Python Image.getNumpy方法代码示例

# 需要导入模块: from SimpleCV.ImageClass import Image [as 别名]
# 或者: from SimpleCV.ImageClass.Image import getNumpy [as 别名]
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
#.........这里部分代码省略.........

# 需要导入模块: from SimpleCV.ImageClass import Image [as 别名]
# 或者: from SimpleCV.ImageClass.Image import getNumpy [as 别名]

#.........这里部分代码省略.........
            warnings.warn("Empty Filter. Returning the image.")
            return image
        w, h = image.size()
        if grayscale:
            image = image.toGray()
        fltImg = self._image
        if fltImg.size() != image.size():
            fltImg = fltImg.resize(w, h)
        filteredImage = image.applyDFTFilter(fltImg)
        return filteredImage

    def getImage(self):
        """
        **SUMMARY**

        Get the SimpleCV Image of the filter

        **RETURNS**

        Image of the filter.

        **EXAMPLE**

        >>> notch = DFT.createNotchFilter(dia1=200, cen=(200, 200),
                                          size=(512, 512), type="highpass")
        >>> notch.getImage().show()
        """
        if isinstance(self._image, type(None)):
            if isinstance(self._numpy, type(None)):
                warnings.warn("Filter doesn't contain any image")
            self._image = Image(self._numpy)
        return self._image

    def getNumpy(self):
        """
        **SUMMARY**

        Get the numpy array of the filter

        **RETURNS**

        numpy array of the filter.

        **EXAMPLE**

        >>> notch = DFT.createNotchFilter(dia1=200, cen=(200, 200),
                                          size=(512, 512), type="highpass")
        >>> notch.getNumpy()
        """
        if isinstance(self._numpy, type(None)):
            if isinstance(self._image, type(None)):
                warnings.warn("Filter doesn't contain any image")
            self._numpy = self._image.getNumpy()
        return self._numpy

    def getOrder(self):
        """
        **SUMMARY**

        Get order of the butterworth filter

        **RETURNS**

        order of the butterworth filter

        **EXAMPLE**