def show_clusters_over_time(task_output_path=None,
query_laps=[0, 1, 2, 5, 10, None],
nrows=2):
ncols = int(np.ceil(len(query_laps) // float(nrows)))
fig_handle, ax_handle_list = pylab.subplots(figsize=(FIG_SIZE[0] * ncols,
FIG_SIZE[1] * nrows),
nrows=nrows,
ncols=ncols,
sharex=True,
sharey=True)
for plot_id, lap_val in enumerate(query_laps):
cur_model, lap_val = bnpy.load_model_at_lap(task_output_path, lap_val)
# Plot the current model
cur_ax_handle = ax_handle_list.flatten()[plot_id]
bnpy.viz.PlotComps.plotCompsFromHModel(
cur_model,
Data=dataset,
) #ax_handle=cur_ax_handle)
cur_ax_handle.set_xticks([-2, -1, 0, 1, 2])
cur_ax_handle.set_yticks([-2, -1, 0, 1, 2])
cur_ax_handle.set_xlabel("lap: %d" % lap_val)
pylab.tight_layout()
pylab.savefig("results/covMat1.png")
pylab.waitforbuttonpress()
pylab.show()
def augmentation_demo(filename, it=20, mean_RGB=None):
"""
Little demo to show how data augmentation is performed on a single image.
Parameters
----------
filename : str
Path of the image
it : int
Number of examples of data augmentation
"""
if mean_RGB is None:
mean_RGB = np.array([107.59348955, 112.1047813, 80.9982362])
else:
mean_RGB = np.array(mean_RGB)
batch = data_augmentation([filename]*it, mean_RGB=mean_RGB)
plt.ion()
fig, [ax1, ax2] = plt.subplots(1, 2, num=1)
ax1.set_title('Original image')
ax2.set_title('Transformed image')
image = Image.open(filename)
ax1.imshow(np.asarray(image))
mean_RGB = mean_RGB.astype(np.float32)
for im in batch:
im = im[::-1, :, :]
im = np.transpose(im, (1, 2, 0))
im = im + mean_RGB[None, None, :]
ax2.imshow(im.astype(np.uint8))
plt.waitforbuttonpress(1)
def plot(s):
s.plot(style='k--')
s=pd.rolling_mean(s, 20, min_periods=1)
s.plot()
m=extremeOf(s)
m.plot(style='ro')
plt.waitforbuttonpress()
plt.close('all')
def plot_boundary(X, Y, model, title=''):
"""
Represents the boundaries of a generic learning model over data
:param X: np.array
Data points. Shape = (num_samples, dim)
:param Y: np.array
Groundtruth labels. (Shape = (num_samples,)
:param model: SVC
A sklearn.SVC fit model
:param title: str
An optional title for the plot
"""
# Initialize subplots
fig, ax = plt.subplots(1, 2)
ax[0].scatter(X[:, 0],
X[:, 1],
c=Y,
s=40,
zorder=10,
cmap=cmap,
edgecolors='k')
# Evaluate limits
x_min = np.min(X[:, 0])
x_max = np.max(X[:, 0])
y_min = np.min(X[:, 1])
y_max = np.max(X[:, 1])
# Predict all over a grid
XX, YY = np.mgrid[x_min:x_max:500j, y_min:y_max:500j]
Z = model.predict(np.c_[XX.ravel(), YY.ravel()])
# Put the result into a color plot
Z = Z.reshape(XX.shape)
ax[1].pcolormesh(XX, YY, Z, cmap=plt.cm.Paired)
ax[1].scatter(X[:, 0],
X[:, 1],
c=Y,
s=40,
zorder=10,
cmap=cmap,
edgecolors='k')
# Set Stuff for subplots
for s in [0, 1]:
ax[s].set_xlim([x_min, x_max])
ax[s].set_ylim([y_min, y_max])
ax[s].set_xticks([])
ax[s].set_yticks([])
ax[0].set_title('Data')
ax[1].set_title('Boundary')
plt.waitforbuttonpress()
def main():
random_start = 1
do_pause = 1
if random_start:
teams1 = np.random.random((DIMENSION, DIMENSION))
teams1 = teams1 / teams1.sum()
teams2 = np.random.random((DIMENSION, DIMENSION))
teams2 = teams2 / teams2.sum()
else:
teams1 = [STARTING_PCT] * DIMENSION**2
teams1 = np.reshape(teams1, (DIMENSION, DIMENSION))
teams2 = [STARTING_PCT] * DIMENSION**2
teams2 = np.reshape(teams2, (DIMENSION, DIMENSION))
fig, axarr = plot_init(np.matrix(teams1), np.matrix(teams2))
for k in range(rounds):
print "round:", k
teams1, teams2 = update(teams1, teams2, k)
if k % 15 == 0 and do_pause: plt.waitforbuttonpress()
plot_update(fig, axarr, teams1, teams2, k)
pause = raw_input("press enter to exit")
def main():
random_start = 1
do_pause = 1
if random_start:
teams1 = np.random.random((DIMENSION, DIMENSION))
teams1 = teams1 / teams1.sum()
teams2 = np.random.random((DIMENSION, DIMENSION))
teams2 = teams2 / teams2.sum()
else:
teams1 = [STARTING_PCT] * DIMENSION ** 2
teams1 = np.reshape(teams1, (DIMENSION, DIMENSION))
teams2 = [STARTING_PCT] * DIMENSION ** 2
teams2 = np.reshape(teams2, (DIMENSION, DIMENSION))
fig, axarr = plot_init(np.matrix(teams1), np.matrix(teams2))
for k in range(rounds):
print "round:", k
teams1, teams2 = update(teams1, teams2, k)
if k % 15 == 0 and do_pause:
plt.waitforbuttonpress()
plot_update(fig, axarr, teams1, teams2, k)
pause = raw_input("press enter to exit")
def _wait_for_movement_key_press():
"""Get key press."""
ready = False
while not ready:
x = plt.waitforbuttonpress(timeout=self._timeout)
if x is None:
logging.info('Timed out. You took longer than %d seconds to click.',
self._timeout)
elif not x:
logging.info('You clicked, but you are supposed to use the Keyboard.')
self.help()
elif self._movement is not None:
ready = True
def main():
# Whether or not to have a noisy start. This should be a proper parameter.
random_start = 0
# What does this do?
do_pause=1
if random_start:
# Randomize strategy distribution
teams = np.random.random((DIMENSION,DIMENSION))
# Normalize
teams = teams / teams.sum()
else:
# Create uniform strategy distribution
teams = [STARTING_PCT] * DIMENSION ** 2
teams = np.reshape(teams, (DIMENSION, DIMENSION))
# Initialize plot with initial matrix? Not 100% sure
fig, axarr = plot_init(np.matrix(teams))
# Main loop
for k in range(rounds):
# Print current round to console
print "round:", k
# Update strategy distribution
teams = update(teams, k)
# Pause if appropriate
if k % 25 == 0 and do_pause: plt.waitforbuttonpress()
# Update screen
plot_update(fig ,axarr,teams,k)
# Pause
pause = raw_input("press enter to exit")
def main():
# Whether or not to have a noisy start. This should be a proper parameter.
random_start = 0
# What does this do?
do_pause = 1
if random_start:
# Randomize strategy distribution
teams = np.random.random((DIMENSION, DIMENSION))
# Normalize
teams = teams / teams.sum()
else:
# Create uniform strategy distribution
teams = [STARTING_PCT] * DIMENSION**2
teams = np.reshape(teams, (DIMENSION, DIMENSION))
# Initialize plot with initial matrix? Not 100% sure
fig, axarr = plot_init(np.matrix(teams))
# Main loop
for k in range(rounds):
# Print current round to console
print "round:", k
# Update strategy distribution
teams = update(teams, k)
# Pause if appropriate
if k % 25 == 0 and do_pause: plt.waitforbuttonpress()
# Update screen
plot_update(fig, axarr, teams, k)
# Pause
pause = raw_input("press enter to exit")
def _get_click():
"""Get mouse click."""
while True:
x = plt.waitforbuttonpress(timeout=self._timeout)
if x is None:
logging.info('Timed out. You took longer than %d seconds to click.',
self._timeout)
click = None
elif x:
logging.info('You pressed a key, but were supposed to click with the '
'mouse.')
self.help()
click = None
else:
click = self._click
return click
def main():
# Camera parameters
im_size = (640, 480)
K = np.array([[572.41140, 0, 325.26110],
[0, 573.57043, 242.04899],
[0, 0, 0]])
objs = ['Ape', 'Can', 'Cat', 'Driller', 'Duck', 'Eggbox', 'Glue', 'Holepuncher']
model_fpath_mask = os.path.join('../../train_data/OcclusionChallengeICCV2015',
'models_ply', '{0}.ply')
pose_fpath_mask = os.path.join('../../train_data/OcclusionChallengeICCV2015',
'poses', '{0}', '*.txt')
models = []
gt_poses = []
for obj in objs:
print 'Loading data:', obj
model_fpath = model_fpath_mask.format(obj)
models.append(inout.load_ply(model_fpath))
gt_fpaths = sorted(glob.glob(pose_fpath_mask.format(obj)))
gt_poses_obj = []
for gt_fpath in gt_fpaths:
gt_poses_obj.append(
inout.load_gt_pose_dresden(gt_fpath))
gt_poses.append(gt_poses_obj)
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(16, 10))
repeats = 100
for i in range(repeats):
print "multi object rendering {} / {}".format(i, repeats)
rot_list = []
pos_list = []
indices = np.random.choice(len(objs), 1)
model_list = []
labels = []
for obj_id, obj_name in enumerate(objs):
# pos = np.random.rand(3) - 0.5
# pos = np.zeros(3)
# pos[2] += -2.0
# pos = np.array([0,0,-1.5]).T
# rot = np.eye(3)
pose = gt_poses[obj_id][int(i)]
if pose['R'].size != 0 and pose['t'].size != 0:
pos = pose['t']
rot = pose['R']
pos_list.append(pos)
rot_list.append(rot)
model_list.append(models[obj_id])
# rgb_ren, depth_ren = renderer.render(
# models[obj_id], im_size, K, rot, pos, 0.1, 4.0, mode='rgb+depth')
labels.append(obj_id + 1)
rgb_ren, depth_ren, label_ren = multi_object_renderer.render(
model_list, im_size, K, rot_list, pos_list, 0.1, 4.0,
labels=labels, mode='rgb+depth+label')
label_img = np.zeros((im_size[1], im_size[0], 3))
n_colors = len(DEFAULT_COLORS)
for lbl_id in labels:
color = color_dict[DEFAULT_COLORS[lbl_id % n_colors]]
label_img[(label_ren == lbl_id), :] = color
# Clear axes
for ax in axes.flatten():
ax.clear()
axes[0].imshow(rgb_ren.astype(np.uint8))
axes[0].set_title('RGB image')
axes[1].imshow(depth_ren)
axes[1].set_title('Depth image')
axes[2].imshow(label_img)
axes[2].set_title('Label image')
fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95,
hspace=0.15, wspace=0.15)
plt.draw()
# # plt.pause(0.01)
plt.waitforbuttonpress()
for line in lines:
Lectura = np.fromstring(line, dtype=float, sep=' ')
Y = np.vstack([Y, Lectura[0]])
Indmin = 1.1
Indmax = 2
Yhist = np.histogram(Y, bins=10, range=(int(Indmin), int(Indmax)))
print(Yhist)
#Analisis de caracteristicas para identificar si es carro o camioneta
#plt.plot(Xhist, Ynew, marker='.') # c='b')
pyl.figure()
pyl.title("Histograma de toda la señal completa")
pyl.hist(Y)
#pyl.show()
pyl.waitforbuttonpress()
plt.figure()
plt.title("Histograma de toda la señal recortada")
#plt.hist(Y)
plt.hist(Y, bins=10, range=(int(Indmin), int(Indmax)))
plt.xlim(1, 1.35)
# pyl.show()
plt.waitforbuttonpress()
except:
print("Error al abrir el archivo")
out_file.close()
def waitforbuttonpress():
while plt.waitforbuttonpress(0.2) is None:
if closed:
return False
return True
decoder_op = decoder(encoder_op)
# Prediction
y_pred = decoder_op
y_true = X
cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))
optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
total_batch = int(mnist.train.num_examples / batch_size)
for epoch in range(training_epochs):
for i in range(total_batch):
batch_x, batch_y = mnist.train.next_batch(batch_size)
_, c = sess.run([optimizer, cost], feed_dict={X: batch_x})
if epoch % display_step == 0:
print("Epoch:{0},cost={1}".format(epoch + 1, c))
print("Optimization Done!")
encode_decode = sess.run(
y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})
f, a = plt.subplots(2, 10, figsize=(10, 2))
for i in range(examples_to_show):
a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))
a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))
f.show()
plt.draw()
plt.waitforbuttonpress()
[ 2 0 0 3 0 883 2 0 2 0]
[ 5 2 0 0 1 3 944 0 3 0]
[ 1 0 9 2 0 0 0 1013 1 2]
[ 2 0 1 1 1 0 1 1 965 2]
[ 1 1 0 0 2 3 0 4 3 995]]
"""
net.load('example.h5')
print('Loaded...')
net.plot(os.path.basename(sys.argv[0]).split(".")[0] + ".png")
out = net.predict({'Input': images_test[:num_examples]})
rrand = list(range(images_test.shape[0]))[:10]
shuffle(rrand)
for i in rrand:
o = out['Output'][i, 0]
# plot
plt.title("number: " + str(o))
plt.imshow(images_test[i][:, :, 0], cmap='gray')
plt.draw()
plt.show(block=False)
while plt.waitforbuttonpress(0) is None:
pass
"""np.set_printoptions(threshold=np.nan)
a = out['Output']
b = labels_test
eq = a == b
accuracy = np.sum(eq)/eq.shape[0]
from sklearn.metrics import confusion_matrix
print('Accuracy: ' + str(accuracy))
print('Confusion Matrix:')
print(confusion_matrix(b, a))"""
def select_outlets(dem,
fd,
prefix,
hs=None,
outlet_filename='outlets.p',
color='r'):
if hs is None:
hs = d.Hillshade(elevation=dem, azimuth=320, inclination=20)
plot_dem(dem, hs)
keep_going = True
while keep_going:
zoom_ok = False
print(
'\nZoom or pan to view, \npress spacebar when ready to select point upstream of outlet:\n'
)
while not zoom_ok:
zoom_ok = plt.waitforbuttonpress()
print('\nClick on point upstream of outlet.')
xy = plt.ginput(1)[0]
xy_path = fd.search_down_flow_direction_from_xy_location(xy)
plt.plot(xy)
plt.plot(xy_path)
xy_path_plot = list(zip(*xy_path))
path = plt.plot(xy_path_plot[0], xy_path_plot[1], color + '-')
print('\nClick on the outlet location.')
xy = plt.ginput(1)[0]
min_distance = 1E12
for loc in xy_path:
if (np.sqrt(
np.power(loc[0] - xy[0], 2) + np.power(loc[1] - xy[1], 2))
< min_distance) or min_distance == 1E12:
outlet_loc = loc
min_distance = np.sqrt(
np.power(loc[0] - xy[0], 2) + np.power(loc[1] - xy[1], 2))
plt.figure(1)
plt.plot(outlet_loc[0], outlet_loc[1], color + 'o')
path.pop(0).remove()
outlet_prefix = raw_input(
'Type a name for this outlet (leaving this blank will prevent outlet from being saved and will complete the selection process: '
)
if outlet_prefix != '':
import os.path
if os.path.isfile(outlet_filename):
outlets = p.load(open(outlet_filename, 'rb'))
else:
outlets = dict()
this_tile = outlets.get(prefix, dict())
this_tile[outlet_prefix] = outlet_loc
outlets[prefix] = this_tile
p.dump(outlets, open(outlet_filename, 'wb'))
else:
keep_going = False