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Analyzing Images in HDF 2.0 Using Tensorflow

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Analyzing Images in HDF 2.0 Using Tensorflow

Take a look at using Google's TensorFlow Deep Learning library for identifying images taken in drones. There is even a NiFi programming example provided.

· Big Data Zone
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Need to build an application around your data? Learn more about dataflow programming for rapid development and greater creativity. 

As part of a live Drone ingest, I want to identify what in the image. The metadata provides a ton of information on GPS, altitude and image characteristics, but not what's in the image.

IBM, Microsoft, and Google all have APIs that do a good job of this and they for the most part of "free" tiers. I wanted to run something locally using libraries installed on my cluster. For my first option, I used TensorFlow Inception-v3 Image Recognition. In future articles, I will cover PaddlePaddle, OpenCV and some other Deep Learning and non-deep learning options for Image Recognition. Also, I will show the entire Drone to Front-End flow including Phoenix, Spring Boot, Zeppelin, LeafletJS and more. This will be done as part of a meetup presentation with a certified drone pilot.

To Run My TensorFlow Binary From HDF 2.0 I use the ExecuteStreamCommand to run a shell script containing the information below:

source /usr/local/lib/bazel/bin/bazel-complete.bash
export JAVA_HOME=/opt/jdk1.8.0_101/

hdfs dfs -get /drone/raw/$@ /tmp/

/opt/demo/tensorflow/bazel-bin/tensorflow/examples/label_image/label_image --image="/tmp/$@" --output_layer="softmax:0" --input_layer="Mul:0"  --input_std=128 --input_mean=128 --graph=/opt/demo/tensorflow/tensorflow/examples/label_image/data/tensorflow_inception_graph.pb --labels=/opt/demo/tensorflow/tensorflow/examples/label_image/data/imagenet_comp_graph_label_strings.txt 2>&1| cut -c48-

In my script, I pull the file out of HDFS (that was loaded by HDF 2.0) and then run the binary version of TensorFlow that I compiled for Centos7. If you can't or don't want to install Bezel and build that, then you can run the Python script, it's a little bit slower and has slightly different output.

python /usr/lib/python2.7/site-packages/tensorflow/models/image/imagenet/classify_image.py --image_file

The C++ version that I compiled is Google's example and you can take a look at it:



It's very clean code if you wish to tweak it.

Installing TensorFlow

You must have JDK 1.8 (and know the path), not just JRE 1.8. Also, you need Python 2.7 or 3.+ and PIP. You need to install Google's Bezel build tool.

sudo yum groupinstall "Development Tools" 
sudo yum install gettext-devel openssl-devel perl-CPAN perl-devel zlib-develsudo yum -y install epel_release
sudo yum -y install gcc gcc-c++ python-pip python-devel atlas atlas-devel gcc-gfortran openssl-devel libffi-devel
pip install --upgrade numpy scipy wheel cryptography
export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp27-none-linux_x86_64.whl
pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0-cp27-none-linux_x86_64.whl

git clone https://github.com/tensorflow/tensorflow
cd tensorflow/
wget https://storage.googleapis.com/download.tensorflow.org/models/inception_dec_2015.zip -O tensorflow/examples/label_image/data/inception_dec_2015.zip
unzip tensorflow/examples/label_image/data/inception_dec_2015.zip -d tensorflow/examples/label_image/data/
cd tensorflow/examples/label_image
/opt/demo/bazel/output/bazel label_image

Run Results of TensorFlow

python classify_image.py --image_file /opt/demo/dronedataold/Bebop2_20160920083655-0400.jpg
solar dish, solar collector, solar furnace (score = 0.98316)
window screen (score = 0.00196)
manhole cover (score = 0.00070)
radiator (score = 0.00041)
doormat, welcome mat (score = 0.00041)

bazel-bin/tensorflow/examples/label_image/label_image --image=/opt/demo/dronedataold/Bebop2_20160920083655-0400.jpg
solar dish (577): 0.983162I 
window screen (912): 0.00196204I 
manhole cover (763): 0.000704005I 
radiator (571): 0.000408321I 
doormat (972): 0.000406186

The image is a picture of solar panels on a residential black tar roof.

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image recognition ,drone ,tensorflow ,big data

Published at DZone with permission of Tim Spann, DZone MVB. See the original article here.

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