Mosquito Breeding Sites Detector (Deprecated)

This project is about detecting Dengue’s vectors breeding site from Google Street View images using deep learning. The main vector is Aedes Egypti mosquito.

Table of contents

• Pipeline of process
• Description of Code
• Directory Structure
• Getting Started
  • Prerequisites
  • Instalation
  • Running
  • Evaluating model accuracy
• Demo
• Built With

Pipeline of process

Code Description

All source codes are in scripts directory.

• data_collection.py used for retrieve google street view images of village that you want. The image size is 600x600 pixels.

• image_recognition.py used for recognize images , it will return top five classification results.

• image_segmentation.py used for extract segmented of image.

• feature_vector_classification.py used for classify the result of image recognition and image segmentation again for increasing more accuracy.

Directory Structure

+Mosquito_Breeding_Sites_Detector
  +SegNet-Tutorial
  +caffe-segnet
  +GSV
  +xgboost
  +dataset
  +geojson
  +scripts
    +feature_vector
      -to_geojson.py
      -xgb_classifier.py
    +image_processing
      +model
      -image_divider.py
      -image_recognizer.py
      -inception.py
    +image_retreival
      -GSV_loader.py
      -get_village_points.py
      -polygon_to_points.py
    +segnet
      +Models
      -pysegnet.py
    -data_collection.py
    -image_recognition.py
    -image_segmentation.py
    -feature_vector_classification.py
  -README.md
  -INSTALL.md

Getting Started

These instructions is about how you copy this project up and running on your local machine for development and testing purposes.

Prerequisites

• Python 2.7
• GPU card with CUDA Compute Capability 3.0 or higher NVIDIA's documentation
• Install CUDA Toolkit 8.0
• Download cuDNN v5.1
• Install TensorFlow with GPU
• Install Caffe-Segnet with GPU, caffe Installation ins here

Installing

• see Instalation instruction

Running

• see Running instruction

Evaluating model accuracy

• see Training model and Evaluation

Demo

• see Demo

Built With

• TensorFlow - Image Recognition
• Caffe-Segnet - Image Segmentation
• XGBoost - Feature Vector Classifier
• Overpass API - Road/Street Geojson
• Google Map API - Street View Image and Visualization