flutter_lwp library Null safety
flutter_lwp contains the functionality to interact with Lego PoweredUp hubs using the LWP protocol.
Disclaimer: This code was not written nor endorsed by Lego. It was written based on the protocol definitions published by Lego here.
Getting started
The entry point for the library is the IHubScanner and Hub.
Classes
- AccelerationPeripheral API
- AccelerationPeripheral class using AccelerationInput mixin.
- CurrentPeripheral API
- CurrentPeripheral class using CurrentInput mixin.
- ErrorMessage
- Error message from the hub.
- FreeFormMessage
- FreeForm message can be used to build a message on the fly.
- GesturePeripheral API
- GesturePeripheral class using GestureInput mixin.
- GotoAbsolutePositionMessage
- Move motor to an absolute rotation [...]
- GyroPeripheral API
- GyroPeripheral class using GyroInput mixin.
- Hub API
- The main hub interaction class.
- HubAttachedIOBaseMessage
- Base class for HubAttachedIOMessage, HubDetachedIOMessage and HubAttachedVirtualIOMessage
- HubAttachedIOMessage
- Fired when a new peripheral is attached or on first connection.
- HubAttachedVirtualIOMessage
- Fired when a new virtual peripheral is created or on first connection.
- HubBatteryPropertyMessage
- Battery State message.
- HubButtonPropertyMessage
- Button State message
- HubDetachedIOMessage
- Fired when a peripheral is detached.
- HubPropertyMessage
- Bases class for hub properties.
- HubState API
- HubState contains the state of the hub This includes which peripherals are attached and if the hub is currently connected.
- HubStatusLightPeripheral API
- HubPeripheral class using Led mixin.
- IHubScanner API
- The main class used to discover hubs.
- IHubTransport backend
- HUB Transport interface
- Message
- Base class for all messages in and out of the Hub.
- MotorPeripheral API
- MotorPeripheral class using Motor mixin.
- OrientationPeripheral API
- OrientationPeripheral class using OrientationInput mixin.
- Peripheral API
- Master Peripheral class. [...]
- PeripheralMode API
- Mode container
- PeripheralModeRange API
- Range container.
- PeripheralValueNotification
- Class that is used by Peripheral to send notification when a values is updated. [...]
- PeripheralVersion
- PortInformationMessage
- Port Information Message.
- PortInformationModeCombinationsMessage
- Get Port Combinations
- PortInformationModesMessage
- Get Port Modes
- PortInformationRequestMessage
- Request Port information. Responds with PortModeInformationMessage
- PortInputFormatMessage
- Port Input format setup message
- PortInputFormatSetupMessage
- Port Input format setup message [...]
- PortModeInformationMessage
- Information about port modes.
- PortModeInformationMessageMotorBias
- PortModeInformationMessageNAME
- PortModeInformationMessagePCT
- PortModeInformationMessageRAW
- PortModeInformationMessageRAWBase
- PortModeInformationMessageRequest
- PortModeInformationMessageSI
- PortModeInformationMessageSYMBOL
- PortModeInformationMessageValueFormat
- https://lego.github.io/lego-ble-wireless-protocol-docs/index.html#value-format
- PortOutputCommandFeedback
- PortOutputCommandFeedbackPort
- PortOutputCommandMessage
- Parent class for PortOutputCommand's.
- PortValueMessage
- RetryException API
- If a transaction has failed and should retry, throw RetryException inside the transaction. The Transaction class will retry Transaction.maxTries number of times.
-
SimpleTransaction<
T> API - Simple transaction provides an easy way to interact with the Hub using the request/response pattern with retries. [...]
- StartSpeedForDegreesMessage
- Move motor for a certain number of revolutions specified in degrees. [...]
- StartSpeedForTimeMessage
- Move motor for a certain number of milliseconds. [...]
- StartSpeedMessage
- Move motor at certain speed indefinitely. [...]
- TemperaturePeripheral API
- TemperaturePeripheral class using TemperatureInput mixin.
-
Transaction<
T> API - transactions are used to facilitate communications with the hub. [...]
- ValueFormat
- https://lego.github.io/lego-ble-wireless-protocol-docs/index.html#value-format
- VoltagePeripheral API
- VoltagePeripheral class using VoltageInput mixin.
- WriteDirectModeDataMessage
- Write Direct Mode data to a port. [...]
Mixins
- AccelerationInput API
- Acceleration input for Peripherals.
- CurrentInput API
- Current input for Peripherals.
- GestureInput API
- Gesture input for Peripherals.
- GyroInput API
- Gyro input for Peripherals.
- Led API
- LED actions for Peripherals.
- Motor API
- Motor actions for Peripherals.
- OrientationInput API
- Orientation input for Peripherals.
- TemperatureInput API
- Temperature input for Peripherals.
- VoltageInput API
- Voltage input for Peripherals.
Extensions
- ErrorCodeValue
- HubAttachedIOEventValue
- HubOperationValue
- HubPropertyValue
- IOTypeValue
- MessageTypeValue
- MotorAccelerationProfileValue
- MotorEndStateValue
- PortInformationRequestTypeValue
- PortModeInformationTypeValue
- PortOutputCompletionValue
- PortOutputFeedbackValue
- PortOutputStartupValue
- PortSubCommandValue
Enums
- ErrorCode
- Protocol Errors Codes [...]
- HubAttachedIOEvent
- Enums detailing if ports are attached or detached. [...]
- HubOperation
- Hub Operation enum [...]
- HubProperty
- Hub Property [...]
- IOType
- IOTypes for attached Peripherals. [...]
- MessageType
- The top level message type for messages to and from the Hub. Used by Message [...]
- MotorAccelerationProfile
- Acceleration/Deceleration profile to use for Motor Commands [...]
- MotorEndState
- Controls the state of the motor at the end of a command used by for example StartSpeedForDegreesMessage. [...]
- PortCapabilities
- Port Capabilities. [...]
- PortInformationRequestType
- Information Request [...]
- PortModeInformationType
- PortOutputCompletion
- Completion options. [...]
- PortOutputFeedback
- Enum detailing the result of a PortOutputCommandMessage. [...]
- PortOutputStartup
- Controls queue behavior for PortOutputCommandMessage [...]
- PortSubCommand
- PortSubCommand values for PortOutputCommandMessage [...]
- ValueFormatDataType
- https://lego.github.io/lego-ble-wireless-protocol-docs/index.html#value-format


Unordered List with Disc Bullets
Apples
Bananas
Lemons
Oranges
Unordered List with Square Bullets
Apples
Bananas
Lemons
Oranges
Unordered List with Circle Bullets
Apples
Bananas
Lemons
Oranges


WEEK 13 TUESDAY
Year 1 (Year 2)
FIRST READING Genesis
19:15-29
A reading from the Book of Genesis
In those days: The angels urged Lot, saying,
"Arise, take your wife and your two daughters who are here, lest you be
consumed in the punishment of Sodom."
But he lingered; so the men seized him and his wife and his two daughters by
the hand, the LORD being merciful to him, and they brought him forth and set
him outside the city. And when they had brought them forth, they said,
"Flee for your life; do not look back or stop anywhere in the valley; flee
to the hills, lest you be consumed." And Lot
said to them, "Oh, no, my lords; behold, your servant has found favour in
your sight, and you have shown me great kindness in saving my life; but I
cannot flee to the hills, lest the disaster overtake me, and I die. Behold,
yonder city is near enough to flee to, and it is a little one. Let me escape
there--is it not a little one?--and my life will be saved!" He said to
him, "Behold, I grant you this favour also, that I will not overthrow the
city of which you have spoken. Make haste, escape
there; for I can do nothing till you arrive there." Therefore the name of
the city was called Zoar. The sun had risen on the earth when Lot
came to Zoar. Then the LORD rained on Sodom and Gomorrah brimstone and
fire from the LORD out of heaven; and he overthrew those cities, and all the valley, and all the inhabitants of the cities, and
what grew on the ground. But Lot's wife behind
him looked back, and she became a pillar of salt. And Abraham went early in the
morning to the place where he had stood before the LORD; and he looked down
toward Sodom and Gomorrah and toward all the land of the valley, and beheld,
and behold, the smoke of the land went up like the smoke of a furnace. So it
was that, when God destroyed the cities of the valley, God remembered Abraham,
and sent Lot out of the midst of the overthrow, when he overthrew the cities in
which Lot dwelt.
The word of the Lord
RESPONSORIAL PSALM Psalm 26:2-3.9-10.11-12 (R. 3a)
R. O Lord, your mercy is before my eyes.
Examine me, LORD, and try me. O test
my heart and my mind. Your mercy is before my eyes, and I walk according to
your truth.
R. O Lord, your mercy is before my eyes.

R. O Lord, your mercy is before my eyes.
As for me, I have walked in my integrity. Redeem me
and have mercy on me. My foot stands on level ground: I will bless the LORD in
the assembly.
R. O Lord, your mercy is before my eyes.
(Gospel Acclamation)
Year 2
FIRST READING Amos
3:1-8; 4:11-12
A reading from the Book of the Prophet Amos
Hear this word that the LORD has spoken against
you, O sons of Israel,
against the whole family which I brought up out of the land of Egypt:
"You only have I known of all the families of the earth; therefore I will
punish you for all your iniquities. "Do two walk
together, unless they have made an appointment? Does a lion roar in the forest,
when he has no prey? Does a young lion cry out from his den, if he has taken
nothing? Does a bird fall in a snare on the earth, when there is no trap for
it? Does a snare spring up from the ground, when it has taken nothing? Is a
trumpet blown in a city, and the people are not afraid? Does evil befall a city,
unless the LORD has done it? Surely the Lord GOD does nothing, without
revealing his secret to his servants the prophets. The lion has roared; who
will not fear? The Lord GOD has spoken; who can but prophesy? "I overthrew
some of you, as when God overthrew Sodom and Gomorrah, and you were as
a brand plucked out of the burning; yet you did not return to me," says
the LORD.
The word of the Lord
RESPONSORIAL PSALM Psalm 5:5-6.7.8 (R. 9a)
R. Lead me, Lord, in your justice.
 The boastful shall not stand their ground before your eyes.
R. Lead me, Lord, in your justice.
 The deceitful and those who shed blood, the
LORD detests.
R. Lead me, Lord, in your justice.
Yet through the greatness of your merciful love, I enter
your house. I bow down before your holy temple, in awe of you.
R. Lead me, Lord, in your justice.
ALLELUIA Psalm 130:5
R. Alleluia. I
wait for the Lord, in his word I hope. R. Alleluia.
GOSPEL
Matthew 8:23-27
A reading from the holy Gospel according to Matthew
At that time: When Jesus got into the boat, his
disciples followed him. And behold, there arose a great storm on the sea, so
that the boat was being swamped by the waves; but he was asleep. And they went
and woke him, saying, "Save us, Lord; we are perishing." And he said
to them, "Why are you afraid, O men of little faith?" Then he rose
and rebuked the winds and the sea; and there was a great calm. And the men
marvelled, saying, "What sort of man is this, that even winds and sea obey
him?"
The Gospel of the Lord


| Interface | Description |
|---|---|
| AlternativeEffectGunFlowState |
Represents the state of an AlternativeEffectGun.
|
| BaseEffectGunFlowState |
Represents the state of a BaseEffectGun.
|
| GunFlowState |
It represents the state of the flow during the use of a gun.
|
| SkippableGunFlowNode<T extends GunFlowState> |
It represents a flow node that can be skipped when certain conditions
- loaded from configuration as a predicate - are verified.
|
| Class | Description |
|---|---|
| AlternativeEffectGunFlowStateImpl | |
| AlternativeGunChooseMovementFlowNode |
This node manage the movement of the killer as an effect of an AlternativeEffectGun.
|
| ApplyAlternativeGunFlowNode |
This class represent the flow node which effectively applies damages
and marks of an AlternativeEffectGun.
|
| ApplyBaseGunFlowNode |
This class represents the flow node which effectively applies damages
and marks of a BaseEffectGun.
|
| ApplyGrenadeLauncherEffectFlowNode |
This class represents a flow node which is a custom effect applier for
the gun GrenadeLauncher.
|
| ApplyScopeFlowNode |
This node represents the Scope powerup applier.
|
| BaseEffectGunFlowStateImpl | |
| BaseGunChooseMovementFlowNode |
This class manages player movement as an effect of some guns.
|
| BaseGunChooseMovementFlowNode.GunChooseMovementFlowState | |
| ChooseAlternativeEffectForGunFlowNode |
This node helps the player choosing the AlternativeGun effect he want to apply.
|
| ChooseBaseEffectForGunFlowNode |
This node helps the player choosing the BaseEffectGun effect he want to apply.
|
| ChooseCellToHitFlowNode |
This node helps the player choosing a cell to hit.
|
| ChoosePlayersToHitFlowNode |
This node helps the player choosing the enemies he want to/could hit.
|
| ChooseRoomToHitFlowNode |
This node helps the player choosing the room he want to hit, as an effect of a gun.
|
| GunChooseEnemyMovementFlowNode |
This node manages the movement of an enemy.
|
| GunFlowStateImpl | |
| TagbackGrenadeFlowNode |
This node helps the player using the Tagback Grenade powerup.
|
| TagbackGrenadeFlowNode.TagbackGrenadeFlowState |
Copyright © 2019. All rights reserved.


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History
A little about our history.
Brukner Nature Center opened to the public in 1974 and is the vision of Clayton J. Brukner, local philanthropist who was born December 18, 1896 in Ravenna, Nebraska. With high school chum and business partner, E.J. "Sam" Junkin, he brought the Weaver Aircraft Company to Troy, Ohio in 1923. The company name was eventually changed to the WACO Aircraft Company, which became the most successful civil aircraft manufacturer of the pre World War II era. Clayton Brukner was an entrepreneur, mechanical genius, and community philanthropist. He had a great love for nature and in 1933 bought 146 acres of land along the Stillwater River, not known for its productivity, but rather for its natural beauty. In 1967 he unveiled his plans to build a nature center on this site and in May of 1974, Brukner Nature Center opened to the public. The 165 acre preserve is traversed by 6 miles of hiking trails, Interpretive Building and the 1804 Iddings Log House.
Brukner Nature Center is a privately funded, nonprofit organization promoting the appreciation and understanding of wildlife conservation through preservation, education, and rehabilitation. The 1804 Iddings log house was renovated in 1980 and again in 2006. It is on the registry of Historic Places as the oldest structure on its original site in Miami County! Under the guidance of our first Directors, Bob & Joan Heidelberg, we grew to a 165-acre preserve with services reaching 21 counties. Today, we annually touch over 45,000 people with our innovative hands-on wildlife programming. Each year over 120,000 visitors enjoy our public events, take a stroll in the woods or visit with our wildlife ambassadors. In 1998 an addition to Interpretive Building doubled the size and provides handicap accessibility to all three floors including our popular Tree-top Bird Vista. In May of 2011, we renovated the interpretive area in our downstairs wildlife exhibits. Our main lobby renovation was completed in May 2016 and we just finished our wildlife rehabilitation quarantine building in 2019! Thank you to all of our supporters who have helped us reach all of our goals!
Iddings Log House:
An integral part of Brukner Nature Center’s interpretive programming, the 1804 Iddings Log House also holds an important place in the history of Miami County. The Iddings family is originally descended from the Norsemen and came to America from Radnorshire, Wales in 1683. Richard Iddings and his wife, Sarah Thomas, arrived in Philadelphia, Pennsylvania and settled in Nantmeal Township, Pennsylvania, between the forks of the Brandywine River and French Creek, very near a little place called Valley Forge. Richard and Sarah Iddings were the grandparents of two famous figures in Ohio history, General “Mad” Anthony Wayne and Benjamin Iddings. Benjamin Iddings married Phebe Wilkinson in 1775, in Pennsylvania. Their children, listed in the Quaker Monthly Meeting records were Rachel, Ruth, Talbot, Joseph, Mary, Benjamin Jr., Phoebe, William, Millie and Hannah. The Iddings were a prominent and very active family in the Quaker community. They began their life on the road in 1779, moving frequently to escape the fighting in the area. They settled in Greene County, Tennessee in 1795. Here they helped found the New Hope Monthly Meeting, were Benjamin was appointed recorder in 1796 and was an Elder. Following the Treaty of Greeneville in 1795, the Ohio territory was opened for settlement. Benjamin, perhaps because of his famous cousin’s experience in the area, made an exploratory trip up the Stillwater River into what is now Newton Township. He moved his family to Miami County, acquiring a patent on 160 acres on SE 1/4 Sec. 33-7-5E. Here, in 1804, Benjamin constructed the log house on what is now Brukner Nature Center property. Benjamin and Phoebe lived here with six of their ten children, as Rachel had passed away and Ruth, Talbot & Joseph were married and on their own.
Interestingly, Benjamin built his house on the property of his neighbor, Benjamin Pearson and later paid him the sum of $63.11 ½ for an additional four acres upon which his house was built. Although this sounds strange for modern times, it was not unusual for property lines to be mistaken in the wilderness of 1800 Ohio. Phoebe died in 1818 and in 1822, Benjamin married his widowed neighbor, Ruth Pierce. Benjamin died in 1826, and both he and Ruth are buried in Old Union Cemetery, on Horseshoe Bend Road. Through extensive research, the underlying historical value of the structure itself was realized, not only for its handsome construction but for the significant family history. In 1976, the 1804 Iddings Log House was placed on the National Register of Historic Places and a successful restoration was completed in 1981. Through its doors have passed many visitors stepping back in time, from Iddings relatives looking for a link to the past to children too numerous to count making memories to last a lifetime.


{% extends 'layout-bsa.html' %} {% block pageTitle %} Do you get money from any of these? - LIS training - NHSBSA {% endblock %} {% block outerContent %} {% endblock %} {% block content %}
We'll review and process your application and let you know the outcome by post. This can take up to 18 working days.


Ocean [ab58], ocean, in Crystal Sea
Last updated Turn #72
Ocean [ab58], ocean, in Crystal Sea
------------------------------------------------------------------------
1: Fast Sail [ht2u], ship, 3 hulls, 1 rowing port, 9 sails, departed for
1: Ocean [aa58].
3: Sharks circle in the water a short distance from the ship.
Routes leaving Ocean:
North, to Ocean [aa58], 6 days
East, to Ocean [ab59], 6 days
South, to Ocean [ac58], 6 days
West, to Ocean [ab57], 6 days
Inner locations:
Island [aa5d], island, 1 day, owner:
Cyclops [dn7v], cyclops, number: 14


There is a triangle shape-inside applied on the green box. The content should be inside the shape-inside. You should not see any red.
Issue 333423: [CSS Shapes] First line gets incorrectly adjusted in shape-inside due to rounding in some polygons


Class UnityEnvironment
Properties:
academy_name
brain_names
brains
curriculum
external_brain_names
global_done
logfile_path
number_brains
number_external_brains
Methods:
__init__(self, file_name, worker_id=0, base_port=5005, curriculum=None, seed=0, docker_training=False)
Starts a new unity environment and establishes a connection with the environment. Notice: Currently communication between Unity and Python takes place over an open socket without authentication. Ensure that the network where training takes place is secure.
:string file_name: Name of Unity environment binary. :int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this. :int worker_id: Number to add to communication port (5005) [0]. Used for asynchronous agent scenarios.
| Parameter | Description |
|---|---|
| docker_training | Informs this class whether the process is being run within a container. |
close(self)
Sends a shutdown signal to the unity environment, and closes the socket connection.
reset(self, train_mode=True, config=None, lesson=None) -> Dict[str, unityagents.brain.BrainInfo]
Sends a signal to reset the unity environment.
Returns AllBrainInfo : A Data structure corresponding to the initial reset state of the environment.
step(self, vector_action=None, memory=None, text_action=None) -> Dict[str, unityagents.brain.BrainInfo]
Provides the environment with an action, moves the environment dynamics forward accordingly, and returns observation, state, and reward information to the agent.
| Parameter | Description |
|---|---|
| vector_action | Agent's vector action to send to environment. Can be a scalar or vector of int/floats. |
| memory | Vector corresponding to memory used for RNNs, frame-stacking, or other auto-regressive process. |
| text_action | Text action to send to environment for. |
Returns AllBrainInfo : A Data structure corresponding to the new state of the environment.


Segment images using a clustering method - each segment is the cluster center to which a pixel belongs.
Image pixels are represented by their r, g, and b values. EM algorithm is applied to the mixture of normal distribution model to cluster image pixels, then the image is segmented by mapping each pixel to the cluster center with the highest value of the posterior probability for that pixel.
# import libs
import sys
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from scipy import misc
from sklearn.cluster import KMeans
from sklearn import preprocessing as pre
def logsumexp(X):
"""
log sum exp trick for approximation to avoid underflow/ overflow
:param X: data matrix
:return: log sum exp applied to each row of input matrix X
"""
x_max = X.max(1)
return x_max + np.log(np.exp(X - x_max[:, None]).sum(1))
We use k-means to compute rough cluster centers and mixture weights.
Caution: pi should not have a zero-element. An infinitesimal smoothing must be applied in such situations, else no documents may be assigned to the corresponding topic.
We define a function so we can repeat the process for different images
CONVERGENCE_THRESHOLD = 100
def EM(X, img_name, W, H, center, scale):
"""
Function that performs EM for image segmentation for [10, 20, 50] segments and displays results as image
:param X: data matrix where each row is [R,G,B]
"""
plt_num = 1
estimators = {'k_means_10': KMeans(n_clusters=10),
'k_means_20': KMeans(n_clusters=20),
'k_means_50': KMeans(n_clusters=50)}
for name, est in estimators.items():
print(name)
J = int(name[8:])
NUM_PIXELS = X.shape[0]
# perform k means
est.fit(X)
segments_id = est.labels_
# get initial cluster centers/ means from k-means
means = est.cluster_centers_
# get initial pi from k-means
pi = np.array([np.sum(segments_id == i) for i in range(J)])
pi = pi / float(NUM_PIXELS)
# check that there are no zero values in pi's
print("check there are no zero values in pi", 0 not in pi)
### EM ###
prev_Q = sys.maxsize
list_of_q = []
while True:
## E-Step ##
ll = np.zeros((NUM_PIXELS, J))
for j in range(J):
ll[:,j] = -0.5 * np.sum((X - means[j,])**2, 1)
# compute w_ij
w = np.exp(ll) @ np.diag(pi)
w = (w.T / np.sum(w,1)).T
# compute Q without constant K
Q = np.sum(ll * w)
list_of_q.append(Q)
# check for convergence
if abs(Q - prev_Q) <= CONVERGENCE_THRESHOLD:
break
else:
prev_Q = Q
## M-Step ##
# update means
for j in range(J):
means[j,] = np.sum((X.T * w[:,j]).T, 0) / np.sum(w[:,j])
# update pi
pi = np.sum(w, 0)/ NUM_PIXELS
# plot convergence of Q as we progress through EM
plt.figure(plt_num)
plt.plot(list_of_q)
plt.xlabel("Number of Iterations")
plt.ylabel("Q at E-step")
plt.show()
plt_num += 1
# display result as segmented image
segmented_img_rgb = np.zeros((H, W, 3), dtype=np.uint8)
for i in range(H):
for j in range(W):
idx = (i-1) * W + j
pixel = X[idx,]
pixel_segment_id = np.argmax(w[idx,])
segmented_img_rgb[i,j,] = means[pixel_segment_id,] * scale + center
plt.figure(plt_num)
plt.imshow(segmented_img_rgb) # show segmented image
plt.show()
plt_num += 1
misc.imsave('images/' + img_name + name + '.jpg', segmented_img_rgb) # save segmented image
Note: We scale data below so that mean along columns is zero and variance is 1 to simplify computation in E-Step
Therefore, we have to un-scale our means before we generate segmented image
# read data
goby_img = misc.imread('images/goby.jpg')
(H, W, N) = goby_img.shape
data = goby_img.reshape((H * W, N))
data_centers = np.mean(data, 0)
data_scale = np.std(data, 0)
data = pre.scale(data)
# run EM
EM(data, 'goby_', W, H, data_centers, data_scale)
CONVERGENCE_THRESHOLD = 0.01
# read data
strelitzia_img = misc.imread('images/strelitzia.jpg')
(H, W, N) = strelitzia_img.shape
data = strelitzia_img.reshape((H * W, N))
data_centers = np.mean(data, 0)
data_scale = np.std(data, 0)
data = pre.scale(data)
# run EM
EM(data, 'strelitzia_', W, H, data_centers, data_scale)
CONVERGENCE_THRESHOLD = 0.1
# read data
sunset_img = misc.imread('images/sunset.jpg')
(H, W, N) = sunset_img.shape
data = sunset_img.reshape((H * W, N))
data_centers = np.mean(data, 0)
data_scale = np.std(data, 0)
data = pre.scale(data)
# run EM
EM(data, 'sunset_', W, H, data_centers, data_scale)
We start with different k-means settings. Each setting will give us different starting point. When we set n_init = 1 we should expect a bad starting point.
We copy above function for EM and change estimators.
CONVERGENCE_THRESHOLD = 10
def EM(X, img_name, W, H, center, scale):
"""
Function that performs EM for image segmentation for [10, 20, 50] segments and displays results as image
:param X: data matrix where each row is [R,G,B]
"""
plt_num = 1
estimators = {'k_means++' : KMeans(n_clusters=20),
'k_means_random_1': KMeans(n_clusters=20, init='random', random_state=1),
'k_means_random_2': KMeans(n_clusters=20, init='random', random_state=2),
'k_means_random_3': KMeans(n_clusters=20, n_init=1, init='random', random_state=3),
'k_means_random_4': KMeans(n_clusters=20, n_init=1, init='random', random_state=4)}
for name, est in estimators.items():
print(name)
J = 20
NUM_PIXELS = X.shape[0]
# perform k means
est.fit(X)
segments_id = est.labels_
# get initial cluster centers/ means from k-means
means = est.cluster_centers_
# get initial pi from k-means
pi = np.array([np.sum(segments_id == i) for i in range(J)])
pi = pi / float(NUM_PIXELS)
# check that there are no zero values in pi's
if (0 in pi):
print("ABORT: one of the segments has mixture weight set to zero initially")
return
### EM ###
prev_Q = sys.maxsize
list_of_q = []
while True:
## E-Step ##
ll = np.zeros((NUM_PIXELS, J))
for j in range(J):
ll[:,j] = -0.5 * np.sum((X - means[j,])**2, 1)
# compute w_ij
w = np.exp(ll) @ np.diag(pi)
w = (w.T / np.sum(w,1)).T
# compute Q without constant K
Q = np.sum(ll * w)
list_of_q.append(Q)
# check for convergence
if abs(Q - prev_Q) <= CONVERGENCE_THRESHOLD:
break
else:
prev_Q = Q
## M-Step ##
# update means
for j in range(J):
means[j,] = np.sum((X.T * w[:,j]).T, 0) / np.sum(w[:,j])
# update pi
pi = np.sum(w, 0)/ NUM_PIXELS
# plot convergence of Q as we progress through EM
plt.figure(plt_num)
plt.plot(list_of_q)
plt.xlabel("Number of Iterations")
plt.ylabel("Q at E-step")
plt.show()
plt_num += 1
# display result as segmented image
segmented_img_rgb = np.zeros((H, W, 3), dtype=np.uint8)
for i in range(H):
for j in range(W):
idx = (i-1) * W + j
pixel = X[idx,]
pixel_segment_id = np.argmax(w[idx,])
segmented_img_rgb[i,j,] = means[pixel_segment_id,] * scale + center
plt.figure(plt_num)
plt.imshow(segmented_img_rgb) # show segmented image
plt.show()
plt_num += 1
misc.imsave('images/' + img_name + name + '.jpg', segmented_img_rgb) # save segmented image
# read data
sunset_img = misc.imread('images/sunset.jpg')
(H, W, N) = sunset_img.shape
data = sunset_img.reshape((H * W, N))
data_centers = np.mean(data, 0)
data_scale = np.std(data, 0)
data = pre.scale(data)
# EM
EM(data, 'sunset_', W, H, data_centers, data_scale)


FISCHER SCIENTIFIC COMPANY -- T-349 TRIKETOHYDRINDENE HYDRATE -- 6810-00D000202
===================== Product Identification =====================
Product ID:T-349 TRIKETOHYDRINDENE HYDRATE
MSDS Date:01/01/1985
FSC:6810
NIIN:00D000202
MSDS Number: BBCLM
=== Responsible Party ===
Company Name:FISCHER SCIENTIFIC COMPANY
Emergency Phone Num:201-796-7100
CAGE:80015
=== Contractor Identification ===
Company Name:FISCHER SCIENTIFIC
CAGE:80015
Company Name:FISHER SCIENTIFIC COMPANY
Address:52 FADEM ROAD.DOMESTIC DIVISION
City:SPRINGFIELD
State:NJ
ZIP:07081
Country:US
Phone:201-796-7100
CAGE:94480
===================== Hazards Identification =====================
LD50 LC50 Mixture:NONE SPECIFIED BY MANUFACTURER.
Routes of Entry: Inhalation:YES Skin:YES Ingestion:YES
Health Hazards Acute and Chronic:ACUTE:INHAL: IRRIT,HDCH,
DIZZ,NAUS,POSS UNCON & ASPHYX.EYE: IRRIT.SKIN: PRLNGD/RPTD CONT MAY
CAUSE DEFAT OF SKIN,LEADING TO IRRIT & DERM.CHRONIC:ING 4:MAY CAUSE
CARBOXYHEMOGLOB NA , I EI X OND &[ @B$ $(2P % $
@@ B " @& L @ @D " B L @" B
$N @D " F @$@
Explanation of Carcinogenicity: $ @ B LB @$ $ $
B $ @ @ B $ LB @$ $ $ B
$
Effects of Overexposure: @ B D @@"
@ $ $ " " @D @ @N @ F L$@ ?
6S D @ 6 F F F D & ? D & ?]


Welcome, dear visitor!
If you, like me, enjoy the hilarity and witty satire of The Onion's Horoscopes, but have been looking for a (forgiving) JSON API, look no further!
I've exposed a couple of resources, all accessible via GET
requests.
http://a.knrz.co/horoscope-api/current
will return an array of all the latest horoscopes.
http://a.knrz.co/horoscope-api/current/:sign
where :sign
is your astrological sign (example, gemini), will return just the horoscope for that sign.
http://a.knrz.co/horoscope-api/archive/:year/:week
where :year
and :week
are self-explanatory (example, 2015 and 32). This will return an array of the last published horoscopes before that week.
http://a.knrz.co/horoscope-api/archive/:year/:week/:sign
the same as the previous one, but it will only return the horoscope for that week.
How it works
By default, any request for a particular week (even the current week) will always return horoscopes, regardless of whether or not The Onion actually published horoscopes for that week. For example, requesting the horoscopes for the 39th week of 2015 (for which The Onion did not publish anything) will return the horoscopes for the 37th week, as those were the last ones published. If you would like to be strict in your requests, for whatever reason, add ?human=false
to the end of your request.


|
ReactPhysics3D
0.6.0
Physics engine library
|
This class represents a pair of two proxy collision shapes that are overlapping during the broad-phase collision detection. More...
#include <OverlappingPair.h>
Public Member Functions | |
| OverlappingPair (ProxyShape *shape1, ProxyShape *shape2, int nbMaxContactManifolds, MemoryAllocator &memoryAllocator) | |
| Constructor. | |
| ~OverlappingPair () | |
| Destructor. | |
| ProxyShape * | getShape1 () const |
| Return the pointer to first proxy collision shape. | |
| ProxyShape * | getShape2 () const |
| Return the pointer to second body. | |
| void | addContact (ContactPoint *contact) |
| Add a contact to the contact cache. | |
| void | update () |
| Update the contact cache. | |
| Vector3 | getCachedSeparatingAxis () const |
| Return the cached separating axis. | |
| void | setCachedSeparatingAxis (const Vector3 &axis) |
| Set the cached separating axis. | |
| uint | getNbContactPoints () const |
| Return the number of contacts in the cache. | |
| const ContactManifoldSet & | getContactManifoldSet () |
| Return the a reference to the contact manifold set. | |
| void | clearContactPoints () |
| Clear the contact points of the contact manifold. | |
Static Public Member Functions | |
| static overlappingpairid | computeID (ProxyShape *shape1, ProxyShape *shape2) |
| Return the pair of bodies index. | |
| static bodyindexpair | computeBodiesIndexPair (CollisionBody *body1, CollisionBody *body2) |
| Return the pair of bodies index of the pair. | |
Friends | |
| class | DynamicsWorld |
This class represents a pair of two proxy collision shapes that are overlapping during the broad-phase collision detection.
It is created when the two proxy collision shapes start to overlap and is destroyed when they do not overlap anymore. This class contains a contact manifold that store all the contact points between the two bodies.


--- title: Breadcrumb layout: layout-docs category: General breadcrumbList: - name: arrow - name: bullet - name: dot - name: succeeds --- {% capture default_breadcrumb_example %} {% endcapture %} {% capture breadcrumb_with_bg %} {% endcapture %} {% capture arrow_example %} {% endcapture %} {% capture bullet_example %} {% endcapture %} {% capture dot_example %} {% endcapture %} {% capture succeeds_example %} {% endcapture %}
Breadcrumbs, typically placed at the top of web pages, aid users in navigation by helping users understand where they are on the current website or service.
The breadcrumb component should be used when you need to help users understand where they are in the website and move between different levels.
Breadcrumbs must not be used as a replacement for your primary navigation. It should also not be used to display progress such as completing a transaction. In those cases, using the progress bar component would be more appropriate
If you have multiple navigational items on the website or page such as side nav, you may want to evaluate whether using the breadcrumb component is necessary.
With Arrowhas-arrow-separator
With Bullethas-bullet-separator
With Dothas-dot-separator
With Succeedshas-succeeds-separator


by Anton Efimov
⌘ ⇧ P → Align
⌥ =
⌘ K, V
⌥ ⇧ ↓
⌥ ⇧ ↑
⌥ ↓
⌥ ↑
⌘ K, ⌘ F
⌘ K, ⌘ X
⌘ I
⌥ ⌘ ↑
⌥ ⌘ ↓
⌘ D → ⌘ ⇧ L
⌘ /
Sensible to language/syntax
⌘ ⇧ P
⌘ P
⌥ F12
F12
⇧ F12
F2
“Linting is the process of running a program that will analyse code for potential errors. ”
"markdownlint.config": {
"MD013": false,
"MD014": false,
"MD024": false,
"MD033": false,
"MD040": false,
"MD007": { "indent": 4 }
},
$ > z solution
Orion specific
alias rdep="rgrav deploy -R unison_frontend:0 -R unison_backend:0 -R unison_haproxy:0 -R connect:0 -p ec2"
alias rinv="rgrav invoke -p ec2"
$ > rdep
More examples
# Recursively delete `.DS_Store` files and Clean up LaunchServices to remove duplicates in the “Open With” menu
alias cleanup="/System/Library/Frameworks/CoreServices.framework/Frameworks/LaunchServices.framework/Support/lsregister -kill -r -domain local -domain system -domain user && killall Finder && find . -type f -name '*.DS_Store' -ls -delete"
# Get macOS Software Updates, and update installed Ruby gems, Homebrew and their installed packages
alias update='sudo softwareupdate -i -a; brew update; brew upgrade; brew cleanup; gem update; gem cleanup; npm update'
To see your current aliases
$ > alias
This presentation was made using the following tools:
- This presentation's source code
- Export PDF of this presentation (Chrome only)
- Thanks for watching


 More...
|
| |
| | refreshInstance ($instance, $isNew) |
| |
| | getAdminData () |
| | To override for defining a new adminData. More...
|
| |
| | getModelViewer () |
| | To override for defining a new ModelViewer. More...
|
| |
| | getFiles () |
| | To override for changing view files. More...
|
| |
| | getEvents () |
| | To override for changing events. More...
|
| |
| | getInstanceToString ($instance) |
| |
◆ _deleteMultiple()
| _deleteMultiple |
( |
|
$data, |
|
|
|
$action, |
|
|
|
$target, |
|
|
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$condition |
|
) |
| |
|
protected |
Helper to delete multiple objects.
- Parameters
-
| mixed | $data | |
| string | $action | |
| string | $target | the css selector for refreshing |
| callable | string | $condition | the callback for generating the SQL where (for deletion) with the parameter data, or a simple string |
Definition at line 135 of file CRUDControllerUtilitiesTrait.php.
◆ _edit()
| _edit |
( |
|
$instance, |
|
|
|
$modal = "no" |
|
) |
| |
|
protected |
◆ _getAdminData()
◆ _getBaseRoute()
◆ _getEvents()
◆ _getFiles()
◆ _getModelViewer()
◆ _renderDataTableForRefresh()
| _renderDataTableForRefresh |
( |
|
$instances, |
|
|
|
$model, |
|
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$totalCount |
|
) |
| |
|
private |
◆ _showConfMessage()
| _showConfMessage |
( |
CRUDMessage |
$message, |
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$url, |
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$responseElement, |
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$data, |
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$attributes = NULL |
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) |
| |
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abstractprotected |
◆ _showModel()
◆ _showSimpleMessage()
| _showSimpleMessage |
( |
CRUDMessage |
$message, |
|
|
|
$staticName = null |
|
) |
| |
|
abstractprotected |
◆ crudLoadView()
| crudLoadView |
( |
|
$viewName, |
|
|
|
$vars = [] |
|
) |
| |
|
private |
◆ getAdminData()
◆ getEvents()
◆ getFiles()
◆ getInstances()
| getInstances |
( |
& |
$totalCount, |
|
|
|
$page = 1 , |
|
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|
$id = null |
|
) |
| |
|
protected |
◆ getInstanceToString()
| getInstanceToString |
( |
|
$instance | ) |
|
|
protected |
◆ getModelInstance()
| getModelInstance |
( |
|
$ids, |
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$transform = true , |
|
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$included = true |
|
) |
| |
|
private |
◆ getModelViewer()
◆ getSingleton()
| getSingleton |
( |
|
$value, |
|
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$method |
|
) |
| |
|
private |
◆ index()
◆ loadView()
| loadView |
( |
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$viewName, |
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$pData = NULL , |
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$asString = false |
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) |
| |
|
abstract |
◆ refreshInstance()
| refreshInstance |
( |
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$instance, |
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$isNew |
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) |
| |
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protected |
◆ search()
| search |
( |
|
$model, |
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$search |
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) |
| |
|
protected |
◆ updateMemberDataElement()
| updateMemberDataElement |
( |
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$member, |
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|
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$instance |
|
) |
| |
|
protected |
◆ $adminDatas
◆ $crudFiles
◆ $events
◆ $modelViewer
The documentation for this class was generated from the following file:


Dusk Priestess:
[ Exchange Failure ]
You don't have the quantity of seal stones you entered. Please try again.
Go Back


Return the auto managed folder directory, if set.
Get the Antfarm options.
Sets the options and creates other environmental objects if necessary.
Get the server instance.
Adds a webhook to the webhook server.
Adds a webhook interface to the webhook server.
Creates an Emailer object to send emails.
Creates the server.
Adds a log entry to the Logger instance.
The log level. 0 = debug, 1 = info, 2 = warning, 3 = error
Log message.
Instance which triggers the action being logged.
Array of of other involved instances.
job.e.log(1, `Transferred to Tunnel "${tunnel.getName()}".`, job, [oldTunnel]);
Generated using TypeDoc
The environment class controls all aspects of the antfarm environment, like options, logging, and constructing globally referenced objects.


{% extends "signup_layout.html" %} {% block content %} {% include "partials/nav.html" ignore missing %} < back to your learning Your learning record Date Name Score Type Expiry {% from "partials/course_list.html" import build_record_item %} {% for course in courses.completed %} {{ build_record_item(course) }} {% endfor %} {% endblock %}


Movies Application
View source on Github
View source on Github
menu
loading...
Current Movies
Add a movie
Title
Rating
add


Tic Tac Toe
Game Menu
Board Size
Width
Height
Game Rule
Win Condition
Player
s
One
Two
Player One
Name:
Symbol:
Player Two
Name:
Symbol:
Reset
Start Game
End Game
Play Again?


| No. | Player | Position |
|---|---|---|
| 1. | Anamul Hasan | All-rounder |
| 2. | Abdullah Moin | Batsman |
| 3. | Safat Zaman | Batsman |
| 4. | Asif Uddin | All-rounder |
| 5. | Samarth Trivedi | Batsman |
| 6. | Aniruddha Tiwari | All-rounder |
| 7. | Omar Chowdhury | All-rounder |
| 8. | Didar Khan | All-rounder |
| 9. | Shaon Ahmed | Bowler |
| 10. | Rashed Zidni | Batsman |
| 11. | Tharindu Lokukat | All-rounder |
| 12. | Diroshan Thevaraj | Bowler |
| 13. | Tharuka | Batsman |


You currently pay Monthly on the 5th day of the month
Change frequency to
Day of the month
By changing to annual payments you will pay $420 every year.
Since your policy is paid up to 05/16/2016. you next payment will be for the lesser amount of $400
We are scheduling you next payment for 04/01/2016. Subsequent payments will be every year after that.
Your payment frequency has been changed and we have emailed you a confirmation at
<EMAIL_ADDRESS>You currently pay Monthly on the 5th day of the month
Please provide your new billing information
Your payment method has been changed and we have emailed you a confirmation at
<EMAIL_ADDRESS>The current owner of policy STI-xxx-1234 is Beatrize Nallar
If you change ownership of this policy you are transferring ownership of this policy to another individual, company or trust. In doing so you give up your ownership rights which include the right to change ownership or choose or change beneficiaries. Once ownership is transferred the new owner will be responsible for all policy premium payments.
To complete a transfer of ownership an email will be sent to the new owner who must consent to taking ownership and provide details to verify their identity. Upon completion of this step you will receive notice of the policy transfer. Do you want to proceed?
The current owner of policy STI-xxx-1234 is Beatrize Nallar
Please provide the new owner's information.
Signature and Date
I hereby transfer absolutely to the "New Owner" all incidents of ownership and control, and all right, little and interest in and to the policy(policy number STI-xxx-12345)
The policy transfer request has been sent to the new owner. Once they have completed the authorization, the policy transfer will be complete.
Your Beneficiaries
Name
Relationship
Date of Birth
Distribution
Mark
Sister
05/07/1988
65%
Your changes have been implemented and a confirmation email has been sent to Beatriz.nallar@example.com.


Data Table Filtering
- Filtering lets users to see the data which belongs to a specified criteria.
- There are multiple list of configurations are available when working with data table filtering.
- To Enable filtering, it is required to set the following property into true in the required columns.
[filterable]="true"
- Note that, The default value for filterable property is false. When there is no value or false value is
assigned to the filterable property, both dropdown & text box filtering will be off for the specified columns.
Filter Dropdown
The following content shows the available configurations when using column dropdown filter.
- [showDropdownFilter]="true or false" => Enable/disable table column dropdown filter. The default value
for this config is false.
- [dropdownFilterMenuPosition]="bottom-right" | "bottom-left" | "top-left" | "top-right" => Enables users to configure the dropdown filter position. Note that, there are
4 positions users can configure to align the dropdown. The default value for this config is
"bottom-left". According to the following example,
- Station Name column filter has the position of BOTTOM_RIGHT
- Status Value column filter has the position of BOTTOM_LEFT
- Available Bikes column filter has the position of TOP_LEFT
- Address column filter has the position of TOP_RIGHT
- [dropDownFilterShowOptionSelectCheckbox]="true or false" => Show/hide dropdown filter's option selection
check boxes. If value is not specified check boxes won't be shown.
- [dropdownFilterCloseMenuOnSelect]="true or false" => Enable/disable dropdown filter close after an
option is selected. The default value for this config is true. According to the following example,
- Station Name column filter has false value for this config
- Station Name column filter has true value for this config
- [dropdownFilterShowSelectAll]="true or false" => Show/hide dropdown filter all option selection check
box.
If value is not specified select all check box won't be shown.
- [dropdownFilterSelectMode]="multi" | "single" | "single-toggle" => Enables users to configure the dropdown filter option selection type.
The default value for this config is "multi". There are 3 types of selection types available in
ornamentum data table dropdown filter.
- MULTI mode: Enable multiple option selection
- SINGLE mode: Enable single option selection
- SINGLE_TOGGLE mode: Enable single option selection with toggle
- [dropdownFilterWrapDisplaySelectLimit]="5 (limit in number)" => Used to configure the number of options
to
be shown in the dropdown at a time. The default value for this config is 1.
- [dropdownFilterShowSelectedOptionRemoveButton]="true or false" => Used to show/hide the filtered option
removal from the dropdown. The default value for this config is false.
- [dropdownFilterGroupByField]="group field name" => Enable dropdown filter options grouping under a
specified name. If the group name is not specified all the values are shown as a single group.
- [dropdownFilterMenuHeight]="200 (height in number)" => Used to specify the column dropdown filter menu
height. The default value for this config is 250.
- [dropdownFilterMenuWidth]="200 (width in number)" => Used to specify the column dropdown filter menu
width. The default value for this config is 320.
- [dropdownFilterMultiSelectOptionMaxWidth]="300 (max width in number)" => Used to specify the multiple
option selection dropdown option max width. The default value for this config is 135.
- [dropdownFilterSearchable]="true or false" => Used to show/hide dropdown filter search box. The default
value for this config is true.
- [dropdownFilterSearchDebounce]="true or false" => Used to enable/disable dropdown filter debounce. The
default value for this config is true.
- [dropdownFilterSearchDebounceTime]="100 (time in number)" => Used to specify the dropdown filter search
debounce time. The default value for this config is 500.
- [dropdownFilterShowClearSelectionButton]="true or false" => Used to show/hide drop down filter selection
clear button. The default value for this config is true.
- [filter]="Column value filter" => Used to provide a filter to see the values matched for specified
criteria. In here [{{ '{' }} key: "In Service", value: "In Service" {{ '}' }}] filter is applied for the Status
Value column.
Filter Text Box
The following content shows the available configurations when using column search filter text box.
- [filterPlaceholder]="Filter place holder text to display" => Used to specify placeholder name for search
text box. If value is npt specified, filter text value is shown as empty.
- [showFilterClearButton]="true or false" => Used to show/hide text box search filter clear button. If
default value for this config is true.
Suggested Links
API Doc for Data Filtering (refer
filtering associated configurations)


Suraj Rai, Date of Birth 1993
Suraj rai is a Erp student from Nepal. He is 27 years old man. He completed his bachelor form hile campus under T.U University.Now he is student in kyoto college of graduate studies for informatics(kcgi). He is livng in Kyoto,Japan He really likes to spends time in electronic devices like laptop, Mobiles, and meet new people, traval different palces etc He believe on himself and want to challege every sitution of his life no matters what might be solution.
The birth of Suraj is located in easter Nepal, Dhankuta-hile which is reknowned as the nature of beauty. Actually it was place like heaven where as famouse for the different religion and culture all over the nepal. There are lots of stunning placees that makes facinated for those who loves to traval. People are kind,humble and very much sensior for the new visitors so that this place is still one of the best destination for people.You can see the beautiful video of place.
For Information and send a friend request
| Weeks | Lecture Class | Time | Vaccation | public holidays | Savings |
|---|---|---|---|---|---|
| Sunday | Web programming | 11:10-3:30 | |||
| Monday | Fundamental of database technologies | 1:30-4:00 | |||
| Tuesday | No class | ||||
| Wednesday | No class | > | |||
| Thursday | No class | ||||
| Friday | Statistic IT | 9:30-12:00 | |||
| Saturday | No class |


Mobile Recharge, Electricity Bill payment, Data Top up, Cabel TV Subscription
OneApp gives you up to 5% cashback on Data and Airtime top up on every Purchases or Transactions.
Become a premium OneApp member and save even more on every Purchase or Transaction.


think architecture arranges house in a park as series of indepentant volumes in zurich
by sofia lekka angelopoulouswiss firm think architecture has built house in a park in zurich as a series of single-story, freely arranged spaces, held together by a continuous roof. shaped according to the natural contours of the area, the independent residential volumes blend harmoniously into the surrounding environment of this park-like plot. the actual size of the house is disguised thanks to its basic meandering shape, and its projections and recesses allow it to merge with the landscape of the surrounding parkland.
all images by simone bossi
think architecture has arranged the series of single-story spaces freely on the hilltop site, connecting them together through a continuous roof. the volumes are clad with natural stone, while a continuous, exposed concrete facing around the edge of the roof ties them all together. all units are internally connected, and have direct access to the garden, while they also benefit from views of the park toward the mountain or toward the lake zurich basin, depending on their orientation.
floor-to-ceiling wood and metal windows and occasional skylights throughout the house bring plenty of natural light within the interior, and emphasize the relation to the exterior landscape. on top of living spaces, bedrooms, a home office, and a music room, the private residence contains an indoor pool, as well as other leisure spaces, which are located below ground level.
project info:
name: house in a park
architect: think architecture
type: study commission 1st price | new building
main use: private house
landscape: cadrage LA
location: zurich, switzerland


Kent Brian Nyanchoga -finished my KCSE few years ago and decided to venture and explore the IT world this is because of the following reasons.with programming I Have Freedom to Make My Own Schedule I Have a Sense of Self-Reliance and Empowerment,I Have Extra Time to Pursue Interests,I Earn More, But Work Fewer Hours and much more. my current skills include freelance photography-exploring codes-gaming and relaxing.
Copyright © 2017. All Rights Reserved.
A theme by SLACKA


How Unai Emery's Arsenal record compared to end of Arsene Wenger's reign
Unai Emery was ousted from his role as Arsenal's head coach on Friday morning after only 18 months in the job.
Spaniard Emery was left with big boots to fill when replacing Arsene Wenger, the longest-serving and most successful manager in the club's history.
Emery was sacked after 78 games across all competitions and the PA news agency looks at how his record compares to that of Wenger over his final 78 matches at the helm.
Emery was in charge for 51 Premier League matches, and chalked up 25 wins, with 13 draws and 13 losses.
Wenger's matches-won record over his final 51 league games is marginally better, with the Gunners claiming 27 victories. They lost four more matches than Emery (17) and only drew seven.
Emery and Wenger collected the exact same number of points over those 51 league games – 88.
Emery has overseen 78 matches across all competitions, winning 43, drawing 16 and losing 19.
This is an improvement on Wenger's fortunes over the final 78 games of his reign, as the Frenchman led the team to two fewer victories and four more losses.
Wenger delivered three Premier League titles and seven FA Cups over the course of his Arsenal tenure.
body check tags ::


--- layout: post title: PyCrypto on Windows 7 64-bit date: '2012-08-10T20:25:00.000+02:00' author: Libor Nenadál tags: - easy_install - pip - PyCrypto - Windows - 64-bit - python modified_time: '2012-08-10T20:25:10.242+02:00' blogger_id: tag:blogger.com,1999:blog-17453343.post-748794523672869106 blogger_orig_url: http://saxuality.blogspot.com/2012/08/pycrypto-on-windows-7-64-bit.html ---
easy_install pycrypto-2.5-py2.7-win-amd64.eggSimple right? Big thanks goes to tahoe-lafs.org for providing the binaries!


| DOSE: T+ 0:00 |
1.75 g |
oral |
Mushrooms |
(cookie / food) |
| T+ 2:15 |
1 bowl |
smoked |
Cannabis |
(plant material) |
| T+ 4:00 |
1 bowl |
smoked |
Cannabis |
(plant material) |
It started out in the middle of April in Minnesota, my friend, who I will call S, told be and his brother, who I will call N, that he had come across some chocolates with Psilocybe Cubensis Mushrooms in them. Having not dabbled in any other substance before besides: Marijuana, my daily dosage of Adderall, many painkillers, Salvia Divinorum, and Alcohol, I was quite intrigued. I went online and did some research on it and weighed the pros and cons and decided that my reason to do them was not simply to trip, but to get personal insight. The next day we drove out to our source and bought ourselves two chocolate bars of it, each bar containing about 1/16 of a gram. I decided not to do them until I felt I was in a perfect mood from the moment I woke up.
The day came, I felt perfect when I woke up and decided not to take my Adderall, for I wanted nothing to possibly interfere with the mushrooms. I also was on an empty stomach to combat possible puking. My friend M, who was my trip sitter, came and picked my friend N and myself up and we decided to go out in the woods for a day to experience the journey, I came equipped with my iPod to listen to in case I felt that I wanted to be in my own world and a pipe loaded with marijuana, plus another bud for another bowl if needed. We made our way in to the heavily forested area known only as 'The Bog', the area was covered with trees that were in bloom and there were quite a few bogs in the area hence the name.
When we found an area that was comfortable and had a good log to sit on we ingested our chocolates, they tasted good inside these chocolates and melted in my mouth. After we consumed them we sat around and spoke with each other about normal things just talked and were waiting in anticipation, my friend M, who had prior experience with this substance, told me 'You will never forget this.' He was right. About 40 minutes had passed and all of the sudden in the middle of a conversation I laughed at nothing that was funny at all N said 'Dude, what is so funny?' I could not respond, all I did was laugh, I was crying because of the laughter.
After the laughter had subsided, about an hour after ingestion, I decided to look at my hands and lines in my hands and fingers were moving and I started to see the trees around me bend unnaturally. I went to be by my lonesome down a path, I put my iPod on and listened to 'Strawberry Fields Forever' and 'Lucy in the Sky with Diamonds'. I felt a lot of meaning come from Strawberry Fields, it's words were trying to tell me something, the line 'It's getting hard to be someone but it all works out.' gave me personal insight. It told me to be myself no matter how hard it may be and how much people do not like it. I realized that there was no point in trying to please others with fakeness, it was a revelation.
Another 45 minutes had passed and it kept getting more intense, everything was moving and movement was slow, watching my feet while walking seemed like it took days for just one step. I walked over to N, by then it had already been 2 hour and 15 minutes since we started, it took me thirty minutes to walk to him, just down a hill by a bog. We smoked a bowl of some good nugget, this made the experience blow up. Everything was moving and was moving slow, we stared at the bog and it looked like it had a tide, yellow particles emerged and moved around clockwise, I stared at this and just sat in thought. I thought about everything, my problems with my father, my demeanor, my choice of friends. It was all becoming clear, I had the urge to make mends with my father and wanted to be the bigger man by ending our conflict.
Time had passed, we were coming down at about 4 hours, we decided to walk to the car, on our way out of the bog we smoked another bowl, this took us back to the peak it seemed. Movement became slow, M yelled to us 'Come on guys' those words echoed in my mind and it seemed like he was running while we were just walking though we had just been moving very slowly. It took about 30 minutes to get to the car, when we got to it we decided to get some food. We went to Buffalo Wild Wings, being in a crowded place brought out my paranoia, my trip was coming down a lot by the time we got there it was about 5 hours after ingestion. I thought everyone in the place knew, and that we were going to get caught by some non existent force. We ate and the trip was pretty much over.
As the day went on I had a feeling of tranquility and peace in my mind. I called my father and we have been speaking since. My realizations have improved my quality of life, now I do not try to be someone different, I am who I am. That is because a simple substance and a simple line, 'It's getting hard to be someone but it all works out.'.
This substance was a great learning experience and I am very appreciative for the chance to experience it.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


You can choose between the thin crunchy crust @50 or if you prefer the touch of speciality, the hand tossed @100, rolled and tossed by our chefs at King Pizziera.
The meat extras come at price of Kes 30 each while for the Vegeterian at Kes 20 each


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OpenM++ compiler (omc)
TypeSymbol.cpp File Reference
Definitions for the
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1.9.1


Why do we use it?
It is a long established fact that a reader will be distracted by the readable content of a page when
looking at
its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as
opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing
packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem
ipsum'
will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes
by
accident, sometimes on purpose (injected humour and the like).
Lorem Ipsum
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Contrary to popular belief, Lorem Ipsum is not simply random text.  Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32.
| Marka | Rocznik | Limit km | |
|---|---|---|---|
| Fiat | 2010 | 600 | |
| BMW | 2015 | 600 | |
| Subaru | 2008 | 400 |


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public class Dnn
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
static int |
DNN_BACKEND_DEFAULT |
static int |
DNN_BACKEND_HALIDE |
static int |
DNN_BACKEND_INFERENCE_ENGINE |
static int |
DNN_BACKEND_OPENCV |
static int |
DNN_TARGET_CPU |
static int |
DNN_TARGET_FPGA |
static int |
DNN_TARGET_MYRIAD |
static int |
DNN_TARGET_OPENCL |
static int |
DNN_TARGET_OPENCL_FP16 |
| Constructor and Description |
|---|
Dnn() |
| Modifier and Type | Method and Description |
|---|---|
static Mat |
blobFromImage(Mat image)
Creates 4-dimensional blob from image.
|
static Mat |
blobFromImage(Mat image,
double scalefactor)
Creates 4-dimensional blob from image.
|
static Mat |
blobFromImage(Mat image,
double scalefactor,
Size size)
Creates 4-dimensional blob from image.
|
static Mat |
blobFromImage(Mat image,
double scalefactor,
Size size,
Scalar mean)
Creates 4-dimensional blob from image.
|
static Mat |
blobFromImage(Mat image,
double scalefactor,
Size size,
Scalar mean,
boolean swapRB)
Creates 4-dimensional blob from image.
|
static Mat |
blobFromImage(Mat image,
double scalefactor,
Size size,
Scalar mean,
boolean swapRB,
boolean crop)
Creates 4-dimensional blob from image.
|
static Mat |
blobFromImage(Mat image,
double scalefactor,
Size size,
Scalar mean,
boolean swapRB,
boolean crop,
int ddepth)
Creates 4-dimensional blob from image.
|
static Mat |
blobFromImages(java.util.List<Mat> images)
Creates 4-dimensional blob from series of images.
|
static Mat |
blobFromImages(java.util.List<Mat> images,
double scalefactor)
Creates 4-dimensional blob from series of images.
|
static Mat |
blobFromImages(java.util.List<Mat> images,
double scalefactor,
Size size)
Creates 4-dimensional blob from series of images.
|
static Mat |
blobFromImages(java.util.List<Mat> images,
double scalefactor,
Size size,
Scalar mean)
Creates 4-dimensional blob from series of images.
|
static Mat |
blobFromImages(java.util.List<Mat> images,
double scalefactor,
Size size,
Scalar mean,
boolean swapRB)
Creates 4-dimensional blob from series of images.
|
static Mat |
blobFromImages(java.util.List<Mat> images,
double scalefactor,
Size size,
Scalar mean,
boolean swapRB,
boolean crop)
Creates 4-dimensional blob from series of images.
|
static Mat |
blobFromImages(java.util.List<Mat> images,
double scalefactor,
Size size,
Scalar mean,
boolean swapRB,
boolean crop,
int ddepth)
Creates 4-dimensional blob from series of images.
|
static java.util.List<java.lang.Integer> |
getAvailableTargets(int be) |
static java.lang.String |
getInferenceEngineBackendType()
Returns Inference Engine internal backend API.
|
static java.lang.String |
getInferenceEngineCPUType()
Returns Inference Engine CPU type.
|
static java.lang.String |
getInferenceEngineVPUType()
Returns Inference Engine VPU type.
|
static void |
imagesFromBlob(Mat blob_,
java.util.List<Mat> images_)
Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure
(std::vector<cv::Mat>).
|
static void |
NMSBoxes(MatOfRect2d bboxes,
MatOfFloat scores,
float score_threshold,
float nms_threshold,
MatOfInt indices)
Performs non maximum suppression given boxes and corresponding scores.
|
static void |
NMSBoxes(MatOfRect2d bboxes,
MatOfFloat scores,
float score_threshold,
float nms_threshold,
MatOfInt indices,
float eta)
Performs non maximum suppression given boxes and corresponding scores.
|
static void |
NMSBoxes(MatOfRect2d bboxes,
MatOfFloat scores,
float score_threshold,
float nms_threshold,
MatOfInt indices,
float eta,
int top_k)
Performs non maximum suppression given boxes and corresponding scores.
|
static void |
NMSBoxesRotated(MatOfRotatedRect bboxes,
MatOfFloat scores,
float score_threshold,
float nms_threshold,
MatOfInt indices) |
static void |
NMSBoxesRotated(MatOfRotatedRect bboxes,
MatOfFloat scores,
float score_threshold,
float nms_threshold,
MatOfInt indices,
float eta) |
static void |
NMSBoxesRotated(MatOfRotatedRect bboxes,
MatOfFloat scores,
float score_threshold,
float nms_threshold,
MatOfInt indices,
float eta,
int top_k) |
static Net |
readNet(java.lang.String model)
Read deep learning network represented in one of the supported formats.
|
static Net |
readNet(java.lang.String framework,
MatOfByte bufferModel)
Read deep learning network represented in one of the supported formats.
|
static Net |
readNet(java.lang.String framework,
MatOfByte bufferModel,
MatOfByte bufferConfig)
Read deep learning network represented in one of the supported formats.
|
static Net |
readNet(java.lang.String model,
java.lang.String config)
Read deep learning network represented in one of the supported formats.
|
static Net |
readNet(java.lang.String model,
java.lang.String config,
java.lang.String framework)
Read deep learning network represented in one of the supported formats.
|
static Net |
readNetFromCaffe(MatOfByte bufferProto)
Reads a network model stored in Caffe model in memory.
|
static Net |
readNetFromCaffe(MatOfByte bufferProto,
MatOfByte bufferModel)
Reads a network model stored in Caffe model in memory.
|
static Net |
readNetFromCaffe(java.lang.String prototxt)
Reads a network model stored in Caffe framework's format.
|
static Net |
readNetFromCaffe(java.lang.String prototxt,
java.lang.String caffeModel)
Reads a network model stored in Caffe framework's format.
|
static Net |
readNetFromDarknet(MatOfByte bufferCfg)
Reads a network model stored in Darknet model files.
|
static Net |
readNetFromDarknet(MatOfByte bufferCfg,
MatOfByte bufferModel)
Reads a network model stored in Darknet model files.
|
static Net |
readNetFromDarknet(java.lang.String cfgFile)
Reads a network model stored in Darknet model files.
|
static Net |
readNetFromDarknet(java.lang.String cfgFile,
java.lang.String darknetModel)
Reads a network model stored in Darknet model files.
|
static Net |
readNetFromModelOptimizer(MatOfByte bufferModelConfig,
MatOfByte bufferWeights)
Load a network from Intel's Model Optimizer intermediate representation.
|
static Net |
readNetFromModelOptimizer(java.lang.String xml,
java.lang.String bin)
Load a network from Intel's Model Optimizer intermediate representation.
|
static Net |
readNetFromONNX(MatOfByte buffer)
Reads a network model from ONNX
in-memory buffer.
|
static Net |
readNetFromONNX(java.lang.String onnxFile)
Reads a network model ONNX.
|
static Net |
readNetFromTensorflow(MatOfByte bufferModel)
Reads a network model stored in TensorFlow framework's format.
|
static Net |
readNetFromTensorflow(MatOfByte bufferModel,
MatOfByte bufferConfig)
Reads a network model stored in TensorFlow framework's format.
|
static Net |
readNetFromTensorflow(java.lang.String model)
Reads a network model stored in TensorFlow framework's format.
|
static Net |
readNetFromTensorflow(java.lang.String model,
java.lang.String config)
Reads a network model stored in TensorFlow framework's format.
|
static Net |
readNetFromTorch(java.lang.String model)
Reads a network model stored in Torch7 framework's format.
|
static Net |
readNetFromTorch(java.lang.String model,
boolean isBinary)
Reads a network model stored in Torch7 framework's format.
|
static Net |
readNetFromTorch(java.lang.String model,
boolean isBinary,
boolean evaluate)
Reads a network model stored in Torch7 framework's format.
|
static Mat |
readTensorFromONNX(java.lang.String path)
Creates blob from .pb file.
|
static Mat |
readTorchBlob(java.lang.String filename)
Loads blob which was serialized as torch.Tensor object of Torch7 framework.
|
static Mat |
readTorchBlob(java.lang.String filename,
boolean isBinary)
Loads blob which was serialized as torch.Tensor object of Torch7 framework.
|
static void |
resetMyriadDevice()
Release a Myriad device (binded by OpenCV).
|
static java.lang.String |
setInferenceEngineBackendType(java.lang.String newBackendType)
Specify Inference Engine internal backend API.
|
static void |
shrinkCaffeModel(java.lang.String src,
java.lang.String dst)
Convert all weights of Caffe network to half precision floating point.
|
static void |
shrinkCaffeModel(java.lang.String src,
java.lang.String dst,
java.util.List<java.lang.String> layersTypes)
Convert all weights of Caffe network to half precision floating point.
|
static void |
writeTextGraph(java.lang.String model,
java.lang.String output)
Create a text representation for a binary network stored in protocol buffer format.
|
public static final int DNN_BACKEND_DEFAULT
public static final int DNN_BACKEND_HALIDE
public static final int DNN_BACKEND_INFERENCE_ENGINE
public static final int DNN_BACKEND_OPENCV
public static final int DNN_TARGET_CPU
public static final int DNN_TARGET_FPGA
public static final int DNN_TARGET_MYRIAD
public static final int DNN_TARGET_OPENCL
public static final int DNN_TARGET_OPENCL_FP16
public static Mat blobFromImage(Mat image)
image
from center,
subtract mean
values, scales values by scalefactor
, swap Blue and Red channels.image
- input image (with 1-, 3- or 4-channels).
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.
in 3-channel image is necessary.
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static Mat blobFromImage(Mat image, double scalefactor)
image
from center,
subtract mean
values, scales values by scalefactor
, swap Blue and Red channels.image
- input image (with 1-, 3- or 4-channels).
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.scalefactor
- multiplier for image
values.
in 3-channel image is necessary.
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static Mat blobFromImage(Mat image, double scalefactor, Size size)
image
from center,
subtract mean
values, scales values by scalefactor
, swap Blue and Red channels.image
- input image (with 1-, 3- or 4-channels).size
- spatial size for output image
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.scalefactor
- multiplier for image
values.
in 3-channel image is necessary.
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean)
image
from center,
subtract mean
values, scales values by scalefactor
, swap Blue and Red channels.image
- input image (with 1-, 3- or 4-channels).size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.scalefactor
- multiplier for image
values.
in 3-channel image is necessary.
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB)
image
from center,
subtract mean
values, scales values by scalefactor
, swap Blue and Red channels.image
- input image (with 1-, 3- or 4-channels).size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.scalefactor
- multiplier for image
values.swapRB
- flag which indicates that swap first and last channels
in 3-channel image is necessary.
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop)
image
from center,
subtract mean
values, scales values by scalefactor
, swap Blue and Red channels.image
- input image (with 1-, 3- or 4-channels).size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.scalefactor
- multiplier for image
values.swapRB
- flag which indicates that swap first and last channels
in 3-channel image is necessary.crop
- flag which indicates whether image will be cropped after resize or not
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static Mat blobFromImage(Mat image, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop, int ddepth)
image
from center,
subtract mean
values, scales values by scalefactor
, swap Blue and Red channels.image
- input image (with 1-, 3- or 4-channels).size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.scalefactor
- multiplier for image
values.swapRB
- flag which indicates that swap first and last channels
in 3-channel image is necessary.crop
- flag which indicates whether image will be cropped after resize or notddepth
- Depth of output blob. Choose CV_32F or CV_8U.
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static Mat blobFromImages(java.util.List<Mat> images)
images
from center, subtract mean
values, scales values by scalefactor
,
swap Blue and Red channels.images
- input images (all with 1-, 3- or 4-channels).
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.
in 3-channel image is necessary.
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static Mat blobFromImages(java.util.List<Mat> images, double scalefactor)
images
from center, subtract mean
values, scales values by scalefactor
,
swap Blue and Red channels.images
- input images (all with 1-, 3- or 4-channels).
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.scalefactor
- multiplier for images
values.
in 3-channel image is necessary.
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static Mat blobFromImages(java.util.List<Mat> images, double scalefactor, Size size)
images
from center, subtract mean
values, scales values by scalefactor
,
swap Blue and Red channels.images
- input images (all with 1-, 3- or 4-channels).size
- spatial size for output image
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.scalefactor
- multiplier for images
values.
in 3-channel image is necessary.
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static Mat blobFromImages(java.util.List<Mat> images, double scalefactor, Size size, Scalar mean)
images
from center, subtract mean
values, scales values by scalefactor
,
swap Blue and Red channels.images
- input images (all with 1-, 3- or 4-channels).size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.scalefactor
- multiplier for images
values.
in 3-channel image is necessary.
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static Mat blobFromImages(java.util.List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB)
images
from center, subtract mean
values, scales values by scalefactor
,
swap Blue and Red channels.images
- input images (all with 1-, 3- or 4-channels).size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.scalefactor
- multiplier for images
values.swapRB
- flag which indicates that swap first and last channels
in 3-channel image is necessary.
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static Mat blobFromImages(java.util.List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop)
images
from center, subtract mean
values, scales values by scalefactor
,
swap Blue and Red channels.images
- input images (all with 1-, 3- or 4-channels).size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.scalefactor
- multiplier for images
values.swapRB
- flag which indicates that swap first and last channels
in 3-channel image is necessary.crop
- flag which indicates whether image will be cropped after resize or not
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static Mat blobFromImages(java.util.List<Mat> images, double scalefactor, Size size, Scalar mean, boolean swapRB, boolean crop, int ddepth)
images
from center, subtract mean
values, scales values by scalefactor
,
swap Blue and Red channels.images
- input images (all with 1-, 3- or 4-channels).size
- spatial size for output imagemean
- scalar with mean values which are subtracted from channels. Values are intended
to be in (mean-R, mean-G, mean-B) order if image
has BGR ordering and swapRB
is true.scalefactor
- multiplier for images
values.swapRB
- flag which indicates that swap first and last channels
in 3-channel image is necessary.crop
- flag which indicates whether image will be cropped after resize or notddepth
- Depth of output blob. Choose CV_32F or CV_8U.
if crop
is true, input image is resized so one side after resize is equal to corresponding
dimension in size
and another one is equal or larger. Then, crop from the center is performed.
If crop
is false, direct resize without cropping and preserving aspect ratio is performed.public static java.util.List<java.lang.Integer> getAvailableTargets(int be)
public static java.lang.String getInferenceEngineBackendType()
CV_DNN_BACKEND_INFERENCE_ENGINE_*
macros.
Default value is controlled through OPENCV_DNN_BACKEND_INFERENCE_ENGINE_TYPE
runtime parameter (environment variable).public static java.lang.String getInferenceEngineCPUType()
public static java.lang.String getInferenceEngineVPUType()
CV_DNN_INFERENCE_ENGINE_VPU_TYPE_*
macros.public static void imagesFromBlob(Mat blob_, java.util.List<Mat> images_)
blob_
- 4 dimensional array (images, channels, height, width) in floating point precision (CV_32F) from
which you would like to extract the images.images_
- array of 2D Mat containing the images extracted from the blob in floating point precision
(CV_32F). They are non normalized neither mean added. The number of returned images equals the first dimension
of the blob (batch size). Every image has a number of channels equals to the second dimension of the blob (depth).public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
bboxes
- a set of bounding boxes to apply NMS.scores
- a set of corresponding confidences.score_threshold
- a threshold used to filter boxes by score.nms_threshold
- a threshold used in non maximum suppression.indices
- the kept indices of bboxes after NMS.public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
bboxes
- a set of bounding boxes to apply NMS.scores
- a set of corresponding confidences.score_threshold
- a threshold used to filter boxes by score.nms_threshold
- a threshold used in non maximum suppression.indices
- the kept indices of bboxes after NMS.eta
- a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).public static void NMSBoxes(MatOfRect2d bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)
bboxes
- a set of bounding boxes to apply NMS.scores
- a set of corresponding confidences.score_threshold
- a threshold used to filter boxes by score.nms_threshold
- a threshold used in non maximum suppression.indices
- the kept indices of bboxes after NMS.eta
- a coefficient in adaptive threshold formula: \(nms\_threshold_{i+1}=eta\cdot nms\_threshold_i\).top_k
- if >0
, keep at most top_k
picked indices.public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta)
public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices, float eta, int top_k)
public static Net readNet(java.lang.String model)
model
- Binary file contains trained weights. The following file
extensions are expected for models from different frameworks:
* *.caffemodel
(Caffe, http://caffe.berkeleyvision.org/)
* *.pb
(TensorFlow, https://www.tensorflow.org/)
* *.t7
| *.net
(Torch, http://torch.ch/)
* *.weights
(Darknet, https://pjreddie.com/darknet/)
* *.bin
(DLDT, https://software.intel.com/openvino-toolkit)
* *.onnx
(ONNX, https://onnx.ai/)
file with the following extensions:
* *.prototxt
(Caffe, http://caffe.berkeleyvision.org/)
* *.pbtxt
(TensorFlow, https://www.tensorflow.org/)
* *.cfg
(Darknet, https://pjreddie.com/darknet/)
* *.xml
(DLDT, https://software.intel.com/openvino-toolkit)model
and config
arguments does not matter.public static Net readNet(java.lang.String framework, MatOfByte bufferModel)
framework
- Name of origin framework.bufferModel
- A buffer with a content of binary file with weightspublic static Net readNet(java.lang.String framework, MatOfByte bufferModel, MatOfByte bufferConfig)
framework
- Name of origin framework.bufferModel
- A buffer with a content of binary file with weightsbufferConfig
- A buffer with a content of text file contains network configuration.public static Net readNet(java.lang.String model, java.lang.String config)
model
- Binary file contains trained weights. The following file
extensions are expected for models from different frameworks:
* *.caffemodel
(Caffe, http://caffe.berkeleyvision.org/)
* *.pb
(TensorFlow, https://www.tensorflow.org/)
* *.t7
| *.net
(Torch, http://torch.ch/)
* *.weights
(Darknet, https://pjreddie.com/darknet/)
* *.bin
(DLDT, https://software.intel.com/openvino-toolkit)
* *.onnx
(ONNX, https://onnx.ai/)config
- Text file contains network configuration. It could be a
file with the following extensions:
* *.prototxt
(Caffe, http://caffe.berkeleyvision.org/)
* *.pbtxt
(TensorFlow, https://www.tensorflow.org/)
* *.cfg
(Darknet, https://pjreddie.com/darknet/)
* *.xml
(DLDT, https://software.intel.com/openvino-toolkit)model
and config
arguments does not matter.public static Net readNet(java.lang.String model, java.lang.String config, java.lang.String framework)
model
- Binary file contains trained weights. The following file
extensions are expected for models from different frameworks:
* *.caffemodel
(Caffe, http://caffe.berkeleyvision.org/)
* *.pb
(TensorFlow, https://www.tensorflow.org/)
* *.t7
| *.net
(Torch, http://torch.ch/)
* *.weights
(Darknet, https://pjreddie.com/darknet/)
* *.bin
(DLDT, https://software.intel.com/openvino-toolkit)
* *.onnx
(ONNX, https://onnx.ai/)config
- Text file contains network configuration. It could be a
file with the following extensions:
* *.prototxt
(Caffe, http://caffe.berkeleyvision.org/)
* *.pbtxt
(TensorFlow, https://www.tensorflow.org/)
* *.cfg
(Darknet, https://pjreddie.com/darknet/)
* *.xml
(DLDT, https://software.intel.com/openvino-toolkit)framework
- Explicit framework name tag to determine a format.model
and config
arguments does not matter.public static Net readNetFromCaffe(MatOfByte bufferProto)
bufferProto
- buffer containing the content of the .prototxt filepublic static Net readNetFromCaffe(MatOfByte bufferProto, MatOfByte bufferModel)
bufferProto
- buffer containing the content of the .prototxt filebufferModel
- buffer containing the content of the .caffemodel filepublic static Net readNetFromCaffe(java.lang.String prototxt)
prototxt
- path to the .prototxt file with text description of the network architecture.public static Net readNetFromCaffe(java.lang.String prototxt, java.lang.String caffeModel)
prototxt
- path to the .prototxt file with text description of the network architecture.caffeModel
- path to the .caffemodel file with learned network.public static Net readNetFromDarknet(MatOfByte bufferCfg)
bufferCfg
- A buffer contains a content of .cfg file with text description of the network architecture.public static Net readNetFromDarknet(MatOfByte bufferCfg, MatOfByte bufferModel)
bufferCfg
- A buffer contains a content of .cfg file with text description of the network architecture.bufferModel
- A buffer contains a content of .weights file with learned network.public static Net readNetFromDarknet(java.lang.String cfgFile)
cfgFile
- path to the .cfg file with text description of the network architecture.public static Net readNetFromDarknet(java.lang.String cfgFile, java.lang.String darknetModel)
cfgFile
- path to the .cfg file with text description of the network architecture.darknetModel
- path to the .weights file with learned network.public static Net readNetFromModelOptimizer(MatOfByte bufferModelConfig, MatOfByte bufferWeights)
bufferModelConfig
- Buffer contains XML configuration with network's topology.bufferWeights
- Buffer contains binary data with trained weights.public static Net readNetFromModelOptimizer(java.lang.String xml, java.lang.String bin)
xml
- XML configuration file with network's topology.bin
- Binary file with trained weights.public static Net readNetFromONNX(MatOfByte buffer)
buffer
- in-memory buffer that stores the ONNX model bytes.public static Net readNetFromONNX(java.lang.String onnxFile)
onnxFile
- path to the .onnx file with text description of the network architecture.public static Net readNetFromTensorflow(MatOfByte bufferModel)
bufferModel
- buffer containing the content of the pb filepublic static Net readNetFromTensorflow(MatOfByte bufferModel, MatOfByte bufferConfig)
bufferModel
- buffer containing the content of the pb filebufferConfig
- buffer containing the content of the pbtxt filepublic static Net readNetFromTensorflow(java.lang.String model)
model
- path to the .pb file with binary protobuf description of the network architecture
Resulting Net object is built by text graph using weights from a binary one that
let us make it more flexible.public static Net readNetFromTensorflow(java.lang.String model, java.lang.String config)
model
- path to the .pb file with binary protobuf description of the network architectureconfig
- path to the .pbtxt file that contains text graph definition in protobuf format.
Resulting Net object is built by text graph using weights from a binary one that
let us make it more flexible.public static Net readNetFromTorch(java.lang.String model)
model
- path to the file, dumped from Torch by using torch.save() function.long
type of C language,
which has various bit-length on different systems.
The loading file must contain serialized nn.Module object
with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.
List of supported layers (i.e. object instances derived from Torch nn.Module class):
- nn.Sequential
- nn.Parallel
- nn.Concat
- nn.Linear
- nn.SpatialConvolution
- nn.SpatialMaxPooling, nn.SpatialAveragePooling
- nn.ReLU, nn.TanH, nn.Sigmoid
- nn.Reshape
- nn.SoftMax, nn.LogSoftMax
Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.public static Net readNetFromTorch(java.lang.String model, boolean isBinary)
model
- path to the file, dumped from Torch by using torch.save() function.isBinary
- specifies whether the network was serialized in ascii mode or binary.long
type of C language,
which has various bit-length on different systems.
The loading file must contain serialized nn.Module object
with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.
List of supported layers (i.e. object instances derived from Torch nn.Module class):
- nn.Sequential
- nn.Parallel
- nn.Concat
- nn.Linear
- nn.SpatialConvolution
- nn.SpatialMaxPooling, nn.SpatialAveragePooling
- nn.ReLU, nn.TanH, nn.Sigmoid
- nn.Reshape
- nn.SoftMax, nn.LogSoftMax
Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.public static Net readNetFromTorch(java.lang.String model, boolean isBinary, boolean evaluate)
model
- path to the file, dumped from Torch by using torch.save() function.isBinary
- specifies whether the network was serialized in ascii mode or binary.evaluate
- specifies testing phase of network. If true, it's similar to evaluate() method in Torch.long
type of C language,
which has various bit-length on different systems.
The loading file must contain serialized nn.Module object
with importing network. Try to eliminate a custom objects from serialazing data to avoid importing errors.
List of supported layers (i.e. object instances derived from Torch nn.Module class):
- nn.Sequential
- nn.Parallel
- nn.Concat
- nn.Linear
- nn.SpatialConvolution
- nn.SpatialMaxPooling, nn.SpatialAveragePooling
- nn.ReLU, nn.TanH, nn.Sigmoid
- nn.Reshape
- nn.SoftMax, nn.LogSoftMax
Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.public static Mat readTensorFromONNX(java.lang.String path)
path
- to the .pb file with input tensor.public static Mat readTorchBlob(java.lang.String filename)
filename
- automatically generatedpublic static Mat readTorchBlob(java.lang.String filename, boolean isBinary)
filename
- automatically generatedisBinary
- automatically generatedpublic static void resetMyriadDevice()
public static java.lang.String setInferenceEngineBackendType(java.lang.String newBackendType)
CV_DNN_BACKEND_INFERENCE_ENGINE_*
macros.newBackendType
- automatically generatedpublic static void shrinkCaffeModel(java.lang.String src, java.lang.String dst)
src
- Path to origin model from Caffe framework contains single
precision floating point weights (usually has .caffemodel
extension).dst
- Path to destination model with updated weights.
By default, converts only Convolutional and Fully-Connected layers'
weights.
Note: Shrinked model has no origin float32 weights so it can't be used
in origin Caffe framework anymore. However the structure of data
is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe.
So the resulting model may be used there.public static void shrinkCaffeModel(java.lang.String src, java.lang.String dst, java.util.List<java.lang.String> layersTypes)
src
- Path to origin model from Caffe framework contains single
precision floating point weights (usually has .caffemodel
extension).dst
- Path to destination model with updated weights.layersTypes
- Set of layers types which parameters will be converted.
By default, converts only Convolutional and Fully-Connected layers'
weights.
Note: Shrinked model has no origin float32 weights so it can't be used
in origin Caffe framework anymore. However the structure of data
is taken from NVidia's Caffe fork: https://github.com/NVIDIA/caffe.
So the resulting model may be used there.public static void writeTextGraph(java.lang.String model, java.lang.String output)
model
- A path to binary network.output
- A path to output text file to be created.
Note: To reduce output file size, trained weights are not included.Generated on 2021-04-02 03:15:03 / OpenCV 3.4.14


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2 Samuel 23:12
But he stood in the midst of the ground, and defended it, and slew the Philistines: and the LORD wrought a great victory.


#include <globalplatform.h>
Public Attributes | |
| BYTE | AIDLength |
| The length of the AID. | |
| BYTE | AID [16] |
| The AID. | |
| BYTE | lifeCycleState |
| The Issuer Security Domain, Security Domains, Executable Load Files and Application life cycle state. | |
| BYTE | privileges |
| The Issuer Security Domain, Security Domains or Application privileges. Has no meaning for Executable Load Files. |
The structure containing Issuer Security Domain, Security Domains, Executable Load Files and Application life cycle states and privileges returned by get_status().
The AID.
The length of the AID.
The Issuer Security Domain, Security Domains, Executable Load Files and Application life cycle state.
The Issuer Security Domain, Security Domains or Application privileges. Has no meaning for Executable Load Files.