = 1, + dist_center_detector = 1, + channels = im_size) + ad3d_test = ag3d_test.allocate() + + reader3d = NEXUSDataReader() + reader3d.set_up(nexus_file = os.path.join(os.getcwd(), 'test_nexus_ad3d.nxs')) + ad3d = reader3d.load_data() + ag3d = reader3d.get_geometry() + + numpy.testing.assert_array_equal(ad3d.as_array(), ad3d_test.as_array(), 'Loaded image is not correct') + numpy.testing.assert_array_equal(ag3d.angles, ag3d_test.angles, 'AcquisitionGeometry.angles is not correct') + self.assertEqual(ag3d.geom_type, ag3d_test.geom_type, 'AcquisitionGeometry.geom_type is not correct') + self.assertEqual(ag3d.dimension, ag3d_test.dimension, 'AcquisitionGeometry.dimension is not correct') + self.assertEqual(ag3d.pixel_num_h, ag3d_test.pixel_num_h, 'AcquisitionGeometry.pixel_num_h is not correct') + self.assertEqual(ag3d.pixel_size_h, ag3d_test.pixel_size_h, 'AcquisitionGeometry.pixel_size_h is not correct') + self.assertEqual(ag3d.pixel_num_v, ag3d_test.pixel_num_v, 'AcquisitionGeometry.pixel_num_v is not correct') + self.assertEqual(ag3d.pixel_size_v, ag3d_test.pixel_size_v, 'AcquisitionGeometry.pixel_size_v is not correct') + self.assertEqual(ag3d.dist_source_center, ag3d_test.dist_source_center, 'AcquisitionGeometry.dist_source_center is not correct') + self.assertEqual(ag3d.dist_center_detector, ag3d_test.dist_center_detector, 'AcquisitionGeometry.dist_center_detector is not correct') + self.assertEqual(ag3d.channels, ag3d_test.channels, 'AcquisitionGeometry.channels is not correct') + + def tearDown(self): + os.remove(os.path.join(os.getcwd(), 'test_nexus_im.nxs')) + os.remove(os.path.join(os.getcwd(), 'test_nexus_ad2d.nxs')) + os.remove(os.path.join(os.getcwd(), 'test_nexus_ad3d.nxs')) + +if __name__ == '__main__': + unittest.main()
\ No newline at end of file diff --git a/Wrappers/Python/test/test_TestData.py b/Wrappers/Python/test/test_TestData.py new file mode 100755 index 0000000..3b26612 --- /dev/null +++ b/Wrappers/Python/test/test_TestData.py @@ -0,0 +1,19 @@ + +import numpy +from ccpi.framework import TestData +import os, sys +sys.path.append( os.path.dirname( os.path.abspath(__file__) ) ) +from testclass import CCPiTestClass + + +class TestTestData(CCPiTestClass): + def test_random_noise(self): + #loader = TestData(data_dir=os.path.join(sys.prefix, 'share','ccpi')) + #data_dir=os.path.join(os.path.dirname(__file__),'..', 'data') + loader = TestData() + + camera = loader.load(TestData.CAMERA) + + noisy_camera = TestData.random_noise(camera, seed=1) + norm = (camera-noisy_camera).norm() + self.assertAlmostEqual(norm, 48.881268, places=4) diff --git a/Wrappers/Python/test/testclass.py b/Wrappers/Python/test/testclass.py new file mode 100755 index 0000000..51b9f3f --- /dev/null +++ b/Wrappers/Python/test/testclass.py @@ -0,0 +1,41 @@ +import unittest +from ccpi.framework import ImageGeometry, ImageData, BlockDataContainer, DataContainer +import numpy + +def dt(steps): + return steps[-1] - steps[-2] + +class CCPiTestClass(unittest.TestCase): + def assertBlockDataContainerEqual(self, container1, container2): + print ("assert Block Data Container Equal") + self.assertTrue(issubclass(container1.__class__, container2.__class__)) + for col in range(container1.shape[0]): + if issubclass(container1.get_item(col).__class__, DataContainer): + print ("Checking col ", col) + self.assertNumpyArrayEqual( + container1.get_item(col).as_array(), + container2.get_item(col).as_array() + ) + else: + self.assertBlockDataContainerEqual(container1.get_item(col),container2.get_item(col)) + + def assertNumpyArrayEqual(self, first, second): + res = True + try: + numpy.testing.assert_array_equal(first, second) + except AssertionError as err: + res = False + print(err) + self.assertTrue(res) + + def assertNumpyArrayAlmostEqual(self, first, second, decimal=6): + res = True + try: + numpy.testing.assert_array_almost_equal(first, second, decimal) + except AssertionError as err: + res = False + print(err) + print("expected " , second) + print("actual " , first) + + self.assertTrue(res)
\ No newline at end of file |