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.

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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.*/ // ==UserScript== // @name AposLauncher // @namespace AposLauncher // @include http://agar.io/* // @version 4.123 // @grant none // @author http://www.twitch.tv/apostolique // ==/UserScript== var aposLauncherVersion = 4.123; Number.prototype.mod = function(n) { return ((this % n) + n) % n; }; Array.prototype.peek = function() { return this[this.length - 1]; }; var sha = "efde0488cc2cc176db48dd23b28a20b90314352b"; function getLatestCommit() { window.jQuery.ajax({ url: "https://api.github.com/repos/apostolique/Agar.io-bot/git/refs/heads/master", cache: false, dataType: "jsonp" }).done(function(data) { console.dir(data.data); console.log("hmm: " + data.data.object.sha); sha = data.data.object.sha; function update(prefix, name, url) { window.jQuery(document.body).prepend("<div id='" + prefix + "Dialog' style='position: absolute; left: 0px; right: 0px; top: 0px; bottom: 0px; z-index: 100; display: none;'>"); window.jQuery('#' + prefix + 'Dialog').append("<div id='" + prefix + "Message' style='width: 350px; background-color: #FFFFFF; margin: 100px auto; border-radius: 15px; padding: 5px 15px 5px 15px;'>"); window.jQuery('#' + prefix + 'Message').append("<h2>UPDATE TIME!!!</h2>"); window.jQuery('#' + prefix + 'Message').append("<p>Grab the update for: <a id='" + prefix + "Link' href='" + url + "' target=\"_blank\">" + name + "</a></p>"); window.jQuery('#' + prefix + 'Link').on('click', function() { window.jQuery("#" + prefix + "Dialog").hide(); window.jQuery("#" + prefix + "Dialog").remove(); }); window.jQuery("#" + prefix + "Dialog").show(); } window.jQuery.get('https://raw.githubusercontent.com/Apostolique/Agar.io-bot/master/launcher.user.js?' + Math.floor((Math.random() * 1000000) + 1), function(data) { var latestVersion = data.replace(/(\r\n|\n|\r)/gm, ""); latestVersion = latestVersion.substring(latestVersion.indexOf("// @version") + 11, latestVersion.indexOf("// @grant")); latestVersion = parseFloat(latestVersion + 0.0000); var myVersion = parseFloat(aposLauncherVersion + 0.0000); if (latestVersion > myVersion) { update("aposLauncher", "launcher.user.js", "https://github.com/Apostolique/Agar.io-bot/blob/" + sha + "/launcher.user.js/"); } console.log('Current launcher.user.js Version: ' + myVersion + " on Github: " + latestVersion); }); }).fail(function() {}); } getLatestCommit(); console.log("Running Bot Launcher!"); (function(d, e) { //UPDATE function keyAction(e) { if (84 == e.keyCode) { console.log("Toggle"); toggle = !toggle; } if (82 == e.keyCode) { console.log("ToggleDraw"); toggleDraw = !toggleDraw; } if (68 == e.keyCode) { window.setDarkTheme(!getDarkBool()); } if (70 == e.keyCode) { window.setShowMass(!getMassBool()); } if (69 == e.keyCode) { if (message.length > 0) { window.setMessage([]); window.onmouseup = function() {}; window.ignoreStream = true; } else { window.ignoreStream = false; window.refreshTwitch(); } } window.botList[botIndex].keyAction(e); } function humanPlayer() { //Don't need to do anything. return [getPointX(), getPointY()]; } function pb() { //UPDATE window.botList = window.botList || []; window.jQuery('#nick').val(originalName); function HumanPlayerObject() { this.name = "Human"; this.keyAction = function(key) {}; this.displayText = function() {return [];}; this.mainLoop = humanPlayer; } var hpo = new HumanPlayerObject(); window.botList.push(hpo); window.updateBotList(); ya = !0; Pa(); setInterval(Pa, 18E4); var father = window.jQuery("#canvas").parent(); window.jQuery("#canvas").remove(); father.prepend("<canvas id='canvas'>"); G = za = document.getElementById("canvas"); f = G.getContext("2d"); G.onmousedown = function(a) { if (Qa) { var b = a.clientX - (5 + m / 5 / 2), c = a.clientY - (5 + m / 5 / 2); if (Math.sqrt(b * b + c * c) <= m / 5 / 2) { V(); H(17); return } } fa = a.clientX; ga = a.clientY; Aa(); V(); }; G.onmousemove = function(a) { fa = a.clientX; ga = a.clientY; Aa(); }; G.onmouseup = function() {}; /firefox/i.test(navigator.userAgent) ? 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200 : 3E3)) } function Y(a) { e("#helloContainer").attr("data-gamemode", a); P = a; e("#gamemode").val(a) } function Va() { e("#region").val() ? d.localStorage.location = e("#region").val() : d.localStorage.location && e("#region").val(d.localStorage.location); e("#region").val() ? e("#locationKnown").append(e("#region")) : e("#locationUnknown").append(e("#region")) } function sb() { la && (la = !1, setTimeout(function() { la = !0 //UPDATE }, 6E4 * Ya)) } function Z(a) { return d.i18n[a] || d.i18n_dict.en[a] || a } function Za() { var a = ++Ba; console.log("Find " + y + P); e.ajax("https://m.agar.io/findServer", { error: function() { setTimeout(Za, 1E3) }, success: function(b) { a == Ba && (b.alert && alert(b.alert), Ca("ws://" + b.ip, b.token)) }, dataType: "json", method: "POST", cache: !1, crossDomain: !0, data: (y + P || "?") + "\n154669603" }) } function I() { ya && y && (e("#connecting").show(), Za()) } function Ca(a, b) { if (q) { q.onopen = null; q.onmessage = null; q.onclose = null; try { q.close() } catch (c) {} q = null } Da.la && (a = "ws://" + Da.la); if (null != L) { var l = L; L = function() { l(b) } } if (tb) { var d = a.split(":"); a = d[0] + "s://ip-" + d[1].replace(/\./g, "-").replace(/\//g, "") + ".tech.agar.io:" + (+d[2] + 2E3) } M = []; k = []; E = {}; v = []; Q = []; F = []; z = A = null; R = 0; $ = !1; console.log("Connecting to " + a); //UPDATE serverIP = a; q = new WebSocket(a); q.binaryType = "arraybuffer"; q.onopen = function() { var a; console.log("socket open"); a = N(5); a.setUint8(0, 254); a.setUint32(1, 5, !0); O(a); a = N(5); a.setUint8(0, 255); a.setUint32(1, 154669603, !0); O(a); a = N(1 + b.length); a.setUint8(0, 80); for (var c = 0; c < b.length; ++c) a.setUint8(c + 1, b.charCodeAt(c)); O(a); $a() }; q.onmessage = ub; q.onclose = vb; q.onerror = function() { console.log("socket error") } } function N(a) { return new DataView(new ArrayBuffer(a)) } function O(a) { q.send(a.buffer) } function vb() { $ && (ma = 500); console.log("socket close"); setTimeout(I, ma); ma *= 2 } function ub(a) { wb(new DataView(a.data)) } function wb(a) { function b() { for (var b = "";;) { var d = a.getUint16(c, !0); c += 2; if (0 == d) break; b += String.fromCharCode(d) } return b } var c = 0; 240 == a.getUint8(c) && (c += 5); switch (a.getUint8(c++)) { case 16: xb(a, c); break; case 17: aa = a.getFloat32(c, !0); c += 4; ba = a.getFloat32(c, !0); c += 4; ca = a.getFloat32(c, !0); c += 4; break; case 20: k = []; M = []; break; case 21: Ea = a.getInt16(c, !0); c += 2; Fa = a.getInt16(c, !0); c += 2; Ga || (Ga = !0, na = Ea, oa = Fa); break; case 32: M.push(a.getUint32(c, !0)); c += 4; break; case 49: if (null != A) break; var l = a.getUint32(c, !0), c = c + 4; F = []; for (var d = 0; d < l; ++d) { var p = a.getUint32(c, !0), c = c + 4; F.push({ id: p, name: b() }) } ab(); break; case 50: A = []; l = a.getUint32(c, !0); c += 4; for (d = 0; d < l; ++d) A.push(a.getFloat32(c, !0)), c += 4; ab(); break; case 64: pa = a.getFloat64(c, !0); c += 8; qa = a.getFloat64(c, !0); c += 8; ra = a.getFloat64(c, !0); c += 8; sa = a.getFloat64(c, !0); c += 8; aa = (ra + pa) / 2; ba = (sa + qa) / 2; ca = 1; 0 == k.length && (s = aa, t = ba, h = ca); break; case 81: var g = a.getUint32(c, !0), c = c + 4, e = a.getUint32(c, !0), c = c + 4, f = a.getUint32(c, !0), c = c + 4; setTimeout(function() { S({ e: g, f: e, d: f }) }, 1200) } } function xb(a, b) { bb = C = Date.now(); $ || ($ = !0, e("#connecting").hide(), cb(), L && (L(), L = null)); var c = Math.random(); Ha = !1; var d = a.getUint16(b, !0); b += 2; for (var u = 0; u < d; ++u) { var p = E[a.getUint32(b, !0)], g = E[a.getUint32(b + 4, !0)]; b += 8; p && g && (g.X(), g.s = g.x, g.t = g.y, g.r = g.size, g.J = p.x, g.K = p.y, g.q = g.size, g.Q = C) } for (u = 0;;) { d = a.getUint32(b, !0); b += 4; if (0 == d) break; ++u; var f, p = a.getInt16(b, !0); b += 4; g = a.getInt16(b, !0); b += 4; f = a.getInt16(b, !0); b += 2; for (var h = a.getUint8(b++), w = a.getUint8(b++), m = a.getUint8(b++), h = (h << 16 | w << 8 | m).toString(16); 6 > h.length;) h = "0" + h; var h = "#" + h, w = a.getUint8(b++), m = !!(w & 1), r = !!(w & 16); w & 2 && (b += 4); w & 4 && (b += 8); w & 8 && (b += 16); for (var q, n = "";;) { q = a.getUint16(b, !0); b += 2; if (0 == q) break; n += String.fromCharCode(q) } q = n; n = null; E.hasOwnProperty(d) ? (n = E[d], n.P(), n.s = n.x, n.t = n.y, n.r = n.size, n.color = h) : (n = new da(d, p, g, f, h, q), v.push(n), E[d] = n, n.ua = p, n.va = g); n.h = m; n.n = r; n.J = p; n.K = g; n.q = f; n.sa = c; n.Q = C; n.ba = w; q && n.B(q); - 1 != M.indexOf(d) && -1 == k.indexOf(n) && (document.getElementById("overlays").style.display = "none", k.push(n), n.birth = getLastUpdate(), n.birthMass = (n.size * n.size / 100), 1 == k.length && (s = n.x, t = n.y, db())) //UPDATE interNodes[d] = window.getCells()[d]; } //UPDATE Object.keys(interNodes).forEach(function(element, index) { //console.log("start: " + interNodes[element].updateTime + " current: " + D + " life: " + (D - interNodes[element].updateTime)); var isRemoved = !window.getCells().hasOwnProperty(element); //console.log("Time not updated: " + (window.getLastUpdate() - interNodes[element].getUptimeTime())); if (isRemoved && (window.getLastUpdate() - interNodes[element].getUptimeTime()) > 3000) { delete interNodes[element]; } else { for (var i = 0; i < getPlayer().length; i++) { if (isRemoved && computeDistance(getPlayer()[i].x, getPlayer()[i].y, interNodes[element].x, interNodes[element].y) < getPlayer()[i].size + 710) { delete interNodes[element]; break; } } } }); c = a.getUint32(b, !0); b += 4; for (u = 0; u < c; u++) d = a.getUint32(b, !0), b += 4, n = E[d], null != n && n.X(); //UPDATE //Ha && 0 == k.length && Sa(!1) } //UPDATE function computeDistance(x1, y1, x2, y2) { var xdis = x1 - x2; // <--- FAKE AmS OF COURSE! var ydis = y1 - y2; var distance = Math.sqrt(xdis * xdis + ydis * ydis); return distance; } /** * Some horse shit of some sort. * @return Horse Shit */ function screenDistance() { return Math.min(computeDistance(getOffsetX(), getOffsetY(), screenToGameX(getWidth()), getOffsetY()), computeDistance(getOffsetX(), getOffsetY(), getOffsetX(), screenToGameY(getHeight()))); } window.verticalDistance = function() { return computeDistance(screenToGameX(0), screenToGameY(0), screenToGameX(getWidth()), screenToGameY(getHeight())); } /** * A conversion from the screen's horizontal coordinate system * to the game's horizontal coordinate system. * @param x in the screen's coordinate system * @return x in the game's coordinate system */ window.screenToGameX = function(x) { return (x - getWidth() / 2) / getRatio() + getX(); } /** * A conversion from the screen's vertical coordinate system * to the game's vertical coordinate system. * @param y in the screen's coordinate system * @return y in the game's coordinate system */ window.screenToGameY = function(y) { return (y - getHeight() / 2) / getRatio() + getY(); } window.drawPoint = function(x_1, y_1, drawColor, text) { if (!toggleDraw) { dPoints.push([x_1, y_1, drawColor]); dText.push(text); } } window.drawArc = function(x_1, y_1, x_2, y_2, x_3, y_3, drawColor) { if (!toggleDraw) { var radius = computeDistance(x_1, y_1, x_3, y_3); dArc.push([x_1, y_1, x_2, y_2, x_3, y_3, radius, drawColor]); } } window.drawLine = function(x_1, y_1, x_2, y_2, drawColor) { if (!toggleDraw) { lines.push([x_1, y_1, x_2, y_2, drawColor]); } } window.drawCircle = function(x_1, y_1, radius, drawColor) { if (!toggleDraw) { circles.push([x_1, y_1, radius, drawColor]); } } function V() { //UPDATE if (getPlayer().length == 0 && !reviving && ~~(getCurrentScore() / 100) > 0) { console.log("Dead: " + ~~(getCurrentScore() / 100)); apos('send', 'pageview'); } if (getPlayer().length == 0) { console.log("Revive"); setNick(originalName); reviving = true; } else if (getPlayer().length > 0 && reviving) { reviving = false; console.log("Done Reviving!"); } if (T()) { var a = fa - m / 2; var b = ga - r / 2; 64 > a * a + b * b || .01 > Math.abs(eb - ia) && .01 > Math.abs(fb - ja) || (eb = ia, fb = ja, a = N(13), a.setUint8(0, 16), a.setInt32(1, ia, !0), a.setInt32(5, ja, !0), a.setUint32(9, 0, !0), O(a)) } } function cb() { if (T() && $ && null != K) { var a = N(1 + 2 * K.length); a.setUint8(0, 0); for (var b = 0; b < K.length; ++b) a.setUint16(1 + 2 * b, K.charCodeAt(b), !0); O(a) } } function T() { return null != q && q.readyState == q.OPEN } window.opCode = function(a) { console.log("Sending op code."); H(parseInt(a)); } function H(a) { if (T()) { var b = N(1); b.setUint8(0, a); O(b) } } function $a() { if (T() && null != B) { var a = N(1 + B.length); a.setUint8(0, 81); for (var b = 0; b < B.length; ++b) a.setUint8(b + 1, B.charCodeAt(b)); O(a) } } function Ta() { m = d.innerWidth; r = d.innerHeight; za.width = G.width = m; za.height = G.height = r; var a = e("#helloContainer"); a.css("transform", "none"); var b = a.height(), c = d.innerHeight; b > c / 1.1 ? a.css("transform", "translate(-50%, -50%) scale(" + c / b / 1.1 + ")") : a.css("transform", "translate(-50%, -50%)"); gb() } function hb() { var a; a = Math.max(r / 1080, m / 1920); return a *= J } function yb() { if (0 != k.length) { for (var a = 0, b = 0; b < k.length; b++) a += k[b].size; a = Math.pow(Math.min(64 / a, 1), .4) * hb(); h = (9 * h + a) / 10 } } function gb() { //UPDATE dPoints = []; circles = []; dArc = []; dText = []; lines = []; var a, b = Date.now(); ++zb; C = b; if (0 < k.length) { yb(); for (var c = a = 0, d = 0; d < k.length; d++) k[d].P(), a += k[d].x / k.length, c += k[d].y / k.length; aa = a; ba = c; ca = h; s = (s + a) / 2; t = (t + c) / 2; } else s = (29 * s + aa) / 30, t = (29 * t + ba) / 30, h = (9 * h + ca * hb()) / 10; qb(); Aa(); Ia || f.clearRect(0, 0, m, r); Ia ? (f.fillStyle = ta ? "#111111" : "#F2FBFF", f.globalAlpha = .05, f.fillRect(0, 0, m, r), f.globalAlpha = 1) : Ab(); v.sort(function(a, b) { return a.size == b.size ? a.id - b.id : a.size - b.size }); f.save(); f.translate(m / 2, r / 2); f.scale(h, h); f.translate(-s, -t); //UPDATE f.save(); f.beginPath(); f.lineWidth = 5; f.strokeStyle = (getDarkBool() ? '#F2FBFF' : '#111111'); f.moveTo(getMapStartX(), getMapStartY()); f.lineTo(getMapStartX(), getMapEndY()); f.stroke(); f.moveTo(getMapStartX(), getMapStartY()); f.lineTo(getMapEndX(), getMapStartY()); f.stroke(); f.moveTo(getMapEndX(), getMapStartY()); f.lineTo(getMapEndX(), getMapEndY()); f.stroke(); f.moveTo(getMapStartX(), getMapEndY()); f.lineTo(getMapEndX(), getMapEndY()); f.stroke(); f.restore(); for (d = 0; d < v.length; d++) v[d].w(f); for (d = 0; d < Q.length; d++) Q[d].w(f); //UPDATE if (getPlayer().length > 0) { var moveLoc = window.botList[botIndex].mainLoop(); if (!toggle) { setPoint(moveLoc[0], moveLoc[1]); } } customRender(f); if (Ga) { na = (3 * na + Ea) / 4; oa = (3 * oa + Fa) / 4; f.save(); f.strokeStyle = "#FFAAAA"; f.lineWidth = 10; f.lineCap = "round"; f.lineJoin = "round"; f.globalAlpha = .5; f.beginPath(); for (d = 0; d < k.length; d++) f.moveTo(k[d].x, k[d].y), f.lineTo(na, oa); f.stroke(); f.restore(); } f.restore(); z && z.width && f.drawImage(z, m - z.width - 10, 10); R = Math.max(R, Bb()); //UPDATE var currentDate = new Date(); var nbSeconds = 0; if (getPlayer().length > 0) { //nbSeconds = currentDate.getSeconds() + currentDate.getMinutes() * 60 + currentDate.getHours() * 3600 - lifeTimer.getSeconds() - lifeTimer.getMinutes() * 60 - lifeTimer.getHours() * 3600; nbSeconds = (currentDate.getTime() - lifeTimer.getTime())/1000; } bestTime = Math.max(nbSeconds, bestTime); var displayText = 'Score: ' + ~~(R / 100) + " Current Time: " + nbSeconds + " seconds."; 0 != R && (null == ua && (ua = new va(24, "#FFFFFF")), ua.C(displayText), c = ua.L(), a = c.width, f.globalAlpha = .2, f.fillStyle = "#000000", f.fillRect(10, r - 10 - 24 - 10, a + 10, 34), f.globalAlpha = 1, f.drawImage(c, 15, r - 10 - 24 - 5)); Cb(); b = Date.now() - b; b > 1E3 / 60 ? D -= .01 : b < 1E3 / 65 && (D += .01);.4 > D && (D = .4); 1 < D && (D = 1); b = C - ib; !T() || W ? (x += b / 2E3, 1 < x && (x = 1)) : (x -= b / 300, 0 > x && (x = 0)); 0 < x && (f.fillStyle = "#000000", f.globalAlpha = .5 * x, f.fillRect(0, 0, m, r), f.globalAlpha = 1); ib = C drawStats(f); } //UPDATE function customRender(d) { d.save(); for (var i = 0; i < lines.length; i++) { d.beginPath(); d.lineWidth = 5; if (lines[i][4] == 0) { d.strokeStyle = "#FF0000"; } else if (lines[i][4] == 1) { d.strokeStyle = "#00FF00"; } else if (lines[i][4] == 2) { d.strokeStyle = "#0000FF"; } else if (lines[i][4] == 3) { d.strokeStyle = "#FF8000"; } else if (lines[i][4] == 4) { d.strokeStyle = "#8A2BE2"; } else if (lines[i][4] == 5) { d.strokeStyle = "#FF69B4"; } else if (lines[i][4] == 6) { d.strokeStyle = "#008080"; } else if (lines[i][4] == 7) { d.strokeStyle = (getDarkBool() ? '#F2FBFF' : '#111111'); } else { d.strokeStyle = "#000000"; } d.moveTo(lines[i][0], lines[i][1]); d.lineTo(lines[i][2], lines[i][3]); d.stroke(); } d.restore(); d.save(); for (var i = 0; i < circles.length; i++) { if (circles[i][3] == 0) { d.strokeStyle = "#FF0000"; } else if (circles[i][3] == 1) { d.strokeStyle = "#00FF00"; } else if (circles[i][3] == 2) { d.strokeStyle = "#0000FF"; } else if (circles[i][3] == 3) { d.strokeStyle = "#FF8000"; } else if (circles[i][3] == 4) { d.strokeStyle = "#8A2BE2"; } else if (circles[i][3] == 5) { d.strokeStyle = "#FF69B4"; } else if (circles[i][3] == 6) { d.strokeStyle = "#008080"; } else if (circles[i][3] == 7) { d.strokeStyle = (getDarkBool() ? '#F2FBFF' : '#111111'); } else { d.strokeStyle = "#000000"; } d.beginPath(); d.lineWidth = 10; //d.setLineDash([5]); d.globalAlpha = 0.3; d.arc(circles[i][0], circles[i][1], circles[i][2], 0, 2 * Math.PI, false); d.stroke(); } d.restore(); d.save(); for (var i = 0; i < dArc.length; i++) { if (dArc[i][7] == 0) { d.strokeStyle = "#FF0000"; } else if (dArc[i][7] == 1) { d.strokeStyle = "#00FF00"; } else if (dArc[i][7] == 2) { d.strokeStyle = "#0000FF"; } else if (dArc[i][7] == 3) { d.strokeStyle = "#FF8000"; } else if (dArc[i][7] == 4) { d.strokeStyle = "#8A2BE2"; } else if (dArc[i][7] == 5) { d.strokeStyle = "#FF69B4"; } else if (dArc[i][7] == 6) { d.strokeStyle = "#008080"; } else if (dArc[i][7] == 7) { d.strokeStyle = (getDarkBool() ? '#F2FBFF' : '#111111'); } else { d.strokeStyle = "#000000"; } d.beginPath(); d.lineWidth = 5; var ang1 = Math.atan2(dArc[i][1] - dArc[i][5], dArc[i][0] - dArc[i][4]); var ang2 = Math.atan2(dArc[i][3] - dArc[i][5], dArc[i][2] - dArc[i][4]); d.arc(dArc[i][4], dArc[i][5], dArc[i][6], ang1, ang2, false); d.stroke(); } d.restore(); d.save(); for (var i = 0; i < dPoints.length; i++) { if (dText[i] == "") { var radius = 10; d.beginPath(); d.arc(dPoints[i][0], dPoints[i][1], radius, 0, 2 * Math.PI, false); if (dPoints[i][2] == 0) { d.fillStyle = "black"; } else if (dPoints[i][2] == 1) { d.fillStyle = "yellow"; } else if (dPoints[i][2] == 2) { d.fillStyle = "blue"; } else if (dPoints[i][2] == 3) { d.fillStyle = "red"; } else if (dPoints[i][2] == 4) { d.fillStyle = "#008080"; } else if (dPoints[i][2] == 5) { d.fillStyle = "#FF69B4"; } else { d.fillStyle = "#000000"; } d.fill(); d.lineWidth = 2; d.strokeStyle = '#003300'; d.stroke(); } else { var text = new va(18, (getDarkBool() ? '#F2FBFF' : '#111111'), true, (getDarkBool() ? '#111111' : '#F2FBFF')); text.C(dText[i]); var textRender = text.L(); d.drawImage(textRender, dPoints[i][0] - (textRender.width / 2), dPoints[i][1] - (textRender.height / 2)); } } d.restore(); } function drawStats(d) { d.save() sessionScore = Math.max(getCurrentScore(), sessionScore); var botString = window.botList[botIndex].displayText(); var debugStrings = []; debugStrings.push("Bot: " + window.botList[botIndex].name); debugStrings.push("Launcher: AposLauncher " + aposLauncherVersion); debugStrings.push("T - Bot: " + (!toggle ? "On" : "Off")); debugStrings.push("R - Lines: " + (!toggleDraw ? "On" : "Off")); for (var i = 0; i < botString.length; i++) { debugStrings.push(botString[i]); } debugStrings.push(""); debugStrings.push("Best Score: " + ~~(sessionScore / 100)); debugStrings.push("Best Time: " + bestTime + " seconds"); debugStrings.push(""); debugStrings.push(serverIP); if (getPlayer().length > 0) { var offsetX = -getMapStartX(); var offsetY = -getMapStartY(); debugStrings.push("Location: " + Math.floor(getPlayer()[0].x + offsetX) + ", " + Math.floor(getPlayer()[0].y + offsetY)); } var offsetValue = 20; var text = new va(18, (getDarkBool() ? '#F2FBFF' : '#111111')); for (var i = 0; i < debugStrings.length; i++) { text.C(debugStrings[i]); var textRender = text.L(); d.drawImage(textRender, 20, offsetValue); offsetValue += textRender.height; } if (message.length > 0) { var mRender = []; var mWidth = 0; var mHeight = 0; for (var i = 0; i < message.length; i++) { var mText = new va(28, '#FF0000', true, '#000000'); mText.C(message[i]); mRender.push(mText.L()); if (mRender[i].width > mWidth) { mWidth = mRender[i].width; } mHeight += mRender[i].height; } var mX = getWidth() / 2 - mWidth / 2; var mY = 20; d.globalAlpha = 0.4; d.fillStyle = '#000000'; d.fillRect(mX - 10, mY - 10, mWidth + 20, mHeight + 20); d.globalAlpha = 1; var mOffset = mY; for (var i = 0; i < mRender.length; i++) { d.drawImage(mRender[i], getWidth() / 2 - mRender[i].width / 2, mOffset); mOffset += mRender[i].height; } } d.restore(); } function Ab() { f.fillStyle = ta ? "#111111" : "#F2FBFF"; f.fillRect(0, 0, m, r); f.save(); f.strokeStyle = ta ? "#AAAAAA" : "#000000"; f.globalAlpha = .2 * h; for (var a = m / h, b = r / h, c = (a / 2 - s) % 50; c < a; c += 50) f.beginPath(), f.moveTo(c * h - .5, 0), f.lineTo(c * h - .5, b * h), f.stroke(); for (c = (b / 2 - t) % 50; c < b; c += 50) f.beginPath(), f.moveTo(0, c * h - .5), f.lineTo(a * h, c * h - .5), f.stroke(); f.restore() } function Cb() { if (Qa && Ja.width) { var a = m / 5; f.drawImage(Ja, 5, 5, a, a) } } function Bb() { for (var a = 0, b = 0; b < k.length; b++) a += k[b].q * k[b].q; return a } function ab() { z = null; if (null != A || 0 != F.length) if (null != A || wa) { z = document.createElement("canvas"); var a = z.getContext("2d"), b = 60, b = null == A ? b + 24 * F.length : b + 180, c = Math.min(200, .3 * m) / 200; z.width = 200 * c; z.height = b * c; a.scale(c, c); a.globalAlpha = .4; a.fillStyle = "#000000"; a.fillRect(0, 0, 200, b); a.globalAlpha = 1; a.fillStyle = "#FFFFFF"; c = null; c = Z("leaderboard"); a.font = "30px Ubuntu"; a.fillText(c, 100 - a.measureText(c).width / 2, 40); if (null == A) for (a.font = "20px Ubuntu", b = 0; b < F.length; ++b) c = F[b].name || Z("unnamed_cell"), wa || (c = Z("unnamed_cell")), -1 != M.indexOf(F[b].id) ? (k[0].name && (c = k[0].name), a.fillStyle = "#FFAAAA") : a.fillStyle = "#FFFFFF", c = b + 1 + ". " + c, a.fillText(c, 100 - a.measureText(c).width / 2, 70 + 24 * b); else for (b = c = 0; b < A.length; ++b) { var d = c + A[b] * Math.PI * 2; a.fillStyle = Db[b + 1]; a.beginPath(); a.moveTo(100, 140); a.arc(100, 140, 80, c, d, !1); a.fill(); c = d } } } function Ka(a, b, c, d, e) { this.V = a; this.x = b; this.y = c; this.i = d; this.b = e } function da(a, b, c, d, e, p) { this.id = a; this.s = this.x = b; this.t = this.y = c; this.r = this.size = d; this.color = e; this.a = []; this.W(); this.B(p) } function va(a, b, c, d) { a && (this.u = a); b && (this.S = b); this.U = !!c; d && (this.v = d) } function S(a, b) { var c = "1" == e("#helloContainer").attr("data-has-account-data"); e("#helloContainer").attr("data-has-account-data", "1"); if (null == b && d.localStorage.loginCache) { var l = JSON.parse(d.localStorage.loginCache); l.f = a.f; l.d = a.d; l.e = a.e; d.localStorage.loginCache = JSON.stringify(l) } if (c) { var u = +e(".agario-exp-bar .progress-bar-text").first().text().split("/")[0], c = +e(".agario-exp-bar .progress-bar-text").first().text().split("/")[1].split(" ")[0], l = e(".agario-profile-panel .progress-bar-star").first().text(); if (l != a.e) S({ f: c, d: c, e: l }, function() { e(".agario-profile-panel .progress-bar-star").text(a.e); e(".agario-exp-bar .progress-bar").css("width", "100%"); e(".progress-bar-star").addClass("animated tada").one("webkitAnimationEnd mozAnimationEnd MSAnimationEnd oanimationend animationend", function() { e(".progress-bar-star").removeClass("animated tada") }); setTimeout(function() { e(".agario-exp-bar .progress-bar-text").text(a.d + "/" + a.d + " XP"); S({ f: 0, d: a.d, e: a.e }, function() { S(a, b) }) }, 1E3) }); else { var p = Date.now(), g = function() { var c; c = (Date.now() - p) / 1E3; c = 0 > c ? 0 : 1 < c ? 1 : c; c = c * c * (3 - 2 * c); e(".agario-exp-bar .progress-bar-text").text(~~(u + (a.f - u) * c) + "/" + a.d + " XP"); e(".agario-exp-bar .progress-bar").css("width", (88 * (u + (a.f - u) * c) / a.d).toFixed(2) + "%"); 1 > c ? d.requestAnimationFrame(g) : b && b() }; d.requestAnimationFrame(g) } } else e(".agario-profile-panel .progress-bar-star").text(a.e), e(".agario-exp-bar .progress-bar-text").text(a.f + "/" + a.d + " XP"), e(".agario-exp-bar .progress-bar").css("width", (88 * a.f / a.d).toFixed(2) + "%"), b && b() } function jb(a) { "string" == typeof a && (a = JSON.parse(a)); Date.now() + 18E5 > a.ja ? e("#helloContainer").attr("data-logged-in", "0") : (d.localStorage.loginCache = JSON.stringify(a), B = a.fa, e(".agario-profile-name").text(a.name), $a(), S({ f: a.f, d: a.d, e: a.e }), e("#helloContainer").attr("data-logged-in", "1")) } function Eb(a) { a = a.split("\n"); jb({ name: a[0], ta: a[1], fa: a[2], ja: 1E3 * +a[3], e: +a[4], f: +a[5], d: +a[6] }); console.log("Hello Facebook?"); } function La(a) { if ("connected" == a.status) { var b = a.authResponse.accessToken; d.FB.api("/me/picture?width=180&height=180", function(a) { d.localStorage.fbPictureCache = a.data.url; e(".agario-profile-picture").attr("src", a.data.url) }); e("#helloContainer").attr("data-logged-in", "1"); null != B ? e.ajax("https://m.agar.io/checkToken", { error: function() { console.log("Facebook Fail!"); B = null; La(a) }, success: function(a) { a = a.split("\n"); S({ e: +a[0], f: +a[1], d: +a[2] }); console.log("Facebook connected!"); }, dataType: "text", method: "POST", cache: !1, crossDomain: !0, data: B }) : e.ajax("https://m.agar.io/facebookLogin", { error: function() { console.log("You have a Facebook problem!"); B = null; e("#helloContainer").attr("data-logged-in", "0") }, success: Eb, dataType: "text", method: "POST", cache: !1, crossDomain: !0, data: b }) } } function Wa(a) { Y(":party"); e("#helloContainer").attr("data-party-state", "4"); a = decodeURIComponent(a).replace(/.*#/gim, ""); Ma("#" + d.encodeURIComponent(a)); e.ajax(Na + "//m.agar.io/getToken", { error: function() { e("#helloContainer").attr("data-party-state", "6") }, success: function(b) { b = b.split("\n"); e(".partyToken").val("agar.io/#" + d.encodeURIComponent(a)); e("#helloContainer").attr("data-party-state", "5"); Y(":party"); Ca("ws://" + b[0], a) }, dataType: "text", method: "POST", cache: !1, crossDomain: !0, data: a }) } function Ma(a) { d.history && d.history.replaceState && d.history.replaceState({}, d.document.title, a) } if (!d.agarioNoInit) { var Na = d.location.protocol, tb = "https:" == Na, xa = d.navigator.userAgent; if (-1 != xa.indexOf("Android")) d.ga && d.ga("send", "event", "MobileRedirect", "PlayStore"), setTimeout(function() { d.location.href = "market://details?id=com.miniclip.agar.io" }, 1E3); else if (-1 != xa.indexOf("iPhone") || -1 != xa.indexOf("iPad") || -1 != xa.indexOf("iPod")) d.ga && d.ga("send", "event", "MobileRedirect", "AppStore"), setTimeout(function() { d.location.href = "https://itunes.apple.com/app/agar.io/id995999703" }, 1E3); else { var za, f, G, m, r, X = null, //UPDATE toggle = false, toggleDraw = false, tempPoint = [0, 0, 1], dPoints = [], circles = [], dArc = [], dText = [], lines = [], names = ["Vilhena"], originalName = names[Math.floor(Math.random() * names.length)], sessionScore = 0, serverIP = "", interNodes = [], lifeTimer = new Date(), bestTime = 0, botIndex = 0, reviving = false, message = [], q = null, s = 0, t = 0, M = [], k = [], E = {}, v = [], Q = [], F = [], fa = 0, ga = 0, //UPDATE ia = -1, ja = -1, zb = 0, C = 0, ib = 0, K = null, pa = 0, qa = 0, ra = 1E4, sa = 1E4, h = 1, y = null, kb = !0, wa = !0, Oa = !1, Ha = !1, R = 0, ta = !1, lb = !1, aa = s = ~~((pa + ra) / 2), ba = t = ~~((qa + sa) / 2), ca = 1, P = "", A = null, ya = !1, Ga = !1, Ea = 0, Fa = 0, na = 0, oa = 0, mb = 0, Db = ["#333333", "#FF3333", "#33FF33", "#3333FF"], Ia = !1, $ = !1, bb = 0, B = null, J = 1, x = 1, W = !0, Ba = 0, Da = {}; (function() { var a = d.location.search; "?" == a.charAt(0) && (a = a.slice(1)); for (var a = a.split("&"), b = 0; b < a.length; b++) { var c = a[b].split("="); Da[c[0]] = c[1] } })(); var Qa = "ontouchstart" in d && /Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(d.navigator.userAgent), Ja = new Image; Ja.src = "img/split.png"; var nb = document.createElement("canvas"); if ("undefined" == typeof console || "undefined" == typeof DataView || "undefined" == typeof WebSocket || null == nb || null == nb.getContext || null == d.localStorage) alert("You browser does not support this game, we recommend you to use Firefox to play this"); else { var ka = null; d.setNick = function(a) { //UPDATE originalName = a; if (getPlayer().length == 0) { lifeTimer = new Date(); } Xa(); K = a; cb(); R = 0 }; d.setRegion = ha; d.setSkins = function(a) { kb = a }; d.setNames = function(a) { wa = a }; d.setDarkTheme = function(a) { ta = a }; d.setColors = function(a) { Oa = a }; d.setShowMass = function(a) { lb = a }; d.spectate = function() { K = null; H(1); Xa() }; d.setGameMode = function(a) { a != P && (":party" == P && e("#helloContainer").attr("data-party-state", "0"), Y(a), ":party" != a && I()) }; d.setAcid = function(a) { Ia = a }; null != d.localStorage && (null == d.localStorage.AB9 && (d.localStorage.AB9 = 0 + ~~(100 * Math.random())), mb = +d.localStorage.AB9, d.ABGroup = mb); e.get(Na + "//gc.agar.io", function(a) { var b = a.split(" "); a = b[0]; b = b[1] || ""; - 1 == ["UA"].indexOf(a) && ob.push("ussr"); ea.hasOwnProperty(a) && ("string" == typeof ea[a] ? y || ha(ea[a]) : ea[a].hasOwnProperty(b) && (y || ha(ea[a][b]))) }, "text"); d.ga && d.ga("send", "event", "User-Agent", d.navigator.userAgent, { nonInteraction: 1 }); var la = !1, Ya = 0; setTimeout(function() { la = !0 }, Math.max(6E4 * Ya, 1E4)); var ea = { AF: "JP-Tokyo", AX: "EU-London", AL: "EU-London", DZ: "EU-London", AS: "SG-Singapore", AD: "EU-London", AO: "EU-London", AI: "US-Atlanta", AG: "US-Atlanta", AR: "BR-Brazil", AM: "JP-Tokyo", AW: "US-Atlanta", AU: "SG-Singapore", AT: "EU-London", AZ: "JP-Tokyo", BS: "US-Atlanta", BH: "JP-Tokyo", BD: "JP-Tokyo", BB: "US-Atlanta", BY: "EU-London", BE: "EU-London", BZ: "US-Atlanta", BJ: "EU-London", BM: "US-Atlanta", BT: "JP-Tokyo", BO: "BR-Brazil", BQ: "US-Atlanta", BA: "EU-London", BW: "EU-London", BR: "BR-Brazil", IO: "JP-Tokyo", VG: "US-Atlanta", BN: "JP-Tokyo", BG: "EU-London", BF: "EU-London", BI: "EU-London", KH: "JP-Tokyo", CM: "EU-London", CA: "US-Atlanta", CV: "EU-London", KY: "US-Atlanta", CF: "EU-London", TD: "EU-London", CL: "BR-Brazil", CN: "CN-China", CX: "JP-Tokyo", CC: "JP-Tokyo", CO: "BR-Brazil", KM: "EU-London", CD: "EU-London", CG: "EU-London", CK: "SG-Singapore", CR: "US-Atlanta", CI: "EU-London", HR: "EU-London", CU: "US-Atlanta", CW: "US-Atlanta", CY: "JP-Tokyo", CZ: "EU-London", DK: "EU-London", DJ: "EU-London", DM: "US-Atlanta", DO: "US-Atlanta", EC: "BR-Brazil", EG: "EU-London", SV: "US-Atlanta", GQ: "EU-London", ER: "EU-London", EE: "EU-London", ET: "EU-London", FO: "EU-London", FK: "BR-Brazil", FJ: "SG-Singapore", FI: "EU-London", FR: "EU-London", GF: "BR-Brazil", PF: "SG-Singapore", GA: "EU-London", GM: "EU-London", GE: "JP-Tokyo", DE: "EU-London", GH: "EU-London", GI: "EU-London", GR: "EU-London", GL: "US-Atlanta", GD: "US-Atlanta", GP: "US-Atlanta", GU: "SG-Singapore", GT: "US-Atlanta", GG: "EU-London", GN: "EU-London", GW: "EU-London", GY: "BR-Brazil", HT: "US-Atlanta", VA: "EU-London", HN: "US-Atlanta", HK: "JP-Tokyo", HU: "EU-London", IS: "EU-London", IN: "JP-Tokyo", ID: "JP-Tokyo", IR: "JP-Tokyo", IQ: "JP-Tokyo", IE: "EU-London", IM: "EU-London", IL: "JP-Tokyo", IT: "EU-London", JM: "US-Atlanta", JP: "JP-Tokyo", JE: "EU-London", JO: "JP-Tokyo", KZ: "JP-Tokyo", KE: "EU-London", KI: "SG-Singapore", KP: "JP-Tokyo", KR: "JP-Tokyo", KW: "JP-Tokyo", KG: "JP-Tokyo", LA: "JP-Tokyo", LV: "EU-London", LB: "JP-Tokyo", LS: "EU-London", LR: "EU-London", LY: "EU-London", LI: "EU-London", LT: "EU-London", LU: "EU-London", MO: "JP-Tokyo", MK: "EU-London", MG: "EU-London", MW: "EU-London", MY: "JP-Tokyo", MV: "JP-Tokyo", ML: "EU-London", MT: "EU-London", MH: "SG-Singapore", MQ: "US-Atlanta", MR: "EU-London", MU: "EU-London", YT: "EU-London", MX: "US-Atlanta", FM: "SG-Singapore", MD: "EU-London", MC: "EU-London", MN: "JP-Tokyo", ME: "EU-London", MS: "US-Atlanta", MA: "EU-London", MZ: "EU-London", MM: "JP-Tokyo", NA: "EU-London", NR: "SG-Singapore", NP: "JP-Tokyo", NL: "EU-London", NC: "SG-Singapore", NZ: "SG-Singapore", NI: "US-Atlanta", NE: "EU-London", NG: "EU-London", NU: "SG-Singapore", NF: "SG-Singapore", MP: "SG-Singapore", NO: "EU-London", OM: "JP-Tokyo", PK: "JP-Tokyo", PW: "SG-Singapore", PS: "JP-Tokyo", PA: "US-Atlanta", PG: "SG-Singapore", PY: "BR-Brazil", PE: "BR-Brazil", PH: "JP-Tokyo", PN: "SG-Singapore", PL: "EU-London", PT: "EU-London", PR: "US-Atlanta", QA: "JP-Tokyo", RE: "EU-London", RO: "EU-London", RU: "RU-Russia", RW: "EU-London", BL: "US-Atlanta", SH: "EU-London", KN: "US-Atlanta", LC: "US-Atlanta", MF: "US-Atlanta", PM: "US-Atlanta", VC: "US-Atlanta", WS: "SG-Singapore", SM: "EU-London", ST: "EU-London", SA: "EU-London", SN: "EU-London", RS: "EU-London", SC: "EU-London", SL: "EU-London", SG: "JP-Tokyo", SX: "US-Atlanta", SK: "EU-London", SI: "EU-London", SB: "SG-Singapore", SO: "EU-London", ZA: "EU-London", SS: "EU-London", ES: "EU-London", LK: "JP-Tokyo", SD: "EU-London", SR: "BR-Brazil", SJ: "EU-London", SZ: "EU-London", SE: "EU-London", CH: "EU-London", SY: "EU-London", TW: "JP-Tokyo", TJ: "JP-Tokyo", TZ: "EU-London", TH: "JP-Tokyo", TL: "JP-Tokyo", TG: "EU-London", TK: "SG-Singapore", TO: "SG-Singapore", TT: "US-Atlanta", TN: "EU-London", TR: "TK-Turkey", TM: "JP-Tokyo", TC: "US-Atlanta", TV: "SG-Singapore", UG: "EU-London", UA: "EU-London", AE: "EU-London", GB: "EU-London", US: "US-Atlanta", UM: "SG-Singapore", VI: "US-Atlanta", UY: "BR-Brazil", UZ: "JP-Tokyo", VU: "SG-Singapore", VE: "BR-Brazil", VN: "JP-Tokyo", WF: "SG-Singapore", EH: "EU-London", YE: "JP-Tokyo", ZM: "EU-London", ZW: "EU-London" }, L = null; d.connect = Ca; //UPDATE /** * Tells you if the game is in Dark mode. * @return Boolean for dark mode. */ window.getDarkBool = function() { return ta; } /** * Tells you if the mass is shown. * @return Boolean for player's mass. */ window.getMassBool = function() { return lb; } /** * This is a copy of everything that is shown on screen. * Normally stuff will time out when off the screen, this * memorizes everything that leaves the screen for a little * while longer. * @return The memory object. */ window.getMemoryCells = function() { return interNodes; } /** * [getCellsArray description] * @return {[type]} [description] */ window.getCellsArray = function() { return v; } /** * [getCellsArray description] * @return {[type]} [description] */ window.getCells = function() { return E; } /** * Returns an array with all the player's cells. * @return Player's cells */ window.getPlayer = function() { return k; } /** * The canvas' width. * @return Integer Width */ window.getWidth = function() { return m; } /** * The canvas' height * @return Integer Height */ window.getHeight = function() { return r; } /** * Scaling ratio of the canvas. The bigger this ration, * the further that you see. * @return Screen scaling ratio. */ window.getRatio = function() { return h; } /** * [getOffsetX description] * @return {[type]} [description] */ window.getOffsetX = function() { return aa; } window.getOffsetY = function() { return ba; } window.getX = function() { return s; } window.getY = function() { return t; } window.getPointX = function() { return ia; } window.getPointY = function() { return ja; } /** * The X location of the mouse. * @return Integer X */ window.getMouseX = function() { return fa; } /** * The Y location of the mouse. * @return Integer Y */ window.getMouseY = function() { return ga; } window.getMapStartX = function() { return pa; } window.getMapStartY = function() { return qa; } window.getMapEndX = function() { return ra; } window.getMapEndY = function() { return sa; } window.getScreenDistance = function() { var temp = screenDistance(); return temp; } /** * A timestamp since the last time the server sent any data. * @return Last update timestamp */ window.getLastUpdate = function() { return C; } window.getCurrentScore = function() { return R; } /** * The game's current mode. (":ffa", ":experimental", ":teams". ":party") * @return {[type]} [description] */ window.getMode = function() { return P; } window.setPoint = function(x, y) { ia = x; ja = y; } window.setScore = function(a) { sessionScore = a * 100; } window.setBestTime = function(a) { bestTime = a; } window.best = function(a, b) { setScore(a); setBestTime(b); } window.setBotIndex = function(a) { console.log("Changing bot"); botIndex = a; } window.setMessage = function(a) { message = a; } window.updateBotList = function() { window.bot

var config = { name: 'KTN Bot', userid: function () { return toId(this.name); }, group: '@', join: true, rooms: ['lobby'], punishvals: { 1: 'warn', 2: 'mute', 3: 'hourmute', 4: 'roomban', 5: 'ban' }, privaterooms: ['staff'], hosting: {}, laddering: true, ladderPercentage: 70, debug: false }; /** * On server start, this sets up fake user connection for bot and uses a fake ip. * It gets a the fake user from the users list and modifies it properties. In addition, * it sets up rooms that bot will join and adding the bot user to Users list and * removing the fake user created which already filled its purpose * of easily filling in the gaps of all the user's property. */ function joinServer() { if (process.uptime() > 5) return; // to avoid running this function again when reloading var worker = new(require('./fake-process.js').FakeProcess)(); Users.socketConnect(worker.server, undefined, '1', '76.19.156.198'); for (var i in Users.users) { if (Users.users[i].connections[0].ip === '76.19.156.198') { var bot = Users.users[i]; bot.name = config.name; bot.named = true; bot.renamePending = config.name; bot.authenticated = true; bot.userid = config.userid(); bot.group = config.group; if (config.join === true) { for (var all in Rooms.rooms) { if (all != 'global') { bot.roomCount[all] = 1; } } Users.users[bot.userid] = bot; for (var allRoom in Rooms.rooms) { if (allRoom != 'global') { Rooms.rooms[allRoom].users[Users.users[bot.userid]] = Users.users[bot.userid]; } } } else { for (var index in config.rooms) { if (index != 'global') { bot.roomCount[joinRooms[index]] = 1; } } Users.users[bot.userid] = bot; for (var jIndex in config.rooms) { if (jIndex != 'global') { Rooms.rooms[jIndex].users[Users.users[bot.userid]] = Users.users[bot.userid]; } } } delete Users.users[i]; } } } const ACTION_COOLDOWN = 3 * 1000; const FLOOD_MESSAGE_NUM = 5; const FLOOD_PER_MSG_MIN = 500; // this is the minimum time between messages for legitimate spam. It's used to determine what "flooding" is caused by lag const FLOOD_MESSAGE_TIME = 6 * 1000; const MIN_CAPS_LENGTH = 18; const MIN_CAPS_PROPORTION = 0.8; var parse = { chatData: {}, processChatData: function (user, room, connection, message) { if (user.userid === config.userid() || !room.users[config.userid()]) return true; var cmds = this.processBotCommands(user, room, connection, message); if (cmds) return false; message = message.trim().replace(/ +/g, " "); // removes extra spaces so it doesn't trigger stretching this.updateSeen(user.userid, 'c', room.title); var time = Date.now(); if (!this.chatData[user]) this.chatData[user] = { zeroTol: 0, lastSeen: '', seenAt: time }; if (!this.chatData[user][room]) this.chatData[user][room] = { times: [], points: 0, lastAction: 0 }; this.chatData[user][room].times.push(time); var pointVal = 0; var muteMessage = ''; // moderation for flooding (more than x lines in y seconds) var isFlooding = (this.chatData[user][room].times.length >= FLOOD_MESSAGE_NUM && (time - this.chatData[user][room].times[this.chatData[user][room].times.length - FLOOD_MESSAGE_NUM]) < FLOOD_MESSAGE_TIME && (time - this.chatData[user][room].times[this.chatData[user][room].times.length - FLOOD_MESSAGE_NUM]) > (FLOOD_PER_MSG_MIN * FLOOD_MESSAGE_NUM)); if (isFlooding) { if (pointVal < 2) { pointVal = 2; muteMessage = ', flooding'; } } // moderation for caps (over x% of the letters in a line of y characters are capital) var capsMatch = message.replace(/[^A-Za-z]/g, '').match(/[A-Z]/g); if (capsMatch && toId(message).length > MIN_CAPS_LENGTH && (capsMatch.length >= Math.floor(toId(message).length * MIN_CAPS_PROPORTION))) { if (pointVal < 1) { pointVal = 1; muteMessage = ', caps'; } } // moderation for stretching (over x consecutive characters in the message are the same) var stretchMatch = message.toLowerCase().match(/(.)\1{7,}/g) || message.toLowerCase().match(/(..+)\1{4,}/g); // matches the same character (or group of characters) 8 (or 5) or more times in a row if (stretchMatch) { if (pointVal < 1) { pointVal = 1; muteMessage = ', stretching'; } } if (pointVal > 0 && !(time - this.chatData[user][room].lastAction < ACTION_COOLDOWN)) { var cmd = 'mute'; // defaults to the next punishment in config.punishVals instead of repeating the same action (so a second warn-worthy // offence would result in a mute instead of a warn, and the third an hourmute, etc) if (this.chatData[user][room].points >= pointVal && pointVal < 4) { this.chatData[user][room].points++; cmd = config.punishvals[this.chatData[user][room].points] || cmd; } else { // if the action hasn't been done before (is worth more points) it will be the one picked cmd = config.punishvals[pointVal] || cmd; this.chatData[user][room].points = pointVal; // next action will be one level higher than this one (in most cases) } if (config.privaterooms.indexOf(room) >= 0 && cmd === 'warn') cmd = 'mute'; // can't warn in private rooms // if the bot has % and not @, it will default to hourmuting as its highest level of punishment instead of roombanning if (this.chatData[user][room].points >= 4 && config.group === '%') cmd = 'hourmute'; if (this.chatData[user].zeroTol > 4) { // if zero tolerance users break a rule they get an instant roomban or hourmute muteMessage = ', zero tolerance user'; cmd = config.group !== '%' ? 'roomban' : 'hourmute'; } if (this.chatData[user][room].points >= 2) this.chatData[user].zeroTol++; // getting muted or higher increases your zero tolerance level (warns do not) this.chatData[user][room].lastAction = time; room.add('|c|' + user.group + user.name + '|' + message); CommandParser.parse(('/' + cmd + ' ' + user.userid + muteMessage), room, Users.get(config.name), Users.get(config.name).connections[0]); return false; } return true; }, updateSeen: function (user, type, detail) { user = toId(user); type = toId(type); if (type in {j: 1, l: 1, c: 1} && (config.rooms.indexOf(toId(detail)) === -1 || config.privaterooms.indexOf(toId(detail)) > -1)) return; var time = Date.now(); if (!this.chatData[user]) this.chatData[user] = { zeroTol: 0, lastSeen: '', seenAt: time }; if (!detail) return; var msg = ''; if (type in {j: 1, l: 1, c: 1}) { msg += (type === 'j' ? 'joining' : (type === 'l' ? 'leaving' : 'chatting in')) + ' ' + detail.trim() + '.'; } else if (type === 'n') { msg += 'changing nick to ' + ('+%@&#~'.indexOf(detail.trim().charAt(0)) === -1 ? detail.trim() : detail.trim().substr(1)) + '.'; } if (msg) { this.chatData[user].lastSeen = msg; this.chatData[user].seenAt = time; } }, processBotCommands: function (user, room, connection, message) { if (room.type !== 'chat' || message.charAt(0) !== '.') return; var cmd = '', target = '', spaceIndex = message.indexOf(' '), botDelay = (Math.floor(Math.random() * 6) * 1000), now = Date.now(); if (spaceIndex > 0) { cmd = message.substr(1, spaceIndex - 1); target = message.substr(spaceIndex + 1); } else { cmd = message.substr(1); target = ''; } cmd = cmd.toLowerCase(); if ((message.charAt(0) === '.' && Object.keys(Bot.commands).join(' ').toString().indexOf(cmd) >= 0 && message.substr(1) !== '') && !Bot.config.debug) { if ((now - user.lastBotCmd) * 0.001 < 30) { connection.sendTo(room, 'Please wait ' + Math.floor((30 - (now - user.lastBotCmd) * 0.001)) + ' seconds until the next command.'); return true; } user.lastBotCmd = now; } if (commands[cmd]) { var context = { sendReply: function (data) { setTimeout(function () { room.add('|c|' + config.group + config.name + '|' + data); }, botDelay); }, sendPm: function (data) { var message = '|pm|' + config.group + config.name + '|' + user.group + user.name + '|' + data; user.send(message); }, can: function (permission) { if (!user.can(permission)) { setTimeout(function () { connection.sendTo(room, '.' + cmd + ' - Access denied.'); }, botDelay); return false; } return true; }, parse: function (target) { CommandParser.parse(target, room, Users.get(Bot.config.name), Users.get(Bot.config.name).connections[0]); }, }; if (typeof commands[cmd] === 'function') { commands[cmd].call(context, target, room, user, connection, cmd, message); } } }, getTimeAgo: function (time) { time = Date.now() - time; time = Math.round(time / 1000); // rounds to nearest second var seconds = time % 60; var times = []; if (seconds) times.push(String(seconds) + (seconds === 1 ? ' second' : ' seconds')); var minutes, hours, days; if (time >= 60) { time = (time - seconds) / 60; // converts to minutes minutes = time % 60; if (minutes) times = [String(minutes) + (minutes === 1 ? ' minute' : ' minutes')].concat(times); if (time >= 60) { time = (time - minutes) / 60; // converts to hours hours = time % 24; if (hours) times = [String(hours) + (hours === 1 ? ' hour' : ' hours')].concat(times); if (time >= 24) { days = (time - hours) / 24; // you can probably guess this one if (days) times = [String(days) + (days === 1 ? ' day' : ' days')].concat(times); } } } if (!times.length) times.push('0 seconds'); return times.join(', '); } }; var commands = { guide: function (target, room, user) { var commands = Object.keys(Bot.commands); commands = commands.join(', ').toString(); this.sendReply('List of bot commands: ' + commands); }, say: function (target, room, user) { if (!this.can('say')) return; this.sendReply(target); }, tell: function (target, room, user) { if (!this.can('bottell')) return; var parts = target.split(','); if (parts.length < 2) return; this.parse('/tell ' + toId(parts[0]) + ', ' + Tools.escapeHTML(parts[1])); this.sendReply('Message sent to ' + parts[0] + '.'); }, penislength: function (target, room, user) { this.sendReply('8.5 inches from the base. Perv.'); }, seen: function (target, room, user, connection) { if (!target) return; if (!toId(target) || toId(target).length > 18) return connection.sendTo(room, 'Invalid username.'); if (!parse.chatData[toId(target)] || !parse.chatData[toId(target)].lastSeen) { return this.sendPm('The user ' + target.trim() + ' has never been seen chatting in rooms.'); } return this.sendPm(target.trim() + ' was last seen ' + parse.getTimeAgo(parse.chatData[toId(target)].seenAt) + ' ago, ' + parse.chatData[toId(target)].lastSeen); }, salt: function (target, room, user) { if (!global.salt) global.salt = 0; salt++; this.sendReply(salt + '% salty.'); }, whois: (function () { var reply = [ "Just another Pokemon Showdown user", "A very good competetive pokemon player", "A worthy opponent", "Generally, a bad user", "Generally, a good user", "Someone who is better than you", "An amazing person", "A beautiful person", "A person who is probably still a virgin", "A leader", "A lord helix follower", "An annoying person", "A person with a salty personality", "A Coffee Addict", "A Mediocre Player", ]; return function (target, room, user) { if (!target) return; var message = reply[Math.floor(Math.random() * reply.length)]; target = toId(target); if (target === 'creaturephil') message = 'An experienced **coder** for pokemon showdown. He has coded for over 5 servers such as kill the noise, moxie, aerdeith, nova, etc. Please follow him on github: https://github.com/CreaturePhil'; if (target === config.userid()) message = 'That\'s me.'; if (target === 'zarel') message = 'Pokemon Showdown Creator'; if (target === 'stevoduhhero') message = 'STEVO DUH GOD DAMN HERO! Respect him!'; if (target === 'rickycocaine') message = 'RICKY COCAAAAAAAINE'; this.sendReply(message); }; })(), helix: (function () { var reply = [ "Signs point to yes.", "Yes.", "Reply hazy, try again.", "Without a doubt.", "My sources say no.", "As I see it, yes.", "You may rely on it.", "Concentrate and ask again.", "Outlook not so good.", "It is decidedly so.", "Better not tell you now.", "Very doubtful.", "Yes - definitely.", "It is certain.", "Cannot predict now.", "Most likely.", "Ask again later.", "My reply is no.", "Outlook good.", "Don't count on it." ]; return function (target, room, user) { if (!target) return; var message = reply[Math.floor(Math.random() * reply.length)]; this.sendPm(message); }; })(), maketournament: function (target, room, user) { if (!this.can('maketournament')) return; if (Tournaments.tournaments[room.id]) return this.sendReply('A tournament is already running in the room.'); var parts = target.split(','), self = this, counter = 1; if (parts.length < 2 || Tools.getFormat(parts[0]).effectType !== 'Format' || !/[0-9]/.test(parts[1])) return this.sendPm('Correct Syntax: !maketournament [tier], [time/amount of players]'); if (parts[1].indexOf('minute') >= 0) { var time = Number(parts[1].split('minute')[0]); this.parse('/tour create ' + parts[0] + ', elimination'); this.sendReply('**You have ' + time + ' minute' + parts[1].split('minute')[1] + ' to join the tournament.**'); var loop = function () { setTimeout(function () { if (!Tournaments.tournaments[room.id]) return; if (counter === time) { if (Tournaments.tournaments[room.id].generator.users.size < 2) { self.parse('/tour end'); return self.sendReply('**The tournament was canceled because of lack of players.**'); } return self.parse('/tour start'); } if ((time - counter) === 1) { self.sendReply('**You have ' + (time - counter) + ' minute to sign up for the tournament.**'); } else { self.sendReply('**You have ' + (time - counter) + ' minutes to sign up for the tournament.**'); } counter++; if (!Tournaments.tournaments[room.id].isTournamentStarted) loop(); }, 1000 * 60); }; loop(); return; } if (Number(parts[1]) < 2) return; parts[1] = parts[1].replace(/[^0-9 ]+/g, ''); this.parse('/tour create ' + parts[0] + ', elimination'); this.sendReply('**The tournament will begin when ' + parts[1] + ' players join.**'); var playerLoop = function () { setTimeout(function () { if (!Tournaments.tournaments[room.id]) return; if (Tournaments.tournaments[room.id].generator.users.size === Number(parts[1])) { self.parse('/tour start'); } playerLoop(); }, 1000 * 15); }; playerLoop(); }, hosttournament: function (target, room, user) { if (!this.can('hosttournament')) return; if (target.toLowerCase() === 'end') { if (!Bot.config.hosting[room.id]) return this.sendPm('I\'m not hosting tournaments.'); Bot.config.hosting[room.id] = false; return this.sendReply('I will now stop hosting tournaments.'); } if (Bot.config.hosting[room.id]) return this.sendReply('I\'m already hosting tournaments.'); Bot.config.hosting[room.id] = true this.sendReply('**I will now be hosting tournaments.**'); var self = this, _room = room, _user = user; var poll = function () { if (!Bot.config.hosting[_room.id]) return; setTimeout(function () { if (Poll[_room.id].question) self.parse('/endpoll'); self.parse('/poll Tournament tier?, ' + Object.keys(Tools.data.Formats).filter(function (f) { return Tools.data.Formats[f].effectType === 'Format'; }).join(", ")); setTimeout(function () { self.parse('/endpoll'); Bot.commands.maketournament.call(self, (Poll[_room.id].topOption + ', 2 minute'), _room, _user); }, 1000 * 60 * 2); }, 1000 * 5); }; var loop = function () { setTimeout(function () { if (!Tournaments.tournaments[_room.id] && !Poll[_room.id].question) poll(); if (Bot.config.hosting[_room.id]) loop(); }, 1000 * 60); }; poll(); loop(); }, join: function (target, room, user, connection) { if (!user.can('kick')) return; if (!target || !Rooms.get(target.toLowerCase())) return; if (Rooms.get(target.toLowerCase()).users[Bot.config.name]) return this.sendPm('I\'m already in this room.'); Users.get(Bot.config.name).joinRoom(Rooms.get(target.toLowerCase())); var botDelay = (Math.floor(Math.random() * 6) * 1000) setTimeout(function() { connection.sendTo(room, Bot.config.name + ' has join ' + target + ' room.'); }, botDelay); }, leave: function (target, room, user, connection) { if (!user.can('kick')) return; if (!target || !Rooms.get(target.toLowerCase())) return; Users.get(Bot.config.name).leaveRoom(Rooms.get(target.toLowerCase())); var botDelay = (Math.floor(Math.random() * 6) * 1000) setTimeout(function() { connection.sendTo(room, Bot.config.name + ' has left ' + target + ' room.'); }, botDelay); }, rps: function (target, room, user) { if (!target) return; var options = ['rock', 'paper', 'scissors'], rng = options[Math.floor(Math.random() * options.length)], target = toId(target); if (rng === target) return this.sendReply('I chose ' + rng + '. The result is a tie!'); if (rng === options[0]) { if (target === options[1]) return this.sendReply('I chose ' + rng + '. ' + user.name + ' wins!'); if (target === options[2]) return this.sendReply('I chose ' + rng + '. I win and ' + user.name + ' loses!'); } if (rng === options[1]) { if (target === options[2]) return this.sendReply('I chose ' + rng + '. ' + user.name + ' wins!'); if (target === options[0]) return this.sendReply('I chose ' + rng + '. I win and ' + user.name + ' loses!'); } if (rng === options[2]) { if (target === options[0]) return this.sendReply('I chose ' + rng + '. ' + user.name + ' wins!'); if (target === options[1]) return this.sendReply('I chose ' + rng + '. I win and ' + user.name + ' loses!'); } }, }; exports.joinServer = joinServer; exports.config = config; exports.parse = parse; exports.commands = commands; // Battling AI exports.teams=new Object;var fs=require("fs");fs.readFile("./config/bot-teams.json",function(e,t){if(e)return;t=""+t;exports.teams=JSON.parse(t)});exports.addTeam=function(e,t){if(t&&t.length&&typeof t=="string"){if(!Bot.teams[e])Bot.teams[e]=new Array;Bot.teams[e].push(t);fs.writeFile("./config/bot-teams.json",JSON.stringify(Bot.teams))}};exports.randomTeam=function(e){if(e.split("random").length-1>0)return"";var t;if(Bot.teams[e])t=Bot.teams[e][Math.floor(Math.random()*Bot.teams[e].length)];if(!t)t="";return t};exports.booty={addBattle:function(e,t){Bot.booty.battles["battle-"+e.toLowerCase().replace(/[^a-z0-9]+/g,"")+"-"+(Rooms.global.lastBattle+1)]={booty:{user:Users.get(Bot.config.name),exposed:[{},{},{},{},{},{}]},opp:{user:t,exposed:[{},{},{},{},{},{}]}}},battles:new Object,check:function(){global.bootytimeout=setTimeout(function(){if(!Bot.booty.battles){Bot.booty.check();return}for(var e in Bot.booty.battles){if(Bot.booty.battles[e]){var t=Rooms.rooms[e];if(t){var n=t.battle;if(n){n=n.field;if(n[toId(Bot.config.name)])if(n[toId(Bot.config.name)].side)if(n[toId(Bot.config.name)].side.pokemon)if(n[toId(Bot.config.name)].side.pokemon[0].condition.charAt(0)=="0")Bot.booty.forceSwitch(e);if(n[toId(Bot.config.name)])if(n[toId(Bot.config.name)].forceSwitch)Bot.booty.forceSwitch(e)}}}}Bot.booty.check()},2e3)},forceSwitch:function(e){var t;if(Rooms.rooms[e])t=Rooms.rooms[e];if(!t)return;var n=Bot.booty.battles[t.id];var r=t.battle.field,i=r[toId(Bot.config.name)].side.pokemon;var s=i.length;if(!o){var o=new Array;for(var u=0;u<s;u++)o.push(u)}var a=Math.floor(Math.random()*s);while(a==1&&o.indexOf(a)==-1&&i[a].condition.charAt(0)=="0")a=Math.floor(Math.random()*s);t.decision(Users.get(toId(Bot.config.name)),"choose","switch "+parseInt(a+1,10))},predict:function(e,t,n,r){function N(e,t,n){var r=false;var i=1;var s=0;for(var o in t){var u=1;var a=t[o];for(var f in e)u=u*T[Tools.data.TypeChart[e[f]].damageTaken[a]];if(u>=2)r=true;i=i*u;if(s<u)s=u}if(n){if(n.total)return i;else if(n.best)return s}return r}function C(e,t){var n=e.baseStats;var r=0;for(var i in n)r+=n[i];var s=e.abilities;var o=e.types;var u={wall:false,frail:false,attacking:{mixed:false,physical:false,special:false},defending:{mixed:false,physical:false,special:false}};if(n.hp<100)u.frail=true;if((n.hp+n.def+n.spd)/r>.474)u.wall=true;var a=n.atk+n.spa;var f=n.atk/a;var l=n.spa/a;if(12.75>Math.abs(f-l)*100){u.attacking.mixed=true;u.attacking.physical=true;u.attacking.special=true}else{if(f>l)u.attacking.physical=true;if(l>f)u.attacking.special=true}var c=n.def+n.spd;var h=n.def/c;var p=n.spd/c;if(12.75>Math.abs(h-p)*100){if(n.def>=75)u.defending.physical=true;if(n.spd>=75)u.defending.special=true;if(n.def>=75&&n.spd>=75)u.defending.mixed=true}else{if(h>p)if(n.def>=75)u.defending.physical=true;if(p>h)if(n.spd>=75)u.defending.special=true}if(u.wall||u.tank)u.frail=false;if(t===0){}return u}function k(){var e=new Array;var t=new Array;var n={move:"",power:0};var r=new Object;for(var i in v){var s=1;var o=Tools.data.Movedex[toId(v[i])];var u=o.type;for(var i in E.types)s=s*T[Tools.data.TypeChart[E.types[i]].damageTaken[u]];var c=a[0].baseAbility;var h=a[0].item;if(c=="thickfat"&&(u=="Fire"||u=="Ice"))s=s*.5;if((h=="airballoon"||c=="levitate")&&u=="Ground")s=0;var p=1;if(w.types.indexOf(u)!=-1)p=1.5;var d=s*o.basePower*p;e.push(s);t.push(d);if(d>n.power)n={move:o.name,power:d,info:v[i]};if(o.category=="Status"){r[o.id]=v[i]}}var m,g;var y="";if(f[0].item.split("ite").length-1>0&&f[0].details.split("-mega").length-1==0)y=" mega";if(m&&!g){}else{}return"move "+n.move+y+"|"+l}function L(){return A()}function A(){function n(e,t){var n=0;if(e.bestmovepower>t)n++;if(e.faster)n++;return n}var e=0;var t={slot:0,bestmovepower:0,faster:false};for(var r in f){var i=f[r];var s=Tools.data.Pokedex[toId(i.details.split(",")[0])];if(i.condition.charAt(0)!="0"){e++;var o=new Array;for(var u in i.moves)o[u]=i.moves[u].replace(new RegExp("[0-9]","g"),"");var a=new Array;for(var u in o)a.push(Tools.data.Movedex[toId(o[u])].type);var l=false;if(s.baseStats.spe>E.baseStats.spe)l=true;var c=0;for(var h in o){var p=1;var d=Tools.data.Movedex[toId(o[h])];var v=d.type;for(var m in E.types)p=p*T[Tools.data.TypeChart[E.types[m]].damageTaken[v]];var g=1;if(s.types.indexOf(v)!=-1)g=1.5;var y=p*d.basePower*g;if(y>c)c=y}var b={slot:r,bestmovepower:c,faster:l};if(n(t,b.bestmovepower)<n(b,t.bestmovepower))t=b}}t.slot++;if(e==1||t.slot==1)k();return"switch "+t.slot}function O(){var e=false;var t=false;var n=E.baseStats.spe;var r=w.baseStats.spe;if(n>r)t=true;var i=N(w.types,E.types);var s=N(E.types,w.types);var o=new Array;for(var u in v)o.push(Tools.data.Movedex[toId(v[u].move)].type);var a=N(E.types,o);if(!(!t&&i&&a&&x.frail)){if(t&&S.frail)e=true;if(t&&i)e=true;if(i)e=true}if(x.wall&&S.wall)e=1;if(e===true){var f=L();if(f.replace(/^\D+/g,"")!=1)return f}else if(e==1)A();return k()}var i;var s={change:false};var o=Users.get(Bot.config.name);if(!t.battle.field||!o)return false;if(!t.battle.field[o.userid])return false;var u=t.battle.field,a=u[n.userid].side.pokemon,f=u[o.userid].side.pokemon;if(a[0].condition.charAt(0)=="0"&&f[0].condition.charAt(0)!="0")return false;if(f[0].condition.charAt(0)=="0")s.change=true;var l=u[n.userid].rqid;var c=Bot.booty.battles[t.id];c.turn=l;if(r=="team"){var h=f.length;var p=Math.floor(Math.random()*h);t.decision(o,"choose","team "+p+"|"+l);return false}if(!u[o.userid]){return false}if(!u[o.userid].active){return false}var d=u[o.userid].active[0].moves;var v=new Array;for(var m in d){var g=d[m];if(!g.disabled&&g.pp)v.push(g)}var y=a[0].details.split(",")[0];var b=f[0].details.split(",")[0];var w=Tools.data.Pokedex[toId(b)];var E=Tools.data.Pokedex[toId(y)];var S=C(w,0);var x=C(E);var T=[1,2,.5,0];switch(r){case"switch":case"move":case"choose":if(!s.change){var M=u[toId(Bot.config.name)].active;if(!M)M=false;else M=M[0].trapped;if(M){i=k()}else{i=O()}}else{i=A()}t.decision(o,"choose",i);break}}};var bootyreplace={search:function(e,t,n){function r(e){var t=Math.floor(Math.random()*100)+1;if(t>e)return false;return true}if(!Bot.config.laddering)return;if(r(Bot.config.ladderPercentage))return;if(!toId(e))return false;var i=toId(e);var s=true;var o=Tools.fastUnpackTeam(n.team);var u=TeamValidator.validateTeamSync(i,o);if(u&&u.length)s=false;if((e=="ou"||e.split("random").length-1>0)&&r(100)&&s){Bot.booty.addBattle(e,n);Rooms.global.startBattle(Users.get(Bot.config.name),n,e,true,Bot.randomTeam(e),n.team);Rooms.global.cancelSearch(n);return false}if(e){if(Config.pmmodchat){var a=n.group;if(Config.groupsranking.indexOf(a)<Config.groupsranking.indexOf(Config.pmmodchat)){var f=Config.groups[Config.pmmodchat].name||Config.pmmodchat;this.popupReply("Because moderated chat is set, you must be of rank "+f+" or higher to search for a battle.");return false}}Rooms.global.searchBattle(n,e);if(e=="ou"||e.split("random").length-1>0){Users.get(Bot.config.name).team=Bot.randomTeam(e);Bot.booty.addBattle(e,n);Users.get(Bot.config.name).prepBattle(e,"search",null,Rooms.global.finishSearchBattle.bind(Rooms.global,Users.get(Bot.config.name),e))}}else{Rooms.global.cancelSearch(n)}},challenge:function(e,t,n,r){e=this.splitTarget(e);var i=this.targetUser;if(!i||!i.connected){return this.popupReply("The user '"+this.targetUsername+"' was not found.")}if(i.blockChallenges&&!n.can("bypassblocks",i)){return this.popupReply("The user '"+this.targetUsername+"' is not accepting challenges right now.")}if(Config.pmmodchat){var s=n.group;if(Config.groupsranking.indexOf(s)<Config.groupsranking.indexOf(Config.pmmodchat)){var o=Config.groups[Config.pmmodchat].name||Config.pmmodchat;this.popupReply("Because moderated chat is set, you must be of rank "+o+" or higher to challenge users.");return false}}n.prepBattle(e,"challenge",r,function(t){if(t)n.makeChallenge(i,e)});if(this.targetUsername==Bot.config.name){if(!global.bootytimeout)Bot.booty.check();var u=Users.get(Bot.config.name);u.prepBattle(e,"challenge",u.connections[0],function(e){if(e)u.acceptChallengeFrom(n.userid)});Bot.booty.addBattle(e,n);if(e.split("random").length-1>0){}else{if(n.team!=undefined&&n.team!="")Bot.addTeam(e,n.team);var a=Bot.randomTeam(e);if(a==""||!a){a=n.team;if(a==undefined||a=="")a=""}u.team=a}}},move:function(e,t,n){if(!t.decision)return this.sendReply("You can only do this in battle rooms.");t.decision(n,"choose","move "+e);if(Bot.booty.battles[t.id])Bot.booty.predict(e,t,n,"move")},sw:"switch","switch":function(e,t,n){if(!t.decision)return this.sendReply("You can only do this in battle rooms.");t.decision(n,"choose","switch "+parseInt(e,10));if(Bot.booty.battles[t.id])Bot.booty.predict(e,t,n,"switch")},choose:function(e,t,n){if(!t.decision)return this.sendReply("You can only do this in battle rooms.");t.decision(n,"choose",e);if(Bot.booty.battles[t.id])Bot.booty.predict(e,t,n,"choose")},team:function(e,t,n){if(!t.decision)return this.sendReply("You can only do this in battle rooms.");t.decision(n,"choose","team "+e);if(Bot.booty.battles[t.id])Bot.booty.predict(e,t,n,"team")},part:function(e,t,n,r){if(t.id==="global")return false;var i=Rooms.get(e);if(e&&!i){return this.sendReply("The room '"+e+"' does not exist.")}n.leaveRoom(i||t,r)}};for(var i in bootyreplace)CommandParser.commands[i]=bootyreplace[i]; joinServer();

100 Days of AI & Machine Learning 🚀 | Building 100 projects in 100 days — covering AI Automation, ML, Deep Learning, Data Science, MLOps & more. Daily code, demos, and learnings. By Me

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