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FROM ubuntu:16.04
# Install system packages
RUN apt-get -qq update && apt-get -qq install --no-install-recommends -y python3 \
python3-dev \
python-pil \
python-lxml \
python-tk \
build-essential \
cmake \
git \
libgtk2.0-dev \
pkg-config \
libavcodec-dev \
libavformat-dev \
libswscale-dev \
libtbb2 \
libtbb-dev \
libjpeg-dev \
libpng-dev \
libtiff-dev \
libjasper-dev \
libdc1394-22-dev \
x11-apps \
wget \
vim \
ffmpeg \
unzip \
&& rm -rf /var/lib/apt/lists/*
# Install core packages
RUN wget -q -O /tmp/get-pip.py --no-check-certificate https://bootstrap.pypa.io/get-pip.py && python3 /tmp/get-pip.py
RUN pip install -U pip \
numpy \
matplotlib \
notebook \
jupyter \
pandas \
moviepy \
tensorflow \
keras \
autovizwidget \
Flask
# Install tensorflow models object detection
RUN GIT_SSL_NO_VERIFY=true git clone -q https://github.com/tensorflow/models /usr/local/lib/python3.5/dist-packages/tensorflow/models
RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/google/protobuf/releases/download/v3.5.1/protobuf-python-3.5.1.tar.gz
# Download & build protobuf-python
RUN cd /usr/local/src/ \
&& tar xf protobuf-python-3.5.1.tar.gz \
&& rm protobuf-python-3.5.1.tar.gz \
&& cd /usr/local/src/protobuf-3.5.1/ \
&& ./configure \
&& make \
&& make install \
&& ldconfig \
&& rm -rf /usr/local/src/protobuf-3.5.1/
# Add dataframe display widget
RUN jupyter nbextension enable --py --sys-prefix widgetsnbextension
# Download & build OpenCV
RUN wget -q -P /usr/local/src/ --no-check-certificate https://github.com/opencv/opencv/archive/3.4.1.zip
RUN cd /usr/local/src/ \
&& unzip 3.4.1.zip \
&& rm 3.4.1.zip \
&& cd /usr/local/src/opencv-3.4.1/ \
&& mkdir build \
&& cd /usr/local/src/opencv-3.4.1/build \
&& cmake -D CMAKE_INSTALL_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local/ .. \
&& make -j4 \
&& make install \
&& rm -rf /usr/local/src/opencv-3.4.1
# Minimize image size
RUN (apt-get autoremove -y; \
apt-get autoclean -y)
# Set TF object detection available
ENV PYTHONPATH "$PYTHONPATH:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research/slim"
RUN cd /usr/local/lib/python3.5/dist-packages/tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=.
COPY detect_objects.py .
CMD ["python3", "-u", "detect_objects.py"]

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# Realtime Object Detection for RTSP Cameras
- Prioritizes realtime processing over frames per second. Dropping frames is fine.
- OpenCV runs in a separate process so it can grab frames as quickly as possible to ensure there aren't old frames in the buffer
- Object detection with Tensorflow runs in a separate process and ignores frames that are more than 0.5 seconds old
- Uses shared memory arrays for handing frames between processes
- Provides a url for viewing the video feed at a hard coded ~5FPS as an mjpeg stream
- Frames are only encoded into mjpeg stream when it is being viewed
## Getting Started
Build the container with
```
docker build -t realtime-od .
```
Download a model from the [zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md).
Download the cooresponding label map from [here](https://github.com/tensorflow/models/tree/master/research/object_detection/data).
Run the container with
```
docker run -it --rm \
-v <path_to_frozen_detection_graph.pb>:/frozen_inference_graph.pb:ro \
-v <path_to_labelmap.pbtext>:/label_map.pbtext:ro \
-p 5000:5000 \
-e RTSP_URL='<rtsp_url>' \
realtime-od:latest
```
Access the mjpeg stream at http://localhost:5000
## Future improvements
- MQTT messages when detected objects change
- Dynamic changes to processing speed, ie. only process 1FPS unless motion detected
- Break incoming frame into multiple smaller images and run detection in parallel for lower latency (rather than input a lower resolution)
- Parallel processing to increase FPS

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import os
import cv2
import time
import datetime
import ctypes
import logging
import multiprocessing as mp
from contextlib import closing
import numpy as np
import tensorflow as tf
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
from flask import Flask, Response, make_response
RTSP_URL = os.getenv('RTSP_URL')
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = '/label_map.pbtext'
# TODO: make dynamic?
NUM_CLASSES = 90
# Loading label map
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,
use_display_name=True)
category_index = label_map_util.create_category_index(categories)
def detect_objects(image_np, sess, detection_graph):
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# build an array of detected objects
objects = []
for index, value in enumerate(classes[0]):
object_dict = {}
if scores[0, index] > 0.5:
object_dict[(category_index.get(value)).get('name').encode('utf8')] = \
scores[0, index]
objects.append(object_dict)
# draw boxes for detected objects on image
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=4)
return objects, image_np
def main():
# capture a single frame and check the frame shape so the correct array
# size can be allocated in memory
video = cv2.VideoCapture(RTSP_URL)
ret, frame = video.read()
if ret:
frame_shape = frame.shape
else:
print("Unable to capture video stream")
exit(1)
video.release()
# create shared value for storing the time the frame was captured
# note: this must be a double even though the value you are storing
# is a float. otherwise it stops updating the value in shared
# memory. probably something to do with the size of the memory block
shared_frame_time = mp.Value('d', 0.0)
# compute the flattened array length from the array shape
flat_array_length = frame_shape[0] * frame_shape[1] * frame_shape[2]
# create shared array for passing the image data from capture to detect_objects
shared_arr = mp.Array(ctypes.c_uint16, flat_array_length)
# create shared array for passing the image data from detect_objects to flask
shared_output_arr = mp.Array(ctypes.c_uint16, flat_array_length)
# create a numpy array with the image shape from the shared memory array
# this is used by flask to output an mjpeg stream
frame_output_arr = tonumpyarray(shared_output_arr).reshape(frame_shape)
capture_process = mp.Process(target=fetch_frames, args=(shared_arr, shared_frame_time, frame_shape))
capture_process.daemon = True
detection_process = mp.Process(target=process_frames, args=(shared_arr, shared_output_arr, shared_frame_time, frame_shape))
detection_process.daemon = True
capture_process.start()
print("capture_process pid ", capture_process.pid)
detection_process.start()
print("detection_process pid ", detection_process.pid)
app = Flask(__name__)
@app.route('/')
def index():
# return a multipart response
return Response(imagestream(),
mimetype='multipart/x-mixed-replace; boundary=frame')
def imagestream():
while True:
# max out at 5 FPS
time.sleep(0.2)
# convert back to BGR
frame_bgr = cv2.cvtColor(frame_output_arr, cv2.COLOR_RGB2BGR)
# encode the image into a jpg
ret, jpg = cv2.imencode('.jpg', frame_bgr)
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + jpg.tobytes() + b'\r\n\r\n')
app.run(host='0.0.0.0', debug=False)
capture_process.join()
detection_process.join()
# convert shared memory array into numpy array
def tonumpyarray(mp_arr):
return np.frombuffer(mp_arr.get_obj(), dtype=np.uint16)
# fetch the frames as fast a possible, only decoding the frames when the
# detection_process has consumed the current frame
def fetch_frames(shared_arr, shared_frame_time, frame_shape):
# convert shared memory array into numpy and shape into image array
arr = tonumpyarray(shared_arr).reshape(frame_shape)
# start the video capture
video = cv2.VideoCapture(RTSP_URL)
# keep the buffer small so we minimize old data
video.set(cv2.CAP_PROP_BUFFERSIZE,1)
while True:
# grab the frame, but dont decode it yet
ret = video.grab()
# snapshot the time the frame was grabbed
frame_time = datetime.datetime.now()
if ret:
# if the detection_process is ready for the next frame decode it
# otherwise skip this frame and move onto the next one
if shared_frame_time.value == 0.0:
# go ahead and decode the current frame
ret, frame = video.retrieve()
if ret:
# copy the frame into the numpy array
arr[:] = frame
# signal to the detection_process by setting the shared_frame_time
shared_frame_time.value = frame_time.timestamp()
video.release()
# do the actual object detection
def process_frames(shared_arr, shared_output_arr, shared_frame_time, frame_shape):
# shape shared input array into frame for processing
arr = tonumpyarray(shared_arr).reshape(frame_shape)
# shape shared output array into frame so it can be copied into
output_arr = tonumpyarray(shared_output_arr).reshape(frame_shape)
# Load a (frozen) Tensorflow model into memory before the processing loop
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
no_frames_available = -1
while True:
# if there isnt a frame ready for processing
if shared_frame_time.value == 0.0:
# save the first time there were no frames available
if no_frames_available == -1:
no_frames_available = datetime.datetime.now().timestamp()
# if there havent been any frames available in 30 seconds,
# sleep to avoid using so much cpu if the camera feed is down
if no_frames_available > 0 and (datetime.datetime.now().timestamp() - no_frames_available) > 30:
time.sleep(1)
print("sleeping because no frames have been available in a while")
continue
# we got a valid frame, so reset the timer
no_frames_available = -1
# if the frame is more than 0.5 second old, discard it
if (datetime.datetime.now().timestamp() - shared_frame_time.value) > 0.5:
# signal that we need a new frame
shared_frame_time.value = 0.0
continue
# make a copy of the frame
frame = arr.copy()
frame_time = shared_frame_time.value
# signal that the frame has been used so a new one will be ready
shared_frame_time.value = 0.0
# convert to RGB
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# do the object detection
objects, frame_overlay = detect_objects(frame_rgb, sess, detection_graph)
# copy the output frame with the bounding boxes to the output array
output_arr[:] = frame_overlay
if(len(objects) > 0):
print(objects)
if __name__ == '__main__':
mp.freeze_support()
main()