def main():
displayer = Displayer()
orig = fetch_image(sys.argv[1], 0)
displayer.add_image(orig, 'Original')
square = np.ones((9, 9), np.float32)
rectangle = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 13))
ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
cross = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))
kernel = cross
displayer.add_image(cv2.erode(orig, kernel, iterations=1), 'Erosion')
displayer.add_image(cv2.dilate(orig, kernel, iterations=1), 'Dilation')
displayer.add_image(cv2.morphologyEx(orig, cv2.MORPH_OPEN, kernel),
'Opening')
closing = cv2.morphologyEx(orig, cv2.MORPH_CLOSE, kernel)
displayer.add_image(closing, 'Closing')
displayer.add_image(cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel),
'OpeningClosing')
displayer.add_image(cv2.morphologyEx(orig, cv2.MORPH_GRADIENT, kernel),
'Gradient')
displayer.add_image(cv2.morphologyEx(orig, cv2.MORPH_TOPHAT, kernel),
'Top Hat')
displayer.add_image(cv2.morphologyEx(orig, cv2.MORPH_BLACKHAT, kernel),
'Black Hat')
displayer.display(cmap='gray')
def main():
displayer = Displayer()
A = cv2.cvtColor(fetch_image('apple.jpg'), cv2.COLOR_BGR2RGB)
B = cv2.cvtColor(fetch_image('orange.jpg'), cv2.COLOR_BGR2RGB)
# generate Gaussian pyramid for A
G = A.copy()
gpA = [G]
for i in xrange(6):
G = cv2.pyrDown(G)
gpA.append(G)
# generate Gaussian pyramid for B
G = B.copy()
gpB = [G]
for i in xrange(6):
G = cv2.pyrDown(G)
gpB.append(G)
# generate Laplacian Pyramid for A
lpA = [gpA[5]]
for i in xrange(5, 0, -1):
GE = cv2.pyrUp(gpA[i])
L = cv2.subtract(gpA[i - 1], GE)
lpA.append(L)
# generate Laplacian Pyramid for B
lpB = [gpB[5]]
for i in xrange(5, 0, -1):
GE = cv2.pyrUp(gpB[i])
L = cv2.subtract(gpB[i - 1], GE)
lpB.append(L)
# Now add left and right halves of images in each level
LS = []
for la, lb in zip(lpA, lpB):
rows, cols, dpt = la.shape
ls = np.hstack((la[:, 0:cols / 2], lb[:, cols / 2:]))
LS.append(ls)
# now reconstruct
ls_ = LS[0]
for i in xrange(1, 6):
ls_ = cv2.pyrUp(ls_)
displayer.add_image(ls_, i)
ls_ = cv2.add(ls_, LS[i])
# image with direct connecting each half
real = np.hstack((A[:, :cols / 2], B[:, cols / 2:]))
displayer.add_image(ls_, "pyramid")
#displayer.add_image(real, "stacked")
#for i in range(len(lpA)):
# displayer.add_image(LS[i], i)
displayer.display()
def main():
displayer = Displayer()
A = cv2.cvtColor(fetch_image('apple.jpg'), cv2.COLOR_BGR2RGB)
B = cv2.cvtColor(fetch_image('orange.jpg'), cv2.COLOR_BGR2RGB)
# generate Gaussian pyramid for A
G = A.copy()
gpA = [G]
for i in xrange(6):
G = cv2.pyrDown(G)
gpA.append(G)
# generate Gaussian pyramid for B
G = B.copy()
gpB = [G]
for i in xrange(6):
G = cv2.pyrDown(G)
gpB.append(G)
# generate Laplacian Pyramid for A
lpA = [gpA[5]]
for i in xrange(5,0,-1):
GE = cv2.pyrUp(gpA[i])
L = cv2.subtract(gpA[i-1],GE)
lpA.append(L)
# generate Laplacian Pyramid for B
lpB = [gpB[5]]
for i in xrange(5,0,-1):
GE = cv2.pyrUp(gpB[i])
L = cv2.subtract(gpB[i-1],GE)
lpB.append(L)
# Now add left and right halves of images in each level
LS = []
for la,lb in zip(lpA,lpB):
rows,cols,dpt = la.shape
ls = np.hstack((la[:,0:cols/2], lb[:,cols/2:]))
LS.append(ls)
# now reconstruct
ls_ = LS[0]
for i in xrange(1,6):
ls_ = cv2.pyrUp(ls_)
displayer.add_image(ls_, i)
ls_ = cv2.add(ls_, LS[i])
# image with direct connecting each half
real = np.hstack((A[:,:cols/2],B[:,cols/2:]))
displayer.add_image(ls_, "pyramid")
#displayer.add_image(real, "stacked")
#for i in range(len(lpA)):
# displayer.add_image(LS[i], i)
displayer.display()
def main():
displayer = Displayer()
orig = cv2.cvtColor(fetch_image(sys.argv[1]), cv2.COLOR_BGR2GRAY)
displayer.add_image(orig, 'Original')
img = cv2.bilateralFilter(np.float32(orig), 9, 75, 75)
x_kernel = np.array([[-1, 0, 1],
[-2, 0, 2],
[-1, 0, 1]])
y_kernel = np.array([[ 1, 2, 1],
[ 0, 0, 0],
[-1, -2, -1]])
"""
x_kernel = np.array([[3, 10, 3],
[0, 0, 0],
[-3, -10, -3]])
y_kernel = np.array([[ -3, -0, 3],
[ -10, 0, 10],
[ -3, -0, 3]])
"""
"""
y_kernel = x_kernel = np.array([[ 0.5, 1, 0.5],
[ 1, -6, 1],
[ 0.5, 1, 0.5]])
"""
x_gradient = cv2.filter2D(img, -1, x_kernel)
abs_x = np.absolute(x_gradient)
x_grad_u8 = np.uint8(abs_x)
displayer.add_image(x_grad_u8, 'manual x gradient')
y_gradient = cv2.filter2D(img, -1, y_kernel)
abs_y = np.absolute(y_gradient)
y_grad_u8 = np.uint8(abs_y)
displayer.add_image(y_grad_u8, 'manual y gradient')
magnitude = cv2.magnitude(abs_x, abs_y)
magnitude_u8 = np.uint8(magnitude)
displayer.add_image(magnitude_u8, 'magnitude')
"""
lap = cv2.Laplacian(orig, -1, cv2.CV_64F)
displayer.add_image(np.uint8(lap), 'laplacian')
"""
canny = cv2.Canny(orig, 100, 200)
displayer.add_image(np.uint8(canny), 'Canny')
displayer.display(cmap='gray')
def main():
img = fetch_image(sys.argv[1])
displayer = Displayer()
kernel = np.ones((5, 5), np.float32)/25
displayer.add_image(img, 'Original')
displayer.add_image(cv2.filter2D(img, -1, kernel), 'Average')
displayer.add_image(cv2.GaussianBlur(img, (5, 5), 0), 'Gaussian')
displayer.add_image(cv2.medianBlur(img, 5), 'Median')
displayer.add_image(cv2.bilateralFilter(img, 9, 75, 75), 'Bilateral')
displayer.display()
def main():
displayer = Displayer()
orig = cv2.cvtColor(fetch_image(sys.argv[1]), cv2.COLOR_BGR2GRAY)
displayer.add_image(orig, 'Original')
img = cv2.bilateralFilter(np.float32(orig), 9, 75, 75)
x_kernel = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
y_kernel = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])
"""
x_kernel = np.array([[3, 10, 3],
[0, 0, 0],
[-3, -10, -3]])
y_kernel = np.array([[ -3, -0, 3],
[ -10, 0, 10],
[ -3, -0, 3]])
"""
"""
y_kernel = x_kernel = np.array([[ 0.5, 1, 0.5],
[ 1, -6, 1],
[ 0.5, 1, 0.5]])
"""
x_gradient = cv2.filter2D(img, -1, x_kernel)
abs_x = np.absolute(x_gradient)
x_grad_u8 = np.uint8(abs_x)
displayer.add_image(x_grad_u8, 'manual x gradient')
y_gradient = cv2.filter2D(img, -1, y_kernel)
abs_y = np.absolute(y_gradient)
y_grad_u8 = np.uint8(abs_y)
displayer.add_image(y_grad_u8, 'manual y gradient')
magnitude = cv2.magnitude(abs_x, abs_y)
magnitude_u8 = np.uint8(magnitude)
displayer.add_image(magnitude_u8, 'magnitude')
"""
lap = cv2.Laplacian(orig, -1, cv2.CV_64F)
displayer.add_image(np.uint8(lap), 'laplacian')
"""
canny = cv2.Canny(orig, 100, 200)
displayer.add_image(np.uint8(canny), 'Canny')
displayer.display(cmap='gray')
def main():
displayer = Displayer()
orig = fetch_image(sys.argv[1], 0)
displayer.add_image(orig, "Original")
square = np.ones((9, 9), np.float32)
rectangle = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 13))
ellipse = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
cross = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))
kernel = cross
displayer.add_image(cv2.erode(orig, kernel, iterations=1), "Erosion")
displayer.add_image(cv2.dilate(orig, kernel, iterations=1), "Dilation")
displayer.add_image(cv2.morphologyEx(orig, cv2.MORPH_OPEN, kernel), "Opening")
closing = cv2.morphologyEx(orig, cv2.MORPH_CLOSE, kernel)
displayer.add_image(closing, "Closing")
displayer.add_image(cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel), "OpeningClosing")
displayer.add_image(cv2.morphologyEx(orig, cv2.MORPH_GRADIENT, kernel), "Gradient")
displayer.add_image(cv2.morphologyEx(orig, cv2.MORPH_TOPHAT, kernel), "Top Hat")
displayer.add_image(cv2.morphologyEx(orig, cv2.MORPH_BLACKHAT, kernel), "Black Hat")
displayer.display(cmap="gray")