e26c8504cb
- Add Micrometer GAUGE Meter metrics. Add new counter.meter-type property to select between counter and gauge meter types. - Add tests and stock-exchange demo application. - Update counter-consumer README. Resolves: https://github.com/spring-cloud/stream-applications/issues/69
42 lines
2.9 KiB
Plaintext
42 lines
2.9 KiB
Plaintext
//tag::ref-doc[]
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= Semantic Segmentation Processor
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Image Semantic Segmentation based on the state-of-art https://github.com/tensorflow/models/tree/master/research/deeplab[DeepLab] Tensorflow model.
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The `Semantic Segmentation` is the process of associating each pixel of an image with a class label, (such as flower, person, road, sky, ocean, or car).
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Unlike the `Instance Segmentation`, which produces instance-aware region masks, the `Semantic Segmentation` produces class-aware masks.
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For implementing `Instance Segmentation` consult the https://github.com/spring-cloud/stream-applications/tree/master/functions/function/object-detection-function[Object Detection Service] instead.
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The `Semantic Segmentation Processor` uses the https://github.com/spring-cloud/stream-applications/tree/master/functions/function/semantic-segmentation-function[Semantic Segmentation Function] library and the https://github.com/spring-cloud/stream-applications/tree/master/functions/common/tensorflow-common[TensorFlow Service].
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=== Payload
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The incoming type is `byte[]`, and the content type is `application/octet-stream`. The processor processes the input `byte[]` image and outputs augmented `byte[]` image payload and json header.
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Processor's input is an image byte array, and the output is an augmented image byte array, and a JSON header `semantic_segmentation` in this format:
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```json
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[
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[ 0, 0, 0 ],
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[ 127, 127, 127 ],
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[ 255, 255, 255 ]
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...
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]
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```
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The output header json format represents the color pixel map computed from the input image.
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== Options
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//tag::configuration-properties[]
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$$semantic.segmentation.color-map-uri$$:: $$Every pre-trained model is based on certain object color maps. The pre-defined options are: - classpath:/colormap/citymap_colormap.json - classpath:/colormap/ade20k_colormap.json - classpath:/colormap/black_white_colormap.json - classpath:/colormap/mapillary_colormap.json$$ *($$String$$, default: `$$classpath:/colormap/citymap_colormap.json$$`)*
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$$semantic.segmentation.debug-output$$:: $$save output image inn the local debugOutputPath path.$$ *($$Boolean$$, default: `$$false$$`)*
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$$semantic.segmentation.debug-output-path$$:: $$<documentation missing>$$ *($$String$$, default: `$$semantic-segmentation-result.png$$`)*
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$$semantic.segmentation.mask-transparency$$:: $$The alpha color of the computed segmentation mask image.$$ *($$Float$$, default: `$$0.45$$`)*
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$$semantic.segmentation.model$$:: $$pre-trained tensorflow semantic segmentation model.$$ *($$String$$, default: `$$https://download.tensorflow.org/models/deeplabv3_mnv2_cityscapes_train_2018_02_05.tar.gz#frozen_inference_graph.pb$$`)*
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$$semantic.segmentation.output-type$$:: $$Specifies the output image type. You can return either the input image with the computed mask overlay, or the mask alone.$$ *($$OutputType$$, default: `$$<none>$$`, possible values: `blended`,`mask`)*
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//end::configuration-properties[]
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//end::ref-doc[]
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