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Keeping track of where a cat is can be a tricky task. In this article, we'll design and prototype a smart IoT cat sensor which detects when a cat is in proximity. This sensor is meant to be part of a larger network of cat sensors covering a target space.

Flow Design

62579-catflow.png

Sensor input

We start by polling an image sensor (camera) for image data. We use the GetUSBCamera processor configured for the USB camera device attached to our sensor controller.

On our system, we had to open up permissions on the USB camera device, otherwise MiNiFi's GetUSBCamera processor would record an access denied error in the logs:

chown user /dev/bus/usb/001/014

We then configure the sensor processor as such:

Processors:
  - name: Get
    class: GetUSBCamera
    Properties:
      FPS: .5
      Format: RAW
      USB Vendor ID: 0x045e
      USB Product ID: 0x0779

Machine Learning Inference on the Edge

We use TensorFlow to perform class inference on the image data. We do this at the sensor rather than in a centralized system in order to significantly reduce inference latency and network bandwidth consumption. This is a three-step process:

  1. Convert image data to a tensor using TFConvertImageToTensor
  2. Perform inference using a pre-trained NASNet Large model applied via TFApplyGraph
  3. Extract inferred classes using TFExtractTopLabels

Preparation of NASNet Graph

We must perform some preliminary steps to get the NASNet graph into form that MiNiFi can use. First, we export the inference graph using the export_inference_graph.py script from TensorFlow models research/slim:

python export_inference_graph.py --model_name=nasnet_large --output_file=./nasnet_inf_graph.pb

This will also create a labels.txt file, which we will save for later use.

Next, we download and extract the checkpoint nasnet-a_large_04_10_2017.tar.gz.

Next, we use freeze_graph to integrate the pre-trained checkopint with the inference graph, and save the whole thing as a frozen graph:

from tensorflow.python.tools import freeze_graph

freeze_graph.freeze_graph(input_graph='./nasnet_inf_graph.pb',
                          input_saver='',
                          input_binary=True,
                          input_checkpoint='./model.ckpt',
                          output_node_names='final_layer/predictions',
                          restore_op_name='save/restore_all',
                          filename_tensor_name='save/Const:0',
                          output_graph='./frozen_nasnet.pb',
                          clear_devices=True,
                          initializer_nodes='')

MiNiFi Inference Flow

We use the following processors and connections to perform inference on images provided via our camera:

Processors:

  - name: Convert
    class: TFConvertImageToTensor
    Properties:
      Input Format: RAW
      Input Width: 1280
      Input Height: 800
      Crop Offset X: 240
      Crop Offset Y: 0
      Crop Size X: 800
      Crop Size Y: 800
      Output Width: 331
      Output Height: 331
      Channels: 3

  - name: Apply
    class: TFApplyGraph
    Properties:
      Input Node: input:0
      Output Node: final_layer/predictions:0

  - name: Extract
    class: TFExtractTopLabels

  - name: Log
    class: LogAttribute

Connections:

  - source name: Get
    source relationship name: success
    destination name: Convert

  - source name: Convert
    source relationship name: success
    destination name: Apply

  - source name: Apply
    source relationship name: success
    destination name: Extract

  - source name: Extract
    source relationship name: success
    destination name: Log

We use the following processors and connections to supply TFApplyGraph with the inference graph and TFExtractTopLabels with the labels file:

Processors:

  - name: GraphGet
    class: GetFile
    scheduling strategy: TIMER_DRIVEN
    scheduling period: 120 sec
    Properties:
      Keep Source File: true
      Input Directory: .
      File Filter: "frozen_nasnet.pb"
  
  - name: GraphUpdate
    class: UpdateAttribute
    Properties:
      tf.type: graph

  - name: LabelsGet
    class: GetFile
    scheduling strategy: TIMER_DRIVEN
    scheduling period: 120 sec
    Properties:
      Keep Source File: true
      Input Directory: .
      File Filter: "labels.txt"

  - name: LabelsUpdate
    class: UpdateAttribute
    Properties:
      tf.type: labels

Connections:

  - source name: GraphGet
    source relationship name: success
    destination name: GraphUpdate

  - source name: GraphUpdate
    source relationship name: success
    destination name: Apply

  - source name: LabelsGet
    source relationship name: success
    destination name: LabelsUpdate

  - source name: LabelsUpdate
    source relationship name: success
    destination name: Extract

Route/Store/Forward Inferences

For the purposes of this prototype, we'll use RouteOnAttribute in conjunction with the NiFi Expression Language forwarded to an ExecuteProcess using notify-send to notify us of a CAT_DETECTED event. In a production system, we may want to use Remote Processing Groups to forward data of interest to a centralzed system.

Our prototype flow looks like this:

Processors:

  - name: Route
    class: RouteOnAttribute
    Properties:
      cat: ${"tf.top_label_0":matches('(282|283|284|285|286|287|288|289|290|291|292|293|294):.*')}
    auto-terminated relationships list:
      - unmatched

  - name: Notify
    class: ExecuteProcess
    Properties:
      Command: notify-send CAT_DETECTED
    auto-terminated relationships list:
      - success

Connections:

  - source name: Log
    source relationship name: success
    destination name: Route

  - source name: Route
    source relationship name: cat
    destination name: Notify

Conclusion

We can now hold a cat up to our sensor and confirm that it detects a cat and triggers our notification:

62580-cat.jpg

----------
Standard FlowFile Attributes
UUID:0143d35c-1be5-11e8-a6f9-b06ebf2c6de8
EntryDate:2018-02-27 12:38:21.748
lineageStartDate:2018-02-27 12:38:21.748
Size:4020 Offset:0
FlowFile Attributes Map Content
key:filename value:1519753101748318191
key:path value:.
key:tf.top_label_0 value:284:Persian cat
key:tf.top_label_1 value:259:Samoyed, Samoyede
key:tf.top_label_2 value:357:weasel
key:tf.top_label_3 value:360:black-footed ferret, ferret, Mustela nigripes
key:tf.top_label_4 value:158:papillon
key:uuid value:0143d35c-1be5-11e8-a6f9-b06ebf2c6de8
FlowFile Resource Claim Content
Content Claim:/home/achristianson/workspace/minifi-article-2018-02-22/flow/contentrepository/1519753075140-43
----------

[2018-02-27 12:38:23.958] [org::apache::nifi::minifi::core::ProcessSession] [info] Transferring 02951658-1be5-11e8-9218-b06ebf2c6de8 from Route to relationship cat
[2018-02-27 12:38:25.754] [org::apache::nifi::minifi::processors::ExecuteProcess] [info] Execute Command notify-send CAT_DETECTED

MiNiFi - C++ makes it easy to create an IoT cat sensor. To complete our cat tracking system, we simply need to deploy a network of these sensors in the target space and configure the flow to deliver inferences to a centralized NiFi instance for storage and further analysis. We might also consider combining the image data with other data such as GPS sensor data using the GetGPS processor.

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Comments
Super Guru

can you add a complete config.yml?

Super Guru

which version of TensorFlow needs to be installed? Can you link the installation document?

Not applicable

Sure, here's the complete yml (some paths, USB ID, image resolution, etc. are specific to my system): https://gist.github.com/achristianson/1dea217e5fcbc88b87e526d919dad2c0. The Tensorflow install process I used is documented here: https://github.com/apache/nifi-minifi-cpp/blob/master/extensions/tensorflow/BUILDING.md. There are multiple ways this could be done, but tensorflow_cc has worked well. The TensorFlow version was used according to the latest master of tensorflow_cc as of the publish date of this article. I believe the version is 1.5.0.

Not applicable
Super Guru

Thanks!!!! That's very helpful.

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Last update:
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