def main():
for img in range(2, 3):
print("Denoising for image " + str(img))
data, image = read_data("../a1/" + str(img) + "_noise.txt", True)
print(data.shape)
print(image.shape)
image[image == 0] = -1
image[image == 255] = 1
avg = denoise(image, 0.7, 5, 10)
avg[avg >= 0] = 255
avg[avg < 0] = 0
print(avg.shape)
width = avg.shape[0]
height = avg.shape[1]
counter = 0
for i in range(0, width):
for j in range(0, height):
data[counter][2] = avg[i][j][0]
counter = counter + 1
write_data(data, "../output/" + str(img) + "_denoise.txt")
read_data("../output/" + str(img) + "_denoise.txt",
True,
save=True,
save_name="../output/" + str(img) + "_denoise.jpg")
print("Finished writing data. Please check " + str(img) +
"_denoise.jpg \n")
def main(filename):
data, image = read_data(filename, True)
image = image.transpose(1, 0, 2)
#Converting gray to binary (-1,1)
image[image == 0] = -1
image[image == 255] = 1
burn_in_itr = 10
itr = 30
q = 0.73
l1_norm = 0.5 * np.log(q / (1 - q))
denoise_sub = gibbs(image, l1_norm * image, 3)
avg = np.zeros_like(image).astype(np.float32)
for i in range(burn_in_itr + itr):
print("Iteration - " + str(i))
for j in range(image.shape[0]):
for k in range(image.shape[1]):
if (random.uniform(0, 1) <= 0.73):
denoise_sub.gibbs_sample(j, k)
if (i > burn_in_itr):
avg += denoise_sub.image
avg / itr
#converting back to gray scale
avg[avg >= 0] = 255
avg[avg < 0] = 0
row = avg.shape[0]
column = avg.shape[1]
cnt = 0
print(avg.shape)
for i in range(0, row):
for j in range(0, column):
data[cnt][2] = avg[i][j][0]
cnt = cnt + 1
write_data(data, filename + "_denoise.txt")
read_data(filename + "_denoise.txt",
True,
save=True,
save_name=filename + "_denoise.jpg")
print("Iterations completed - Please check your folder for output -" +
filename + "_denoise.jpg")
iterations = 10
q = 0.7 # 0.x or None # for logit - external filed factor
threshold = 0.7
d_img = denoising(image,
burn_in=burn_in,
iterations=iterations,
q=q,
threshold=threshold)
d_img[d_img >= 0] = 255
d_img[d_img < 0] = 0
print(d_img.shape)
height = d_img.shape[0]
width = d_img.shape[1]
counter = 0
for i in range(0, width):
for j in range(0, height):
data[counter][2] = d_img[j][i][0]
counter = counter + 1
write_data(data, "../output/gibbs/" + str(img) + "_denoise.txt")
read_data("../output/gibbs/" + str(img) + "_denoise.txt",
True,
save=True,
save_name="../output/gibbs/" + str(img) + "_denoise.png")
print("Finished writing data. Please check " + str(img) +
"_denoise.png \n")
def fit_EM(m1, m2, filename, data, image):
mean1 = m1 #Mean 1
mean2 = m2 #Mean 2
eps = 0.5 #Threshold
z = np.asarray([[13, 20, 29], [13, 23, 37], [13, 23, 29]])
cov1 = np.cov(z) #covariance 1
z = np.asarray([[9, -58, 7], [8, -7, 10], [6, -4, 6]])
cov2 = np.cov(z) #covariance 2
mix1 = 0.4 #mixing co-efficient 1
mix2 = 0.6 #mixing co-efficient 2
N = image.shape[0] * image.shape[1] #Total number of samples
log_likelihoods = []
print(
"Initialization of mean,covariance and mixing co-efficient statement done for "
+ str(filename))
print("")
print("Starting EM algorithm for " + str(filename))
# Expectation Step :
iteration_number = 0
while (1):
iteration_number += 1
print("Iteration: " + str(iteration_number))
N1 = 0
N2 = 0
resp1_list = []
resp2_list = []
mu_sum1 = [0, 0, 0]
mu_sum2 = [0, 0, 0]
for y in image:
for x in y:
prob1 = multivariate_normal.pdf(
x, mean=mean1, cov=cov1,
allow_singular=True) # gaussian density 1
prob2 = multivariate_normal.pdf(
x, mean=mean2, cov=cov2,
allow_singular=True) # gaussian density 2
Numerator1 = mix1 * prob1
Numerator2 = mix2 * prob2
denom = Numerator1 + Numerator2
resp1 = Numerator1 / denom #responsibility for 1st cluster
resp2 = Numerator2 / denom #responsibility for 2nd cluster
resp1_list.append(resp1)
resp2_list.append(resp2)
mu_sum1 += resp1 * x
mu_sum2 += resp2 * x
N1 += resp1
N2 += resp2
# Maximization Step :
mu_new1 = mu_sum1 / N1 #updated mean 1
mu_new2 = mu_sum2 / N2 #updated mean 2
var_1 = np.zeros((3, 3))
var_2 = np.zeros((3, 3))
i = 0
for y in image:
for x in y:
var_1 += resp1_list[i] * np.outer((x - mu_new1), (x - mu_new1))
var_2 += resp2_list[i] * np.outer((x - mu_new2), (x - mu_new2))
i = i + 1
var_new1 = var_1 / N1 #updated covariance1
var_new2 = var_2 / N2 #updated covariance2
mix_new1 = N1 / N #updated mixing co-efficient1
mix_new2 = N2 / N #updated mixing co-efficient2
mean1 = mu_new1
mean2 = mu_new2
cov1 = var_new1
cov2 = var_new2
mix1 = mix_new1
mix2 = mix_new2
#Calculate Log Likelihood
Z = [0, 0]
ll = 0
sumList = []
for y in image:
for x in y:
prob1 = multivariate_normal.pdf(x,
mu_new1,
var_new1,
allow_singular=True)
prob2 = multivariate_normal.pdf(x,
mu_new2,
var_new2,
allow_singular=True)
sum = (mix_new1 * prob1) + (mix_new2 * prob2)
sumList.append(np.log(sum))
ll = np.sum(np.asarray(sumList))
log_likelihoods.append(ll)
print("Log Likelihood: " + str(ll))
if len(log_likelihoods) < 2: continue
if np.abs(ll - log_likelihoods[-2]) < eps: break
#Break loop if log likelihoods dont change more than threshold over 2 iterations
print("")
print("End of iterations for: " + str(filename))
print("")
#Write to File
print("Writing to file for: " + str(filename))
back_data = data.copy()
front_data = data.copy()
mask_data = data.copy()
for i in range(0, len(data) - 1):
cell = data[i]
point = [cell[2], cell[3], cell[4]]
prob1 = multivariate_normal.pdf(point,
mean=mean1,
cov=cov1,
allow_singular=True)
resp1 = mix1 * prob1
prob2 = multivariate_normal.pdf(point,
mean=mean2,
cov=cov2,
allow_singular=True)
resp2 = mix2 * prob2
resp1 = resp1 / (resp1 + resp2)
resp2 = resp2 / (resp1 + resp2)
if (resp1 < resp2):
back_data[i][2] = back_data[i][3] = back_data[i][4] = 0
mask_data[i][2] = mask_data[i][3] = mask_data[i][4] = 0
else:
front_data[i][2] = front_data[i][3] = front_data[i][4] = 0
mask_data[i][2] = 100
mask_data[i][3] = mask_data[i][4] = 0
write_data(back_data, "../output/" + str(filename) + "_back.txt")
read_data(str(filename) + "_back.txt",
False,
save=True,
save_name="../output/" + str(filename) + "_background.jpg")
write_data(front_data, "../output/" + str(filename) + "_fore.txt")
read_data(str(filename) + "_fore.txt",
False,
save=True,
save_name="../output/" + str(filename) + "_foreground.jpg")
write_data(mask_data, "../output/" + str(filename) + "_mask.txt")
read_data(str(filename) + "_mask.txt",
False,
save=True,
save_name="../output/" + str(filename) + "_masked.jpg")
print("Finished writing data. Please check " + str(filename) +
"_background.jpg, " + str(filename) + "_foreground.jpg and " +
str(filename) + "_masked.jpg ")