def _normalization(image):
assert image.__class__ == numpy.ndarray
row, col = image.shape
#stdImag standardized image
stdImg = numpy.zeros((row, col))
"""
What image.sum() do is the same as the following code
but more faster than this.
for i in xrange(self.Row):
for j in xrange(self.Col):
sigma += image[i][j]
"""
#sigma = image.sum()
meanVal = image.mean()
stdValue = image.std()
if stdValue == 0:
stdValue = 1
stdImg = (image - meanVal) / stdValue
return stdImg
def _normalization(image):
assert image.__class__ == numpy.ndarray
row, col = image.shape
#stdImag standardized image
stdImg = numpy.zeros((row, col))
"""
What image.sum() do is the same as the following code
but more faster than this.
for i in xrange(self.Row):
for j in xrange(self.Col):
sigma += image[i][j]
"""
#sigma = image.sum()
meanVal = image.mean()
stdValue = image.std()
if stdValue == 0:
stdValue = 1
stdImg = (image - meanVal) / stdValue
return stdImg
def Normimg(image):
row, col = image.shape
img_new = np.zeros((row, col))
meanVal = image.mean()
stdValue = image.std()
if stdValue == 0:
stdValue = 1
img_new = (image - meanVal) / stdValue
return img_new
def _normalization(image):
assert image.__class__ == numpy.ndarray
row, col = image.shape
stdImg = numpy.zeros((row, col))
meanVal = image.mean()
stdValue = image.std()
if stdValue == 0:
stdValue = 1
stdImg = (image - meanVal)/stdValue
return stdImg
def vertical_edge(width, height, matrix):
sosm = 3
vef = np.array([[1, 0, -1], [1, 0, -1], [1, 0, -1]])
row = (height - sosm) + 1
column = (width - sosm) + 1
result = []
r = (height - sosm) + 1
c = (width - sosm) + 1
for i in range(row):
for j in range(column):
sq = matrix[i:i + sosm, j:j + sosm]
sum = 0
for k in range(3):
for l in range(3):
sum += (sq[k, l] * vef[k, l])
result.append(sum)
result_matrix = np.asarray(result).reshape(r, c)
return result_matrix
img = img.imread('rubiks-cube.jpg')
black_n_white = img.mean(axis=2) #converting to grayscale image
h, w = black_n_white.shape #shape of the matrix
vertical = vertical_edge(w, h, black_n_white)
horizontal = horizontal_edge(w, h, black_n_white)
image = np.sqrt((vertical**2) + (horizontal**2)) #combining both
plt.imshow(image, cmap='gray',
interpolation='nearest') #collecting the final image
plt.show()