def plot(df,
t,
kappas,
width=map_base.shape[1],
height=map_base.shape[0],
crop=False,
is_gt=False):
fig, ax = plt.subplots(1,
1,
figsize=np.array(
(width, height)) / float(dpi),
dpi=dpi)
ax.imshow(map_base, cmap='gray', vmin=0, vmax=255)
# rgb = list(map(hsv_to_rgb, [(f_star, kappa * 0.5, 0.5) for (f_star, kappa) in zip(df.f_star, minmax_normalization(df.kappa))]))
rgb = list(
map(hsv_to_rgb, [(t, 0.5 + kappa * 0.5, 0.8)
for (t, kappa) in zip(t, kappas)]))
# print (rgb)
sc = ax.scatter(
df.x, df.y, c=rgb, s=8, marker="x", linewidths=1
) # , vmin=0, vmax=1) # , cmap=cmap) # cmap = "hsv") #
if crop:
plt.axis([
np.min(df.x) - 100,
np.max(df.x) + 100,
np.max(df.y) + 100,
np.min(df.y) - 100
])
plt.hsv()
# sc._A = np.array([])
# plt.colorbar(sc)
mappable = plt.cm.ScalarMappable(norm=Normalize(0,
1,
clip=False),
cmap=cmap)
mappable.set_array([])
if is_gt:
plt.title("Ground Truth: user %02d" % (id))
else:
plt.title("%s: user %02d" % (kernel, id))
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(mappable, cax=cax)
manager = plt.get_current_fig_manager()
manager.window.showMaximized()
def plot2():
this_dir = os.path.dirname(os.path.realpath(__file__))
lena = Image.open(os.path.join(this_dir, 'lena.png'))
dpi = rcParams['figure.dpi']
figsize = lena.size[0] / dpi, lena.size[1] / dpi
fig = pp.figure(figsize=figsize)
ax = pp.axes([0, 0, 1, 1], frameon=False)
ax.set_axis_off()
pp.imshow(lena, cmap='viridis', origin='lower')
# Set the current color map to HSV.
pp.hsv()
pp.colorbar()
return fig
def pca_on_bird():
bird_image = image.imread(
"ml_course_material/machine-learning-ex7/ex7/bird_small.png")
plt.imshow(bird_image)
plt.show(block=False)
im_shape = bird_image.shape
X = bird_image.reshape([im_shape[0] * im_shape[1], 3])
k = 16
(centroids,
closest_centroids) = k_means.k_means(X,
k_means.init_random_centroids(X, k))
nr_samples = 1000
rng = np.random.default_rng()
random_indices = rng.integers(0, X.shape[0], nr_samples)
colors = []
X_rand = np.empty((nr_samples, X.shape[1]))
for i in range(nr_samples):
colors.append(closest_centroids[random_indices[i]])
X_rand[i, :] = X[random_indices[i], :]
plt.hsv()
fig = plt.figure(3)
ax = fig.gca(projection='3d')
ax.scatter(X_rand[:, 0], X_rand[:, 1], X_rand[:, 2], c=colors)
plt.show(block=False)
X_norm = feature.FeatureNormalizer(X).normalized_x_m
while True:
try:
(U, S) = pca.pca(X_norm)
break
except:
pass
Z = pca.project(X, U, 2)
Z_rand = np.empty((nr_samples, Z.shape[1]))
for i in range(nr_samples):
Z_rand[i, :] = Z[random_indices[i], :]
plt.figure(4)
plt.scatter(Z_rand[:, 0], Z_rand[:, 1], c=colors)
plt.show()
def image_plot():
from matplotlib import rcParams
try:
import Image
except ImportError:
raise SystemExit('PIL must be installed to run this example')
lena = Image.open('lena.png')
dpi = rcParams['figure.dpi']
figsize = lena.size[0] / dpi, lena.size[1] / dpi
pp.figure(figsize=figsize)
ax = pp.axes([0, 0, 1, 1], frameon=False)
ax.set_axis_off()
pp.imshow(lena, origin='lower')
# Set the current color map to HSV.
pp.hsv()
pp.colorbar()
return 'An \\texttt{imshow} plot'
def plot2():
from matplotlib import rcParams
import matplotlib.pyplot as plt
from PIL import Image
import os
this_dir = os.path.dirname(os.path.realpath(__file__))
lena = Image.open(os.path.join(this_dir, 'lena.png'))
dpi = rcParams['figure.dpi']
figsize = lena.size[0] / dpi, lena.size[1] / dpi
fig = plt.figure(figsize=figsize)
ax = plt.axes([0, 0, 1, 1], frameon=False)
ax.set_axis_off()
plt.imshow(lena, cmap='viridis', origin='lower')
# Set the current color map to HSV.
plt.hsv()
plt.colorbar()
return fig
def image_plot():
from matplotlib import rcParams
try:
import Image
except ImportError:
raise SystemExit('PIL must be installed to run this example')
lena = Image.open('lena.png')
dpi = rcParams['figure.dpi']
figsize = lena.size[0]/dpi, lena.size[1]/dpi
pp.figure(figsize=figsize)
ax = pp.axes([0, 0, 1, 1], frameon=False)
ax.set_axis_off()
pp.imshow(lena, origin='lower')
# Set the current color map to HSV.
pp.hsv()
pp.colorbar()
return 'An \\texttt{imshow} plot'
def plot_lower():
from matplotlib import rcParams
import matplotlib.image as mpimg
import os
this_dir = os.path.dirname(os.path.realpath(__file__))
img = mpimg.imread(os.path.join(this_dir, "lena.png"))
dpi = rcParams["figure.dpi"]
figsize = img.shape[0] / dpi, img.shape[1] / dpi
fig = plt.figure(figsize=figsize)
ax = plt.axes([0, 0, 1, 1], frameon=False)
ax.set_axis_off()
plt.imshow(img, cmap="viridis", origin="lower")
# Set the current color map to HSV.
plt.hsv()
plt.colorbar()
return fig
def plot():
from matplotlib import rcParams
import matplotlib.image as mpimg
import os
this_dir = os.path.dirname(os.path.realpath(__file__))
img = mpimg.imread(os.path.join(this_dir, "lena.png"))
dpi = rcParams["figure.dpi"]
figsize = img.shape[0] / dpi, img.shape[1] / dpi
fig = plt.figure(figsize=figsize)
ax = plt.axes([0, 0, 1, 1], frameon=False)
ax.set_axis_off()
plt.imshow(img, cmap="viridis", origin="lower")
# Set the current color map to HSV.
plt.hsv()
plt.colorbar()
return fig
def plot():
from matplotlib import rcParams
from matplotlib import pyplot as pp
import os
try:
from PIL import Image
except ImportError:
raise RuntimeError('PIL must be installed to run this example')
this_dir = os.path.dirname(os.path.realpath(__file__))
lena = Image.open(os.path.join(this_dir, 'lena.png'))
dpi = rcParams['figure.dpi']
figsize = lena.size[0]/dpi, lena.size[1]/dpi
fig = pp.figure(figsize=figsize)
ax = pp.axes([0, 0, 1, 1], frameon=False)
ax.set_axis_off()
pp.imshow(lena, origin='lower')
# Set the current color map to HSV.
pp.hsv()
pp.colorbar()
return fig
def plot():
from matplotlib import rcParams
from matplotlib import pyplot as pp
import os
try:
from PIL import Image
except ImportError:
raise RuntimeError('PIL must be installed to run this example')
this_dir = os.path.dirname(os.path.realpath(__file__))
lena = Image.open(os.path.join(this_dir, 'lena.png'))
dpi = rcParams['figure.dpi']
figsize = lena.size[0] / dpi, lena.size[1] / dpi
fig = pp.figure(figsize=figsize)
ax = pp.axes([0, 0, 1, 1], frameon=False)
ax.set_axis_off()
pp.imshow(lena, origin='lower')
# Set the current color map to HSV.
pp.hsv()
pp.colorbar()
return fig
def plotTSNE(self, ax, tsneData, dataLabels, perplexity, theta=0.5):
'''
- plot and fencify
'''
lineWidth = 1.2
tsneData = tsneData.astype('float64')
tsneEmbedded = tsne(tsneData, perplexity=perplexity, theta=theta)
plt.hsv() # color model
ax.scatter(tsneEmbedded[:, 0],
tsneEmbedded[:, 1],
s=45,
c=dataLabels,
edgecolors='w',
linewidth=0.30,
alpha=0.75)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
#ax.yaxis.set_ticks_position('left'); ax.xaxis.set_ticks_position('bottom')
ax.tick_params(axis='x',
which='both',
bottom='off',
top='off',
labelbottom='off')
ax.tick_params(axis='y',
which='both',
left='off',
right='off',
labelleft='off')
xmin, xmax = ax.get_xlim()
ax.axvline((xmax - np.abs(xmin)) / 2.0,
color='black',
linewidth=lineWidth)
ymin, ymax = ax.get_ylim()
ax.axhline((ymax - np.abs(ymin)) / 2.0,
color='black',
linewidth=lineWidth)
plt.tight_layout()
plt.show()
def visualize_vertex_field_old(verPred, segPred, keypointIdx=0):
print('Visualizing Vertex Field.')
verWeight = segPred.float().cpu().detach()
verWeight = np.argmax(verWeight, axis=1)
verWeight = verWeight[None, :, :, :]
_, _, h, w = verWeight.detach().cpu().numpy().shape
angle = np.zeros((h, w))
pred = verPred.detach().cpu().numpy()
for i in range(h):
for j in range(w):
x, y = pred[0, [2 * keypointIdx, 2 * keypointIdx + 1], i, j]
angle[i, j] = np.arctan2(-y, x)
angle = angle * verWeight.detach().cpu().numpy()[0, 0, :, :]
plt.imshow(angle)
plt.hsv()
plt.show()
print(' ')
def plot_poses(poses, conseq=True, wait=True, arrow_len=1):
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
if conseq:
plt.hsv()
#ax.set_prop_cycle('color', map(lambda c: '%f' % c, np.linspace(.7, 0, len(poses))))
for i, pose in enumerate(poses):
if pose is not None:
q = np.quaternion(*pose[3:])
lat, lon, _ = q_to_ypr(q)
v1 = spherical2cartesian(lat, lon, 1)*arrow_len
v2 = (v1 + normalize_v(np.cross(np.cross(v1, np.array([0, 0, 1])), v1))*0.1*arrow_len)*0.85
v2 = q_times_v(q, v2)
ax.plot((pose[0], v1[0], v2[0]), (pose[1], v1[1], v2[1]), (pose[2], v1[2], v2[2]))
while(wait and not plt.waitforbuttonpress()):
pass
def visualize_vertex_field(verPred, mask, keypointIdx=0):
print('Visualizing Vertex Field.')
verWeight = mask.float().cpu()
verWeight = verWeight[None, :, :, :]
_, _, h, w = verWeight.numpy().shape
angle = np.zeros((h, w))
pred = verPred.cpu().numpy()
# for i in range(h):
# for j in range(w):
# x,y = pred[0,[2*keypointIdx,2*keypointIdx+1],i,j]
# angle[i,j] = np.arctan2(-y,x)
x, y = verPred.cpu().numpy()[0,
[2 * keypointIdx, 2 * keypointIdx + 1], :, :]
angle = np.arctan2(-y, x)
angle = angle * verWeight.numpy()[0, 0, :, :]
plt.imshow(angle)
plt.hsv()
plt.show()
print(' ')
# zu weit draussen, abbrechen und neu starten
running = False
elif world[x, y] == 1:
# Angelagert!
world[xalt, yalt] = 1
# Radius anpassen
R = max(R, center_dist(xalt, yalt) + 5)
running = False
attached = True
return path, R, attached
# Ausgabe
##########################################
pyplot.hsv()
figure = pyplot.figure(figsize=(4,4))
figure.subplots_adjust(left=0.1, bottom=0.1, right=0.9, top=0.9)
graph = figure.add_subplot(111)
pfad, = graph.plot([],[], "r-")
kreis, = graph.plot([],[], "k:")
# Plot-Routine
##########################################
# Fuer die Farbe
cnt = 0.0
def animate():
def main():
# Make sure the matlab AES scripts are in the path
# #
# Create two 128-bit plaintexts (exactly 16 byte)
pt1 = []
pt2 = []
for b1 in bytes('Attack at 12:56!', 'utf-8'):
pt1 += [b1]
for b2 in bytes('Attack at 12:57!', 'utf-8'):
pt2 += [b2]
plaintext_1 = np.uint8(pt1)
plaintext_1_1 = np.asmatrix(plaintext_1)
plaintext_2 = np.uint8(pt2)
plaintext_2_2 = np.asmatrix(plaintext_2)
# how many bits are different between the two?
res = hamming_weight(np.bitwise_xor(plaintext_1_1, plaintext_2_2))
print(res)
# #
# Create a key
key_bytes = []
for byte in bytes('1234512345123456', 'utf-8'):
key_bytes += [byte]
key = np.uint8(key_bytes)
key = np.asmatrix(key)
ENCRYPT = 1
DECRYPT = 0
# #
# Encrypt the two plaintexts
ciphertext_1 = aes_crypt_8bit(plaintext_1_1, key, ENCRYPT)
ciphertext_2 = aes_crypt_8bit(plaintext_2_2, key, ENCRYPT)
# even though the plaintexts were very similar...
print(plaintext_1)
print(plaintext_2)
# ... the ciphertexts are very different
print(ciphertext_1)
print(ciphertext_2)
# #
# how many bits are different between the two?
print(hamming_weight(np.bitwise_xor(ciphertext_1, ciphertext_2)))
# #
# Decrypt the two ciphertexts
decrypted_1 = aes_crypt_8bit(ciphertext_1, key, DECRYPT)
decrypted_2 = aes_crypt_8bit(ciphertext_2, key, DECRYPT)
# Did we get the plaintext again?
print(plaintext_1)
print(plaintext_2)
print(decrypted_1)
print(decrypted_2)
print()
# #
# Look at the internals of AES now
[ciphertext_1, state_1, _,
leak_1] = aes_crypt_8bit_and_leak(plaintext_1_1, key, ENCRYPT)
[ciphertext_2, state_2, _,
leak_2] = aes_crypt_8bit_and_leak(plaintext_2_2, key, ENCRYPT)
# Show an image showing the two leaks side by size
plt.subplot(1, 3, 1)
# plt.imshow(np.squeeze(leak_1), interpolation='nearest')
plt.imshow(np.squeeze(state_1), interpolation='nearest')
plt.hsv()
plt.subplot(1, 3, 3)
# plt.imshow(np.squeeze(leak_2), interpolation='nearest')
plt.imshow(np.squeeze(state_2), interpolation='nearest')
plt.hsv()
plt.figure()
# #
# Show the difference in the middle
plt.subplot(1, 3, 2)
# plt.imshow(np.squeeze(np.bitwise_xor(leak_1, leak_2)), interpolation='nearest')
plt.imshow(np.squeeze(np.bitwise_xor(state_1, state_2)),
interpolation='nearest')
plt.hsv()
plt.figure()
# #
# plot the HW of the difference
plt.subplot(1, 1, 1)
# plt.bar(hamming_weight(np.bitwise_xor(leak_1, leak_2)))
plt.bar(range(1, (np.shape(state_1)[0]) + 1),
hamming_weight(np.bitwise_xor(state_1, state_2)))
plt.show()
from matplotlib import rcParams
try:
import Image
except ImportError, exc:
raise SystemExit('PIL must be installed to run this example')
lena = Image.open('lena.png')
dpi = rcParams['figure.dpi']
figsize = lena.size[0]/dpi, lena.size[1]/dpi
pp.figure( figsize=figsize )
ax = pp.axes([0,0,1,1], frameon=False)
ax.set_axis_off()
im = pp.imshow(lena, origin='lower')
# Set the current color map to HSV.
pp.hsv()
pp.colorbar()
return 'An \\texttt{imshow} plot'
# =============================================================================
def noise():
from numpy.random import randn
# Make plot with vertical (default) colorbar
fig = pp.figure()
ax = fig.add_subplot(111)
data = np.clip(randn(250, 250), -1, 1)
cax = ax.imshow(data, interpolation='nearest')
ax.set_title('Gaussian noise with vertical colorbar')
segPred = segPred.cpu().detach().numpy()
verPred = verPred.cpu().detach().numpy()
t2 = time.time()
thisClassMask = segpred_to_mask(segPred)
classMask[iClass] = thisClassMask
thisClassAngles = vertexfield_to_angles(verPred,
thisClassMask,
keypointIdx=iKeypoint)
classAngles[iClass] = thisClassAngles
print('Other shit time elapsed: {}'.format(time.time() - t2))
# Create the seg and ver image
print('......................')
t3 = time.time()
if classIdx is not None:
segImg = Image.fromarray(classMask[iClass][0, :, :])
segTargetPath = os.path.join(targetDir, 'seg',
str(iImage).zfill(5) + '.png')
segImg.save(segTargetPath)
plt.imshow(classAngles[iClass])
plt.hsv()
verTargetPath = os.path.join(targetDir, 'ver',
str(iImage).zfill(5) + '.png')
plt.savefig(verTargetPath)
plt.clf()
print('Saving image time elapsed: {}'.format(time.time() - t3))
print('Fininshed image {}/{}.'.format(iImage, nImages))
pfo = np.polyfit(ox, loy, polyorder)
ploy = np.polyval(pfo, ox)
fx, fy = bindata(reslist[1][0], reslist[1][1])
lfy = np.log(fy)
pff = np.polyfit(fx, lfy, polyorder)
plfy = np.polyval(pff, fx)
diffy = loy - lfy
pdiffterm = np.polyfit(ox, diffy, polyorder)
pdiffy = np.polyval(pdiffterm, ox)
plt.figure()
plt.clf()
plt.hsv()
ax1 = plt.axes([0.125,0.1,0.775,0.15])
plt.plot(ox, diffy, 'kx')
plt.plot(ox, pdiffy, 'k-')
aln = plt.axis()
plt.axis([al[0],al[1],aln[2],aln[3]])
plt.annotate('$A_1 = %e$\n$A_0 = %e$'%tuple(-pdiffterm), [0.9*(al[1]-al[0])+aln[0], 0.4*(aln[3]-aln[2])+aln[2]])
plt.ylabel('$\Delta \ln (A)$')
plt.xlabel('Offset (m)')
ax2 = plt.axes([0.125,0.3,0.775,0.6], sharex=ax1)
plt.hexbin(reslist[0][0], reslist[0][2], mincnt=threshcnt)
plt.plot(ox, loy, 'kx')
plt.plot(ox, ploy, 'k-')
plt.annotate('Field', [0.95*al[1], loy.mean()])