def normCrossCorrelation(img1, img2, pt0, pt1, status, winsize, method=cv2.cv.CV_TM_CCOEFF_NORMED):
"""
**SUMMARY**
(Dev Zone)
Calculates normalized cross correlation for every point.
**PARAMETERS**
img1 - Image 1.
img2 - Image 2.
pt0 - vector of points of img1
pt1 - vector of points of img2
status - Switch which point pairs should be calculated.
if status[i] == 1 => match[i] is calculated.
else match[i] = 0.0
winsize- Size of quadratic area around the point
which is compared.
method - Specifies the way how image regions are compared. see cv2.matchTemplate
**RETURNS**
match - Output: Array will contain ncc values.
0.0 if not calculated.
"""
nPts = len(pt0)
match = np.zeros(nPts)
for i in np.argwhere(status):
i = i[0]
patch1 = cv2.getRectSubPix(img1,(winsize,winsize),tuple(pt0[i]))
patch2 = cv2.getRectSubPix(img2,(winsize,winsize),tuple(pt1[i]))
match[i] = cv2.matchTemplate(patch1,patch2,method)
return match
def _initData(self):
"""
Initialize the cluster centers and initial values of the pixel-wise
cluster assignment and distance values.
"""
self.clusters = -1 * np.ones(self.img.shape[:2])
self.distances = self.FLT_MAX * np.ones(self.img.shape[:2])
centers = []
for i in xrange(self.step, self.width - self.step/2, self.step):
for j in xrange(self.step, self.height - self.step/2, self.step):
nc = self._findLocalMinimum(center=(i, j))
color = self.labimg[nc[1], nc[0]]
center = [color[0], color[1], color[2], nc[0], nc[1]]
centers.append(center)
self.center_counts = np.zeros(len(centers))
self.centers = np.array(centers)
def normCrossCorrelation(img1,
img2,
pt0,
pt1,
status,
winsize,
method=cv2.cv.CV_TM_CCOEFF_NORMED):
"""
**SUMMARY**
(Dev Zone)
Calculates normalized cross correlation for every point.
**PARAMETERS**
img1 - Image 1.
img2 - Image 2.
pt0 - vector of points of img1
pt1 - vector of points of img2
status - Switch which point pairs should be calculated.
if status[i] == 1 => match[i] is calculated.
else match[i] = 0.0
winsize- Size of quadratic area around the point
which is compared.
method - Specifies the way how image regions are compared. see cv2.matchTemplate
**RETURNS**
match - Output: Array will contain ncc values.
0.0 if not calculated.
"""
nPts = len(pt0)
match = np.zeros(nPts)
for i in np.argwhere(status):
i = i[0]
patch1 = cv2.getRectSubPix(img1, (winsize, winsize), tuple(pt0[i]))
patch2 = cv2.getRectSubPix(img2, (winsize, winsize), tuple(pt1[i]))
match[i] = cv2.matchTemplate(patch1, patch2, method)
return match
def createLowpassFilter(self, xCutoff, yCutoff=None, size=(64, 64)):
"""
**SUMMARY**
Creates a lowpass filter of given size and order.
**PARAMETERS**
* *xCutoff* - int - horizontal cut off frequency
- list - provide a list of three cut off frequencies
to create a 3 channel filter
* *yCutoff* - int - vertical cut off frequency
- list - provide a list of three cut off frequencies
to create a 3 channel filter
* *size* - size of the filter (width, height)
**RETURNS**
DFT filter.
**EXAMPLE**
>>> flt = DFT.createLowpassFilter(xCutoff=75, size=(320, 280))
>>> flt = DFT.createLowpassFilter(xCutoff=[75], size=(320, 280))
>>> flt = DFT.createLowpassFilter(xCutoff=[75, 100, 120],
size=(320, 280))
>>> flt = DFT.createLowpassFilter(xCutoff=75, yCutoff=35,
size=(320, 280))
>>> flt = DFT.createLowpassFilter(xCutoff=[75], yCutoff=[35],
size=(320, 280))
>>> flt = DFT.createLowpassFilter(xCutoff=[75, 100, 125], yCutoff=35,
size=(320, 280))
>>> # yCutoff will be [35, 35, 35]
>>> flt = DFT.createLowpassFilter(xCutoff=[75, 113, 124],
yCutoff=[35, 45, 90],
size=(320, 280))
>>> img = Image('lenna')
>>> flt.applyFilter(img).show()
"""
if isinstance(xCutoff, list):
if len(xCutoff) != 3 and len(xCutoff) != 1:
warnings.warn("xCutoff list must be of size 3 or 1")
return None
if isinstance(yCutoff, list):
if len(yCutoff) != 3 and len(yCutoff) != 1:
warnings.warn("yCutoff list must be of size 3 or 1")
return None
if len(yCutoff) == 1:
yCutoff = [yCutoff[0]] * len(xCutoff)
else:
yCutoff = [yCutoff] * len(xCutoff)
stackedfilter = DFT()
for xfreq, yfreq in zip(xCutoff, yCutoff):
stackedfilter = stackedfilter._stackFilters(
self.createLowpassFilter(xfreq, yfreq, size))
image = Image(stackedfilter._numpy)
retVal = DFT(numpyarray=stackedfilter._numpy,
image=image,
xCutoffLow=xCutoff,
yCutoffLow=yCutoff,
channels=len(xCutoff),
size=size,
type=stackedfilter._type,
order=self._order,
frequency=stackedfilter._freqpass)
return retVal
w, h = size
xCutoff = np.clip(int(xCutoff), 0, w / 2)
if yCutoff is None:
yCutoff = xCutoff
yCutoff = np.clip(int(yCutoff), 0, h / 2)
flt = np.zeros((w, h))
flt[0:xCutoff, 0:yCutoff] = 255
flt[0:xCutoff, h - yCutoff:h] = 255
flt[w - xCutoff:w, 0:yCutoff] = 255
flt[w - xCutoff:w, h - yCutoff:h] = 255
img = Image(flt)
lowpassFilter = DFT(size=size,
numpyarray=flt,
image=img,
type="Lowpass",
xCutoffLow=xCutoff,
yCutoffLow=yCutoff,
frequency="lowpass")
return lowpassFilter
def createLowpassFilter(self, xCutoff, yCutoff=None, size=(64, 64)):
"""
**SUMMARY**
Creates a lowpass filter of given size and order.
**PARAMETERS**
* *xCutoff* - int - horizontal cut off frequency
- list - provide a list of three cut off frequencies
to create a 3 channel filter
* *yCutoff* - int - vertical cut off frequency
- list - provide a list of three cut off frequencies
to create a 3 channel filter
* *size* - size of the filter (width, height)
**RETURNS**
DFT filter.
**EXAMPLE**
>>> flt = DFT.createLowpassFilter(xCutoff=75, size=(320, 280))
>>> flt = DFT.createLowpassFilter(xCutoff=[75], size=(320, 280))
>>> flt = DFT.createLowpassFilter(xCutoff=[75, 100, 120],
size=(320, 280))
>>> flt = DFT.createLowpassFilter(xCutoff=75, yCutoff=35,
size=(320, 280))
>>> flt = DFT.createLowpassFilter(xCutoff=[75], yCutoff=[35],
size=(320, 280))
>>> flt = DFT.createLowpassFilter(xCutoff=[75, 100, 125], yCutoff=35,
size=(320, 280))
>>> # yCutoff will be [35, 35, 35]
>>> flt = DFT.createLowpassFilter(xCutoff=[75, 113, 124],
yCutoff=[35, 45, 90],
size=(320, 280))
>>> img = Image('lenna')
>>> flt.applyFilter(img).show()
"""
if isinstance(xCutoff, list):
if len(xCutoff) != 3 and len(xCutoff) != 1:
warnings.warn("xCutoff list must be of size 3 or 1")
return None
if isinstance(yCutoff, list):
if len(yCutoff) != 3 and len(yCutoff) != 1:
warnings.warn("yCutoff list must be of size 3 or 1")
return None
if len(yCutoff) == 1:
yCutoff = [yCutoff[0]]*len(xCutoff)
else:
yCutoff = [yCutoff]*len(xCutoff)
stackedfilter = DFT()
for xfreq, yfreq in zip(xCutoff, yCutoff):
stackedfilter = stackedfilter._stackFilters(self.createLowpassFilter(xfreq, yfreq, size))
image = Image(stackedfilter._numpy)
retVal = DFT(numpyarray=stackedfilter._numpy, image=image,
xCutoffLow=xCutoff, yCutoffLow=yCutoff,
channels=len(xCutoff), size=size,
type=stackedfilter._type, order=self._order,
frequency=stackedfilter._freqpass)
return retVal
w, h = size
xCutoff = np.clip(int(xCutoff), 0, w/2)
if yCutoff is None:
yCutoff = xCutoff
yCutoff = np.clip(int(yCutoff), 0, h/2)
flt = np.zeros((w, h))
flt[0:xCutoff, 0:yCutoff] = 255
flt[0:xCutoff, h-yCutoff:h] = 255
flt[w-xCutoff:w, 0:yCutoff] = 255
flt[w-xCutoff:w, h-yCutoff:h] = 255
img = Image(flt)
lowpassFilter = DFT(size=size, numpyarray=flt, image=img,
type="Lowpass", xCutoffLow=xCutoff,
yCutoffLow=yCutoff, frequency="lowpass")
return lowpassFilter