feature_list = 1;
-};
diff --git a/function/spring-object-detection-function/src/main/proto/string_int_label_map.proto b/function/spring-object-detection-function/src/main/proto/string_int_label_map.proto
deleted file mode 100644
index bc5ccf47..00000000
--- a/function/spring-object-detection-function/src/main/proto/string_int_label_map.proto
+++ /dev/null
@@ -1,25 +0,0 @@
-// Message to store the mapping from class label strings to class id. Datasets
-// use string labels to represent classes while the object detection framework
-// works fromMemory class ids. This message maps them so they can be converted back
-// and forth as needed.
-syntax = "proto2";
-
-package org.springframework.cloud.fn.object.detection.protos;
-
-message StringIntLabelMapItem {
- // String name. The most common practice is to set this to a MID or synsets
- // id. Synset: a set of synonyms that share a common meaning.
- // https://en.wikipedia.org/wiki/WordNet
- optional string name = 1;
-
- // Integer id that maps to the string name above. Label ids should start
- // from 1.
- optional int32 id = 2;
-
- // Human readable string label.
- optional string display_name = 3;
-};
-
-message StringIntLabelMap {
- repeated StringIntLabelMapItem item = 1;
-};
diff --git a/function/spring-object-detection-function/src/main/resources/images/object-detection-augmented.jpg b/function/spring-object-detection-function/src/main/resources/images/object-detection-augmented.jpg
deleted file mode 100644
index f17adfd8..00000000
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diff --git a/function/spring-object-detection-function/src/main/resources/images/object-detection-segmentation-augmented.jpg b/function/spring-object-detection-function/src/main/resources/images/object-detection-segmentation-augmented.jpg
deleted file mode 100644
index e9466f1e..00000000
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diff --git a/function/spring-object-detection-function/src/main/resources/images/object-detection.jpg b/function/spring-object-detection-function/src/main/resources/images/object-detection.jpg
deleted file mode 100644
index 9eb325ac..00000000
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diff --git a/function/spring-object-detection-function/src/main/resources/images/object_detection_1.jpg b/function/spring-object-detection-function/src/main/resources/images/object_detection_1.jpg
deleted file mode 100644
index 913b2e6d..00000000
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diff --git a/function/spring-object-detection-function/src/test/java/org/springframework/cloud/fn/object/detection/examples/ExampleInstanceSegmentation.java b/function/spring-object-detection-function/src/test/java/org/springframework/cloud/fn/object/detection/examples/ExampleInstanceSegmentation.java
deleted file mode 100644
index 0b5d892c..00000000
--- a/function/spring-object-detection-function/src/test/java/org/springframework/cloud/fn/object/detection/examples/ExampleInstanceSegmentation.java
+++ /dev/null
@@ -1,102 +0,0 @@
-/*
- * Copyright 2020-2020 the original author or authors.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * https://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.springframework.cloud.fn.object.detection.examples;
-
-import java.io.File;
-import java.io.FileOutputStream;
-import java.io.IOException;
-import java.util.List;
-
-import org.apache.commons.io.IOUtils;
-
-import org.springframework.cloud.fn.common.tensorflow.deprecated.GraphicsUtils;
-import org.springframework.cloud.fn.common.tensorflow.deprecated.JsonMapperFunction;
-import org.springframework.cloud.fn.object.detection.ObjectDetectionImageAugmenter;
-import org.springframework.cloud.fn.object.detection.ObjectDetectionService;
-import org.springframework.cloud.fn.object.detection.domain.ObjectDetection;
-import org.springframework.core.io.DefaultResourceLoader;
-import org.springframework.core.io.ResourceLoader;
-
-/**
- * 4 of the pre-trained model in the model zoo
- * (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md)
- * can also compute the masks of the detected objects, providing instance segmentation.
- *
- * Here are the models that can be used for instance segmentation.
- *
- * mask_rcnn_inception_resnet_v2_atrous_coco 771 36 Masks mask_rcnn_inception_v2_coco 79
- * 25 Masks mask_rcnn_resnet101_atrous_coco 470 33 Masks mask_rcnn_resnet50_atrous_coco
- * 343 29 Masks
- *
- * @author Christian Tzolov
- */
-public class ExampleInstanceSegmentation {
-
- public static void main(String[] args) throws IOException {
-
- ResourceLoader resourceLoader = new DefaultResourceLoader();
-
- // You can download pre-trained models directly from the zoo:
- // https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
- // Just use the notation #
- // For performance reasons you may consider downloading the model locally and use
- // the file:/ URI instead!
- String model = "https://download.tensorflow.org/models/object_detection/mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28.tar.gz#frozen_inference_graph.pb";
-
- // All labels for the pre-trained models are available at:
- // https://github.com/tensorflow/models/tree/master/research/object_detection/data
- // Use the labels applicable for the model.
- // Also, for performance reasons you may consider to download the labels and load
- // them from file: instead.
- String labels = "https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/mscoco_label_map.pbtxt";
-
- // You can cache the TF model on the local file system to improve the bootstrap
- // performance on consecutive runs!
- boolean CACHE_TF_MODEL = true;
-
- // For the pre-trained models fromMemory mask you can set the
- // INSTANCE_SEGMENTATION to enable object instance segmentation as well
- boolean INSTANCE_SEGMENTATION = true;
-
- // Only object fromMemory confidence above the threshold are returned
- float CONFIDENCE_THRESHOLD = 0.4f;
-
- ObjectDetectionService detectionService = new ObjectDetectionService(model, labels, CONFIDENCE_THRESHOLD,
- INSTANCE_SEGMENTATION, CACHE_TF_MODEL);
-
- // You can use file:, http: or classpath: to provide the path to the input image.
- byte[] image = GraphicsUtils.loadAsByteArray("classpath:/images/object-detection.jpg");
-
- // Returns a list ObjectDetection domain classes to allow programmatic accesses to
- // the detected objects's metadata
- List detectedObjects = detectionService.detect(image);
-
- // Get JSON representation of the detected objects
- String jsonObjectDetections = new JsonMapperFunction().apply(detectedObjects);
- System.out.println(jsonObjectDetections);
-
- // Draw the detected object metadata on top of the original image and store the
- // result
- byte[] annotatedImage = new ObjectDetectionImageAugmenter(INSTANCE_SEGMENTATION).apply(image, detectedObjects);
- File projectDir = new File("functions/function/object-detection-function");
- File output = new File(projectDir, "target/object-detection-segmentation-augmented.jpg");
- IOUtils.write(annotatedImage, new FileOutputStream(output));
- System.out.println("Created:" + output.getPath());
- }
-
-}
diff --git a/function/spring-object-detection-function/src/test/java/org/springframework/cloud/fn/object/detection/examples/ExampleObjectDetection.java b/function/spring-object-detection-function/src/test/java/org/springframework/cloud/fn/object/detection/examples/ExampleObjectDetection.java
deleted file mode 100644
index e4df6e11..00000000
--- a/function/spring-object-detection-function/src/test/java/org/springframework/cloud/fn/object/detection/examples/ExampleObjectDetection.java
+++ /dev/null
@@ -1,98 +0,0 @@
-/*
- * Copyright 2020-2020 the original author or authors.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * https://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.springframework.cloud.fn.object.detection.examples;
-
-import java.io.FileOutputStream;
-import java.io.IOException;
-import java.io.InputStream;
-import java.util.List;
-
-import org.apache.commons.io.IOUtils;
-
-import org.springframework.cloud.fn.common.tensorflow.deprecated.JsonMapperFunction;
-import org.springframework.cloud.fn.object.detection.ObjectDetectionImageAugmenter;
-import org.springframework.cloud.fn.object.detection.ObjectDetectionService;
-import org.springframework.cloud.fn.object.detection.domain.ObjectDetection;
-import org.springframework.core.io.DefaultResourceLoader;
-import org.springframework.util.StreamUtils;
-
-/**
- * @author Christian Tzolov
- */
-public class ExampleObjectDetection {
-
- public static void main(String[] args) throws IOException {
-
- // You can download pre-trained models directly from the zoo:
- // https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
- // Just use the notation #
- // For performance reasons you may consider downloading the model locally and use
- // the file:/ URI instead!
- String model = "https://download.tensorflow.org/models/object_detection/faster_rcnn_nas_coco_2018_01_28.tar.gz#frozen_inference_graph.pb";
- // Resource model =
- // resourceLoader.getResource("https://download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_fgvc_2018_07_19.tar.gz#frozen_inference_graph.pb");
- // Resource model =
- // resourceLoader.getResource("https://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_fgvc_2018_07_19.tar.gz#frozen_inference_graph.pb");
-
- // All labels for the pre-trained models are available at:
- // https://github.com/tensorflow/models/tree/master/research/object_detection/data
- // Use the labels applicable for the model.
- // Also, for performance reasons you may consider to download the labels and load
- // them from file: instead.
- String labels = "https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/mscoco_label_map.pbtxt";
- // Resource labels =
- // resourceLoader.getResource("https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/fgvc_2854_classes_label_map.pbtxt");
-
- // You can cache the TF model on the local file system to improve the bootstrap
- // performance on consecutive runs!
- boolean CACHE_TF_MODEL = true;
-
- // For the pre-trained models fromMemory mask you can set the
- // INSTANCE_SEGMENTATION to enable object instance segmentation as well
- boolean NO_INSTANCE_SEGMENTATION = false;
-
- // Only object fromMemory confidence above the threshold are returned
- float CONFIDENCE_THRESHOLD = 0.4f;
-
- ObjectDetectionService detectionService = new ObjectDetectionService(model, labels, CONFIDENCE_THRESHOLD,
- NO_INSTANCE_SEGMENTATION, CACHE_TF_MODEL);
-
- // You can use file:, http: or classpath: to provide the path to the input image.
- String inputImageUri = "classpath:/images/object-detection.jpg";
- try (InputStream is = new DefaultResourceLoader().getResource(inputImageUri).getInputStream()) {
-
- byte[] image = StreamUtils.copyToByteArray(is);
-
- // Returns a list ObjectDetection domain classes to allow programmatic
- // accesses to the detected objects's metadata
- List detectedObjects = detectionService.detect(image);
-
- // Get JSON representation of the detected objects
- String jsonObjectDetections = new JsonMapperFunction().apply(detectedObjects);
- System.out.println(jsonObjectDetections);
-
- // Draw the detected object metadata on top of the original image and store
- // the result
- byte[] annotatedImage = new ObjectDetectionImageAugmenter(NO_INSTANCE_SEGMENTATION).apply(image,
- detectedObjects);
- IOUtils.write(annotatedImage,
- new FileOutputStream("./object-detection-function/target/object-detection-augmented.jpg"));
- }
- }
-
-}
diff --git a/function/spring-object-detection-function/src/test/java/org/springframework/cloud/fn/object/detection/examples/SimpleExample.java b/function/spring-object-detection-function/src/test/java/org/springframework/cloud/fn/object/detection/examples/SimpleExample.java
deleted file mode 100644
index 2d1d41ed..00000000
--- a/function/spring-object-detection-function/src/test/java/org/springframework/cloud/fn/object/detection/examples/SimpleExample.java
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright 2020-2020 the original author or authors.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * https://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.springframework.cloud.fn.object.detection.examples;
-
-import java.util.List;
-
-import org.springframework.cloud.fn.object.detection.ObjectDetectionService;
-import org.springframework.cloud.fn.object.detection.domain.ObjectDetection;
-
-/**
- * @author Christian Tzolov
- */
-public class SimpleExample {
-
- public static void main(String[] args) {
- // Select a pre-trained model from the model zoo:
- // https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
- // Just use the notation #
- String model = "https://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync_2018_07_03.tar.gz#frozen_inference_graph.pb";
-
- // All labels for the pre-trained models are available at:
- // https://github.com/tensorflow/models/tree/master/research/object_detection/data
- String labels = "https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/mscoco_label_map.pbtxt";
-
- ObjectDetectionService detectionService = new ObjectDetectionService(model, labels, 0.4f, // Only
- // object
- // fromMemory
- // confidence
- // above
- // the
- // threshold
- // are
- // returned.
- // Confidence
- // range
- // is
- // [0,
- // 1].
- false, // No instance segmentation
- true); // cache the TF model locally
-
- // You can use file:, http: or classpath: to provide the path to the input image.
- List detectedObjects = detectionService.detect("classpath:/images/object-detection.jpg");
- detectedObjects.stream().map(o -> o.toString()).forEach(System.out::println);
- }
-
-}
diff --git a/function/spring-semantic-segmentation-function/README.adoc b/function/spring-semantic-segmentation-function/README.adoc
deleted file mode 100644
index 6876b5a6..00000000
--- a/function/spring-semantic-segmentation-function/README.adoc
+++ /dev/null
@@ -1,104 +0,0 @@
-:images-asciidoc: https://raw.githubusercontent.com/spring-cloud/stream-applications/master/functions/function/semantic-segmentation-function/src/main/resources/images/
-# Semantic Segmentation
-
-[.lead]
-Image Semantic Segmentation based on the state-of-art https://github.com/tensorflow/models/tree/master/research/deeplab[DeepLab] Tensorflow model.
-
-[cols="1,2", frame=none, grid=none]
-|===
-| image:{images-asciidoc}/VikiMaxiAdi-all.png[width=100%]
-|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).
-Unlike the `Instance Segmentation`, which produces instance-aware region masks, the `Semantic Segmentation` produces class-aware masks.
-For implementing `Instance Segmentation` consult the https://github.com/spring-cloud/stream-applications/tree/master/functions/function/object-detection-function[Object Detection Service] instead.
-|===
-
-The https://github.com/spring-cloud/stream-applications/blob/master/functions/common/tensorflow-common/src/main/java/org/springframework/cloud/fn/common/tensorflow/deprecated/JsonMapperFunction.java[JsonMapperFunction] permits
-converting the `List` into JSON objects, and the
-https://github.com/spring-cloud/stream-applications/blob/master/functions/function/object-detection-function/src/main/java/org/springframework/cloud/fn/object/detection/ObjectDetectionImageAugmenter.java[ObjectDetectionImageAugmenter]
-allow to augment the input image with the detected bounding boxes and segmentation masks.
-
-## Usage
-
-Add the `semantic-segmentation` dependency to your pom (_use the latest version available_):
-
-[source,xml]
-----
-
- org.springframework.cloud.fn
- semantic-segmentation-function
- ${revision}
-
-
-
- org.springframework.cloud.fn
- object-detection-function
- ${revision}
-
-----
-
-Following snippet demos how to use the PASCAL VOC model to apply mask to an input image
-
-[source,java,linenums]
-----
-
-SemanticSegmentation segmentationService = new SemanticSegmentation(
- "https://download.tensorflow.org/models/deeplabv3_mnv2_pascal_trainval_2018_01_29.tar.gz#frozen_inference_graph.pb", // <1>
- true); // <2>
-
-byte[] inputImage = GraphicsUtils.loadAsByteArray("classpath:/images/VikiMaxiAdi.jpg"); // <3>
-
-byte[] imageMask = segmentationService.masksAsImage(inputImage); // <4>
-BufferedImage bi = ImageIO.read(new ByteArrayInputStream(imageMask));
-ImageIO.write(bi, "png", new FileOutputStream("./semantic-segmentation-function/target/VikiMaxiAdi_masks.png"));
-
-byte[] augmentedImage = segmentationService.augment(inputImage); // <5>
-IOUtils.write(augmentedImage, new FileOutputStream("./semantic-segmentation-function/target/VikiMaxiAdi_augmented.jpg"));
-----
-<1> Download the PASCAL 2012 trained model directly from the web. The `frozen_inference_graph.pb` is the name of the model
-file inside the `tar.gz` archive.
-<2> Cache the downloaded model locally
-<3> Load the input image as byte array
-<4> Read get the segmentation mask as separate image
-<5> Blend the segmentation mask on top of the original image
-
-## Models
-
-Based on the training datasets, three groups of pre-trained models provided:
-
-[cols="1,2", frame=none, grid=none]
-|===
-| image:{images-asciidoc}/VikiMaxiAdi-all.png[width=100%]
-| https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md#deeplab-models-trained-on-pascal-voc-2012[DeepLab models trained on PASCAL VOC 2012]
-
-| image:{images-asciidoc}/cityscape-all-small.png[width=100%]
-| https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md#deeplab-models-trained-on-cityscapes[DeepLab models trained on Cityscapes]
-
-| image:{images-asciidoc}/ADE20K-all-small.png[width=100%]
-| https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md#deeplab-models-trained-on-ade20k[DeepLab models trained on ADE20K]
-|===
-
-Select the model you want to use, copy its archive download Url and add a `#frozen_inference_graph.pb` fragment to it.
-Later fragment is the frozen model's file name inside the archive
-
-TIP: Download the archive and uncompress the `frozen_inference_graph.pb` for required model. Then use the `file://` URI schema.
-
-Also, convenience there are a couple of models, extracted from the archive and uploaded to bintray:
-
-[cols=2*,, frame=none, grid=none]
-|===
-|PASCAL VOC 2012 (default)
-|https://dl.bintray.com/big-data/generic/deeplabv3_mnv2_pascal_train_aug_frozen_inference_graph.pb
-
-|CITYSCAPE
-|https://dl.bintray.com/big-data/generic/deeplabv3_mnv2_cityscapes_train_2018_02_05_frozen_inference_graph.pb
-
-|ADE20K
-|https://dl.bintray.com/big-data/generic/deeplabv3_xception_ade20k_train_2018_05_29_frozen_inference_graph.pb
-|===
-
-## References:
-[.small]
-* https://ai.googleblog.com/2018/03/semantic-image-segmentation-with.html[Semantic Image Segmentation with DeepLab in TensorFlow]
-* https://github.com/tensorflow/models/tree/master/research/deeplab[DeepLab Project]
-* https://medium.freecodecamp.org/how-to-use-deeplab-in-tensorflow-for-object-segmentation-using-deep-learning-a5777290ab6b[How to re-train DeepLab Segmentation models using Transfer Learning]
-
diff --git a/function/spring-semantic-segmentation-function/build.gradle b/function/spring-semantic-segmentation-function/build.gradle
deleted file mode 100644
index 498498ef..00000000
--- a/function/spring-semantic-segmentation-function/build.gradle
+++ /dev/null
@@ -1,3 +0,0 @@
-dependencies {
- api project(':spring-tensorflow-common')
-}
diff --git a/function/spring-semantic-segmentation-function/src/main/java/org/springframework/cloud/fn/semantic/segmentation/NativeImageUtils.java b/function/spring-semantic-segmentation-function/src/main/java/org/springframework/cloud/fn/semantic/segmentation/NativeImageUtils.java
deleted file mode 100644
index a8e5be3a..00000000
--- a/function/spring-semantic-segmentation-function/src/main/java/org/springframework/cloud/fn/semantic/segmentation/NativeImageUtils.java
+++ /dev/null
@@ -1,114 +0,0 @@
-/*
- * Copyright 2020-2020 the original author or authors.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * https://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.springframework.cloud.fn.semantic.segmentation;
-
-import java.util.Arrays;
-
-import org.tensorflow.Operand;
-import org.tensorflow.op.Ops;
-import org.tensorflow.op.core.Concat;
-import org.tensorflow.op.core.ExpandDims;
-import org.tensorflow.op.core.Gather;
-import org.tensorflow.op.core.Range;
-import org.tensorflow.op.core.ReduceMax;
-import org.tensorflow.op.core.Tile;
-import org.tensorflow.op.math.Add;
-import org.tensorflow.op.math.Mul;
-import org.tensorflow.op.math.Sub;
-
-/**
- * @author Christian Tzolov
- */
-public final class NativeImageUtils {
-
- private NativeImageUtils() {
- }
-
- /**
- * grayscaleToRgb.
- * https://github.com/tensorflow/tensorflow/blob/r1.13/tensorflow/python/ops/image_ops_impl.py#L1536
- */
- public static Operand grayscaleToRgb(Ops tf, Operand images) {
- ExpandDims rank_1 = tf.expandDims(tf.math.sub(tf.rank(images), tf.constant(1)), tf.constant(0));
- // Create once 1D vector of the shape defined by the rank_1.
- // E.g. for rank [2] will produce matrix [1, 1]. For [3] rank will produce a cube
- // [1, 1, 1]
- Add ones = tf.math.add(tf.zeros(rank_1, Integer.class), tf.constant(1));
- // Convert scalar 3 into 1D array [3]
- ExpandDims channelsAs1D = tf.expandDims(tf.constant(3), tf.constant(0));
- Concat shapeList = tf.concat(Arrays.asList(ones, channelsAs1D), tf.constant(0));
- Tile tile = tf.withName("grayscaleToRgb").tile(images, shapeList);
- return tile;
- }
-
- public static Operand normalizeMask(Ops tf, Operand mask, float newValue) {
- // generate array representing the axis indexes.
- // For example of tensor of rank K the axisRange is {0, 1, 2 ...K}
- Range axisRange = tf.range(tf.constant(0), // from
- tf.dtypes.cast(tf.rank(mask), Integer.class), // to
- tf.constant(1)); // step
-
- ReduceMax max = tf.reduceMax(mask, axisRange);
- // Mul input2Float1 = tf.math.mul(tf.math.div(input2Float, max),
- // tf.constant(1f));
- Mul normalizedMask = tf.math.mul(tf.math.div(mask, max), tf.constant(newValue));
-
- return normalizedMask;
- }
-
- /**
- * Alpha Blending . https://en.wikipedia.org/wiki/Alpha_compositing#Alpha_blending
- */
- public static Operand alphaBlending(Ops tf, Operand srcRgb, Operand dstRgb,
- Operand srcAlpha) {
- Sub alpha = tf.math.sub(tf.onesLike(srcRgb), srcAlpha);
- Mul src = tf.math.mul(srcRgb, alpha);
- Mul dst = tf.math.mul(dstRgb, tf.math.sub(tf.constant(1.0f), alpha));
- Add out = tf.math.add(dst, src);
-
- // Mul out = tf.math.mul(srcRgbNormalized, dstRgb);
- // Squeeze squeeze = tf.withName("squeeze").squeeze(out,
- // Squeeze.axis(Arrays.asList(0L)));
-
- return out;
- }
-
- /**
- * The mask can contain label values larger than the list of colors provided in the
- * color map. To avoid out-of-index errors we will "normalize" the label values in the
- * mask to MOD max-color-table-value.
- * @param tf - tensorflow
- * @param colorTable Color map of shape [n, 3]. n is the count of label entries and 3
- * is the RGB color assigned to that label.
- * @param mask Mask of shape [h, w] containing label vales.
- * @return Mask of shape [h, w] fromMemory values normalized between [0, n]
- */
- public static Operand normalizeMaskLabels(Ops tf, Operand colorTable, Operand mask) {
- // The mask can contain label values larger than the list of colors provided in
- // the color map.
- // To avoid out-of-index errors we will "normalize" the label values in the mask
- // to MOD max-color-table-value.
- Sub colorTableShape = tf.math.sub(tf.shape(colorTable, Long.class), tf.constant(1L));
- // Color tables have shape [N, 3], where N is the count of label entries.
- // Therefore the max label id is (N - 1).
- Gather colorTableSize = tf.gather(colorTableShape, tf.constant(new int[] { 0 }), tf.constant(0));
- // Normalize the label values in the mask so they don't exceed the max value in
- // the color map.
- return tf.math.mod(mask, colorTableSize);
- }
-
-}
diff --git a/function/spring-semantic-segmentation-function/src/main/java/org/springframework/cloud/fn/semantic/segmentation/SegmentationColorMap.java b/function/spring-semantic-segmentation-function/src/main/java/org/springframework/cloud/fn/semantic/segmentation/SegmentationColorMap.java
deleted file mode 100644
index fd71a9e6..00000000
--- a/function/spring-semantic-segmentation-function/src/main/java/org/springframework/cloud/fn/semantic/segmentation/SegmentationColorMap.java
+++ /dev/null
@@ -1,162 +0,0 @@
-/*
- * Copyright 2020-2020 the original author or authors.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * https://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.springframework.cloud.fn.semantic.segmentation;
-
-import java.io.InputStream;
-import java.util.Arrays;
-
-import com.fasterxml.jackson.databind.ObjectMapper;
-
-import org.springframework.core.io.DefaultResourceLoader;
-
-/**
- *
- * Visualizes the segmentation results via specified color map. Color maps helping to
- * visualize the semantic segmentation results for the different datasets.
- *
- * Supported colormaps are: - ADE20K
- * (http://groups.csail.mit.edu/vision/datasets/ADE20K/). - Cityscapes dataset
- * (https://www.cityscapes-dataset.com). - Mapillary Vistas
- * (https://research.mapillary.com). - PASCAL VOC 2012
- * (http://host.robots.ox.ac.uk/pascal/VOC/).
- *
- * Based on:
- * https://github.com/tensorflow/models/blob/master/research/deeplab/utils/get_dataset_colormap.py
- *
- * @author Christian Tzolov
- */
-public final class SegmentationColorMap {
-
- private SegmentationColorMap() {
-
- }
-
- /** MAPILLARY_COLORMAP . */
- public static final int[][] MAPILLARY_COLORMAP = new int[][] { { 165, 42, 42 }, { 0, 192, 0 }, { 196, 196, 196 },
- { 190, 153, 153 }, { 180, 165, 180 }, { 102, 102, 156 }, { 102, 102, 156 }, { 128, 64, 255 },
- { 140, 140, 200 }, { 170, 170, 170 }, { 250, 170, 160 }, { 96, 96, 96 }, { 230, 150, 140 },
- { 128, 64, 128 }, { 110, 110, 110 }, { 244, 35, 232 }, { 150, 100, 100 }, { 70, 70, 70 }, { 150, 120, 90 },
- { 220, 20, 60 }, { 255, 0, 0 }, { 255, 0, 0 }, { 255, 0, 0 }, { 200, 128, 128 }, { 255, 255, 255 },
- { 64, 170, 64 }, { 128, 64, 64 }, { 70, 130, 180 }, { 255, 255, 255 }, { 152, 251, 152 }, { 107, 142, 35 },
- { 0, 170, 30 }, { 255, 255, 128 }, { 250, 0, 30 }, { 0, 0, 0 }, { 220, 220, 220 }, { 170, 170, 170 },
- { 222, 40, 40 }, { 100, 170, 30 }, { 40, 40, 40 }, { 33, 33, 33 }, { 170, 170, 170 }, { 0, 0, 142 },
- { 170, 170, 170 }, { 210, 170, 100 }, { 153, 153, 153 }, { 128, 128, 128 }, { 0, 0, 142 }, { 250, 170, 30 },
- { 192, 192, 192 }, { 220, 220, 0 }, { 180, 165, 180 }, { 119, 11, 32 }, { 0, 0, 142 }, { 0, 60, 100 },
- { 0, 0, 142 }, { 0, 0, 90 }, { 0, 0, 230 }, { 0, 80, 100 }, { 128, 64, 64 }, { 0, 0, 110 }, { 0, 0, 70 },
- { 0, 0, 192 }, { 32, 32, 32 }, { 0, 0, 0 }, { 0, 0, 0 }, };
-
- /**
- * Label colormap used in ADE20K segmentation benchmark.
- */
- public static final int[][] ADE20K_COLORMAP = new int[][] { { 0, 0, 0 }, { 120, 120, 120 }, { 180, 120, 120 },
- { 6, 230, 230 }, { 80, 50, 50 }, { 4, 200, 3 }, { 120, 120, 80 }, { 140, 140, 140 }, { 204, 5, 255 },
- { 230, 230, 230 }, { 4, 250, 7 }, { 224, 5, 255 }, { 235, 255, 7 }, { 150, 5, 61 }, { 120, 120, 70 },
- { 8, 255, 51 }, { 255, 6, 82 }, { 143, 255, 140 }, { 204, 255, 4 }, { 255, 51, 7 }, { 204, 70, 3 },
- { 0, 102, 200 }, { 61, 230, 250 }, { 255, 6, 51 }, { 11, 102, 255 }, { 255, 7, 71 }, { 255, 9, 224 },
- { 9, 7, 230 }, { 220, 220, 220 }, { 255, 9, 92 }, { 112, 9, 255 }, { 8, 255, 214 }, { 7, 255, 224 },
- { 255, 184, 6 }, { 10, 255, 71 }, { 255, 41, 10 }, { 7, 255, 255 }, { 224, 255, 8 }, { 102, 8, 255 },
- { 255, 61, 6 }, { 255, 194, 7 }, { 255, 122, 8 }, { 0, 255, 20 }, { 255, 8, 41 }, { 255, 5, 153 },
- { 6, 51, 255 }, { 235, 12, 255 }, { 160, 150, 20 }, { 0, 163, 255 }, { 140, 140, 140 }, { 250, 10, 15 },
- { 20, 255, 0 }, { 31, 255, 0 }, { 255, 31, 0 }, { 255, 224, 0 }, { 153, 255, 0 }, { 0, 0, 255 },
- { 255, 71, 0 }, { 0, 235, 255 }, { 0, 173, 255 }, { 31, 0, 255 }, { 11, 200, 200 }, { 255, 82, 0 },
- { 0, 255, 245 }, { 0, 61, 255 }, { 0, 255, 112 }, { 0, 255, 133 }, { 255, 0, 0 }, { 255, 163, 0 },
- { 255, 102, 0 }, { 194, 255, 0 }, { 0, 143, 255 }, { 51, 255, 0 }, { 0, 82, 255 }, { 0, 255, 41 },
- { 0, 255, 173 }, { 10, 0, 255 }, { 173, 255, 0 }, { 0, 255, 153 }, { 255, 92, 0 }, { 255, 0, 255 },
- { 255, 0, 245 }, { 255, 0, 102 }, { 255, 173, 0 }, { 255, 0, 20 }, { 255, 184, 184 }, { 0, 31, 255 },
- { 0, 255, 61 }, { 0, 71, 255 }, { 255, 0, 204 }, { 0, 255, 194 }, { 0, 255, 82 }, { 0, 10, 255 },
- { 0, 112, 255 }, { 51, 0, 255 }, { 0, 194, 255 }, { 0, 122, 255 }, { 0, 255, 163 }, { 255, 153, 0 },
- { 0, 255, 10 }, { 255, 112, 0 }, { 143, 255, 0 }, { 82, 0, 255 }, { 163, 255, 0 }, { 255, 235, 0 },
- { 8, 184, 170 }, { 133, 0, 255 }, { 0, 255, 92 }, { 184, 0, 255 }, { 255, 0, 31 }, { 0, 184, 255 },
- { 0, 214, 255 }, { 255, 0, 112 }, { 92, 255, 0 }, { 0, 224, 255 }, { 112, 224, 255 }, { 70, 184, 160 },
- { 163, 0, 255 }, { 153, 0, 255 }, { 71, 255, 0 }, { 255, 0, 163 }, { 255, 204, 0 }, { 255, 0, 143 },
- { 0, 255, 235 }, { 133, 255, 0 }, { 255, 0, 235 }, { 245, 0, 255 }, { 255, 0, 122 }, { 255, 245, 0 },
- { 10, 190, 212 }, { 214, 255, 0 }, { 0, 204, 255 }, { 20, 0, 255 }, { 255, 255, 0 }, { 0, 153, 255 },
- { 0, 41, 255 }, { 0, 255, 204 }, { 41, 0, 255 }, { 41, 255, 0 }, { 173, 0, 255 }, { 0, 245, 255 },
- { 71, 0, 255 }, { 122, 0, 255 }, { 0, 255, 184 }, { 0, 92, 255 }, { 184, 255, 0 }, { 0, 133, 255 },
- { 255, 214, 0 }, { 25, 194, 194 }, { 102, 255, 0 }, { 92, 0, 255 }, };
-
- /** BLACK_WHITE_COLORMAP . */
- public static int[][] BLACK_WHITE_COLORMAP = new int[][] { { 0, 0, 0 }, { 127, 127, 127 }, { 255, 255, 255 }, };
-
- /** CITYMAP_COLORMAP . */
- public static final int[][] CITYMAP_COLORMAP = new int[255][3];
-
- static {
-
- // Initialize citymap
- int[][] _CITYMAP_COLORMAP = new int[][] { { 128, 64, 128 }, { 244, 35, 232 }, { 70, 70, 70 }, { 102, 102, 156 },
- { 190, 153, 153 }, { 153, 153, 153 }, { 250, 170, 30 }, { 220, 220, 0 }, { 107, 142, 35 },
- { 152, 251, 152 }, { 70, 130, 180 }, { 220, 20, 60 }, { 255, 0, 0 }, { 0, 0, 142 }, { 0, 0, 70 },
- { 0, 60, 100 }, { 0, 80, 100 }, { 0, 0, 230 }, { 119, 11, 32 } };
-
- for (int i = 0; i < _CITYMAP_COLORMAP.length; i++) {
- System.arraycopy(_CITYMAP_COLORMAP[i], 0, CITYMAP_COLORMAP[i], 0, _CITYMAP_COLORMAP[i].length);
- }
- }
-
- public static int[][] loadColorMap(String resourceUri) {
- try {
- InputStream colorMapIs = new DefaultResourceLoader().getResource(resourceUri).getInputStream();
- ColorMap colorMap = new ObjectMapper().readValue(colorMapIs, ColorMap.class);
- return colorMap.getColormap();
- }
- catch (Exception exception) {
- throw new RuntimeException(exception);
- }
- }
-
- public static class ColorMap {
-
- private String name;
-
- private String info;
-
- private int[][] colormap;
-
- public String getName() {
- return name;
- }
-
- public void setName(String name) {
- this.name = name;
- }
-
- public String getInfo() {
- return info;
- }
-
- public void setInfo(String info) {
- this.info = info;
- }
-
- public int[][] getColormap() {
- return colormap;
- }
-
- public void setColormap(int[][] colormap) {
- this.colormap = colormap;
- }
-
- @Override
- public String toString() {
- return "ColorMap{" + "name='" + name + '\'' + "info='" + info + '\'' + ", colormap="
- + Arrays.deepToString(colormap) + '}';
- }
-
- }
-
-}
diff --git a/function/spring-semantic-segmentation-function/src/main/java/org/springframework/cloud/fn/semantic/segmentation/SemanticSegmentation.java b/function/spring-semantic-segmentation-function/src/main/java/org/springframework/cloud/fn/semantic/segmentation/SemanticSegmentation.java
deleted file mode 100644
index 7bb8c018..00000000
--- a/function/spring-semantic-segmentation-function/src/main/java/org/springframework/cloud/fn/semantic/segmentation/SemanticSegmentation.java
+++ /dev/null
@@ -1,348 +0,0 @@
-/*
- * Copyright 2020-2020 the original author or authors.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * https://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.springframework.cloud.fn.semantic.segmentation;
-
-import java.io.ByteArrayInputStream;
-import java.io.File;
-import java.io.IOException;
-import java.util.Arrays;
-import java.util.Collections;
-import java.util.Map;
-
-import javax.imageio.ImageIO;
-
-import com.fasterxml.jackson.databind.ObjectMapper;
-import org.tensorflow.Operand;
-import org.tensorflow.Tensor;
-import org.tensorflow.op.Ops;
-import org.tensorflow.op.core.Gather;
-import org.tensorflow.op.core.Placeholder;
-import org.tensorflow.op.core.Squeeze;
-import org.tensorflow.op.core.ZerosLike;
-import org.tensorflow.op.dtypes.Cast;
-import org.tensorflow.op.image.DecodeJpeg;
-import org.tensorflow.op.image.ExtractJpegShape;
-import org.tensorflow.op.math.Add;
-import org.tensorflow.op.math.Div;
-import org.tensorflow.op.math.Equal;
-import org.tensorflow.types.UInt8;
-
-import org.springframework.cloud.fn.common.tensorflow.Functions;
-import org.springframework.cloud.fn.common.tensorflow.GraphRunner;
-import org.springframework.cloud.fn.common.tensorflow.GraphRunnerMemory;
-import org.springframework.cloud.fn.common.tensorflow.ProtoBufGraphDefinition;
-import org.springframework.cloud.fn.common.tensorflow.deprecated.GraphicsUtils;
-import org.springframework.core.io.DefaultResourceLoader;
-
-/**
- * @author Christian Tzolov
- */
-public class SemanticSegmentation implements AutoCloseable {
-
- private static final long CHANNELS = 3;
-
- private static final float REQUIRED_INPUT_IMAGE_SIZE = 513f;
-
- private final GraphRunner imageNormalization;
-
- private final GraphRunner semanticSegmentation;
-
- private final GraphRunner maskImageEncoding;
-
- private final GraphRunner alphaBlending;
-
- private final Tensor colorMapTensor;
-
- private final Tensor maskTransparencyTensor;
-
- @Override
- public void close() {
- this.imageNormalization.close();
- this.semanticSegmentation.close();
- this.maskImageEncoding.close();
- this.alphaBlending.close();
-
- this.colorMapTensor.close();
- this.maskTransparencyTensor.close();
- }
-
- public SemanticSegmentation(String modelUrl, int[][] colorMap, long[] labelFilter, float maskTransparency) {
-
- this.imageNormalization = new GraphRunner("input_image", "resized_image").withGraphDefinition(tf -> {
- Placeholder input = tf.withName("input_image").placeholder(String.class);
- ExtractJpegShape imageShapeAndChannel = tf.image.extractJpegShape(input);
- Gather imageShape = tf.gather(imageShapeAndChannel, tf.constant(new int[] { 0, 1 }),
- tf.constant(0));
-
- Cast maxSize = tf.dtypes.cast(tf.max(imageShape, tf.constant(0)), Float.class);
- Div scale = tf.math.div(tf.constant(REQUIRED_INPUT_IMAGE_SIZE), maxSize);
- Cast newSize = tf.dtypes.cast(tf.math.mul(scale, tf.dtypes.cast(imageShape, Float.class)),
- Integer.class);
-
- final Operand decodedImage = tf.dtypes
- .cast(tf.image.decodeJpeg(input, DecodeJpeg.channels(CHANNELS)), Float.class);
-
- final Operand resizedImageFloat = tf.image
- .resizeBilinear(tf.expandDims(decodedImage, tf.constant(0)), newSize);
-
- tf.withName("resized_image").dtypes.cast(resizedImageFloat, UInt8.class);
- });
-
- this.semanticSegmentation = new GraphRunner("ImageTensor:0", "SemanticPredictions:0")
- .withGraphDefinition(new ProtoBufGraphDefinition(new DefaultResourceLoader().getResource(modelUrl), true));
-
- this.colorMapTensor = Tensor.create(colorMap).expect(Integer.class);
-
- this.maskImageEncoding = new GraphRunner(Arrays.asList("color_map", "mask_pixels"),
- Arrays.asList("mask_png", "mask_rgb"))
- .withGraphDefinition(tf -> {
- Placeholder colorTable = tf.withName("color_map").placeholder(Integer.class);
-
- Placeholder batchedMask = tf.withName("mask_pixels").placeholder(Long.class);
- // Remove batch dimension
- Squeeze mask = tf.squeeze(batchedMask, Squeeze.axis(Arrays.asList(0L)));
-
- Operand filteredMask = labelFilter(tf, mask, labelFilter);
-
- // The mask can contain label values larger than the list of colors
- // provided in the color map.
- // To avoid out-of-index errors we will "normalize" the label values in
- // the mask to MOD max-color-table-value.
- Operand mask3 = NativeImageUtils.normalizeMaskLabels(tf, colorTable, filteredMask);
-
- Gather maskRgb = tf.withName("mask_rgb").gather(colorTable, mask3, tf.constant(0));
-
- Operand png = tf.withName("mask_png").image.encodePng(tf.dtypes.cast(maskRgb, UInt8.class));
-
- });
-
- this.maskTransparencyTensor = Tensor.create(maskTransparency).expect(Float.class);
-
- this.alphaBlending = new GraphRunner(Arrays.asList("input_image", "mask_image", "mask_transparency"),
- Arrays.asList("blended_png"))
- .withGraphDefinition(tf -> {
- // Input image [B, H, W, 3]
- Cast inputImageRgb = tf.dtypes.cast(tf.withName("input_image").placeholder(UInt8.class),
- Float.class);
-
- Placeholder a = tf.withName("mask_image").placeholder(Integer.class);
- Cast maskRgb = tf.dtypes.cast(a, Float.class);
-
- Squeeze inputImageRgb2 = tf.squeeze(inputImageRgb, Squeeze.axis(Arrays.asList(0L)));
-
- Placeholder maskTransparencyHolder = tf.withName("mask_transparency").placeholder(Float.class);
-
- // Blend the transparent maskImage on top of the input image.
- Operand blended = NativeImageUtils.alphaBlending(tf, maskRgb, inputImageRgb2,
- maskTransparencyHolder);
-
- // Cut
- // Operand condition = tf.math.equal(a, tf.zerosLike(a));
- // Operand blended = tf.where3(condition, tf.zerosLike(maskRgb),
- // inputImageRgb2);
-
- // Encode PNG
- tf.withName("blended_png").image.encodePng(tf.dtypes.cast(blended, UInt8.class));
-
- });
- }
-
- public byte[] blendMask(byte[] image) {
- try (Tensor inputTensor = Tensor.create(image); GraphRunnerMemory memory = new GraphRunnerMemory()) {
-
- Map> blendedTensors = this.imageNormalization.andThen(memory) // (input_image)
- // ->
- // (resized_image)
- // and
- // memorize
- // (resized_image)
- .andThen(this.semanticSegmentation)
- .andThen(memory) // (ImageTensor:0) -> (SemanticPredictions:0) and
- // memorize (SemanticPredictions:0)
- .andThen(Functions.rename("SemanticPredictions:0", "mask_pixels")) // (SemanticPredictions:0)
- // ->
- // (mask_pixels)
- .andThen(Functions.enrichWith("color_map", this.colorMapTensor)) // (mask_pixels)
- // ->
- // (mask_pixels,
- // color_map)
- .andThen(this.maskImageEncoding)
- .andThen(memory) // (color_map, mask_pixels) -> (mask_png, mask_rgb) and
- // memorize (mask_png, mask_rgb)
- .andThen(Functions.enrichFromMemory(memory, "resized_image")) // (mask_png,
- // mask_rgb)
- // ->
- // (mask_png,
- // mask_rgb,
- // resized_image),
- // e.g.
- // join
- // the
- // normalizedImageTensor
- .andThen(Functions.rename("resized_image", "input_image", "mask_rgb", "mask_image")) // (mask_png,
- // mask_rgb,
- // resized_image)
- // ->
- // (mask_image,
- // input_image)
- .andThen(Functions.enrichWith("mask_transparency", this.maskTransparencyTensor)) // (mask_image,
- // input_image)
- // ->
- // (mask_image,
- // input_image,
- // mask_transparency)
- .andThen(this.alphaBlending)
- .andThen(memory) // (mask_image, input_image, mask_transparency) ->
- // (blended_png)
- .apply(Collections.singletonMap("input_image", inputTensor)); // () ->
- // (input_image)
-
- byte[] blendedImage = blendedTensors.get("blended_png").bytesValue();
-
- memory.getTensorMap().entrySet().stream().forEach(e -> System.out.println(e));
-
- return blendedImage;
- }
- }
-
- public long[][] maskPixels(byte[] image) {
- try (Tensor inputTensor = Tensor.create(image); GraphRunnerMemory memory = new GraphRunnerMemory()) {
-
- return this.imageNormalization.andThen(memory) // (input_image) ->
- // (resized_image) and
- // memorize (resized_image)
- .andThen(this.semanticSegmentation)
- .andThen(memory) // (ImageTensor:0) -> (SemanticPredictions:0) and
- // memorize (SemanticPredictions:0)
- .andThen(tensorMap -> {
- Tensor maskTensor = tensorMap.get("SemanticPredictions:0");
- int width = (int) maskTensor.shape()[1];
- int height = (int) maskTensor.shape()[2];
- return maskTensor.copyTo(new long[1][width][height])[0]; // 1 == batch
- // size
- })
- .apply(Collections.singletonMap("input_image", inputTensor)); // () ->
- // (input_image)
- }
- }
-
- public byte[] maskImage(byte[] image) {
-
- try (Tensor inputTensor = Tensor.create(image); GraphRunnerMemory memory = new GraphRunnerMemory()) {
-
- return this.imageNormalization.andThen(memory) // (input_image) ->
- // (resized_image) and
- // memorize (resized_image)
- .andThen(this.semanticSegmentation)
- .andThen(memory) // (ImageTensor:0) -> (SemanticPredictions:0) and
- // memorize (SemanticPredictions:0)
- .andThen(Functions.rename("SemanticPredictions:0", "mask_pixels")) // (SemanticPredictions:0)
- // ->
- // (mask_pixels)
- .andThen(Functions.enrichWith("color_map", this.colorMapTensor)) // (mask_pixels)
- // ->
- // (mask_pixels,
- // color_map)
- .andThen(this.maskImageEncoding)
- .andThen(memory) // (color_map, mask_pixels) -> (mask_png, mask_rgb) and
- // memorize (mask_png, mask_rgb)
- .andThen(tensorMap -> tensorMap.get("mask_png").bytesValue())
- .apply(Collections.singletonMap("input_image", inputTensor)); // () ->
- // (input_image)
- }
- }
-
- private Operand labelFilter(Ops tf, Operand mask, long[] labels) {
-
- if (labels == null || labels.length == 0) {
- return mask;
- }
-
- ZerosLike zeroMask = tf.zerosLike(mask);
- Operand result = zeroMask;
- for (long label : labels) {
- Add labelMask = tf.math.add(tf.zerosLike(mask), tf.constant(label));
- Equal condition = tf.math.equal(mask, labelMask);
- result = tf.math.add(result, tf.where3(condition, labelMask, zeroMask));
- }
- return result;
- }
-
- public static void main(String[] args) throws IOException {
-
- try (SemanticSegmentation segmentationService = new SemanticSegmentation(
- "https://download.tensorflow.org/models/deeplabv3_mnv2_cityscapes_train_2018_02_05.tar.gz#frozen_inference_graph.pb",
- SegmentationColorMap.loadColorMap("classpath:/colormap/citymap_colormap.json"), null, 0.45f)) {
- byte[] inputImage = GraphicsUtils.loadAsByteArray("classpath:/images/amsterdam-cityscape1.jpg");
-
- // 1. Mask pixels
- long[][] maskPixels = segmentationService.maskPixels(inputImage);
-
- String json = new ObjectMapper().writeValueAsString(maskPixels);
-
- // 2. Alpha Blending
- byte[] blended = segmentationService.blendMask(inputImage);
- ImageIO.write(ImageIO.read(new ByteArrayInputStream(blended)), "png",
- new File("./functions/function/semantic-segmentation-function/target/blendedImage.png"));
-
- // 3. Mask Image
- byte[] maskImage = segmentationService.maskImage(inputImage);
- ImageIO.write(ImageIO.read(new ByteArrayInputStream(maskImage)), "png",
- new File("./functions/function/semantic-segmentation-function/target/maskImage.png"));
- }
-
- try (SemanticSegmentation segmentationService = new SemanticSegmentation(
- "https://download.tensorflow.org/models/deeplabv3_xception_ade20k_train_2018_05_29.tar.gz#frozen_inference_graph.pb",
- SegmentationColorMap.loadColorMap("classpath:/colormap/ade20k_colormap.json"), null, 0.45f)) {
- byte[] inputImage = GraphicsUtils.loadAsByteArray("classpath:/images/interior.jpg");
-
- // 1. Mask pixels
- long[][] maskPixels = segmentationService.maskPixels(inputImage);
-
- // 2. Alpha Blending
- byte[] blended = segmentationService.blendMask(inputImage);
- ImageIO.write(ImageIO.read(new ByteArrayInputStream(blended)), "png",
- new File("./functions/function/semantic-segmentation-function/target/inventory-blendedImage.png"));
-
- // 3. Mask Image
- byte[] maskImage = segmentationService.maskImage(inputImage);
- ImageIO.write(ImageIO.read(new ByteArrayInputStream(maskImage)), "png",
- new File("./functions/function/semantic-segmentation-function/target/inventory-MaskImage.png"));
- }
-
- try (SemanticSegmentation segmentationService = new SemanticSegmentation(
- "https://download.tensorflow.org/models/deeplabv3_mnv2_pascal_trainval_2018_01_29.tar.gz#frozen_inference_graph.pb",
- SegmentationColorMap.loadColorMap("classpath:/colormap/black_white_colormap.json"), null, 0.45f)) {
- byte[] inputImage = GraphicsUtils.loadAsByteArray("classpath:/images/VikiMaxiAdi.jpg");
-
- // 1. Mask pixels
- long[][] maskPixels = segmentationService.maskPixels(inputImage);
-
- // 2. Alpha Blending
- byte[] blended = segmentationService.blendMask(inputImage);
- ImageIO.write(ImageIO.read(new ByteArrayInputStream(blended)), "png",
- new File("./functions/function/semantic-segmentation-function/target/pascal-blendedImage.png"));
-
- // 3. Mask Image
- byte[] maskImage = segmentationService.maskImage(inputImage);
- ImageIO.write(ImageIO.read(new ByteArrayInputStream(maskImage)), "png",
- new File("./functions/function/semantic-segmentation-function/target/pascal-MaskImage.png"));
- }
-
- }
-
-}
diff --git a/function/spring-semantic-segmentation-function/src/main/java/org/springframework/cloud/fn/semantic/segmentation/attic/SemanticSegmentationUtils.java b/function/spring-semantic-segmentation-function/src/main/java/org/springframework/cloud/fn/semantic/segmentation/attic/SemanticSegmentationUtils.java
deleted file mode 100644
index bf2f2e92..00000000
--- a/function/spring-semantic-segmentation-function/src/main/java/org/springframework/cloud/fn/semantic/segmentation/attic/SemanticSegmentationUtils.java
+++ /dev/null
@@ -1,305 +0,0 @@
-/*
- * Copyright 2020-2020 the original author or authors.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * https://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.springframework.cloud.fn.semantic.segmentation.attic;
-
-import java.awt.Color;
-import java.awt.Graphics2D;
-import java.awt.Image;
-import java.awt.image.BufferedImage;
-import java.awt.image.DataBufferByte;
-import java.io.ByteArrayInputStream;
-import java.io.File;
-import java.io.IOException;
-import java.nio.ByteBuffer;
-import java.util.Arrays;
-import java.util.Base64;
-import java.util.Collections;
-import java.util.Map;
-
-import javax.imageio.ImageIO;
-
-import com.fasterxml.jackson.databind.ObjectMapper;
-import org.tensorflow.Tensor;
-import org.tensorflow.types.UInt8;
-
-import org.springframework.cloud.fn.common.tensorflow.deprecated.GraphicsUtils;
-import org.springframework.cloud.fn.common.tensorflow.deprecated.TensorFlowService;
-import org.springframework.core.io.DefaultResourceLoader;
-
-import static java.awt.image.BufferedImage.TYPE_3BYTE_BGR;
-
-/**
- *
- * Semantic image segmentation - the task of assigning a semantic label, such as "road",
- * "sky", "person", "dog", to every pixel in an image.
- *
- * https://ai.googleblog.com/2018/03/semantic-image-segmentation-with.html
- * https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
- * https://github.com/tensorflow/models/tree/master/research/deeplab
- * https://github.com/tensorflow/models/blob/master/research/deeplab/deeplab_demo.ipynb
- * http://presentations.cocodataset.org/Places17-GMRI.pdf
- *
- * http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html
- * https://www.cityscapes-dataset.com/dataset-overview/#class-definitions
- * http://groups.csail.mit.edu/vision/datasets/ADE20K/
- *
- * https://github.com/mapillary/inplace_abn
- *
- * @author Christian Tzolov
- */
-public class SemanticSegmentationUtils {
-
- /** INPUT_TENSOR_NAME . */
- public static final String INPUT_TENSOR_NAME = "ImageTensor:0";
-
- /** OUTPUT_TENSOR_NAME . */
- public static final String OUTPUT_TENSOR_NAME = "SemanticPredictions:0";
-
- private static final int BATCH_SIZE = 1;
-
- private static final long CHANNELS = 3;
-
- private static final int REQUIRED_INPUT_IMAGE_SIZE = 513;
-
- public static BufferedImage scaledImage(String imagePath) {
- try {
- return scaledImage(ImageIO.read(new DefaultResourceLoader().getResource(imagePath).getInputStream()));
- }
- catch (IOException e) {
- throw new IllegalStateException("Failed to load Image from: " + imagePath, e);
- }
- }
-
- public static BufferedImage scaledImage(byte[] image) {
- try {
- return scaledImage(ImageIO.read(new ByteArrayInputStream(image)));
- }
- catch (IOException e) {
- throw new IllegalStateException("Failed to load Image from byte array", e);
- }
- }
-
- public static BufferedImage scaledImage(BufferedImage image) {
- double scaleRatio = 1.0 * REQUIRED_INPUT_IMAGE_SIZE / Math.max(image.getWidth(), image.getHeight());
- return scale(image, scaleRatio);
- }
-
- private static BufferedImage scale(BufferedImage originalImage, double scale) {
- int newWidth = (int) (originalImage.getWidth() * scale);
- int newHeight = (int) (originalImage.getHeight() * scale);
-
- Image tmpImage = originalImage.getScaledInstance(newWidth, newHeight, Image.SCALE_DEFAULT);
- // BufferedImage resizedImage = new BufferedImage(newWidth, newHeight,
- // TYPE_INT_BGR);
- BufferedImage resizedImage = new BufferedImage(newWidth, newHeight, TYPE_3BYTE_BGR);
- // BufferedImage resizedImage = new BufferedImage(newWidth, newHeight,
- // originalImage.getType());
-
- Graphics2D g2d = resizedImage.createGraphics();
- g2d.drawImage(tmpImage, 0, 0, null);
- g2d.dispose();
-
- return resizedImage;
- }
-
- public static BufferedImage blendMask(BufferedImage mask, BufferedImage background) {
- GraphicsUtils.overlayImages(background, mask, 0, 0);
- return background;
- }
-
- public static Tensor createInputTensor(BufferedImage scaledImage) {
- if (scaledImage.getType() != TYPE_3BYTE_BGR) {
- throw new IllegalArgumentException(
- String.format("Expected 3-byte BGR encoding in BufferedImage, found %d", scaledImage.getType()));
- }
-
- // ImageIO.read produces BGR-encoded images, while the model expects RGB.
- byte[] data = bgrToRgb(toBytes(scaledImage));
-
- // Expand dimensions since the model expects images to have shape: [1, None, None,
- // 3]
- long[] shape = new long[] { BATCH_SIZE, scaledImage.getHeight(), scaledImage.getWidth(), CHANNELS };
-
- return Tensor.create(UInt8.class, shape, ByteBuffer.wrap(data));
- }
-
- private static byte[] bgrToRgb(byte[] brgImage) {
- byte[] rgbImage = new byte[brgImage.length];
- for (int i = 0; i < brgImage.length; i += 3) {
- rgbImage[i] = brgImage[i + 2];
- rgbImage[i + 1] = brgImage[i + 1];
- rgbImage[i + 2] = brgImage[i];
- }
- return rgbImage;
- }
-
- private static byte[] toBytes(BufferedImage bufferedImage) {
- return ((DataBufferByte) bufferedImage.getRaster().getDataBuffer()).getData();
- }
-
- public static BufferedImage createMaskImage(int[][] maskPixels, int width, int height, double transparency) {
-
- maskPixels = rotate(maskPixels);
-
- int maskWidth = maskPixels.length;
- int maskHeight = maskPixels[0].length;
-
- int[] maskArray = new int[maskWidth * maskHeight];
- int k = 0;
- for (int i = 0; i < maskHeight; i++) {
- for (int j = 0; j < maskWidth; j++) {
- Color c = (maskPixels[j][i] == 0) ? Color.BLACK : GraphicsUtils.getClassColor(maskPixels[j][i]);
- int t = (int) (255 * (1 - transparency));
- maskArray[k++] = new Color(c.getRed(), c.getGreen(), c.getBlue(), t).getRGB();
- }
- }
-
- // Turn the pixel array into image;
- BufferedImage maskImage = new BufferedImage(maskWidth, maskHeight, BufferedImage.TYPE_INT_ARGB);
- maskImage.setRGB(0, 0, maskWidth, maskHeight, maskArray, 0, maskWidth);
-
- // Stretch the image to fit the target box width and height!
- return GraphicsUtils.toBufferedImage(maskImage.getScaledInstance(width, height, Image.SCALE_SMOOTH));
- }
-
- /**
- * rotate clockwise in 90 degree.
- * @param input The 2D matrix to be rotated
- * @return The input matrix rotated clockwise in 90 degrees
- */
- private static int[][] rotate(int[][] input) {
-
- int w = input.length;
- int h = input[0].length;
-
- int[][] output = new int[h][w];
- for (int y = 0; y < h; y++) {
- for (int x = w - 1; x >= 0; x--) {
- output[y][x] = input[x][y];
- }
- }
- return output;
- }
-
- public static int[][] toIntArray(long[][] longArray) {
- int[][] intArray = new int[longArray.length][longArray[0].length];
- for (int i = 0; i < longArray.length; i++) {
- for (int j = 0; j < longArray[0].length; j++) {
- intArray[i][j] = (int) longArray[i][j];
- }
- }
- return intArray;
- }
-
- public String serializeToJson(int[][] pixels) {
- String masksBase64 = Base64.getEncoder().encodeToString(toBytes(pixels));
- return String.format("{ \"columns\":%d, \"rows\":%d, \"masks\":\"%s\"}", pixels.length, pixels[0].length,
- masksBase64);
- }
-
- public int[][] deserializeToMasks(String json) throws IOException {
- Map map = new ObjectMapper().readValue(json, Map.class);
- int cols = (int) map.get("columns");
- int rows = (int) map.get("rows");
- String masksBase64 = (String) map.get("masks");
- byte[] masks = Base64.getDecoder().decode(masksBase64);
- return toInts(masks, cols, rows);
- }
-
- private byte[] toBytes(int[][] pixels) {
- byte[] b = new byte[pixels.length * pixels[0].length * 4];
- int bi = 0;
- for (int i = 0; i < pixels.length; i++) {
- for (int j = 0; j < pixels[0].length; j++) {
- b[bi + 0] = (byte) (i >> 24);
- b[bi + 1] = (byte) (i >> 16);
- b[bi + 2] = (byte) (i >> 8);
- b[bi + 3] = (byte) (i /* >> 0 */);
- bi = bi + 4;
- }
- }
- return b;
- }
-
- private int[][] toInts(byte[] b, int ic, int jc) {
- int[][] intResult = new int[ic][jc];
- int bi = 0;
- for (int i = 0; i < ic; i++) {
- for (int j = 0; j < jc; j++) {
- intResult[i][j] = (b[bi] << 24) + (b[bi + 1] << 16) + (b[bi + 2] << 8) + b[bi + 3];
- bi = bi + 4;
- }
- }
- return intResult;
- }
-
- public static void main(String[] args) throws IOException {
-
- // PASCAL VOC 2012
- // String tensorflowModelLocation =
- // "file:/Users/ctzolov/Downloads/deeplabv3_mnv2_pascal_train_aug/frozen_inference_graph.pb";
- // String imagePath = "classpath:/images/VikiMaxiAdi.jpg";
-
- // CITYSCAPE
- // String tensorflowModelLocation =
- // "file:/Users/ctzolov/Downloads/deeplabv3_mnv2_cityscapes_train/frozen_inference_graph.pb";
- // String imagePath = "classpath:/images/amsterdam-cityscape1.jpg";
- // String imagePath = "classpath:/images/amsterdam-channel.jpg";
- // String imagePath = "classpath:/images/landsmeer.png";
-
- // ADE20K
- String tensorflowModelLocation = "file:/Users/ctzolov/Downloads/deeplabv3_xception_ade20k_train/frozen_inference_graph.pb";
- String imagePath = "classpath:/images/interior.jpg";
-
- BufferedImage inputImage = ImageIO.read(new DefaultResourceLoader().getResource(imagePath).getInputStream());
-
- TensorFlowService tf = new TensorFlowService(new DefaultResourceLoader().getResource(tensorflowModelLocation),
- Arrays.asList(OUTPUT_TENSOR_NAME));
-
- SemanticSegmentationUtils segmentationService = new SemanticSegmentationUtils();
-
- BufferedImage scaledImage = segmentationService.scaledImage(inputImage);
-
- Tensor inTensor = segmentationService.createInputTensor(scaledImage);
-
- Map> output = tf.apply(Collections.singletonMap(INPUT_TENSOR_NAME, inTensor));
-
- Tensor maskPixelsTensor = output.get(OUTPUT_TENSOR_NAME);
-
- int height = (int) maskPixelsTensor.shape()[1];
- int width = (int) maskPixelsTensor.shape()[2];
- long[][] maskPixels = maskPixelsTensor.copyTo(new long[BATCH_SIZE][height][width])[0]; // take
- // 0
- // because
- // the
- // batch
- // size
- // is
- // 1.
-
- int[][] maskPixelsInt = segmentationService.toIntArray(maskPixels);
-
- BufferedImage maskImage = segmentationService.createMaskImage(maskPixelsInt, scaledImage.getWidth(),
- scaledImage.getHeight(), 0.35);
-
- BufferedImage blended = segmentationService.blendMask(maskImage, scaledImage);
-
- ImageIO.write(maskImage, "png", new File("./semantic-segmentation/target/java2Dmask.jpg"));
- ImageIO.write(blended, "png", new File("./semantic-segmentation/target/java2Dblended.jpg"));
- }
-
-}
diff --git a/function/spring-semantic-segmentation-function/src/main/resources/colormap/ade20k_colormap.json b/function/spring-semantic-segmentation-function/src/main/resources/colormap/ade20k_colormap.json
deleted file mode 100644
index 51648f4e..00000000
--- a/function/spring-semantic-segmentation-function/src/main/resources/colormap/ade20k_colormap.json
+++ /dev/null
@@ -1,156 +0,0 @@
-{
- "name" : "ade20k",
- "info" : "ADE20K (http://groups.csail.mit.edu/vision/datasets/ADE20K/)",
- "colormap" :[
- [ 0, 0, 0 ],
- [ 120, 120, 120 ],
- [ 180, 120, 120 ],
- [ 6, 230, 230 ],
- [ 80, 50, 50 ],
- [ 4, 200, 3 ],
- [ 120, 120, 80 ],
- [ 140, 140, 140 ],
- [ 204, 5, 255 ],
- [ 230, 230, 230 ],
- [ 4, 250, 7 ],
- [ 224, 5, 255 ],
- [ 235, 255, 7 ],
- [ 150, 5, 61 ],
- [ 120, 120, 70 ],
- [ 8, 255, 51 ],
- [ 255, 6, 82 ],
- [ 143, 255, 140 ],
- [ 204, 255, 4 ],
- [ 255, 51, 7 ],
- [ 204, 70, 3 ],
- [ 0, 102, 200 ],
- [ 61, 230, 250 ],
- [ 255, 6, 51 ],
- [ 11, 102, 255 ],
- [ 255, 7, 71 ],
- [ 255, 9, 224 ],
- [ 9, 7, 230 ],
- [ 220, 220, 220 ],
- [ 255, 9, 92 ],
- [ 112, 9, 255 ],
- [ 8, 255, 214 ],
- [ 7, 255, 224 ],
- [ 255, 184, 6 ],
- [ 10, 255, 71 ],
- [ 255, 41, 10 ],
- [ 7, 255, 255 ],
- [ 224, 255, 8 ],
- [ 102, 8, 255 ],
- [ 255, 61, 6 ],
- [ 255, 194, 7 ],
- [ 255, 122, 8 ],
- [ 0, 255, 20 ],
- [ 255, 8, 41 ],
- [ 255, 5, 153 ],
- [ 6, 51, 255 ],
- [ 235, 12, 255 ],
- [ 160, 150, 20 ],
- [ 0, 163, 255 ],
- [ 140, 140, 140 ],
- [ 250, 10, 15 ],
- [ 20, 255, 0 ],
- [ 31, 255, 0 ],
- [ 255, 31, 0 ],
- [ 255, 224, 0 ],
- [ 153, 255, 0 ],
- [ 0, 0, 255 ],
- [ 255, 71, 0 ],
- [ 0, 235, 255 ],
- [ 0, 173, 255 ],
- [ 31, 0, 255 ],
- [ 11, 200, 200 ],
- [ 255, 82, 0 ],
- [ 0, 255, 245 ],
- [ 0, 61, 255 ],
- [ 0, 255, 112 ],
- [ 0, 255, 133 ],
- [ 255, 0, 0 ],
- [ 255, 163, 0 ],
- [ 255, 102, 0 ],
- [ 194, 255, 0 ],
- [ 0, 143, 255 ],
- [ 51, 255, 0 ],
- [ 0, 82, 255 ],
- [ 0, 255, 41 ],
- [ 0, 255, 173 ],
- [ 10, 0, 255 ],
- [ 173, 255, 0 ],
- [ 0, 255, 153 ],
- [ 255, 92, 0 ],
- [ 255, 0, 255 ],
- [ 255, 0, 245 ],
- [ 255, 0, 102 ],
- [ 255, 173, 0 ],
- [ 255, 0, 20 ],
- [ 255, 184, 184 ],
- [ 0, 31, 255 ],
- [ 0, 255, 61 ],
- [ 0, 71, 255 ],
- [ 255, 0, 204 ],
- [ 0, 255, 194 ],
- [ 0, 255, 82 ],
- [ 0, 10, 255 ],
- [ 0, 112, 255 ],
- [ 51, 0, 255 ],
- [ 0, 194, 255 ],
- [ 0, 122, 255 ],
- [ 0, 255, 163 ],
- [ 255, 153, 0 ],
- [ 0, 255, 10 ],
- [ 255, 112, 0 ],
- [ 143, 255, 0 ],
- [ 82, 0, 255 ],
- [ 163, 255, 0 ],
- [ 255, 235, 0 ],
- [ 8, 184, 170 ],
- [ 133, 0, 255 ],
- [ 0, 255, 92 ],
- [ 184, 0, 255 ],
- [ 255, 0, 31 ],
- [ 0, 184, 255 ],
- [ 0, 214, 255 ],
- [ 255, 0, 112 ],
- [ 92, 255, 0 ],
- [ 0, 224, 255 ],
- [ 112, 224, 255 ],
- [ 70, 184, 160 ],
- [ 163, 0, 255 ],
- [ 153, 0, 255 ],
- [ 71, 255, 0 ],
- [ 255, 0, 163 ],
- [ 255, 204, 0 ],
- [ 255, 0, 143 ],
- [ 0, 255, 235 ],
- [ 133, 255, 0 ],
- [ 255, 0, 235 ],
- [ 245, 0, 255 ],
- [ 255, 0, 122 ],
- [ 255, 245, 0 ],
- [ 10, 190, 212 ],
- [ 214, 255, 0 ],
- [ 0, 204, 255 ],
- [ 20, 0, 255 ],
- [ 255, 255, 0 ],
- [ 0, 153, 255 ],
- [ 0, 41, 255 ],
- [ 0, 255, 204 ],
- [ 41, 0, 255 ],
- [ 41, 255, 0 ],
- [ 173, 0, 255 ],
- [ 0, 245, 255 ],
- [ 71, 0, 255 ],
- [ 122, 0, 255 ],
- [ 0, 255, 184 ],
- [ 0, 92, 255 ],
- [ 184, 255, 0 ],
- [ 0, 133, 255 ],
- [ 255, 214, 0 ],
- [ 25, 194, 194 ],
- [ 102, 255, 0 ],
- [ 92, 0, 255 ]]
-}
diff --git a/function/spring-semantic-segmentation-function/src/main/resources/colormap/black_white_colormap.json b/function/spring-semantic-segmentation-function/src/main/resources/colormap/black_white_colormap.json
deleted file mode 100644
index ca1c33ea..00000000
--- a/function/spring-semantic-segmentation-function/src/main/resources/colormap/black_white_colormap.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
- "name" : "black_white",
- "info" : "Black and white color map",
- "colormap" :[
- [ 0, 0, 0 ],
- [ 127, 127, 127 ],
- [ 255, 255, 255 ]]
-}
diff --git a/function/spring-semantic-segmentation-function/src/main/resources/colormap/citymap_colormap.json b/function/spring-semantic-segmentation-function/src/main/resources/colormap/citymap_colormap.json
deleted file mode 100644
index dbd1ef2c..00000000
--- a/function/spring-semantic-segmentation-function/src/main/resources/colormap/citymap_colormap.json
+++ /dev/null
@@ -1,24 +0,0 @@
-{
- "name" : "citymap",
- "info" : "Cityscapes dataset (https://www.cityscapes-dataset.com).",
- "colormap" :[
- [ 128, 64, 128 ],
- [ 244, 35, 232 ],
- [ 70, 70, 70 ],
- [ 102, 102, 156 ],
- [ 190, 153, 153 ],
- [ 153, 153, 153 ],
- [ 250, 170, 30 ],
- [ 220, 220, 0 ],
- [ 107, 142, 35 ],
- [ 152, 251, 152 ],
- [ 70, 130, 180 ],
- [ 220, 20, 60 ],
- [ 255, 0, 0 ],
- [ 0, 0, 142 ],
- [ 0, 0, 70 ],
- [ 0, 60, 100 ],
- [ 0, 80, 100 ],
- [ 0, 0, 230 ],
- [ 119, 11, 32 ]]
-}
diff --git a/function/spring-semantic-segmentation-function/src/main/resources/colormap/mapillary_colormap.json b/function/spring-semantic-segmentation-function/src/main/resources/colormap/mapillary_colormap.json
deleted file mode 100644
index f089268b..00000000
--- a/function/spring-semantic-segmentation-function/src/main/resources/colormap/mapillary_colormap.json
+++ /dev/null
@@ -1,71 +0,0 @@
-{
- "name" : "mapillary",
- "info" : "Mapillary Vistas (https://research.mapillary.com).",
- "colormap" :[
- [ 165, 42, 42 ],
- [ 0, 192, 0 ],
- [ 196, 196, 196 ],
- [ 190, 153, 153 ],
- [ 180, 165, 180 ],
- [ 102, 102, 156 ],
- [ 102, 102, 156 ],
- [ 128, 64, 255 ],
- [ 140, 140, 200 ],
- [ 170, 170, 170 ],
- [ 250, 170, 160 ],
- [ 96, 96, 96 ],
- [ 230, 150, 140 ],
- [ 128, 64, 128 ],
- [ 110, 110, 110 ],
- [ 244, 35, 232 ],
- [ 150, 100, 100 ],
- [ 70, 70, 70 ],
- [ 150, 120, 90 ],
- [ 220, 20, 60 ],
- [ 255, 0, 0 ],
- [ 255, 0, 0 ],
- [ 255, 0, 0 ],
- [ 200, 128, 128 ],
- [ 255, 255, 255 ],
- [ 64, 170, 64 ],
- [ 128, 64, 64 ],
- [ 70, 130, 180 ],
- [ 255, 255, 255 ],
- [ 152, 251, 152 ],
- [ 107, 142, 35 ],
- [ 0, 170, 30 ],
- [ 255, 255, 128 ],
- [ 250, 0, 30 ],
- [ 0, 0, 0 ],
- [ 220, 220, 220 ],
- [ 170, 170, 170 ],
- [ 222, 40, 40 ],
- [ 100, 170, 30 ],
- [ 40, 40, 40 ],
- [ 33, 33, 33 ],
- [ 170, 170, 170 ],
- [ 0, 0, 142 ],
- [ 170, 170, 170 ],
- [ 210, 170, 100 ],
- [ 153, 153, 153 ],
- [ 128, 128, 128 ],
- [ 0, 0, 142 ],
- [ 250, 170, 30 ],
- [ 192, 192, 192 ],
- [ 220, 220, 0 ],
- [ 180, 165, 180 ],
- [ 119, 11, 32 ],
- [ 0, 0, 142 ],
- [ 0, 60, 100 ],
- [ 0, 0, 142 ],
- [ 0, 0, 90 ],
- [ 0, 0, 230 ],
- [ 0, 80, 100 ],
- [ 128, 64, 64 ],
- [ 0, 0, 110 ],
- [ 0, 0, 70 ],
- [ 0, 0, 192 ],
- [ 32, 32, 32 ],
- [ 0, 0, 0 ],
- [ 0, 0, 0 ]]
-}
diff --git a/function/spring-semantic-segmentation-function/src/main/resources/images/ADE20K-all-small.png b/function/spring-semantic-segmentation-function/src/main/resources/images/ADE20K-all-small.png
deleted file mode 100644
index bede9db4..00000000
Binary files a/function/spring-semantic-segmentation-function/src/main/resources/images/ADE20K-all-small.png and /dev/null differ
diff --git a/function/spring-semantic-segmentation-function/src/main/resources/images/VikiMaxiAdi-all.png b/function/spring-semantic-segmentation-function/src/main/resources/images/VikiMaxiAdi-all.png
deleted file mode 100644
index 719814e1..00000000
Binary files a/function/spring-semantic-segmentation-function/src/main/resources/images/VikiMaxiAdi-all.png and /dev/null differ
diff --git a/function/spring-semantic-segmentation-function/src/main/resources/images/VikiMaxiAdi.jpg b/function/spring-semantic-segmentation-function/src/main/resources/images/VikiMaxiAdi.jpg
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