def findBlobs(ccmap,
thresh,
maxsize=500,
minsize=1,
maxpeaks=1500,
border=10,
maxmoment=6.0,
elim="highest",
summary=False):
"""
calls leginon's imagefun.find_blobs
"""
totalarea = (ccmap.shape)[0] * (ccmap.shape)[1]
ccthreshmap = imagefun.threshold(ccmap, thresh)
percentcov = round(100.0 * float(ccthreshmap.sum()) / float(totalarea), 2)
#imagefun.find_blobs(image,mask,border,maxblobs,maxblobsize,minblobsize,maxmoment,method)
if percentcov > 15:
apDisplay.printWarning("too much coverage in threshold: " +
str(percentcov))
return [], percentcov
#apImage.arrayToJpeg(ccmap, "dogmap2.jpg")
#apImage.arrayToJpeg(ccthreshmap, "threshmap2.jpg")
blobtree = imagefun.find_blobs(ccmap, ccthreshmap, border, maxpeaks * 4,
maxsize, minsize, maxmoment, elim, summary)
return blobtree, percentcov
def findSquares(self):
if self.mosaicimagedata is None:
message = 'You must save the current mosaic image before finding squares on it.'
self.logger.error(message)
return
original_image = self.mosaicimagedata['image']
message = 'finding squares'
self.logger.info(message)
sigma = self.settings['lpf']['sigma']
kernel = convolver.gaussian_kernel(sigma)
self.convolver.setKernel(kernel)
image = self.convolver.convolve(image=original_image)
self.setImage(image, 'Filtered')
## threshold grid bars
squares_thresh = self.settings['threshold']
image = imagefun.threshold(image, squares_thresh)
self.setImage(image, 'Thresholded')
## find blobs
blobs = imagefun.find_blobs(original_image, image,
self.settings['blobs']['border'],
self.settings['blobs']['max'],
self.settings['blobs']['max size'],
self.settings['blobs']['min size'])
## use stats to find good ones
mean_min = self.settings['blobs']['min mean']
mean_max = self.settings['blobs']['max mean']
std_min = self.settings['blobs']['min stdev']
std_max = self.settings['blobs']['max stdev']
targets = []
prefs = self.storeSquareFinderPrefs()
rows, columns = image.shape
for blob in blobs:
row = blob.stats['center'][0]
column = blob.stats['center'][1]
mean = blob.stats['mean']
std = blob.stats['stddev']
stats = leginondata.SquareStatsData(prefs=prefs,
row=row,
column=column,
mean=mean,
stdev=std)
if (mean_min <= mean <= mean_max) and (std_min <= std <= std_max):
stats['good'] = True
## create a display target
targets.append((column, row))
else:
stats['good'] = False
self.publish(stats, database=True)
## display them
self.setTargets(targets, 'acquisition')
message = 'found %s squares' % (len(targets), )
self.logger.info(message)
def find_edges(self):
'''
find edges on the original image
'''
if self.__results['original'] is None:
raise RuntimeError('no original image to find edges on')
sourceim = self.__results['original']
filt = self.edges_config['filter']
sigma = self.edges_config['sigma']
ab = self.edges_config['abs']
lpsig = self.edges_config['lpsig']
edgethresh = self.edges_config['thresh']
edgesflag = self.edges_config['edges']
kernel = convolver.gaussian_kernel(lpsig)
n = len(kernel)
self.edgefinder.setKernel(kernel)
smooth = self.edgefinder.convolve(image=sourceim)
if not edgesflag:
edges = smooth
elif filt == 'laplacian3':
kernel = convolver.laplacian_kernel3
self.edgefinder.setKernel(kernel)
edges = self.edgefinder.convolve(image=smooth)
elif filt == 'laplacian5':
kernel = convolver.laplacian_kernel5
self.edgefinder.setKernel(kernel)
edges = self.edgefinder.convolve(image=smooth)
elif filt == 'laplacian-gaussian':
kernel = convolver.laplacian_of_gaussian_kernel(n,sigma)
self.edgefinder.setKernel(kernel)
edges = self.edgefinder.convolve(image=smooth)
elif filt == 'sobel':
self.edgefinder.setImage(smooth)
kernel1 = convolver.sobel_row_kernel
kernel2 = convolver.sobel_col_kernel
edger = self.edgefinder.convolve(kernel=kernel1)
edgec = self.edgefinder.convolve(kernel=kernel2)
edges = numpy.hypot(edger,edgec)
## zero the image edge effects
edges[:n] = 0
edges[:,:n] = 0
edges[:,-n:] = 0
edges[-n:] = 0
else:
raise RuntimeError('no such filter type: %s' % (filt,))
if ab and edgesflag:
edges = numpy.absolute(edges)
if edgethresh and edgesflag:
edges = imagefun.threshold(edges, edgethresh)
self.__update_result('edges', edges)
if self.save_mrc:
mrc.write(edges, 'edges.mrc')
def findSquares(self):
if self.mosaicimagedata is None:
message = 'You must save the current mosaic image before finding squares on it.'
self.logger.error(message)
return
original_image = self.mosaicimagedata['image']
message = 'finding squares'
self.logger.info(message)
sigma = self.settings['lpf']['sigma']
kernel = convolver.gaussian_kernel(sigma)
self.convolver.setKernel(kernel)
image = self.convolver.convolve(image=original_image)
self.setImage(image, 'Filtered')
## threshold grid bars
squares_thresh = self.settings['threshold']
image = imagefun.threshold(image, squares_thresh)
self.setImage(image, 'Thresholded')
## find blobs
blobs = imagefun.find_blobs(original_image, image,
self.settings['blobs']['border'],
self.settings['blobs']['max'],
self.settings['blobs']['max size'],
self.settings['blobs']['min size'])
## use stats to find good ones
mean_min = self.settings['blobs']['min mean']
mean_max = self.settings['blobs']['max mean']
std_min = self.settings['blobs']['min stdev']
std_max = self.settings['blobs']['max stdev']
targets = []
prefs = self.storeSquareFinderPrefs()
rows, columns = image.shape
for blob in blobs:
row = blob.stats['center'][0]
column = blob.stats['center'][1]
mean = blob.stats['mean']
std = blob.stats['stddev']
stats = leginondata.SquareStatsData(prefs=prefs, row=row, column=column, mean=mean, stdev=std)
if (mean_min <= mean <= mean_max) and (std_min <= std <= std_max):
stats['good'] = True
## create a display target
targets.append((column,row))
else:
stats['good'] = False
self.publish(stats, database=True)
## display them
self.setTargets(targets, 'acquisition')
message = 'found %s squares' % (len(targets),)
self.logger.info(message)
def threshold_correlation(self, threshold=None):
'''
Threshold the correlation image.
'''
if self.__results['correlation'] is None:
raise RuntimeError('need correlation image to threshold')
self.configure_threshold(threshold)
cc = self.__results['correlation']
t = imagefun.threshold(cc, self.threshold)
self.__update_result('threshold', t)
if self.save_mrc:
mrc.write(t, 'threshold.mrc')
def threshold_correlation(self, threshold=None):
'''
Threshold the correlation image.
'''
if self.__results['correlation'] is None:
raise RuntimeError('need correlation image to threshold')
self.configure_threshold(threshold)
cc = self.__results['correlation']
mean = arraystats.mean(cc)
std = arraystats.std(cc)
thresh = mean + self.threshold * std
t = imagefun.threshold(cc, thresh)
self.__update_result('threshold', t)
if self.save_mrc:
mrc.write(t, 'threshold.mrc')
def findBlobs(ccmap, thresh, maxsize=500, minsize=1, maxpeaks=1500, border=10,
maxmoment=6.0, elim= "highest", summary=False):
"""
calls leginon's imagefun.find_blobs
"""
totalarea = (ccmap.shape)[0]*(ccmap.shape)[1]
ccthreshmap = imagefun.threshold(ccmap, thresh)
percentcov = round(100.0*float(ccthreshmap.sum())/float(totalarea),2)
#imagefun.find_blobs(image,mask,border,maxblobs,maxblobsize,minblobsize,maxmoment,method)
if percentcov > 15:
apDisplay.printWarning("too much coverage in threshold: "+str(percentcov))
return [],percentcov
#apImage.arrayToJpeg(ccmap, "dogmap2.jpg")
#apImage.arrayToJpeg(ccthreshmap, "threshmap2.jpg")
blobtree = imagefun.find_blobs(ccmap, ccthreshmap, border, maxpeaks*4,
maxsize, minsize, maxmoment, elim, summary)
return blobtree, percentcov
def fitFirstCTFNode(pow, rpixelsize, defocus, ht): filter = ndimage.gaussian_filter(pow,3) grad = ndimage.gaussian_gradient_magnitude(filter,3) thr = imagefun.threshold(grad,grad.mean()+3*grad.std()) if defocus: z = abs(defocus) s = calculateFirstNode(ht,z) dmean = max(0.8*s/rpixelsize, 30) else: shape = pow.shape r = 20 center = ( shape[0] / 2, shape[1] / 2 ) grad[center[0]-r: center[0]+r, center[1]-r: center[1]+r] = 0 peak = ndimage.maximum_position(grad) dmean = math.hypot(peak[0] - center[0], peak[1] - center[1]) drange = max(dmean / 4, 10) eparams = find_ast_ellipse(grad,thr,dmean,drange) if eparams: z0, zast, ast_ratio, alpha = getAstigmaticDefocii(eparams,rpixelsize, ht) return z0,zast,ast_ratio, alpha, eparams
def fitFirstCTFNode(pow, rpixelsize, defocus, ht):
filter = ndimage.gaussian_filter(pow, 3)
grad = ndimage.gaussian_gradient_magnitude(filter, 3)
thr = imagefun.threshold(grad, grad.mean() + 3 * grad.std())
if defocus:
z = abs(defocus)
s = calculateFirstNode(ht, z)
dmean = max(0.8 * s / rpixelsize, 30)
else:
shape = pow.shape
r = 20
center = (shape[0] / 2, shape[1] / 2)
grad[center[0] - r:center[0] + r, center[1] - r:center[1] + r] = 0
peak = ndimage.maximum_position(grad)
dmean = math.hypot(peak[0] - center[0], peak[1] - center[1])
drange = max(dmean / 4, 10)
eparams = find_ast_ellipse(grad, thr, dmean, drange)
if eparams:
z0, zast, ast_ratio, alpha = getAstigmaticDefocii(
eparams, rpixelsize, ht)
return z0, zast, ast_ratio, alpha, eparams
def findEdges(self, image): gk = gaussian(image.shape, 1.2) srk = sobel_row(image.shape) sck = sobel_col(image.shape) imfft = ffteng.transform(image) gkfft = ffteng.transform(gk) srkfft = ffteng.transform(srk) sckfft = ffteng.transform(sck) smooth = Numeric.multiply(imfft, gkfft) refft = Numeric.multiply(smooth, srkfft) cefft = Numeric.multiply(smooth, sckfft) re = ffteng.itransform(refft) ce = ffteng.itransform(cefft) edges = Numeric.hypot(re,ce) ## convolution leaves invalid borders # copy rows print 'clean up' edges[0] = edges[1] edges[-1] = edges[-2] # copy cols edges[:,0] = edges[:,1] edges[:,-1] = edges[:,-2] # threshold print 'threshold' edges = imagefun.zscore(edges) print 'clip' # works for everything except test_square7 #edges = Numeric.clip(edges, -0.5, 1.0) edges = imagefun.threshold(edges, 0.8) print 'done' return edges
def findEdges(self, image):
gk = gaussian(image.shape, 1.2)
srk = sobel_row(image.shape)
sck = sobel_col(image.shape)
imfft = ffteng.transform(image)
gkfft = ffteng.transform(gk)
srkfft = ffteng.transform(srk)
sckfft = ffteng.transform(sck)
smooth = Numeric.multiply(imfft, gkfft)
refft = Numeric.multiply(smooth, srkfft)
cefft = Numeric.multiply(smooth, sckfft)
re = ffteng.itransform(refft)
ce = ffteng.itransform(cefft)
edges = Numeric.hypot(re,ce)
## convolution leaves invalid borders
# copy rows
print 'clean up'
edges[0] = edges[1]
edges[-1] = edges[-2]
# copy cols
edges[:,0] = edges[:,1]
edges[:,-1] = edges[:,-2]
# threshold
print 'threshold'
edges = imagefun.zscore(edges)
print 'clip'
# works for everything except test_square7
#edges = Numeric.clip(edges, -0.5, 1.0)
edges = imagefun.threshold(edges, 0.8)
print 'done'
return edges
def find_edges(self):
'''
find edges on the original image
'''
if self.__results['original'] is None:
raise RuntimeError('no original image to find edges on')
sourceim = self.__results['original']
sigma = self.edges_config['lpsig']
edgethresh = self.edges_config['thresh']
smooth = scipy.ndimage.gaussian_filter(sourceim, sigma)
mrc.write(smooth, 'smooth.mrc')
edges = scipy.ndimage.generic_gradient_magnitude(smooth, derivative=scipy.ndimage.sobel)
if edgethresh:
edges = imagefun.threshold(edges, edgethresh)
self.__update_result('edges', edges)
if self.save_mrc:
mrc.write(edges, 'edges.mrc')
def find_edges(self):
'''
find edges on the original image
'''
if self.__results['original'] is None:
raise RuntimeError('no original image to find edges on')
sourceim = self.__results['original']
sigma = self.edges_config['lpsig']
edgethresh = self.edges_config['thresh']
smooth = scipy.ndimage.gaussian_filter(sourceim, sigma)
if self.save_mrc:
mrc.write(smooth, 'smooth.mrc')
edges = scipy.ndimage.generic_gradient_magnitude(smooth, derivative=scipy.ndimage.sobel)
if edgethresh:
edges = imagefun.threshold(edges, edgethresh)
self.__update_result('edges', edges)
if self.save_mrc:
mrc.write(edges, 'edges.mrc')
def find_ast_ellipse(grad,thr,dmean,drange):
mrc.write(grad,'grad.mrc')
mrc.write(thr,'thr.mrc')
maxsize=4*(drange)*(drange)
blobs = imagefun.find_blobs(grad,thr,maxblobsize=maxsize,minblobsize=0)
points = []
goods = []
# find isolated blobs in the range
for blob in blobs:
if blob.stats['size']==0:
points.append(blob.stats['center'])
else:
points.append(blob.stats['maximum_position'])
shape = grad.shape
center = map((lambda x: x / 2), shape)
for point in points:
d = math.hypot(point[0]-center[0],point[1]-center[1])
if d > dmean-drange and d < dmean+drange:
goods.append(point)
# divide the continuous blob into wedges and find centers of each
offsets = []
angles = []
division = 32
for i in range(0,division):
angles.append(i * math.pi / division)
while angles:
angle = angles.pop()
extra = 0
blobs = []
while len(blobs)==0 and extra < dmean:
# move out until a blob is found
offset = ((dmean+extra)*math.cos(angle)-(drange+extra)+center[0],(dmean+extra)*math.sin(angle)-(drange+extra)+center[1])
dim = (drange+extra,drange+extra)
gsample = grad[offset[0]:offset[0]+2*dim[0], offset[1]:offset[1]+2*dim[1]]
sample = imagefun.threshold(gsample,grad.mean()+3*grad.std())
# pad the edge to 0 so that blobs including the edge can be found
sample[0:1,:]=0
sample[-1:,:]=0
sample[:,0:1]=0
sample[:,-1:]=0
#mrc.write(sample,os.path.join('sample%d.mrc'%(division-len(angles))))
maxblobsize = dim[0]*dim[1]
blobs = imagefun.find_blobs(gsample,sample,maxblobsize=maxsize,border=5+extra)
distances = []
gooddistances = []
for blob in blobs:
position = blob.stats['maximum_position']
newposition = (position[0]+offset[0],position[1]+offset[1])
d = math.hypot(newposition[0]-center[0],newposition[1]-center[1])
distances.append(d)
if d > dmean-drange:
gooddistances.append(d)
if len(gooddistances) > 0:
for i,blob in enumerate(blobs):
position = blob.stats['maximum_position']
newposition = (position[0]+offset[0],position[1]+offset[1])
if distances[i] == min(gooddistances):
#print division - len(angles),distances[i],position
symposition = (center[0]*2-newposition[0],center[1]*2-newposition[1])
goods.append(newposition)
goods.append(symposition)
break
extra += drange / 2
if len(goods) > 6:
eparams = ellipse.solveEllipseB2AC(goods)
return eparams
else:
return None
def findPeaksInMapPlus(ccmaxmap, file, num, params, template, tmplmask,
anglemap):
threshold = float(params["thresh"])
bin = int(params["bin"])
diam = float(params["diam"])
apix = float(params["apix"])
olapmult = float(params["overlapmult"])
maxpeaks = int(params["maxpeaks"])
pixrad = diam / apix / 2.0 / float(bin)
#MAXBLOBSIZE ==> 2x AREA OF PARTICLE
maxblobsize = int(round(math.pi *
(apix * diam / float(bin))**2 / 2.0, 0)) + 1
totalarea = (ccmaxmap.shape)[0]**2
print " ... threshold", threshold
outfile = "pikfiles/" + file + "." + str(num) + ".pik"
if (os.path.exists(outfile)):
os.remove(outfile)
print " ... removed existing file:", outfile
for i in range(5):
thresh = threshold + float(i - 2) * 0.05
ccthreshmap = imagefun.threshold(ccmaxmap, thresh)
percentcover = round(
100.0 * float(ccthreshmap.sum()) / float(totalarea), 3)
blobs = imagefun.find_blobs(ccmaxmap, ccthreshmap, 6, maxpeaks * 4,
maxblobsize, 2, 5, "highest")
tstr = "%.2f" % thresh
lbstr = "%4d" % len(blobs)
pcstr = "%.2f" % percentcover
if (thresh == threshold):
print " ... *** selected threshold: "+tstr+" gives "+lbstr+" peaks ("+\
pcstr+"% coverage ) ***"
else:
print " ... varying threshold: "+tstr+" gives "+lbstr+" peaks ("+\
pcstr+"% coverage )"
ccthreshmap = imagefun.threshold(ccmaxmap, threshold)
percentcover = round(100.0 * float(ccthreshmap.sum()) / float(totalarea),
2)
#numeric_to_jpg(ccthreshmap,"ccthreshmap.jpg")
blobs = imagefun.find_blobs(ccmaxmap, ccthreshmap, 6, maxpeaks * 4,
maxblobsize, 2, 5, "highest")
if (len(blobs) > maxpeaks):
print " !!! more than maxpeaks (" + str(
maxpeaks) + ") peaks, selecting only top peaks"
blobs.sort(blob_compare)
blobs = blobs[0:maxpeaks]
del ccthreshmap
#find_blobs(image,mask,border,maxblobs,maxblobsize,minblobsize)
print "Template "+str(num)+": Found",len(blobs),"peaks ("+\
str(percentcover)+"% coverage)"
if (percentcover > 10):
print " !!! WARNING: thresholding covers more than 10% of image"
cutoff = olapmult * pixrad #1.5x particle radius in pixels
removeOverlappingBlobs(blobs, cutoff)
blobs.sort(blob_compare)
#blobs = calc_corrcoeffs(blobs,file,bin,template,tmplmask,anglemap)
blobs = fake_corrcoeffs(blobs, file, bin, template, tmplmask, anglemap)
#WRITE PIK FILE
f = open(outfile, 'w')
f.write(
"#filename x y mean stdev corr_coeff peak_size templ_num angle moment\n"
)
for blob in blobs:
row = blob.stats['center'][0]
col = blob.stats['center'][1]
mean = blob.stats['mean']
std = blob.stats['stddev']
rho = blob.stats['corrcoeff']
mom = blob.stats['moment']
size = blob.stats['n']
mean_str = "%.4f" % mean
std_str = "%.4f" % std
rho_str = "%.4f" % rho
mom_str = "%.4f" % mom
#filename x y mean stdev corr_coeff peak_size templ_num angle moment
out = file+".mrc "+str(int(col)*bin)+" "+str(int(row)*bin)+ \
" "+mean_str+" "+std_str+" "+rho_str+" "+str(int(size))+ \
" "+str(num)+" 0 "+mom_str
f.write(str(out) + "\n")
f.close()
drawBlobs(ccmaxmap, blobs, file, num, bin, pixrad)
return blobs
def find_ast_ellipse(grad,thr,dmean,drange):
mrc.write(grad,'grad.mrc')
mrc.write(thr,'thr.mrc')
maxsize=4*(drange)*(drange)
blobs = imagefun.find_blobs(grad,thr,maxblobsize=maxsize,minblobsize=0)
points = []
goods = []
# find isolated blobs in the range
for blob in blobs:
if blob.stats['size']==0:
points.append(blob.stats['center'])
else:
points.append(blob.stats['maximum_position'])
shape = grad.shape
center = map((lambda x: x / 2), shape)
for point in points:
d = math.hypot(point[0]-center[0],point[1]-center[1])
if d > dmean-drange and d < dmean+drange:
goods.append(point)
# divide the continuous blob into wedges and find centers of each
offsets = []
angles = []
division = 32
for i in range(0,division):
angles.append(i * math.pi / division)
while angles:
angle = angles.pop()
extra = 0
blobs = []
while len(blobs)==0 and extra < dmean:
# move out until a blob is found
offset = ((dmean+extra)*math.cos(angle)-(drange+extra)+center[0],(dmean+extra)*math.sin(angle)-(drange+extra)+center[1])
dim = (drange+extra,drange+extra)
gsample = grad[offset[0]:offset[0]+2*dim[0], offset[1]:offset[1]+2*dim[1]]
sample = imagefun.threshold(gsample,grad.mean()+3*grad.std())
# pad the edge to 0 so that blobs including the edge can be found
sample[0:1,:]=0
sample[-1:,:]=0
sample[:,0:1]=0
sample[:,-1:]=0
#mrc.write(sample,os.path.join('sample%d.mrc'%(division-len(angles))))
maxblobsize = dim[0]*dim[1]
blobs = imagefun.find_blobs(gsample,sample,maxblobsize=maxsize,border=5+extra)
distances = []
gooddistances = []
for blob in blobs:
position = blob.stats['maximum_position']
newposition = (position[0]+offset[0],position[1]+offset[1])
d = math.hypot(newposition[0]-center[0],newposition[1]-center[1])
distances.append(d)
if d > dmean-drange:
gooddistances.append(d)
if len(gooddistances) > 0:
for i,blob in enumerate(blobs):
position = blob.stats['maximum_position']
newposition = (position[0]+offset[0],position[1]+offset[1])
if distances[i] == min(gooddistances):
#print division - len(angles),distances[i],position
symposition = (center[0]*2-newposition[0],center[1]*2-newposition[1])
goods.append(newposition)
goods.append(symposition)
break
extra += drange / 2
if len(goods) > 6:
eparams = ellipse.solveEllipseB2AC(goods)
return eparams
else:
return None
def findPeaksInMapPlus(ccmaxmap,file,num,params,template,tmplmask,anglemap):
threshold = float(params["thresh"])
bin = int(params["bin"])
diam = float(params["diam"])
apix = float(params["apix"])
olapmult = float(params["overlapmult"])
maxpeaks = int(params["maxpeaks"])
pixrad = diam/apix/2.0/float(bin)
#MAXBLOBSIZE ==> 2x AREA OF PARTICLE
maxblobsize = int(round(math.pi*(apix*diam/float(bin))**2/2.0,0))+1
totalarea = (ccmaxmap.shape)[0]**2
print " ... threshold",threshold
outfile="pikfiles/"+file+"."+str(num)+".pik"
if (os.path.exists(outfile)):
os.remove(outfile)
print " ... removed existing file:",outfile
for i in range(5):
thresh = threshold + float(i-2)*0.05
ccthreshmap = imagefun.threshold(ccmaxmap,thresh)
percentcover = round(100.0*float(ccthreshmap.sum())/float(totalarea),3)
blobs = imagefun.find_blobs(ccmaxmap,ccthreshmap,6,maxpeaks*4,maxblobsize,2,5,"highest")
tstr = "%.2f" % thresh
lbstr = "%4d" % len(blobs)
pcstr = "%.2f" % percentcover
if(thresh == threshold):
print " ... *** selected threshold: "+tstr+" gives "+lbstr+" peaks ("+\
pcstr+"% coverage ) ***"
else:
print " ... varying threshold: "+tstr+" gives "+lbstr+" peaks ("+\
pcstr+"% coverage )"
ccthreshmap = imagefun.threshold(ccmaxmap,threshold)
percentcover = round(100.0*float(ccthreshmap.sum())/float(totalarea),2)
#numeric_to_jpg(ccthreshmap,"ccthreshmap.jpg")
blobs = imagefun.find_blobs(ccmaxmap, ccthreshmap, 6, maxpeaks*4, maxblobsize, 2,5,"highest")
if(len(blobs) > maxpeaks):
print " !!! more than maxpeaks ("+str(maxpeaks)+") peaks, selecting only top peaks"
blobs.sort(blob_compare)
blobs = blobs[0:maxpeaks]
del ccthreshmap
#find_blobs(image,mask,border,maxblobs,maxblobsize,minblobsize)
print "Template "+str(num)+": Found",len(blobs),"peaks ("+\
str(percentcover)+"% coverage)"
if(percentcover > 10):
print " !!! WARNING: thresholding covers more than 10% of image"
cutoff = olapmult*pixrad #1.5x particle radius in pixels
removeOverlappingBlobs(blobs,cutoff)
blobs.sort(blob_compare)
#blobs = calc_corrcoeffs(blobs,file,bin,template,tmplmask,anglemap)
blobs = fake_corrcoeffs(blobs,file,bin,template,tmplmask,anglemap)
#WRITE PIK FILE
f=open(outfile, 'w')
f.write("#filename x y mean stdev corr_coeff peak_size templ_num angle moment\n")
for blob in blobs:
row = blob.stats['center'][0]
col = blob.stats['center'][1]
mean = blob.stats['mean']
std = blob.stats['stddev']
rho = blob.stats['corrcoeff']
mom = blob.stats['moment']
size = blob.stats['n']
mean_str = "%.4f" % mean
std_str = "%.4f" % std
rho_str = "%.4f" % rho
mom_str = "%.4f" % mom
#filename x y mean stdev corr_coeff peak_size templ_num angle moment
out = file+".mrc "+str(int(col)*bin)+" "+str(int(row)*bin)+ \
" "+mean_str+" "+std_str+" "+rho_str+" "+str(int(size))+ \
" "+str(num)+" 0 "+mom_str
f.write(str(out)+"\n")
f.close()
drawBlobs(ccmaxmap,blobs,file,num,bin,pixrad)
return blobs
def _process(self, input):
min = arraystats.min(input.image)
max = arraystats.max(input.image)
cutoff = (max - min) / self.cutoff.get()
return self.outputclass(input.image,
imagefun.threshold(input.image, cutoff))
def _process(self, input):
min = arraystats.min(input.image)
max = arraystats.max(input.image)
cutoff = (max - min) / self.cutoff.get()
return self.outputclass(input.image,
imagefun.threshold(input.image, cutoff))