Hylas will kein Weib nicht haben (Anonymous)

Hylas will kein Weib nicht haben (Anonymous) ... Composer, Anonymous. Key, G major. First Publication. 1651 in Seladons Weltliche Lieder (No.3).

from Google Alert - anonymous http://ift.tt/2pkcg39
via IFTTT

MLB Video: Kendrys Morales hits towering walk-off home run to give Blue Jays 2-1 victory over Orioles (ESPN)

from ESPN http://ift.tt/1eW1vUH
via IFTTT

Turns Out Microsoft Has Already Patched Exploits Leaked By Shadow Brokers

The latest dump of hacking tools allegedly belonged to the NSA is believed to be the most damaging release by the Shadow Brokers till the date. But after analyzing the disclosed exploits, Microsoft security team says most of the windows vulnerabilities exploited by these hacking tools, including EternalBlue, EternalChampion, EternalSynergy, EternalRomance and others, are already patched in

from The Hacker News http://ift.tt/2plitsQ
via IFTTT

Luminous Salar de Uyuni

A scene in high contrast this thoughtful night skyscape is a modern composition inspired by M. C. Escher's lithograph Phosphorescent Sea. In it, bright familiar stars of Orion the Hunter and Aldebaran, eye of Taurus the Bull, hang in clear dark skies above a distant horizon. Below, faintly luminous edges trace an otherworldly constellation of patterns in mineral-crusted mud along the Uyuni Salt Flat of southwest Bolivia. The remains of an ancient lake, the Uyuni Salt Flat, Salar de Uyuni, is planet Earth's largest salt flat, located on the Bolivian Altiplano at an altitude of about 3,600 meters. Escher's 1933 lithograph also featured familiar stars in planet Earth's night, framing The Plough or Big Dipper above waves breaking on a more northern shore. via NASA http://ift.tt/2phdj0I

Orioles Video: Chris Davis says goodbye, takes pitch deep to center for monster HR in 6-4 victory over Blue Jays (ESPN)

from ESPN http://ift.tt/1eW1vUH
via IFTTT

Search Manuscripts/Mixed Material

Results: 1-4 of 4 | Refined by: Original Format: Manuscript/Mixed Material Remove Look Inside: Anonymous to Thomas Jefferson, June 29, 1802 ...

from Google Alert - anonymous http://ift.tt/2pgDec0
via IFTTT

Ravens counting on LB Kamalei Correa and DE Bronson Kaufusi to step up and possibly fill starting roles - Jamison Hensley (ESPN)

from ESPN http://ift.tt/17lH5T2
via IFTTT

Anonymous Donor Pledges $250000 To New Mitchell Pool

An anonymous donor has pledged $250,000 toward the new city indoor aquatic center. The announcement was made at yesterday's Mitchell Parks ...

from Google Alert - anonymous http://ift.tt/2ouUiZ0
via IFTTT

Anonymous

Title details for Calling Maggie May by Anonymous · Calling Maggie May ... Title details for The Book of David by Anonymous · The Book of David.

from Google Alert - anonymous http://ift.tt/2ozlLKD
via IFTTT

Latest Hacking Tools Leak Indicates NSA Was Targeting SWIFT Banking Network

The Shadow Brokers – a hackers group that claimed to have stolen a bunch of hacking tools from the NSA – released today more alleged hacking tools and exploits that target earlier versions of Windows operating system, along with evidence that the Intelligence agency also targeted the SWIFT banking system of several banks around the world. Last week, the hacking group released the password for an

from The Hacker News http://ift.tt/2oHmCZY
via IFTTT

I have a new follower on Twitter

Bryan Clagett
CMO & Investor @Geezeo™, Vice Chair @centerforchild, Speaker. #CMO #Marketing Exec. K#banking #finserv #fintech #HRVA #IoT #payments Old #fintechmafia vet.
Williamsburg, VA
https://t.co/iufv2KmhgZ
Following: 16140 - Followers: 20553

April 14, 2017 at 11:04AM via Twitter http://twitter.com/Clagett

ISS Daily Summary Report – 4/13/2017

Fine Motor Skills (FMS): The crew performed a series of interactive tasks to complete a FMS session. The investigation studies how the fine motor skills are effected by long-term microgravity exposure, different phases of microgravity adaptation, and sensorimotor recovery after returning to Earth gravity. The goal of this investigation is to understand how fine motor performance in microgravity trends/varies over the duration of a six-month and yearlong space mission; how fine motor performance on orbit compare with that of a closely matched participant on Earth; and how performance trend/vary before and after gravitational transitions, including the periods of early flight adaptation and very early/near immediate post-flight periods.  Veg-03 Operations: The crew checked and photo documented status of plants in the Veggie facility. The goal of Veg-03 is to further demonstrate proof of concept for the Veggie plant growth chamber and the planting pillows using Red Romaine lettuce. Future long-duration missions into the solar system will require a fresh food supply to supplement crew diets which entails growing crops in space. Previous investigations focused on improving productivity in controlled environments but the limited quarters of the space shuttle and ISS made it difficult to conduct large-scale crop production tests. Veg-03 expands on previous validation tests of the new Veggie hardware which crew members will soon use to grow cabbage, lettuce and other fresh vegetables. Tests determine which types of microorganisms are present in space-grown cabbage, providing baseline data for future crop-growing efforts. Behavioral health surveys assess the impact of growing plants on crew morale and mood.  Manufacturing Device (MD) Operations: The crew removed and replaced (R&Rd) the MD Feedstock Canister, Extruder, and Print Tray. The MD – Additive Manufacturing Facility (AMF) enables the production of components on the ISS for both NASA and commercial objectives. Parts, entire experiments, and tools can be created on demand utilizing the AMF. The AMF is capable of producing parts out of a wide variety of thermopolymers including engineered plastics. Electrostatic Levitation Furnace (ELF) Sample Holder Exchange: The crew performed a JAXA ELF Sample exchange in the Multi-Purpose Small Payload Rack-2 (MSPR2) facility.  They removed the sample holder and installed a new cartridge into the holder, then installed the Sample Holder and Cartridge into the ELF Work Volume. The ELF is an experimental facility designed to levitate/melt/solidify materials by containerless processing techniques using the Electrostatic Levitation method. With this facility, thermophysical properties of high temperature melts can be measured, and solidification from deeply undercooled melts can be achieved. Microgravity Science Laboratory (MSL) Sample Cartridge Assembly (SCA) Exchange: The crew completed a cartridge exchange in the MSL located in the Material Science Research Rack (MSRR). They removed the used sample cartridge and replaced it with the next test sample. Batch-2b of the Materials Science Laboratory Sample Cartridge Assemblies (MSL SCA-Batch 2b-ESA) serves two projects investigating how different phases organize in a structure when metallic alloys are solidified. The METCOMP project studies the phase formed by the reaction of the remaining liquid phase with an already formed solid to form a second solid phase on cooling. For this purpose, Bronze (Copper-Tin Alloys) of different compositions will be processed. The other project, Solidification along a Eutectic path in Ternary Alloys (SETA), looks at how two phases that form together organize into lamellar, or fiber, structures when cooling Aluminum (Copper-Silver Alloys). Both projects will provide benchmark samples that enables testing of numerical models that aim to predict these structures. 50 Soyuz (50S) Arrival Preparations: The crew reconnected a wireless Station Support Computer (SSC) client to the Joint Station Local Area Network (JSL) to allow ground teams to load and configure the clients for 50S crew use. Distillation Assembly (DA) Remove & Replace (R&R): The crew R&Rd the Water Recovery System (WRS) DA and verified torque level of the rack-side mounting plate fasteners. The R&R was performed as a result of the Urine Processing Assembly producing distillate with an elevated/erratic conductivity.  Analysis of samples has shown that pretreated urine (PTU) is present in the Separator Plumbing Assembly (SPA) distillate.  Fault tree investigation determined that the source of this PTU was either the DA or the Fluids Control and Pump Assembly. The UPA will be activated later today. Remote Power Controller (RPC) Trip: Yesterday RPC 16 on Remote Power Control Module (RPCM) N21B4B_B tripped open. This RPC powers the Node 2 Moderate Temperature Loop (MTL) System Flow Control Assembly (SFCA) Valve which impacts the ability for closed-loop control of the Node2 MTL loop flow.  The current loads on the Node 2 MTL are supported with the valve in its current position. Should thermal loads on this loop change, the crew can manually adjust this valve. The main system loads on this loop are four DC-to-DC Converter Units (DDCU) for the JEM and Columbus modules.  Telemetry reviewed by ground teams indicated ~3Amp overcurrent event. Ground Teams are evaluating a forward plan to recover. Today’s Planned Activities All activities were completed unless otherwise noted. Lighting Effects Sleep Log Entry Cygnus PROX Switch ON On MCC GO, Regeneration of Micropurification unit (БМП) Ф2 cartridge (start) Handhold Exp Platform Shooting Fine Motor Skills Experiment Test VEG-03 Plant Photo Troubleshooting possible cause of generating off-nominal.command in FGB Program-Logic Control Device [УПЛУ] via SM-FGB – X Docking Assembly Electrostatic Levitation Furnace(ELF) Sample Cartridge Removal Electrostatic Levitation Furnace(ELF) Sample Cartridge Installation Robotic Workstation (RWS) Setup Biomolecule Sequencer Surface Pro 3 Software Update Joint Station Local Area Network (LAN) (JSL) Network Information for JSL Administration (NINJA) Print Preparation steps for finding the cause of current overload in FGB ПШО31 exchange bus via SM-FGB Docking Assembly –X, preparation. Cygnus PCS HCP Selfcheck (Side B) Clean Bench (CB) Valve Checkout Cygnus PCS HCP Selfcheck (A Side) JEM System Laptop Terminal (SLT) 4 deactivation JEM System Laptop Terminal (SLT) 4 Hard Drive remove and replacement JEM System Laptop Terminal (SLT) 4 activation Manufacturing Device Feedstock Canister And Extruder Exchange Recharging Soyuz 733 Samsung PC Battery (if charge level is below 80%) Material Science Laboratory SCA Exchange2B #12 Water Recovery System (WRS) Distillation […]

from ISS On-Orbit Status Report http://ift.tt/2of9bfZ
via IFTTT

Ravens: DB Jerraud Powers, 29, announces retirement after eight seasons; team's third player to retire this offseason (ESPN)

from ESPN http://ift.tt/17lH5T2
via IFTTT

[FD] Adobe Creative Cloud Desktop Application <= v4.0.0.185 Privilege Escalation

? Zach Britton escapes 9th-inning jam as Orioles hold on for 2-1 victory over Blue Jays (ESPN)

from ESPN http://ift.tt/1eW1vUH
via IFTTT

Anonymous Contribution to Help Nursing Students at D'Youville

Anonymous Contribution to Help Nursing Students at D'Youville. Gift Honors Work of Sister Donna Del Santo, a D'Youville Graduate. Buffalo, New ...

from Google Alert - anonymous http://ift.tt/2oEHuRR
via IFTTT

How to count anonymous users in fivestar modules?

Hi, My goal is for anonymous users to be able to rate contents. My original plan is to have a question "was it helpful" with yes or no at the end of the ...

from Google Alert - anonymous http://ift.tt/2peP8jx
via IFTTT

Would You Want an Anonymous Couple to Pay Off Your Student Loans?

Then I asked myself if I'd want an anonymous couple to offer to pay off my debt. I'm debt-free at the moment, so this doesn't strictly apply, but I still think ...

from Google Alert - anonymous http://ift.tt/2oEHPEc
via IFTTT

Paul Manafort Allegedly Used Anonymous Shell Companies

These media reports also shine a light on how frequently these same Ukrainian political and economic figures have used anonymous companies and ...

from Google Alert - anonymous http://ift.tt/2oErhfi
via IFTTT

When anonymous sources question Leonard Fournette's passion, show them this hit

One question that's faced LSU RB Leonard Fournette concerns his passion for football. And there's a widespread feeling he's driven by stardom.

from Google Alert - anonymous http://ift.tt/2pyXTEI
via IFTTT

Information services

Stuart Jones in Tax News · Appeals against self assessment penalties. Anonymous replied: Did they reply ? (with 3 other replies). 11 months ago.

from Google Alert - anonymous http://ift.tt/2oEopiO
via IFTTT

Android Trojan Targeting Over 420 Banking Apps Worldwide Found On Google Play Store

Do you like watching funny videos online? I am not kind of a funny person, but I love watching funny videos clips online, and this is one of the best things that people can do in their spare time. But, beware if you have installed a funny video app from Google Play Store. A security researcher has discovered a new variant of the infamous Android banking Trojan hiding in apps under different

from The Hacker News http://ift.tt/2pyxCWY
via IFTTT

I have a new follower on Twitter

Susan Gunelius
President/CEO of KeySplash Creative, Inc., a marketing communications company. Author, speaker, marketing, branding, content marketing, social media, copywriter
Orlando, FL
http://t.co/gDdxrqNReb
Following: 17934 - Followers: 25642

April 13, 2017 at 01:54PM via Twitter http://twitter.com/susangunelius

ISS Daily Summary Report – 4/12/2017

Lighting Effects: The 49S subject conducted three Lighting Effects tests as shown below.  This is the second week of Lighting Effects activities for this subject. Biochem Profile/Repository Test: Upon wakeup, subject provided a sleep log entry before conducting the second half of the 48-hour urine collection process that began yesterday. Each sample was stowed in the Minus Eighty Degree Celsius Laboratory Freezer for ISS (MELFI) for freezing until they are returned for analysis. Cognition Test: Subject completed three runs of the Cognition test including pre-test questions and an array of cognitive tests with performance feedback. Individualized Real-Time Neurocognitive Assessment Toolkit for Space Flight Fatigue (Cognition) is a battery of tests that measure how spaceflight-related physical changes, such as microgravity and lack of sleep, can affect cognitive performance. Cognition includes brief computerized tests that cover a wide range of cognitive functions, and provides immediate feedback on current and past test results. The software allows for real-time measurement of cognitive performance while in space. Visual Performance Test: Subject unstowed the Visual Performance Test hardware and verified that they were testing under the correct Solid State Lighting Assemblies (SSLA) setting. Subject then completed the Numerical Verification Test and Color Discrimination Test and took photographs of the completed tests. The Lighting Effects experiment hopes to better quantify and qualify how lighting can effect habitability of spacecraft. The light bulbs on the ISS are being replaced with a new system designed for improved crew health and wellness. The Lighting Effects investigation studies the impact of the change from fluorescent light bulbs to solid-state light-emitting diodes (LEDs) with adjustable intensity and color and aims to determine if the new lights can improve crew circadian rhythms, sleep, and cognitive performance. Results from this investigation also have major implications for people on Earth who use electric lights.  ESA Active Dosimeter Swap: The crew swapped the worn Mobile Unit (MU) with a charged MU, then initiated the data download to the ESA Active Dosimeter Personal Stowage Device (PSD).  The European Crew Personal Active Dosimeter is an active device worn by European ISS crewmembers in orbit to measure radiation exposure. This device, coupled with other dosimeters in the European Space Agency’s (ESA) Columbus Laboratory, provides radiation dosage information that can be used to support risk assessment and dose management. The goal is to enable the verification of radiation monitoring systems for future medical monitoring of crewmembers in space. Fine Motor Skills (FMS): The crew completed an FMS session this morning, completing a series of interactive tasks. The investigation studies how the fine motor skills are effected by long-term microgravity exposure, different phases of microgravity adaptation, and sensorimotor recovery after returning to Earth gravity. The goal of the investigation is to answer how fine motor performance in microgravity trend/vary over the duration of a six-month and year-long space mission. how fine motor performance on orbit compare with that of a closely matched participant on Earth, and how performance trend/vary before and after gravitational transitions, including the periods of early flight adaptation, and very early/near immediate post-flight periods. Joint Station Local Area Network (LAN) Version 10.0 Transition Review: In preparation for Friday’s planned Node 2 and Lab Router Remove & Replace (R&R), the crew completed a procedures review. The transition involves both hardware/software changes and requires both onboard and ground operations. The entire JSL network will be down during Friday’s activity until the new system is operational.  Systems Operations Data File Updates: The crew completed pen and ink updates to emergency books to account for new pre-treat contamination constraints for Nitrile Gloves. Warning books were also updated to include COL Cycle 14.1 software changes.  Mobile Servicing System (MSS) Operations:  Last evening, Robotics Ground Controllers powered up the MSS and walked the Space Station Remote Manipulator System (SSRMS) off Mobile Base System (MBS) Power Data Grapple Fixture #4 (PDGF4) onto the Lab PDGF then onto the Node2 PDGF. While the SSRMS was based on the Lab PDGF with Latching End Effector A (LEE-A) as the base LEE, a Direct Drive Test was performed on SSRMS Joint 7 (the End B Roll Joint) for mechanism health trending.  Data was collected on the full range of motion of this joint to help characterize its behavior. MSS performance was nominal.  Today’s Planned Activities All activities were completed unless otherwise noted. Lighting Effects Sleep Log Entry HRF Generic Urine Collection HRF Generic Sample MELFI Retrieval Insertion Operations Fine Motor Skills Experiment Test Lighting Effects Cognition Test 1 Cupola Window Shutter 7 Close JSL Version 10 Procedure Review RGN WSTA Fill In Flight Maintenance Cupola Window 7 Scratch Pane Remove and Replace Crew Provisions Audit Portable Workstation Activation PWS2 Telecommand and Telemetry Checkout RFID Logistics Reader JSL Version 10 Procedure Review Deactivation of Camcorder, Video Control Monitor [ВКУ], and Closing CP SSC Applications HRF Generic Urine Collection Stow Lighting Effects Cognition Test 2 – Subject TOCA WRS Sample Analysis Cargo Water Container – Iodinated Degas Extravehicular Mobility Unit (EMU) Swap Lighting Effects Visual Performance Tests GLA Setting  – Subject EVA Extravehicular Mobility Unit (EMU) Full Water Tank Dump and Fill ESA ACTIVE DOSIMETER MOBILE UNIT SWAP Fluids Integrated Rack Doors Open Fluids Integrated Rack White Light Replace Fluids Integrated Rack Doors Close Metal Oxide (METOX) Regeneration Initiation Total Organic Carbon Analyzer (TOCA) Sample Data Record Lighting Effects Cognition Test 3 – Subject      Completed Task List Items LAB1S4 Microgravity Rack Barrier Bridge Bracket Assembly R&R Ground Activities All activities were completed unless otherwise noted. EWIS NCU troubleshooting Three-Day Look Ahead: Thursday, 04/13: Rodent Research inventory, ELF removal, MSL sample cartridge exchange Friday, 04/14: Lab/N2 JSL router R&R, CB2 hardware setup Saturday, 04/15: Crew off duty, housekeeping QUICK ISS Status – Environmental Control Group:   Component Status Elektron On Vozdukh Manual [СКВ] 1 – SM Air Conditioner System (“SKV1”) Off           [СКВ] 2 – SM Air Conditioner System (“SKV2”) On Carbon Dioxide Removal Assembly (CDRA) Lab Standby Carbon Dioxide Removal Assembly (CDRA) Node 3 Operate Major Constituent Analyzer (MCA) Lab Operate Major Constituent Analyzer (MCA) Node 3 Operate Oxygen […]

from ISS On-Orbit Status Report http://ift.tt/2nIxJTz
via IFTTT

Get 9 Popular Online Hacking Training Course Package for Just $49

Since the Internet is filled with hackers and cyber criminals keen on hacking networks for valuable information, ethical hackers are in huge demand and being hired by almost every industry to help them keep their networks protected. These ethical hackers, penetration testers, and information security analysts not only gain reputation in the IT industry but are also one of the most well-paid

from The Hacker News http://ift.tt/2oaowyC
via IFTTT

Anonymous Jobs

Look at Anonymous profile and browse the latest full & part-time jobs and vacancies in the UK - 300395 - CV-Library.

from Google Alert - anonymous http://ift.tt/2oan2o5
via IFTTT

Goldholics Anonymous

Courtesy of ZeroHedge. View original post here. Authored by Kevin Muir via The Macro Tourist blog, With the rising global political tensions, gold has ...

from Google Alert - anonymous http://ift.tt/2ovatXR
via IFTTT

Here's How Hacker Activated All Dallas Emergency Sirens On Friday Night

Last weekend when outdoor emergency sirens in Dallas cried loudly for over 90 minutes, many researchers concluded that some hackers hijacked the alarm system by exploiting an issue in a vulnerable computer network. But it turns out that the hackers did not breach Dallas' emergency services computer systems to trigger the city's outdoor sirens for tornado warnings and other emergencies, rather

from The Hacker News http://ift.tt/2p9IYnL
via IFTTT

[InsideNothing] Dennis N. liked your post "[FD] Kajona 4.7: XSS & Directory Traversal"

Dennis N. liked your post on InsideNothing They thought [FD] Kajona 4.7: XSS & Directory Traversal was pretty awesome. You should go see what they're up to. Maybe you'll like their blog as much as they liked yours! Great posts worth seeing from Dennis N.: About Mediterranean Harvest: Vegetarian Recipes from the World's Healthiest Cuisine True Strength: My Journey from Hercules to Mere Mortal--and How Nearly Dying Saved My Life Want less email? Modify your Notification Options. Moderate comments on the go Check out the mobile WordPress Apps. Thanks for flying with WordPress.com

Source: Gmail -> IFTTT-> Blogger

[InsideNothing] Dennis N. liked your post "[InsideNothing] hitebook.net liked your post "[FD] Kajona 4.7: XSS & Directory Traversal""

Dennis N. liked your post on InsideNothing They thought [InsideNothing] hitebook.net liked your post "[FD] Kajona 4.7: XSS & Directory Traversal" was pretty awesome. You should go see what they're up to. Maybe you'll like their blog as much as they liked yours! Great posts worth seeing from Dennis N.: About Mediterranean Harvest: Vegetarian Recipes from the World's Healthiest Cuisine True Strength: My Journey from Hercules to Mere Mortal--and How Nearly Dying Saved My Life Want less email? Modify your Notification Options. Moderate comments on the go Check out the mobile WordPress Apps. Thanks for flying with WordPress.com

Source: Gmail -> IFTTT-> Blogger

Not Just Criminals, But Governments Were Also Using MS Word 0-Day Exploit

Recently we reported about a critical code execution vulnerability in Microsoft Word that was being exploited in the wild by cyber criminal groups to distribute malware like Dridex banking trojans and Latentbot. Now, it turns out that the same previously undisclosed vulnerability in Word (CVE-2017-0199) was also actively being exploited by the government-sponsored hackers to spy on Russian

from The Hacker News http://ift.tt/2oC8JMF
via IFTTT

Moons and Jupiter

On April 10, a Full Moon and Jupiter shared this telephoto field of view. Both were near opposition, opposite the Sun in Earth's night sky. Captured when a passing cloud bank dimmmed the bright moonlight slightly, the single exposure reveals the familiar face of our fair planet's own large natural satellite, along with a line up of the ruling gas giant's four Galilean moons. Labeled top to bottom, the tiny pinpricks of light above bright Jupiter are Callisto, Europa, Ganymede, and Io. Closer and brighter, our own natural satellite appears to loom large. But Callisto, Ganymede, and Io are physically larger than Earth's Moon, while water world Europa is only slightly smaller. In fact, of the Solar System's six largest planetary satellites, only Saturn's moon Titan is missing from the scene. via NASA http://ift.tt/2prWH5V

? Trey Mancini, Jonathan Schoop take Red Sox P Steven Wright deep on back-to-back pitches to spark 6-run first inning in 12-5 win at Fenway (ESPN)

from ESPN http://ift.tt/1eW1vUH
via IFTTT

How you stay anonymous when providing a tip to Crime Stoppers

KENNEWICK, WA - Over the years, Crime Stoppers has been successful in the amount of crime and drugs they've taken off of the streets. At the end of ...

from Google Alert - anonymous http://ift.tt/2o7yWQd
via IFTTT

"Am I trying to needle the Red Sox? No, that's ridiculous" - Orioles' Buck Showalter insists he wasn't taking shots at Boston about flu (ESPN)

from ESPN http://ift.tt/1eW1vUH
via IFTTT

Prison Inmates Built PCs from e-Waste and Connected Online Using Prison Network

Can you imagine your world without the Internet? I know it's hard to imagine your life without the Internet, and the same was the case of two Ohio prisoners who built personal computers from parts from e-waste, hid them in the ceiling, and connected those PCs to the Internet via the prison's network. The incident occurred in 2015 but has now been made public by the State of Ohio's Office of

from The Hacker News http://ift.tt/2nFBg50
via IFTTT

Anonymous - Executive Chef

Established upscale restaurant located in the heart of the ever growing Quincy Center seeks an experienced Executive Chef. They are looking for a ...

from Google Alert - anonymous http://ift.tt/2ozMKGt
via IFTTT

McShay's Three-Round Mock: Ravens land WR Mike Williams at No. 16, followed by CB, LB and OT, generating plenty excitement - Jamison Hensley (ESPN)

from ESPN http://ift.tt/17lH5T2
via IFTTT

ISS Daily Summary Report – 4/11/2017

Lighting Effects:  Upon wakeup, the 49S subject provided a sleep log entry and began the first half of a 48-hour urine collection to support the Sleep Shift and Biochem Profile/Repository portion of the Lighting Effects investigation.  Subject conducted four urine collections which are performed within a 24-hour period. Each sample was stowed in the Minus Eighty Degree Celsius Laboratory Freezer for ISS (MELFI) for freezing for return and analysis. The Lighting Effects experiment hopes to better quantify and qualify how lighting can effect habitability of spacecraft. The light bulbs on the ISS are being replaced with a new system designed for improved crew health and wellness. The Lighting Effects investigation studies the impact of the change from fluorescent light bulbs to solid-state light-emitting diodes (LEDs) with adjustable intensity and color and aims to determine if the new lights can improve crew circadian rhythms, sleep, and cognitive performance. Results from this investigation also have major implications for people on Earth who use electric lights  Device for the study of Critical Liquids and Crystallization (DECLIC) Hard Drive Exchange:  The crew exchanged hard drives for the DECLIC investigation. DECLIC High Temperature Insert (HTI)-Reflight (R) studies water near its critical point, the point beyond which water loses its distinction between liquid and vapor and begins to behave as a dense gas. Salt tends to precipitate out from water at temperatures and pressures beyond its critical point. Understanding this behavior will assist designers in building extended-life and low-maintenance supercritical water oxidation (SCWO) reactors that will provide more environmentally friendly waste management systems and reduce operating costs of power plants that use supercritical water for its working fluid. Dose Tracker: The crew launched the Dose Tracker app before completing entries for medication tracking on an iPad. This investigation documents the medication usage of crewmembers before and during their missions by capturing data regarding medication use during spaceflight, including side effect qualities, frequencies and severities. The data is expected to either support or counter anecdotal evidence of medication ineffectiveness during flight and unusual side effects experienced during flight. It is also expected that specific, near-real-time questioning about symptom relief and side effects will provide the data required to establish whether spaceflight-associated alterations in pharmacokinetics (PK) or pharmacodynamics (PD) is occurring during missions. Expedite the Processing of Experiments to the Space Station (Express) Rack 6 (ER6) Improved Payload Ethernet Hub Gateway (iPEHG) Installation: The crew removed the Payload Ethernet Hub Bridge (PEHB) from ER6 and installed an Improved Payload Ethernet Hub Gateway (iPEHG) in its place.  The iPEHG provides additional payload data band width needed for the TangoLab payload arriving on SpX-11. The iPEHG was checked out and is performing nominally. The PWD was removed and reinstalled back in the same ER6 location, to allow access for installing the iPEHG.  While preparing for the iPEHG installation, the crew found damaged threads on one of the Microgravity Rack Barrier Posts and its threaded insert. The plan is to replace the Standoff Bridge Bracket to provide a good installation location for a spare Microgravity Rack Barrier Post. Remote Power Control Module (RPCM) S11A_C Remote Power Controller (RPC) 3 Status: Yesterday, RPCM S11A-C RPC 3 tripped, removing power from the Starboard Thermal Radiator (STR) Multiplexer/Demultiplexer (MDM). Due to unusual temperatures in the RPCM after the trip, specialists suspected that the trip might have been caused by an RPCM failure rather than a true overcurrent. Multiple attempts to reclose RPC 3 were unsuccessful.  Teams are reviewing data to attempt to isolate the failure to the RPCM or the STR MDM. Nitrogen Repress from Nitrogen/Oxygen Recharge System (NORS): Today, the crew performed the first nitrogen repress from a NORS N2 tank. The tank is now empty and ready for return on SpX-11.  Today’s Planned Activities All activities were completed unless otherwise noted. Troubleshooting possible cause of generating inadvertent command in FGB Program-Logic Control Device [УПЛУ] via SM-FGB – X Docking Assembly, preparation.  ISS HAM Radio Power Up COTS UHF Communications Unit Removal Dose Tracker Data Entry Subject HRF Generic Urine Collection – Subject Meteor Shutter Open WHC UR and Insert Filter (IF) R&R PILOT-T. Preparation for the experiment. HRF Generic Sample MELFI Retrieval Insertion Operations ExPRESS Rack 6 Rack Preparation PILOT-T. Experiment Ops ARED Cylinder Flywheel Evacuation IPEHG Hardware Install PILOT-T. Closeout Ops Verification of ИП-1 Flow Indicator Position Filling (separation) of ЕДВ (КОВ) for Elektron or ЕДВ-СВ. HRF Generic Sample MELFI Retrieval Insertion Operations Microgravity Science Glovebox Equipment Inspection and Cleaning EMU Radio Frequency (RF) Camera Assembly (ERCA) Inspect CONTENT. Experiment Ops ExPRESS Rack 6 Rack Reconfiguration HRF Generic Urine Collection Female – Subject Galley Potable Water Dispenser (PWD) Installation HRF Generic Sample MELFI Retrieval Insertion Operations DECLIC Removable Hard Disk Drive Exchange Atmosphere Control and Supply (ACS) Nitrogen Manual Valve Open Completed Task List Items Advanced Resistive Exercise Device (ARED) Quarterly Inspection [Completed GMT 98] Treadmill 2 System (T2) Monthly Inspection [Completed GMT 98] Data Prep for Return [Completed GMT 98] ESA Active Dosimeter Area Monitoring Mobile Unit Stow [Completed GMT 98] ESA PAO Recorded Message “150 e du Canada” [Completed GMT 98] ESA PAO Message – Martian Meteorite video [Completed GMT 98] ESA PAO Message – St. Exupery writing contest winners [Completed GMT 98] EVA Hardware Audit [Completed GMT 98] Health Maintenance System (HMS) Automated External Defibrillator (AED) Inspection [Completed GMT 98] NOD1S4 Zero-G Stowage Rack Strap Install [Completed GMT 98] Station Support Computer 1 Reseat Hard Disk Drive [Completed GMT 98] Health Maintenance System (HMS) Spaceflight Cognitive Assessment Tool for Windows (WinSCAT) Test [Completed GMT 99] RED Video Card Swap [Completed GMT 99] SSC 4 Primary Hard Drive Swap [Completed GMT 99] EXPRESS Rack 6 Locker Removal [Completed GMT 100] NOD1D4 Audit – Part 2 [Completed GMT 100] Rodent Research Inventory Audit [Completed GMT 100] Strata Final Card Changeout [Completed GMT 100] Ground Activities All activities were completed unless otherwise noted. SSRMS walkoff to N2 PDGF IPEHG ER6 Activation and Checkout NORS N2 repress Three-Day Look Ahead: Wednesday, 04/12: Cognition, ER6 locker install/CUCU install, Man. Device feedstock canister, extruder, print tray exchange, GLA […]

from ISS On-Orbit Status Report http://ift.tt/2ooM3gQ
via IFTTT

Overeaters Anonymous Friday 5:30

Overeaters Anonymous Friday Night 5:30 pm Literature and Spirituality Meeting Location near Anderson Middle School 2889 Vernon Street Anderson, ...

from Google Alert - anonymous http://ift.tt/2p7yAKw
via IFTTT

Microsoft Issues Patches for Actively Exploited Critical Vulnerabilities

Besides a previously undisclosed code-execution flaw in Microsoft Word, the tech giant patches two more zero-day vulnerabilities that attackers had been exploiting in the wild for months, as part of this month's Patch Tuesday. In total, Microsoft patches 45 unique vulnerabilities in its nine products, including three previously undisclosed vulnerabilities under active attack. The first

from The Hacker News http://ift.tt/2nDB8CU
via IFTTT

Hackers Can Steal Your Passwords Just by Monitoring SmartPhone Sensors

Do you know how many kinds of sensors your smartphone has inbuilt? And what data they gather about your physical and digital activities? An average smartphone these days is packed with a wide array of sensors such as GPS, Camera, microphone, accelerometer, magnetometer, proximity, gyroscope, pedometer, and NFC, to name a few. Now, according to a team of scientists from Newcastle University

from The Hacker News http://ift.tt/2os05jw
via IFTTT

Anonymous's Activity

Anonymous started a new thread symptoms for recurrence of colorectal cancer. It is just over a year when I had an operation to remove cancer from ...

from Google Alert - anonymous http://ift.tt/2p4lG2q
via IFTTT

Leo Trio

This group is popular in the northern spring. Famous as the Leo Triplet, the three magnificent galaxies gather in one field of view. Crowd pleasers when imaged with even modest telescopes, they can be introduced individually as NGC 3628 (left), M66 (bottom right), and M65 (top). All three are large spiral galaxies but they tend to look dissimilar because their galactic disks are tilted at different angles to our line of sight. NGC 3628 is seen edge-on, with obscuring dust lanes cutting across the plane of the galaxy, while the disks of M66 and M65 are both inclined enough to show off their spiral structure. Gravitational interactions between galaxies in the group have also left telltale signs, including the warped and inflated disk of NGC 3628 and the drawn out spiral arms of M66. This gorgeous view of the region spans about one degree (two full moons) on the sky. The field covers over 500 thousand light-years at the trio's estimated distance of 30 million light-years. via NASA http://ift.tt/2p3iAJs

Anonymous Biker Avatar

Buy Anonymous Biker Avatar [DLC] for PS4 from PlayStation®Store US for $1.99. Download PlayStation® games and DLC to PS4™, PS3™, and PS ...

from Google Alert - anonymous http://ift.tt/2o3j6p0
via IFTTT

A Midwife Discusses Birth Schmutz

Beautiful Stories From Anonymous People #56 April 10, 2017. Chris asks a midwife some real questions: has she ever seen someone in labor punch ...

from Google Alert - anonymous http://ift.tt/2p3BAKq
via IFTTT

Anonymous Rex by Eric Garcia

Great deals on Anonymous Rex by Eric Garcia. Limited-time free and discounted ebook deals for Anonymous Rex and other great books.

from Google Alert - anonymous http://ift.tt/2prgna6
via IFTTT

Orioles: OF Michael Bourn likely to rejoin team after reaching agreement on restructured contract - Jerry Crasnick (ESPN)

from ESPN http://ift.tt/1eW1vUH
via IFTTT

ISS Daily Summary Report – 4/10/2017

48 Soyuz (48S) Undock and Landing: Shane Kimbrough, Sergey Ryzhikov, and Andrei Borisenko undocked from the ISS at 3:00AM CDT and landed nominally at 6:21AM CDT today. Kimbrough and support personnel are aboard the G5 plane enroute to Houston. The ISS will be in 3-crew operations until the arrival of 50S scheduled to launch on April 20, 2017. Today is an on board crew day off. Nominal reporting will resume tomorrow, Tuesday, April 11.

from ISS On-Orbit Status Report http://ift.tt/2pppOH8
via IFTTT

Anonymous pledge helps funeral fund

Anonymous pledge helps funeral fund ... The £6,000 target was smashed yesterday when an anonymous backer offered up a £2,000 donation.

from Google Alert - anonymous http://ift.tt/2on4CUm
via IFTTT

Unpatched Microsoft Word Flaw is Being Used to Spread Dridex Banking Trojan

If you are a regular reader of The Hacker News, you might be aware of an ongoing cyber attack — detected in the wild by McAfee and FireEye — that silently installs malware on fully-patched computers by exploiting an unpatched Microsoft Word vulnerability in all current versions of Microsoft Office. Now, according to security firm Proofpoint, the operators of the Dridex malware started

from The Hacker News http://ift.tt/2p2kA4C
via IFTTT

Eye on Health with Suzanne Radford: Alcoholics Anonymous #DubaiToday

Suzanne Radford is discussing alcohol addiction with members of Alcholics Anonymous from the UAE. April marks Alcohol Awareness Month and ...

from Google Alert - anonymous http://ift.tt/2omrnaZ
via IFTTT

U.S. Takes Down Kelihos Botnet After Its Russian Operator Arrested in Spain

A Russian computer hacker arrested over the weekend in Barcelona was apparently detained for his role in a massive computer botnet, and not for last year's US presidential election hack as reported by the Russian media. Peter Yuryevich Levashov, 32-years-old Russian computer programmer, suspected of operating the Kelihos botnet — a global network of over 100,000 infected computers that was

from The Hacker News http://ift.tt/2o2uq5v
via IFTTT

Anonymous read, list data, how is it crawled or returned to users via Search

I have a SharePoint 2010 Farm with a public (anonymous = read) facing site. When I search for text in list items when I am logged I, the list items.

from Google Alert - anonymous http://ift.tt/2otNJaV
via IFTTT

Man, Dog, Sun

This was supposed to be a shot of trees in front of a setting Sun. Sometimes, though, the unexpected can be photogenic. During some planning shots, a man walking his dog unexpected crossed the ridge. The result was so striking that, after cropping, it became the main shot. The reason the Sun appears so large is that the image was taken from about a kilometer away through a telephoto lens. Scattering of blue light by the Earth's atmosphere makes the bottom of the Sun appear slightly more red that the top. Also, if you look closely at the Sun, just above the man's head, a large group of sunspots is visible. The image was taken just last week in Bad Mergentheim, Germany. via NASA http://ift.tt/2nw42oh

April Fool's Day Snow Storm

NASA's Global Precipitation Measurement mission or GPM core observatory satellite flew over the United States northeast coast during a snow storm on April 1, 2017. This snow storm delivered up to 18 inches of snow in some parts of New England. The GPM Core Observatory carries two instruments that show the location and intensity of rain and snow, which defines a crucial part of the storm structure - and how it will behave. The GPM Microwave Imager sees through the tops of clouds to observe how much and where precipitation occurs, and the Dual-frequency Precipitation Radar observes precise details of precipitation in 3-dimensions. GPM data is part of the toolbox of satellite data used by forecasters and scientists to understand how storms behave. GPM is a joint mission between NASA and the Japan Aerospace Exploration Agency. Current and future data sets are available with free registration to users from NASA Goddard's Precipitation Processing Center website.

from NASA's Scientific Visualization Studio: Most Recent Items http://ift.tt/2p1nxT2
via IFTTT

Ravens 2017 preseason schedule released: Host Redskins in Week 1 (August 10-14), visit Saints in Week 4 (August 31-Sept 1) ?? (ESPN)

from ESPN http://ift.tt/17lH5T2
via IFTTT

Anonymous

Anonymous. informations. Lang : fr; Status : Visitor; Posts count : 0; ChatBox : 0. © 2010 - 2017. Root Me : Hacking and Information Security learning ...

from Google Alert - anonymous http://ift.tt/2oS4IEM
via IFTTT

Symantec Connects 40 Cyber Attacks to CIA Hacking Tools Exposed by Wikileaks

Security researchers have confirmed that the alleged CIA hacking tools recently exposed by WikiLeaks have been used against at least 40 governments and private organizations across 16 countries. Since March, as part of its "Vault 7" series, Wikileaks has published over 8,761 documents and other confidential information that the whistleblower group claims came from the US Central Intelligence

from The Hacker News http://ift.tt/2oYFmF8
via IFTTT

Hacker Caused Panic in Dallas by Turning ON Every Emergency Siren at Once

We have seen hackers flooding 911 emergency service with rogue requests to knock the service offline for an entire state, but some hacking incidents are worse than others. One such incident took place in Dallas on Friday night when hacker triggered a network of 156 emergency warning sirens for about two hours, waking up residents and sparking fears of a disaster. The emergency warning sirens

from The Hacker News http://ift.tt/2oiZD76
via IFTTT

MLB Power Rankings: Cubs remains No. 1 after first week of season, followed by Indians, Nationals; Orioles jump to No. 4 from 13 (ESPN)

from ESPN http://ift.tt/1eW1vUH
via IFTTT

Detect eyes, nose, lips, and jaw with dlib, OpenCV, and Python

Today’s blog post is part three in our current series on facial landmark detection and their applications to computer vision and image processing.

Two weeks ago I demonstrated how to install the dlib library which we are using for facial landmark detection.

Then, last week I discussed how to use dlib to actually detect facial landmarks in images.

Today we are going to take the next step and use our detected facial landmarks to help us label and extract face regions, including:

• Mouth
• Right eyebrow
• Left eyebrow
• Right eye
• Left eye
• Nose
• Jaw

To learn how to extract these face regions individually using dlib, OpenCV, and Python, just keep reading.

Looking for the source code to this post?
Jump right to the downloads section.

Detect eyes, nose, lips, and jaw with dlib, OpenCV, and Python

Today’s blog post will start with a discussion on the (x, y)-coordinates associated with facial landmarks and how these facial landmarks can be mapped to specific regions of the face.

We’ll then write a bit of code that can be used to extract each of the facial regions.

We’ll wrap up the blog post by demonstrating the results of our method on a few example images.

By the end of this blog post, you’ll have a strong understanding of how face regions are (automatically) extracted via facial landmarks and will be able to apply this knowledge to your own applications.

Facial landmark indexes for face regions

The facial landmark detector implemented inside dlib produces 68 (x, y)-coordinates that map to specific facial structures. These 68 point mappings were obtained by training a shape predictor on the labeled iBUG 300-W dataset.

Below we can visualize what each of these 68 coordinates map to:

Figure 1: Visualizing each of the 68 facial coordinate points from the iBUG 300-W dataset (higher resolution).

Examining the image, we can see that facial regions can be accessed via simple Python indexing (assuming zero-indexing with Python since the image above is one-indexed):

• The mouth can be accessed through points [48, 68].
• The right eyebrow through points [17, 22].
• The left eyebrow through points [22, 27].
• The right eye using [36, 42].
• The left eye with [42, 48].
• The nose using [27, 35].
• And the jaw via [0, 17].

These mappings are encoded inside the

FACIAL_LANDMARKS_IDXS

  dictionary inside face_utils of the imutils library:

# import the necessary packages
from collections import OrderedDict
import numpy as np
import cv2

# define a dictionary that maps the indexes of the facial
# landmarks to specific face regions
FACIAL_LANDMARKS_IDXS = OrderedDict([
        ("mouth", (48, 68)),
        ("right_eyebrow", (17, 22)),
        ("left_eyebrow", (22, 27)),
        ("right_eye", (36, 42)),
        ("left_eye", (42, 48)),
        ("nose", (27, 35)),
        ("jaw", (0, 17))
])

Using this dictionary we can easily extract the indexes into the facial landmarks array and extract various facial features simply by supplying a string as a key.

Visualizing facial landmarks with OpenCV and Python

A slightly harder task is to visualize each of these facial landmarks and overlay the results on an input image.

To accomplish this, we’ll need the

visualize_facial_landmarks

  function, already included in the imutils library:

# import the necessary packages
from collections import OrderedDict
import numpy as np
import cv2

# define a dictionary that maps the indexes of the facial
# landmarks to specific face regions
FACIAL_LANDMARKS_IDXS = OrderedDict([
        ("mouth", (48, 68)),
        ("right_eyebrow", (17, 22)),
        ("left_eyebrow", (22, 27)),
        ("right_eye", (36, 42)),
        ("left_eye", (42, 48)),
        ("nose", (27, 35)),
        ("jaw", (0, 17))
])

def rect_to_bb(rect):
        # take a bounding predicted by dlib and convert it
        # to the format (x, y, w, h) as we would normally do
        # with OpenCV
        x = rect.left()
        y = rect.top()
        w = rect.right() - x
        h = rect.bottom() - y

        # return a tuple of (x, y, w, h)
        return (x, y, w, h)

def shape_to_np(shape, dtype="int"):
        # initialize the list of (x, y)-coordinates
        coords = np.zeros((68, 2), dtype=dtype)

        # loop over the 68 facial landmarks and convert them
        # to a 2-tuple of (x, y)-coordinates
        for i in range(0, 68):
                coords[i] = (shape.part(i).x, shape.part(i).y)

        # return the list of (x, y)-coordinates
        return coords

def visualize_facial_landmarks(image, shape, colors=None, alpha=0.75):
        # create two copies of the input image -- one for the
        # overlay and one for the final output image
        overlay = image.copy()
        output = image.copy()

        # if the colors list is None, initialize it with a unique
        # color for each facial landmark region
        if colors is None:
                colors = [(19, 199, 109), (79, 76, 240), (230, 159, 23),
                        (168, 100, 168), (158, 163, 32),
                        (163, 38, 32), (180, 42, 220)]

Our

visualize_facial_landmarks

  function requires two arguments, followed by two optional ones, each detailed below:

• image
  

   : The image that we are going to draw our facial landmark visualizations on.

• shape
  

   : The NumPy array that contains the 68 facial landmark coordinates that map to various facial parts.

• colors
  

   : A list of BGR tuples used to color-code each of the facial landmark regions.

• alpha
  

   : A parameter used to control the opacity of the overlay on the original image.

Lines 45 and 46 create two copies of our input image — we’ll need these copies so that we can draw a semi-transparent overlay on the output image.

Line 50 makes a check to see if the

colors

  list is

None

 , and if so, initializes it with a preset list of BGR tuples (remember, OpenCV stores colors/pixel intensities in BGR order rather than RGB).

We are now ready to visualize each of the individual facial regions via facial landmarks:

# import the necessary packages
from collections import OrderedDict
import numpy as np
import cv2

# define a dictionary that maps the indexes of the facial
# landmarks to specific face regions
FACIAL_LANDMARKS_IDXS = OrderedDict([
        ("mouth", (48, 68)),
        ("right_eyebrow", (17, 22)),
        ("left_eyebrow", (22, 27)),
        ("right_eye", (36, 42)),
        ("left_eye", (42, 48)),
        ("nose", (27, 35)),
        ("jaw", (0, 17))
])

def rect_to_bb(rect):
        # take a bounding predicted by dlib and convert it
        # to the format (x, y, w, h) as we would normally do
        # with OpenCV
        x = rect.left()
        y = rect.top()
        w = rect.right() - x
        h = rect.bottom() - y

        # return a tuple of (x, y, w, h)
        return (x, y, w, h)

def shape_to_np(shape, dtype="int"):
        # initialize the list of (x, y)-coordinates
        coords = np.zeros((68, 2), dtype=dtype)

        # loop over the 68 facial landmarks and convert them
        # to a 2-tuple of (x, y)-coordinates
        for i in range(0, 68):
                coords[i] = (shape.part(i).x, shape.part(i).y)

        # return the list of (x, y)-coordinates
        return coords

def visualize_facial_landmarks(image, shape, colors=None, alpha=0.75):
        # create two copies of the input image -- one for the
        # overlay and one for the final output image
        overlay = image.copy()
        output = image.copy()

        # if the colors list is None, initialize it with a unique
        # color for each facial landmark region
        if colors is None:
                colors = [(19, 199, 109), (79, 76, 240), (230, 159, 23),
                        (168, 100, 168), (158, 163, 32),
                        (163, 38, 32), (180, 42, 220)]

        # loop over the facial landmark regions individually
        for (i, name) in enumerate(FACIAL_LANDMARKS_IDXS.keys()):
                # grab the (x, y)-coordinates associated with the
                # face landmark
                (j, k) = FACIAL_LANDMARKS_IDXS[name]
                pts = shape[j:k]

                # check if are supposed to draw the jawline
                if name == "jaw":
                        # since the jawline is a non-enclosed facial region,
                        # just draw lines between the (x, y)-coordinates
                        for l in range(1, len(pts)):
                                ptA = tuple(pts[l - 1])
                                ptB = tuple(pts[l])
                                cv2.line(overlay, ptA, ptB, colors[i], 2)

                # otherwise, compute the convex hull of the facial
                # landmark coordinates points and display it
                else:
                        hull = cv2.convexHull(pts)
                        cv2.drawContours(overlay, [hull], -1, colors[i], -1)

On Line 56 we loop over each entry in the

FACIAL_LANDMARKS_IDXS

  dictionary.

For each of these regions, we extract the indexes of the given facial part and grab the (x, y)-coordinates from the

shape

  NumPy array.

Lines 63-69 make a check to see if we are drawing the jaw, and if so, we simply loop over the individual points, drawing a line connecting the jaw points together.

Otherwise, Lines 73-75 handle computing the convex hull of the points and drawing the hull on the overlay.

The last step is to create a transparent overlay via the

cv2.addWeighted

  function:

# import the necessary packages
from collections import OrderedDict
import numpy as np
import cv2

# define a dictionary that maps the indexes of the facial
# landmarks to specific face regions
FACIAL_LANDMARKS_IDXS = OrderedDict([
        ("mouth", (48, 68)),
        ("right_eyebrow", (17, 22)),
        ("left_eyebrow", (22, 27)),
        ("right_eye", (36, 42)),
        ("left_eye", (42, 48)),
        ("nose", (27, 35)),
        ("jaw", (0, 17))
])

def rect_to_bb(rect):
        # take a bounding predicted by dlib and convert it
        # to the format (x, y, w, h) as we would normally do
        # with OpenCV
        x = rect.left()
        y = rect.top()
        w = rect.right() - x
        h = rect.bottom() - y

        # return a tuple of (x, y, w, h)
        return (x, y, w, h)

def shape_to_np(shape, dtype="int"):
        # initialize the list of (x, y)-coordinates
        coords = np.zeros((68, 2), dtype=dtype)

        # loop over the 68 facial landmarks and convert them
        # to a 2-tuple of (x, y)-coordinates
        for i in range(0, 68):
                coords[i] = (shape.part(i).x, shape.part(i).y)

        # return the list of (x, y)-coordinates
        return coords

def visualize_facial_landmarks(image, shape, colors=None, alpha=0.75):
        # create two copies of the input image -- one for the
        # overlay and one for the final output image
        overlay = image.copy()
        output = image.copy()

        # if the colors list is None, initialize it with a unique
        # color for each facial landmark region
        if colors is None:
                colors = [(19, 199, 109), (79, 76, 240), (230, 159, 23),
                        (168, 100, 168), (158, 163, 32),
                        (163, 38, 32), (180, 42, 220)]

        # loop over the facial landmark regions individually
        for (i, name) in enumerate(FACIAL_LANDMARKS_IDXS.keys()):
                # grab the (x, y)-coordinates associated with the
                # face landmark
                (j, k) = FACIAL_LANDMARKS_IDXS[name]
                pts = shape[j:k]

                # check if are supposed to draw the jawline
                if name == "jaw":
                        # since the jawline is a non-enclosed facial region,
                        # just draw lines between the (x, y)-coordinates
                        for l in range(1, len(pts)):
                                ptA = tuple(pts[l - 1])
                                ptB = tuple(pts[l])
                                cv2.line(overlay, ptA, ptB, colors[i], 2)

                # otherwise, compute the convex hull of the facial
                # landmark coordinates points and display it
                else:
                        hull = cv2.convexHull(pts)
                        cv2.drawContours(overlay, [hull], -1, colors[i], -1)

        # apply the transparent overlay
        cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output)

        # return the output image
        return output

After applying

visualize_facial_landmarks

  to an image and associated facial landmarks, the output would look similar to the image below:

Figure 2: A visualization of each facial landmark region overlaid on the original image.

To learn how to glue all the pieces together (and extract each of these facial regions), let’s move on to the next section.

Extracting parts of the face using dlib, OpenCV, and Python

Before you continue with this tutorial, make sure you have:

1. Installed dlib according to my instructions in this blog post.
2. Have installed/upgraded imutils to the latest version, ensuring you have access to the

   face_utils
   

     submodule: 

   pip install --upgrade imutils
   

From there, open up a new file, name it

detect_face_parts.py

 , and insert the following code:

# import the necessary packages
from imutils import face_utils
import numpy as np
import argparse
import imutils
import dlib
import cv2

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor", required=True,
        help="path to facial landmark predictor")
ap.add_argument("-i", "--image", required=True,
        help="path to input image")
args = vars(ap.parse_args())

# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])

# load the input image, resize it, and convert it to grayscale
image = cv2.imread(args["image"])
image = imutils.resize(image, width=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# detect faces in the grayscale image
rects = detector(gray, 1)

The first code block in this example is identical to the one in our previous tutorial.

We are simply:

• Importing our required Python packages (Lines 2-7).
• Parsing our command line arguments (Lines 10-15).
• Instantiating dlib’s HOG-based face detector and loading the facial landmark predictor (Lines 19 and 20).
• Loading and pre-processing our input image (Lines 23-25).
• Detecting faces in our input image (Line 28).

Again, for a more thorough, detailed overview of this code block, please see last week’s blog post on facial landmark detection with dlib, OpenCV, and Python.

Now that we have detected faces in the image, we can loop over each of the face ROIs individually:

# import the necessary packages
from imutils import face_utils
import numpy as np
import argparse
import imutils
import dlib
import cv2

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor", required=True,
        help="path to facial landmark predictor")
ap.add_argument("-i", "--image", required=True,
        help="path to input image")
args = vars(ap.parse_args())

# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])

# load the input image, resize it, and convert it to grayscale
image = cv2.imread(args["image"])
image = imutils.resize(image, width=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# detect faces in the grayscale image
rects = detector(gray, 1)

# loop over the face detections
for (i, rect) in enumerate(rects):
        # determine the facial landmarks for the face region, then
        # convert the landmark (x, y)-coordinates to a NumPy array
        shape = predictor(gray, rect)
        shape = face_utils.shape_to_np(shape)

        # loop over the face parts individually
        for (name, (i, j)) in face_utils.FACIAL_LANDMARKS_IDXS.items():
                # clone the original image so we can draw on it, then
                # display the name of the face part on the image
                clone = image.copy()
                cv2.putText(clone, name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
                        0.7, (0, 0, 255), 2)

                # loop over the subset of facial landmarks, drawing the
                # specific face part
                for (x, y) in shape[i:j]:
                        cv2.circle(clone, (x, y), 1, (0, 0, 255), -1)

For each face region, we determine the facial landmarks of the ROI and convert the 68 points into a NumPy array (Lines 34 and 35).

Then, for each of the face parts, we loop over them and on Line 38.

We draw the name/label of the face region on Lines 42 and 43, then draw each of the individual facial landmarks as circles on Lines 47 and 48.

To actually extract each of the facial regions we simply need to compute the bounding box of the (x, y)-coordinates associated with the specific region and use NumPy array slicing to extract it:

# import the necessary packages
from imutils import face_utils
import numpy as np
import argparse
import imutils
import dlib
import cv2

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor", required=True,
        help="path to facial landmark predictor")
ap.add_argument("-i", "--image", required=True,
        help="path to input image")
args = vars(ap.parse_args())

# initialize dlib's face detector (HOG-based) and then create
# the facial landmark predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])

# load the input image, resize it, and convert it to grayscale
image = cv2.imread(args["image"])
image = imutils.resize(image, width=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# detect faces in the grayscale image
rects = detector(gray, 1)

# loop over the face detections
for (i, rect) in enumerate(rects):
        # determine the facial landmarks for the face region, then
        # convert the landmark (x, y)-coordinates to a NumPy array
        shape = predictor(gray, rect)
        shape = face_utils.shape_to_np(shape)

        # loop over the face parts individually
        for (name, (i, j)) in face_utils.FACIAL_LANDMARKS_IDXS.items():
                # clone the original image so we can draw on it, then
                # display the name of the face part on the image
                clone = image.copy()
                cv2.putText(clone, name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
                        0.7, (0, 0, 255), 2)

                # loop over the subset of facial landmarks, drawing the
                # specific face part
                for (x, y) in shape[i:j]:
                        cv2.circle(clone, (x, y), 1, (0, 0, 255), -1)

                # extract the ROI of the face region as a separate image
                (x, y, w, h) = cv2.boundingRect(np.array([shape[i:j]]))
                roi = image[y:y + h, x:x + w]
                roi = imutils.resize(roi, width=250, inter=cv2.INTER_CUBIC)

                # show the particular face part
                cv2.imshow("ROI", roi)
                cv2.imshow("Image", clone)
                cv2.waitKey(0)

        # visualize all facial landmarks with a transparent overlay
        output = face_utils.visualize_facial_landmarks(image, shape)
        cv2.imshow("Image", output)
        cv2.waitKey(0)

Computing the bounding box of the region is handled on Line 51 via

cv2.boundingRect

 .

Using NumPy array slicing we can extract the ROI on Line 52.

This ROI is then resized to have a width of 250 pixels so we can better visualize it (Line 53).

Lines 56-58 display the individual face region to our screen.

Lines 61-63 then apply the

visualize_facial_landmarks

  function to create a transparent overlay for each facial part.

Face part labeling results

Now that our example has been coded up, let’s take a look at some results.

Be sure to use the “Downloads” section of this guide to download the source code + example images + dlib facial landmark predictor model.

From there, you can use the following command to visualize the results:

$ python detect_face_parts.py --shape-predictor shape_predictor_68_face_landmarks.dat \
        --image images/example_01.jpg

Notice how my mouth is detected first:

Figure 3: Extracting the mouth region via facial landmarks.

Followed by my right eyebrow:

Figure 4: Determining the right eyebrow of an image using facial landmarks and dlib.

Then the left eyebrow:

Figure 5: The dlib library can extract facial regions from an image.

Next comes the right eye:

Figure 6: Extracting the right eye of a face using facial landmarks, dlib, OpenCV, and Python.

Along with the left eye:

Figure 7: Extracting the left eye of a face using facial landmarks, dlib, OpenCV, and Python.

And finally the jawline:

Figure 8: Automatically determining the jawline of a face with facial landmarks.

As you can see, the bounding box of the jawline is m entire face.

The last visualization for this image are our transparent overlays with each facial landmark region highlighted with a different color:

Figure 9: A transparent overlay that displays the individual facial regions extracted via the image with facial landmarks.

Let’s try another example:

$ python detect_face_parts.py --shape-predictor shape_predictor_68_face_landmarks.dat \
        --image images/example_02.jpg

This time I have created a GIF animation of the output:

Figure 10: Extracting facial landmark regions with computer vision.

The same goes for our final example:

$ python detect_face_parts.py --shape-predictor shape_predictor_68_face_landmarks.dat \
        --image images/example_03.jpg

Figure 11: Automatically labeling eyes, eyebrows, nose, mouth, and jaw using facial landmarks.

Summary

In this blog post I demonstrated how to detect various facial structures in an image using facial landmark detection.

Specifically, we learned how to detect and extract the:

• Mouth
• Right eyebrow
• Left eyebrow
• Right eye
• Left eye
• Nose
• Jawline

This was accomplished using dlib’s pre-trained facial landmark detector along with a bit of OpenCV and Python magic.

At this point you’re probably quite impressed with the accuracy of facial landmarks — and there are clear advantages of using facial landmarks, especially for face alignment, face swapping, and extracting various facial structures.

…but the big question is:

“Can facial landmark detection run in real-time?”

To find out, you’ll need to stay tuned for next week’s blog post.

To be notified when next week’s blog post on real-time facial landmark detection is published, be sure to enter your email address in the form below!

See you then.

Downloads:

If you would like to download the code and images used in this post, please enter your email address in the form below. Not only will you get a .zip of the code, I’ll also send you a FREE 11-page Resource Guide on Computer Vision and Image Search Engines, including exclusive techniques that I don’t post on this blog! Sound good? If so, enter your email address and I’ll send you the code immediately!

Email address:

The post Detect eyes, nose, lips, and jaw with dlib, OpenCV, and Python appeared first on PyImageSearch.

from PyImageSearch http://ift.tt/2oq9UyR
via IFTTT