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,
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 __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()
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()
