STT-tensorflow/tensorflow/examples/multibox_detector

TensorFlow C++ MultiBox Object Detection Demo

This example shows how you can load a pre-trained TensorFlow network and use it to detect objects in images in C++. For an alternate implementation see the Android TensorFlow demo

Description

This demo uses a model based on Scalable Object Detection using Deep NeuralNetworks to detect people in images passed in from the command line. This is the same model also used in the Android TensorFlow demo for real-time person detection and tracking in the camera preview.

To build/install/run

The TensorFlow GraphDef that contains the model definition and weights is not packaged in the repo because of its size. Instead, you must first download the file to the data directory in the source tree:

$ wget https://storage.googleapis.com/download.tensorflow.org/models/mobile_multibox_v1a.zip -O tensorflow/examples/multibox_detector/data/mobile_multibox_v1a.zip

$ unzip tensorflow/examples/multibox_detector/data/mobile_multibox_v1a.zip -d tensorflow/examples/multibox_detector/data/

Then, as long as you've managed to build the main TensorFlow framework, you should have everything you need to run this example installed already.

Once extracted, see the box priors file in the data directory. This file contains means and standard deviations for all 784 possible detections, normalized from 0-1 in left top right bottom order.

To build it, run this command:

$ bazel build --config opt tensorflow/examples/multibox_detector/...

That should build a binary executable that you can then run like this:

$ bazel-bin/tensorflow/examples/multibox_detector/detect_objects --image_out=$HOME/x20/surfers_labeled.png

This uses the default example image that ships with the framework, and should output something similar to this:

I0125 18:24:13.804047    8677 main.cc:293] ===== Top 5 Detections ======
I0125 18:24:13.804058    8677 main.cc:307] Detection 0: L:324.542 T:76.5764 R:373.26 B:214.957 (635) score: 0.267425
I0125 18:24:13.804077    8677 main.cc:307] Detection 1: L:332.896 T:76.2751 R:372.116 B:204.614 (523) score: 0.245334
I0125 18:24:13.804087    8677 main.cc:307] Detection 2: L:306.605 T:76.2228 R:371.356 B:217.32 (634) score: 0.216121
I0125 18:24:13.804096    8677 main.cc:307] Detection 3: L:143.918 T:86.0909 R:187.333 B:195.885 (387) score: 0.171368
I0125 18:24:13.804104    8677 main.cc:307] Detection 4: L:144.915 T:86.2675 R:185.243 B:165.246 (219) score: 0.169244

In this case, we're using a public domain stock image of surfers walking on the beach, and the top two few detections are of the two on the right. Adding more detections with --num_detections=N will also include the surfer on the left, and eventually non-person boxes below a certain threshold.

You can visually inspect the detections by viewing the resulting png file '~/surfers_labeled.png'.

Next, try it out on your own images by supplying the --image= argument, e.g.

$ bazel-bin/tensorflow/examples/multibox_detector/detect_objects --image=my_image.png

For another implementation of this work, you can check out the Android TensorFlow demo.