This example shows how you can load a pre-trained TensorFlow network and use it to recognize objects in images in C++. For Java see the Java README, and for Go see the godoc example.
This demo uses a Google Inception model to classify image files that are passed in on the command line.
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:
$ curl -L "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz" | tar -C tensorflow/examples/label_image/data -xz
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 labels file in the data directory for the possible classifications, which are the 1,000 categories used in the Imagenet competition.
To build it, run this command:
$ bazel build tensorflow/examples/label_image/...
That should build a binary executable that you can then run like this:
$ bazel-bin/tensorflow/examples/label_image/label_image
This uses the default example image that ships with the framework, and should output something similar to this:
I tensorflow/examples/label_image/main.cc:206] military uniform (653): 0.834306 I tensorflow/examples/label_image/main.cc:206] mortarboard (668): 0.0218692 I tensorflow/examples/label_image/main.cc:206] academic gown (401): 0.0103579 I tensorflow/examples/label_image/main.cc:206] pickelhaube (716): 0.00800814 I tensorflow/examples/label_image/main.cc:206] bulletproof vest (466): 0.00535088
In this case, we‘re using the default image of Admiral Grace Hopper, and you can see the network correctly spots she’s wearing a military uniform, with a high score of 0.8.
Next, try it out on your own images by supplying the --image= argument, e.g.
$ bazel-bin/tensorflow/examples/label_image/label_image --image=my_image.png
For a more detailed look at this code, you can check out the C++ section of the Inception tutorial.
label_image.py is a python implementation that provides code corresponding to the C++ code here. This gives more intuitive mapping between C++ and Python than the Python code mentioned in the Inception tutorial. and could be easier to add visualization or debug code.
bazel-bin/tensorflow/examples/label_image/label_image_py should be there after
$ bazel build tensorflow/examples/label_image/...
Run
$ bazel-bin/tensorflow/examples/label_image/label_image_py
Or, with tensorflow python package installed, you can run it like:
$ python3 tensorflow/examples/label_image/label_image.py
And get result similar to this:
military uniform 0.834305 mortarboard 0.0218694 academic gown 0.0103581 pickelhaube 0.00800818 bulletproof vest 0.0053509