• Introduction
• Navigation
• Functions
• • 1.Image segmentation
  • 2.Green vision rate calculation
  • 3.Health benefit evaluation
• Quick start
• • Requirements
  • • Basic requirement
    • Development environment
  • 1.Run on Windows
  • 2.Run on Source Code
  • • 2.1 Install Python 3.7+
    • 2.2 Install Dependency Package
    • 2.3 Download Model
    • 2.4 Run
  • Batch Processing
  • • API Description
    • Curl
    • PHP
    • Python
• Others
• • Structure
  • Model List
  • License
  • About us
  • Thanks

Landscape-Health Score

English | 简体中文

Introduction

Landscape-Health Score (LHS) is an auxiliary evaluation tool based on Paddlepaddle framework. You can simply drag and drop image files to automatically analyze images and generate simple evaluation reports. The currently supported Bisenet and PSPNet models can realize the identification and segmentation of 9 kinds of environmental elements, and real-time statistics of the proportion of environmental elements, the green vision rate and the scores of 6 psychological indicators such as happy / relaxed / depressive / social / motivated / focused.

Navigation

• Introduction
• Navigation
• Functions
  • 1.Image segmentation
  • 2.Green vision rate calculation
  • 3.Health benefit evaluation
• Quick start
  • Requirements
    • Basic requirement
    • Development environment
  • 1.Run on Windows
  • 2.Run on Source Code
    • 2.1 Install Python 3.7+
    • 2.2 Install Dependency Package
    • 2.3 Download Model
    • 2.4 Run
  • Batch Processing
    • API Description
    • Curl
    • PHP
    • Python
• Others
  • Structure
  • Model List
  • License
  • About us
  • Thanks

Functions

1.Image segmentation

The image segmentation function is based on the principle of image semantic segmentation technology, using deep learning technology and Cityscapes data set training model, through the model to identify and classify pixels and count the area ratio of each element on the image.
Currently, the environmental elements that support identification are：

• sky
• terrain
• vegetation
• pole
• fence
• wall
• building
• sidewalk
• road
• water
• hill/mountain

2.Green vision rate calculation

Green vision rate is one of the reference indicators of environmental greening evaluation. Traditional green vision rate is counted by using the principle of RGB value of image pixels, which is obviously affected by environmental factors. Based on deep learning technology, green plants in the image can be identified, and other interference factors (such as green clothes / billboards, etc.) can be eliminated, so as to achieve more accurate proportion calculation.

3.Health benefit evaluation

It supports the evaluation and analysis of six psychological dimensions such as happy / relaxed / depressive / social / motivated / focused for the input image. At present, the function is still in the research and testing stage, and the algorithm will continue to upgrade and iterate.

Quick start

Requirements

Basic requirement

• Windows10(Recommended)
• CPU Intel i5 or AMD equivalent processor
• GPU RTX1060 6G and above configuration (optional)

Development environment

• npm >= 7.15
• Vite 2
• VUE 3.0

1.Run on Windows

Packing……

2.Run on Source Code

2.1 Install Python 3.7+

2.2 Install Dependency Package

Press Win+R , enter cmd
Enter cd [LHS folder]
Enter pip install -r requirement.txt
Wait patiently for the installation to complete.

2.3 Download Model

Go to Model List to download and decompress package of the model,and copy all the files to the folder named model.We recommend downloading BiSeNetv2 model.

2.4 Run

Press Win+R,enter cmd
Enter cd [LHS folder]
Enter python app.py to run the program
Open any browser except IE and enterhttp://127.0.0.1:5000 to the operation interface.

Batch Processing

In order to meet the requirements for batch processing of images, I added a http API, which can initiate POST requests through any codes while running the program.

API Description

HTTP Method：POST
Request URL：http://127.0.0.1:5000/api/v1/json
Header:

Parameter	Value
Content-Type	application/json

Request Parameter:

Parameter	Type	Description
image	string	Image information, Base64 encoding, bytes type need to be converted to UTF-8

Return Parameter:

Parameter	Type	Description
image	string	Base64 coding of image segmentation
gvr	double	Green vision rate
proportion	json	Proportion of environmental elements
score	json	6 dimensions of psychological index score

The following three request cases are provided: curl / PHP and python:

Curl

curl -i -k 'http://127.0.0.1:5000/api/v1/json' --data 'image=[Image Base64 encoding, need URLEncode]' -H 'Content-Type:application/x-www-form-urlencoded'

PHP

<?php
function request_post($url = '', $param = '')
{
    if (empty($url) || empty($param)) {
        return false;
    }
    $postUrl = $url;
    $curlPost = $param;
    $curl = curl_init();
    curl_setopt($curl, CURLOPT_URL, $postUrl);
    curl_setopt($curl, CURLOPT_HEADER, 0);
    curl_setopt($curl, CURLOPT_RETURNTRANSFER, 1);
    curl_setopt($curl, CURLOPT_SSL_VERIFYPEER, false);
    curl_setopt($curl, CURLOPT_POST, 1);
    curl_setopt($curl, CURLOPT_POSTFIELDS, $curlPost);
    $data = curl_exec($curl);
    curl_close($curl);

    return $data;
}

$url = 'http://127.0.0.1:5000/api/v1/json';
$img = file_get_contents('[Image filepath]');
$img = base64_encode($img);
$bodys = array(
    'image' => $img
);
$res = request_post($url, $bodys);

var_dump($res);

Python

import requests
import base64
import json

request_url = "http://127.0.0.1:5000/api/v1/json"
f = open('[Image filepath]', 'rb')
img = base64.b64encode(f.read()).decode('utf-8')

params = {'image':img}
headers = {'content-type': 'application/json'}
response = requests.post(request_url, data=json.dumps(params), headers=headers)
if response:
    print (response.json())

Others

Structure

* requirement.txt  --list of dependent files
* app.py  --main program for app
* infer_api.py  --program for inference
* Qscore_algorithm.py  --program for calculating health scores
* dist --front-end program (compiled)
* model --deep learning model 
* frontend  --front-end program (source code)

Model List

Currently, the BiSeNetv2 and PSPNet models are supported. The BiSeNetv2 model is small in size, has a fast predictive operation speed, and is suitable for a CPU operating environment, but the accuracy is poor. The PSPNet model has higher requirements for GPU, slower running speed, but higher accuracy. Note: Before using PSPNet, you need to install and configure the CUDA9.0+ .

Network Structure	Size	Requirement	Download Link
BiSeNetv2	8.4 MB	CPU	http://www.gis.show/index/article/id/18
PSPNet	307.7 MB	GPU	http://www.gis.show/index/article/id/19

License

This project uses The GNU General Public License v3.0 (GPL3.0). Any program using this project needs to follow the content of the GPL3.0 and add citation sources.

About us

SCULAB https://www.scurbanlab.com/

Thanks

Paddlepaddle http://www.paddlepaddle.org.cn
GT https://github.com/GT-ZhangAcer