class RecorderIface(TrackerIface):
"""
This class extends the TrackerIface to provide video acquisition and live detection (tracking).
It uses openCV to run the camera and thus should work with any camera supported by openCV.
It uses the first available USB/firewire camera unless the platform is a raspberry pi,
in which case it will use the pi camera.
"""
@pyqtSlot()
def load(self): # TODO: check if worth keeping
pass
@pyqtSlot(result=QVariant)
def start(self):
"""
Start the recording and tracking.
:returns: The recording was started status code
"""
if not hasattr(self.params, 'destPath'):
return False
vidExt = os.path.splitext(self.params.destPath)[1]
if vidExt not in VIDEO_FORMATS:
print(('Unknow format: {}'.format(vidExt)))
return False
self.positions = [] # reset between runs
self.distancesFromArena = []
bgStart = self.params.bgFrameIdx
nBackgroundFrames = self.params.nBgFrames
trackFrom = self.params.startFrameIdx
trackTo = self.params.endFrameIdx if (
self.params.endFrameIdx > 0) else None
threshold = self.params.detectionThreshold
minArea = self.params.objectsMinArea
maxArea = self.params.objectsMaxArea
teleportationThreshold = self.params.teleportationThreshold
nSds = self.params.nSds
clearBorders = self.params.clearBorders
normalise = self.params.normalise
extractArena = self.params.extractArena
self.tracker = GuiTracker(
self,
srcFilePath=None,
destFilePath=self.params.destPath,
threshold=threshold,
minArea=minArea,
maxArea=maxArea,
teleportationThreshold=teleportationThreshold,
bgStart=bgStart,
trackFrom=trackFrom,
trackTo=trackTo,
nBackgroundFrames=nBackgroundFrames,
nSds=nSds,
clearBorders=clearBorders,
normalise=normalise,
plot=True,
fast=False,
extractArena=extractArena,
cameraCalibration=self.params.calib,
callback=None)
self.stream = self.tracker # To comply with BaseInterface
self._setDisplay()
self._updateImgProvider()
self.tracker.setRoi(self.roi)
self.timer.start(self.timerSpeed)
return True
def getSamplingFreq(self):
"""
Return the sampling frequency (note this is a maximum and can be limited by a slower CPU)
"""
return 1.0 / (self.timerSpeed / 1000.0) # timer speed in ms
@pyqtSlot(result=QVariant)
def camDetected(self):
"""
Check if a camera is available
"""
cap = cv2.VideoCapture(0)
detected = False
if cap.isOpened():
detected = True
cap.release()
return detected
def getImg(self):
self.display.reload()
class RecorderIface(TrackerIface):
"""
This class extends the TrackerIface to provide video acquisition and live detection (tracking).
It uses openCV to run the camera and thus should work with any camera supported by openCV.
It uses the first available USB/firewire camera unless the platform is a raspberry pi,
in which case it will use the pi camera.
"""
@pyqtSlot()
def load(self): # TODO: check if worth keeping
pass
@pyqtSlot(result=QVariant)
def start(self):
"""
Start the recording and tracking.
:returns: The recording was started status code
"""
if not hasattr(self.params, 'destPath'):
return False
vidExt = os.path.splitext(self.params.destPath)[1]
if vidExt not in VIDEO_FORMATS:
print('Unknow format: {}'.format(vidExt))
return False
self.positions = [] # reset between runs
self.distancesFromArena = []
bgStart = self.params.bgFrameIdx
nBackgroundFrames = self.params.nBgFrames
trackFrom = self.params.startFrameIdx
trackTo = self.params.endFrameIdx if (self.params.endFrameIdx > 0) else None
threshold = self.params.detectionThreshold
minArea = self.params.objectsMinArea
maxArea = self.params.objectsMaxArea
teleportationThreshold = self.params.teleportationThreshold
nSds = self.params.nSds
clearBorders = self.params.clearBorders
normalise = self.params.normalise
extractArena = self.params.extractArena
self.tracker = GuiTracker(self, srcFilePath=None, destFilePath=self.params.destPath,
threshold=threshold, minArea=minArea, maxArea=maxArea,
teleportationThreshold=teleportationThreshold,
bgStart=bgStart, trackFrom=trackFrom, trackTo=trackTo,
nBackgroundFrames=nBackgroundFrames, nSds=nSds,
clearBorders=clearBorders, normalise=normalise,
plot=True, fast=False, extractArena=extractArena,
cameraCalibration=self.params.calib,
callback=None)
self.stream = self.tracker # To comply with BaseInterface
self._setDisplay()
self._updateImgProvider()
self.tracker.setRoi(self.roi)
self.timer.start(self.timerSpeed)
return True
def getSamplingFreq(self):
"""
Return the sampling frequency (note this is a maximum and can be limited by a slower CPU)
"""
return 1.0 / (self.timerSpeed / 1000.0) # timer speed in ms
@pyqtSlot(result=QVariant)
def camDetected(self):
"""
Check if a camera is available
"""
cap = cv2.VideoCapture(0)
detected = False
if cap.isOpened():
detected = True
cap.release()
return detected
def getImg(self):
self.display.reload()
class TrackerIface(BaseInterface):
"""
This class implements the BaseInterface to provide a qml interface
to the GuiTracker object of the tracking module.
"""
def __init__(self, app, context, parent, params, displayName, providerName,
analysisProvider1, analysisProvider2):
BaseInterface.__init__(self, app, context, parent, params, displayName,
providerName)
self.positions = []
self.roi = None
self.analysisImageProvider = analysisProvider1
self.analysisImageProvider2 = analysisProvider2
@pyqtSlot(QVariant, result=QVariant)
def getRow(self, idx):
"""
Get the data (position and distancesFromArena) at row idx
:param int idx: The index of the row to return
"""
idx = int(idx)
if 0 <= idx < len(self.positions):
row = [idx] + list(self.positions[idx]) + list(
self.distancesFromArena[idx])
return [str(e) for e in row]
else:
return -1
@pyqtSlot()
def load(self):
"""
Load the video and create the GuiTracker object
Also registers the analysis image providers (for the analysis tab) with QT
"""
self.tracker = GuiTracker(self,
srcFilePath=self.params.srcPath,
destFilePath=None,
nBackgroundFrames=1,
plot=True,
fast=False,
cameraCalibration=self.params.calib,
callback=None)
self.stream = self.tracker # To comply with BaseInterface
self.tracker.roi = self.roi
self.nFrames = self.tracker._stream.nFrames - 1
self.currentFrameIdx = self.tracker._stream.currentFrameIdx
if self.params.endFrameIdx == -1:
self.params.endFrameIdx = self.nFrames
self._setDisplay()
self._setDisplayMax()
self._updateImgProvider()
@pyqtSlot()
def start(self):
"""
Start the tracking of the loaded video with the parameters from self.params
"""
self.positions = [] # reset between runs
self.distancesFromArena = []
self.tracker._stream.bgStartFrame = self.params.bgFrameIdx
nBackgroundFrames = self.params.nBgFrames
self.tracker._stream.bgEndFrame = self.params.bgFrameIdx + nBackgroundFrames - 1
self.tracker.trackFrom = self.params.startFrameIdx
self.tracker.trackTo = self.params.endFrameIdx if (
self.params.endFrameIdx > 0) else None
self.tracker.threshold = self.params.detectionThreshold
self.tracker.minArea = self.params.objectsMinArea
self.tracker.maxArea = self.params.objectsMaxArea
self.tracker.teleportationThreshold = self.params.teleportationThreshold
self.tracker.nSds = self.params.nSds
self.tracker.clearBorders = self.params.clearBorders
self.tracker.normalise = self.params.normalise
self.tracker.extractArena = self.params.extractArena
self.tracker.setRoi(self.roi)
self.timer.start(self.timerSpeed)
@pyqtSlot()
def stop(self):
"""
The qt slot to self._stop()
"""
self._stop('Recording stopped manually')
def _stop(self, msg):
"""
Stops the tracking gracefully
:param string msg: The message to print upon stoping
"""
self.timer.stop()
self.tracker._stream.stopRecording(msg)
@pyqtSlot(QVariant, QVariant, QVariant, QVariant, QVariant)
def setRoi(self, width, height, x, y, diameter):
"""
Sets the ROI (in which to check for the specimen) from the one drawn in QT
Scaling is applied to match the (resolution difference) between the representation
of the frames in the GUI (on which the user draws the ROI) and the internal representation
used to compute the position of the specimen.
:param width: The width of the image representation in the GUI
:param height: The height of the image representation in the GUI
:param x: The center of the roi in the first dimension
:param y: The center of the roi in the second dimension
:param diameter: The diameter of the ROI
"""
if hasattr(self, 'tracker'):
streamWidth, streamHeight = self.tracker._stream.size # flipped for openCV
horizontalScalingFactor = streamWidth / width
verticalScalingFactor = streamHeight / height
radius = diameter / 2.0
scaledX = (x + radius) * horizontalScalingFactor
scaledY = (y + radius) * verticalScalingFactor
scaledRadius = radius * horizontalScalingFactor
self.roi = Circle((scaledX, scaledY), scaledRadius)
@pyqtSlot()
def removeRoi(self):
self.roi = None
@pyqtSlot(QVariant)
def save(self, defaultDest):
"""
Save the data (positions and distancesFromArena) as a csv style file
"""
diag = QFileDialog()
if defaultDest:
defaultDest = os.path.splitext(defaultDest)[0] + '.csv'
else:
defaultDest = os.getenv('HOME')
destPath = diag.getSaveFileName(parent=diag,
caption='Save file',
directory=defaultDest,
filter="Text (*.txt *.dat *.csv)",
initialFilter="Text (*.csv)")
destPath = destPath[0]
if destPath:
self.write(destPath)
def write(self, dest):
"""
The method called by save() to write the csv file
"""
with open(dest, 'w') as outFile:
writer = csv.writer(outFile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
for fid, row in enumerate(self.positions):
writer.writerow([fid] + list(row))
@pyqtSlot(QVariant)
def setFrameType(self, outputType):
"""
Set the type of frame to display. (As source, difference with background or binary mask)
:param string outputType: The type of frame to display. One of ['Raw', 'Diff', 'Mask']
"""
self.outputType = outputType.lower()
@pyqtSlot()
def analyseAngles(self):
"""
Compute and plot the angles between the segment Pn -> Pn+1 and Pn+1 -> Pn+2
"""
fig, ax = plt.subplots()
angles = video_analysis.getAngles(self.positions)
video_analysis.plotAngles(angles, self.getSamplingFreq())
self.analysisImageProvider._fig = fig
@pyqtSlot()
def analyseDistances(self):
"""
Compute and plot the distances between the points Pn and Pn+1
"""
fig, ax = plt.subplots()
distances = video_analysis.posToDistances(self.positions)
video_analysis.plotDistances(distances, self.getSamplingFreq())
self.analysisImageProvider2._fig = fig
@pyqtSlot()
def saveAnglesFig(self):
"""
Save the graph as a png or jpeg image
"""
diag = QFileDialog()
destPath = diag.getSaveFileName(parent=diag,
caption='Save file',
directory=os.getenv('HOME'),
filter="Image (*.png *.jpg)")
destPath = destPath[0]
if destPath:
imsave(destPath, self.analysisImageProvider.getArray())
def getSamplingFreq(self):
return self.tracker._stream.fps
def getImg(self):
if self.tracker._stream.currentFrameIdx < self.nFrames:
self.display.reload()
self._updateDisplayIdx()
else:
self._stop('End of recording reached')
class TrackerIface(BaseInterface):
"""
This class implements the BaseInterface to provide a qml interface
to the GuiTracker object of the tracking module.
"""
def __init__(self, app, context, parent, params, displayName, providerName, analysisProvider1, analysisProvider2):
BaseInterface.__init__(self, app, context, parent, params, displayName, providerName)
self.positions = []
self.roi = None
self.analysisImageProvider = analysisProvider1
self.analysisImageProvider2 = analysisProvider2
@pyqtSlot(QVariant, result=QVariant)
def getRow(self, idx):
"""
Get the data (position and distancesFromArena) at row idx
:param int idx: The index of the row to return
"""
idx = int(idx)
if 0 <= idx < len(self.positions):
row = [idx] + list(self.positions[idx]) + list(self.distancesFromArena[idx])
return [str(e) for e in row]
else:
return -1
@pyqtSlot()
def load(self):
"""
Load the video and create the GuiTracker object
Also registers the analysis image providers (for the analysis tab) with QT
"""
self.tracker = GuiTracker(self, srcFilePath=self.params.srcPath, destFilePath=None,
nBackgroundFrames=1, plot=True,
fast=False, cameraCalibration=self.params.calib,
callback=None)
self.stream = self.tracker # To comply with BaseInterface
self.tracker.roi = self.roi
self.nFrames = self.tracker._stream.nFrames - 1
self.currentFrameIdx = self.tracker._stream.currentFrameIdx
if self.params.endFrameIdx == -1:
self.params.endFrameIdx = self.nFrames
self._setDisplay()
self._setDisplayMax()
self._updateImgProvider()
@pyqtSlot()
def start(self):
"""
Start the tracking of the loaded video with the parameters from self.params
"""
self.positions = [] # reset between runs
self.distancesFromArena = []
self.tracker._stream.bgStartFrame = self.params.bgFrameIdx
nBackgroundFrames = self.params.nBgFrames
self.tracker._stream.bgEndFrame = self.params.bgFrameIdx + nBackgroundFrames - 1
self.tracker.trackFrom = self.params.startFrameIdx
self.tracker.trackTo = self.params.endFrameIdx if (self.params.endFrameIdx > 0) else None
self.tracker.threshold = self.params.detectionThreshold
self.tracker.minArea = self.params.objectsMinArea
self.tracker.maxArea = self.params.objectsMaxArea
self.tracker.teleportationThreshold = self.params.teleportationThreshold
self.tracker.nSds = self.params.nSds
self.tracker.clearBorders = self.params.clearBorders
self.tracker.normalise = self.params.normalise
self.tracker.extractArena = self.params.extractArena
self.tracker.setRoi(self.roi)
self.timer.start(self.timerSpeed)
@pyqtSlot()
def stop(self):
"""
The qt slot to self._stop()
"""
self._stop('Recording stopped manually')
def _stop(self, msg):
"""
Stops the tracking gracefully
:param string msg: The message to print upon stoping
"""
self.timer.stop()
self.tracker._stream.stopRecording(msg)
@pyqtSlot(QVariant, QVariant, QVariant, QVariant, QVariant)
def setRoi(self, width, height, x, y, diameter):
"""
Sets the ROI (in which to check for the specimen) from the one drawn in QT
Scaling is applied to match the (resolution difference) between the representation
of the frames in the GUI (on which the user draws the ROI) and the internal representation
used to compute the position of the specimen.
:param width: The width of the image representation in the GUI
:param height: The height of the image representation in the GUI
:param x: The center of the roi in the first dimension
:param y: The center of the roi in the second dimension
:param diameter: The diameter of the ROI
"""
if hasattr(self, 'tracker'):
streamWidth, streamHeight = self.tracker._stream.size # flipped for openCV
horizontalScalingFactor = streamWidth / width
verticalScalingFactor = streamHeight / height
radius = diameter / 2.0
scaledX = (x + radius) * horizontalScalingFactor
scaledY = (y + radius) * verticalScalingFactor
scaledRadius = radius * horizontalScalingFactor
self.roi = Circle((scaledX, scaledY), scaledRadius)
@pyqtSlot()
def removeRoi(self):
self.roi = None
@pyqtSlot(QVariant)
def save(self, defaultDest):
"""
Save the data (positions and distancesFromArena) as a csv style file
"""
diag = QFileDialog()
if defaultDest:
defaultDest = os.path.splitext(defaultDest)[0] + '.csv'
else:
defaultDest = os.getenv('HOME')
destPath = diag.getSaveFileName(parent=diag,
caption='Save file',
directory=defaultDest,
filter="Text (*.txt *.dat *.csv)",
initialFilter="Text (*.csv)")
destPath = destPath[0]
if destPath:
self.write(destPath)
def write(self, dest):
"""
The method called by save() to write the csv file
"""
with open(dest, 'w') as outFile:
writer = csv.writer(outFile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
for fid, row in enumerate(self.positions):
writer.writerow([fid]+list(row))
@pyqtSlot(QVariant)
def setFrameType(self, outputType):
"""
Set the type of frame to display. (As source, difference with background or binary mask)
:param string outputType: The type of frame to display. One of ['Raw', 'Diff', 'Mask']
"""
self.outputType = outputType.lower()
@pyqtSlot()
def analyseAngles(self):
"""
Compute and plot the angles between the segment Pn -> Pn+1 and Pn+1 -> Pn+2
"""
fig, ax = plt.subplots()
angles = video_analysis.getAngles(self.positions)
video_analysis.plotAngles(angles, self.getSamplingFreq())
self.analysisImageProvider._fig = fig
@pyqtSlot()
def analyseDistances(self):
"""
Compute and plot the distances between the points Pn and Pn+1
"""
fig, ax = plt.subplots()
distances = video_analysis.posToDistances(self.positions)
video_analysis.plotDistances(distances, self.getSamplingFreq())
self.analysisImageProvider2._fig = fig
@pyqtSlot()
def saveAnglesFig(self):
"""
Save the graph as a png or jpeg image
"""
diag = QFileDialog()
destPath = diag.getSaveFileName(parent=diag,
caption='Save file',
directory=os.getenv('HOME'),
filter="Image (*.png *.jpg)")
destPath = destPath[0]
if destPath:
imsave(destPath, self.analysisImageProvider.getArray())
def getSamplingFreq(self):
return self.tracker._stream.fps
def getImg(self):
if self.tracker._stream.currentFrameIdx < self.nFrames:
self.display.reload()
self._updateDisplayIdx()
else:
self._stop('End of recording reached')