def __init__(self,
history=200,
nMixtures=5,
backgroundRatio=0.7,
noiseSigma=15,
learningRate=0.7):
try:
import cv2
except ImportError:
raise ImportError(
"Cannot load OpenCV library which is required by SimpleCV")
return
if not hasattr(cv2, 'BackgroundSubtractorMOG'):
raise ImportError("A newer version of OpenCV is needed")
return
self.mError = False
self.mReady = False
self.mDiffImg = None
self.mColorImg = None
self.mBlobMaker = BlobMaker()
self.history = history
self.nMixtures = nMixtures
self.backgroundRatio = backgroundRatio
self.noiseSigma = noiseSigma
self.learningRate = learningRate
self.mBSMOG = cv2.BackgroundSubtractorMOG(history, nMixtures,
backgroundRatio, noiseSigma)
def findBlobs(images, masks, minArea=1, maxArea=-1):
""" images and masks should contain SimpleCV images!
"""
maker = BlobMaker()
# TODO: Automatically update the recursion limit!
return [maker.extractFromBinary(m, i, minArea, maxArea)
for m, i in zip(masks, images)]
def extract(self, img):
"""
This method takes in a image and returns some basic morphology
characteristics about the largest blob in the image. The
if a color image is provided the threshold operation is applied.
"""
retVal = None
if (self.mThresholdOpeation is not None):
bwImg = self.mThresholdOpeation(img)
else:
bwImg = img.binarize()
if (self.mBlobMaker is None):
self.mBlobMaker = BlobMaker()
fs = self.mBlobMaker.extractFromBinary(bwImg, img)
if (fs is not None and len(fs) > 0):
fs = fs.sortArea()
retVal = []
retVal.append(fs[0].mArea / fs[0].mPerimeter)
retVal.append(fs[0].mAspectRatio)
retVal.append(fs[0].mHu[0])
retVal.append(fs[0].mHu[1])
retVal.append(fs[0].mHu[2])
retVal.append(fs[0].mHu[3])
retVal.append(fs[0].mHu[4])
retVal.append(fs[0].mHu[5])
retVal.append(fs[0].mHu[6])
return retVal
def __init__(self, grayOnly=False, threshold=(10, 10, 10)):
self.mGrayOnlyMode = grayOnly
self.mThreshold = threshold
self.mError = False
self.mCurrImg = None
self.mLastImg = None
self.mDiffImg = None
self.mColorImg = None
self.mBlobMaker = BlobMaker()
def __init__(self, thresholdOperation=None):
"""
The threshold operation is a function of the form
binaryimg = threshold(img)
the simplest example would be:
def binarize_wrap(img):
"""
self.mNBins = 12
self.mBlobMaker = BlobMaker()
self.mThresholdOpeation = thresholdOperation
def extract(self, img):
"""
This method takes in a image and returns some basic morphology
characteristics about the largest blob in the image. The
if a color image is provided the threshold operation is applied.
"""
retVal = None
if(self.mThresholdOpeation is not None):
bwImg = self.mThresholdOpeation(img)
else:
bwImg = img.binarize()
if( self.mBlobMaker is None ):
self.mBlobMaker = BlobMaker()
fs = self.mBlobMaker.extractFromBinary(bwImg,img)
if( fs is not None and len(fs) > 0 ):
fs = fs.sortArea()
retVal = []
retVal.append(fs[0].mArea/fs[0].mPerimeter)
retVal.append(fs[0].mAspectRatio)
retVal.append(fs[0].mHu[0])
retVal.append(fs[0].mHu[1])
retVal.append(fs[0].mHu[2])
retVal.append(fs[0].mHu[3])
retVal.append(fs[0].mHu[4])
retVal.append(fs[0].mHu[5])
retVal.append(fs[0].mHu[6])
return retVal
def __init__(self, alpha=0.7, thresh=(20,20,20)):
"""
Create an running background difference.
alpha - the update weighting where:
accumulator = ((1-alpha)input_image)+((alpha)accumulator)
threshold - the foreground background difference threshold.
"""
self.mError = False
self.mReady = False
self.mAlpha = alpha
self.mThresh = thresh
self.mModelImg = None
self.mDiffImg = None
self.mColorImg = None
self.mBlobMaker = BlobMaker()
def __init__(self, grayOnly=False, threshold = (10,10,10) ):
self.mGrayOnlyMode = grayOnly
self.mThreshold = threshold
self.mError = False
self.mCurrImg = None
self.mLastImg = None
self.mDiffImg = None
self.mColorImg = None
self.mBlobMaker = BlobMaker()
def __init__(self, thresholdOperation=None):
"""
The threshold operation is a function of the form
binaryimg = threshold(img)
the simplest example would be:
def binarize_wrap(img):
"""
self.mNBins = 12
self.mBlobMaker = BlobMaker()
self.mThresholdOpeation = thresholdOperation
def __init__(self, alpha=0.7, thresh=(20,20,20)):
"""
Create an running background difference.
alpha - the update weighting where:
accumulator = ((1-alpha)input_image)+((alpha)accumulator)
threshold - the foreground background difference threshold.
"""
self.mError = False
self.mReady = False
self.mAlpha = alpha
self.mThresh = thresh
self.mModelImg = None
self.mDiffImg = None
self.mColorImg = None
self.mBlobMaker = BlobMaker()
class MorphologyFeatureExtractor(FeatureExtractorBase):
"""
This feature extractor collects some basic morphology infromation about a given
image. It is assumed that the object to be recognized is the largest object
in the image. The user must provide a segmented white on black blob image.
This operation then straightens the image and collects the data.
"""
mNBins = 12
mBlobMaker = None
mThresholdOpeation = None
def __init__(self, thresholdOperation=None):
"""
The threshold operation is a function of the form
binaryimg = threshold(img)
the simplest example would be:
def binarize_wrap(img):
"""
self.mNBins = 12
self.mBlobMaker = BlobMaker()
self.mThresholdOpeation = thresholdOperation
def setThresholdOperation(self, threshOp):
"""
The threshold operation is a function of the form
binaryimg = threshold(img)
the simplest example would be:
def binarize_wrap(img):
return img.binarize()
"""
self.mThresholdOperation = threshOp
def extract(self, img):
"""
This method takes in a image and returns some basic morphology
characteristics about the largest blob in the image. The
if a color image is provided the threshold operation is applied.
"""
retVal = None
if(self.mThresholdOpeation is not None):
bwImg = self.mThresholdOpeation(img)
else:
bwImg = img.binarize()
if( self.mBlobMaker is None ):
self.mBlobMaker = BlobMaker()
fs = self.mBlobMaker.extractFromBinary(bwImg,img)
if( fs is not None and len(fs) > 0 ):
fs = fs.sortArea()
retVal = []
retVal.append(fs[0].mArea/fs[0].mPerimeter)
retVal.append(fs[0].mAspectRatio)
retVal.append(fs[0].mHu[0])
retVal.append(fs[0].mHu[1])
retVal.append(fs[0].mHu[2])
retVal.append(fs[0].mHu[3])
retVal.append(fs[0].mHu[4])
retVal.append(fs[0].mHu[5])
retVal.append(fs[0].mHu[6])
return retVal
def getFieldNames(self):
"""
This method gives the names of each field in the feature vector in the
order in which they are returned. For example, 'xpos' or 'width'
"""
retVal = []
retVal.append('area over perim')
retVal.append('AR')
retVal.append('Hu0')
retVal.append('Hu1')
retVal.append('Hu2')
retVal.append('Hu3')
retVal.append('Hu4')
retVal.append('Hu5')
retVal.append('Hu6')
return retVal
def getNumFields(self):
"""
This method returns the total number of fields in the feature vector.
"""
return self.mNBins
def __getstate__(self):
mydict = self.__dict__.copy()
self.mBlobMaker = None
del mydict['mBlobMaker']
return mydict
def __setstate__(self, mydict):
self.__dict__ = mydict
self.mBlobMaker = BlobMaker()
def __setstate__(self, mydict):
self.__dict__ = mydict
self.mBlobMaker = BlobMaker()
class MOGSegmentation(SegmentationBase):
"""
Background subtraction using mixture of gausians.
For each pixel store a set of gaussian distributions and try to fit new pixels
into those distributions. One of the distributions will represent the background.
history - length of the pixel history to be stored
nMixtures - number of gaussian distributions to be stored per pixel
backgroundRatio - chance of a pixel being included into the background model
noiseSigma - noise amount
learning rate - higher learning rate means the system will adapt faster to new backgrounds
"""
mError = False
mDiffImg = None
mColorImg = None
mReady = False
# OpenCV default parameters
history = 200
nMixtures = 5
backgroundRatio = 0.7
noiseSigma = 15
learningRate = 0.7
bsMOG = None
def __init__(self, history = 200, nMixtures = 5, backgroundRatio = 0.7, noiseSigma = 15, learningRate = 0.7):
try:
import cv2
except ImportError:
raise ImportError("Cannot load OpenCV library which is required by SimpleCV")
return
if not hasattr(cv2, 'BackgroundSubtractorMOG'):
raise ImportError("A newer version of OpenCV is needed")
return
self.mError = False
self.mReady = False
self.mDiffImg = None
self.mColorImg = None
self.mBlobMaker = BlobMaker()
self.history = history
self.nMixtures = nMixtures
self.backgroundRatio = backgroundRatio
self.noiseSigma = noiseSigma
self.learningRate = learningRate
self.mBSMOG = cv2.BackgroundSubtractorMOG(history, nMixtures, backgroundRatio, noiseSigma)
def addImage(self, img):
"""
Add a single image to the segmentation algorithm
"""
if( img is None ):
return
self.mColorImg = img
self.mDiffImg = Image(self.mBSMOG.apply(img.getNumpyCv2(), None, self.learningRate), cv2image=True)
self.mReady = True
return
def isReady(self):
"""
Returns true if the camera has a segmented image ready.
"""
return self.mReady
def isError(self):
"""
Returns true if the segmentation system has detected an error.
Eventually we'll consruct a syntax of errors so this becomes
more expressive
"""
return self.mError #need to make a generic error checker
def resetError(self):
"""
Clear the previous error.
"""
self.mError = false
return
def reset(self):
"""
Perform a reset of the segmentation systems underlying data.
"""
self.mModelImg = None
self.mDiffImg = None
def getRawImage(self):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
return self.mDiffImg
def getSegmentedImage(self, whiteFG=True):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
return self.mDiffImg
def getSegmentedBlobs(self):
"""
return the segmented blobs from the fg/bg image
"""
retVal = []
if( self.mColorImg is not None and self.mDiffImg is not None ):
retVal = self.mBlobMaker.extractFromBinary(self.mDiffImg ,self.mColorImg)
return retVal
def __getstate__(self):
mydict = self.__dict__.copy()
self.mBlobMaker = None
self.mDiffImg = None
del mydict['mBlobMaker']
del mydict['mDiffImg']
return mydict
def __setstate__(self, mydict):
self.__dict__ = mydict
self.mBlobMaker = BlobMaker()
class RunningSegmentation(SegmentationBase):
"""
RunningSegmentation performs segmentation using a running background model.
This model uses an accumulator which performs a running average of previous frames
where:
accumulator = ((1-alpha)input_image)+((alpha)accumulator)
"""
mError = False
mAlpha = 0.1
mThresh = 10
mModelImg = None
mDiffImg = None
mCurrImg = None
mBlobMaker = None
mGrayOnly = True
mReady = False
def __init__(self, alpha=0.7, thresh=(20,20,20)):
"""
Create an running background difference.
alpha - the update weighting where:
accumulator = ((1-alpha)input_image)+((alpha)accumulator)
threshold - the foreground background difference threshold.
"""
self.mError = False
self.mReady = False
self.mAlpha = alpha
self.mThresh = thresh
self.mModelImg = None
self.mDiffImg = None
self.mColorImg = None
self.mBlobMaker = BlobMaker()
def addImage(self, img):
"""
Add a single image to the segmentation algorithm
"""
if( img is None ):
return
self.mColorImg = img
if( self.mModelImg == None ):
self.mModelImg = Image(cv.CreateImage((img.width,img.height), cv.IPL_DEPTH_32F, 3))
self.mDiffImg = Image(cv.CreateImage((img.width,img.height), cv.IPL_DEPTH_32F, 3))
else:
# do the difference
cv.AbsDiff(self.mModelImg.getBitmap(),img.getFPMatrix(),self.mDiffImg.getBitmap())
#update the model
cv.RunningAvg(img.getFPMatrix(),self.mModelImg.getBitmap(),self.mAlpha)
self.mReady = True
return
def isReady(self):
"""
Returns true if the camera has a segmented image ready.
"""
return self.mReady
def isError(self):
"""
Returns true if the segmentation system has detected an error.
Eventually we'll consruct a syntax of errors so this becomes
more expressive
"""
return self.mError #need to make a generic error checker
def resetError(self):
"""
Clear the previous error.
"""
self.mError = false
return
def reset(self):
"""
Perform a reset of the segmentation systems underlying data.
"""
self.mModelImg = None
self.mDiffImg = None
def getRawImage(self):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
return self._floatToInt(self.mDiffImg)
def getSegmentedImage(self, whiteFG=True):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
retVal = None
img = self._floatToInt(self.mDiffImg)
if( whiteFG ):
retVal = img.binarize(thresh=self.mThresh)
else:
retVal = img.binarize(thresh=self.mThresh).invert()
return retVal
def getSegmentedBlobs(self):
"""
return the segmented blobs from the fg/bg image
"""
retVal = []
if( self.mColorImg is not None and self.mDiffImg is not None ):
eightBit = self._floatToInt(self.mDiffImg)
retVal = self.mBlobMaker.extractFromBinary(eightBit.binarize(thresh=self.mThresh),self.mColorImg)
return retVal
def _floatToInt(self,input):
"""
convert a 32bit floating point cv array to an int array
"""
temp = cv.CreateImage((input.width,input.height), cv.IPL_DEPTH_8U, 3)
cv.Convert(input.getBitmap(),temp)
return Image(temp)
def __getstate__(self):
mydict = self.__dict__.copy()
self.mBlobMaker = None
self.mModelImg = None
self.mDiffImg = None
del mydict['mBlobMaker']
del mydict['mModelImg']
del mydict['mDiffImg']
return mydict
def __setstate__(self, mydict):
self.__dict__ = mydict
self.mBlobMaker = BlobMaker()
class DiffSegmentation(SegmentationBase):
"""
This method will do image segmentation by looking at the difference between
two frames.
grayOnly - use only gray images.
threshold - The value at which we consider the color difference to
be significant enough to be foreground imagery.
The general usage is
>>> segmentor = DiffSegmentation()
>>> cam = Camera()
>>> while(1):
>>> segmentor.addImage(cam.getImage())
>>> if(segmentor.isReady()):
>>> img = segmentor.getSegmentedImage()
"""
mError = False
mLastImg = None
mCurrImg = None
mDiffImg = None
mColorImg = None
mGrayOnlyMode = True
mThreshold = 10
mBlobMaker = None
def __init__(self, grayOnly=False, threshold = (10,10,10) ):
self.mGrayOnlyMode = grayOnly
self.mThreshold = threshold
self.mError = False
self.mCurrImg = None
self.mLastImg = None
self.mDiffImg = None
self.mColorImg = None
self.mBlobMaker = BlobMaker()
def addImage(self, img):
"""
Add a single image to the segmentation algorithm
"""
if( img is None ):
return
if( self.mLastImg == None ):
if( self.mGrayOnlyMode ):
self.mLastImg = img.toGray()
self.mDiffImg = Image(self.mLastImg.getEmpty(1))
self.mCurrImg = None
else:
self.mLastImg = img
self.mDiffImg = Image(self.mLastImg.getEmpty(3))
self.mCurrImg = None
else:
if( self.mCurrImg is not None ): #catch the first step
self.mLastImg = self.mCurrImg
if( self.mGrayOnlyMode ):
self.mColorImg = img
self.mCurrImg = img.toGray()
else:
self.mColorImg = img
self.mCurrImg = img
self.mDiffImg = Image(cv2.absdiff(self.mCurrImg.getNumpy(), self.mLastImg.getNumpy()))
return
def isReady(self):
"""
Returns true if the camera has a segmented image ready.
"""
if( self.mDiffImg is None ):
return False
else:
return True
def isError(self):
"""
Returns true if the segmentation system has detected an error.
Eventually we'll consruct a syntax of errors so this becomes
more expressive
"""
return self.mError #need to make a generic error checker
def resetError(self):
"""
Clear the previous error.
"""
self.mError = False
return
def reset(self):
"""
Perform a reset of the segmentation systems underlying data.
"""
self.mCurrImg = None
self.mLastImg = None
self.mDiffImg = None
def getRawImage(self):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
return self.mDiffImg
def getSegmentedImage(self, whiteFG=True):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
retVal = None
if( whiteFG ):
retVal = self.mDiffImg.binarize(thresh=self.mThreshold)
else:
retVal = self.mDiffImg.binarize(thresh=self.mThreshold).invert()
return retVal
def getSegmentedBlobs(self):
"""
return the segmented blobs from the fg/bg image
"""
retVal = []
if( self.mColorImg is not None and self.mDiffImg is not None ):
retVal = self.mBlobMaker.extractFromBinary(self.mDiffImg.binarize(thresh=self.mThreshold),self.mColorImg)
return retVal
def __getstate__(self):
mydict = self.__dict__.copy()
self.mBlobMaker = None
del mydict['mBlobMaker']
return mydict
def __setstate__(self, mydict):
self.__dict__ = mydict
self.mBlobMaker = BlobMaker()
def __setstate__(self, mydict):
self.__dict__ = mydict
self.mBlobMaker = BlobMaker()
def __init__(self):
self.mColorModel = ColorModel()
self.mError = False
self.mCurImg = Image()
self.mTruthImg = Image()
self.mBlobMaker = BlobMaker()
class ColorSegmentation(SegmentationBase):
"""
Perform color segmentation based on a color model or color provided. This class
uses ColorModel.py to create a color model.
"""
mColorModel = []
mError = False
mCurImg = []
mTruthImg = []
mBlobMaker = []
def __init__(self):
self.mColorModel = ColorModel()
self.mError = False
self.mCurImg = Image()
self.mTruthImg = Image()
self.mBlobMaker = BlobMaker()
def addImage(self, img):
"""
Add a single image to the segmentation algorithm
"""
self.mTruthImg = img
self.mCurImg = self.mColorModel.threshold(img)
return
def isReady(self):
"""
Returns true if the camera has a segmented image ready.
"""
return True;
def isError(self):
"""
Returns true if the segmentation system has detected an error.
Eventually we'll consruct a syntax of errors so this becomes
more expressive
"""
return self.mError #need to make a generic error checker
def resetError(self):
"""
Clear the previous error.
"""
self.mError = false
return
def reset(self):
"""
Perform a reset of the segmentation systems underlying data.
"""
self.mColorModel.reset()
def getRawImage(self):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
return self.mCurImg
def getSegmentedImage(self, whiteFG=True):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
return self.mCurImg
def getSegmentedBlobs(self):
"""
return the segmented blobs from the fg/bg image
"""
return self.mBlobMaker.extractFromBinary(self.mCurImg,self.mTruthImg)
# The following are class specific methods
def addToModel(self, data):
self.mColorModel.add(data)
def subtractModel(self, data):
self.mColorModel.remove(data)
def __getstate__(self):
mydict = self.__dict__.copy()
self.mBlobMaker = None
del mydict['mBlobMaker']
return mydict
def __setstate__(self, mydict):
self.__dict__ = mydict
self.mBlobMaker = BlobMaker()
def __init__(self):
self.mColorModel = ColorModel()
self.mError = False
self.mCurImg = Image()
self.mTruthImg = Image()
self.mBlobMaker = BlobMaker()
class RunningSegmentation(SegmentationBase):
"""
RunningSegmentation performs segmentation using a running background model.
This model uses an accumulator which performs a running average of previous frames
where:
accumulator = ((1-alpha)input_image)+((alpha)accumulator)
"""
mError = False
mAlpha = 0.1
mThresh = 10
mModelImg = None
mDiffImg = None
mCurrImg = None
mBlobMaker = None
mGrayOnly = True
mReady = False
def __init__(self, alpha=0.7, thresh=(20,20,20)):
"""
Create an running background difference.
alpha - the update weighting where:
accumulator = ((1-alpha)input_image)+((alpha)accumulator)
threshold - the foreground background difference threshold.
"""
self.mError = False
self.mReady = False
self.mAlpha = alpha
self.mThresh = thresh
self.mModelImg = None
self.mDiffImg = None
self.mColorImg = None
self.mBlobMaker = BlobMaker()
def addImage(self, img):
"""
Add a single image to the segmentation algorithm
"""
if( img is None ):
return
self.mColorImg = img
if( self.mModelImg == None ):
self.mModelImg = Image(np.zeros((img.height, img.width, 3)).astype(np.float32))
self.mDiffImg = Image(np.zeros((img.height, img.width, 3)).astype(np.float32))
else:
# do the difference
self.mDiffImg = Image(cv2.absdiff(self.mModelImg.getNumpy(), self.mDiffImg.getNumpy()))
#update the model
npimg = np.zeros((img.height, img.width, 3)).astype(np.float32)
npimg = self.mModelImg.getFPNumpy()
cv2.accumulateWeighted(img.getFPNumpy(), npimg, self.mAlpha)
print npimg
self.mModelImg = Image(npimg)
#cv.RunningAvg(img.getFPMatrix(),self.mModelImg.getBitmap(),self.mAlpha)
self.mReady = True
return
def isReady(self):
"""
Returns true if the camera has a segmented image ready.
"""
return self.mReady
def isError(self):
"""
Returns true if the segmentation system has detected an error.
Eventually we'll consruct a syntax of errors so this becomes
more expressive
"""
return self.mError #need to make a generic error checker
def resetError(self):
"""
Clear the previous error.
"""
self.mError = false
return
def reset(self):
"""
Perform a reset of the segmentation systems underlying data.
"""
self.mModelImg = None
self.mDiffImg = None
def getRawImage(self):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
return self._floatToInt(self.mDiffImg)
def getSegmentedImage(self, whiteFG=True):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
retVal = None
img = self._floatToInt(self.mDiffImg)
if( whiteFG ):
retVal = img.binarize(thresh=self.mThresh)
else:
retVal = img.binarize(thresh=self.mThresh).invert()
return retVal
def getSegmentedBlobs(self):
"""
return the segmented blobs from the fg/bg image
"""
retVal = []
if( self.mColorImg is not None and self.mDiffImg is not None ):
eightBit = self._floatToInt(self.mDiffImg)
retVal = self.mBlobMaker.extractFromBinary(eightBit.binarize(thresh=self.mThresh),self.mColorImg)
return retVal
def _floatToInt(self,inputimg):
"""
convert a 32bit floating point cv array to an int array
"""
temp = inputimg.getNumpy()
temp = temp * 255.0
temp = temp.astype(np.uint8)
#temp = cv.CreateImage((input.width,input.height), cv.IPL_DEPTH_8U, 3)
#cv.Convert(input.getBitmap(),temp)
return Image(temp)
def __getstate__(self):
mydict = self.__dict__.copy()
self.mBlobMaker = None
self.mModelImg = None
self.mDiffImg = None
del mydict['mBlobMaker']
del mydict['mModelImg']
del mydict['mDiffImg']
return mydict
def __setstate__(self, mydict):
self.__dict__ = mydict
self.mBlobMaker = BlobMaker()
class ColorSegmentation(SegmentationBase):
"""
Perform color segmentation based on a color model or color provided. This class
uses ColorModel.py to create a color model.
"""
mColorModel = []
mError = False
mCurImg = []
mTruthImg = []
mBlobMaker = []
def __init__(self):
self.mColorModel = ColorModel()
self.mError = False
self.mCurImg = Image()
self.mTruthImg = Image()
self.mBlobMaker = BlobMaker()
def addImage(self, img):
"""
Add a single image to the segmentation algorithm
"""
self.mTruthImg = img
self.mCurImg = self.mColorModel.threshold(img)
return
def isReady(self):
"""
Returns true if the camera has a segmented image ready.
"""
return True
def isError(self):
"""
Returns true if the segmentation system has detected an error.
Eventually we'll consruct a syntax of errors so this becomes
more expressive
"""
return self.mError #need to make a generic error checker
def resetError(self):
"""
Clear the previous error.
"""
self.mError = false
return
def reset(self):
"""
Perform a reset of the segmentation systems underlying data.
"""
self.mColorModel.reset()
def getRawImage(self):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
return self.mCurImg
def getSegmentedImage(self, whiteFG=True):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
return self.mCurImg
def getSegmentedBlobs(self):
"""
return the segmented blobs from the fg/bg image
"""
return self.mBlobMaker.extractFromBinary(self.mCurImg, self.mTruthImg)
# The following are class specific methods
def addToModel(self, data):
self.mColorModel.add(data)
def subtractModel(self, data):
self.mColorModel.remove(data)
def __getstate__(self):
mydict = self.__dict__.copy()
self.mBlobMaker = None
del mydict['mBlobMaker']
return mydict
def __setstate__(self, mydict):
self.__dict__ = mydict
self.mBlobMaker = BlobMaker()
class DiffSegmentation(SegmentationBase):
"""
This method will do image segmentation by looking at the difference between
two frames.
grayOnly - use only gray images.
threshold - The value at which we consider the color difference to
be significant enough to be foreground imagery.
The general usage is
>>> segmentor = DiffSegmentation()
>>> cam = Camera()
>>> while(1):
>>> segmentor.addImage(cam.getImage())
>>> if(segmentor.isReady()):
>>> img = segmentor.getSegmentedImage()
"""
mError = False
mLastImg = None
mCurrImg = None
mDiffImg = None
mColorImg = None
mGrayOnlyMode = True
mThreshold = 10
mBlobMaker = None
def __init__(self, grayOnly=False, threshold=(10, 10, 10)):
self.mGrayOnlyMode = grayOnly
self.mThreshold = threshold
self.mError = False
self.mCurrImg = None
self.mLastImg = None
self.mDiffImg = None
self.mColorImg = None
self.mBlobMaker = BlobMaker()
def addImage(self, img):
"""
Add a single image to the segmentation algorithm
"""
if (img is None):
return
if (self.mLastImg == None):
if (self.mGrayOnlyMode):
self.mLastImg = img.toGray()
self.mDiffImg = Image(self.mLastImg.getEmpty(1))
self.mCurrImg = None
else:
self.mLastImg = img
self.mDiffImg = Image(self.mLastImg.getEmpty(3))
self.mCurrImg = None
else:
if (self.mCurrImg is not None): #catch the first step
self.mLastImg = self.mCurrImg
if (self.mGrayOnlyMode):
self.mColorImg = img
self.mCurrImg = img.toGray()
else:
self.mColorImg = img
self.mCurrImg = img
cv.AbsDiff(self.mCurrImg.getBitmap(), self.mLastImg.getBitmap(),
self.mDiffImg.getBitmap())
return
def isReady(self):
"""
Returns true if the camera has a segmented image ready.
"""
if (self.mDiffImg is None):
return False
else:
return True
def isError(self):
"""
Returns true if the segmentation system has detected an error.
Eventually we'll consruct a syntax of errors so this becomes
more expressive
"""
return self.mError #need to make a generic error checker
def resetError(self):
"""
Clear the previous error.
"""
self.mError = False
return
def reset(self):
"""
Perform a reset of the segmentation systems underlying data.
"""
self.mCurrImg = None
self.mLastImg = None
self.mDiffImg = None
def getRawImage(self):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
return self.mDiffImg
def getSegmentedImage(self, whiteFG=True):
"""
Return the segmented image with white representing the foreground
and black the background.
"""
retVal = None
if (whiteFG):
retVal = self.mDiffImg.binarize(thresh=self.mThreshold)
else:
retVal = self.mDiffImg.binarize(thresh=self.mThreshold).invert()
return retVal
def getSegmentedBlobs(self):
"""
return the segmented blobs from the fg/bg image
"""
retVal = []
if (self.mColorImg is not None and self.mDiffImg is not None):
retVal = self.mBlobMaker.extractFromBinary(
self.mDiffImg.binarize(thresh=self.mThreshold), self.mColorImg)
return retVal
def __getstate__(self):
mydict = self.__dict__.copy()
self.mBlobMaker = None
del mydict['mBlobMaker']
return mydict
def __setstate__(self, mydict):
self.__dict__ = mydict
self.mBlobMaker = BlobMaker()
class MorphologyFeatureExtractor(FeatureExtractorBase):
"""
This feature extractor collects some basic morphology infromation about a given
image. It is assumed that the object to be recognized is the largest object
in the image. The user must provide a segmented white on black blob image.
This operation then straightens the image and collects the data.
"""
mNBins = 12
mBlobMaker = None
mThresholdOpeation = None
def __init__(self, thresholdOperation=None):
"""
The threshold operation is a function of the form
binaryimg = threshold(img)
the simplest example would be:
def binarize_wrap(img):
"""
self.mNBins = 12
self.mBlobMaker = BlobMaker()
self.mThresholdOpeation = thresholdOperation
def setThresholdOperation(self, threshOp):
"""
The threshold operation is a function of the form
binaryimg = threshold(img)
the simplest example would be:
def binarize_wrap(img):
return img.binarize()
"""
self.mThresholdOperation = threshOp
def extract(self, img):
"""
This method takes in a image and returns some basic morphology
characteristics about the largest blob in the image. The
if a color image is provided the threshold operation is applied.
"""
retVal = None
if (self.mThresholdOpeation is not None):
bwImg = self.mThresholdOpeation(img)
else:
bwImg = img.binarize()
if (self.mBlobMaker is None):
self.mBlobMaker = BlobMaker()
fs = self.mBlobMaker.extractFromBinary(bwImg, img)
if (fs is not None and len(fs) > 0):
fs = fs.sortArea()
retVal = []
retVal.append(fs[0].mArea / fs[0].mPerimeter)
retVal.append(fs[0].mAspectRatio)
retVal.append(fs[0].mHu[0])
retVal.append(fs[0].mHu[1])
retVal.append(fs[0].mHu[2])
retVal.append(fs[0].mHu[3])
retVal.append(fs[0].mHu[4])
retVal.append(fs[0].mHu[5])
retVal.append(fs[0].mHu[6])
return retVal
def getFieldNames(self):
"""
This method gives the names of each field in the feature vector in the
order in which they are returned. For example, 'xpos' or 'width'
"""
retVal = []
retVal.append('area over perim')
retVal.append('AR')
retVal.append('Hu0')
retVal.append('Hu1')
retVal.append('Hu2')
retVal.append('Hu3')
retVal.append('Hu4')
retVal.append('Hu5')
retVal.append('Hu6')
return retVal
def getNumFields(self):
"""
This method returns the total number of fields in the feature vector.
"""
return self.mNBins
def __getstate__(self):
mydict = self.__dict__.copy()
self.mBlobMaker = None
del mydict['mBlobMaker']
return mydict
def __setstate__(self, mydict):
self.__dict__ = mydict
self.mBlobMaker = BlobMaker()
