Vectorizing a pixel-averaging operation in Numpy

I am reading from a file containing some segments (irregular parcels of the image) and trying to average the entire segment to have one pixel value. This is the code I use:

band = band[:,:,0] #take the first band of the image 

for i in range(numSegments): #for every segment
    tx = band[segments==i]   #select all the pixels in segment
    avg = np.average(tx)     #average the values
    band[segments==i] = avg  #write the average back into the image

I am omiting some transformation steps and code for printing running time from the snippet.

This takes quite sometime to run for even one band. Almost 1000 seconds. I was wondering if there is a way to vectorize this operation to make it faster?

Data:
Segment2009: an image of all the segments in the image.
This is what the segments look like:

Bands: 3000x3000 pixels, float32 values.

Full context:

workFolder = '/home/shaunak/Work/ChangeDet_2016/SLIC/003_lee_m0_alpha'
bandlist=os.path.join(workFolder,'bandlist.txt')
configfile = os.path.join(workFolder,'config.txt')

segmentfile = os.path.join(workFolder,'Segments2009')

#%% Load the bands -- can refer to subfolders in the bandlist
files = utilities.readBandList(bandlist)
destinations = []
for f in files:
    destinations.append(f.split('.')[0]+"_SP."+f.split('.')[1])


(lines,samples,bands) = utilities.readConfigImSizeBand(configfile)
#%% Superpixel file
segments = np.fromfile(segmentfile,dtype='float32')
segments = np.reshape(segments,(lines,samples))
numSegments = int(np.max(segments))

#%% simple avg
for idx,f in enumerate(files):
    band = np.fromfile(f,dtype='float32').reshape((lines,samples))
    start = time.time()
    for i in range(numSegments):
        tx = band[segments==i]
        avg = np.average(tx)
        band[segments==i] = avg

    band.tofile(destinations[idx])

I am writing the values back to the original after averaging. It is not necessary, also not the most expensive part -- and helps me visualize the results better so I kept it in. I used the following approach also:

avgOut = np.zeros((numSegments,bands),dtype='float32')
#avgOutJoined = np.zeros((lines,samples,bands),dtype='float32')

for i in range(numSegments):
    tx = band[segments==i]
    avgOut[i,:] = np.average(tx,axis=0)
#    avgOutJoined[segments==i,:] = np.average(tx,axis=0)


np.tofile(outputSeperated,avgOut)       
#np.tofile(outputJoined,avgOutJoined)

Since not writing the averaged result back did not save much time, I kept it in.