.. ===============LICENSE_START======================================================= .. Acumos CC-BY-4.0 .. =================================================================================== .. Copyright (C) 2017-2018 AT&T Intellectual Property & Tech Mahindra. All rights reserved. .. =================================================================================== .. This Acumos documentation file is distributed by AT&T and Tech Mahindra .. under the Creative Commons Attribution 4.0 International License (the "License"); .. you may not use this file except in compliance with the License. .. You may obtain a copy of the License at .. .. http://creativecommons.org/licenses/by/4.0 .. .. This file is distributed on an "AS IS" BASIS, .. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. .. See the License for the specific language governing permissions and .. limitations under the License. .. ===============LICENSE_END========================================================= .. _demonstration-image_classification: ======================================================== Demonstrations: Tutorial for Image Classification Models ======================================================== To extend functionality into a usable web-demo, a second set of assets were created within the ``web_demo`` directory. This web page sample uses an HTML5 player to play an mp4 video and submit its frames to an image classification service. * `video/stock-footage-bicycles.mp4 `_ * `video/stock-footage-city-cars.mp4 `_ * `video/stock-footage-coast-time.mp4 `_ * `video/stock-footage-dogs.mp4 `_ * `video/stock-footage-squirrel.mp4 `_ * `video/stock-footage-scuba.mp4 `_ Browser Interaction =================== Most browsers should have no CORS or other cross-domain objections to dropping the file ``image-classes.html`` into the browser and accesing a locally hosted server API, as configured in :ref:`deployment_image-classification`. Open-source hosted run ---------------------- Utilizing the generous `htmlpreview function `_ available on GitHub, you can also experiment with the respository-based web resource. This resource will proxy the repository ``web_demo`` directory into a live resource. Navigate to the `default webhost page `_ and confirm that the resource load properly. The image at the bottom of this guide is a good reference for correct page loading and display. After confirming correct page load, simply replace the value in the ``Transform URL`` field to point at your deployed instance. For example, if you've created a dumped model locally, it might be a localhost port. Local webserver run ------------------- If you want to run the test locally, you can use a supplied python webserver with the line below while working in the ``web_demo`` directory (assuming you're running python3). :: python simple-cors-http-server-python3.py 5000 Afterwards, just point your browser at ``http://localhost:5000/image-classes.html``. Usage of protobuf binaries for testing -------------------------------------- Binary (protobuf encoded) data can be downloaded from the web page or directly with curl. Two demonstration binaries have been included in the source repository for testing, as captured from the :ref:`demonstration-image_classification_running_example` image below. - ``protobuf.Image.bin`` - a protobuf-encoded image of a coastal-lapse video - ``protobuf.ImageTagSet.bin`` - a protobuf-encoded classification tag set for the coastal-lapse video Within the webpage demo, simply select the correct protobuf method and then drag and drop the binary file into the ``Protobuf Payload Input`` file uploader. It will be immediately uploaded through javascript to your specified ``Transform Url``. Example image classification demo (docker and protobuf) ======================================================= To customize this demo, one should change either the included javascript or simply update the primary classification URL on the page itself during runtime. This demo utilizes the `javascript protobuf library `_ to encode parameters into proto binaries in the browser. ** NOTE ** One version of the model's protobuf schema is included with this web page, but it may change over time. If you receive encoding errors or unexpected results, please verify that your target model and this web page are using the same ``.proto`` file. * confirm that your target docker instance is configured and running * download this directory to your local machine * confirm the host port and classification service URL in the file ``image-classes.js`` .. code:: bash classificationServer: "http://localhost:8886/classify", * view the page ``image-classes.html`` in a Crome or Firefox browser * you can switch between a few sample images or upload your own by clicking on the buttons below the main image window .. _demonstration-image_classification_running_example: .. image:: example_running.jpg :alt: example web application classifying costal video :width: 200 Special decoding example ------------------------ You can also download a binary, encoded version of the last image or output that was sent to the remote service. When available, the **Download Encoded Message** button will be enabled and a binary file will be generated in the browser. .. code:: bash protoc --decode=ZmazgwcYOzRPSlAKlNLcoITKjByZchTo.ImageTagSet model.scene.proto < protobuf.out.bin protoc --decode=ZmazgwcYOzRPSlAKlNLcoITKjByZchTo.Image model.scene.proto < protobuf.in.bin **NOTE** The specific package name may have changed since the time of writing, so be sure to check the contents of the current ``.proto`` file. Reuse with object detectors --------------------------- This framework can be used to demonstrate other object detector and manipulation models as well. Although the source for the model is not included in this repo, an object detection model based on the `common Objects in Context (COCO) `_ dataset was created and tested with this content. The example below shows use of the relevant endpoint and ``.proto`` file (also included in this sample). .. _demonstration-image_classification_running_example_obj: .. image:: example_running_object.jpg :alt: example web application classifying bicycle image :width: 200