def LAT(img, threshold=None):
'''Adaptive thresholding'''
imArray = utils.im2array(img)
# set default threshold if not specified
if threshold is None:
T=imArray.mean()
else:
T=threshold
Tdiff=1
while(Tdiff):
# Segment image into object and background
G1_mask=np.array(imArray>T, dtype='uint8') # object segments mask
G2_mask=np.array(imArray<=T, dtype='uint8') # background mask
G1=G1_mask*imArray # object image pixel
G2=G2_mask*imArray # background image pixel
# A new threshold is created using the mean values of the two segments
Tnew= (G1.mean()+G2.mean())/2
Tdiff=Tnew-T
T=Tnew # set the new threshold as threshold
# iterate until new threshold aproximately matches the old threshold
if Tdiff<0.0002:
Tdiff=0
G1=np.array(imArray>T, dtype='uint8')
G1*=255
G, imG1 = utils.formatimage(G1)
return imG1
def gaborwavelet(self, img):
'''
Gabor wavelet (GW) filter
(Is this working?)
'''
imArray = utils.im2array(img)
row_size, col_size = imArray.shape
x = np.array(range(1, row_size + 1), dtype='float')
x = x.reshape((row_size, 1))
y = np.array(range(1, col_size + 1), dtype='float')
y = y.reshape((1, col_size))
orientations = []
for i in range(8):
orientations.append(i * pi / 8)
w = signal.freqz() # spatial frequency
G = np.exp(-(x**2 + y**2) / (2 * imArray.std()**2))
GList = [] # image edge container for different directions
for theta in orientations:
wave_vector = x * np.cos(theta) + y * np.sin(theta)
G *= np.cos(w * wave_vector) + 1j * np.sin(w * wave_vector)
# Apply Gabor filter to image
sigma = convolve.convolve2d(imArray, G)
GList.append(sigma)
# Get the imaginary part of GW
# format the output image
sigma, output_image = utils.formatimage(sigma)
return output_image
def gaborwavelet(self, img):
'''
Gabor wavelet (GW) filter
(Is this working?)
'''
imArray = utils.im2array(img)
row_size, col_size = imArray.shape
x = np.array(range(1, row_size + 1), dtype='float')
x = x.reshape((row_size, 1))
y = np.array(range(1, col_size + 1), dtype='float')
y = y.reshape((1, col_size))
orientations = []
for i in range(8):
orientations.append(i * pi / 8)
w = signal.freqz()# spatial frequency
G = np.exp(-(x ** 2 + y ** 2) / (2 * imArray.std()**2))
GList = [] # image edge container for different directions
for theta in orientations:
wave_vector = x * np.cos(theta) + y * np.sin(theta)
G *= np.cos(w * wave_vector) + 1j * np.sin(w * wave_vector)
# Apply Gabor filter to image
sigma = convolve.convolve2d(imArray, G)
GList.append(sigma)
# Get the imaginary part of GW
# format the output image
sigma, output_image = utils.formatimage(sigma)
return output_image
def BEI(img, scale=6):
'''
Compute sum of SBEIs. Default scale is 6.
'''
sumSBEI = np.zeros((112, 92)) # initialization
imageList = [] # wavelet transformed images container list
#print "Processing image's BEI"
for level in range(1, scale):
SBEI, image_WT, im_SBEI = wtHighFreq(img, level=level)
imageList.append(image_WT) # store wave transformed image
sumSBEI += SBEI
#imageList.insert(0, imgData[5]) # insert the original image into the list
imgSize = imageList[-1].size[-1] * imageList[-1].size[-1]
NfpList = []
TList = []
for t in range(1, scale - 1):
threshold = np.array(sumSBEI >= t, dtype='uint8')
Nfp = threshold.sum()
T = abs(Nfp / imgSize - 0.2)
NfpList.append(Nfp)
TList.append(T)
threshold = min(TList) # set threshold
BEI = np.array(sumSBEI > threshold, dtype='uint8') # binary image
BEI *= 255
BEI, imBEI = utils.formatimage(BEI)
return [BEI, imBEI]
def BEI(img, scale=6):
'''
Compute sum of SBEIs. Default scale is 6.
'''
sumSBEI=np.zeros((112,92)) # initialization
imageList=[] # wavelet transformed images container list
#print "Processing image's BEI"
for level in range(1, scale):
SBEI, image_WT, im_SBEI = wtHighFreq(img,level=level)
imageList.append(image_WT) # store wave transformed image
sumSBEI+=SBEI
#imageList.insert(0, imgData[5]) # insert the original image into the list
imgSize=imageList[-1].size[-1]*imageList[-1].size[-1]
NfpList=[]
TList=[]
for t in range(1, scale-1):
threshold=np.array(sumSBEI>=t, dtype='uint8')
Nfp=threshold.sum()
T=abs(Nfp/imgSize-0.2)
NfpList.append(Nfp)
TList.append(T)
threshold=min(TList) # set threshold
BEI = np.array(sumSBEI > threshold, dtype='uint8') # binary image
BEI *= 255
BEI, imBEI = utils.formatimage(BEI)
return [BEI, imBEI]
def LAT(img, threshold=None):
'''Adaptive thresholding'''
imArray = utils.im2array(img)
# set default threshold if not specified
if threshold is None:
T = imArray.mean()
else:
T = threshold
Tdiff = 1
while (Tdiff):
# Segment image into object and background
G1_mask = np.array(imArray > T, dtype='uint8') # object segments mask
G2_mask = np.array(imArray <= T, dtype='uint8') # background mask
G1 = G1_mask * imArray # object image pixel
G2 = G2_mask * imArray # background image pixel
# A new threshold is created using the mean values of the two segments
Tnew = (G1.mean() + G2.mean()) / 2
Tdiff = Tnew - T
T = Tnew # set the new threshold as threshold
# iterate until new threshold aproximately matches the old threshold
if Tdiff < 0.0002:
Tdiff = 0
G1 = np.array(imArray > T, dtype='uint8')
G1 *= 255
G, imG1 = utils.formatimage(G1)
return imG1
def sobel(img, threshold=0, mode=None):
imArray = utils.im2array(img)
Gx=ndimage.sobel(imArray, axis=0) # the horizontal derivative approx.
Gy=ndimage.sobel(imArray, axis=1) # the vertical derivative approx.
G_mag = np.sqrt( Gx**2 + Gy**2)
G_angle = np.arctan2(Gy, Gx)
G_mag -= threshold
G_mag, imG_mag = utils.formatimage(G_mag, mode=mode)
return [G_mag, imG_mag]
def sobelLAT(img):
'''
Edge extraction of image using 4-directional
Sobel edge operators with LAT
'''
imArray = utils.im2array(img)
# low pass filtering of input image
LowPassFilter=np.array([[1., 2., 1.],[2., 4., 2.],[1., 2., 1.]])
LowPassFilter /= 16
imArray_lowpass=ndimage.convolve(imArray, LowPassFilter)
# sobel edge operators
z0=np.array([[1., 2., 1.], [0., 0., 0.], [-1., -2., -1]])
z1=np.array([[2., 1., 0.], [1., 0., -1.], [0., -1., -2.]])
z2=np.array([[1., 0., -1.], [2., 0., -2.], [1., 0., -1.]])
z3=np.array([[0., -1., -2.], [1., 0., -1.], [2., 1., 0.]])
SobelEdgeList=[z0, z1, z2, z3]
G=np.array(np.zeros(imArray.shape))
for zk in SobelEdgeList:
Gx = ndimage.convolve(imArray, zk)
Gy = ndimage.convolve(imArray, zk.transpose())
Gnew = np.sqrt( Gx**2 + Gy**2 )
# keeping max values
left_mask = np.array( Gnew >= G, dtype='uint8')
right_mask = np.array( left_mask==0, dtype='uint8')
G = Gnew * left_mask + G * right_mask
LAT = G/imArray_lowpass
LAT, imLAT = utils.formatimage(LAT, dtype='uint8') # format image as uint8
binary_edge_map=np.array(LAT>1, dtype='uint8')
binary_edge_map *= 255 # grayscaling
binary_edge_map, imOutput = utils.formatimage(binary_edge_map)
return [binary_edge_map, imOutput]
def sobel_handson(img):
imArray = utils.im2array(img)
gx = np.array([[1, 0, -1], [2, 0, -2] , [1, 0, -1]], dtype='float')
gy = gx.transpose()
Gx = ndimage.convolve(imArray, gx) # the horizontal derivative approx.
Gy = ndimage.convolve(imArray, gy) # the vertical derivative approx.
G_mag = np.sqrt(Gx**2 + Gy**2)
G_angle = np.arctan2(Gy, Gx)
G_mag, output_image = utils.formatimage(G_mag)
return [output_image]
def wtHighFreq(img, mode='haar', level=1):
'''
Apply Wavelet Transform to an image
Author: Lee Seongjoo [email protected]
2009 (c) Lee Seongjoo
'''
imArray = utils.im2array(img)
# compute coefficients of multiresolution WT
coeffs=pywt.wavedec2(imArray, mode, level=level)
# high frequency coeffs
coeffs_H=list(coeffs)
# discarding the low frequency
# Approximation coeffs are from the low-pass filter
coeffs_H[0]=np.zeros(coeffs_H[0].shape)
# multilevel reconstruction
imArray_H=pywt.waverec2(coeffs_H, mode)
#Compute binarization of image
HA=imArray_H.mean() # mean value of high frequency part imArray_H
SBEI=np.array(imArray_H<HA+5, dtype='uint8') # binarized image
#msg='Multiresolution Wavelet Transform level: '+ str(level)
#print msg
#print "execution took", t_end-t0, "seconds"
# converting resulting ndarray into an image
imArray_H, image_wavetransformed = utils.formatimage(imArray_H)
SBEI *= 255 # SBEI to grayscale
SBEI, image_binarized = utils.formatimage(SBEI)
imgData=[SBEI,image_wavetransformed, image_binarized]
return imgData
def wtHighFreq(img, mode='haar', level=1):
'''
Apply Wavelet Transform to an image
Author: Lee Seongjoo [email protected]
2009 (c) Lee Seongjoo
'''
imArray = utils.im2array(img)
# compute coefficients of multiresolution WT
coeffs = pywt.wavedec2(imArray, mode, level=level)
# high frequency coeffs
coeffs_H = list(coeffs)
# discarding the low frequency
# Approximation coeffs are from the low-pass filter
coeffs_H[0] = np.zeros(coeffs_H[0].shape)
# multilevel reconstruction
imArray_H = pywt.waverec2(coeffs_H, mode)
#Compute binarization of image
HA = imArray_H.mean() # mean value of high frequency part imArray_H
SBEI = np.array(imArray_H < HA + 5, dtype='uint8') # binarized image
#msg='Multiresolution Wavelet Transform level: '+ str(level)
#print msg
#print "execution took", t_end-t0, "seconds"
# converting resulting ndarray into an image
imArray_H, image_wavetransformed = utils.formatimage(imArray_H)
SBEI *= 255 # SBEI to grayscale
SBEI, image_binarized = utils.formatimage(SBEI)
imgData = [SBEI, image_wavetransformed, image_binarized]
return imgData
def EdgeImage(img, threshold=0):
imArray = utils.im2array(img)
G_mag, imG_mag = sobels.sobel(img, threshold=threshold)
row_size, col_size = G_mag.shape
EdgeMap = np.zeros(imArray.shape)
# thresholding and Edge map
#EdgeMap = np.array(G_mag > threshold, dtype='uint8')
for row in xrange(0, row_size - 3):
for col in xrange(0, col_size - 3):
if G_mag[row, col] > threshold:
EdgeMap[row:row + 3, col:col + 3] = np.ones((3, 3))
EdgeIntensity = imArray * EdgeMap
EdgeIntensity, imEdgeIntensity = utils.formatimage(EdgeIntensity)
return [EdgeIntensity, EdgeMap, imEdgeIntensity]
def EdgeImage(img, threshold=0):
imArray = utils.im2array(img)
G_mag, imG_mag = sobels.sobel(img, threshold=threshold)
row_size, col_size = G_mag.shape
EdgeMap = np.zeros(imArray.shape)
# thresholding and Edge map
#EdgeMap = np.array(G_mag > threshold, dtype='uint8')
for row in xrange(0,row_size-3):
for col in xrange(0, col_size-3):
if G_mag[row, col] > threshold:
EdgeMap[row:row+3, col:col+3] = np.ones((3,3))
EdgeIntensity = imArray * EdgeMap
EdgeIntensity, imEdgeIntensity = utils.formatimage(EdgeIntensity)
return [EdgeIntensity, EdgeMap, imEdgeIntensity]
def BEI2(img, scale=6):
''' noise filtered enhanced BEI
Algorithm based on Song et al 2007
'''
arrayBEI, imBEI = BEI(img, scale)
binary_image_map, imsobelLAT = sobels.sobelLAT(img)
# noise filtering
BEI2 = arrayBEI * binary_image_map
# clustering
# finding all eight-connected components from BEI
# then
# image formatting
BEI2 *= 255 # Black-and-White image value
BEI2, imBEI2 = utils.formatimage(BEI2)
return [BEI2, imBEI2]
def BEI2(img, scale=6):
''' noise filtered enhanced BEI
Algorithm based on Song et al 2007
'''
arrayBEI, imBEI = BEI(img, scale)
binary_image_map, imsobelLAT = sobels.sobelLAT(img)
# noise filtering
BEI2 = arrayBEI * binary_image_map
# clustering
# finding all eight-connected components from BEI
# then
# image formatting
BEI2 *= 255 # Black-and-White image value
BEI2, imBEI2 = utils.formatimage(BEI2)
return [BEI2, imBEI2]
def outputImage(self, response):
output, imOutput = utils.formatimage(response)
return [output, imOutput]
def outputImage(self, response):
output, imOutput = utils.formatimage(response)
return [output, imOutput]
