---
title: "streetscape"
author: "Xiaohao Yang"
date: "08/30/2024"
vignette: >
  %\VignetteIndexEntry{streetscape}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## 1 Get metadata of street view image from Mapillary 

### 1.1 search data using a bounding box

```{r eval = FALSE}
bbox <- c(-83.751812,42.272984,-83.741255,42.279716)
data <- streetscape::strview_searchByGeo(bbox = bbox,
                                         epsg = 2253,
                                         token = "")

```

### 1.2 search data using a proximity given coordinates in degree

```{r eval = FALSE}
data <- streetscape::strview_searchByGeo(x = -83.743460634278,
                                         y = 42.277848830294,
                                         r = 100,
                                         epsg = 2253,
                                         token = "")
```

### 1.3 search data with filters

```{r eval = FALSE}
# check supported filters
streetscape::available_filter()
# only search for 360-degree street views
data <- streetscape::strview_searchByGeo(bbox = bbox,
                                         epsg = 2253,
                                         token = "",
                                         is_pano = TRUE)
```

### 1.4 search the nearest data given coordinates in degree (within a 10m buffer)

```{r eval = FALSE}
data <- streetscape::strview_search_nnb(
  x = -83.743460634278,
  y = 42.277848830294,
  epsg = 2253,
  token = '')
```

### 1.5 batch search data using OSM road line

```{r eval = FALSE}
bbox <- c(-83.752041,42.274896,-83.740711,42.281945)
data <- streetscape::strview_search_osm(
        bbox = bbox,
        epsg = 2253,
        token = '',
        size = 100)
```

## 2 Calculate the Green View Index

```{r eval = FALSE}
data$gvi()
```

## 3 Extract semantic segmentation 

```{r eval = FALSE}
streetviewdata <- streetscape::scdataframe
# calculate the percentage of each segmentation
data$decodeDetection()
data$data$segmentation[[1]]
# extract the semantic segmentation of a street view
mask <- streetviewdata$get_mask(1)
```

## 4 Visualize the data in maps

```{r eval = FALSE}
map1 <- data$mapPreview('meta')
print(map1)
# assume that one has run data$gvi() and data$decodeDetection()
map2 <- data$mapPreview('seg')
print(map2)
map3 <- data$mapPreview('gvi')
print(map3)
```

## 5 Download data

```{r eval = FALSE}
# download street view images
data$download_data(path = 'path/to/download', items = 'image')
# download images and masks in sf format
data$download_data(path = 'path/to/download', items = c('image', 'mask'))
```

## 6 Generate Qualtrics survey text file

```{r eval = FALSE}
# general survey for understanding subjective perception from streetviews
questions <- c('1. To what extent you feel pleasant if you were in this environment', 
              '2. To what extent you feel safe if you were in this environment')
choices <- list(c('Unpleasant','Less pleasant', 'Pleasant', 'More pleasant'), 
                c('Unsafe', 'Less safe','Safe','Safer'))
header <- "Please review the following picture(s):"
streetscape::strview2rate(data, header, questions, choices, file = 'folder/filename')

# pair-wised comparison survey for ranking some specific property (such as perceived safety) of street views
questions <- c('which one is more beautiful?', 'which one is safer?')
streetscape::strview2pwc(data, k=1, header, questions, file = 'folder/filename')
```
