synapse.ml.opencv package
Submodules
synapse.ml.opencv.ImageSetAugmenter module
- class synapse.ml.opencv.ImageSetAugmenter.ImageSetAugmenter(java_obj=None, flipLeftRight=True, flipUpDown=False, inputCol='image', outputCol='ImageSetAugmenter_bcd70db584cc_output')[source]
Bases:
ComplexParamsMixin
,JavaMLReadable
,JavaMLWritable
,JavaTransformer
- Parameters:
- flipLeftRight = Param(parent='undefined', name='flipLeftRight', doc='Symmetric Left-Right')
- flipUpDown = Param(parent='undefined', name='flipUpDown', doc='Symmetric Up-Down')
- inputCol = Param(parent='undefined', name='inputCol', doc='The name of the input column')
- outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')
synapse.ml.opencv.ImageTransformer module
- class synapse.ml.opencv.ImageTransformer.ImageTransformer(java_obj=None, autoConvertToColor=False, colorScaleFactor=None, ignoreDecodingErrors=False, inputCol='image', normalizeMean=None, normalizeStd=None, outputCol='ImageTransformer_61033c8182d7_output', stages=None, tensorChannelOrder='RGB', tensorElementType=None, toTensor=False)[source]
Bases:
_ImageTransformer
Transformer for common image processing stages.
- crop(x, y, height, width)[source]
Crops the image given the starting x,y coordinates and the width and height
- flip(flip_code=1)[source]
Flips the image :param int _sphinx_paramlinks_synapse.ml.opencv.ImageTransformer.ImageTransformer.flip.flip_code: a flag to specify how to flip the image - 0 means flipping around the x-axis (up-down) - positive value (for example, 1) means flipping around y-axis (left-right, default) - negative value (for example, -1) means flipping around both axes (diagonally) See OpenCV documentation for details.
- normalize(mean, std, color_scale_factor)[source]
Normalizes the image by multiplying the color_scale_factor, substracting mean and dividing by std
Module contents
SynapseML is an ecosystem of tools aimed towards expanding the distributed computing framework Apache Spark in several new directions. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK), LightGBM and OpenCV. These tools enable powerful and highly-scalable predictive and analytical models for a variety of datasources.
SynapseML also brings new networking capabilities to the Spark Ecosystem. With the HTTP on Spark project, users can embed any web service into their SparkML models. In this vein, SynapseML provides easy to use SparkML transformers for a wide variety of Microsoft Cognitive Services. For production grade deployment, the Spark Serving project enables high throughput, sub-millisecond latency web services, backed by your Spark cluster.
SynapseML requires Scala 2.12, Spark 3.0+, and Python 3.6+.