def E_step(number_of_image, priblijenie, weigths):
#print("process {}".format(number_of_image))
#print('calculating log(L) for {} picture'.format(number_of_image))
#REF = priblijenie
IMG = np.array(Image.open('img{}.png'.format(number_of_image)))
step = np.zeros(number_of_angles)
for i in range(number_of_angles):
angle = i * term
#print('angle = {}'.format(angle))
#REF = ndimage.rotate(priblijenie, angle, reshape=False)
#REF = np.rot90(priblijenie, -i)#при +i вертит против часовой
REF = polar_rotate(priblijenie, -angle)
#log_L = 0
#for x in range(REF.shape[0]):
# for y in range(REF.shape[1]):
# log_L -= (int(IMG[x, y]) - int(REF[x, y])) ** 2/(2*sigma**2)
a = (REF.astype(int) - IMG.astype(int)) ** 2/(2*sigma**2)
log_L = -a.sum()
step[i] = log_L
vector, SUM = WeightsG(step,weigths)
return vector, SUM
def M_step_Matrix_calc(vect, number_of_img, a):
IMG_arr = np.array(Image.open(way_to_img.format(number_of_img)))
argmax = vect.argmax()
new_vect = np.roll(vect, -argmax)
matrix = np.zeros(a)
for i in range(number_of_angles):
angle = term * (i + argmax)
IMG = polar_rotate(IMG_arr, angle)
matrix[i] = IMG
matrix = matrix * np.float64(
new_vect[:, None, None]) #multiply by g_ij IMG* * g_ij
divider = (matrix != 0).astype(int) # true\false to 1\0
return matrix, divider
def E_step(number_of_image, priblijenie, weigths):
#print('calculating log(L) for {} picture'.format(number_of_image))
IMG = np.array(Image.open(way_to_img.format(number_of_image)))
step = np.zeros(number_of_angles)
for i in range(number_of_angles):
angle = i * term
REF = polar_rotate(priblijenie, -angle)
step[i] = LogL(REF, IMG)
#print(step)
vector, SUM = WeightsG(step, weigths)
#if number_of_image==3:
# print(weigths)
# print("{} IMG:".format(number_of_image), vector)
# print(SUM)
return vector, SUM
def Generate1(arr, Num):
#angle = int(90*np.random.randint(3)) #Loses values on edjes of pic
#rotated_arr = ndimage.rotate(arr, angle, reshape=False)
arr = put_normal_distribution(arr)
arr = Del_pixels1(arr, del_percent)
angle = np.random.randint(number_of_angles)*term
polar_arr = reproject_image_into_polar(arr, 100, 100)
rotated_arr = polar_rotate(polar_arr, angle)
im = Image.fromarray(rotated_arr)
if im.mode != 'L':
im = im.convert('L')
im.save(way_to_img.format(Num))
print("saved img{}.png".format(Num))
return 1
def M_step(number_of_image,vector,weights):
#print('calculating pixels for {} picture {}'.format(number_of_image,vector))
#IMG = np.array(Image.open('rot{}.png'.format(number_of_image)).convert('RGB'))
IMG_arr = np.array(Image.open('img{}.png'.format(number_of_image)))
priblij = np.zeros(IMG_arr.shape)
for i in range(number_of_angles):
angle = i * term
#print('angle = {}'.format(angle))
#REF = ndimage.rotate(IMG, -angle, reshape=False)
IMG = polar_rotate(IMG_arr, angle)
a = np.float64(vector[i]/weights[i])
priblij += a * IMG
#print(f"{vector[i]} * {REF[55,55]} = {priblij[55,55]}")
#plt.imshow(priblij)
#plt.show()
return priblij