Why not work globally ?
calculate the distances between all points
union the resulting lines pointx - pointy with a distance < 14m
I will use Shapely, much easier for resolving these kinds of problems.
You must iterate through all pairs of points to calculate the distance once (as distance point1-point2 = distance point2-point1). There are many ...
I modified the original code a little bit to avoid some confusion when defining the RasterCenter function, since the argument named raster used in def RasterCenter(raster) and the variable named raster used in raster = arcpy.Raster(raster) within the function can cause confusion and make things not working properly. I modified parsing the path when reading ...
If all you have is the jpg, then I would suggest georeferencing the jpg and then manually digitizing the roads/rivers/etc vectors. That will give you the best control over the result, I personally have never had good luck with using raster-vector conversions in a situation like this.
Unfortunately, sometimes the best option is to just do it the hard way.
If you look at the example on the man page for ST_PixelAsPolygons you will see how you can access the geometries using table_alias.geom syntax (similar in spirit to how ST_Dump works to turn a set or records into individual rows). Following on from that example, you can pass (gv).geom to the ST_AsGeoJSON function, eg,
As GDAL supports writing to X,Y,Z (CSV) ascii, you could use gdal_translate:
gdal_translate -of xyz -co ADD_HEADER_LINE=YES -co COLUMN_SEPARATOR="," input_raster output.csv
To avoid writing NoData values to your output you can write the output to stdout then pipe to grep/findstr to filter it before writing to your csv:
gdal_translate -q -of xyz -co ...
I came across a thread Smoothing a 2-D figure. The answers make reference to this paper Chaikin's algorithm for curves
For a given polygon with vertices as P0, P1, ...P(N-1), the corner cutting algorithm will generate 2 new vertices for each line segment defined by P(i) and P(i+1) as
Q(i) = (3/4)P(i) + (1/4)P(i+1)
R(i) = (1/4)P(i) + (3/4)P(i+1)
Make bit rasters for each of the unique classes. This can be a 1-band rasters for each class, or a single raster with a band for each class (e.g. GeoTIFF). If using GTiff, you can use the creation option NBITS=1 to conserve space. You may also want to consider twobit rasters to store three-valued logic where the third (e.g. 2) is NODATA, which would ...
In answer to my own question, I've written a program to "vectorize" an ArcInfo Grid ASCII files as an ESRI shapefile with a single layer containing oriented polygonal grid squares centered at the points of the grid, with an attribute value equal to the value at the coordinates of the centroid. The program (still under development) is available on GitHub. An ...
You can't use GDALFPolygonize with the GDAL python bindings without modifying the source code and recompiling as it isn't exposed in the GDAL swig interface.
Note: as at Feb 2016, GDALFPolygonize IS exposed in the GDAL SVN trunk source, but is not in either of the latest releases (1.11.4/2.0.2).
To polygonize your raster, you will need to convert from ...
ArcGIS has a Raster to Polygon tool that will do this for you. It's in the conversion toolbox.
I suspect there's a better way to approach your problem, but it's hard to say without knowing more about your analysis.
I assume that your graphs came from a R-script and that you are capable of using R. Here is a solution in R, which finds local maxima and minima along a data sequence
x <- rnorm(50,mean=1500,sd=800) # Example-Data
r <- rle(x) # Generate run sequence object
min <- which(rep(x = diff(sign(diff(c(-Inf, r$values, -Inf)))) == 2,
To generalize, try running a majority filter. This is available in saga (and grass as well, check markusN his answer).
An explanation for how it works from arcgis:
Try using rasterio, which uses GDALFPolygonize on float arrays.
import numpy as np
from affine import Affine
from shapely.geometry import shape
# triangular array
ar = np.tri(5, dtype='f')
for shp, val in rasterio.features.shapes(ar, transform=Affine(1, 0, 0, 0, -1, 5)):
print('%s: %s' % (val, shape(shp)))
Rather than converting that image to vector, you should get the OpenStreetMap data from Mapzen:
Bring that into QGIS and check out what data is there, and then start to style it as your raster map is styled.
As mentioned in the comments by @Knightshound, you seem to lack some experience in GIS. Still, i'm going to answer your question so you get some of this experience you need.
What you want to do is Polygonize, in the Raster menu/Conversion/Polygonize (Raster to Vector), but you'll quickly realize it doesn't produce what you expect ;)
you can use gdal_polygonize.py for converting raster to vector, if u previously use .
some information is here.
produces a polygon feature layer from a raster
gdal_polygonize.py [-o name=value] [-nomask] [-mask filename]
raster_file [-b band]
[-q] [-f ogr_format] out_file [layer] [fieldname]
beside this in qgis you ...
Why don't you crop again with your clipper after vectorizing? That way any combined polygons derived from your raster layer will match your original clipping layer. If you don't want to add an extra step you can vectorize the entire raster layer then clip to vector layer (though this could take more computation time).
Any other option I can think of would ...
The final batch file, based on Logan Byers answer, which I managed to get working on a Windows machine.
I should note that I had to amend the output of gdal2xyz.py. For some reason the version I had (installed with FWTools 2.4.7) would not accept the -csv option as valid syntax and you can see it is omitted from the code below. I had to open the gdal2xyz....
The data was in float, so I found after some research it needs to be converted into integer first. Using Raster Calculator I used the following syntax to convert into new raster layer. It created an attribute table and I can convert it into vector data now.
Your question is kind of similar to one I asked before about land cover extraction. The solution that I was given was to use the open source GIS software called GRASS (see my question/answer below).
Land Cover Feature Extraction from Satellite Imagery
I say you should be very careful when using data like this. You can easily get wrong values since your image, more than the values you want to extract have town names, streets, etc... Even worst, it might be antialiased producing lots of diferent rgb combinations, that would be difficult to reclassify.
I would say that the safest way would be manually ...
The model below should get you started in the right direction. I used 4-band 1m NAIP imagery as the model input. The imagery below shows the CIR tiff on the left followed by the generalized raster in the middle and the final vector product with four vegetation zones is on the right. Unsupervised maximum likelihood classification was used on the CIR ...
You can only vectorize lines, not continuous fields. Hence use r.mapcalc with a threshold to extract line structures from that map, then subsequently r.thin. Then it will work as expected.
See also this Wiki page for more possibilities: http://grass.osgeo.org/wiki/R.stream.*
There is no need to clean a map to vectorize it.
Printed raster maps have resolutions, scales. Editing and redrawing make much sense since you planning create a new map composition using data from another map source.
If you do not have the original data used to create the map or if it is old and need be converted to digital format( raster, vector )