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()])