Topic about How To Prevent Access To Drives From Your Computer only video tutorial

Hlw everyone, this is really amazing tutorial and you can easily create security in your HardDrive disk after watch this videos..

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PC user does not know the password changed despite his previous password WITH VIDEO TUTORIAL

Follow my image instruction:

 if you have facing any problems then watch this videos

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Improving YouTube video thumbnails with deep neural nets

Posted by Weilong Yang and Min-hsuan Tsai, Video Content Analysis team and the YouTube Creator team

Video thumbnails are often the first things viewers see when they look for something interesting to watch. A strong, vibrant, and relevant thumbnail draws attention, giving viewers a quick preview of the content of the video, and helps them to find content more easily. Better thumbnails lead to more clicks and views for video creators.

Inspired by the recent remarkable advances of deep neural networks (DNNs) in computer vision, such as image and video classification, our team has recently launched an improved automatic YouTube "thumbnailer" in order to help creators showcase their video content. Here is how it works.

The Thumbnailer Pipeline

While a video is being uploaded to YouTube, we first sample frames from the video at one frame per second. Each sampled frame is evaluated by a quality model and assigned a single quality score. The frames with the highest scores are selected, enhanced and rendered as thumbnails with different sizes and aspect ratios. Among all the components, the quality model is the most critical and turned out to be the most challenging to develop. In the latest version of the thumbnailer algorithm, we used a DNN for the quality model. So, what is the quality model measuring, and how is the score calculated?

The main processing pipeline of the thumbnailer.

(Training) The Quality Model

Unlike the task of identifying if a video contains your favorite animal, judging the visual quality of a video frame can be very subjective - people often have very different opinions and preferences when selecting frames as video thumbnails. One of the main challenges we faced was how to collect a large set of well-annotated training examples to feed into our neural network. Fortunately, on YouTube, in addition to having algorithmically generated thumbnails, many YouTube videos also come with carefully designed custom thumbnails uploaded by creators. Those thumbnails are typically well framed, in-focus, and center on a specific subject (e.g. the main character in the video). We consider these custom thumbnails from popular videos as positive (high-quality) examples, and randomly selected video frames as negative (low-quality) examples. Some examples of the training images are shown below.

Example training images.

The visual quality model essentially solves a problem we call "binary classification": given a frame, is it of high quality or not? We trained a DNN on this set using a similar architecture to the Inception network in GoogLeNet that achieved the top performance in the ImageNet 2014 competition.

Results

Compared to the previous automatically generated thumbnails, the DNN-powered model is able to select frames with much better quality. In a human evaluation, the thumbnails produced by our new models are preferred to those from the previous thumbnailer in more than 65% of side-by-side ratings. Here are some examples of how the new quality model performs on YouTube videos:

Example frames with low and high quality score from the DNN quality model, from video “Grand Canyon Rock Squirrel”.

Thumbnails generated by old vs. new thumbnailer algorithm.

We recently launched this new thumbnailer across YouTube, which means creators can start to choose from higher quality thumbnails generated by our new thumbnailer. Next time you see an awesome YouTube thumbnail, don’t hesitate to give it a thumbs up. ;)

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Leadtek	Leadtek video card drivers
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Maxtech	Maxtech video card drivers
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NVIDIA	NVIDIA video drivers
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PC Chips	PC Chips video card drivers
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PNY Technologies	PNY Technologies video card drivers
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Streaming Other HD Video Sites on the Raspberry PI

For those that are interested, this will allow you to stream internet shows like The Daily Show on your Raspberry Pi in HD with no lag.

A week or so ago I posted a hack which allowed people to stream youtube videos in the browser here. Unfortunately, this same method didnt work out of the box for some other video sites. They would stream a couple seconds then just end (I found this to happen with the daily show).



I now have a fix for that and have a script called youtube-safe, which can stream (high-quality with no lag on a decent internet connection) any video site that works with youtube-dl.

If you already have the Youtube scripts in my PiAUISuite installed, you can just update, otherwise you have to go through the install script and select which parts you want (this one being under youtube).

Install Instructions

sudo apt-get install git-core
git clone git://github.com/StevenHickson/PiAUISuite.git
cd PiAUISuite/Install/
./InstallAUISuite.sh

**NOTE, this will ask you if you want to install a lot of different scripts because it is a SUITE. You only have to pick the ones you want to use. If you only want to use the youtube scripts, press n on any other question except for the dependencies and youtube.

Update Instructions 

cd PiAUISuite
git pull
cd Install
sudo ./UpdateAUISuite.sh



Once that is done, you can watch the daily show in 1080p like so:
youtube-safe "http://www.thedailyshow.com/full-episodes/"

Even better, if you have voicecommand installed on your system, add the following line to the bottom of your config file for it to play the newest daily show with your voice:
~Daily Show==youtube-safe "http://www.thedailyshow.com/full-episodes/"

Ill work on making a browser plugin for this as well.

Consider donating to further my tinkering

Places you can find me

• Facebook
• Google+
• Linkedin
• Blogspot
• Google-Code
• Github
• YouTube
• Twitter

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Beyond Short Snippets Deep Networks for Video Classification

Posted by Software Engineers George Toderici and Sudheendra Vijayanarasimhan

Convolutional Neural Networks (CNNs) have recently shown rapid progress in advancing the state of the art of detecting and classifying objects in static images, automatically learning complex features in pictures without the need for manually annotated features. But what if one wanted not only to identify objects in static images, but also analyze what a video is about? After all, a video isn’t much more than a string of static images linked together in time.

As it turns out, video analysis provides even more information to the object detection and recognition task performed by CNN’s by adding a temporal component through which motion and other information can be also be used to improve classification. However, analyzing entire videos is challenging from a modeling perspective because one must model variable length videos with a fixed number of parameters. Not to mention that modeling variable length videos is computationally very intensive.

In Beyond Short Snippets: Deep Networks for Video Classification, to be presented at the 2015 Computer Vision and Pattern Recognition conference (CVPR 2015), we¹ evaluated two approaches - feature pooling networks and recurrent neural networks (RNNs) - capable of modeling variable length videos with a fixed number of parameters while maintaining a low computational footprint. In doing so, we were able to not only show that learning a high level global description of the video’s temporal evolution is very important for accurate video classification, but that our best networks exhibited significant performance improvements over previously published results on the Sports 1 million dataset (Sports-1M).

In previous work, we employed 3D-convolutions (meaning convolutions over time and space) over short video clips - typically just a few seconds - to learn motion features from raw frames implicitly and then aggregate predictions at the video level. For purposes of video classification, the low level motion features were only marginally outperforming models in which no motion was modeled.

To understand why, consider the following two images which are very similar visually but obtain drastically different scores from a CNN model trained on static images:

Slight differences in object poses/context can change the predicted class/confidence of CNNs trained on static images.

Since each individual video frame forms only a small part of the video’s story, static frames and short video snippets (2-3 secs) use incomplete information and could easily confuse subtle fine-grained distinctions between classes (e.g: Tae Kwon Do vs. Systema) or use portions of the video irrelevant to the action of interest.

To get around this frame-by-frame confusion, we used feature pooling networks that independently process each frame and then pool/aggregate the frame-level features over the entire video at various stages. Another approach we took was to utilize an RNN (derived from Long Short Term Memory units) instead of feature pooling, allowing the network itself to decide which parts of the video are important for classification. By sharing parameters through time, both feature pooling and RNN architectures are able to maintain a constant number of parameters while capturing a global description of the video’s temporal evolution.

In order to feed the two aggregation approaches, we compute an image “pixel-based” CNN model, based on the raw pixels in the frames of a video. We processed videos for the “pixel-based” CNNs at one frame per second to reduce computational complexity. Of course, at this frame rate implicit motion information is lost.

To compensate, we incorporate explicit motion information in the form of optical flow - the apparent motion of objects across a cameras viewfinder due to the motion of the objects or the motion of the camera. We compute optical flow images over adjacent frames to learn an additional “optical flow” CNN model.

Left: Image used for the pixel-based CNN; Right: Dense optical flow image used for optical flow CNN

The pixel-based and optical flow based CNN model outputs are provided as inputs to both the RNN and pooling approaches described earlier. These two approaches then separately aggregate the frame-level predictions from each CNN model input, and average the results. This allows our video-level prediction to take advantage of both image information and motion information to accurately label videos of similar activities even when the visual content of those videos varies greatly.

Badminton (top 25 videos according to the max-pooling model). Our methods accurately label all 25 videos as badminton despite the variety of scenes in the various videos because they use the entire video’s context for prediction.

We conclude by observing that although very different in concept, the max-pooling and the recurrent neural network methods perform similarly when using both images and optical flow. Currently, these two architectures are the top performers on the Sports-1M dataset. The main difference between the two was that the RNN approach was more robust when using optical flow alone on this dataset. Check out a short video showing some example outputs from the deep convolutional networks presented in our paper.

---

1 Research carried out in collaboration with University of Maryland, College Park PhD student Joe Yue-Hei Ng and University of Texas at Austin PhD student Matthew Hausknecht, as part of a Google Software Engineering Internship^?

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Fast download youtube videos only video tutorial

watch this video the you can easily download youtube videos without any problems...

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Raspberry Pi Automatic Video Looper

Download the image here:
https://onedrive.live.com/redir?resid=e0f17bd2b1ffe81!411&authkey=!AGW37ozZuaeyjDw&ithint=file%2czip

MIRROR: https://mega.co.nz/#!JBcDxLhQ!z41lixcpCS0-zvF2X9SkX-T98Gj5I4m3QIFjXKiZ5p4

Recently I helped out with an Architecture exhibition where they needed 8 projectors looping videos all at the same time. Deciding they didnt want to pay for 8 computers and pay for the electricity for 8 computers, they contacted me to see if I could help (Pictures to come soon!). They got 8 Raspberry Pis, which together cost less than one PC and use less an energy than one PC. Thats over 16 times the cost savings and energy savings.

I found a link that did this here. However, it has a couple big issues. It only supports certain file types, doesnt allow spaces in the files and a couple of other things, the escape key stops the whole loop, and their is a longer delay than I want. It also doesnt have the newest version of omxplayer, which plays subtitles and has other new fixes.

So I started creating my own and my version fixes these problems. So I now have decided to publish my image here.

How to set up the looper

1. Copy this image to an SD card following these directions
2. Put your video files in the /home/pi/videos directory
3. Plug it in

Features

• Supports all raspberry pi video types (mp4,avi,mkv,mp3,mov,mpg,flv,m4v)
• Supports subtitles (just put the srt file in the same directory as the videos)
• Reduces time between videos
• Allows spaces and special characters in the filename
• Allows pausing and skipping
• Full screen with a black background and no flicker
• SSH automatically enabled with user:pi and password:raspberry
• Allows easy video conversion using ffmpeg (ffmpeg INFILE -sameq OUTFILE)
• Has a default of HDMI audio output with one quick file change (replace -o hdmi with -o local in startvideos.sh).
• Can support external HDDs and other directories easily with one quick file change (Change FILES=/home/pi/videos/ to FILES=/YOUR DIRECTORY/ in startvideos.sh)

Source code

The source code can be found on github here. There are a couple main files listed below:

startvideos.sh
startfullscreen.sh
videoloop

This is perfect if you are working on a museum or school exhibit. Dont spend a lot of money and energy on a PC running windows and have problems like below (courtesy of the Atlanta Aquarium).

If you are a museum or other educationally based program and need help, you can contact me by e-mail at help@stevenhickson.com

Consider donating to further my tinkering since I do all this and help people out for free.

Places you can find me

• Facebook
• Google+
• Linkedin
• Blogspot
• Google-Code
• Github
• YouTube
• Twitter

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RPi Video Looper 2 0

I have a brand new version of the Raspberry Pi Videolooper that solves a lot of the common issues Im emailed about. Hopefully this solves problems for a lot of you. You can download the new image here:

https://onedrive.live.com/redir?resid=e0f17bd2b1ffe81!411&authkey=!AGW37ozZuaeyjDw&ithint=file%2czip

MIRROR: https://mega.co.nz/#!JBcDxLhQ!z41lixcpCS0-zvF2X9SkX-T98Gj5I4m3QIFjXKiZ5p4

For help you can post on the Raspberry Pi subreddit (probably the best way to get fast help)

How to set up the looper

1. Copy the above image to an SD card following these directions
2. If you want to use USB, change usb=0 to usb=1 in looperconfig.txt on the SD card (It is in the boot partition which can be read by Windows and Mac).
3. If you want to disable the looping autostart to make copying files easier, change autostart=1 to autostart=0 in looperconfig.txt
4. If you arent using a USB (NTFS) put your video files in the /home/pi/videos directory with SFTP or by turning autostart off. Otherwise, put your video files in a directory named videos on the root directory of your USB.
5. Set your config options and plug it in!

Features

• NEW: Has a config file in the boot directory (looperconfig.txt)
• NEW: Has a autostart flag in the config file (autostart=0,autostart=1)
• NEW: Has a USB flag in the config file (usb=0,usb=1), just set usb=1, then plug a USB (NTFS) with a videos folder on it and boot.
• NEW: Updated all packages (no heartbleed vulnerability, new omxplayer version).
• NEW: Only requires 4GB SD card and has a smaller zipped download file.
• Supports all raspberry pi video types (mp4,avi,mkv,mp3,mov,mpg,flv,m4v)
• Supports subtitles (just put the srt file in the same directory as the videos)
• Reduces time between videos
• Allows spaces and special characters in the filename
• Allows pausing and skipping
• Full screen with a black background and no flicker
• SSH automatically enabled with user:pi and password:raspberry
• Allows easy video conversion using ffmpeg (ffmpeg INFILE -sameq OUTFILE)
• Has a default of HDMI audio output with one quick file change (replace -o hdmi with -o local in startvideos.sh).
• Can support external HDDs and other directories easily with one quick file change (Change FILES=/home/pi/videos/ to FILES=/YOUR DIRECTORY/ in startvideos.sh)

Source code

The source code can be found on github here. 

This is perfect if you are working on a museum or school exhibit. Dont spend a lot of money and energy on a PC running windows and have problems like below (courtesy of the Atlanta Aquarium)!

If you are a museum or other educationally based program and need help, you can post on the Raspberry Pi subreddit (probably the best way to get fast help) or contact me by e-mail at help@stevenhickson.com

Consider donating to further my tinkering since I do all this and help people out for free.

Places you can find me

• Facebook
• Google+
• Linkedin
• Blogspot
• Google-Code
• Github
• YouTube
• Twitter

Read More..

ZOTAC Expands GeForce GTX 200 Series Lineup with GTX 275 Video Cards

ZOTAC today unleashes the latest addition to its GeForce GTX 200 series lineup – the new ZOTAC GeForce GTX 275 and GeForce GTX 275 AMP! Edition – the latest generation of graphics cards that deliver class-leading performance at value prices.

Powered by a 2nd Generation NVIDIA Unified Architecture with 240 screaming-fast stream processors, the ZOTAC GeForce GTX 275 series is ready to take on the latest DirectX 10, OpenGL 3.0, NVIDIA CUDA and PhysX powered games and applications for phenomenal performance and breathtaking visuals. High-speed GDDR3 memory technology feeds the GPU with high-resolution textures across an ultra-wide 448-bit memory interface. With 896MB of memory and ultra-wide memory interface, the ZOTAC GeForce GTX 275 series is prepared to take on the latest demanding game titles with ultra quality settings.

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Leadtek Announces the WinFast PX9600 GSO Extreme video card

Leadtek just announced the launch of the WinFast PX9600 GSO Extreme video card. Based on the high quality NVIDIA GeForce 9600 GSO core chip, PX9600 GSO is equipped with 384MB of memory and 96 Stream Processing Units. The Leadtek exclusive S-FANPIPE fansink design is also applied to PX9600 GSO. This innovative cooling system can effectively conduct GPU heat through an S-shape copper heat pipe to the fan for a dramatic cool down effect that creates a stable temperature for the graphic card to deliver a superior visual performance.

Leadtek WinFast PX9600 GSO Extreme has adopted 65nm technology and is powered by NVIDIAs GeForce 9600 GSO graphics processing unit. With 384GMB of GDDR3 on a 192-bit memory bus design, the GPU and memory clock can run at the amazing speed of 600MHz and 1800MHz respectively. Furthermore, PX9600 GSO Extreme has 96 Stream Processing Units, which combine with the new PCI Express 2.0 bus architecture to create the fastest 3D graphic processing speed. To offer users an even better and more convenient experience using a graphic card, PX9600 GSO Extreme not only supports Microsoft Windows Vista and Direct X 10 with full Shader Model 4.0, but also fully supportsthe existing PCI Express 1.0 motherboards.

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