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Intel® OpenVINOTM integration with TensorFlow - C++ and Python Examples

These examples demonstrate how to use Intel® OpenVINOTM integration with Tensorflow to recognize and detect objects in images and videos.

Quick Links for examples

Demos showcased in the examples

  • Classification demo uses Google's Inception v3 model to classify a given image, video, directory of images or camera input.
  • Object detection demo uses YOLOv3 model converted from Darknet to detect objects in a given image, video, directory of images or camera input.

Setup for the examples

Before you proceed to run the examples, you will have to clone the openvino_tensorflow repository to your local machine. For this, run the following commands:

$ git clone https://github.com/openvinotoolkit/openvino_tensorflow.git
$ cd openvino_tensorflow
$ git submodule init
$ git submodule update --recursive

Python implementation for classification

For this example, we assume that you've already:

  • Installed TensorFlow on your system
  • Installed Intel® OpenVINOTM integration with Tensorflow on your system

Refer to this page for quick installation using pip.

The TensorFlow model used in this demo is not packaged in the repo because of its size. So, download the model to the <path-to-openvino_tensorflow-repository>/examples/data directory in your cloned repo of openvino_tensorflow and extract the file:

$ curl -L "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz" |
  tar -C <path-to-openvino_tensorflow-repository>/examples/data -xz

Once extracted, the <path-to-openvino_tensorflow-repository>/examples/data folder will contain two new files:

  • imagenet_slim_labels.txt
  • inception_v3_2016_08_28_frozen.pb

Open imagenet_slim_labels.txt to read the labels in the <path-to-openvino_tensorflow-repository>/examples/data directory for the possible classifications. In the .txt file, you'll find 1,000 categories that were used in the Imagenet competition.

Install the pre-requisites

$ cd <path-to-openvino_tensorflow-repository>/examples
$ pip3 install -r requirements.txt

Now, you can run classification sample using the instructions below:

$ cd <path-to-openvino_tensorflow-repository>
$ python3 examples/classification_sample.py

classification_sample.py does inference on the default example image that comes with this repository and should output something similar to:

military uniform (653): 0.834306
mortarboard (668): 0.0218693
academic gown (401): 0.010358
pickelhaube (716): 0.00800814
bulletproof vest (466): 0.00535091

Note: use --no_show flag to disable the application display window. By default the display window is enabled.

In this case, we are using the default image of Admiral Grace Hopper. As you can see, the network correctly spots that she's wearing a military uniform, with a high score of 0.8.

Next, try it out by passing the path to your new input. You can provide either absolute or relative path to an image or video or directory of images. e.g.

$ python3 examples/classification_sample.py --input=<absolute-or-relative-path-to-your-input>

If you add the new image (e.g, my_image.png) to the existing <path-to-openvino_tensorflow-repository>/examples/data directory in the openvino_tensorflow repository, it will look like this:

$ python3 examples/classification_sample.py --input=examples/data/my_image.png

To see more options for various backends (Intel® hardware), invoke:

$ python3 examples/classification_sample.py --help

Next, try it out on your own video file by passing the path to your input video. You can provide either absolute or relative path e.g.

$ python3 examples/classification_sample.py --input=<absolute-or-relative-path-to-your-video-file>

If you add the new video (e.g, examples/data/people-detection.mp4) to the existing <path-to-openvino_tensorflow-repository>/examples/data directory in the openvino_tensorflow repository, it will look like this:

$ python3 examples/classification_sample.py --input=examples/data/people-detection.mp4

For using camera as input use --input=0. Here '0' refers to the camera present at /dev/video0. If the camera is connected to a different port, change it appropriately.

Python implementation for object detection

For this example, we assume that you've already:

  • Installed TensorFlow on your system.
  • Installed Intel® OpenVINOTM integration with Tensorflow on your system

Note: Refer to this page for the conversion of yolov3 darknet model using Tensorflow 1.15 and it's python object detection sample.

Refer to this page for quick installation using pip.

Install the pre-requisites

$ cd <path-to-openvino_tensorflow-repository>/examples
$ pip3 install -r requirements.txt

The TensorFlow Yolo v3 darknet model used in this demo is not packaged in the repository because of its size. So, follow the instructions below to convert the model from DarkNet to TensorFlow and download the labels and weights to the <path-to-openvino_tensorflow-repository>/examples/data directory in your cloned repo of openvino_tensorflow:

$ cd <path-to-openvino_tensorflow-repository>/examples
$ chmod +x convert_yolov3.sh
$ ./convert_yolov3.sh

Once completed, the <path-to-openvino_tensorflow-repository>/examples/data folder will contain following files needed to run the object detection example:

  • coco.names
  • yolo_v3_darknet_2.pb

Run the object detection example using the instructions below:

$ cd <path-to-openvino_tensorflow-repository>
$ python3 examples/object_detection_sample.py

Note:

  • use --no_show flag to disable the application display window. By default the display window is enabled.
  • use --rename to rename the input image or the directory of the images based on the content of image, for example noexiftags-1person-1uniform-intelOpenVINO.jpg

This uses the default example image that comes with this repository, and should output something similar as below and the result is written to detections.jpg:

In this case, we're using the default image of Admiral Grace Hopper. As you can see, the network detects and draws the bounding box around the person correctly.

Next, try it out on your own image by passing the path to your new input. You can provide either absolute or relative path e.g.

$ python3 examples/object_detection_sample.py --input=<absolute-or-relative-path-to-your-image>

If you add the new image (e.g, my_image.png) to the existing <path-to-openvino_tensorflow-repository>/examples/data directory in the openvino_tensorflow repository, it will look like this:

$ cd <path-to-openvino_tensorflow-repository>
$ python3 examples/object_detection_sample.py --input=examples/data/my_image.png

To see more options for various backends (Intel® hardware), invoke:

$ python3 examples/object_detection_sample.py --help

Next, try it out on your own video file by passing the path to your input video. You can provide either absolute or relative path e.g.

$ python3 examples/object_detection_sample.py --input=<absolute-or-relative-path-to-your-video-file>

If you add the new video (e.g, examples/data/people-detection.mp4) to the existing <path-to-openvino_tensorflow-repository>/examples/data directory in the openvino_tensorflow repository, it will look like this:

$ python3 examples/object_detection_sample.py --input=examples/data/people-detection.mp4

For using camera as input use --input=0. Here '0' refers to the camera present at /dev/video0. If the camera is connected to a different port, change it appropriately.

Note: The results with input as an image or a directory of images, are written to output images. For video or camera input, use the application display window for the results.

C++ Implementation for classification

For running C++ examples, we need to build TensorFlow framework from source since examples have a dependency on the TensorFlow libraries.

Before you start building from source, you have to make sure that the prerequisites are installed.

The TensorFlow model used in this demo is not packaged in the repo because of its size. So, download the model to the <path-to-openvino_tensorflow-repository>/examples/data directory in your cloned repo of openvino_tensorflow and extract the file:

$ curl -L "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz" |
  tar -C <path-to-openvino_tensorflow-repository>/examples/data -xz

Run the following commands to build openvino_tensorflow with samples:

$ cd <path-to-openvino_tensorflow-repository>
$ python3 build_tf.py --output_dir <path-to-tensorflow-dir>
$ python3 build_ovtf.py --use_tensorflow_from_location <path-to-tensorflow-dir>

For detailed build instructions read BUILD.md.

Now, a binary executable for classification_sample is built. Update the LD_LIBRARY_PATH and run the sample:

$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<path-to-openvino_tensorflow-repository>/build_cmake/artifacts/lib:<path-to-openvino_tensorflow-repository>/build_cmake/artifacts/tensorflow
$ ./build_cmake/examples/classification_sample/infer_image

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

military uniform (653): 0.834306
mortarboard (668): 0.0218693
academic gown (401): 0.010358
pickelhaube (716): 0.00800814
bulletproof vest (466): 0.00535091

In this case, we're using the default image of Admiral Grace Hopper. As 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 image by passing the path to your new input. You can provide either absolute or relative path e.g.

$ ./build_cmake/examples/classification_sample/infer_image --image=<absolute-or-relative-path-to-your-image>

If you add the new image (e.g, my_image.png) to the existing <path-to-openvino_tensorflow-repository>/examples/data directory in the openvino_tensorflow repository, it will look like this:

$ ./build_cmake/examples/classification_sample/infer_image --image=examples/data/my_image.png

To see more options for various backends (Intel® hardware), invoke:

$ ./build_cmake/examples/classification_sample/infer_image --help