September 21, 2019

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uvipen/QuickDraw

uvipen/QuickDraw

Implementation of Quickdraw - an online game developed by Google

repo name uvipen/QuickDraw
repo link https://github.com/uvipen/QuickDraw
homepage
language Python
size (curr.) 147031 kB
stars (curr.) 656
created 2019-01-31
license MIT License

[PYTHON] QuickDraw

Introduction

Here is my python source code for QuickDraw - an online game developed by google. with my code, you could:

  • Run an app which you could draw in front of a camera (If you use laptop, your webcam will be used by default)
  • Run an app which you could draw on a canvas

Camera app

In order to use this app, you need a pen (or any object) with blue, red or green color. When the pen (object) appears in front of camera, it will be catched and highlighted by an yellow circle. When you are ready for drawing, you need to press space button. When you want to stop drawing, press space again Below is the demo by running the sript camera_app.py:

Drawing app

The script and demo will be released soon

Dataset

The dataset used for training my model could be found at [Quick Draw dataset] https://console.cloud.google.com/storage/browser/quickdraw_dataset/sketchrnn. Here I only picked up 20 files for 20 categories

Categories:

The table below shows 20 categories my model used:

apple book bowtie candle
cloud cup door envelope
eyeglasses guitar hammer hat
ice cream leaf scissors star
t-shirt pants lightning tree

Trained models

You could find my trained model at trained_models/whole_model_quickdraw

Training

You need to download npz files corresponding to 20 classes my model used and store them in folder data. If you want to train your model with different list of categories, you only need to change the constant CLASSES at src/config.py and download necessary npz files. Then you could simply run python3 train.py

Experiments:

For each class, I take the first 10000 images, and then split them to training and test sets with ratio 8:2. The training/test loss/accuracy curves for the experiment are shown below:

Requirements

  • python 3.6
  • cv2
  • pytorch
  • numpy
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