NanoNets Object Detection PHP Sample

Golang Sample	Python Sample	Node.js Sample	PHP Sample

Tracking the Millenium Falcon

Images and annotations taken from - https://github.com/bourdakos1/Custom-Object-Detection

Images consists of frames taken from a clip from Star Wars: The Force Awakens.

Annotations are present for each frame and have the same name as the image name. You can find the example to train a model in python and node, by updating the api-key and model id in corresponding file. There is also a pre-processed json annotations folder that are ready payload for nanonets api.

Build an Object Detector for the Millenium Falcon

Step 1: Clone the Repo

git clone https://github.com/NanoNets/object-detection-sample-php.git
cd object-detection-sample-php

Need to install php-cli and php-curl: Here are the command to do same on Ubuntu

sudo apt-get install php<version>-cli
sudo apt-get install php<version>-curl

for PHP5

sudo apt-get install php5-cli
sudo apt-get install php5-curl

for PHP7

sudo apt-get install php7.0-cli
sudo apt-get install php7.0-curl

Step 2: Get your free API Key

Get your free API Key from http://app.nanonets.com/user/api_key

Step 3: Set the API key as an Environment Variable

export NANONETS_API_KEY=YOUR_API_KEY_GOES_HERE

Step 4: Create a New Model

php ./code/create-model.php

_Note: This generates a MODEL_ID that you need for the next step

Step 5: Add Model Id as Environment Variable

export NANONETS_MODEL_ID=YOUR_MODEL_ID

_Note: you will get YOUR_MODEL_ID from the previous step

Step 6: Upload the Training Data

The training data is found in images (image files) and annotations (annotations for the image files)

php ./code/upload-training.php

Step 7: Train Model

Once the Images have been uploaded, begin training the Model

php ./code/train-model.php

Step 8: Get Model State

The model takes ~2 hours to train. You will get an email once the model is trained. In the meanwhile you check the state of the model

php ./code/model-state.php

Step 9: Make Prediction

Once the model is trained. You can make predictions using the model

php ./code/prediction.php PATH_TO_YOUR_IMAGE.jpg

Sample Usage:

php ./code/prediction.php ./images/videoplayback0051.jpg

Note the php sample uses the comverted json instead of the xml payload for convenience purposes, hence it has no dependencies.