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detect_objects.py
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detect_objects.py
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"""Object Detection on Spark using TensorFlow.
Consumes video frames from an Kafka Endpoint, process it on spark, produces
a result containing annotate video frame and sends it to another topic of the
same Kafka Endpoint.
Run with: /usr/bin/spark-submit detect_objects.py
Build on information from:
- https://stackoverflow.com/questions/37337086/how-to-properly-use-pyspark-to-send-data-to-kafka-broker
- https://scotch.io/tutorials/build-a-distributed-streaming-system-with-apache-kafka-and-python
- https://databricks.com/blog/2016/01/25/deep-learning-with-apache-spark-and-tensorflow.html
- https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
"""
# Utility imports
from __future__ import print_function
import base64
import json
import numpy as np
from StringIO import StringIO
from timeit import default_timer as timer
from PIL import Image
import datetime as dt
from random import randint
# Streaming imports
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
from kafka import KafkaProducer
# Object detection imports
import tensorflow as tf
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
class Spark_Object_Detector():
"""Stream WebCam Images to Kafka Endpoint.
Keyword arguments:
source -- Index of Video Device or Filename of Video-File
interval -- Interval for capturing images in seconds (default 5)
server -- Host + Port of Kafka Endpoint (default '127.0.0.1:9092')
"""
def __init__(self,
interval=10,
model_file='',
labels_file='',
number_classes=90,
detect_treshold=.5,
topic_to_consume='pycturestream',
topic_for_produce='resultstream',
kafka_endpoint='127.0.0.1:9092'):
"""Initialize Spark & TensorFlow environment."""
self.topic_to_consume = topic_to_consume
self.topic_for_produce = topic_for_produce
self.kafka_endpoint = kafka_endpoint
self.treshold = detect_treshold
self.v_sectors = ['top', 'middle', 'bottom']
self.h_sectors = ['left', 'center', 'right']
# Create Kafka Producer for sending results
self.producer = KafkaProducer(bootstrap_servers=kafka_endpoint)
# Load Labels & Categories
label_map = label_map_util.load_labelmap(labels_file)
categories = label_map_util.convert_label_map_to_categories(label_map,
max_num_classes=number_classes,
use_display_name=True
)
self.category_index = label_map_util.create_category_index(categories)
# Load Spark Context
sc = SparkContext(appName='PyctureStream')
self.ssc = StreamingContext(sc, interval) # , 3)
# Make Spark logging less extensive
log4jLogger = sc._jvm.org.apache.log4j
log_level = log4jLogger.Level.ERROR
log4jLogger.LogManager.getLogger('org').setLevel(log_level)
log4jLogger.LogManager.getLogger('akka').setLevel(log_level)
log4jLogger.LogManager.getLogger('kafka').setLevel(log_level)
self.logger = log4jLogger.LogManager.getLogger(__name__)
# Load Frozen Network Model & Broadcast to Worker Nodes
with tf.gfile.FastGFile(model_file, 'rb') as f:
model_data = f.read()
self.model_data_bc = sc.broadcast(model_data)
# Load Graph Definition
self.graph_def = tf.GraphDef()
self.graph_def.ParseFromString(self.model_data_bc.value)
def start_processing(self):
"""Start consuming from Kafka endpoint and detect objects."""
kvs = KafkaUtils.createDirectStream(self.ssc,
[self.topic_to_consume],
{'metadata.broker.list': self.kafka_endpoint}
)
kvs.foreachRDD(self.handler)
self.ssc.start()
self.ssc.awaitTermination()
def load_image_into_numpy_array(self, image):
"""Convert PIL image to numpy array."""
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
def box_to_sector(self, box):
"""Transform object box in image into sector description."""
width = box[3] - box[1]
h_pos = box[1] + width / 2.0
height = box[2] - box[0]
v_pos = box[0] + height / 2.0
h_sector = min(int(h_pos * 3), 2) # 0: left, 1: center, 2: right
v_sector = min(int(v_pos * 3), 2) # 0: top, 1: middle, 2: bottom
return (self.v_sectors[v_sector], self.h_sectors[h_sector])
def get_annotated_image_as_text(self, image_np, output):
"""Paint the annotations on the image and serialize it into text."""
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output['detection_boxes'],
output['detection_classes'],
output['detection_scores'],
self.category_index,
instance_masks=output.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=3)
# Serialize into text, so it can be send as json
img = Image.fromarray(image_np)
text_stream = StringIO()
img.save(text_stream, 'JPEG')
contents = text_stream.getvalue()
text_stream.close()
img_as_text = base64.b64encode(contents).decode('utf-8')
return img_as_text
def format_object_desc(self, output):
"""Transform object detection output into nice list of dicts."""
objs = []
for i in range(len(output['detection_classes'])):
# just for for nice output
score = round(output['detection_scores'][i], 2)
if score > self.treshold: # Only keep objects over treshold
# Get label for category id
cat_id = output['detection_classes'][i]
label = self.category_index[cat_id]['name']
# Get position of object box as [ymin, xmin, ymax, xmax]
box = output['detection_boxes'][i]
objs.append({
'label': label,
'score': score,
'sector': self.box_to_sector(box)
})
return objs
def detect_objects(self, event):
"""Use TensorFlow Model to detect objects."""
# Load the image data from the json into PIL image & numpy array
decoded = base64.b64decode(event['image'])
stream = StringIO(decoded)
image = Image.open(stream)
image_np = self.load_image_into_numpy_array(image)
stream.close()
# Load Network Graph
tf.import_graph_def(self.graph_def, name='') # ~ 2.7 sec
# Runs a tensor flow session
with tf.Session() as sess:
# Get handles to input and output tensors
ops = tf.get_default_graph().get_operations()
all_tensor_names = {
output.name for op in ops for output in op.outputs
}
tensor_dict = {}
for key in ['num_detections',
'detection_boxes',
'detection_scores',
'detection_classes',
'detection_masks'
]:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = (tf.get_default_graph()
.get_tensor_by_name(tensor_name)
)
if 'detection_masks' in tensor_dict:
# The following processing is only for single image
detection_boxes = tf.squeeze(
tensor_dict['detection_boxes'], [0]
)
detection_masks = tf.squeeze(
tensor_dict['detection_masks'], [0]
)
# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
real_num_detection = tf.cast(
tensor_dict['num_detections'][0], tf.int32
)
detection_boxes = tf.slice(
detection_boxes,
[0, 0],
[real_num_detection, -1]
)
detection_masks = tf.slice(
detection_masks,
[0, 0, 0],
[real_num_detection, -1, -1]
)
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks,
detection_boxes,
image.shape[0],
image.shape[1]
)
detection_masks_reframed = tf.cast(
tf.greater(detection_masks_reframed, 0.5),
tf.uint8
)
# Follow the convention by adding back the batch dimension
tensor_dict['detection_masks'] = tf.expand_dims(
detection_masks_reframed,
0
)
image_tensor = (tf.get_default_graph()
.get_tensor_by_name('image_tensor:0')
)
# Run inference
output = sess.run(
tensor_dict,
feed_dict={image_tensor: np.expand_dims(image, 0)}
) # ~4.7 sec
# all outputs are float32 numpy arrays, so convert types as appropriate
output['num_detections'] = int(output['num_detections'][0])
output['detection_classes'] = output['detection_classes'][0].astype(
np.uint8)
output['detection_boxes'] = output['detection_boxes'][0]
output['detection_scores'] = output['detection_scores'][0]
# END tf.session
# Prevent Memory Leaking
tf.reset_default_graph()
# Prepare object for sending to endpoint
result = {'timestamp': event['timestamp'],
'camera_id': event['camera_id'],
'objects': self.format_object_desc(output),
'image': self.get_annotated_image_as_text(image_np, output)
}
return json.dumps(result)
def handler(self, timestamp, message):
"""Collect messages, detect object and send to kafka endpoint."""
records = message.collect()
# For performance reasons, we only want to process the newest message
# for every camera_id
to_process = {}
self.logger.info( '\033[3' + str(randint(1, 7)) + ';1m' + # Color
'-' * 25 +
'[ NEW MESSAGES: ' + str(len(records)) + ' ]'
+ '-' * 25 +
'\033[0m' # End color
)
dt_now = dt.datetime.now()
for record in records:
event = json.loads(record[1])
self.logger.info('Received Message: ' +
event['camera_id'] + ' - ' + event['timestamp'])
dt_event = dt.datetime.strptime(
event['timestamp'], '%Y-%m-%dT%H:%M:%S.%f')
delta = dt_now - dt_event
if delta.seconds > 5:
continue
to_process[event['camera_id']] = event
if len(to_process) == 0:
self.logger.info('Skipping processing...')
for key, event in to_process.items():
self.logger.info('Processing Message: ' +
event['camera_id'] + ' - ' + event['timestamp'])
start = timer()
detection_result = self.detect_objects(event)
end = timer()
delta = end - start
self.logger.info('Done after ' + str(delta) + ' seconds.')
self.producer.send(self.topic_for_produce, detection_result)
self.producer.flush()
if __name__ == '__main__':
sod = Spark_Object_Detector(
interval=3,
model_file='/home/cloudera/PyctureStream-master/frozen_inference_graph.pb',
labels_file='/home/cloudera/PyctureStream-master/mscoco_label_map.pbtxt',
number_classes=90,
detect_treshold=.5, # .5 This is default treshold for annotations in image
topic_to_consume='pycturestream',
topic_for_produce='resultstream',
kafka_endpoint='127.0.0.1:9092')
sod.start_processing()