Python – Add raster image to HDF5 file using h5py

h5pyhdf5numpypythonraster

I apologize if this is sort of a newbie question, but I am fairly new to Python and HDF5. I am using h5py, numpy, and Python 2.7. I have data from various files that need to be imported into one HDF5 file. The data from each file is to be stored in a different group. Each of these groups needs to contain 1) the raw data from the file as an m x n matrix and 2) an image raster generated from normalized raw data.

I am able to accomplish part 1, and am able to normalize the data, but I am not able to write this normalized data to a raster image because I don't know how to add a raster image to a group. It seems like there should be an easy, straightforward way to do this, but I have read the documentation and haven't found one. How would one do this in h5py, and if it can't be done using h5py, what should I use to accomplish this?

Thanks!!

Best Answer

There is nothing special about images in HDF5. The link you provided is for the high level library bindings. You can just as easily use the specifications of images in HDF5, which are just attributes.

Here is a very quick and dirty example:

#!/usr/bin/env python

import numpy as np
import h5py

# Define a color palette
pal =  np.array([[0,     0, 168],
                 [0,     0, 252],
                 [0,   168, 252],
                 [84,  252, 252],
                 [168, 252, 168],
                 [0,   252, 168],
                 [252, 252,  84],
                 [252, 168,   0],
                 [252,   0,   0]],
                 dtype=np.uint8
               )

# Generate some data/image
x = np.linspace(0,pal.shape[0]-1)
data,Y = np.meshgrid(x,x)

# Create the HDF5 file
f = h5py.File('test.h5', 'w')

# Create the image and palette dataspaces
dset = f.create_dataset('img', data=data)
pset = f.create_dataset('palette', data=pal)

# Set the image attributes
dset.attrs['CLASS'] = 'IMAGE'
dset.attrs['IMAGE_VERSION'] = '1.2'
dset.attrs['IMAGE_SUBCLASS'] =  'IMAGE_INDEXED'
dset.attrs['IMAGE_MINMAXRANGE'] = np.array([0,255], dtype=np.uint8)
dset.attrs['PALETTE'] = pset.ref

# Set the palette attributes
pset.attrs['CLASS'] = 'PALETTE'
pset.attrs['PAL_VERSION'] = '1.2'
pset.attrs['PAL_COLORMODEL'] = 'RGB'
pset.attrs['PAL_TYPE'] = 'STANDARD8'

# Close the file
f.close()

Run the example and then look at the image in HDFView:

Image within HDF5 file

Note that you have to open the image data with "Open As" in order to see it as an image, as the table view is the default.

Related Solutions

Python – Peak detection in a 2D array

I detected the peaks using a local maximum filter. Here is the result on your first dataset of 4 paws: Peaks detection result

I also ran it on the second dataset of 9 paws and it worked as well.

Here is how you do it:

import numpy as np
from scipy.ndimage.filters import maximum_filter
from scipy.ndimage.morphology import generate_binary_structure, binary_erosion
import matplotlib.pyplot as pp

#for some reason I had to reshape. Numpy ignored the shape header.
paws_data = np.loadtxt("paws.txt").reshape(4,11,14)

#getting a list of images
paws = [p.squeeze() for p in np.vsplit(paws_data,4)]


def detect_peaks(image):
    """
    Takes an image and detect the peaks usingthe local maximum filter.
    Returns a boolean mask of the peaks (i.e. 1 when
    the pixel's value is the neighborhood maximum, 0 otherwise)
    """

    # define an 8-connected neighborhood
    neighborhood = generate_binary_structure(2,2)

    #apply the local maximum filter; all pixel of maximal value 
    #in their neighborhood are set to 1
    local_max = maximum_filter(image, footprint=neighborhood)==image
    #local_max is a mask that contains the peaks we are 
    #looking for, but also the background.
    #In order to isolate the peaks we must remove the background from the mask.

    #we create the mask of the background
    background = (image==0)

    #a little technicality: we must erode the background in order to 
    #successfully subtract it form local_max, otherwise a line will 
    #appear along the background border (artifact of the local maximum filter)
    eroded_background = binary_erosion(background, structure=neighborhood, border_value=1)

    #we obtain the final mask, containing only peaks, 
    #by removing the background from the local_max mask (xor operation)
    detected_peaks = local_max ^ eroded_background

    return detected_peaks


#applying the detection and plotting results
for i, paw in enumerate(paws):
    detected_peaks = detect_peaks(paw)
    pp.subplot(4,2,(2*i+1))
    pp.imshow(paw)
    pp.subplot(4,2,(2*i+2) )
    pp.imshow(detected_peaks)

pp.show()

All you need to do after is use scipy.ndimage.measurements.label on the mask to label all distinct objects. Then you'll be able to play with them individually.

Note that the method works well because the background is not noisy. If it were, you would detect a bunch of other unwanted peaks in the background. Another important factor is the size of the neighborhood. You will need to adjust it if the peak size changes (the should remain roughly proportional).

Python – Save plot to image file instead of displaying it using Matplotlib

While the question has been answered, I'd like to add some useful tips when using matplotlib.pyplot.savefig. The file format can be specified by the extension:

from matplotlib import pyplot as plt

plt.savefig('foo.png')
plt.savefig('foo.pdf')

Will give a rasterized or vectorized output respectively, both which could be useful. In addition, there's often an undesirable, whitespace around the image, which can be removed with:

plt.savefig('foo.png', bbox_inches='tight')

Note that if showing the plot, plt.show() should follow plt.savefig(), otherwise the file image will be blank.

Best Answer

Related Solutions

Python – Peak detection in a 2D array

Python – Save plot to image file instead of displaying it using Matplotlib

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