hist = np.zeros( 256, dtype="uint32" )

for p in ndata.flatten() :

hist[p] += 1

# 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)

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

fig = Figure(figsize=(8.5,11))

# plot the histogram

ax = fig.add_subplot(211)

ax.grid()

# don't plot min/max (avoid the clipped pixels)

# ax.plot(n[1:255])

ax.plot(n)

# plot the peaks

x = peaks

y = [ n[p] for p in peaks ]

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

ax = fig.add_subplot(212)

ax.grid()

ax.plot( np.diff(n) )

# plot the row avg down the page

# ax = fig.add_subplot(313)

# ax.grid()

# ax.plot([ np.mean(row) for row in ndata[:,]])

outfilename = "out.png"

canvas = FigureCanvasAgg(fig)

canvas.print_figure(outfilename)

fig = Figure(figsize=(8.5,11))

# plot the row avg down the page

ax = fig.add_subplot(211)

ax.grid()

ax.plot([ np.mean(row) for row in ndata[:,]])

ax = fig.add_subplot(212)

ax.grid()

ax.plot([ np.mean(col) for col in ndata.T[:,]])

outfilename = "xy.png"

canvas = FigureCanvasAgg(fig)

canvas.print_figure(outfilename)

line_slope = float(y2-y1)/float(x2-x1)

line_b = y1 - x1 * line_slope

eq = lambda x : line_slope * x + line_b

eq,m,b = line_equation( black_clip, 0, white_clip, 255 )

# print white_clip,black_clip

# print eq

# print m,b

lin = np.linspace(0,255,256)

lut = np.asarray( [ int(round(eq(l))) for l in lin ] )

# print len(lut)

assert len(lut)==256,len(lut)

lut[ np.where(lut<0) ] = 0

lut[ np.where(lut>255) ] =255

lut8 = lut.astype(np.uint8)

output = lut8[ndata]

lut8 = lut.astype(np.uint8)

# outfilename = "{0}_gray.tif".format(basename)

# outfilename = "clipped.tif"

img = Image.fromarray(output)

img2 = img.resize( (img.size[0]/2,img.size[1]/2), Image.BICUBIC )

img2.save(outfilename)

# img.save(outfilename)

# kill the clipped-to-white pixels

# ndata = ndata[:-1]

smoothed = savitzky_golay.savitzky_golay( ndata, 21, 5, 1 )

# print "smoothed=", smoothed

fig = Figure()

ax = fig.add_subplot(111)

ax.grid()

ax.plot(ndata)

# ax = fig.add_subplot(212)

# ax.grid()

ax.plot(smoothed)

outfilename = "svgy.pdf"

canvas = FigureCanvasAgg(fig)

canvas.print_figure(outfilename)

print "wrote",outfilename

ndata = imtools.load_image(imgfilename,dtype="uint8")

print ndata.shape

# hist = mkhisto( ndata )

# print hist

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

# n,bins = np.histogram(ndata,256)

print n

# nz = np.where(ndata==0)

# print nz

# print len(nz[0]),len(nz[1])

# row_means = [ np.mean(row) for row in ndata[:,]]

# row_std = [ np.std(row) for row in ndata[:,]]

# print n

# peaks = peakdetect.peakdetect_zero_crossing( n[1:255] )

# peaks = find_peaks(n)

# print peaks

# last_peak = peaks[-1]

# print n[last_peak-10:last_peak+10]

# print n[last_peak]

# plots( ndata, peaks )

# xyplot( ndata )

smoothed = plot_savitzky_golay( n )

peaks = find_peaks(smoothed)

print "peaks=",peaks

last_peak = peaks[-1]

print "last_peak=",last_peak

# print "

plots( ndata, peaks )

basename = imtools.get_basename(imgfilename)

outfilename = "{0}_out.png".format(basename)

white_clip = last_peak

ndata2 = np.copy(ndata)

# get the data as grayscale

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

basename = imtools.get_basename(imgfilename)

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

peaks_list, = peaks.find_histogram_peaks(ndata)

print "peaks_list=",peaks_list

white_peak = peaks_list[-1]

black_peak = peaks_list[0]

print "white_peak={0} black_peak={1}".format(white_peak, black_peak)

# get the data from the original image

ndata = imtools.load_image(imgfilename,dtype="uint8")

outfilename = "{0}_autowhite.png".format(basename)

white_clip = white_peak

black_clip = black_peak