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[samples]: Samples using the Java API.
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‎samples/java/README.md

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# TensorFlow for Java: Examples
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Examples using the TensorFlow Java API.

‎samples/java/docker/Dockerfile

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FROM tensorflow/tensorflow:1.4.0
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WORKDIR /
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RUN apt-get update
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RUN apt-get -y install maven openjdk-8-jdk
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RUN mvn dependency:get -Dartifact=org.tensorflow:tensorflow:1.4.0
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RUN mvn dependency:get -Dartifact=org.tensorflow:proto:1.4.0
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CMD ["/bin/bash", "-l"]

‎samples/java/docker/README.md

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Dockerfile for building an image suitable for running the Java examples.
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Typical usage:
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```
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docker build -t java-tensorflow .
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docker run -it --rm -v ${PWD}/..:/examples java-tensorflow
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```
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That second command will pop you into a shell which has all
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the dependencies required to execute the scripts and Java
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examples.

‎samples/java/label_image/.gitignore

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images
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src/main/resources
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target

‎samples/java/label_image/README.md

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# Image Classification Example
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1. Download the model:
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- If you have [TensorFlow 1.4+ for Python installed](https://www.tensorflow.org/install/),
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run `python ./download.py`
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- If not, but you have [docker](https://www.docker.com/get-docker) installed,
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run `download.sh`.
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2. Compile [`LabelImage.java`](src/main/java/LabelImage.java):
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```
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mvn compile
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```
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3. Download some sample images:
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If you already have some images, great. Otherwise `download_sample_images.sh`
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gets a few.
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3. Classify!
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```
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mvn -q exec:java -Dexec.args="<path to image file>"
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```

‎samples/java/label_image/download.py

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"""Create an image classification graph.
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Script to download a pre-trained image classifier and tweak it so that
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the model accepts raw bytes of an encoded image.
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Doing so involves some model-specific normalization of an image.
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Ideally, this would have been part of the image classifier model,
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but the particular model being used didn't include this normalization,
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so this script does the necessary tweaking.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from six.moves import urllib
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import os
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import zipfile
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import tensorflow as tf
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URL = 'https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip'
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LABELS_FILE = 'imagenet_comp_graph_label_strings.txt'
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GRAPH_FILE = 'tensorflow_inception_graph.pb'
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GRAPH_INPUT_TENSOR = 'input:0'
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GRAPH_PROBABILITIES_TENSOR = 'output:0'
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IMAGE_HEIGHT = 224
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IMAGE_WIDTH = 224
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MEAN = 117
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SCALE = 1
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LOCAL_DIR = 'src/main/resources'
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def download():
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print('Downloading %s' % URL)
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zip_filename, _ = urllib.request.urlretrieve(URL)
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with zipfile.ZipFile(zip_filename) as zip:
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zip.extract(LABELS_FILE)
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zip.extract(GRAPH_FILE)
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os.rename(LABELS_FILE, os.path.join(LOCAL_DIR, 'labels.txt'))
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os.rename(GRAPH_FILE, os.path.join(LOCAL_DIR, 'graph.pb'))
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def create_graph_to_decode_and_normalize_image():
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"""See file docstring.
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Returns:
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input: The placeholder to feed the raw bytes of an encoded image.
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y: A Tensor (the decoded, normalized image) to be fed to the graph.
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"""
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image = tf.placeholder(tf.string, shape=(), name='encoded_image_bytes')
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with tf.name_scope("preprocess"):
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y = tf.image.decode_image(image, channels=3)
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y = tf.cast(y, tf.float32)
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y = tf.expand_dims(y, axis=0)
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y = tf.image.resize_bilinear(y, (IMAGE_HEIGHT, IMAGE_WIDTH))
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y = (y - MEAN) / SCALE
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return (image, y)
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def patch_graph():
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"""Create graph.pb that applies the model in URL to raw image bytes."""
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with tf.Graph().as_default() as g:
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input_image, image_normalized = create_graph_to_decode_and_normalize_image()
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original_graph_def = tf.GraphDef()
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with open(os.path.join(LOCAL_DIR, 'graph.pb')) as f:
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original_graph_def.ParseFromString(f.read())
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softmax = tf.import_graph_def(
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original_graph_def,
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name='inception',
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input_map={GRAPH_INPUT_TENSOR: image_normalized},
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return_elements=[GRAPH_PROBABILITIES_TENSOR])
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# We're constructing a graph that accepts a single image (as opposed to a
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# batch of images), so might as well make the output be a vector of
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# probabilities, instead of a batch of vectors with batch size 1.
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output_probabilities = tf.squeeze(softmax, name='probabilities')
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# Overwrite the graph.
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with open(os.path.join(LOCAL_DIR, 'graph.pb'), 'w') as f:
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f.write(g.as_graph_def().SerializeToString())
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print('------------------------------------------------------------')
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print('MODEL GRAPH : graph.pb')
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print('LABELS : labels.txt')
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print('INPUT TENSOR : %s' % input_image.op.name)
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print('OUTPUT TENSOR: %s' % output_probabilities.op.name)
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if __name__ == '__main__':
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if not os.path.exists(LOCAL_DIR):
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os.makedirs(LOCAL_DIR)
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download()
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patch_graph()

‎samples/java/label_image/download.sh

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#!/bin/bash
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DIR="$(cd "$(dirname "$0")" && pwd -P)"
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docker run -it -v ${DIR}:/x -w /x --rm tensorflow/tensorflow:1.4.0 python download.py

‎samples/java/label_image/download_sample_images.sh

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#!/bin/bash
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DIR=$(dirname $0)
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mkdir -p ${DIR}/images
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cd ${DIR}/images
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# Some random images
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curl -o "porcupine.jpg" -L "https://cdn.pixabay.com/photo/2014/11/06/12/46/porcupines-519145_960_720.jpg"
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curl -o "whale.jpg" -L "https://static.pexels.com/photos/417196/pexels-photo-417196.jpeg"
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curl -o "terrier1u.jpg" -L "https://upload.wikimedia.org/wikipedia/commons/3/34/Australian_Terrier_Melly_%282%29.JPG"
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curl -o "terrier2.jpg" -L "https://cdn.pixabay.com/photo/2014/05/13/07/44/yorkshire-terrier-343198_960_720.jpg"

‎samples/java/label_image/pom.xml

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<project>
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<modelVersion>4.0.0</modelVersion>
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<groupId>org.myorg</groupId>
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<artifactId>label-image</artifactId>
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<version>1.0-SNAPSHOT</version>
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<properties>
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<exec.mainClass>LabelImage</exec.mainClass>
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<!-- The sample code requires at least JDK 1.7. -->
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<!-- The maven compiler plugin defaults to a lower version -->
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<maven.compiler.source>1.7</maven.compiler.source>
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<maven.compiler.target>1.7</maven.compiler.target>
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</properties>
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<dependencies>
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow</artifactId>
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<version>1.4.0</version>
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</dependency>
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<!-- For ByteStreams.toByteArray: https://google.github.io/guava/releases/23.0/api/docs/com/google/common/io/ByteStreams.html -->
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<dependency>
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<groupId>com.google.guava</groupId>
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<artifactId>guava</artifactId>
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<version>23.6-jre</version>
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</dependency>
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</dependencies>
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</project>

‎samples/java/label_image/src/main/java/LabelImage.java

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/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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import com.google.common.io.ByteStreams;
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import java.io.BufferedReader;
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import java.io.IOException;
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import java.io.InputStream;
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import java.io.InputStreamReader;
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import java.nio.file.Files;
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import java.nio.file.Path;
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import java.nio.file.Paths;
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import java.util.ArrayList;
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import java.util.List;
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import org.tensorflow.Graph;
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import org.tensorflow.Session;
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import org.tensorflow.Tensor;
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import org.tensorflow.Tensors;
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/**
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* Simplified version of
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* https://github.com/tensorflow/tensorflow/blob/r1.4/tensorflow/java/src/main/java/org/tensorflow/examples/LabelImage.java
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*/
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public class LabelImage {
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public static void main(String[] args) throws Exception {
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if (args.length < 1) {
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System.err.println("USAGE: Provide a list of image filenames");
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System.exit(1);
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}
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final List<String> labels = loadLabels();
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try (Graph graph = new Graph();
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Session session = new Session(graph)) {
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graph.importGraphDef(loadGraphDef());
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float[] probabilities = null;
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for (String filename : args) {
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byte[] bytes = Files.readAllBytes(Paths.get(filename));
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try (Tensor<String> input = Tensors.create(bytes);
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Tensor<Float> output =
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session
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.runner()
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.feed("encoded_image_bytes", input)
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.fetch("probabilities")
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.run()
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.get(0)
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.expect(Float.class)) {
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if (probabilities == null) {
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probabilities = new float[(int) output.shape()[0]];
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}
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output.copyTo(probabilities);
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int label = argmax(probabilities);
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System.out.printf(
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"%-30s --> %-15s (%.2f%% likely)\n",
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filename, labels.get(label), probabilities[label] * 100.0);
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}
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}
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}
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}
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private static byte[] loadGraphDef() throws IOException {
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try (InputStream is = LabelImage.class.getClassLoader().getResourceAsStream("graph.pb")) {
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return ByteStreams.toByteArray(is);
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}
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}
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private static ArrayList<String> loadLabels() throws IOException {
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ArrayList<String> labels = new ArrayList<String>();
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String line;
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final InputStream is = LabelImage.class.getClassLoader().getResourceAsStream("labels.txt");
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try (BufferedReader reader = new BufferedReader(new InputStreamReader(is))) {
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while ((line = reader.readLine()) != null) {
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labels.add(line);
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}
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}
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return labels;
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}
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private static int argmax(float[] probabilities) {
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int best = 0;
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for (int i = 1; i < probabilities.length; ++i) {
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if (probabilities[i] > probabilities[best]) {
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best = i;
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}
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}
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return best;
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}
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}

‎samples/java/object_detection/.gitignore

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images
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labels
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models
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src/main/protobuf
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target

‎samples/java/object_detection/README.md

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# Object Detection in Java
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Example of using pre-trained models of the [TensorFlow Object Detection
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API](https://github.com/tensorflow/models/tree/master/research/object_detection)
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in Java.
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## Quickstart
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1. Download some metadata files:
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```
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./download.sh
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```
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2. Download a model from the [object detection API model
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zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md).
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For example:
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```
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mkdir -p models
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curl -L \
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http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz \
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| tar -xz -C models/
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```
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3. Locate the corresponding labels file in the `data/` directory.
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3. Have some test images handy. For example:
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```
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mkdir -p images
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curl -L -o images/test.jpg \
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https://pixnio.com/free-images/people/mother-father-and-children-washing-dog-labrador-retriever-outside-in-the-fresh-air-725x483.jpg
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```
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4. Compile and run!
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```
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mvn -q compile exec:java \
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-Dexec.args="models/ssd_inception_v2_coco_2017_11_17/saved_model labels/mscoco_label_map.pbtxt images/test.jpg"
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```
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## Notes
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- This example demonstrates the use of the TensorFlow [SavedModel
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format](https://www.tensorflow.org/programmers_guide/saved_model). If you have
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TensorFlow for Python installed, you could explore the model to get the names
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of the tensors using `saved_model_cli` command. For example:
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```
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saved_model_cli show --dir models/ssd_inception_v2_coco_2017_11_17/saved_model/ --all
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```
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- The file in `src/main/object_detection/protos/` was generated using:
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```
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./download.sh
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protoc -Isrc/main/protobuf --java_out=src/main/java src/main/protobuf/string_int_label_map.proto
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```
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Where `protoc` was downloaded from
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https://github.com/google/protobuf/releases/tag/v3.5.1

‎samples/java/object_detection/download.sh

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#!/bin/bash
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set -ex
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DIR="$(cd "$(dirname "$0")" && pwd -P)"
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cd "${DIR}"
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# The protobuf file needed for mapping labels to human readable names.
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# From:
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# https://github.com/tensorflow/models/blob/f87a58c/research/object_detection/protos/string_int_label_map.proto
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mkdir -p src/main/protobuf
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curl -L -o src/main/protobuf/string_int_label_map.proto "https://raw.githubusercontent.com/tensorflow/models/f87a58cd96d45de73c9a8330a06b2ab56749a7fa/research/object_detection/protos/string_int_label_map.proto"
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# Labels from:
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# https://github.com/tensorflow/models/tree/865c14c/research/object_detection/data
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mkdir -p labels
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curl -L -o labels/mscoco_label_map.pbtxt "https://raw.githubusercontent.com/tensorflow/models/865c14c1209cb9ae188b2a1b5f0883c72e050d4c/research/object_detection/data/mscoco_label_map.pbtxt"
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curl -L -o labels/oid_bbox_trainable_label_map.pbtxt "https://raw.githubusercontent.com/tensorflow/models/865c14c1209cb9ae188b2a1b5f0883c72e050d4c/research/object_detection/data/oid_bbox_trainable_label_map.pbtxt"

‎samples/java/object_detection/pom.xml

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<project>
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<modelVersion>4.0.0</modelVersion>
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<groupId>org.myorg</groupId>
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<artifactId>detect-objects</artifactId>
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<version>1.0-SNAPSHOT</version>
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<properties>
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<exec.mainClass>DetectObjects</exec.mainClass>
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<!-- The sample code requires at least JDK 1.7. -->
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<!-- The maven compiler plugin defaults to a lower version -->
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<maven.compiler.source>1.7</maven.compiler.source>
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<maven.compiler.target>1.7</maven.compiler.target>
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</properties>
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<dependencies>
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>tensorflow</artifactId>
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<version>1.4.0</version>
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</dependency>
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<dependency>
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<groupId>org.tensorflow</groupId>
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<artifactId>proto</artifactId>
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<version>1.4.0</version>
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</dependency>
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</dependencies>
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</project>

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