def testWormIdentification(self):
plt.figure()
for i, region in enumerate(self.regions):
plt.subplot(4, np.ceil(np.float64(self.numberOfRegions)/4.0), i+1)
ip = self.imageProcessor
cropped = wp.cropImageToRegion(self.firstFrame, region.cropRegion)
filtered = ip.applyBackgroundFilter(cropped)
thresholded = ip.applyThreshold(filtered)
cleaned = ip.applyMorphologicalCleaning(thresholded)
# remove ignored areas
if region.ignoredAreas is not None:
cleaned[region.ignoredAreas==255] = False
possibleWorms = ip.identifyPossibleWorms(cleaned)
if len(possibleWorms) > 0:
likelyWorm = max(possibleWorms, key=lambda worm: worm[1])
if likelyWorm is not None:
try:
wormImage = WormImage(region, filtered, cleaned,
likelyWorm[0])
wormImage.measureWorm()
wormImage.plot(bodyPtMarkerSize=30)
print '{0} {1}: worm {2} um long, {3} um wide'.format(region.strainName,
region.wormName,
wormImage.length,
wormImage.width)
except(Exception) as e:
Logger.logPrint('Error in {0} {1}: {2}'.format(region.strainName,
region.wormName,
str(e)))
plt.title(region.strainName + ' ' + region.wormName)
plt.show()
def testWormIdentification(self):
plt.figure()
for i, region in enumerate(self.regions):
plt.subplot(4, np.ceil(np.float64(self.numberOfRegions) / 4.0),
i + 1)
ip = self.imageProcessor
cropped = wp.cropImageToRegion(self.firstFrame, region.cropRegion)
filtered = ip.applyBackgroundFilter(cropped)
thresholded = ip.applyThreshold(filtered)
cleaned = ip.applyMorphologicalCleaning(thresholded)
# remove ignored areas
if region.ignoredAreas is not None:
cleaned[region.ignoredAreas == 255] = False
possibleWorms = ip.identifyPossibleWorms(cleaned)
if len(possibleWorms) > 0:
likelyWorm = max(possibleWorms, key=lambda worm: worm[1])
if likelyWorm is not None:
try:
wormImage = WormImage(region, filtered, cleaned,
likelyWorm[0])
wormImage.measureWorm()
wormImage.plot(bodyPtMarkerSize=30)
print '{0} {1}: worm {2} um long, {3} um wide'.format(
region.strainName, region.wormName,
wormImage.length, wormImage.width)
except (Exception) as e:
Logger.logPrint('Error in {0} {1}: {2}'.format(
region.strainName, region.wormName, str(e)))
plt.title(region.strainName + ' ' + region.wormName)
plt.show()
def defineFoodRegions(self):
if self.firstFrame is None:
self.readFirstFrame()
raw_input("Draw a circle covering the food for each region...")
for region in self.regions:
crop = region.cropRegion
plt.imshow(wp.cropImageToRegion(self.firstFrame, crop), plt.gray())
plt.title(region.strainName + " " + region.wormName)
sel = roitools.CircleRegionSelector()
plt.show()
region.foodCircle = sel.asXYR()
def defineFoodRegions(self):
if self.firstFrame is None:
self.readFirstFrame()
raw_input("Draw a circle covering the food for each region...")
for region in self.regions:
crop = region.cropRegion
plt.imshow(wp.cropImageToRegion(self.firstFrame, crop),
plt.gray())
plt.title(region.strainName + " " + region.wormName)
sel = roitools.CircleRegionSelector()
plt.show()
region.foodCircle = sel.asXYR()
def testMorphologicalCleaning(self, regionIdx):
plt.figure()
region = self.regions[regionIdx]
ip = self.imageProcessor
cropped = wp.cropImageToRegion(self.firstFrame, region.cropRegion)
filtered = ip.applyBackgroundFilter(cropped)
ax1 = plt.subplot(1, 2, 1)
thresholded = ip.applyThreshold(filtered)
plt.imshow(thresholded, plt.gray())
plt.subplot(1, 2, 2, sharex=ax1, sharey=ax1)
cleaned = ip.applyMorphologicalCleaning(thresholded)
# remove ignored areas
if region.ignoredAreas is not None:
cleaned[region.ignoredAreas == 255] = False
plt.imshow(cleaned, plt.gray())
plt.show()
def testMorphologicalCleaning(self, regionIdx):
plt.figure()
region = self.regions[regionIdx]
ip = self.imageProcessor
cropped = wp.cropImageToRegion(self.firstFrame, region.cropRegion)
filtered = ip.applyBackgroundFilter(cropped)
ax1 = plt.subplot(1,2,1)
thresholded = ip.applyThreshold(filtered)
plt.imshow(thresholded, plt.gray())
plt.subplot(1,2,2, sharex=ax1, sharey=ax1)
cleaned = ip.applyMorphologicalCleaning(thresholded)
# remove ignored areas
if region.ignoredAreas is not None:
cleaned[region.ignoredAreas==255] = False
plt.imshow(cleaned, plt.gray())
plt.show()
def selectNonWormRegionsToIgnore(self):
if self.firstFrame is None:
self.readFirstFrame()
raw_input("Click on the worm (if visible) in the thresholded image for each region...")
for region in self.regions:
ip = self.imageProcessor
cropped = wp.cropImageToRegion(self.firstFrame, region.cropRegion)
filtered = ip.applyBackgroundFilter(cropped)
ax1 = plt.subplot(1,2,1)
plt.imshow(filtered, plt.gray())
plt.subplot(1,2,2, sharex=ax1, sharey=ax1)
thresholded = ip.applyThreshold(filtered)
cleaned = ip.applyMorphologicalCleaning(thresholded)
selector = roitools.ThresholdedImageSelector(cleaned)
plt.show()
region.ignoredAreas = selector.getImageNonSelectedRegions()
def selectNonWormRegionsToIgnore(self):
if self.firstFrame is None:
self.readFirstFrame()
raw_input(
"Click on the worm (if visible) in the thresholded image for each region..."
)
for region in self.regions:
ip = self.imageProcessor
cropped = wp.cropImageToRegion(self.firstFrame, region.cropRegion)
filtered = ip.applyBackgroundFilter(cropped)
ax1 = plt.subplot(1, 2, 1)
plt.imshow(filtered, plt.gray())
plt.subplot(1, 2, 2, sharex=ax1, sharey=ax1)
thresholded = ip.applyThreshold(filtered)
cleaned = ip.applyMorphologicalCleaning(thresholded)
selector = roitools.ThresholdedImageSelector(cleaned)
plt.show()
region.ignoredAreas = selector.getImageNonSelectedRegions()
def processFrame(self, frame, idx):
# crop frame
cFrame = wp.cropImageToRegion(frame, self.cropRegion)
# filter frame
fFrame = self.imageProcessor.applyBackgroundFilter(cFrame)
# threshold frame
tFrame = self.imageProcessor.applyThreshold(fFrame)
# morphological cleaning
clFrame = self.imageProcessor.applyMorphologicalCleaning(tFrame)
# remove ignored areas
if self.ignoredAreas is not None:
clFrame[self.ignoredAreas == 255] = False
# worm identification
ip = self.imageProcessor
# identify possible worms in image
# returns contours, areas
possibleWorms = ip.identifyPossibleWorms(clFrame)
if (possibleWorms is not None and len(possibleWorms) > 0):
# likely worm is the largest area
likelyWorm = max(possibleWorms, key=lambda worm: worm[1])
else:
likelyWorm = None
if likelyWorm is not None:
# Create worm object which will measure
# the properties of the worm
try:
worm = self.measureWorm(fFrame,
clFrame,
likelyWorm[0])
# write results to HDF5 store
pre = (self.resultsStorePath + '/' +
self.strainName + '/' +
self.wormName)
worm.store(self.resultsStoreFile,
pre, idx)
except(Exception) as e:
Logger.logPrint('Error in {0} {1} frame {2} analyzing worm: {3}'.format(
self.strainName, self.wormName, str(idx), str(e)))
def readFirstFrame(self):
if self.videoFile is None:
self.firstFrame = None
return
video = cv2.VideoCapture()
if video.open(self.videoFile):
success, firstFrame = video.read()
if not success:
raise Exception("Couldn't read video")
else:
firstFrameChannels = cv2.split(firstFrame)
frame = firstFrameChannels[0]
crop = self.h5ref['cropRegion'][...]
frame = wip.cropImageToRegion(frame, crop)
ip = self.getImageProcessor()
frame = ip.applyBackgroundFilter(frame)
self.firstFrame = cv2.normalize(frame,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX)
else:
raise Exception("Couldn't open video")
def readFirstFrame(self):
if self.videoFile is None:
self.firstFrame = None
return
video = cv2.VideoCapture()
if video.open(self.videoFile):
success, firstFrame = video.read()
if not success:
raise Exception("Couldn't read video")
else:
firstFrameChannels = cv2.split(firstFrame)
frame = firstFrameChannels[0]
crop = self.h5ref['cropRegion'][...]
frame = wip.cropImageToRegion(frame, crop)
ip = self.getImageProcessor()
frame = ip.applyBackgroundFilter(frame)
self.firstFrame = cv2.normalize(frame,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX)
else:
raise Exception("Couldn't open video")
def processFrame(self, frame, idx):
# crop frame
cFrame = wp.cropImageToRegion(frame, self.cropRegion)
# filter frame
fFrame = self.imageProcessor.applyBackgroundFilter(cFrame)
# threshold frame
tFrame = self.imageProcessor.applyThreshold(fFrame)
# morphological cleaning
clFrame = self.imageProcessor.applyMorphologicalCleaning(tFrame)
# remove ignored areas
if self.ignoredAreas is not None:
clFrame[self.ignoredAreas == 255] = False
# worm identification
ip = self.imageProcessor
# identify possible worms in image
# returns contours, areas
possibleWorms = ip.identifyPossibleWorms(clFrame)
if (possibleWorms is not None and len(possibleWorms) > 0):
# likely worm is the largest area
likelyWorm = max(possibleWorms, key=lambda worm: worm[1])
else:
likelyWorm = None
if likelyWorm is not None:
# Create worm object which will measure
# the properties of the worm
try:
worm = self.measureWorm(fFrame, clFrame, likelyWorm[0])
# write results to HDF5 store
pre = (self.resultsStorePath + '/' + self.strainName + '/' +
self.wormName)
worm.store(self.resultsStoreFile, pre, idx)
except (Exception) as e:
Logger.logPrint(
'Error in {0} {1} frame {2} analyzing worm: {3}'.format(
self.strainName, self.wormName, str(idx), str(e)))
