Techday 2018 : "Pop-up" pool detection using ArcGIS, Machine Learning and Deep Learning

This repository is part of the Esri Suisse Techday

Useful links

The general implementation have been made based on this specific open source project : https://github.com/matterport/Mask_RCNN

This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.

In order to implement our own pool detection we followed this great tutorial that aims to find balloons in images. The global idea was to replace "balloons" with "pools" and it worked quite well : https://github.com/matterport/Mask_RCNN/tree/master/samples/balloon

This is an example showing the use of Mask RCNN in a real application. We train the model to detect balloons only, and then we use the generated masks to keep balloons in color while changing the rest of the image to grayscale. This blog post describes this sample in more detail.

Setup

The implementation and training has been done on a Windows 10 computer with the use of a personal Nvidia 1070 GPU for the training part.

It could have been done on any GPU powered cloud instance, could it be Azure, AWS or more "niche" players specialized in deep learning like Crestle or Paperspace

The GPU is not needed for the detection using a trained model : you can do that on your CPU (it's slower but enough to check the result)

We found the setup steps for a Mask RCNN repo here : https://github.com/markjay4k/Mask-RCNN-series/blob/master/Mask_RCNN%20Install%20Instructions.ipynb

Overview on how to install base Mask RCNN

• Step 1: create a conda virtual environment with python 3.6
• Step 2: install the dependencies
• Step 3: Clone the Mask_RCNN repo
• Step 4: install pycocotools

Specific for the pool detection

• Step 5: download the pre-trained weights
• Step 6: Test it

Step 1 - Create a conda virtual environment

we will be using Anaconda with python 3.6.

If you don't have Anaconda, follow this tutorial

https://www.youtube.com/watch?v=T8wK5loXkXg

run this command in a CMD window

conda create -n MaskRCNN python=3.6 pip

Step 2 - Install the Dependencies

place the requirements.txt in your cwdir https://github.com/markjay4k/Mask-RCNN-series/blob/master/requirements.txt run these commands

activate MaskRCN 
pip install -r requirements.txt

NOTE: we're installing these (tf-gpu requires some pre-reqs) : numpy, scipy, cython, h5py, Pillow, scikit-image, tensorflow-gpu==1.5, keras, jupyter

Step 3 - Clone the Mask RCNN Repo

Run this command

git clone https://github.com/matterport/Mask_RCNN.git

Step 4 - Install pycocotools

NOTE: pycocotools requires Visual C++ 2015 Build Tools download here if needed https://www.visualstudio.com/downloads/#build-tools-for-visual-studio-2017

pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI

Step 5 - Download the Pre-trained Weights

File (mask_rcnn_poolall.h5 , 250 Mb) for pool detection can be found here

https://drive.google.com/open?id=1wZbFQKipZFdiukwjg5GgEazlzC1CSVBu

Step 6 - Let's Test it!

open up the Techday 2018.ipynb and run it

Train your own

Using labeled data and images, we trained our own model by launching the following command

python3 pool.py train --dataset=C:\ML\Mask_RCNN\pools --weights=coco