def _get_line_filter(segment_size, variation): """Computes the filters that can be used to enhance vertical lines in an Image. Args: segment_size: Size of the segment variatoin: Variation in horizontal axes if user wants not exact vertical lines. Returns: filters saved in 3D matrices, each 3rd dimension includes a filter """ smalldisk = pymorph.sedisk(1); bigdisk = pymorph.sedisk(2); horizontal_filter = numpy.zeros((variation*2+1, variation*2+1, segment_size)) horizontal_surrounding = numpy.zeros((variation*2+1, variation*2+1, segment_size)) index = -1 # Generates the filters for each direction of lines for variation_index in range(-variation, variation + 1): index = index + 1; points = bresenham(variation + variation_index,0, variation - variation_index, segment_size - 1) tmp = numpy.zeros((variation*2+1)*segment_size).reshape((variation*2+1, segment_size)) for point_ind in range(0, len(points)): tup_point = points[point_ind] tmp[tup_point[0], tup_point[1]] = 1 tmp_filter = pymorph.dilate(pymorph.binary(tmp), smalldisk) tmp_surrounding = pymorph.subm(pymorph.dilate(pymorph.binary(tmp), bigdisk) , \ pymorph.dilate(pymorph.binary(tmp), smalldisk)) horizontal_filter[index,:,:] = tmp_filter horizontal_surrounding[index,:,:] = tmp_surrounding return horizontal_filter, horizontal_surrounding
def refineMask(mask, imageSeries, numDilations=3, thresh=0.5, se=None):
def corrMaskWithSourcePreConv(imageSeriesSmoothed, dilatedBinaryMask, sourceSmoothed):
corrImage = np.zeros((imageSeries.shape[0], imageSeries.shape[1]))
bounds = np.squeeze(pymorph.blob(dilatedMask, 'boundingbox', output='data'))
for x in range(bounds[1], bounds[3]):
for y in range(bounds[0], bounds[2]):
if dilatedBinaryMask[x,y]>0:
corr = stats.pearsonr(sourceSmoothed[1:-1], imageSeriesSmoothed[x,y,:])[0]
corrImage[x,y] = corr
return corrImage
# calculate box for smoothing
box = sig.boxcar(3)
box = box / box.sum()
imageSeriesSmoothed = nd.convolve1d(imageSeries, box, axis=2, mode='mirror')
completeRefinedMask = np.zeros_like(mask)
if se is None:
se = np.array([[0,1,0],[1,1,1],[0,1,0]])
#se = np.array([[1,1,1],[1,1,1],[1,1,1]])
seedMask = mask.copy() > 0
for rep in range(numDilations):
seedMask = pymorph.dilate(seedMask, se)
for maskIndex in range(1,mask.max()+1):
origMask = mask == maskIndex
dilatedOrigMask = origMask.copy() > 0
for rep in range(numDilations):
dilatedOrigMask = pymorph.dilate(dilatedOrigMask, se)
forbiddenMask = np.logical_or(np.logical_and(seedMask, np.logical_not(dilatedOrigMask)), pymorph.dilate(completeRefinedMask))
# make smoothed source
source = avgFromROIInSeries(imageSeries, origMask)
sourceSmoothed = np.convolve(source, box)
dilatedMask = (mask==maskIndex).copy()
for rep in range(numDilations+1):
dilatedMask = pymorph.dilate(dilatedMask)
corrMask = corrMaskWithSourcePreConv(imageSeriesSmoothed, dilatedOrigMask, sourceSmoothed)
threshMask = corrMask >= thresh
newMask = np.logical_and(np.logical_not(forbiddenMask), np.logical_or(threshMask, origMask))
#completeRefinedMask = np.logical_xor(completeRefinedMask, newMask)
completeRefinedMask += (newMask>0)*maskIndex
#pdb.set_trace()
completeRefinedMask[completeRefinedMask > maskIndex] = 0
return completeRefinedMask
def test_dilate():
f = np.zeros((8,8), np.bool)
Bs = [np.reshape(B, (3,3)) for B in (
[1,1,0, 1,1,0, 0,0,0],
[1,0,0, 1,1,0, 0,0,0],
[1,0,0, 0,1,0, 0,0,0],
[0,1,0, 0,1,0, 0,0,0],
)]
for B in Bs:
assert pymorph.dilate(f, B != 0).sum() == 0
assert pymorph.dilate(f, B).sum() == 0
def test_dilate():
f = np.zeros((8, 8), np.bool)
Bs = [
np.reshape(B, (3, 3)) for B in (
[1, 1, 0, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0],
)
]
for B in Bs:
assert pymorph.dilate(f, B != 0).sum() == 0
assert pymorph.dilate(f, B).sum() == 0
def run_one(fname, outname):
N = 1
im = img.open(fname)
#im = im.filter(ImageFilter.BLUR)
im = im.resize((600, 600), img.ANTIALIAS)
im = im.convert('L')
im = invert(im)
x = np.asarray(im)
y = x
size = 3
for i in range(N):
y = morph.dilate(y, morph.sedisk(size))
y = morph.close(y, morph.sedisk(size))
jm = img.fromarray(y)
jm = invert(jm)
jm = jm.resize((400, 400), img.ANTIALIAS)
jm.save(outname)
def create_shad(matte, target):
"""
Creates a shadowed image given target (to be shadowed) and matte.
The matte can be smaller that the target in which case it will be used at
some random location.
"""
# first get a bounding box of the shadow matte
mask = np.array(matte < 1, dtype=int)
mask = dilate(erode(mask, sedisk(3)), sedisk(3))
left, upper, right, lower = PIL.Image.fromarray(mask).getbbox()
# now cut it out
mh, mw = matte.shape[:2]
matte_bbox = matte[upper:lower, left:right]
# import pdb; pdb.set_trace()
# get new dimensions
mh, mw = matte_bbox.shape[:2]
th, tw = target.shape[:2]
new_matte = np.ones(target.shape)
# get random position to insert the matte
matte_x = matte_y = 0
if mh < th:
matte_y = (th - mh) * np.random.random()
if mw < tw:
matte_x = (tw - mw) * np.random.random()
new_matte[matte_y:matte_y + mh, matte_x:matte_x + mw] = matte_bbox
return new_matte * target
def morph_sharp(im):
img = numpy.zeros(im.shape,dtype=numpy.int32)
# Choose a disk structuring element (3x3 disk)
# Function could be modified to pass structuring element in
se = pymorph.sedisk(r=1,dim=2)
# Apply grayscale erosion
Ie = pymorph.erode(im,se)
# Apply grayscale dilation
Id = pymorph.dilate(im,se)
for i in range(0,im.shape[0]):
for j in range(0,im.shape[1]):
# Compute differences between original image and processed
da = Id[i][j] - im[i][j]
db = im[i][j] - Ie[i][j]
if da < db:
img[i][j] = Id[i][j]
elif da > db:
img[i][j] = Ie[i][j]
else:
img[i][j] = im[i][j]
return img
def clean_mask(mask):
# first erode to get rid of noise
mask = erode(mask, sedisk(2))
# then dilate more to capture a slightly larger area
mask = dilate(mask, sedisk(16))
return mask
def morph_sharp(im):
img = numpy.zeros(im.shape, dtype=numpy.int32)
# Choose a disk structuring element (3x3 disk)
# Function could be modified to pass structuring element in
se = pymorph.sedisk(r=1, dim=2)
# Apply grayscale erosion
Ie = pymorph.erode(im, se)
# Apply grayscale dilation
Id = pymorph.dilate(im, se)
for i in range(0, im.shape[0]):
for j in range(0, im.shape[1]):
# Compute differences between original image and processed
da = Id[i][j] - im[i][j]
db = im[i][j] - Ie[i][j]
if da < db:
img[i][j] = Id[i][j]
elif da > db:
img[i][j] = Ie[i][j]
else:
img[i][j] = im[i][j]
return img
def find_ROI(In):
Ain = cv2array(In)[:, :, 0]
T = cv.CloneImage(In)
cv.Threshold(In, T, 1, 255, cv.CV_THRESH_OTSU | cv.CV_THRESH_BINARY)
I = cv2array(T)[:, :, 0]
I = I > 0
# Cadre blanc
M = np.ones(np.shape(I))
M[1: np.shape(I)[0] - 1, 1: np.shape(I)[1] - 1] = 0
# Enleve bord
I = pymorph.erode(pymorph.erode(pymorph.erode(I)))
M2 = M * I > 0
M1 = M2 * 0
while abs(np.sum(M1 - M2)) > 0.1:
M1 = M2
M2 = pymorph.dilate(M2)
M2 = M2 * I
M2 = pymorph.dilate(pymorph.dilate(pymorph.dilate(M2)))
return M2 * Ain
def _getLineFilter(self, segmentSize, variation):
smallDisk = pymorph.sedisk(1);
bigDisk = pymorph.sedisk(2);
horizontal_filter = numpy.zeros((variation*2+1,variation*2+1,segmentSize))
horizontal_surrounding = numpy.zeros((variation*2+1,variation*2+1,segmentSize))
index = -1
for i in range(-variation,variation+1):
index = index + 1;
# find the line between selected points
points = bresenham(variation+i,0,variation-i,segmentSize-1)
tmp = numpy.zeros((variation*2+1)*segmentSize).reshape((variation*2+1, segmentSize))
for l in range(0, len(points)):
tup_point = points[l]
tmp[tup_point[0], tup_point[1]] = 1
tmp_filter = pymorph.dilate(pymorph.binary(tmp), smallDisk)
tmp_surrounding = pymorph.subm(pymorph.dilate(pymorph.binary(tmp), bigDisk) , pymorph.dilate(pymorph.binary(tmp), smallDisk))
horizontal_filter[index,:,:] = tmp_filter
horizontal_surrounding[index,:,:] = tmp_surrounding
return horizontal_filter, horizontal_surrounding
def Enleve_bord(im_lbl, mask=None):
im_lbl = im_lbl * (im_lbl > 0)
cadre = np.ones(im_lbl.shape)
cadre[2: (im_lbl.shape[0] - 2), 2: (im_lbl.shape[1] - 2)] = 0
if mask is not None:
import pymorph
mask = pymorph.dilate(mask)
cadre += mask > 0
print(cadre)
lbl_bord = np.unique(cadre * im_lbl)
for i in lbl_bord:
im_lbl[im_lbl == i] = 0
return im_lbl
def _get_line_filter(segment_size, variation):
"""Computes the filters that can be used to enhance vertical lines in
an Image.
Args:
segment_size: Size of the segment
variatoin: Variation in horizontal axes if user wants not exact
vertical lines.
Returns:
filters saved in 3D matrices, each 3rd dimension includes a filter
"""
smalldisk = pymorph.sedisk(1)
bigdisk = pymorph.sedisk(2)
horizontal_filter = numpy.zeros(
(variation * 2 + 1, variation * 2 + 1, segment_size))
horizontal_surrounding = numpy.zeros(
(variation * 2 + 1, variation * 2 + 1, segment_size))
index = -1
# Generates the filters for each direction of lines
for variation_index in range(-variation, variation + 1):
index = index + 1
points = bresenham(variation + variation_index, 0,
variation - variation_index, segment_size - 1)
tmp = numpy.zeros((variation * 2 + 1) * segment_size).reshape(
(variation * 2 + 1, segment_size))
for point_ind in range(0, len(points)):
tup_point = points[point_ind]
tmp[tup_point[0], tup_point[1]] = 1
tmp_filter = pymorph.dilate(pymorph.binary(tmp), smalldisk)
tmp_surrounding = pymorph.subm(pymorph.dilate(pymorph.binary(tmp), bigdisk) , \
pymorph.dilate(pymorph.binary(tmp), smalldisk))
horizontal_filter[index, :, :] = tmp_filter
horizontal_surrounding[index, :, :] = tmp_surrounding
return horizontal_filter, horizontal_surrounding
def bright_object_detection(image):
""" Perform bright object detection on an array image."""
# Store all intermediate steps in a dictionary. Useful for debugging.
steps = dict()
steps['input'] = image
# Reduce noise using a median filter.
med_filter_size = (MED_SIZE, MED_SIZE, MED_SIZE)
steps['median'] = ndimg.median_filter(steps['input'], med_filter_size)
# Convert median filtered image to grayscale.
steps['luminance'] = scikits.image.color.rgb2gray(steps['median']) * 255.
# Compute local pixel average.
k_avg = np.ones((AVG_SIZE, AVG_SIZE)) / AVG_SIZE**2
steps['average'] = ndimg.convolve(steps['luminance'], k_avg)
# Compute local pixel variance.
steps['diff_mean'] = steps['luminance'] - steps['average']
steps['diff_mean_sq'] = steps['diff_mean'] * steps['diff_mean']
steps['variance'] = ndimg.convolve(steps['diff_mean_sq'], k_avg)
# Compute binary threshold image using mahalonobis distance. Use the sign
# of the difference between the pixel and its local mean to ignore dark
# pixels.
steps['maha_sq'] = (steps['diff_mean'] > 0) * steps['diff_mean_sq'] / \
steps['variance']
steps['thresh_maha'] = (steps['maha_sq'] > (NUM_STDDEV * NUM_STDDEV))
# Integrate global illumination effects by taking a top percentage of
# intensities from the detected light regions.
steps['masked_regions_lum'] = steps['thresh_maha'] * steps['luminance']
steps['masked_regions_hist'] = pymorph.histogram(steps['masked_regions_lum'])
steps['global_bright_thresh'] = int((len(steps['masked_regions_hist']) * \
(1.0 - GLOBAL_BRIGHT_PCT)) + 0.5)
steps['thresh_global'] = steps['masked_regions_lum'] >= \
steps['global_bright_thresh']
# Morphological operations on detected blobs.
steps['detect_erode'] = pymorph.erode(steps['thresh_global'])
steps['detect_dilate'] = pymorph.dilate(steps['detect_erode'])
# Count bright objects. Connected components and raw pixels.
steps['detect_labels'] = pymorph.label(steps['detect_dilate'])
steps['bright_blob_count'] = steps['detect_labels'].max()
steps['bright_pixel_count'] = sum(steps['masked_regions_hist']
[steps['global_bright_thresh']:])
return steps
def b(image, w=2, sigma=25, k=15):
mask = -sigma * np.array([[1,1,1], [1,0,1], [1,1,1]])
erosion = np.copy(image)
dilation = np.copy(image)
for i in range(k):
erosion = erode(erosion, mask)
dilation = dilate(dilation, mask)
filtered = np.copy(image)
for i in range(w, image.shape[0]-w):
for j in range(w, image.shape[1]-w):
slice = np.array(image)[i-w:i+w+1, j-w:j+w+1]
if (dilation[i][j] - image[i][j] > image[i][j] - erosion[i][j]):
filtered[i][j] = 0
alpha = dominant_direction(slice, np.std)
if alpha != None:
alpha = math.radians(alpha + 90)
filtered[i][j] = cv2.dilate(np.array(slice, np.uint8), np.array(direction(slice, alpha), np.uint8), iterations = k)[w][0]
return filtered
def morph_toggleCE(im):
img = numpy.zeros(im.shape,dtype=numpy.int32)
se = pymorph.sedisk(r=1,dim=2)
Ie = pymorph.erode(im,se)
Id = pymorph.dilate(im,se)
for i in range(0,im.shape[0]):
for j in range(0,im.shape[1]):
da = Id[i][j] - im[i][j]
db = im[i][j] - Ie[i][j]
if da < db:
img[i][j] = Id[i][j]
else:
img[i][j] = Ie[i][j]
return img
def morph_toggleCE(im):
img = numpy.zeros(im.shape, dtype=numpy.int32)
se = pymorph.sedisk(r=1, dim=2)
Ie = pymorph.erode(im, se)
Id = pymorph.dilate(im, se)
for i in range(0, im.shape[0]):
for j in range(0, im.shape[1]):
da = Id[i][j] - im[i][j]
db = im[i][j] - Ie[i][j]
if da < db:
img[i][j] = Id[i][j]
else:
img[i][j] = Ie[i][j]
return img
def region_prop(fig, subfig):
# Inspired by:
# http://stackoverflow.com/a/9059648/621449
c = subfig
# set up the 'FilledImage' bit of regionprops.
FilledImage = np.zeros(fig.shape[0:2]).astype('uint8')
# set up the 'ConvexImage' bit of regionprops.
ConvexImage = np.zeros(fig.shape[0:2]).astype('uint8')
# calculate some things useful later:
m = cv2.moments(c)
# ** regionprops **
Area = m['m00']
Perimeter = cv2.arcLength(c,True)
# bounding box: x,y,width,height
BoundingBox = cv2.boundingRect(c)
# centroid = m10/m00, m01/m00 (x,y)
Centroid = ( m['m10']/m['m00'],m['m01']/m['m00'] )
# EquivDiameter: diameter of circle with same area as region
EquivDiameter = np.sqrt(4*Area/np.pi)
# Extent: ratio of area of region to area of bounding box
Extent = Area/(BoundingBox[2]*BoundingBox[3])
# FilledImage: draw the region on in white
cv2.drawContours( FilledImage, [c], 0, color=255, thickness=-1 )
# calculate indices of that region..
regionMask = (FilledImage==255)
# FilledArea: number of pixels filled in FilledImage
FilledArea = np.sum(regionMask)
# PixelIdxList : indices of region.
# (np.array of xvals, np.array of yvals)
PixelIdxList = regionMask.nonzero()
# CONVEX HULL stuff
# convex hull vertices
ConvexHull = cv2.convexHull(c)
ConvexArea = cv2.contourArea(ConvexHull)
# Solidity := Area/ConvexArea
Solidity = Area/ConvexArea
# convexImage -- draw on ConvexImage
cv2.drawContours( ConvexImage, [ConvexHull], -1,
color=255, thickness=-1 )
# ELLIPSE - determine best-fitting ellipse.
centre,axes,angle = cv2.fitEllipse(c)
MAJ = np.argmax(axes) # this is MAJor axis, 1 or 0
MIN = 1-MAJ # 0 or 1, minor axis
# Note: axes length is 2*radius in that dimension
MajorAxisLength = axes[MAJ]
MinorAxisLength = axes[MIN]
Eccentricity = np.sqrt(1-(axes[MIN]/axes[MAJ])**2)
Orientation = angle
EllipseCentre = centre # x,y
Test = FilledImage.astype('uint8')
mf = cv2.moments(Test)
CentroidFilled = ( mf['m10']/mf['m00'],mf['m01']/mf['m00'] )
# # ** if an image is supplied with the fig:
# # Max/Min Intensity (only meaningful for a one-channel img..)
# MaxIntensity = np.max(img[regionMask])
# MinIntensity = np.min(img[regionMask])
# # Mean Intensity
# MeanIntensity = np.mean(img[regionMask],axis=0)
# # pixel value
# PixelValues = img[regionMask]
x0, y0, dx, dy = BoundingBox
x1, y1 = x0 + dx, y0 + dy
Image = fig[y0:y1, x0:x1]
FilledImageFit = FilledImage[y0:y1, x0:x1]
OImage = fig[y0-1:y1+1, x0-1:x1+1]
NumPixels = Image.sum()
Fillity = (NumPixels+0.0)/FilledArea
crx, cry = (CentroidFilled[0]-x0, CentroidFilled[1]-y0)
dxc = crx-(x1-x0)/2.0
dyc = cry-(y1-y0)/2.0
CentLength = math.sqrt(dxc*dxc + dyc*dyc)
e = lambda fig: pymorph.erode(fig)
d = lambda fig: pymorph.dilate(fig)
o = lambda fig: pymorph.open(fig)
c = lambda fig: pymorph.close(fig)
a = lambda fun, n: reduce(lambda f1, f2: lambda x: f1(f2(x)), [fun]*n, lambda x: x)
Thin = pymorph.thin(OImage)
if num_holes(Image) >= 2:
Inner = removeOuter(Thin)
Inner = (a(d,7))(Inner>0)
Outer = OImage > Inner
ret = dict((k,v) for k, v in locals().iteritems() if k[0].isupper())
return ret
import cv2
import numpy
import numpy as np
import scipy
import pylab as pl
import pylab
import pymorph
from scipy import misc
def s(fig): pl.imshow(fig); pl.gray(); pl.show()
e = lambda fig: pymorph.erode(fig)
d = lambda fig: pymorph.dilate(fig)
o = lambda fig: pymorph.open(fig)
c = lambda fig: pymorph.close(fig)
a = lambda fun, n: reduce(lambda f1, f2: lambda x: f1(f2(x)), [fun]*n, lambda x: x)
img= 255-cv2.imread('reps/2/2.png', cv2.CV_LOAD_IMAGE_GRAYSCALE)
imgb = img > 128
BW=imgb
# grab contours
cs,_ = cv2.findContours( BW.astype('uint8'), mode=cv2.RETR_LIST,
method=cv2.CHAIN_APPROX_SIMPLE )
# set up the 'FilledImage' bit of regionprops.
filledI = np.zeros(BW.shape[0:2]).astype('uint8')
# set up the 'ConvexImage' bit of regionprops.
convexI = np.zeros(BW.shape[0:2]).astype('uint8')
# for each contour c in cs:
# will demonstrate with cs[0] but you could use a loop.
steps['global_bright_thresh'] = int((len(steps['masked_regions_hist']) * \
(1.0 - GLOBAL_BRIGHT_PCT)) + 0.5)
steps['thresh_global'] = steps['masked_regions_lum'] >= \
steps['global_bright_thresh']
print "Global filtered mask:"
plab.imshow(pymorph.overlay(steps['luminance'].astype('uint8'),
steps['thresh_global']))
###############################################################################
# Morpohological operations on detected blobs.
# <demo> stop
# <demo> auto
steps['detect_erode'] = pymorph.erode(steps['thresh_global'])
steps['detect_dilate'] = pymorph.dilate(steps['detect_erode'])
print "Morphed mask (erode, dilate):"
plab.imshow(pymorph.overlay(steps['luminance'].astype('uint8'),
steps['detect_dilate']))
# <demo> stop
# <demo> auto
# Count bright objects. Connected components and raw pixels.
steps['detect_labels'] = pymorph.label(steps['detect_dilate'])
steps['bright_blob_count'] = steps['detect_labels'].max()
print "Bright blob count:", steps['bright_blob_count']
steps['bright_pixel_count'] = sum(steps['masked_regions_hist']
[steps['global_bright_thresh']:])
print "Bright pixel count:", steps['bright_pixel_count']
import numpy as np
import scipy
import pylab as pl
import pylab
import pymorph
from scipy import misc
def s(fig):
pl.imshow(fig)
pl.gray()
pl.show()
e = lambda fig: pymorph.erode(fig)
d = lambda fig: pymorph.dilate(fig)
o = lambda fig: pymorph.open(fig)
c = lambda fig: pymorph.close(fig)
a = lambda fun, n: reduce(lambda f1, f2: lambda x: f1(f2(x)), [fun] * n, lambda
x: x)
img = 255 - cv2.imread('reps/2/2.png', cv2.CV_LOAD_IMAGE_GRAYSCALE)
imgb = img > 128
BW = imgb
# grab contours
cs, _ = cv2.findContours(BW.astype('uint8'),
mode=cv2.RETR_LIST,
method=cv2.CHAIN_APPROX_SIMPLE)
# set up the 'FilledImage' bit of regionprops.
filledI = np.zeros(BW.shape[0:2]).astype('uint8')
import pymorph as m
import mahotas
from numpy import where, reshape
image = mahotas.imread('B.png') # Load image
b1 = image[:,:,0] < 100 # Make a binary image from the thresholded red channel
b2 = m.erode(b1, m.sedisk(4)) # Erode to enhance contrast of the bridge
b3 = m.open(b2,m.sedisk(4)) # Remove the bridge
b4 = b2-b3 # Bridge plus small noise
b5 = m.areaopen(b4,1000) # Remove small areas leaving only a thinned bridge
b6 = m.dilate(b3)*b5 # Extend the non-bridge area slightly and get intersection with the bridge.
#b6 is image of end of bridge, now find single points
b7 = m.thin(b6, m.endpoints('homotopic')) # Narrow regions to single points.
labelled = m.label(b7) # Label endpoints.
x1, y1 = reshape(where(labelled == 1),(1,2))[0]
x2, y2 = reshape(where(labelled == 2),(1,2))[0]
outputimage = m.overlay(b1, m.dilate(b7,m.sedisk(5)))
mahotas.imsave('output.png', outputimage)
mask = filter_org > thres_ratio * peak
# ostu threshold
T = mahotas.thresholding.otsu(im16)
thres_list.append(T)
mmask = im16 > thres_ratio * T
im_ma = im16 * mmask
# pymorph
labeled, nr_obj = ndimage.label(mask)
max_label = labeled[max_idx / w][max_idx % w]
max_mask = labeled == max_label
top_hat_mask = ndimage.morphology.white_tophat(mask, (4, 4))
dmask = pm.dilate(max_mask)
ndmask = ~dmask
peak_template = dmask * im16_org
removed_peak = ndmask * im16_org
ratio_peak = removed_peak.max() / (peak * 1.)
second_peak_list.append(ratio_peak)
if False: #ratio_peak > 0.9:
plt.imshow(im16_org)
output_path = output_dir +'/peak_' + str("%0.3f" % ratio_peak)+ \
f[index].rsplit('.', 2)[0] + '.png'
plt.savefig(output_path)
