Implementing DetectionPolicy with existing Inceptionv3 model

.gitignore

@@ -7,3 +7,5 @@ credentials.json
 token.json
 local.db
 node_modules
+#pycharm files
+*/.idea/

google_cloud/setup_vm.sh

@@ -17,6 +17,10 @@ apt-get -y install sqlite3
 pip3 install -U pip
 pip3 install -U psycopg2
 pip3 install -U twilio
+pip3 install -U numpy
+pip3 install -U scipy
+pip3 install -U docker
+
 
 # change timezone
 ln -fs /usr/share/zoneinfo/America/Los_Angeles /etc/localtime

lib/detection_policies/inception_and_threshold.py

@@ -10,7 +10,7 @@
 import settings
 import datetime
 import math
-
+import docker
 
 class InceptionV3AndHistoricalThreshold:
 
@@ -19,6 +19,9 @@ class InceptionV3AndHistoricalThreshold:
 
     def __init__(self, settings, args, google_services, dbManager):
         self.dbManager = dbManager
+        #start up docker daemon with tf serving        
+        client = docker.from_env()
+        client.containers.run("inception_serving", detach=True, ports={'8500/tcp': 8500}, tty=True)
         self.prediction_service = connect_to_prediction_service(settings.server_ip_and_port)
         self.args = args
         self.google_services = google_services

lib/gcp_helper.py

@@ -0,0 +1,62 @@
+# Copyright 2018 The Fuego 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
+#
+#     http://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.
+# ==============================================================================
+"""
+
+Helper functions for Google Cloud Platform
+
+"""
+
+import grpc
+import tensorflow as tf
+import time
+
+from tensorflow_serving.apis import predict_pb2
+from tensorflow_serving.apis import prediction_service_pb2_grpc
+from tensorflow.python.framework import tensor_util
+
+def connect_to_prediction_service(server_ip_and_port):
+    """
+    Connect to a an inference server at given ip address and port. Server could be
+    a single machine or a Kubernetes cluster
+    :param server_ip_and_port: string with ip address followed by port (e.g. '34.82.71.243:8500')
+    :return: PredicitonServiceStub object
+    """
+    # tf.app.flags.DEFINE_string('server', server_ip_and_port, 'PredictionService host:port')
+    # channel = grpc.insecure_channel(tf.app.flags.FLAGS.server)
+    channel = grpc.insecure_channel(server_ip_and_port)
+    # grpc.secure_channel()
+    return prediction_service_pb2_grpc.PredictionServiceStub(channel)
+
+def predict_batch(prediction_service, crops, timing=False):
+    """
+    Run inference on a batch of predicitons
+    :param crops: N x H x W x 3 uint8 numpy array (e.g. set of crops for a single camera)
+    :return: N x 2 numpy array of smoke/nonsmoke probabilities
+    """
+
+    # Send request
+    # See prediction_service.proto for gRPC request/response details.
+    request = predict_pb2.PredictRequest()
+    request.model_spec.name = 'inception'
+    request.model_spec.signature_name = 'serving_default'
+    request.inputs['image_batch:0'].CopyFrom(tf.contrib.util.make_tensor_proto(crops, shape=crops.shape))
+
+    start = time.time()
+    result = prediction_service.Predict(request, 1000.0)  # 1000 secs timeout
+    if timing:
+        print('Inference time {}'.format(time.time() - start))
+    #convert to numpy
+    numpy_result = tensor_util.MakeNdarray(result.outputs["import_1/import/InceptionV3/Predictions/Reshape_1"])
+    return numpy_result

lib/rect_to_squares.py

@@ -14,9 +14,7 @@
 # ==============================================================================
 """
 @author: Kinshuk Govil
-
 Simple utility to break up rectangle into squares
-
 """
 
 import os
@@ -33,7 +31,6 @@ def rect_to_squares(selectionX0, selectionY0, selectionX1, selectionY1, limitX,
     close enough to a square.  Also, squares must meet minimium size requiremet of
     minSize and must be centered around the selected rectangle.  All squares must fit
     between (0,0) and (limitX, limitY)
-
     Returns array/list of coordinates for the squares.  The coordinates are represented
     by 4-tuples (x0,y0,x1,y1)
     """
@@ -130,15 +127,12 @@ def cutBoxesOld(imgOrig, outputDirectory, imageFileName, callBackFn=None):
 
 def getSegmentRanges(fullSize, segmentSize):
     """Break the given fullSize into ranges of segmentSize
-
     Divide the range (0,fullSize) into multiple ranges of size
     segmentSize that are equally spaced apart and have approximately
     10% overlap (overlapRatio)
-
     Args:
         fullSize (int): size of the full range (0, fullSize)
         segmentSize (int): size of each segment
-
     Returns:
         (list): list of tuples (start, end) marking each segment's range
     """
@@ -169,18 +163,15 @@ def getSegmentRanges(fullSize, segmentSize):
 
 def cutBoxesFixed(imgOrig, outputDirectory, imageFileName, callBackFn=None):
     """Cut the given image into fixed size boxes
-
     Divide the given image into square segments of 299x299 (segmentSize below)
     to match the size of images used by InceptionV3 image classification
     machine learning model.  This function uses the getSegmentRanges() function
     above to calculate the exact start and end of each square
-
     Args:
         imgOrig (Image): Image object of the original image
         outputDirectory (str): name of directory to store the segments
         imageFileName (str): nane of image file (used as segment file prefix)
         callBackFn (function): callback function that's called for each square
-
     Returns:
         (list): list of segments with filename and coordinates
     """
@@ -233,4 +224,4 @@ def test():
 
 # for testing
 if __name__=="__main__":
-    test()
+    test()

sample_settings.py

@@ -23,6 +23,8 @@
 downloadDir = 'XXX/orig'
 archive_storage_bucket = "fuego-firecam-a"
 
+server_ip_and_port = 'localhost:8500' #depends on the specific inference server running on GCP
+
 teamDriveID = '0ADX9uPkOmsDJUk9PVA' # Fuego Smoke
 allPictures = '1SCXFLE25EbQUQMvTcWfeHFU29qxN8WMM' # Pictures  - Samples and Full Sets
 # smokePictures = '1jq9p2A5BVLh1oWKktpV1oaWTVUU9KmNJ' # Smoke