def test_dataMeanDoesNotConsiderTestSet(self):
train = [
self.makeImage([10, 20, 40, 80]),
self.makeImage([20, 40, 40, 60])
]
valid = [
self.makeImage([10, 20, 40, 80]),
self.makeImage([20, 40, 40, 60])
]
test = [
self.makeImage([1000, 2000, 4000, 8000]),
self.makeImage([2000, 4000, 4000, 6000])
]
dataset = (train, valid, test)
classesNames = ['myname']
classes = [0]
expected = {}
expected['num_vis'] = 4
expected['data_shape'] = self.data_shape
expected['data_in_rows'] = True
expected['label_names'] = classesNames
expected['data_mean'] = arrayEqualsTo(
numpy.asarray([15, 30, 40, 70]).reshape(-1, 1))
self.assertEqual(expected,
self.target.build(dataset, classes, classesNames))
def test_ReadImageSwapBGR_to_RGB(self):
#openCV uses BGR order instead of RGB
mockCv = Mock()
imageFactory = Mock()
fileSystem = Mock()
fileSystem.joinPath.return_value = 'somefolder/somefile.jpg'
original = numpy.zeros((2, 2, 3))
original[:, :, 2] = [[0, 1], [2, 3]]
original[:, :, 1] = [[4, 5], [6, 7]]
original[:, :, 0] = [[8, 9], [10, 11]]
expected = numpy.zeros((2, 2, 3))
expected[:, :, 0] = [[0, 1], [2, 3]]
expected[:, :, 1] = [[4, 5], [6, 7]]
expected[:, :, 2] = [[8, 9], [10, 11]]
mockCv.imread = Mock()
mockCv.imread.return_value = original
target = OpenCVImgReader(imageFactory=imageFactory,
fileSystem=fileSystem,
openCV=mockCv)
target.read('somefolder', 'somefile.jpg')
fileSystem.joinPath.assert_called_with('somefolder', 'somefile.jpg')
mockCv.imread.assert_called_with('somefolder/somefile.jpg')
imageFactory.create.assert_called_with(arrayEqualsTo(expected),
'somefile.jpg')
def test_singleImage(self):
image = self.makeImage(numpy.arange(12), 0, 'file.jpg')
classes = [0]
expected = {}
expected['data'] = arrayEqualsTo(numpy.arange(12).reshape(-1,1))
expected['labels'] = [0]
expected['filenames'] = ['file.jpg']
target = SingleBatchBuilder()
self.assertEqual(expected,target.build([image], classes))
def test_multipleLabels(self):
image1 = self.makeImage(numpy.ones(12)*1, 0, 'file1.jpg')
image2 = self.makeImage(numpy.ones(12)*2, 3, 'file2.jpg')
image3 = self.makeImage(numpy.ones(12)*3, 5, 'file3.jpg')
classes = [0,3,5]
expected = {}
data = numpy.asarray([numpy.ones(12), numpy.ones(12)*2, numpy.ones(12)*3]).T
expected['data'] = arrayEqualsTo(data)
expected['labels'] = [0,1,2]
expected['filenames'] = ['file1.jpg','file2.jpg','file3.jpg']
target = SingleBatchBuilder()
result = target.build([image1, image2, image3], classes)
self.assertEqual(expected,result)
def test_OnlySelectedClassesAreStored(self):
train = self.makeZeroedImages(qty=2, size=4)
valid = self.makeZeroedImages(qty=2, size=4)
test = self.makeZeroedImages(qty=2, size=4)
dataset = (train, valid, test)
classesNames = ['class0', 'class1', 'class2', 'class3']
classes = [2,1]
expected = {}
expected['num_vis'] = 4
expected['data_in_rows'] = True
expected['data_shape'] = self.data_shape
expected['label_names'] = ['class2', 'class1']
expected['data_mean'] = arrayEqualsTo(numpy.zeros(4).reshape(-1,1))
self.assertEqual(expected, self.target.build(dataset, classes, classesNames))
def test_OnlySelectedClassesAreStored(self):
train = self.makeZeroedImages(qty=2, size=4)
valid = self.makeZeroedImages(qty=2, size=4)
test = self.makeZeroedImages(qty=2, size=4)
dataset = (train, valid, test)
classesNames = ['class0', 'class1', 'class2', 'class3']
classes = [2, 1]
expected = {}
expected['num_vis'] = 4
expected['data_in_rows'] = True
expected['data_shape'] = self.data_shape
expected['label_names'] = ['class2', 'class1']
expected['data_mean'] = arrayEqualsTo(numpy.zeros(4).reshape(-1, 1))
self.assertEqual(expected,
self.target.build(dataset, classes, classesNames))
def test_dataMeanDoesNotConsiderTestSet(self):
train = [self.makeImage([10, 20, 40, 80]),
self.makeImage([20, 40, 40, 60])]
valid = [self.makeImage([10, 20, 40, 80]),
self.makeImage([20, 40, 40, 60])]
test = [self.makeImage([1000, 2000, 4000, 8000]),
self.makeImage([2000, 4000, 4000, 6000])]
dataset = (train, valid, test)
classesNames = ['myname']
classes = [0]
expected = {}
expected['num_vis'] = 4
expected['data_shape'] = self.data_shape
expected['data_in_rows'] = True
expected['label_names'] = classesNames
expected['data_mean'] = arrayEqualsTo(numpy.asarray([15, 30, 40, 70]).reshape(-1,1))
self.assertEqual(expected, self.target.build(dataset, classes, classesNames))
