a296b833
 David Mayerich
added the muve al...
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def press(event):
global i
global ax
global fig
if event.key == 'z':
i = i - 1
if i < 0:
i = 0
if event.key == 'x':
i = i + 1
if i == S.shape[0]:
i = S.shape[0]
if event.key == "up":
for n in range(i, S.shape[0]):
warps[n][1, 2] = warps[n][1, 2] + 1
if event.key == "down":
for n in range(i, S.shape[0]):
warps[n][1, 2] = warps[n][1, 2] - 1
if event.key == "left":
for n in range(i, S.shape[0]):
warps[n][0, 2] = warps[n][0, 2] + 1
if event.key == "right":
for n in range(i, S.shape[0]):
warps[n][0, 2] = warps[n][0, 2] - 1
aligned = cv2.warpAffine(S[i, :, :, :], warps[i], (S.shape[2], S.shape[1]), flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP);
ax.cla()
ax.imshow(aligned.astype(numpy.uint8))
ax.set_title('Slice ' + str(i))
fig.canvas.draw()
sys.stdout.flush
#apply the alignment adjustments to I based on the list of warp matrices in t
def apply_alignment(I, t):
A = numpy.zeros(I.shape, dtype=numpy.uint8)
for i in range(0, I.shape[0]):
A[i, :, :, :] = cv2.warpAffine(I[i, :, :, :], t[i], (I.shape[2], I.shape[1]), flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP);
return A
#get the transform that aligns image B to image A
def align(A, B, max_power=5):
# Define the motion model
warp_mode = cv2.MOTION_TRANSLATION
# Specify the number of iterations.
number_of_iterations = 5000;
# Specify the threshold of the increment
# in the correlation coefficient between two iterations
termination_eps = 1e-10;
# Define termination criteria
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, number_of_iterations, termination_eps)
#attempt to fit the image, increasing the blur as necessary
i = 0
while i <= max_power:
#blur the image used for fitting
im1_blur = scipy.ndimage.filters.gaussian_filter(A, (2 ** i, 2 ** i), mode='constant')
im2_blur = scipy.ndimage.filters.gaussian_filter(B, (2 ** i, 2 ** i), mode='constant')
# Run the ECC algorithm. The results are stored in warp_matrix.
# Define 2x3 matrix and initialize the matrix to identity
warp_matrix = numpy.eye(2, 3, dtype=numpy.float32)
try:
(cc, warp_matrix) = cv2.findTransformECC (im1_blur,im2_blur,warp_matrix, warp_mode, criteria)
except:
#print("Error aligning at p = " + str(i))
i = i + 1
else:
#print("Successful alignment at p = " + str(i))
break
#enforce the fact that the x-axis is already aligned
#warp_matrix[0, 2] = 0
return warp_matrix
#if i > 0:
# (cc, warp_matrix) = cv2.findTransformECC (A,B,warp_matrix, warp_mode, criteria)
#warp_matrix[0, 2] = 0
# return warp_matrix
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# Define the motion model
warp_mode = cv2.MOTION_TRANSLATION
# Define the motion model
warp_mode = cv2.MOTION_TRANSLATION
# Specify the number of iterations.
number_of_iterations = 5000
# Specify the threshold of the increment
# in the correlation coefficient between two iterations
termination_eps = 1e-10
# Define termination criteria
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, number_of_iterations, termination_eps)
print("\n\nCalculating alignment transformations...")
bar = progressbar.ProgressBar(max_value=G.shape[0])
#for each image in the stack, calculate a transformation matrix to align it to the previous image
warps = [numpy.eye(2, 3, dtype=numpy.float32)]
for i in range(1, G.shape[0]):
warps.append(align(G[i-1, :, :], G[i, :, :]))
bar.update(i+1)
print("\n\nApplying alignment transformation...")
# Define 2x3 matrix and initialize the matrix to identity
for i in range(1, G.shape[0]):
warps[i][0, 2] = warps[i][0, 2] + warps[i-1][0, 2]
warps[i][1, 2] = warps[i][1, 2] + warps[i-1][1, 2]
i = 0
aligned = cv2.warpAffine(S[i, :, :, :], warps[i], (S.shape[2], S.shape[1]), flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP)
fig, ax = plt.subplots()
fig.canvas.mpl_connect('key_press_event', press)
ax.imshow(aligned.astype(numpy.uint8))
ax.set_title('Slice ' + str(i))
ax.set_xlabel("Press 'z' and 'x' to change slices and arrow keys to align")
plt.show()
I = apply_alignment(S, warps)
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