# all values in window are less than value

for p in window :

if p > value :

return False

# all values in window are greater than value

for p in window :

if p < value :

return False

ndata = np.asarray( data )

diffs = np.diff( ndata )

# look for zero crossings in a window

peaks = []

window_size = 21

mid = 10

cnt = window_size/2

while cnt < len(data)-window_size/2 :

window = data[cnt-window_size/2 : cnt+window_size/2]

# print "window=",window

# print "mid=",window[mid]

# left size of the window < midpoint?

if not win_less(window[:mid],window[mid]) :

cnt += 1

# print "no match <"

# print window

continue

# right side of the window > midpoint?

if not win_less(window[mid+1:],window[mid]) :

cnt += 1

# print "no match >"

# print window

continue

peaks.append(cnt)

cnt += 1

# sys.exit(0)

if not mkoutfilename :

return

fig = Figure()

ax = fig.add_subplot(111)

ax.grid()

# plot the original histogram

ax.plot(hist,".-")

# plot the SG smoothed derivative if passed (this same function used with

# other peak finding code that doesn't pass the SVG)

if ndata is not None:

ax.plot(ndata,".-")

# plot the peaks

x = zeros_list

# y = [ smoothed[p] for p in zeros_list ]

y = [ hist[p] for p in zeros_list ]

ax.plot( x, y, 'rx' )

# my mkoutfilename lambda defaults to .tif extension

outfilename = mkoutfilename( "hist" ).replace(".tif",".pdf")

canvas = FigureCanvasAgg(fig)

canvas.print_figure(outfilename)

# find locations where data crosses zero

zeros_list = []

for i in range(len(ndata)-1):

if ndata[i]==0 or (ndata[i] < 0 and ndata[i+1] > 0) \

or (ndata[i] > 0 and ndata[i+1] < 0) :

zeros_list.append( i )

# Find all the peaks. Sort. Take 'n' highest.

peaks = []

for z in zeros_list :

peaks.append( (z,ndata[z]) )

peaks.sort( lambda a,b : cmp( a[1],b[1] ) )

# print peaks

# print peaks[-num:]

# return the indices with the highest values

highest_peaks = [ p[0] for p in peaks[-num:] ]

# print highest_peaks

# We could be on either side of the peak or at the peak. Adjust our

# location to exactly the highest peak

for i in range(len(highest_peaks)):

p = highest_peaks[i]

if ndata[p-1] > ndata[p] :

highest_peaks[i] = p-1

elif ndata[p+1] > ndata[p] :

highest_peaks[i] = p+1

# print highest_peaks

argsorted = np.argsort(hist)

print hist

print argsorted

print hist[argsorted]

peaks_list = []

riter = reversed(argsorted)

for n in riter :

print n

if hist[n] < hist[n+1] or hist[n] < hist[n-1] :

continue

print "keep",n

peaks_list.append(n)

if len(peaks_list) >= num_peaks :

break

print peaks_list

hist,bins = np.histogram(ndata,bins=256)

np.save("hist.npy",hist)

# zero pad the end so clipped white will show up as a peak

tmp = np.zeros(257)

tmp[0:256] = hist

hist = tmp

del tmp

if 1 :

# smoothed 1st derivative of the signal

svg = savitzky_golay.savitzky_golay( hist, 21, 5, 1 )

np.save("svg.npy",svg)

peaks_list = find_peaks( svg )

# look for zero crossing

zeros_list = find_zeros( svg )

print "zeros_list=",zeros_list

highest_peaks = find_highest_peaks( zeros_list, hist, num_peaks )

print "highest_peaks=",highest_peaks

else :

# nothing fancy; just use argmax repeatedly

highest_peaks = simple_highest_peaks( ndata, hist, num_peaks )

svg = None

# svg = np.diff(hist)

# 2nd derivative

# svg = np.diff(np.diff(hist))

# highest_peaks = np.argsort(svg)[0:num_peaks]

# highest_peaks = np.argsort(svg)[-num_peaks:]

print highest_peaks

save_gray_histogram_img( hist, svg, highest_peaks )

sorted_peaks = sorted(highest_peaks)

histo_values = [ hist[p] for p in sorted_peaks ]

# return the pixel values of the peaks and the histogram counts

from basename import get_basename

infilename = sys.argv[1]

ndata = imtools.load_image( infilename, mode="L", dtype="uint8" )

print ndata.shape

# aggressive median filter to smooth out as much noise as possible

if 1 :

print "filtering..."

fdata = scipy.ndimage.filters.median_filter( ndata, size=(5,5) )

else :

fdata = np.copy(ndata) # no smoothing

basename = get_basename(infilename)

global mkoutfilename

mkoutfilename = lambda s : "{0}_{1}.tif".format(basename,s)

imtools.clip_and_save( fdata, mkoutfilename("gray"))

np.save("gray.npy",fdata)

peaks_list, pixel_counts = find_histogram_peaks( fdata )

print "peaks=",peaks_list