How YouTube Works Background - Moozonian Search

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GitHub Repo https://github.com/2389736818/Zombie-Outline

2389736818/Zombie-Outline

Zombie Outline Version: 1.0 Description: This mod is based on the old Zombie Horde mod, but with many more features. The ZH mod gives you a nice co-op environment for annihilating the mass of zombies, while this mod offers you a "smooth story", which is actually a month (31 days) of disaster which combine soldiers have to face. Aside from that, zombies yield experience, so allowing you to level up and increase your health, armor, speed, jump rate and evasion rate. There is a moderate number of different types of zombies, each of them having different stats and giving a different amount of experience when taken down. Every day it is harder and harder for the combine to complete their task as the number of zombies is increasing as well as their strength. The type of the day can either be demolition, survival or epidemic. When soldiers are caught in a demolition day, their taks is to annihilate a certain amount of zombies to complete it. There is no time limit for this. In the survival days, if they stay alive a certain amount of time (without all being dead at the same time), they will proceed to the next day. Epidemic days are similar to demolition days, only then you do not want to get hit by a zombie, as the virus that is spread in the air will hurt you through the wound more than you think it will. Ocasionally (in certain days), combine soldiers have to face motherzombies. In the demolition and epidemic days, they will come right after the number of regular zombies reaches zero, while in survival days they will come at the very beginning, but not required to slay them. This mod works in any way, with only bots on the human side and/or with real players. Terrorist team only contains bots as zombies. I personally haven't tested it with multiple players being on CT side at the same time, but it works quite well while I play it with bots. For a better glimpse on this mod, check this video. Zombie Outline Commands: /zomenu - Open up the mod menu. /spawn - Spawn if you haven't, but still it is early game. /return - Don't move for 20 seconds (or so) and return to spawn point. /info - Check your information, like level, experience, etc... /zhp - Show zombie health. /zoadmin - Admin menu for this mod. /fspawn <name> - Admin command that forces a player to spawn. /freturn <name> - Admin command that forces a player to return to spawn. /zoday <value> - Admin command to set next day (must be <= 31 and > 0). /zoendday <ct/t> - Admin command to immediately end the day (argument <ct/t> = winner) /zombies <value> - Admin command to set how many zombies are left. /zoaddexp <name> <amount> - Admin command to give a player experience. /zoaddlvl <name> <amount> - Admin command to give a player level. /zoexpboost <value> - Admin command to set the experience boost. Addon Commands: /weapons - Opets up the weapons menu. [weaponsmenu addon] /ambientson - Turns ambients (background music) on. [ambients addon] /ambientsoff - Turns ambients off. [ambients addon] /quakeon - Turns quake sounds on. [ambients addon] /quakeoff - Turns quake sounds off. [ambients addon] /spree - Check your killing/headshot spree. [ambients addon] /ambients <1/0> - Admin command to toggle ambients across whole server. [ambients addon] /quake <1/0> - Admin command to toggle quake sounds across whole server. [ambients addon] /mapstyle - Admin command to open up the mapstyle menu. [mapstyle addon] Installation: Simply download "Zombie Outline v1.0.zip" and extract it to your game directory. You will need to download all zombie models and other materials from here. Once downloaded, simply extract it to your game directory. I have uploaded few more addons. They are not necessary to have, but they improve the game experience and kinda add few more dimensions to the mod. If you want them, simply download and extract the "cstrike" folder from each addon (zip) to your game directory. Addon descriptions: Serverconfigs are in my opinion very useful, as they fix precaching models in case the Zombie Outline doesn't. Also with it, you can do few more configurations such as setting cvars and execing some cfgs on map start (for example) which automates Zombie Outline preperation. Mapstyle is optional. Adds darkness (customizable) to the map and also the ability to change the sky. For this addon, you will have to download skyboxes, and extract them to your game directory. Ambients sure give good background music (from Half-Life). Quake sounds also included. Another optional, but use it (as well as the mapstyle!). ;) Weaponsmenu, optional. Nothing too special, as the name is selfexplainig. In Zombie Outline, the Blackmarket Shop, which is meant for buying weapons (I think), still isn't working as I am not sure how would I want it to be created. So think of weaponsmenu as a temporary replacement. If you want to be hardcore in game, don't use it ;) Before you start commenting why haven't I put these as seperate plugins in new threads, well, there ARE DEFINITELY better plugins you can find that do the same thing, I have made these only for one sole purpose, and it is for improving Zombie Outline. So, I don't see the point of putting them there. Configuration: Configuring Zombie Outline goes from "cfg/sourcemod/zo" folder. You actually edit .txt files, not .cfg files! But, if you have just installed the mod, you won't have the .txt files. To generate them, you will have to run the mod once on your server, and after that, close the server and get back. Download the "cfg example for v1.0.zip" for more details. Serverconfigs can be configured from "cfg/sourcemod/serverconfigs.cfg" file. There is almost no need for configuring it. But if you really want to, check the file and uhm... good luck (made this addon for some other reasons, but later found it useful here a bit). Mapstyle can be configured from "cfg/sourcemod/mapstyle.txt" and "cfg/sourcemod/mapstyle.cfg". Ambients can be configured from "cfg/sourcemod/ambients.txt". Weaponsmenu can be configured from "cfg/sourcemod/weaponsmenu.cfg". How to play: In order to be able to play, you must create yourself a "workspace" (basically sign yourself in the mod database, so your progress can be saved and similar). Nothing too chumpy, all you have to do is in the mod menu (default "/zomenu"), select the "Configuration" option and you will see "Create Workspace". Select it and you are ready to go! There are plenty of reasons why I didn't automate this. Debugging is the first and most important, but it is a long and boring story, so I will skip it. Known glitches: Sometimes "/spawn" command won't let you spawn, even though you are sure you can. Just use that command few more times, or wait few seconds and then use it, and it should work. It is not a glitch, but there are 2 more commands: client "/debug" and admin "/allowdebug <name>". There is no point of using them (so far), just wanned to let you know that they exist too. If you have any questions, problems or anything else, leave a reply here or a comment on the youtube video.

StackOverflow https://stackoverflow.com/questions/44673202/youtube-background-video-doesnt-expand-vertically

YouTube Background Video doesn't expand vertically

Tags: css, video, youtube

StackOverflow https://stackoverflow.com/questions/70725270/how-to-make-background-audio-pause-when-the-web-is-on-background

How to make background audio pause when the web is on background

Tags: javascript, audio, aframe, 8thwall-web

StackOverflow https://stackoverflow.com/questions/55215315/full-youtube-background-video

Full youtube background video

Tags: html, video, youtube, background

GitHub Repo https://github.com/molyswu/hand_detection

molyswu/hand_detection

using Neural Networks (SSD) on Tensorflow. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. I was interested mainly in detecting hands on a table (egocentric view point). I experimented first with the [Oxford Hands Dataset](http://www.robots.ox.ac.uk/~vgg/data/hands/) (the results were not good). I then tried the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) which was a much better fit to my requirements. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). Better still, provide code that can be adapted to other uses cases. If you use this tutorial or models in your research or project, please cite [this](#citing-this-tutorial). Here is the detector in action. <img src="images/hand1.gif" width="33.3%"><img src="images/hand2.gif" width="33.3%"><img src="images/hand3.gif" width="33.3%"> Realtime detection on video stream from a webcam . <img src="images/chess1.gif" width="33.3%"><img src="images/chess2.gif" width="33.3%"><img src="images/chess3.gif" width="33.3%"> Detection on a Youtube video. Both examples above were run on a macbook pro **CPU** (i7, 2.5GHz, 16GB). Some fps numbers are: | FPS | Image Size | Device| Comments| | ------------- | ------------- | ------------- | ------------- | | 21 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run without visualizing results| | 16 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | | 11 | 640 * 480 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | > Note: The code in this repo is written and tested with Tensorflow `1.4.0-rc0`. Using a different version may result in [some errors](https://github.com/tensorflow/models/issues/1581). You may need to [generate your own frozen model](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/) graph using the [model checkpoints](model-checkpoint) in the repo to fit your TF version. **Content of this document** - Motivation - Why Track/Detect hands with Neural Networks - Data preparation and network training in Tensorflow (Dataset, Import, Training) - Training the hand detection Model - Using the Detector to Detect/Track hands - Thoughts on Optimizations. > P.S if you are using or have used the models provided here, feel free to reach out on twitter ([@vykthur](https://twitter.com/vykthur)) and share your work! ## Motivation - Why Track/Detect hands with Neural Networks? There are several existing approaches to tracking hands in the computer vision domain. Incidentally, many of these approaches are rule based (e.g extracting background based on texture and boundary features, distinguishing between hands and background using color histograms and HOG classifiers,) making them not very robust. For example, these algorithms might get confused if the background is unusual or in situations where sharp changes in lighting conditions cause sharp changes in skin color or the tracked object becomes occluded.(see [here for a review](https://www.cse.unr.edu/~bebis/handposerev.pdf) paper on hand pose estimation from the HCI perspective) With sufficiently large datasets, neural networks provide opportunity to train models that perform well and address challenges of existing object tracking/detection algorithms - varied/poor lighting, noisy environments, diverse viewpoints and even occlusion. The main drawbacks to usage for real-time tracking/detection is that they can be complex, are relatively slow compared to tracking-only algorithms and it can be quite expensive to assemble a good dataset. But things are changing with advances in fast neural networks. Furthermore, this entire area of work has been made more approachable by deep learning frameworks (such as the tensorflow object detection api) that simplify the process of training a model for custom object detection. More importantly, the advent of fast neural network models like ssd, faster r-cnn, rfcn (see [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models) ) etc make neural networks an attractive candidate for real-time detection (and tracking) applications. Hopefully, this repo demonstrates this. > If you are not interested in the process of training the detector, you can skip straight to applying the [pretrained model I provide in detecting hands](#detecting-hands). Training a model is a multi-stage process (assembling dataset, cleaning, splitting into training/test partitions and generating an inference graph). While I lightly touch on the details of these parts, there are a few other tutorials cover training a custom object detector using the tensorflow object detection api in more detail[ see [here](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) and [here](https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9) ]. I recommend you walk through those if interested in training a custom object detector from scratch. ## Data preparation and network training in Tensorflow (Dataset, Import, Training) **The Egohands Dataset** The hand detector model is built using data from the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) dataset. This dataset works well for several reasons. It contains high quality, pixel level annotations (>15000 ground truth labels) where hands are located across 4800 images. All images are captured from an egocentric view (Google glass) across 48 different environments (indoor, outdoor) and activities (playing cards, chess, jenga, solving puzzles etc). <img src="images/egohandstrain.jpg" width="100%"> If you will be using the Egohands dataset, you can cite them as follows: > Bambach, Sven, et al. "Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions." Proceedings of the IEEE International Conference on Computer Vision. 2015. The Egohands dataset (zip file with labelled data) contains 48 folders of locations where video data was collected (100 images per folder). ``` -- LOCATION_X -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder -- LOCATION_Y -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder ``` **Converting data to Tensorflow Format** Some initial work needs to be done to the Egohands dataset to transform it into the format (`tfrecord`) which Tensorflow needs to train a model. This repo contains `egohands_dataset_clean.py` a script that will help you generate these csv files. - Downloads the egohands datasets - Renames all files to include their directory names to ensure each filename is unique - Splits the dataset into train (80%), test (10%) and eval (10%) folders. - Reads in `polygons.mat` for each folder, generates bounding boxes and visualizes them to ensure correctness (see image above). - Once the script is done running, you should have an images folder containing three folders - train, test and eval. Each of these folders should also contain a csv label document each - `train_labels.csv`, `test_labels.csv` that can be used to generate `tfrecords` Note: While the egohands dataset provides four separate labels for hands (own left, own right, other left, and other right), for my purpose, I am only interested in the general `hand` class and label all training data as `hand`. You can modify the data prep script to generate `tfrecords` that support 4 labels. Next: convert your dataset + csv files to tfrecords. A helpful guide on this can be found [here](https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/).For each folder, you should be able to generate `train.record`, `test.record` required in the training process. ## Training the hand detection Model Now that the dataset has been assembled (and your tfrecords), the next task is to train a model based on this. With neural networks, it is possible to use a process called [transfer learning](https://www.tensorflow.org/tutorials/image_retraining) to shorten the amount of time needed to train the entire model. This means we can take an existing model (that has been trained well on a related domain (here image classification) and retrain its final layer(s) to detect hands for us. Sweet!. Given that neural networks sometimes have thousands or millions of parameters that can take weeks or months to train, transfer learning helps shorten training time to possibly hours. Tensorflow does offer a few models (in the tensorflow [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models)) and I chose to use the `ssd_mobilenet_v1_coco` model as my start point given it is currently (one of) the fastest models (read the SSD research [paper here](https://arxiv.org/pdf/1512.02325.pdf)). The training process can be done locally on your CPU machine which may take a while or better on a (cloud) GPU machine (which is what I did). For reference, training on my macbook pro (tensorflow compiled from source to take advantage of the mac's cpu architecture) the maximum speed I got was 5 seconds per step as opposed to the ~0.5 seconds per step I got with a GPU. For reference it would take about 12 days to run 200k steps on my mac (i7, 2.5GHz, 16GB) compared to ~5hrs on a GPU. > **Training on your own images**: Please use the [guide provided by Harrison from pythonprogramming](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) on how to generate tfrecords given your label csv files and your images. The guide also covers how to start the training process if training locally. [see [here] (https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/)]. If training in the cloud using a service like GCP, see the [guide here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_cloud.md). As the training process progresses, the expectation is that total loss (errors) gets reduced to its possible minimum (about a value of 1 or thereabout). By observing the tensorboard graphs for total loss(see image below), it should be possible to get an idea of when the training process is complete (total loss does not decrease with further iterations/steps). I ran my training job for 200k steps (took about 5 hours) and stopped at a total Loss (errors) value of 2.575.(In retrospect, I could have stopped the training at about 50k steps and gotten a similar total loss value). With tensorflow, you can also run an evaluation concurrently that assesses your model to see how well it performs on the test data. A commonly used metric for performance is mean average precision (mAP) which is single number used to summarize the area under the precision-recall curve. mAP is a measure of how well the model generates a bounding box that has at least a 50% overlap with the ground truth bounding box in our test dataset. For the hand detector trained here, the mAP value was **0.9686@0.5IOU**. mAP values range from 0-1, the higher the better. <img src="images/accuracy.jpg" width="100%"> Once training is completed, the trained inference graph (`frozen_inference_graph.pb`) is then exported (see the earlier referenced guides for how to do this) and saved in the `hand_inference_graph` folder. Now its time to do some interesting detection. ## Using the Detector to Detect/Track hands If you have not done this yet, please following the guide on installing [Tensorflow and the Tensorflow object detection api](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). This will walk you through setting up the tensorflow framework, cloning the tensorflow github repo and a guide on - Load the `frozen_inference_graph.pb` trained on the hands dataset as well as the corresponding label map. In this repo, this is done in the `utils/detector_utils.py` script by the `load_inference_graph` method. ```python detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) print("> ====== Hand Inference graph loaded.") ``` - Detect hands. In this repo, this is done in the `utils/detector_utils.py` script by the `detect_objects` method. ```python (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) ``` - Visualize detected bounding detection_boxes. In this repo, this is done in the `utils/detector_utils.py` script by the `draw_box_on_image` method. This repo contains two scripts that tie all these steps together. - detect_multi_threaded.py : A threaded implementation for reading camera video input detection and detecting. Takes a set of command line flags to set parameters such as `--display` (visualize detections), image parameters `--width` and `--height`, videe `--source` (0 for camera) etc. - detect_single_threaded.py : Same as above, but single threaded. This script works for video files by setting the video source parameter videe `--source` (path to a video file). ```cmd # load and run detection on video at path "videos/chess.mov" python detect_single_threaded.py --source videos/chess.mov ``` > Update: If you do have errors loading the frozen inference graph in this repo, feel free to generate a new graph that fits your TF version from the model-checkpoint in this repo. Use the [export_inference_graph.py](https://github.com/tensorflow/models/blob/master/research/object_detection/export_inference_graph.py) script provided in the tensorflow object detection api repo. More guidance on this [here](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/). ## Thoughts on Optimization. A few things that led to noticeable performance increases. - Threading: Turns out that reading images from a webcam is a heavy I/O event and if run on the main application thread can slow down the program. I implemented some good ideas from [Adrian Rosebuck](https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/) on parrallelizing image capture across multiple worker threads. This mostly led to an FPS increase of about 5 points. - For those new to Opencv, images from the `cv2.read()` method return images in [BGR format](https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/). Ensure you convert to RGB before detection (accuracy will be much reduced if you dont). ```python cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) ``` - Keeping your input image small will increase fps without any significant accuracy drop.(I used about 320 x 240 compared to the 1280 x 720 which my webcam provides). - Model Quantization. Moving from the current 32 bit to 8 bit can achieve up to 4x reduction in memory required to load and store models. One way to further speed up this model is to explore the use of [8-bit fixed point quantization](https://heartbeat.fritz.ai/8-bit-quantization-and-tensorflow-lite-speeding-up-mobile-inference-with-low-precision-a882dfcafbbd). Performance can also be increased by a clever combination of tracking algorithms with the already decent detection and this is something I am still experimenting with. Have ideas for optimizing better, please share! <img src="images/general.jpg" width="100%"> Note: The detector does reflect some limitations associated with the training set. This includes non-egocentric viewpoints, very noisy backgrounds (e.g in a sea of hands) and sometimes skin tone. There is opportunity to improve these with additional data. ## Integrating Multiple DNNs. One way to make things more interesting is to integrate our new knowledge of where "hands" are with other detectors trained to recognize other objects. Unfortunately, while our hand detector can in fact detect hands, it cannot detect other objects (a factor or how it is trained). To create a detector that classifies multiple different objects would mean a long involved process of assembling datasets for each class and a lengthy training process. > Given the above, a potential strategy is to explore structures that allow us **efficiently** interleave output form multiple pretrained models for various object classes and have them detect multiple objects on a single image. An example of this is with my primary use case where I am interested in understanding the position of objects on a table with respect to hands on same table. I am currently doing some work on a threaded application that loads multiple detectors and outputs bounding boxes on a single image. More on this soon.

StackOverflow https://stackoverflow.com/questions/36437315/onetone-youtube-background-video-not-playing-in-safari-or-ie11

Onetone YouTube Background Video Not Playing in Safari or IE11

Tags: wordpress, internet-explorer, video, youtube, safari

StackOverflow https://stackoverflow.com/questions/11888171/youtube-video-causing-chrome-css-fixed-background-issue

youtube video causing chrome css fixed background issue

Tags: css, background, youtube

StackOverflow https://stackoverflow.com/questions/38017309/spinner-loading-in-youtube-search-engine

spinner loading in youtube search engine

Tags: javascript, jquery, css, html, youtube

StackOverflow https://stackoverflow.com/questions/58627996/react-native-deeplink-only-works-one-time

react-native deeplink only works one time

Tags: react-native, deep-linking

StackOverflow https://stackoverflow.com/questions/19243440/what-are-the-differences-between-youtube-urls-how-to-convert-from-one-form-to-a

what are the differences between youtube urls. how to convert from one form to another using php code

Tags: php, iframe, youtube

StackOverflow https://stackoverflow.com/questions/34433684/how-to-play-youtube-video-in-webview-after-getting-youtube-status-in-android

How to play YouTube video in webview after getting YouTube status in Android?

Tags: android, video, webview, youtube, android-webview

StackOverflow https://stackoverflow.com/questions/30152658/keeping-youtube-video-running-while-changing-activities

Keeping Youtube video running while changing activities

Tags: youtube-api, android-youtube-api

GitHub Repo https://github.com/mhowerton91/history

mhowerton91/history

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