def ensureSIFTFeatures(filepath,
paramsSIFT,
properties,
csvDir,
validateByFileExists=False):
"""
filepath: to the image from which SIFT features have been or have to be extracted.
params: dict of registration parameters, including the key "scale".
paramsSIFT: FloatArray2DSIFT.Params instance.
csvDir: directory into which serialized features have been or will be saved.
load: function to load an image as an ImageProcessor from the filepath.
validateByFileExists: whether to merely check that the .obj file exists as a quick form of validation.
First check if serialized features exist for the image, and if the Params match.
Otherwise extract the features and store them serialized.
Returns the ArrayList of Feature instances.
"""
path = os.path.join(csvDir,
os.path.basename(filepath) + ".SIFT-features.obj")
if validateByFileExists:
if os.path.exists(path):
return True
# An ArrayList whose last element is a mpicbg.imagefeatures.FloatArray2DSIFT.Param
# and all other elements are mpicbg.imagefeatures.Feature
features = deserialize(path) if os.path.exists(path) else None
if features:
if features.get(features.size() - 1).equals(paramsSIFT):
features.remove(features.size() - 1) # removes the Params
syncPrintQ("Loaded %i SIFT features for %s" %
(features.size(), os.path.basename(filepath)))
return features
else:
# Remove the file: paramsSIFT have changed
os.remove(path)
# Else, extract de novo:
try:
# Extract features
imp = loadImp(filepath)
ip = imp.getProcessor()
paramsSIFT = paramsSIFT.clone()
ijSIFT = SIFT(FloatArray2DSIFT(paramsSIFT))
features = ArrayList() # of Feature instances
ijSIFT.extractFeatures(ip, features)
ip = None
imp.flush()
imp = None
features.add(
paramsSIFT
) # append Params instance at the end for future validation
serialize(features, path)
features.remove(features.size() -
1) # to return without the Params for immediate use
syncPrintQ("Extracted %i SIFT features for %s" %
(features.size(), os.path.basename(filepath)))
except:
printException()
return features
def loadFloatProcessor(filepath, params, paramsSIFT, scale=True):
try:
fp = loadImp(filepath).getProcessor().convertToFloatProcessor()
# Preprocess images: Gaussian-blur to scale down, then normalize contrast
if scale:
fp = Filter.createDownsampled(fp, params["scale"], 0.5,
paramsSIFT.initialSigma)
Util.normalizeContrast(fp) # TODO should be outside the if clause?
return fp
except:
syncPrintQ(sys.exc_info())
def translate(self, dx, dy):
a = zeros(2, 'l')
self.interval.min(a)
width = self.cell_dimensions[0]
height = self.cell_dimensions[1]
x0 = max(0, min(a[0] + dx, self.img_dimensions[0] - width))
y0 = max(0, min(a[1] + dy, self.img_dimensions[1] - height))
self.interval = FinalInterval([x0, y0],
[x0 + width - 1, y0 + height - 1])
syncPrintQ(str(Intervals.dimensionsAsLongArray(self.interval)))
self.cache.clear()
def qualityControl(filepaths,
csvDir,
params,
properties,
paramsTileConfiguration,
imp=None):
"""
Show a 3-column table with the indices of all compared pairs of sections and their pointmatches.
"""
rows = []
"""
for task in loadPointMatchesTasks(filepaths, csvDir, params, paramsTileConfiguration["n_adjacent"]):
i, j, pointmatches = task.call() # pointmatches is a list
rows.append([i, j, len(pointmatches)])
syncPrintQ("Counting pointmatches for sections %i::%i = %i" % (i, j, len(pointmatches)))
"""
# Same, in parallel:
w = ParallelTasks("loadPointMatches")
for i, j, pointmatches in w.chunkConsume(
properties["n_threads"],
loadPointMatchesTasks(filepaths, csvDir, params,
paramsTileConfiguration["n_adjacent"])):
rows.append([i, j, len(pointmatches)])
syncPrintQ("Counting pointmatches for sections %i::%i = %i" %
(i, j, len(pointmatches)))
w.awaitAll()
w.destroy()
if imp is None:
img_title = properties["srcDir"].split('/')[-2]
imp = WindowManager.getImage(img_title)
destroy = None
setStackSlice = None
print imp
if imp:
ob = SetStackSlice(imp)
exe = Executors.newSingleThreadScheduledExecutor()
exe.scheduleWithFixedDelay(ob, 0, 500, TimeUnit.MILLISECONDS)
else:
print "image titled %s is not open." % img_title
table, frame = showTable(
rows,
column_names=["section i", "section j", "n_pointmatches"],
title="Number of pointmatches",
onCellClickFn=ob.setFromTableCell)
frame.addWindowListener(ExecutorCloser(exe))
return table, frame
def deserialize(filepath):
f = None
o = None
obj = None
try:
f = FileInputStream(filepath)
o = ObjectInputStream(f)
obj = o.readObject()
except:
syncPrintQ(sys.exc_info())
finally:
if f:
f.close()
if o:
o.close()
if obj is None:
syncPrintQ("Failed to deserialize object at " + filepath)
return obj
def serialize(obj, filepath):
if not Serializable.isAssignableFrom(obj.getClass()):
syncPrintQ("Object doesn't implement Serializable: " + str(obj))
return False
f = None
o = None
try:
f = FileOutputStream(filepath)
o = ObjectOutputStream(f)
o.writeObject(obj)
o.flush()
f.getFD().sync() # ensure file is written to disk
return True
except:
syncPrintQ(sys.exc_info())
finally:
if o:
o.close()
if f:
f.close()
def extractSIFTMatches(filepath1, filepath2, params, paramsSIFT, properties,
csvDir):
# Skip if pointmatches CSV file exists already:
csvpath = os.path.join(
csvDir,
basename(filepath1) + '.' + basename(filepath2) + ".pointmatches.csv")
if os.path.exists(csvpath):
return False
try:
# Load from CSV files or extract features de novo
features1 = ensureSIFTFeatures(filepath1, paramsSIFT, properties,
csvDir)
features2 = ensureSIFTFeatures(filepath2, paramsSIFT, properties,
csvDir)
#syncPrintQ("Loaded %i features for %s\n %i features for %s" % (features1.size(), os.path.basename(filepath1),
# features2.size(), os.path.basename(filepath2)))
# Vector of PointMatch instances
sourceMatches = FloatArray2DSIFT.createMatches(
features1,
features2,
params.get(
"max_sd",
1.5), # max_sd: maximal difference in size (ratio max/min)
TranslationModel2D(),
params.get(
"max_id",
Double.MAX_VALUE), # max_id: maximal distance in image space
params.get("rod", 0.9)) # rod: ratio of best vs second best
syncPrintQ("Found %i SIFT pointmatches for %s vs %s" %
(sourceMatches.size(), os.path.basename(filepath1),
os.path.basename(filepath2)))
# Store pointmatches
savePointMatches(os.path.basename(filepath1),
os.path.basename(filepath2), sourceMatches, csvDir,
params)
return True
except:
printException()
def makeLinkedTiles(filepaths, csvDir, params, paramsSIFT, n_adjacent,
properties):
if properties.get("precompute", True):
ensurePointMatches(filepaths, csvDir, params, paramsSIFT, n_adjacent,
properties)
try:
tiles = [Tile(TranslationModel2D()) for _ in filepaths]
# FAILS when running in parallel, for mysterious reasons related to jython internals, perhaps syncPrint fails
#w = ParallelTasks("loadPointMatches")
#for i, j, pointmatches in w.chunkConsume(properties["n_threads"], loadPointMatchesTasks(filepaths, csvDir, params, n_adjacent)):
syncPrintQ("Loading all pointmatches.")
if properties.get("use_SIFT"):
params = {"rod": params["rod"]}
for task in loadPointMatchesTasks(filepaths, csvDir, params,
n_adjacent):
i, j, pointmatches = task.call()
syncPrintQ("%i, %i : %i" % (i, j, len(pointmatches)))
tiles[i].connect(tiles[j], pointmatches) # reciprocal connection
syncPrintQ("Finished loading all pointmatches.")
return tiles
except Exception as e:
print i, j
print e
finally:
#w.destroy()
pass
def preload(cachedCellImg, loader, block_size, filepaths, exe):
"""
Find which is the last cell index in the cache, identify to which block
(given the blockSize[2] AKA Z dimension) that index belongs to,
and concurrently load all cells (sections) that the Z dimension of the blockSize will need.
If they are already loaded, these operations are insignificant.
"""
try:
# The SoftRefLoaderCache.map is a ConcurrentHashMap with Long keys, aka numbers
cache = cachedCellImg.getCache()
f1 = cache.getClass().getDeclaredField(
"cache") # LoaderCacheAsCacheAdapter.cache
f1.setAccessible(True)
softCache = f1.get(cache)
cache = None
f2 = softCache.getClass().getDeclaredField(
"map") # SoftRefLoaderCache.map
f2.setAccessible(True)
keys = sorted(f2.get(softCache).keySet())
if 0 == len(keys):
return
first = max(0, keys[-1] - (keys[-1] % block_size[2]))
last = min(len(filepaths), first + block_size[2]) - 1
keys = None
syncPrintQ("### Preloading %i-%i ###" % (first, last))
futures = []
for index in xrange(first, last + 1):
futures.append(
exe.submit(TimeItTask(softCache.get, index, loader)))
softCache = None
# Wait for all
loaded_any = False
count = 0
while len(futures) > 0:
r, t = futures.pop(0).get() # waits for the image to load
if t > 1000: # in miliseconds. Less than this is for sure a cache hit, more a cache miss and reload
loaded_any = True
r = None
# t in miliseconds
syncPrintQ("preloaded index %i in %f ms" % (first + count, t))
count += 1
if not loaded_any:
syncPrintQ("Completed preloading %i-%i" %
(first, first + block_size[2] - 1))
except:
syncPrintQ(sys.exc_info())
def keyPressed(self, event):
try:
dx, dy = self.delta.get(event.getKeyCode(), (0, 0))
if dx + dy == 0:
return
syncPrintQ("Translating source")
if event.isShiftDown():
dx *= self.shift
dy *= self.shift
if event.isAltDown():
dx *= self.alt
dy *= self.alt
syncPrintQ("... by x=%i, y=%i" % (dx, dy))
self.cellGet.translate(dx, dy)
self.imp.updateAndDraw()
event.consume()
except:
syncPrintQ(str(sys.exc_info()))
def extractBlockMatches(filepath1, filepath2, params, paramsSIFT, properties,
csvDir, exeload, load):
"""
filepath1: the file path to an image of a section.
filepath2: the file path to an image of another section.
params: dictionary of parameters necessary for BlockMatching.
exeload: an ExecutorService for parallel loading of image files.
load: a function that knows how to load the image from the filepath.
return False if the CSV file already exists, True if it has to be computed.
"""
# Skip if pointmatches CSV file exists already:
csvpath = os.path.join(
csvDir,
basename(filepath1) + '.' + basename(filepath2) + ".pointmatches.csv")
if os.path.exists(csvpath):
return False
try:
# Load files in parallel
futures = [
exeload.submit(Task(load, filepath1)),
exeload.submit(Task(load, filepath2))
]
fp1 = futures[0].get(
) # FloatProcessor, already Gaussian-blurred, contrast-corrected and scaled!
fp2 = futures[1].get() # FloatProcessor, idem
# Define points from the mesh
sourcePoints = ArrayList()
# List to fill
sourceMatches = ArrayList(
) # of PointMatch from filepath1 to filepath2
# Don't use blockmatching if the dimensions are different
use_blockmatching = fp1.getWidth() == fp2.getWidth() and fp1.getHeight(
) == fp2.getHeight()
# Fill the sourcePoints
mesh = TransformMesh(params["meshResolution"], fp1.width, fp1.height)
PointMatch.sourcePoints(mesh.getVA().keySet(), sourcePoints)
syncPrintQ("Extracting block matches for \n S: " + filepath1 +
"\n T: " + filepath2 + "\n with " +
str(sourcePoints.size()) + " mesh sourcePoints.")
# Run
BlockMatching.matchByMaximalPMCCFromPreScaledImages(
fp1,
fp2,
params["scale"], # float
params["blockRadius"], # X
params["blockRadius"], # Y
params["searchRadius"], # X
params["searchRadius"], # Y
params["minR"], # float
params["rod"], # float
params["maxCurvature"], # float
sourcePoints,
sourceMatches)
# At least some should match to accept the translation
if len(sourceMatches) < max(20, len(sourcePoints) / 5) / 2:
syncPrintQ(
"Found only %i blockmatching pointmatches (from %i source points)"
% (len(sourceMatches), len(sourcePoints)))
syncPrintQ(
"... therefore invoking SIFT pointmatching for:\n S: " +
basename(filepath1) + "\n T: " + basename(filepath2))
# Can fail if there is a shift larger than the searchRadius
# Try SIFT features, which are location independent
#
# Images are now scaled: load originals
futures = [
exeload.submit(
Task(loadFloatProcessor,
filepath1,
params,
paramsSIFT,
scale=False)),
exeload.submit(
Task(loadFloatProcessor,
filepath2,
params,
paramsSIFT,
scale=False))
]
fp1 = futures[0].get() # FloatProcessor, original
fp2 = futures[1].get() # FloatProcessor, original
# Images can be of different size: scale them the same way
area1 = fp1.width * fp1.height
area2 = fp2.width * fp2.height
if area1 == area2:
paramsSIFT1 = paramsSIFT.clone()
paramsSIFT1.maxOctaveSize = int(
max(properties.get("SIFT_max_size", 2048),
fp1.width * params["scale"]))
paramsSIFT1.minOctaveSize = int(paramsSIFT1.maxOctaveSize /
pow(2, paramsSIFT1.steps))
paramsSIFT2 = paramsSIFT1
else:
bigger, smaller = (fp1, fp2) if area1 > area2 else (fp2, fp1)
target_width_bigger = int(
max(1024, bigger.width * params["scale"]))
if 1024 == target_width_bigger:
target_width_smaller = int(1024 * float(smaller.width) /
bigger.width)
else:
target_width_smaller = smaller.width * params["scale"]
#
paramsSIFT1 = paramsSIFT.clone()
paramsSIFT1.maxOctaveSize = target_width_bigger
paramsSIFT1.minOctaveSize = int(paramsSIFT1.maxOctaveSize /
pow(2, paramsSIFT1.steps))
paramsSIFT2 = paramsSIFT.clone()
paramsSIFT2.maxOctaveSize = target_width_smaller
paramsSIFT2.minOctaveSize = int(paramsSIFT2.maxOctaveSize /
pow(2, paramsSIFT2.steps))
ijSIFT1 = SIFT(FloatArray2DSIFT(paramsSIFT1))
features1 = ArrayList() # of Point instances
ijSIFT1.extractFeatures(fp1, features1)
ijSIFT2 = SIFT(FloatArray2DSIFT(paramsSIFT2))
features2 = ArrayList() # of Point instances
ijSIFT2.extractFeatures(fp2, features2)
# Vector of PointMatch instances
sourceMatches = FloatArray2DSIFT.createMatches(
features1,
features2,
params.get(
"max_sd",
1.5), # max_sd: maximal difference in size (ratio max/min)
TranslationModel2D(),
params.get("max_id", Double.MAX_VALUE
), # max_id: maximal distance in image space
params.get("rod", 0.9)) # rod: ratio of best vs second best
# Store pointmatches
savePointMatches(os.path.basename(filepath1),
os.path.basename(filepath2), sourceMatches, csvDir,
params)
return True
except:
printException()
def loadImp(filepath):
""" Returns an ImagePlus """
syncPrintQ("Loading image " + filepath)
return IJ.openImage(filepath)
def ensurePointMatches(filepaths, csvDir, params, paramsSIFT, n_adjacent,
properties):
""" If a pointmatches csv file doesn't exist, will create it. """
w = ParallelTasks("ensurePointMatches",
exe=newFixedThreadPool(properties["n_threads"]))
exeload = newFixedThreadPool()
try:
if properties.get("use_SIFT", False):
syncPrintQ("use_SIFT is True")
# Pre-extract SIFT features for all images first
# ensureSIFTFeatures returns the features list so the Future will hold it in memory: can't hold onto them
# therefore consume the tasks in chunks:
chunk_size = properties["n_threads"] * 2
count = 1
for result in w.chunkConsume(
chunk_size, # tasks to submit before starting to wait for futures
(Task(ensureSIFTFeatures,
filepath,
paramsSIFT,
properties,
csvDir,
validateByFileExists=properties.get(
"SIFT_validateByFileExists"))
for filepath in filepaths)):
count += 1
if 0 == count % chunk_size:
syncPrintQ(
"Completed extracting or validating SIFT features for %i images."
% count)
w.awaitAll()
syncPrintQ(
"Completed extracting or validating SIFT features for all images."
)
# Compute pointmatches across adjacent sections
count = 1
for result in w.chunkConsume(
chunk_size,
generateSIFTMatches(filepaths, n_adjacent, params,
paramsSIFT, properties, csvDir)):
count += 1
syncPrintQ("Completed SIFT pointmatches %i/%i" %
(count, len(filepaths) * n_adjacent))
else:
# Use blockmatches
syncPrintQ("using blockmatches")
loadFPMem = SoftMemoize(lambda path: loadFloatProcessor(
path, params, paramsSIFT, scale=True),
maxsize=properties["n_threads"] +
n_adjacent)
count = 1
for result in w.chunkConsume(
properties["n_threads"],
pointmatchingTasks(filepaths, csvDir, params, paramsSIFT,
n_adjacent, exeload, properties,
loadFPMem)):
if result: # is False when CSV file already exists
syncPrintQ("Completed %i/%i" %
(count, len(filepaths) * n_adjacent))
count += 1
syncPrintQ("Awaiting all remaining pointmatching tasks to finish.")
w.awaitAll()
syncPrintQ("Finished all pointmatching tasks.")
except:
printException()
finally:
exeload.shutdown()
w.destroy()
def savePointMatches(img_filename1,
img_filename2,
pointmatches,
directory,
params,
coords_header=["x1", "y1", "x2", "y2"]):
filename = basename(img_filename1) + '.' + basename(
img_filename2) + ".pointmatches.csv"
path = os.path.join(directory, filename)
msg = [str(len(pointmatches))]
ra = None
try:
"""
with open(path, 'w') as csvfile:
w = csv.writer(csvfile, delimiter=',', quotechar='"', quoting=csv.QUOTE_NONNUMERIC)
# First two rows: parameter names and values
keys = params.keys()
msg.append("keys: " + ",".join(map(str, keys)))
msg.append("vals: " + ",".join(str(params[key]) for key in keys))
#for pm in pointmatches:
# msg.append(", ".join(map(str, PointMatches.asRow(pm))))
w.writerow(keys)
w.writerow(tuple(params[key] for key in keys))
# PointMatches header
if 0 == len(pointmatches):
# Can't know whether there are 2 or 3 dimensions per coordinate
w.writerow(coords_header)
else:
w.writerow(PointMatches.csvHeader(next(iter(pointmatches)))) # support both lists and sets
# One PointMatch per row
for pm in pointmatches:
w.writerow(PointMatches.asRow(pm))
# Ensure it's written
csvfile.flush()
os.fsync(csvfile.fileno())
"""
# DEBUG write differently, the above FAILS for ~20 out of 130,000 files
lines = []
keys = params.keys()
lines.append(",".join(map(str, keys)))
lines.append(",".join(map(str, (params[key] for key in keys))))
header = coords_header if 0 == len(pointmatches) \
else PointMatches.csvHeader(next(iter(pointmatches)))
lines.append(",".join(header))
for pm in pointmatches:
p1 = pm.getP1().getW() # a double[] array
p2 = pm.getP2().getW() # a double[] array
lines.append("%f,%f,%f,%f" % (p1[0], p1[1], p2[0], p2[1]))
body = "\n".join(lines)
ra = RandomAccessFile(path, 'rw')
ra.writeBytes(body)
ra.getFD().sync() # ensure it's written
except:
syncPrintQ("Failed to save pointmatches at %s\n%s" %
(path, "\n".join(msg)))
printException()
if os.path.exists(path):
os.remove(path)
finally:
if ra is not None:
ra.close()
