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Super Guru
Happy Holidays!



Apache NiFi makes it easy to build your own integration tests. So I am generating tests to test Turning On and Off My Christmas Tree Hat. I also testing taking a picture.


My use case is to send an HTTP message to trigger a Raspberry Pi to turn on a physical device like a camera or light. This is pretty cool and secure with Apache MiniFi and Apache NiFi. A little Python script is all the code and that's basic example code.







This code is a modified TensorFlow that adds turning on the Christmas Tree. So we turn on the tree and then take a picture with the PiCamera and then run it through a Tensorflow classifier.

root@vid5:/opt/demo# cat
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Simple image classification with Inception.
Run image classification with Inception trained on ImageNet 2012 Challenge data
This program creates a graph from a saved GraphDef protocol buffer,
and runs inference on an input JPEG image. It outputs human readable
strings of the top 5 predictions along with their probabilities.
Change the --image_file argument to any jpg image to compute a
classification of that image.
Please see the tutorial and website for a detailed description of how
to use this script to perform image recognition.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os.path
import re
import sys
import tarfile
import os
import datetime
import math
import random, string
import base64
import json
import time
import picamera
from time import sleep
from time import gmtime, strftime
import numpy as np
from six.moves import urllib
import tensorflow as tf
from gpiozero import LEDBoard
from import random_values
from signal import pause
tree = LEDBoard(*range(2,28),pwm=True)
for led in tree:
 led.source_delay = 0.1
 led.source = random_values()
FLAGS = None
# pylint: disable=line-too-long
# pylint: enable=line-too-long
# yyyy-mm-dd hh:mm:ss
currenttime= strftime("%Y-%m-%d %H:%M:%S",gmtime())
host = os.uname()[1]
def randomword(length):
  return ''.join(random.choice(string.lowercase) for i in range(length))
class NodeLookup(object):
  """Converts integer node ID's to human readable labels."""
  def __init__(self,
    if not label_lookup_path:
      label_lookup_path = os.path.join(
          FLAGS.model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
    if not uid_lookup_path:
      uid_lookup_path = os.path.join(
          FLAGS.model_dir, 'imagenet_synset_to_human_label_map.txt')
    self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
  def load(self, label_lookup_path, uid_lookup_path):
    """Loads a human readable English name for each softmax node.
      label_lookup_path: string UID to integer node ID.
      uid_lookup_path: string UID to human-readable string.
      dict from integer node ID to human-readable string.
    if not tf.gfile.Exists(uid_lookup_path):
      tf.logging.fatal('File does not exist %s', uid_lookup_path)
    if not tf.gfile.Exists(label_lookup_path):
      tf.logging.fatal('File does not exist %s', label_lookup_path)
    # Loads mapping from string UID to human-readable string
    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
    uid_to_human = {}
    p = re.compile(r'[n\d]*[ \S,]*')
    for line in proto_as_ascii_lines:
      parsed_items = p.findall(line)
      uid = parsed_items[0]
      human_string = parsed_items[2]
      uid_to_human[uid] = human_string
    # Loads mapping from string UID to integer node ID.
    node_id_to_uid = {}
    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
    for line in proto_as_ascii:
      if line.startswith('  target_class:'):
        target_class = int(line.split(': ')[1])
      if line.startswith('  target_class_string:'):
        target_class_string = line.split(': ')[1]
        node_id_to_uid[target_class] = target_class_string[1:-2]
    # Loads the final mapping of integer node ID to human-readable string
    node_id_to_name = {}
    for key, val in node_id_to_uid.items():
      if val not in uid_to_human:
        tf.logging.fatal('Failed to locate: %s', val)
      name = uid_to_human[val]
      node_id_to_name[key] = name
    return node_id_to_name
  def id_to_string(self, node_id):
    if node_id not in self.node_lookup:
      return ''
    return self.node_lookup[node_id]
def create_graph():
  """Creates a graph from saved GraphDef file and returns a saver."""
  # Creates graph from saved graph_def.pb.
  with tf.gfile.FastGFile(os.path.join(
      FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
    graph_def = tf.GraphDef()
    _ = tf.import_graph_def(graph_def, name='')
def run_inference_on_image(image):
  """Runs inference on an image.
    image: Image file name.
  if not tf.gfile.Exists(image):
    tf.logging.fatal('File does not exist %s', image)
  image_data = tf.gfile.FastGFile(image, 'rb').read()
  # Creates graph from saved GraphDef.
  with tf.Session() as sess:
    # Some useful tensors:
    # 'softmax:0': A tensor containing the normalized prediction across
    #   1000 labels.
    # 'pool_3:0': A tensor containing the next-to-last layer containing 2048
    #   float description of the image.
    # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
    #   encoding of the image.
    # Runs the softmax tensor by feeding the image_data as input to the graph.
    softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
    predictions =,
                           {'DecodeJpeg/contents:0': image_data})
    predictions = np.squeeze(predictions)
    # Creates node ID --> English string lookup.
    node_lookup = NodeLookup()
    top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
    row  = []
    for node_id in top_k:
      human_string = node_lookup.id_to_string(node_id)
      score = predictions[node_id]
      row.append( { 'node_id': node_id, 'image': image, 'host': host, 'ts': currenttime, 'human_string': str(human_string),  'score': str(score)} )
    json_string = json.dumps(row)
    print( json_string )
def maybe_download_and_extract():
  """Download and extract model tar file."""
  dest_directory = FLAGS.model_dir
  if not os.path.exists(dest_directory):
  filename = DATA_URL.split('/')[-1]
  filepath = os.path.join(dest_directory, filename)
  if not os.path.exists(filepath):
    def _progress(count, block_size, total_size):
      sys.stdout.write('\r>> Downloading %s %.1f%%' % (
          filename, float(count * block_size) / float(total_size) * 100.0))
    filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
    statinfo = os.stat(filepath)
    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.'), 'r:gz').extractall(dest_directory)
def main(_):
  # Create unique image name
  img_name = '/opt/demo/images/pi_image_{0}_{1}.jpg'.format(randomword(3),strftime("%Y%m%d%H%M%S",gmtime()))
  # Capture Image from Pi Camera
    camera = picamera.PiCamera()
    camera.resolution = (1024,768)
    camera.annotate_text = " Stored with Apache NiFi "
    camera.capture(img_name, resize=(600,400))
#  image = (FLAGS.image_file if FLAGS.image_file else
#           os.path.join(FLAGS.model_dir, 'cropped_panda.jpg'))
if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  # classify_image_graph_def.pb:
  #   Binary representation of the GraphDef protocol buffer.
  # imagenet_synset_to_human_label_map.txt:
  #   Map from synset ID to a human readable string.
  # imagenet_2012_challenge_label_map_proto.pbtxt:
  #   Text representation of a protocol buffer mapping a label to synset ID.
      help="""      Path to classify_image_graph_def.pb,
      imagenet_synset_to_human_label_map.txt, and
      imagenet_2012_challenge_label_map_proto.pbtxt.      """
      help='Absolute path to image file.'
      help='Display this many predictions.'
  FLAGS, unparsed = parser.parse_known_args(), argv=[sys.argv[0]] + unparsed)

This is the information for getting your own Christmas Tree Hat for your RPI.

sudo apt-get install python-gpiozero python3-gpiozero

Results of Running

[{"image": "/opt/demo/images/pi_image_bey_20171218140347.jpg", "ts": "2017-12-18 14:03:32", "host": "vid5", "score": "0.175653", "human_string": "pay-phone, pay-station", "node_id": 843}, {"image": "/opt/demo/images/pi_image_bey_20171218140347.jpg", "ts": "2017-12-18 14:03:32", "host": "vid5", "score": "0.0890657", "human_string": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "node_id": 914}, {"image": "/opt/demo/images/pi_image_bey_20171218140347.jpg", "ts": "2017-12-18 14:03:32", "host": "vid5", "score": "0.0631831", "human_string": "vending machine", "node_id": 558}, {"image": "/opt/demo/images/pi_image_bey_20171218140347.jpg", "ts": "2017-12-18 14:03:32", "host": "vid5", "score": "0.0541551", "human_string": "abacus", "node_id": 547}, {"image": "/opt/demo/images/pi_image_bey_20171218140347.jpg", "ts": "2017-12-18 14:03:32", "host": "vid5", "score": "0.0417486", "human_string": "rotisserie", "node_id": 663}]

To Remote Active the Tree

curl -X POST --data-ascii "tree-on" -v

It's so easy to enable Apache MIniFi to be controlled by any remote HTTP request.

Other Apache MiniFi Requests

root@vid5:/opt/demo/minifi-0.2.0/logs# curl -v http://HW13125.local:8080/nifi-api/system-diagnostics * Hostname was NOT found in DNS cache * Trying * Connected to HW13125.local ( port 8080 (#0) > GET /nifi-api/system-diagnostics HTTP/1.1 > User-Agent: curl/7.38.0 > Host: HW13125.local:8080 > Accept: */* > < HTTP/1.1 200 OK < Date: Mon, 18 Dec 2017 13:42:07 GMT < X-Frame-Options: SAMEORIGIN < Cache-Control: private, no-cache, no-store, no-transform < Content-Type: application/json < Vary: Accept-Encoding < Vary: User-Agent < Content-Length: 1852 * Server Jetty(9.4.3.v20170317) is not blacklisted < Server: Jetty(9.4.3.v20170317) < {"systemDiagnostics":{"aggregateSnapshot":{"totalNonHeap":"390.23 MB","totalNonHeapBytes":409190400,"usedNonHeap":"370.09 MB","usedNonHeapBytes":388065576,"freeNonHeap":"20.15 MB","freeNonHeapBytes":21124824,"maxNonHeap":"-1 bytes","maxNonHeapBytes":-1,"totalHeap":"2 GB","totalHeapBytes":2147483648,"usedHeap":"1.77 GB","usedHeapBytes":1904638968,"freeHeap":"231.59 MB","freeHeapBytes":242844680,"maxHeap":"2 GB","maxHeapBytes":2147483648,"heapUtilization":"89.0%","availableProcessors":8,"processorLoadAverage":2.794921875,"totalThreads":105,"daemonThreads":41,"uptime":"43:00:01.210","flowFileRepositoryStorageUsage":{"freeSpace":"55.76 GB","totalSpace":"931.19 GB","usedSpace":"875.43 GB","freeSpaceBytes":59870846976,"totalSpaceBytes":999860912128,"usedSpaceBytes":939990065152,"utilization":"94.0%"},"contentRepositoryStorageUsage":[{"identifier":"default","freeSpace":"55.76 GB","totalSpace":"931.19 GB","usedSpace":"875.43 GB","freeSpaceBytes":59870846976,"totalSpaceBytes":999860912128,"usedSpaceBytes":939990065152* Connection #0 to host HW13125.local left intact ,"utilization":"94.0%"}],"provenanceRepositoryStorageUsage":[{"identifier":"default","freeSpace":"55.76 GB","totalSpace":"931.19 GB","usedSpace":"875.43 GB","freeSpaceBytes":59870846976,"totalSpaceBytes":999860912128,"usedSpaceBytes":939990065152,"utilization":"94.0%"}],"garbageCollection":[{"name":"G1 Young Generation","collectionCount":742,"collectionTime":"00:00:17.754","collectionMillis":17754},{"name":"G1 Old Generation","collectionCount":0,"collectionTime":"00:00:00.000","collectionMillis":0}],"statsLastRefreshed":"08:42:07 EST","versionInfo":{"niFiVersion":"1.5.0-SNAPSHOT","javaVendor":"Oracle Corporation","javaVersion":"1.8.0_121","osName":"Mac OS X","osVersion":"10.13.2","osArchitecture":"x86_64","buildTag":"HEAD","buildRevision":"a774f1d","buildBranch":"master","buildTimestamp":"12/07/2017 13:37:07 EST"}}}}root@vid5:/opt/d


I will keep my eyes out for Raspberry PI add-ons for other holidays.

For the second christmas tree it's a Sense Hat!

 1.  Setup Raspian Stretch
 2.  Sense-Hat
 sudo apt-get install sense-hat
 sudo apt-get install octave -y
pip install --upgrade sense-hat
pip install --upgrade pillow
pip install rtimulib
pip install psutil
sudo apt-get install oracle-java8-jdk
sudo apt install gstreamer-1.0
sudo apt install python3-gst-1.0
sudo apt-get install gir1.2-gstreamer-1.0
sudo apt-get install gir1.2-gst-plugins-base-1.0

For the Sense Hat

Just run this:

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Version history
Revision #:
2 of 2
Last update:
‎08-17-2019 09:39 AM
Updated by:
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