from scipy.ndimage import rotate import ImageTool as imtl from statistical import * from parameters import * i = 1 pwd = 'examples/Normal_quad_scan_L2/' filename = pwd + 'X111_YAG_' + str(i) + '_bkg_0.png' filenamebg = pwd + 'X111_YAG_' + str(i) + '_img_0.png' data = imtl.Load(filename) bg = imtl.Load(filenamebg) data = data - bg data = data[0:2000, 0:2000] data = imtl.Normalize(data) #experimental data_rot = data.copy() data_rot = rotate(data, skew, reshape=False) x, y = imtl.ImageFit(data_rot, 1.0, True) print "Image center is at: ", int(x[0]), int(y[0]) print "Gaussian fit sizes (px): ", int(x[2]), int(y[2]) print "Gaussian fit sizes (m): ", cal * x[2], cal * y[2] sigma_max = int(np.max((x[2], y[2]))) sigma_min = int(np.min((x[2], y[2]))) #initial crop box = int(4.7 * sigma_max) data = imtl.AutoCrop(data, box, box)
def image_moments(data, semiaxis_a, semiaxis_b):
global radius0
radius0 = (semiaxis_a + semiaxis_b) / 2
global semi_a0, semi_b0
semi_a0 = int(2 * semiaxis_a)
semi_b0 = int(2 * semiaxis_b)
print "Inital mask radius: ", radius0
h, v = data.shape[:2]
global step
step = int((h / 2 - max(semi_a0, semi_b0)) / m)
print "Step size is: ", step
#Retrieve cov(R) data
cov_array = covR(data)
#Find the image center-of-mass
xbar, ybar, cov = image_raw_moments(data)
center = [xbar, ybar]
p2x, p2y = imtl.ImageFit(data, 1.0)
print "Image center is at: ", p2x[0], p2y[0]
center = [p2x[0], p2y[0]]
# ellipse = data.copy()
# mask_ellipse = create_elliptical_mask(h,v,center,radius0,3*radius0,45.0)
# ellipse[~mask_ellipse] = 0
# plt.figure()
# plt.imshow(ellipse)
# plt.show()
#Calculate the correct radii for <xx>,<yy> and <xy> moments. The condition is min(d covR(R) / dR)
# m = len(cov_array)
#Retrieve the positions of max(d covR(R) / dR)
# nx, ny, nxy = dcov_array_max(cov_array)
#Radius is calculated as the location of derivative minimum
# radiusX = radius0 + step*np.argmin(np.gradient(cov_array[nx:m-1,0])) + step*nx
# radiusY = radius0 + step*np.argmin(np.gradient(cov_array[ny:m-1,3])) + step*ny
# radiusXY = radius0 + step*np.argmin(np.abs(np.gradient(cov_array[nxy:m-1,1]))) + step*nxy
minx, miny, minxy = dcov_array_min(cov_array)
semi_aX = semi_a0 + step * minx
semi_bX = semi_b0 + step * minx
semi_aY = semi_a0 + step * miny
semi_bY = semi_b0 + step * miny
semi_aXY = semi_a0 + step * minxy
semi_bXY = semi_b0 + step * minxy
print "Mask ellipse X: ", semi_aX, semi_bX
print "Mask ellipse Y: ", semi_aY, semi_bY
print "Mask ellipse XY: ", semi_aXY, semi_bXY
#Plotting covariance matrix elements for debugging
plot_covarray(cov_array)
#Create masks for <xx>,<yy> and <xy> moments
maskX = create_elliptical_mask(h, v, center, semi_aX, semi_bX, skew)
maskY = create_elliptical_mask(h, v, center, semi_aY, semi_bY, skew)
maskXY = create_elliptical_mask(h, v, center, semi_aXY, semi_bXY, skew)
#Prepare three copies of masked data for final moments calculation
tempX = data.copy()
tempY = data.copy()
tempXY = data.copy()
tempX[~maskX] = 0
tempY[~maskY] = 0
tempXY[~maskXY] = 0
# plt.figure()
fig, ax = plt.subplots(1)
extent = (0, len(tempXY[:, 0]), 0, len(tempXY[:, 1]))
tempXY = np.flip(tempXY, 0)
ax.imshow(imtl.Normalize(tempXY), extent=extent)
cx = patches.Ellipse(center,
2 * semi_aX,
2 * semi_bX,
skew,
color='y',
linewidth=3,
fill=False)
cy = patches.Ellipse(center,
2 * semi_aY,
2 * semi_bY,
skew,
color='g',
linewidth=3,
fill=False)
cxy = patches.Ellipse(center,
2 * semi_aXY,
2 * semi_bXY,
skew,
color='m',
linewidth=3,
fill=False)
ax.add_patch(cx)
ax.add_patch(cy)
ax.add_patch(cxy)
plt.legend((cx, cy, cxy),
(r'Mask $\langle xx \rangle$', r'Mask $\langle yy\rangle$',
r'Mask $\langle xy\rangle$'))
plt.xlabel('x (px)')
plt.ylabel('y (px)')
plt.tight_layout()
plt.show()
#Final calculation
xxt, yyt, covx = image_raw_moments(tempX)
xxt, yyt, covy = image_raw_moments(tempY)
xxt, yyt, covxy = image_raw_moments(tempXY)
return covx[0, 0], covy[1, 1], covxy[0, 1]
