def make_blur_matrix(img, kernel_size=12, debug=True):
n = kernel_size
# k2 = np.zeros(shape=(n,n))
# k2[n//2,n//2] = 1
# sigma = kernel_size/5.0 ## 2.5 sigma
# testk=scipy.ndimage.gaussian_filter(k2,sigma) ## already normalized
r = n // 3
P = fpm.create_source_pattern(shape='circle',
ledmat_shape=[n, n],
radius=r)
S1 = fpm.create_source_pattern(shape='semicircle',
angle=0,
ledmat_shape=[n, n],
radius=r)
# S2 = fpm.create_source_pattern(shape='semicircle', angle=180, ledmat_shape=[n, n], radius=r)
# Hph = 1j*(signal.correlate2d(P, S1*P)-signal.correlate2d(P, S2*P))
# Hph /= np.pi*np.max(np.imag(Hph))
H = fftshift(fft2(fftshift(P)))
H_real = np.real(H)
H_imag = np.imag(H)
if (debug):
plt.imshow(fftshift(P))
plt.show()
# plotkernel(np.abs(Hph), n)
b_real = make_kernel_2D(H_real, img.shape)
b_imag = make_kernel_2D(H_imag, img.shape)
return b_real, b_imag
def get_dpc_reconstruction(color, angles):
image_dict = np.load(get_color_file(color))
image_list = []
pupil_list = []
try:
image_blanks = np.load(get_color_background(color))
except:
image_blanks = np.zeros((1900, 1900))
image_blanks = image_blanks = np.load(get_color_background('R'))
# Get a list of background corrected images
for angle in angles:
image = image_dict[()][angle]
blank = image_blanks[()][angle]
image_corr = image-.3*blank
image_corr -= np.min(image_corr)
# image_corr = image
image_list.append(image_corr)
pupil_list.append(fpm.create_source_pattern(shape='semicircle', angle=angle, ledmat_shape=[1900, 1900], radius=600, int_radius=60))
# Apply the DPC reconstructon to that list
dpc_total = np.zeros_like(image)
for j in range(2):
dpc_total += st.dpc_difference(image_list[i], image_list[i+1])
return dpc_total
def make_blur_matrix(img, kernel_size=12, debug=True):
n = kernel_size
# k2 = np.zeros(shape=(n,n))
# k2[n//2,n//2] = 1
# sigma = kernel_size/5.0 ## 2.5 sigma
# testk=scipy.ndimage.gaussian_filter(k2,sigma) ## already normalized
r = n // 3
P = fpm.create_source_pattern(shape='circle',
ledmat_shape=[n, n],
radius=r)
# S1 = fpm.create_source_pattern(shape='semicircle', angle=0, ledmat_shape=[n, n], radius=r)
# S2 = fpm.create_source_pattern(shape='semicircle', angle=180, ledmat_shape=[n, n], radius=r)
# Hph = 1j*(signal.correlate2d(P, S1*P)-signal.correlate2d(P, S2*P))
# Hph /= np.pi*np.max(np.imag(Hph))
Hph = fftshift(fft2(P))
Hph = np.arange(1, n**2 + 1).reshape(n, -1) * (1 + 2 * 1j)
mydebug("kernel: ", Hph)
# Hph = Hph
if (debug):
plt.imshow(np.abs(Hph))
# plt.show()
# plotkernel(np.abs(Hph), n)
blurmat = make_kernel_2D(Hph, img.shape)
mydebug("Convolution matrix: ", blurmat.toarray())
return (blurmat)
def create_hph(angle1, angle2, npx, radius):
radius = radius // 2
n = npx // 2 + 1
P = fpm.create_source_pattern(shape='circle',
ledmat_shape=[n, n],
radius=radius)
S1 = fpm.create_source_pattern(shape='semicircle',
angle=angle1,
ledmat_shape=[n, n],
radius=radius)
S2 = fpm.create_source_pattern(shape='semicircle',
angle=angle2,
ledmat_shape=[n, n],
radius=radius)
Hph = 1j * (signal.correlate2d(P, S1 * P) - signal.correlate2d(P, S2 * P))
Hph /= np.pi * np.max(np.imag(Hph))
return Hph[0:npx, 0:npx]
def mencoded(angle):
matrix = fpm.create_source_pattern(shape='semicircle', angle=angle, int_radius=2, radius=5,)
pattern = fpm.hex_encode(matrix)
return pattern
# xoff=1250, yoff=950
def acquire_image_pattern(ss, pattern, Nmean=1):
image_mean = np.zeros(cfg.patch_size)
for i in range(Nmean):
image_response = client.acquire_ledmatrix_pattern(pattern=pattern, power=255, color='R', shutter_speed=ss, iso=400, xoff=0, yoff=0)
image_i = np.array(image_response).reshape(cfg.patch_size)
image_mean += image_i
return image_mean/(Nmean)
def mencoded(angle):
matrix = fpm.create_source_pattern(shape='semicircle', angle=angle, int_radius=2, radius=5,)
pattern = fpm.hex_encode(matrix)
return pattern
circ_matrix = fpm.create_source_pattern(shape='circle')
N = 1
# angles = [0, 180, 90, 270, 45, 225, 135, 315]
angles = [0, 0]
pupil_list = []
image_dict = dict()
fig, (ax) = plt.subplots(1, 1, figsize=(25, 15))
fig.show()
fig.canvas.draw()
for angle in angles:
image_dict[angle] = acquire_image_pattern(ss=1E4, pattern=mencoded(angle=angle), Nmean=N)
ax.cla()
ax.imshow(image_dict[angle], cmap=cm.gray)
fig.canvas.draw()
elapsed2 = time.time() - t2
print("Time: %2f s constructing the matrix ; %2f s solving it\n" %
(elapsed1, elapsed2))
return deblurred
npx = 100
radius = 60
mag_array = scipy.misc.imread('sandbox/alambre.png', 'F')[:npx, :npx]
image_phase = scipy.misc.imread('sandbox/lines0_0.png', 'F')[:npx, :npx]
ph_array = np.pi * (image_phase) / np.amax(image_phase)
image_array = mag_array * np.exp(1j * ph_array)
image_fft = fftshift(fft2(image_array))
b = make_complex_blur_matrix(image_array, kernel_size=12, debug=True)
I1 = blur_noise_image(image_array, b, noise_scale=0.0)
N = np.prod(I1.shape)
deblur_tikh = deblur_tikh_sparse(I1, b, 0.025, method='Lap')
fig, (axes) = plt.subplots(2, 2, figsize=(25, 15))
fig.show()
# S1 = fpm.create_source_pattern(shape='semicircle', angle=180, ledmat_shape=[npx, npx], radius=npx//3)
S1 = fpm.create_source_pattern(shape='circle',
angle=180,
ledmat_shape=[npx, npx],
radius=npx // 6)
Ilp1 = np.abs(ifft2(S1 * image_fft))
axes[0][0].imshow(np.abs(I1))
axes[0][1].imshow(np.abs(deblur_tikh))
plt.show()
npx = 32
radius = 10
mag_array = misc.imread('sandbox/alambre.png', 'F')[:npx, :npx]
image_phase = misc.imread('sandbox/lines0_0.png', 'F')[:npx, :npx]
ph_array = np.pi * (image_phase) / np.amax(image_phase)
image_array = mag_array * np.exp(1j * ph_array)
image_fft = fftshift(fft2(image_array))
angles = [0, 180, 90, 270, 45, 225, 135, 315]
pupil_list = []
image_list = []
for angle in angles:
H = (fpm.create_source_pattern(shape='semicircle',
angle=angle,
ledmat_shape=[npx, npx],
radius=radius,
int_radius=20))
pupil_list.append(H)
image_list.append(np.abs(ifft2(H * image_fft)))
fig, (axes) = plt.subplots(2, 2, figsize=(25, 15))
fig.show()
# IDPC2 = np.abs(ifft2(H2*image_fft))
axes[0][0].imshow(pupil_list[0] + pupil_list[2])
axes[0][1].imshow(pupil_list[1] + pupil_list[3])
signal.correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0)
plt.show()
for idpc in image_list:
fft_list.append(fftshift(fft2(idpc)))
for pupil in pupil_list:
H = pupil / (sum_idpc)
H_list.append(np.conjugate(H))
tik_den = np.sum([np.abs(H.ravel())**2 for H in H_list]) + alpha
print(tik_den)
tik_nom = 0
for HC, FFT_IDPC in zip(H_list, fft_list):
tik_nom += HC * FFT_IDPC
tik = (ifft2(tik_nom / tik_den))
return tik
P = fpm.create_source_pattern(shape='circle',
ledmat_shape=[npx, npx],
radius=radius)
S1 = fpm.create_source_pattern(shape='semicircle',
angle=0,
ledmat_shape=[npx, npx],
radius=radius)
S2 = fpm.create_source_pattern(shape='semicircle',
angle=180,
ledmat_shape=[npx, npx],
radius=radius)
S3 = fpm.create_source_pattern(shape='semicircle',
angle=90,
ledmat_shape=[npx, npx],
radius=radius)
S4 = fpm.create_source_pattern(shape='semicircle',
angle=270,