def getMosaicImage(self, maxdimension=None, numtype=numpy.uint16): if not self.tiles: return None bbox = self.getMosaicImageBoundaries() imageshape = [bbox['max'][0] - bbox['min'][0], bbox['max'][1] - bbox['min'][1]] scale = 1.0 scaleoffset = [0, 0] if maxdimension is not None: for value in imageshape: newscale = float(maxdimension)/float(value) if newscale < scale: scale = newscale for i, value in enumerate(imageshape): imageshape[i] = int(numpy.ceil(scale*value)) # scaleoffset[i] = int(numpy.floor((maxdimension - imageshape[i])/2.0)) if self.tiles: numtype = self.tiles[0].image.dtype mosaicimage = numpy.zeros(imageshape, numtype) for tile in self.tiles: position = self.getTilePosition(tile) shape = self.getTileShape(tile) offset = (position[0] - bbox['min'][0], position[1] - bbox['min'][1]) offset = (int(round(offset[0] * scale + scaleoffset[0])), int(round(offset[1] * scale + scaleoffset[1]))) image = imagefun.scale(tile.image, scale) image = numpy.asarray(image, numtype) mosaicimage[offset[0]:offset[0] + image.shape[0], offset[1]:offset[1] + image.shape[1]] = image self.scale = scale return mosaicimage
def getMosaicImage(self, maxdimension=None, numtype=numpy.uint16):
if not self.tiles:
return None
bbox = self.getMosaicImageBoundaries()
imageshape = [bbox['max'][0] - bbox['min'][0],
bbox['max'][1] - bbox['min'][1]]
scale = 1.0
scaleoffset = [0, 0]
if maxdimension is not None:
for value in imageshape:
newscale = float(maxdimension)/float(value)
if newscale < scale:
scale = newscale
for i, value in enumerate(imageshape):
imageshape[i] = int(numpy.ceil(scale*value))
# scaleoffset[i] = int(numpy.floor((maxdimension - imageshape[i])/2.0))
if self.tiles:
numtype = self.tiles[0].image.dtype
mosaicimage = numpy.zeros(imageshape, numtype)
for tile in self.tiles:
position = self.getTilePosition(tile)
shape = self.getTileShape(tile)
offset = (position[0] - bbox['min'][0], position[1] - bbox['min'][1])
offset = (int(round(offset[0] * scale + scaleoffset[0])),
int(round(offset[1] * scale + scaleoffset[1])))
image = imagefun.scale(tile.image, scale)
image = numpy.asarray(image, numtype)
mosaicimage[offset[0]:offset[0] + image.shape[0],
offset[1]:offset[1] + image.shape[1]] = image
self.scale = scale
return mosaicimage
def getMosaicImage(self, maxdimension=None): self.calculateMosaicImage() ### scale the mosaic shape if maxdimension is None: scale = 1.0 else: maxdim = max(self.mosaicshape) self.scale = float(maxdimension) / float(maxdim) ## this is stupid, but avoids rounding problems ## and misaligned matrices errors scale = float(maxdimension-1) / float(maxdim) self.scale = scale numtype = self.tiles[0].image.dtype ### find shape of final mosaic ### optimize me maxrow = maxcol = 0 for tile in self.tiles: scaled_tile = imagefun.scale(tile.image, scale) scaled_tile = numpy.asarray(scaled_tile, numtype) scaled_shape = scaled_tile.shape scaled_pos = self.scaled(tile.corner_pos) rowslice1 = scaled_pos[0],scaled_pos[0]+scaled_shape[0] colslice1 = scaled_pos[1],scaled_pos[1]+scaled_shape[1] rowslice = slice(scaled_pos[0],scaled_pos[0]+scaled_shape[0]) colslice = slice(scaled_pos[1],scaled_pos[1]+scaled_shape[1]) if rowslice1[1] > maxrow: maxrow = rowslice1[1] if colslice1[1] > maxcol: maxcol = colslice1[1] mshape = (maxrow,maxcol) ### create mosaic image mosaicimage = numpy.zeros(mshape, numtype) ### scale and insert tiles for tile in self.tiles: scaled_tile = imagefun.scale(tile.image, scale) scaled_tile = numpy.asarray(scaled_tile, numtype) scaled_shape = scaled_tile.shape scaled_pos = self.scaled(tile.corner_pos) rowslice = slice(scaled_pos[0],scaled_pos[0]+scaled_shape[0]) colslice = slice(scaled_pos[1],scaled_pos[1]+scaled_shape[1]) mosaicimage[rowslice, colslice] = scaled_tile return mosaicimage
def getMosaicImage(self, maxdimension=None):
self.calculateMosaicImage()
### scale the mosaic shape
if maxdimension is None:
scale = 1.0
else:
maxdim = max(self.mosaicshape)
self.scale = float(maxdimension) / float(maxdim)
## this is stupid, but avoids rounding problems
## and misaligned matrices errors
scale = float(maxdimension-1) / float(maxdim)
self.scale = scale
numtype = self.tiles[0].image.dtype
### find shape of final mosaic
### optimize me
maxrow = maxcol = 0
for tile in self.tiles:
scaled_tile = imagefun.scale(tile.image, scale)
scaled_tile = numpy.asarray(scaled_tile, numtype)
scaled_shape = scaled_tile.shape
scaled_pos = self.scaled(tile.corner_pos)
rowslice1 = scaled_pos[0],scaled_pos[0]+scaled_shape[0]
colslice1 = scaled_pos[1],scaled_pos[1]+scaled_shape[1]
rowslice = slice(scaled_pos[0],scaled_pos[0]+scaled_shape[0])
colslice = slice(scaled_pos[1],scaled_pos[1]+scaled_shape[1])
if rowslice1[1] > maxrow:
maxrow = rowslice1[1]
if colslice1[1] > maxcol:
maxcol = colslice1[1]
mshape = (maxrow,maxcol)
### create mosaic image
mosaicimage = numpy.zeros(mshape, numtype)
### scale and insert tiles
for tile in self.tiles:
scaled_tile = imagefun.scale(tile.image, scale)
scaled_tile = numpy.asarray(scaled_tile, numtype)
scaled_shape = scaled_tile.shape
scaled_pos = self.scaled(tile.corner_pos)
rowslice = slice(scaled_pos[0],scaled_pos[0]+scaled_shape[0])
colslice = slice(scaled_pos[1],scaled_pos[1]+scaled_shape[1])
mosaicimage[rowslice, colslice] = scaled_tile
return mosaicimage
def reshapeMask( image, mask ): # make sure the images have the same shape imgshape = numpy.asarray(image.shape) print "MRC Image Shape:" print imgshape imgsize = imgshape[0]*imgshape[1] print "MRC Image Size:" print imgsize maskshape = numpy.shape(mask) print "Mask Image Shape:" print maskshape print "resizing mask image." scaleFactor = float(imgshape[0])/float(maskshape[0]) print scaleFactor outimg = imagefun.scale( mask, scaleFactor ) maskshape = numpy.shape(outimg) print "Mask Image Shape:" print maskshape return outimg
def commitToDatabase(self, imgdata):
sessiondata = imgdata['session']
rundir = self.params['rundir']
maskname = self.params['runname']
assessname = self.params['runname']
bin = self.params['bin']
maskdir = os.path.join(rundir, "masks")
maskrundata, maskparamsdata = apMask.getMaskParamsByRunName(
maskname, sessiondata)
if not maskrundata:
apMask.insertManualMaskRun(sessiondata, rundir, maskname, bin)
maskrundata, maskparamsdata = apMask.getMaskParamsByRunName(
maskname, sessiondata)
try:
apParam.createDirectory(maskdir)
except:
apDisplay.printWarning('can not create mask directory')
massessrundata, exist = apMask.insertMaskAssessmentRun(
sessiondata, maskrundata, assessname)
# The mask file should only exist if the em_hole_finder found a region to mask.
# If it does not exist, do not insert anything to the DB.
if (os.path.exists(self.outfile)):
apDisplay.printMsg("Writing results to database: " +
time.asctime())
# Set black pixels to white and anything else to 0
img1 = Image.open(self.outfile)
img2 = Image.eval(img1, lambda px: 255 if px == 0 else 0)
# make sure the images have the same shape
imgshape = numpy.asarray(imgdata['image'].shape)
apDisplay.printMsg("MRC Image Shape:")
print imgshape
imgsize = imgshape[0] * imgshape[1]
apDisplay.printMsg("MRC Image Size:")
print imgsize
maskshape = numpy.shape(img2)
apDisplay.printMsg("Mask Image Shape:")
print maskshape
apDisplay.printMsg("resizing mask image with scale:")
scaleFactorx = float(imgshape[0]) / float(maskshape[0])
scaleFactory = float(imgshape[1]) / float(maskshape[1])
scale = scaleFactorx, scaleFactory
print scale
img3 = imagefun.scale(img2, scale)
maskshape = numpy.shape(img3)
apDisplay.printMsg("Mask Image Shape:")
print maskshape
#img3 = numpy.resize(img2, imgshape) # not working
img3path = self.outfile + "_tmp.jpg"
scipy.misc.imsave(img3path, img3)
labeled_regions, clabels = ndimage.label(img3)
testlog = [False, 0, ""]
infos = {}
apDisplay.printMsg("getting mask region info.")
infos, testlog = apCrud.getLabeledInfo(imgdata['image'], img3,
labeled_regions,
range(1, clabels + 1),
False, infos, testlog)
apDisplay.printMsg("inserting mask regions to DB.")
print len(infos)
area_max = imgsize * .9
offset = 1
for l1 in range(0, len(infos)):
l = l1 + offset
info = infos[l]
area = info[0]
print area
if (area > 400 and area < area_max):
apDisplay.printMsg("saving a region of size:")
print area
info.append(l)
regiondata = apMask.insertMaskRegion(
maskrundata, imgdata, info)
# Insert mask assessment regions. This keeps track of the mask regions that the user wants to reject.
allregiondata = apMask.getMaskRegions(maskrundata, imgdata)
for regiondata in allregiondata:
apMask.insertMaskAssessment(massessrundata, regiondata, True)
# if self.assess != self.assessold and self.assess is not None:
# #imageaccessor run is always named run1
# apDatabase.insertImgAssessmentStatus(imgdata, 'run1', self.assess)
return
def commitToDatabase(self,imgdata):
sessiondata = imgdata['session']
rundir = self.params['rundir']
maskname = self.params['runname']
assessname = self.params['runname']
bin = self.params['bin']
maskdir = os.path.join(rundir,"masks")
maskrundata,maskparamsdata = apMask.getMaskParamsByRunName(maskname,sessiondata)
if not maskrundata:
apMask.insertManualMaskRun(sessiondata,rundir,maskname,bin)
maskrundata,maskparamsdata = apMask.getMaskParamsByRunName(maskname,sessiondata)
try:
apParam.createDirectory(maskdir)
except:
apDisplay.printWarning('can not create mask directory')
massessrundata,exist = apMask.insertMaskAssessmentRun(sessiondata,maskrundata,assessname)
# The mask file should only exist if the em_hole_finder found a region to mask.
# If it does not exist, do not insert anything to the DB.
if ( os.path.exists(self.outfile) ):
apDisplay.printMsg("Writing results to database: " + time.asctime())
# Set black pixels to white and anything else to 0
img1 = Image.open(self.outfile)
img2 = Image.eval(img1, lambda px: 255 if px==0 else 0)
# make sure the images have the same shape
imgshape = numpy.asarray(imgdata['image'].shape)
apDisplay.printMsg("MRC Image Shape:")
print imgshape
imgsize = imgshape[0]*imgshape[1]
apDisplay.printMsg("MRC Image Size:")
print imgsize
maskshape = numpy.shape(img2)
apDisplay.printMsg("Mask Image Shape:")
print maskshape
apDisplay.printMsg("resizing mask image with scale:")
scaleFactorx = float(imgshape[0])/float(maskshape[0])
scaleFactory = float(imgshape[1])/float(maskshape[1])
scale = scaleFactorx, scaleFactory
print scale
img3 = imagefun.scale( img2, scale )
maskshape = numpy.shape(img3)
apDisplay.printMsg("Mask Image Shape:")
print maskshape
#img3 = numpy.resize(img2, imgshape) # not working
img3path = self.outfile + "_tmp.jpg"
scipy.misc.imsave(img3path, img3)
labeled_regions,clabels = ndimage.label(img3)
testlog = [False,0,""]
infos={}
apDisplay.printMsg("getting mask region info.")
infos,testlog = apCrud.getLabeledInfo( imgdata['image'], img3, labeled_regions, range(1,clabels+1), False, infos, testlog)
apDisplay.printMsg("inserting mask regions to DB.")
print len(infos)
area_max = imgsize*.9
offset = 1
for l1 in range(0,len(infos)):
l = l1 + offset
info = infos[l]
area=info[0]
print area
if (area > 400 and area < area_max):
apDisplay.printMsg("saving a region of size:")
print area
info.append(l)
regiondata = apMask.insertMaskRegion( maskrundata, imgdata, info )
# Insert mask assessment regions. This keeps track of the mask regions that the user wants to reject.
allregiondata = apMask.getMaskRegions(maskrundata,imgdata)
for regiondata in allregiondata:
apMask.insertMaskAssessment(massessrundata,regiondata,True)
# if self.assess != self.assessold and self.assess is not None:
# #imageaccessor run is always named run1
# apDatabase.insertImgAssessmentStatus(imgdata, 'run1', self.assess)
return