Exemplo n.º 1
0
	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
Exemplo n.º 3
0
	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
Exemplo n.º 5
0
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