def client2ServerMessage(message):
if message == 'socket echo test':
emit('server2ClientMessage', message)
elif message == 'initialize':
IS.reset()
DL.reset()
emit('server2ClientMessage', 'server initialized')
l = listfiles(ML_SUBFOLDER_ANNOTATIONS, '.tif')
for i in range(len(l)):
removeFile(l[i])
elif message == 'create label mask':
IS.maskType = 'labelMask'
if IS.labelMask is None: # new annotation from scratch
IS.createLabelMask()
else: # editing annotation, thus just tell client to show label mask
emit('server2ClientMessage', 'fetch plane')
emit('server2ClientMessage', 'label mask created')
elif message == 'save label mask':
IS.saveLabelMask(ML_SUBFOLDER_ANNOTATIONS)
IS.maskType = 'noMask'
emit('server2ClientMessage', 'done saving annotations')
elif message == 'train ml segmenter':
IS.mlSegmenterAnnotationsPath = ML_SUBFOLDER_ANNOTATIONS
IS.trainMLSegmenter()
emit('dialog', 'done training')
elif message[:10] == 'view plane':
if IS.hasImage and len(IS.imageShape) == 3:
newViewPlane = message[11]
# print(IS.currentViewPlane,'->',newViewPlane)
IS.imageProcessing('view plane', {
'plane': newViewPlane,
'currentViewPlane': IS.currentViewPlane
})
IS.currentViewPlane = newViewPlane
emit('server2ClientMessage', 'fetch plane')
# emit('dialog', 'operation %s done' % message)
else:
emit('dialog', 'operation available only for 3D images')
elif message[:13] == 'set mask type':
IS.maskType = message[14:]
print('new mask type: ', IS.maskType)
else:
print('client2ServerMessage', message)
def jsonFileUpload():
if 'file' not in request.files:
print('No file part')
file = request.files['file']
if file.filename == '':
print('No selected file')
I = tifread(file)
IS.setImage(I)
IS.originalImage = IS.I
IS.currentViewPlane = '' # this only sets 2D images
IS.maskType = 'noMask'
IS.labelMask = None
shape = list(I.shape)
uint8I = np.uint8(255 * IS.I)
emit(
'dialog', 'shape: %s | min: %f | max: %f' %
(shape2String(IS.imageShape), np.min(IS.I), np.max(IS.I)))
return jsonify(tcpi=[0, 0, 0],
shape=shape,
data=uint8I.tolist(),
planeView=IS.currentViewPlane,
mask=[],
maskType='noMask')
def jsonUpload():
data = request.form['text']
parsed = json.loads(data)
if parsed['description'] == 'annotation':
IS.updateLabelMask(parsed['content'])
if len(IS.imageShape) == 2:
IS.saveLabelMask(ML_SUBFOLDER_ANNOTATIONS)
IS.maskType = 'noMask'
return jsonify(message='annotations uploaded')
elif parsed[
'description'] == 'mlsegtrainprmts': # ML segmentation training parameters
IS.mlSegmenterTrainParameters = parsed['content']
# training procedure actually started by client after receiving following message
return jsonify(
message=
'training ml segmenter | this might take a while | a message will be shown here when training is done'
)
elif parsed['description'] == 'mlsegprmts': # ml segmentation parameters
mlsegprmts = parsed['content']
pmIdx = mlsegprmts[0] - 1
segBlr, segThr = mlsegprmts[1:3]
IS.mlSegment(pmIdx, segBlr / 100, segThr / 100)
IS.maskType = 'segmMask'
return jsonifyCurrentPlane()
elif parsed[
'description'] == 'thrsegprmts': # threshold segmentation parameters
thrsegprmts = parsed['content']
cIdx = thrsegprmts[0] - 1 # 0: dark, 1: bright
segBlr, segThr = thrsegprmts[1:3]
IS.thresholdSegment(cIdx, segBlr / 100, segThr / 100)
IS.maskType = 'segmMask'
return jsonifyCurrentPlane()
elif parsed[
'description'] == 'plnfrmsvr': # plane from server; content: [time,channel,plane]
IS.planeIndex = parsed['content'][2]
IS.channelIndex = parsed['content'][1]
# time index not supported yet
return jsonifyCurrentPlane()
elif parsed['description'] == 'fetchpln':
return jsonifyCurrentPlane()
def dialog(message):
if message == 'image properties':
if not IS.hasImage:
emit('dialog',
'image not set | enter \'new image\' to set new image')
else:
emit(
'dialog', 'shape: %s | min: %f | max: %f' %
(shape2String(IS.imageShape), np.min(IS.I), np.max(IS.I)))
elif message == 'new image from server':
DL.newImageFromServerStep = 1
IS.imagesOnServer = listfiles(DATA_SUBFOLDER, '.tif')
lstr = 'choose image: ' + pathList2String(IS.imagesOnServer)
emit('dialog', lstr)
emit('server2ClientMessage', 'animate last dialog message')
elif message == 'reset image': # reset to original image (before processing)
if IS.hasImage:
IS.setImage(IS.originalImage)
if len(IS.imageShape) == 3:
IS.currentViewPlane = 'z'
else:
IS.currentViewPlane = ''
IS.maskType = 'noMask'
IS.labelMask = None
emit('server2ClientMessage', 'fetch plane')
emit(
'dialog', 'shape: %s | min: %f | max: %f' %
(shape2String(IS.imageShape), np.min(IS.I), np.max(IS.I)))
else:
emit('server2ClientMessage', 'no image to set')
elif message == 'save current image to server':
if IS.hasImage:
DL.saveCurrentImageToServerStep = 1
emit(
'dialog',
'enter image name | first character must be a letter | all characters should be alphanumeric | do not include extension (such as .tif)'
)
emit('server2ClientMessage', 'animate last dialog message')
else:
emit('dialog', 'no image to save')
elif message == 'save annotations to server':
IS.unsavedAnnotationsOnServer = listfiles(ML_SUBFOLDER_ANNOTATIONS,
'.tif')
if IS.unsavedAnnotationsOnServer:
DL.saveAnnotationsToServerStep = 1
emit(
'dialog',
'enter folder name to save annotations in | first character must be a letter | all characters should be alphanumeric'
)
emit('server2ClientMessage', 'animate last dialog message')
else:
emit('dialog', 'no annotations to save')
elif message == 'load annotations from server':
print('load annotations from server')
IS.annotationSetsOnServer = listsubdirs(ML_SUBFOLDER_ANNOTATIONS)
if IS.annotationSetsOnServer:
DL.loadAnnotationsFromServerStep = 1
lstr = 'choose annotation set: ' + pathList2String(
IS.annotationSetsOnServer)
emit('dialog', lstr)
emit('server2ClientMessage', 'animate last dialog message')
else:
emit('dialog', 'no annotation sets available')
elif message == 'edit annotations':
IS.unsavedAnnotationsOnServer = listfiles(ML_SUBFOLDER_ANNOTATIONS,
'.tif')
if IS.unsavedAnnotationsOnServer:
DL.editAnnotationsStep = 1
emit(
'dialog', 'enter index of annotations to edit [1,...,%d]' %
(len(IS.unsavedAnnotationsOnServer) / 2))
emit('server2ClientMessage', 'animate last dialog message')
else:
emit('dialog', 'no annotations to edit')
elif message == 'save ml model to server':
if IS.mlSegmenterModel:
DL.saveMLModelToServerStep = 1
emit(
'dialog',
'enter model name | first character must be a letter | all characters should be alphanumeric | do not include extension (such as .txt)'
)
emit('server2ClientMessage', 'animate last dialog message')
else:
emit('dialog', 'no model to save')
elif message == 'load ml model from server':
IS.mlModelsOnServer = listfiles(
pathjoin(MAIN_FOLDER, ML_SUBFOLDER_MODELS), '.obj')
l = IS.mlModelsOnServer
if len(l) > 0:
DL.loadMLModelFromServerStep = 1
lstr = 'choose model: ' + pathList2String(l)
emit('dialog', lstr)
emit('server2ClientMessage', 'animate last dialog message')
else:
emit('dialog', 'none available')
elif message == 'ml probability maps':
if IS.hasImage:
if IS.mlSegmenterModel:
emit('dialog', 'computing...')
IS.imageProcessing('ml probability maps', {})
emit('server2ClientMessage', 'fetch plane')
emit('dialog', 'operation ' + message + ' done')
else:
emit('dialog', 'no ml model available')
else:
emit('dialog', 'set image first')
elif message == 'median filter':
ipDialogStep1('median filter', 'which filter radius? [2,3,...]')
elif message == 'maximum filter':
ipDialogStep1('maximum filter', 'which filter radius? [2,3,...]')
elif message == 'minimum filter':
ipDialogStep1('minimum filter', 'which filter radius? [2,3,...]')
elif message == 'blur':
ipDialogStep1('blur', 'which scale? [1,2,3,...]')
elif message == 'log':
ipDialogStep1('log', 'which scale? [1,2,3,...]')
elif message == 'gradient magnitude':
ipDialogStep1('gradient magnitude', 'which scale? [1,2,3,...]')
elif message == 'derivatives':
ipDialogStep1('derivatives', 'which scale? [1,2,3,...]')
elif message == 'extract plane':
extractPlaneDialog()
elif message == 'extract channel':
extractChannelDialog()
elif DL.newImageFromServerStep == 1:
if message.isnumeric():
idx = int(message) - 1
if idx >= 0 and idx < len(IS.imagesOnServer):
IS.setImage(tifread(IS.imagesOnServer[idx]))
IS.originalImage = IS.I
if len(IS.imageShape) == 3:
IS.currentViewPlane = 'z'
else:
IS.currentViewPlane = ''
IS.maskType = 'noMask'
IS.labelMask = None
DL.newImageFromServerStep = 0
emit('server2ClientMessage', 'fetch plane')
emit(
'dialog', 'shape: %s | min: %f | max: %f' %
(shape2String(IS.imageShape), np.min(IS.I), np.max(IS.I)))
else:
emit('dialog', 'index out of bounds')
else:
emit('dialog', 'please enter one of the provided indices')
elif DL.saveCurrentImageToServerStep == 1:
if len(message) > 0:
if message[0].isalpha():
if message.isalnum():
path = pathjoin(DATA_SUBFOLDER, '%s.tif' % message)
tifwrite(np.uint8(255 * normalize(IS.I)), path)
emit('dialog', 'saved to server as %s.tif' % message)
DL.saveCurrentImageToServerStep = 0
else:
emit('dialog', 'not all chars are alphanumeric')
else:
emit('dialog', 'first char should be a letter')
else:
emit('dialog', 'name should have at least one char')
elif DL.saveAnnotationsToServerStep == 1:
if len(message) > 0:
if message[0].isalpha():
if message.isalnum():
path = pathjoin(ML_SUBFOLDER_ANNOTATIONS, '%s' % message)
IS.saveAnnotations(path)
emit('dialog', 'annotations saved under %s' % message)
DL.saveAnnotationsToServerStep = 0
else:
emit('dialog', 'not all chars are alphanumeric')
else:
emit('dialog', 'first char should be a letter')
else:
emit('dialog', 'name should have at least one char')
elif DL.loadAnnotationsFromServerStep == 1:
if len(message) > 0 and message.isnumeric():
idx = int(message)
if idx >= 1 and idx <= len(IS.annotationSetsOnServer):
IS.loadAnnotations(IS.annotationSetsOnServer[idx - 1])
[p, n, e] = fileparts(IS.annotationSetsOnServer[idx - 1])
emit('dialog', 'annotations ' + n + ' loaded')
emit('server2ClientMessage', 'did annotate images')
DL.loadAnnotationsFromServerStep = 0
else:
emit('dialog', 'invalid index')
else:
emit('dialog', 'invalid input')
elif DL.editAnnotationsStep == 1:
if len(message) > 0 and message.isnumeric():
idx = int(message)
if idx >= 1 and idx <= (len(IS.unsavedAnnotationsOnServer) / 2):
I = tifread(
pathjoin(ML_SUBFOLDER_ANNOTATIONS,
'Image%02d_Img.tif' % (idx - 1)))
A = tifread(
pathjoin(ML_SUBFOLDER_ANNOTATIONS,
'Image%02d_Ant.tif' % (idx - 1)))
IS.setImage(I)
IS.originalImage = IS.I
if len(IS.imageShape) == 3:
IS.currentViewPlane = 'z'
else:
IS.currentViewPlane = ''
IS.maskType = 'noMask' # will be corrected once client loads annotation tool
IS.labelMask = A
IS.labelMaskIndex = idx - 1
DL.editAnnotationsStep = 0
emit('server2ClientMessage', 'fetch plane')
emit(
'dialog', 'shape: %s | min: %f | max: %f' %
(shape2String(IS.imageShape), np.min(IS.I), np.max(IS.I)))
emit('server2ClientMessage', 'set_nClasses%d' % np.max(A))
emit('server2ClientMessage', 'load annotation tool')
else:
emit('dialog', 'invalid index')
else:
emit('dialog', 'invalid input')
elif DL.saveMLModelToServerStep == 1:
if len(message) > 0:
if message[0].isalpha():
if message.isalnum():
path = pathjoin(ML_SUBFOLDER_MODELS, '%s.obj' % message)
IS.saveMLModel(path)
emit('dialog', 'model saved as %s.obj' % message)
DL.saveMLModelToServerStep = 0
else:
emit('dialog', 'not all chars are alphanumeric')
else:
emit('dialog', 'first char should be a letter')
else:
emit('dialog', 'name should have at least one char')
elif DL.loadMLModelFromServerStep == 1:
if len(message) > 0 and message.isnumeric():
idx = int(message)
if idx >= 1 and idx <= len(IS.mlModelsOnServer):
IS.loadMLModel(IS.mlModelsOnServer[idx - 1])
emit('dialog', 'model loaded')
emit('server2ClientMessage', 'did train ml segmenter')
emit('server2ClientMessage',
'set_nClasses%d' % IS.mlSegmenterModel['nClasses'])
DL.loadMLModelFromServerStep = 0
else:
emit('dialog', 'invalid index')
else:
emit('dialog', 'invalid input')
elif DL.ipStep == 1: # image processing step
if len(message) > 0 and message.isnumeric():
didSucceed = True
prm = int(message)
if any(DL.ipOperation == ipOp for ipOp in
['median filter', 'maximum filter', 'minimum filter']):
emit('dialog', 'computing...')
IS.imageProcessing(DL.ipOperation, {'size': prm})
elif any(DL.ipOperation == ipOp for ipOp in
['blur', 'log', 'gradient magnitude', 'derivatives']):
emit('dialog', 'computing...')
IS.imageProcessing(DL.ipOperation, {'sigma': prm})
elif DL.ipOperation == 'extract plane':
if prm >= 1 and prm <= IS.nPlanes:
IS.imageProcessing(DL.ipOperation, {'plane': prm})
else:
didSucceed = False
emit('dialog', 'index out of bounds')
elif DL.ipOperation == 'extract channel':
if prm >= 1 and prm <= IS.nChannels:
IS.imageProcessing(DL.ipOperation, {'channel': prm})
else:
didSucceed = False
emit('dialog', 'index out of bounds')
if didSucceed:
DL.ipStep = 0
emit('server2ClientMessage', 'fetch plane')
emit('dialog', 'operation ' + DL.ipOperation + ' done')
else:
emit('dialog', 'invalid input')
else:
emit('dialog', 'invalid input')