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interactions-py / Interactions.pyA highly extensible, easy to use, and feature complete bot framework for Discord
molyswu / Hand Detectionusing 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.
discord / Discord Interactions PythonUseful tools for building interactions in Python
LiBa001 / Discord Interactions.pyA Python library for the Discord Interactions API
discord-py-ui / Discord UiA discord.py extension for sending, receiving and handling ui interactions in discord
panos-stavrianos / Py Directuspy-directus is a Python wrapper for asynchronous interaction with the Directus headless CMS API. It provides a convenient and easy-to-use interface for performing CRUD operations, querying data, and managing resources in Directus.
Nishant-Wadhwani / Intelligent Infotainment System For Automobiles# Intellifotainment assist” – Smart HMI for passenger cars To run the program, download all files and save them in the same directory. After that, simply run 'Master.py' in the terminal. At the moment, the program will only run in linux based systems. # The Idea Infotainment systems have come a long way since the first set of dashboards installed in cars. Through our idea, we aim to create a Human Machine interaction model that takes infotainment systems to a new level. The driver tends to get distracted from the road while performing secondary tasks such as changing the music track, locking/unlocking the door while driving etc. Our system shall enable the driver to focus only on driving. Controlling the secondary tasks will be much easier. Our product primarily comprises of 5 modules: 1) Attention and drowsiness detection: - A camera shall be present on the dashboard, in front of the driver, behind the steering wheel. Through digital image processing techniques , using hough circle algorithm and haarcascade of an eye, we shall keep track of the driver’s sight. If he or she is looking away from the road while driving for more than a specified amount of time, we shall alert the driver to focus. We shall map the head orientation and iris position to accurately identify the driver’s attention. 2) Infotainment control features using blink combination: Through a combination of blinks, the driver can turn on or of the headlights, tail lights as well as indicators. Blinking of the eyes shall be detected using ‘dlib’ features in python. This shall give extremely accurate results. 3) Voice commands to control wipers, car lock, music system and windows A simple, yet extremely useful idea that would make the life of the driver a whole lot easy. Enabling the driver to speak to his car infotainment system would allow him to control and navigate these functionalities with great ease. The car will be enabled with a virtual assistant. 4) Automatic rear view mirror adjustment scheme: Using the camera placed in front of the driver, the system shall detect the position of the driver’s head. This shall also be done using image processing techniques and we shall identify the coordinates of the driver head in 3D space. There will be a mapping between the head position and mirror adjustment scheme. The mirrors will adjust their position using servo motors and shall do so automatically by identifying the head position. 5) A revolutionary reverse-assistance algorithm for smart parking and general reversing: Probably the highlight of our model, this feature shall make driving the car in and out of a parking spot, or rather, even reversing a car in general, far easier and safer than what it already is. Like most other modern cars, our model shall also have a camera installed at the back and the corresponding image displayed at the infotainment screen for parking assistance. Upon activating the reverse gear, the screen shall trace the line of motion of the car corresponding to the current position of the steering wheel. Because of this feature, the driver gets an idea of whether or not he’ll hit an obstacle while reversing if the steering wheel is kept at that position. Taking this feature to another level, the rear camera, after capturing the live video feed from the back of the car, shall perform image processing and machine learning algorithms to find a safe, obstacle-free path for reversing and indicate the driver to move the steering wheel accordingly. So instead of relying solely on the drivers judgement, our system shall actually find the path to be taken while reversing, such that other cars and other obstacles will be avoided, and accordingly recommend the driver to steer the wheel in that direction. This feature shall be extremely useful for new drivers/ learners. During the initial phase, to prevent errors from creeping in, we will always have a manual override button. After a good amount of testing, further modifications and refinements can be made. Our systems adds new dimensions to both precautionary safety measures, as well as convenience. If implemented properly, we are confident that our project will reach new heights of HMI and driver assistance technology. It will give drivers several less reasons to worry about.
shhubhxm / Skin Disease Detection Team TechnophileWe are team technophiles and participated in 48hrs hackathon organized by Nirma University in collabration with Binghamton University. Our Problem Definition : To develop a solution, the first step is to understand the problem. The problem here is to develop an Application Programming Interface which can be easily integrated with Android and IOS to detect the skin disease without any physical interaction with a Dermatologist. The detected skin disease should be sent through whatsapp to a particular patient and doctor. Our college name: Pandit Deendayal Energy University Team Members: Rushabh Thakkar, Divy Patel, Denish Kalariya, Yug Thakkar, and Shubham Vyas. Project Details: We made an application which classifies the skin diseases into these given types healthy, lupus, ringworm and scalp_infections How did we make? The data given was analysed first. We came to conclusion that the data given was not enough so we searched for new datasets. We got these datasets: https://ieee-dataport.org/documents/image-dataset-various-skin-conditions-and-rashes https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T We segregated the datasets of harvard. Combined all the datasets and trained the tensorflow image classification model multiple times. Accuracy was not satisfying. Augmented the data to unbaised the model and the dataset would be balanced. Data Augmentation was done on the data given . We generated 800 images per disease. Again we had trained the model. Accuracy was good. Exported the .tflite and label.txt file. We imported the files into android studio We have used three python codes: data_removal.py This code is used to remove data randomly from the folder if there are more number of images than required. We just need to change total_files_req variable in the code to number of files required after deletion. data_augmentation.py This code is used to augment the data randomly from the folder if there are less number of images than required. We just need to change total_files_req variable in the code to number of files required after augmentation. We change various parameters of images like clearity, rotation, brightness, etc. image_classification_code.py This is the main code in which we have trained the model and exported it to run on the app Models we tried: efficientnet-lite0(USED in our project) efficientnet-lite1 efficientnet-lite2 efficientnet-lite3 efficientnet-lite4 API: TensorFlowLite Used Android studio for App development . Used Language = java We sync all the grade files. Changed the model files and update it with the new model Working model file name is model.tflite Tflite classifier working java files are CameraActivity.java CamerConnectionFragment.java ClasssifierActivity.java LegacyCameraConnectionFreagment.java Dataset: Uploaded on Github WORKING MODEL LINK: https://drive.google.com/file/d/1BnqfFInFkJJDkYDlmdj9VB601f7PjTdj/view?usp=sharing
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You will use commercially reasonable efforts to ensure that a User is provided with clear and comprehensive information about, and consents to, the storing and accessing of cookies or other information on the User’s device where such activity occurs in connection with the Service and where providing such information and obtaining such consent is required by law. You must not circumvent any privacy features (e.g., an opt-out) that are part of the Service. You will comply with all applicable Google Analytics policies located at www.google.com/analytics/policies/ (or such other URL as Google may provide) as modified from time to time (the "Google Analytics Policies"). You may participate in an integrated version of Google Analytics and certain Google advertising services ("Google Analytics Advertising Features"). If You use Google Analytics Advertising Features, You will adhere to the Google Analytics Advertising Features policy (available at support.google.com/analytics/bin/answer.py?hl=en&topic=2611283&answer=2700409). Your access to and use of any Google advertising service is subject to the applicable terms between You and Google regarding that service. If You use the Platform Home, Your use of the Platform Home is subject to the Platform Home Additional Terms (or as subsequently re-named) available at https://support.google.com/marketingplatform/answer/9047313 (or such other URL as Google may provide) as modified from time to time (the "Platform Home Terms"). 8. Indemnification. To the extent permitted by applicable law, You will indemnify, hold harmless and defend Google and its wholly-owned subsidiaries, at Your expense, from any and all third-party claims, actions, proceedings, and suits brought against Google or any of its officers, directors, employees, agents or affiliates, and all related liabilities, damages, settlements, penalties, fines, costs or expenses (including, reasonable attorneys' fees and other litigation expenses) incurred by Google or any of its officers, directors, employees, agents or affiliates, arising out of or relating to (i) Your breach of any term or condition of this Agreement, (ii) Your use of the Service, (iii) Your violations of applicable laws, rules or regulations in connection with the Service, (iv) any representations and warranties made by You concerning any aspect of the Service, the Software or Reports to any Third Party; (v) any claims made by or on behalf of any Third Party pertaining directly or indirectly to Your use of the Service, the Software or Reports; (vi) violations of Your obligations of privacy to any Third Party; and (vii) any claims with respect to acts or omissions of any Third Party in connection with the Service, the Software or Reports. Google will provide You with written notice of any claim, suit or action from which You must indemnify Google. You will cooperate as fully as reasonably required in the defense of any claim. Google reserves the right, at its own expense, to assume the exclusive defense and control of any matter subject to indemnification by You. 9. Third Parties. If You use the Service on behalf of the Third Party or a Third Party otherwise uses the Service through Your Account, whether or not You are authorized by Google to do so, then You represent and warrant that (a) You are authorized to act on behalf of, and bind to this Agreement, the Third Party to all obligations that You have under this Agreement, (b) Google may share with the Third Party any Customer Data that is specific to the Third Party's Properties, and (c) You will not disclose Third Party's Customer Data to any other party without the Third Party's consent. 10. DISCLAIMER OF WARRANTIES. TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, EXCEPT AS EXPRESSLY PROVIDED FOR IN THIS AGREEMENT, GOOGLE MAKES NO OTHER WARRANTY OF ANY KIND, WHETHER EXPRESS, IMPLIED, STATUTORY OR OTHERWISE, INCLUDING WITHOUT LIMITATION WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR USE AND NONINFRINGEMENT. 11. LIMITATION OF LIABILITY. TO THE EXTENT PERMITTED BY APPLICABLE LAW, GOOGLE WILL NOT BE LIABLE FOR YOUR LOST REVENUES OR INDIRECT, SPECIAL, INCIDENTAL, CONSEQUENTIAL, EXEMPLARY, OR PUNITIVE DAMAGES, EVEN IF GOOGLE OR ITS SUBSIDIARIES AND AFFILIATES HAVE BEEN ADVISED OF, KNEW OR SHOULD HAVE KNOWN THAT SUCH DAMAGES WERE POSSIBLE AND EVEN IF DIRECT DAMAGES DO NOT SATISFY A REMEDY. GOOGLE'S (AND ITS WHOLLY OWNED SUBSIDIARIES’) TOTAL CUMULATIVE LIABILITY TO YOU OR ANY OTHER PARTY FOR ANY LOSS OR DAMAGES RESULTING FROM CLAIMS, DEMANDS, OR ACTIONS ARISING OUT OF OR RELATING TO THIS AGREEMENT WILL NOT EXCEED $500 (USD). 12. Proprietary Rights Notice. The Service, which includes the Software and all Intellectual Property Rights therein are, and will remain, the property of Google (and its wholly owned subsidiaries). All rights in and to the Software not expressly granted to You in this Agreement are reserved and retained by Google and its licensors without restriction, including, Google's (and its wholly owned subsidiaries’) right to sole ownership of the Software and Documentation. Without limiting the generality of the foregoing, You agree not to (and not to allow any third party to): (a) sublicense, distribute, or use the Service or Software outside of the scope of the license granted in this Agreement; (b) copy, modify, adapt, translate, prepare derivative works from, reverse engineer, disassemble, or decompile the Software or otherwise attempt to discover any source code or trade secrets related to the Service; (c) rent, lease, sell, assign or otherwise transfer rights in or to the Software, Documentation or the Service; (d) use, post, transmit or introduce any device, software or routine which interferes or attempts to interfere with the operation of the Service or the Software; (e) use the trademarks, trade names, service marks, logos, domain names and other distinctive brand features or any copyright or other proprietary rights associated with the Service for any purpose without the express written consent of Google; (f) register, attempt to register, or assist anyone else to register any trademark, trade name, serve marks, logos, domain names and other distinctive brand features, copyright or other proprietary rights associated with Google (or its wholly owned subsidiaries) other than in the name of Google (or its wholly owned subsidiaries, as the case may be); (g) remove, obscure, or alter any notice of copyright, trademark, or other proprietary right appearing in or on any item included with the Service or Software; or (h) seek, in a proceeding filed during the term of this Agreement or for one year after such term, an injunction of any portion of the Service based on patent infringement. 13. U.S. Government Rights. If the use of the Service is being acquired by or on behalf of the U.S. Government or by a U.S. Government prime contractor or subcontractor (at any tier), in accordance with 48 C.F.R. 227.7202-4 (for Department of Defense (DOD) acquisitions) and 48 C.F.R. 2.101 and 12.212 (for non-DOD acquisitions), the Government's rights in the Software, including its rights to use, modify, reproduce, release, perform, display or disclose the Software or Documentation, will be subject in all respects to the commercial license rights and restrictions provided in this Agreement. 14. Term and Termination. Either party may terminate this Agreement at any time with notice. Upon any termination of this Agreement, Google will stop providing, and You will stop accessing the Service. Additionally, if Your Account and/or Properties are terminated, You will (i) delete all copies of the GAMC from all Properties and/or (ii) suspend any and all use of the SDKs within 3 business days of such termination. In the event of any termination (a) You will not be entitled to any refunds of any usage fees or any other fees, and (b) any outstanding balance for Service rendered through the date of termination will be immediately due and payable in full and (c) all of Your historical Report data will no longer be available to You. 15. Modifications to Terms of Service and Other Policies. Google may modify these terms or any additional terms that apply to the Service to, for example, reflect changes to the law or changes to the Service. You should look at the terms regularly. Google will post notice of modifications to these terms at https://www.google.com/analytics/terms/, the Google Analytics Policies at www.google.com/analytics/policies/, or other policies referenced in these terms at the applicable URL for such policies. Changes will not apply retroactively and will become effective no sooner than 14 days after they are posted. If You do not agree to the modified terms for the Service, You should discontinue Your use Google Analytics. No amendment to or modification of this Agreement will be binding unless (i) in writing and signed by a duly authorized representative of Google, (ii) You accept updated terms online, or (iii) You continue to use the Service after Google has posted updates to the Agreement or to any policy governing the Service. 16. Miscellaneous, Applicable Law and Venue. Google will be excused from performance in this Agreement to the extent that performance is prevented, delayed or obstructed by causes beyond its reasonable control. This Agreement (including any amendment agreed upon by the parties in writing) represents the complete agreement between You and Google concerning its subject matter, and supersedes all prior agreements and representations between the parties. If any provision of this Agreement is held to be unenforceable for any reason, such provision will be reformed to the extent necessary to make it enforceable to the maximum extent permissible so as to effect the intent of the parties, and the remainder of this Agreement will continue in full force and effect. This Agreement will be governed by and construed under the laws of the state of California without reference to its conflict of law principles. In the event of any conflicts between foreign law, rules, and regulations, and California law, rules, and regulations, California law, rules and regulations will prevail and govern. Each party agrees to submit to the exclusive and personal jurisdiction of the courts located in Santa Clara County, California. The United Nations Convention on Contracts for the International Sale of Goods and the Uniform Computer Information Transactions Act do not apply to this Agreement. The Software is controlled by U.S. Export Regulations, and it may be not be exported to or used by embargoed countries or individuals. Any notices to Google must be sent to: Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA, with a copy to Legal Department, via first class or air mail or overnight courier, and are deemed given upon receipt. A waiver of any default is not a waiver of any subsequent default. You may not assign or otherwise transfer any of Your rights in this Agreement without Google's prior written consent, and any such attempt is void. The relationship between Google and You is not one of a legal partnership relationship, but is one of independent contractors. This Agreement will be binding upon and inure to the benefit of the respective successors and assigns of the parties hereto. The following sections of this Agreement will survive any termination thereof: 1, 4, 5, 6 (except the last two sentences), 7, 8, 9, 10, 11, 12, 14, 16, and 17. 17. Google Analytics for Firebase. If You link a Property to Firebase (“Firebase Linkage”) as part of using the Service, the following terms, in addition to Sections 1-16 above, will also apply to You, and will also govern Your use of the Service, including with respect to Your use of Firebase Linkage. Other than as modified below, all other terms will stay the same and continue to apply. In the event of a conflict between this Section 17 and Sections 1-16 above, the terms in Section 17 will govern and control solely with respect to Your use of the Firebase Linkage. The following definition in Section 1 is modified as follows: "Hit" means a collection of interactions that results in data being sent to the Service and processed. Examples of Hits may include page view hits and ecommerce hits. A Hit can be a call to the Service by various libraries, but does not have to be so (e.g., a Hit can be delivered to the Service by other Google Analytics-supported protocols and mechanisms made available by the Service to You). For the sake of clarity, a Hit does not include certain events whose collection reflects interactions with certain Properties capable of supporting multiple data streams, and which may include screen views and custom events (the collection of events, an “Enhanced Packet”). The following sentence is added to the end of Section 7 as follows: If You link a Property to a Firebase project (“Firebase Linkage”) (i) certain data from Your Property, including Customer Data, may be made accessible within or to any other entity or personnel according to permissions set in Firebase and (ii) that Property may have certain Service settings modified by authorized personnel of Firebase (notwithstanding the settings You may have designated for that Property within the Service). Last Updated June 17, 2019 Follow us About Google Marketing Platform Overview For Small Businesses For Enterprise Learning & support Support Blog Analytics Academy Skillshop Google Primer Developers & partners Google Marketing Platform Partners Google Measurement Partners Analytics for developers Tag Manager for developers Surveys for developers Campaign Manager 360 for developers Related products Google Ads Google AdSense Google Ad Manager Google Cloud Firebase More from Google Think with Google Business Solutions Google Workspace PrivacyTermsAbout GoogleGoogle Products Help
MCServerScout / Discord BotServer Scout (Discord Bot)
fortunatoman / Discord AI ChatbotAI-powered Discord chatbot built with Python and discord.py. It uses Selenium-driven automation to generate intelligent responses, supports server and DM interactions, and provides a simple setup with bot authentication, ChromeDriver integration, and scalable message handling.
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