1 files changed, 137 insertions, 0 deletions
diff --git a/silx/opencl/test/test_image.py b/silx/opencl/test/test_image.py
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+++ b/silx/opencl/test/test_image.py
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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+#    Project: image manipulation in  OpenCL
+#             https://github.com/silx-kit/silx
+#
+# Permission is hereby granted, free of charge, to any person
+# obtaining a copy of this software and associated documentation
+# files (the "Software"), to deal in the Software without
+# restriction, including without limitation the rights to use,
+# copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the
+# Software is furnished to do so, subject to the following
+# conditions:
+#
+# The above copyright notice and this permission notice shall be
+# included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+# OTHER DEALINGS IN THE SOFTWARE.
+
+"""
+Simple test of image manipulation
+"""
+
+from __future__ import division, print_function
+
+__authors__ = ["Jérôme Kieffer"]
+__contact__ = "jerome.kieffer@esrf.eu"
+__license__ = "MIT"
+__copyright__ = "2017 European Synchrotron Radiation Facility, Grenoble, France"
+__date__ = "13/02/2018"
+
+import logging
+import numpy
+
+import unittest
+from ..common import ocl, _measure_workgroup_size
+if ocl:
+    import pyopencl
+    import pyopencl.array
+from ...test.utils import utilstest
+from ..image import ImageProcessing
+logger = logging.getLogger(__name__)
+try:
+    from PIL import Image
+except ImportError:
+    Image = None
+
+
+@unittest.skipUnless(ocl and Image, "PyOpenCl/Image is missing")
+class TestImage(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        super(TestImage, cls).setUpClass()
+        if ocl:
+            cls.ctx = ocl.create_context()
+            cls.lena = utilstest.getfile("lena.png")
+            cls.data = numpy.asarray(Image.open(cls.lena))
+            cls.ip = ImageProcessing(ctx=cls.ctx, template=cls.data, profile=True)
+
+    @classmethod
+    def tearDownClass(cls):
+        super(TestImage, cls).tearDownClass()
+        cls.ctx = None
+        cls.lena = None
+        cls.data = None
+        if logger.level <= logging.INFO:
+            logger.warning("\n".join(cls.ip.log_profile()))
+        cls.ip = None
+
+    def setUp(self):
+        if ocl is None:
+            return
+        self.data = numpy.asarray(Image.open(self.lena))
+
+    def tearDown(self):
+        self.img = self.data = None
+
+    @unittest.skipUnless(ocl, "pyopencl is missing")
+    def test_cast(self):
+        """
+        tests the cast kernel
+        """
+        res = self.ip.to_float(self.data)
+        self.assertEqual(res.shape, self.data.shape, "shape")
+        self.assertEqual(res.dtype, numpy.float32, "dtype")
+        self.assertEqual(abs(res - self.data).max(), 0, "content")
+
+    @unittest.skipUnless(ocl, "pyopencl is missing")
+    def test_normalize(self):
+        """
+        tests that all devices are working properly ...
+        """
+        tmp = pyopencl.array.empty(self.ip.ctx, self.data.shape, "float32")
+        res = self.ip.to_float(self.data, out=tmp)
+        res2 = self.ip.normalize(tmp, -100, 100, copy=False)
+        norm = (self.data.astype(numpy.float32) - self.data.min()) / (self.data.max() - self.data.min())
+        ref2 = 200 * norm - 100
+        self.assertLess(abs(res2 - ref2).max(), 3e-5, "content")
+
+    @unittest.skipUnless(ocl, "pyopencl is missing")
+    def test_histogram(self):
+        """
+        Test on a greyscaled image ... of Lena :)
+        """
+        lena_bw = (0.2126 * self.data[:, :, 0] +
+                   0.7152 * self.data[:, :, 1] +
+                   0.0722 * self.data[:, :, 2]).astype("int32")
+        ref = numpy.histogram(lena_bw, 255)
+        ip = ImageProcessing(ctx=self.ctx, template=lena_bw, profile=True)
+        res = ip.histogram(lena_bw, 255)
+        ip.log_profile()
+        delta = (ref[0] - res[0])
+        deltap = (ref[1] - res[1])
+        self.assertEqual(delta.sum(), 0, "errors are self-compensated")
+        self.assertLessEqual(abs(delta).max(), 1, "errors are small")
+        self.assertLessEqual(abs(deltap).max(), 3e-5, "errors on position are small: %s" % (abs(deltap).max()))
+
+
+def suite():
+    testSuite = unittest.TestSuite()
+    testSuite.addTest(TestImage("test_cast"))
+    testSuite.addTest(TestImage("test_normalize"))
+    testSuite.addTest(TestImage("test_histogram"))
+    return testSuite
+
+
+if __name__ == '__main__':
+    unittest.main(defaultTest="suite")