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 response(self, frequency, rotation, gamma=1, etha=1):
gb = self.gabor2DFunction(frequency, rotation, gamma, etha)
gb = gb.reshape(self.image.shape)
# Get magnitude of Gabor function?
# gb_magnitude = np.abs(gb)
response = convolve.convolve2d(self.image, gb.real)
return response
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 response(self, frequency, rotation, gamma=1, etha=1):
gb = self.gabor2DFunction(frequency, rotation, gamma, etha)
gb = gb.reshape(self.image.shape)
# Get magnitude of Gabor function?
# gb_magnitude = np.abs(gb)
response = convolve.convolve2d(self.image, gb.real)
return response
def lapacian(grayarr):
try:
from scipy.stsci.convolve import convolve2d
except ImportError:
print "You probabably didn't install python scipy 0.7.0 correctly."
kernel = -1*numpy.ones((3,3))
kernel[1,1] = 8.0
return convolve2d(grayarr, kernel, fft=True)
def filterResponse(self, params):
'''
compute the response given x,y,f,angle
'''
return convolve.convolve2d(params, self.imageArray)
def filterResponse(self, params):
'''
compute the response given x,y,f,angle
'''
return convolve.convolve2d(params, self.imageArray)
