def homework_3_problem_3():
# load image
img = I.read_gecko_image()
org = deepcopy(img)
fig = plt.gcf()
fig.suptitle("Problem 3: Lloyd's Algorithm")
plt.subplot(221)
plt.title('original')
plt.imshow(org, cm.Greys_r)
# Apply haar transformation
wavelet.forward(h, img)
plt.subplot(222)
plt.title('forward')
plt.imshow(img, cm.Greys_r)
# store sign and apply thresholding
img, sign = quantization.thresh(img, cutoff=0.8)
# logarithmic codebook guess
x = img[np.where(img > 0)]
init = np.array(
[min(x) * 2**i for i in range(int(np.ceil(np.log2(max(x)/min(x)))))])
# Apply k-means to find optimal codebook
codebook, distortion = vq.kmeans(np.sort(img[np.where(img > 0)]), init)
# Encode image
img, dist = vq.vq(img, codebook)
# Decode image
img = np.array([s*codebook[i] for s, i in zip(sign, img)])
img = img.reshape([256, 256])
plt.subplot(223)
plt.title('after quant')
plt.imshow(img, cm.Greys_r)
# Revert haar transformation
wavelet.inverse(h, img)
plt.subplot(224)
plt.title('reverse')
plt.imshow(img, cm.Greys_r)
plt.show()
def homework_3_problem_1():
# Generate X matrix with border
dim = 256
A = M.identity(dim)
A = A + M.fliplr(A)
A[0, :] = 1
A[-1, :] = 1
A[:, 0] = 1
A[:, -1] = 1
A[dim / 2, dim / 2] = 1
A = A * 255
fig = plt.gcf()
fig.suptitle("Problem 1: Haar Transform")
# Display original matrix
plt.subplot(321)
plt.title('original')
plt.imshow(A, cm.Greys_r)
plt.subplot(322)
plt.title('forward')
wavelet.forward(h, A, recursive=False)
plt.imshow(A, cm.Greys_r)
plt.subplot(323)
plt.title('top left')
plt.imshow(A[:dim / 2, :dim / 2], cm.Greys_r)
plt.subplot(324)
plt.title('top right')
plt.imshow(A[:dim / 2, dim / 2:], cm.Greys_r)
plt.subplot(325)
plt.title('bottom left')
plt.imshow(A[dim / 2:, :dim / 2], cm.Greys_r)
plt.subplot(326)
plt.title('bottom right')
plt.imshow(A[dim / 2:, dim / 2:], cm.Greys_r)
plt.show()
from copy import deepcopy
from math import sqrt
img = I.read_gecko_image()
org = deepcopy(img)
fig = plt.gcf()
fig.suptitle('Logarithmic quantizationuantization')
plt.subplot(231)
plt.title('original')
plt.imshow(org, cm.Greys_r)
haar_coefficients = [1/sqrt(2), 1/sqrt(2)]
wavelet.forward(h=haar_coefficients, image=img)
plt.subplot(232)
plt.title('forward')
plt.imshow(img, cm.Greys_r)
offset = 4
for idx, th in enumerate([0.8, 0.9, 0.95]):
img_cp = deepcopy(img)
thresh, lmaxt = quantization.log_thresh(img_cp, th)
img_cp = quantization.encode(img_cp, thresh, lmaxt)
img_cp = quantization.decode(img_cp, thresh, lmaxt)
wavelet.inverse(h=haar_coefficients, image=img_cp)
plt.subplot(eval('23%s' % (offset + idx)))
plt.title('%s' % th)
plt.imshow(img_cp, cm.Greys_r)
from copy import deepcopy
from math import sqrt
img = I.read_gecko_image()
org = deepcopy(img)
fig = plt.gcf()
fig.suptitle('Logarithmic quantizationuantization')
plt.subplot(231)
plt.title('original')
plt.imshow(org, cm.Greys_r)
haar_coefficients = [1 / sqrt(2), 1 / sqrt(2)]
wavelet.forward(h=haar_coefficients, image=img)
plt.subplot(232)
plt.title('forward')
plt.imshow(img, cm.Greys_r)
offset = 4
for idx, th in enumerate([0.8, 0.9, 0.95]):
img_cp = deepcopy(img)
thresh, lmaxt = quantization.log_thresh(img_cp, th)
img_cp = quantization.encode(img_cp, thresh, lmaxt)
img_cp = quantization.decode(img_cp, thresh, lmaxt)
wavelet.inverse(h=haar_coefficients, image=img_cp)
plt.subplot(eval('23%s' % (offset + idx)))
plt.title('%s' % th)
plt.imshow(img_cp, cm.Greys_r)