Emoji Scavenger Hunt is an experimental web based game that makes use of TensorFlow.js to identify objects seen by your webcam or mobile camera in the browser. We show you emojis 🍌 ⏰ ☕️ 📱 and you have to find those objects in the real world before your timer runs out 🏆 👍.
Learn more about the experiment and try it for yourself at g.co/emojiscavengerhunt
yarn prepRunning yarn prep will use yarn to get the right packages and setup the right folders. If you don't have yarn you can install it via homebrew (for Mac). If you’re already running node/npm with nvm (our recommendation) you can install yarn without node using brew install yarn --without-node.
In order to start local development we also require the installation of the Google Cloud SDK and associated App Engine Components. These are used for the local webserver and pushing to app engine for static site hosting.
Once you have both installed you can run the local development server with:
yarn devThis task uses watchify to continually watch for changes to JS and SASS files and recompiles them if any changes are detected. You can access the local development server at http://localhost:3000/
When building assets for production use:
yarn buildThis will minify SASS and JS for serving in production.
You can build your own image recognition model by running a Docker container.
Dockerfiles are in training directory.
Prepare images for training by dividing them into directories for each label
name that you want to train.
For example: the directory structure for training cat and dog will look as
follows assuming image data is stored under data/images.
data
└── images
├── cat
│ ├── cat1.jpg
│ ├── cat2.jpg
│ └── ...
└── dog
├── dog1.jpg
├── dog2.jpg
└── ...
Once the sample images are ready, you can kickstart the training by building and running the Docker container.
$ cd training
$ docker build -t model-builder .
$ docker run -v /path/to/data:/data -it model-builder
After the training is completed, you'll see three files in the
data/saved_model_web directory:
- web_model.pb (the dataflow graph)
- weights_manifest.json (weight manifest file)
- group1-shard*of* (collection of binary weight files)
They are SavedModel files in a web-friendly format converted by the
TensorFlow.js converter.
You can build your own game using your own custom image recognition model by replacing
the corresponding files under the dist/model/ directory with the newly generated ones.
You also need to update src/js/scavenger_classes.ts in order to update the
label outputs from the custom model with human-readable strings.
Update the game logic in src/js/game.ts if needed.
You can boost the training speed by utilizing your GPU. If you want to use the GPU for training, install nvidia-docker and run:
$ cd training
$ nvidia-docker build -f Dockerfile.gpu model-builder
$ nvidia-docker run -v /path/to/data:/data -it model-builder
Copyright 2018 Google LLC
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License 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.
This is an experiment and collaboration between Google Brand Studio and the PAIR teams at Google.
This is not an official Google product. We will do our best to support and maintain this experiment but your mileage may vary.
We encourage open sourcing projects as a way of learning from each other. Please respect our and other creators’ rights, including copyright and trademark rights when present, when sharing these works and creating derivative work.
If you want more info on Google's policy, you can find that here