Fully load raster into a numpy array?

Question

I have been trying to check my filters on DEM raster for pattern recognition and it is always resulting in missing last rows and columns(like..20). I have tried with PIL library, image load. Then with numpy. The output is the same.

I thought, something is wrong with my loops, when checking values in array (just picking pixels with Identification in ArcCatalog) I realized that pixel values were not loaded into an array.

So, just simply opening, puting into array and saving the image from array:

a=numpy.array(Image.open(inraster)) #raster is .tif Float32, size 561x253
newIm=Image.new(Im.mode, Im.size)
Image.fromarray(a).save(outraster)

Results in cuting away the last rows and columns. Sorry, can#t post the image

Anyone could help to understand why? And advise some solution?

EDIT:

So, I succeeded loading small rasters into numpy array with a help of guys, but when having a bigger image I start getting errors. I suppose it's about the limits of numpy array, and so array is automatically reshaped or smth like that... So ex:

Traceback (most recent call last):
  File "<pyshell#36>", line 1, in <module>
    ima=numpy.array(inDs.GetRasterBand(1).ReadAsArray())
  File "C:\Python25\lib\site-packages\osgeo\gdal.py", line 835, in ReadAsArray
    buf_xsize, buf_ysize, buf_obj )
  File "C:\Python25\lib\site-packages\osgeo\gdal_array.py", line 140, in BandReadAsArray
    ar = numpy.reshape(ar, [buf_ysize,buf_xsize])
  File "C:\Python25\lib\site-packages\numpy\core\fromnumeric.py", line 108, in reshape
    return reshape(newshape, order=order)
ValueError: total size of new array must be unchanged

The point is I don't want to read block by block as I need filtering, several times with different filters, different sizes.. Is there any work around or I must learn rading by blocks :O

Answer 1

if you have python-gdal bindings:

import numpy as np
from osgeo import gdal
ds = gdal.Open("mypic.tif")
myarray = np.array(ds.GetRasterBand(1).ReadAsArray())

And you're done:

myarray.shape
(2610,4583)
myarray.size
11961630
myarray
array([[        nan,         nan,         nan, ...,  0.38068664,
     0.37952521,  0.14506227],
   [        nan,         nan,         nan, ...,  0.39791253,
            nan,         nan],
   [        nan,         nan,         nan, ...,         nan,
            nan,         nan],
   ..., 
   [ 0.33243281,  0.33221543,  0.33273876, ...,         nan,
            nan,         nan],
   [ 0.33308044,  0.3337177 ,  0.33416209, ...,         nan,
            nan,         nan],
   [ 0.09213851,  0.09242494,  0.09267616, ...,         nan,
            nan,         nan]], dtype=float32)

Answer 2

You can use rasterio to interface with NumPy arrays. To read a raster to an array:

import rasterio

with rasterio.open('/path/to/raster.tif', 'r') as ds:
    arr = ds.read()  # read all raster values

print(arr.shape)  # this is a 3D numpy array, with dimensions [band, row, col]

This will read everything into a 3D numpy array arr, with dimensions [band, row, col].

---

Here is an advanced example to read, edit a pixel, then save it back to the raster:

with rasterio.open('/path/to/raster.tif', 'r+') as ds:
    arr = ds.read()  # read all raster values
    arr[0, 10, 20] = 3  # change a pixel value on band 1, row 11, column 21
    ds.write(arr)

The raster will be written and closed at the end of the "with" statement.

Answer 3

My solution using gdal looks like this. I think it is very reusable.

import gdal
import osgeo.gdalnumeric as gdn

def img_to_array(input_file, dim_ordering="channels_last", dtype='float32'):
    file  = gdal.Open(input_file)
    bands = [file.GetRasterBand(i) for i in range(1, file.RasterCount + 1)]
    arr = np.array([gdn.BandReadAsArray(band) for band in bands]).astype(dtype)
    if dim_ordering=="channels_last":
        arr = np.transpose(arr, [1, 2, 0])  # Reorders dimensions, so that channels are last
    return arr

Answer 4

Granted I'm reading a plain old png image, but this works using scipy (imsave uses PIL though):

>>> import scipy
>>> import numpy
>>> img = scipy.misc.imread("/home/chad/logo.png")
>>> img.shape
(81, 90, 4)
>>> array = numpy.array(img)
>>> len(array)
81
>>> scipy.misc.imsave('/home/chad/logo.png', array)

My resultant png is also 81 x 90 pixels.