def test_resize(self):
self.assertRaises(ValueError, imageIO.createResizeImageUDF, [1, 2, 3])
make_smaller = imageIO.createResizeImageUDF([4, 5]).func
imgAsRow = imageIO.imageArrayToStruct(array)
smallerImg = make_smaller(imgAsRow)
self.assertEqual(smallerImg.height, 4)
self.assertEqual(smallerImg.width, 5)
# Compare to PIL resizing
imgAsPIL = PIL.Image.fromarray(obj=imageIO._reverseChannels(array)).resize((5, 4))
smallerAry = imageIO._reverseChannels(np.asarray(imgAsPIL))
np.testing.assert_array_equal(smallerAry, imageIO.imageStructToArray(smallerImg))
# Test that resize with the same size is a no-op
sameImage = imageIO.createResizeImageUDF((imgAsRow.height, imgAsRow.width)).func(imgAsRow)
self.assertEqual(imgAsRow, sameImage)
# Test that we have a valid image schema (all fields are in)
for n in ImageSchema.imageSchema['image'].dataType.names:
smallerImg[n]
def test_resize(self):
self.assertRaises(ValueError, imageIO.createResizeImageUDF, [1, 2, 3])
make_smaller = imageIO.createResizeImageUDF([4, 5]).func
imgAsRow = imageIO.imageArrayToStruct(array)
smallerImg = make_smaller(imgAsRow)
self.assertEqual(smallerImg.height, 4)
self.assertEqual(smallerImg.width, 5)
# Compare to PIL resizing
imgAsPIL = PIL.Image.fromarray(
obj=imageIO._reverseChannels(array)).resize((5, 4))
smallerAry = imageIO._reverseChannels(np.asarray(imgAsPIL))
np.testing.assert_array_equal(smallerAry,
imageIO.imageStructToArray(smallerImg))
# Test that resize with the same size is a no-op
sameImage = imageIO.createResizeImageUDF(
(imgAsRow.height, imgAsRow.width)).func(imgAsRow)
self.assertEqual(imgAsRow, sameImage)
# Test that we have a valid image schema (all fields are in)
for n in ImageSchema.imageSchema['image'].dataType.names:
smallerImg[n]
def _transform(self, dataset):
modelGraphSpec = _buildTFGraphForName(self.getModelName(), self.getFeaturize())
inputCol = self.getInputCol()
resizedCol = "__sdl_imagesResized"
tfTransformer = TFImageTransformer(
channelOrder='BGR',
inputCol=resizedCol,
outputCol=self.getOutputCol(),
graph=modelGraphSpec["graph"],
inputTensor=modelGraphSpec["inputTensorName"],
outputTensor=modelGraphSpec["outputTensorName"],
outputMode=modelGraphSpec["outputMode"])
resizeUdf = createResizeImageUDF(modelGraphSpec["inputTensorSize"])
result = tfTransformer.transform(dataset.withColumn(resizedCol, resizeUdf(inputCol)))
return result.drop(resizedCol)
def _transform(self, dataset):
modelGraphSpec = _buildTFGraphForName(self.getModelName(), self.getFeaturize())
inputCol = self.getInputCol()
resizedCol = "__sdl_imagesResized"
tfTransformer = TFImageTransformer(
channelOrder='BGR',
inputCol=resizedCol,
outputCol=self.getOutputCol(),
graph=modelGraphSpec["graph"],
inputTensor=modelGraphSpec["inputTensorName"],
outputTensor=modelGraphSpec["outputTensorName"],
outputMode=modelGraphSpec["outputMode"])
resizeUdf = createResizeImageUDF(modelGraphSpec["inputTensorSize"])
result = tfTransformer.transform(dataset.withColumn(resizedCol, resizeUdf(inputCol)))
return result.drop(resizedCol)