- QGIS Python Programming Cookbook
- Table of Contents
- QGIS Python Programming Cookbook
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Automating QGIS
- Introduction
- Installing QGIS for development
- Using the QGIS Python console for interactive control
- Using the Python ScriptRunner plugin
- Setting up your QGIS IDE
- Debugging QGIS Python scripts
- Navigating the PyQGIS API
- Creating a QGIS plugin
- Distributing a plugin
- Creating a standalone application
- Storing and reading global preferences
- Storing and reading project preferences
- Accessing the script path from within your script
- 2. Querying Vector Data
- Introduction
- Loading a vector layer from a file sample
- Loading a vector layer from a spatial database
- Examining vector layer features
- Examining vector layer attributes
- Filtering a layer by geometry
- Filtering a layer by attributes
- Buffering a feature intermediate
- Measuring the distance between two points
- Measuring the distance along a line sample
- Calculating the area of a polygon
- Creating a spatial index
- Calculating the bearing of a line
- Loading data from a spreadsheet
- 3. Editing Vector Data
- Introduction
- Creating a vector layer in memory
- Adding a point feature to a vector layer
- Adding a line feature to a vector layer
- Adding a polygon feature to a vector layer
- Adding a set of attributes to a vector layer
- Adding a field to a vector layer
- Joining a shapefile attribute table to a CSV file
- Moving vector layer geometry
- Changing a vector layer feature's attribute
- Deleting a vector layer feature
- Deleting a vector layer attribute
- Reprojecting a vector layer
- Converting a shapefile to KML
- Merging shapefiles
- Splitting a shapefile
- Generalizing a vector layer
- Dissolving vector shapes
- Performing a union on vector shapes
- Rasterizing a vector layer
- 4. Using Raster Data
- Introduction
- Loading a raster layer
- Getting the cell size of a raster layer
- Obtaining the width and height of a raster
- Counting raster bands
- Swapping raster bands
- Querying the value of a raster at a specified point
- Reprojecting a raster
- Creating an elevation hillshade
- Creating vector contours from elevation data
- Sampling a raster dataset using a regular grid
- Adding elevation data to line vertices using a digital elevation model
- Creating a common extent for rasters
- Resampling raster resolution
- Counting the unique values in a raster
- Mosaicing rasters
- Converting a TIFF image to a JPEG image
- Creating pyramids for a raster
- Converting a pixel location to a map coordinate
- Converting a map coordinate to a pixel location
- Creating a KML image overlay for a raster
- Classifying a raster
- Converting a raster to a vector
- Georeferencing a raster from control points
- Clipping a raster using a shapefile
- 5. Creating Dynamic Maps
- Introduction
- Accessing the map canvas
- Changing the map units
- Iterating over layers
- Symbolizing a vector layer
- Rendering a single band raster using a color ramp algorithm
- Creating a complex vector layer symbol
- Using icons as vector layer symbols
- Creating a graduated vector layer symbol renderer
- Creating a categorized vector layer symbol
- Creating a map bookmark
- Navigating to a map bookmark
- Setting scale-based visibility for a layer
- Using SVG for layer symbols
- Using pie charts for symbols
- Using the OpenStreetMap service
- Using the Bing aerial image service
- Adding real-time weather data from OpenWeatherMap
- Labeling features
- Changing map layer transparency
- Adding standard map tools to the canvas
- Using a map tool to draw points on the canvas
- Using a map tool to draw polygons or lines on the canvas
- Building a custom selection tool
- Creating a mouse coordinate tracking tool
- 6. Composing Static Maps
- Introduction
- Creating the simplest map renderer
- Using the map composer
- Adding labels to a map for printing
- Adding a scale bar to the map
- Adding a north arrow to the map
- Adding a logo to the map
- Adding a legend to the map
- Adding a custom shape to the map
- Adding a grid to the map
- Adding a table to the map
- Adding a world file to a map image
- Saving a map to a project
- Loading a map from a project
- 7. Interacting with the User
- Introduction
- Using log files
- Creating a simple message dialog
- Creating a warning dialog
- Creating an error dialog
- Displaying a progress bar
- Creating a simple text input dialog
- Creating a file input dialog
- Creating a combobox
- Creating radio buttons
- Creating checkboxes
- Creating tabs
- Stepping the user through a wizard
- Keeping dialogs on top
- 8. QGIS Workflows
- Introduction
- Creating an NDVI
- Geocoding addresses
- Creating raster footprints
- Performing network analysis
- Routing along streets
- Tracking a GPS
- Creating a mapbook
- Finding the least cost path
- Performing nearest neighbor analysis
- Creating a heat map
- Creating a dot density map
- Collecting field data
- Computing road slope using elevation data
- Geolocating photos on the map
- Image change detection
- 9. Other Tips and Tricks
- Introduction
- Creating tiles from a QGIS map
- Adding a layer to geojson.io
- Rendering map layers based on rules
- Creating a layer style file
- Using NULL values in PyQGIS
- Using generators for layer queries
- Using alpha values to show data density
- Using the __geo_interface__ protocol
- Generating points along a line
- Using expression-based labels
- Creating dynamic forms in QGIS
- Calculating length for all selected lines
- Using a different status bar CRS than the map
- Creating HTML labels in QGIS
- Using OpenStreetMap's points of interest in QGIS
- Visualizing data in 3D with WebGL
- Visualizing data on a globe
- Index
The ability to view rasters in a geospatial context relies on the conversion of pixel locations to coordinates on the ground. Sooner or later when you use Python to write geospatial programs, you'll have to perform this conversion yourself.
We will use the SatImage raster available at:
https://geospatialpython.googlecode.com/files/SatImage.zip
Place this raster in your /qgis_data/rasters directory.
We will use GDAL to extract the information needed to convert pixels to coordinates and then use pure Python to perform the calculation. We'll use the center pixel of the image as the location to convert.
- Start QGIS.
- From the Plugins menu select Python Console
- We need to import the
gdalmodule:from osgeo import gdal - Then, we need to define the reusable function that does the conversion accepting a GDAL
GeoTransformobject containing the raster georeferencing information and the pixel's x,y values:def Pixel2world(geoMatrix, x, y): ulX = geoMatrix[0] ulY = geoMatrix[3] xDist = geoMatrix[1] yDist = geoMatrix[5] coorX = (ulX + (x * xDist)) coorY = (ulY + (y * yDist)) return (coorX, coorY)
- Now, we'll open the image in GDAL
src = gdal.Open("/qgis_data/rasters/Satimage.tif") - Next, get the
GeoTransformobject from the image:geoTrans = src.GetGeoTransform() - Now, calculate the center pixel of the image:
centerX = src.RasterXSize/2 centerY = src.RasterYSize/2
- Finally, perform the conversion by calling our function:
Pixel2world(geoTrans, centerX, centerY) - Verify the coordinates returned are close to the following output:
(-89.59486002580364, 30.510227817850406)
Pixel conversion is just a scaling ratio between two planes, the image coordinate system and the Earth coordinate system. When dealing with large areas, this conversion can become a more complex projection because the curvature of the Earth comes into play. The GDAL website has a nice tutorial about the geotransform object at the following URL: http://www.gdal.org/gdal_tutorial.html
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