GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This repository contains a pure Python implementation multi-pose only of the Google TensorFlow. Windows 10 with the latest as of bit Python 3.
If you want to use the webcam demo, a pip version of opencv pip install opencv-python is required instead of the conda version.Realtime Multi-Person 2D Human Pose Estimation using Part Affinity Fields, CVPR 2017 Oral
Also, you may have to force install version 3. There are three demo apps in the root that utilize the PoseNet model. They are very basic and could definitely be improved. The first time these apps are run or the library is used model weights will be downloaded from the TensorFlow. The default is the model. Image demo runs inference on an input folder of images and outputs those images with the keypoints and skeleton overlayed. The webcam demo uses OpenCV to capture images from a connected webcam.
The result is overlayed with the keypoints and skeletons and rendered to the screen. The original model, weights, code, etc.
I would like to use the PoseNet tensorflow model for a project. The browser acquires video data from webcam user and elaborate it. I would like to know if the posenet demo and of course the tensorflow. First of all, you need to know if the model is processing personal data data that can be used to identify the user.
Then, even if personal data is being processed, you have to figure out who is processing that data. This is because GDPR does not apply to the processing of personal data " by a natural person in the course of a purely personal or household activity ". This means that if the user is the one that freely decides to load some data into the web browser, and apply your TensorFlowJS model to that data, you are not a data processor who processes data in behalf of the end user.
An example of this would be this pneumonia detection web app. The user selects an x-ray image, and the app finds out whether the patient has pneumonia, all without sending the image to any server. However, GDPR does apply when you your server receives personal data. Even if your model is run in the browser, your front end may still send data back to the server. So you need to be careful with this.
Finally, I am not a laywer. This answer is based entirely on my understanding of the GDPR text which may, or may not, be accurate. Learn more. PoseNet tensorflow. Asked 4 days ago. Active 3 days ago. Viewed 38 times. Thank you. Alessandro Trinca Tornidor. You can ask about GDPR compliance on law. Active Oldest Votes. New contributor. The Overflow Blog. Featured on Meta.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. PoseNet does not recognize who is in an image, it is simply estimating where key body joints are. This repo contains a set of PoseNet models that are quantized and optimized for use on Coral's Edge TPU, together with some example code to shows how to run it on a camera stream.
Pose estimation has many uses, from interactive installations that react to the body to augmented realityanimationfitness usesand more. We hope the accessibility of this model inspires more developers and makers to experiment and apply pose detection to their own unique projects, to demonstrate how machine learning can be deployed in ways that are anonymous and private.
An input RGB image is fed through a convolutional neural network. In our case this is a MobileNet V1 architecture. Instead of a classification head however, there is a specialized head which produces a set of heatmaps one for each kind of key point and some offset maps.
This step runs on the EdgeTPU. The results are then fed into step 2. A special multi-pose decoding algorithm is used to decode poses, pose confidence scores, keypoint positions, and keypoint confidence scores. Note that unlike in the TensorflowJS version we have created a custom OP in Tensorflow Lite and appended it to the network graph itself. The advantage is that we don't have to deal with the heatmaps directly and when we then call this network through the Coral Python API we simply get a series of keypoints from the network.
If you're interested in the gory details of the decoding algorithm and how PoseNet works under the hood, I recommend you take a look at the original research paper or this medium post whihch describes the raw heatmaps produced by the convolutional model. Pose : at the highest level, PoseNet will return a pose object that contains a list of keypoints and an instance-level confidence score for each detected person.
It contains both a position and a keypoint confidence score. PoseNet currently detects 17 keypoints illustrated in the following diagram:. Keypoint Confidence Score : this determines the confidence that an estimated keypoint position is accurate. It ranges between 0. It can be used to hide keypoints that are not deemed strong enough. Keypoint Position : 2D x and y coordinates in the original input image where a keypoint has been detected. For more information on updating see:.
A camera example that streams the camera image through posenet and draws the pose on top as an overlay. This is a great first example to run to familiarize yourself with the network and its outputs. In this repo we have included 3 posenet model files for differnet input resolutions. The larger resolutions are slower of course, but allow a wider field of view, or further-away poses to be processed correctly.Tutorials show you how to use TensorFlow.
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Use a Python model in Node. You may even see a performance boost too. Install Learn Introduction. TensorFlow Lite for mobile and embedded devices. TensorFlow Extended for end-to-end ML components. API r2. API r1 r1. Pre-trained models and datasets built by Google and the community.
How it works. Use official TensorFlow. Retrain existing models Retrain pre-existing ML models using your own data.
Use Transfer Learning to customize models. Get started with TensorFlow. Performance RNN Enjoy a real-time piano performance by a neural network.
Webcam Controller Play Pac-Man using images trained in your browser. Move Mirror Explore pictures in a fun new way, just by moving around. See all demos. Sign up. Mar 18, Watch the video. Cancel Continue. Feb 24, Read the blog.July 19, — Posted by Jane Friedhoff and Irene AlvaradoCreative Technologists, Google Creative Lab Pose estimation, or the ability to detect humans and their poses from image data, is one of the most exciting — and most difficult — topics in machine learning and computer vision.
Recently, Google shared PoseNet : a state-of-the-art pose estimation model that provides highly accurate pose data from image data…. Return to TensorFlow Home.
Body, Movement, Language: AI Sketches With Bill T. Jones
July 19, Posted by Jane Friedhoff and Irene AlvaradoCreative Technologists, Google Creative Lab Pose estimation, or the ability to detect humans and their poses from image data, is one of the most exciting — and most difficult — topics in machine learning and computer vision.
Recently, Google shared PoseNet : a state-of-the-art pose estimation model that provides highly accurate pose data from image data even when those images are blurry, low-resolution, or in black and white. This is the story of the experiment that prompted us to create this pose estimation library for the web in the first place. Months ago, we prototyped a fun experiment called Move Mirror that lets you explore images in your browser, just by moving around.
The experiment creates a unique, flipbook-like experience that follows your moves and reflects them with images of all kinds of human movement — from sports and dance to martial arts, acting, and beyond. We wanted to release the experience on the web, let others play with it, learn about machine learning, and share the experience with friends.
Unfortunately we faced a problem: a publicly accessible web-specific model for pose estimation did not exist.
We thus saw a unique opportunity to make pose estimation more widely accessible by porting an in-house model to TensorFlow. With PoseNet out in the wild, we can finally release Move Mirror — a project that is a testament to the value that experimentation and play can add to serious engineering work. It was only through a true collaboration between research, product, and creative teams that we were able to build PoseNet and Move Mirror.
What is pose estimation? What is posenet? We want our machine learning models to be able to understand and smartly infer data about all these different bodies. In the past, technologists have approached the problem of pose estimation using special cameras and sensors like stereoscopic imagery, mocap suits, and infrared cameras as well as computer vision techniques that can extract pose estimation from 2d images like OpenPose. This makes it harder for the average developer to quickly get started with playful pose experiments.
This was the perfect opportunity, we realized, to connect TensorFlow. By porting PoseNet to TensorFlow. You can read more about that process here. A few things that made us super excited about PoseNet in TensorFlow.
Shareability: Because everything can run in the browser, TensorFlow. No need to make operating-system-specific builds — just upload your webpage and go. Privacy: Because all of the pose estimation can be done in the browser, that means none of your image data ever has to leave your computer. Rather than sending your photos to some server in the sky to do pose analysis on a centralized service i. With Move Mirror, we match the x,y joint data that PoseNet spits out with our bank of poses on our backend — but your image stays entirely on your computer.
Design and Inspiration We spent a few weeks just goofing around with different pose estimation prototypes. We played with trails, puppets, and all sorts of other silly things before we landed on the concept that would become Move Mirror.
In talking about what we could do with pose estimation, we were tickled by the idea of being able to search an archive by pose.
Law Stack Exchange is a question and answer site for legal professionals, students, and others with experience or interest in law. It only takes a minute to sign up. I would like to use the PoseNet tensorflow. When the user loads the demo project, the browser ask to acquire video data from webcam user.
I would like to know what's the Google general position about GDPR compliance when their web services elaborate users data and, if possible, also how Google web services handle the collected user data. I can't find nothing about this argument, only this generic announcement.
Someone have accurate information about this topic? Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Asked 3 days ago. Active 3 days ago. Viewed 26 times. Thank you. New contributor. I am not an expert on PoseNet but my understanding is that it runs entirely within the client's browser and no images or other data are transmitted to Google.
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Alessandro Trinca Tornidor is a new contributor. Be nice, and check out our Code of Conduct. Sign up or log in Sign up using Google.Image-to-Image Demo. Interactive Image Translation with pix2pix-tensorflow. Written by Christopher Hesse — February 19 th Recently, I made a Tensorflow port of pix2pix by Isola et al.
I've taken a few pre-trained models and made an interactive web thing for trying them out. Chrome is recommended.
The pix2pix model works by training on pairs of images such as building facade labels to building facades, and then attempts to generate the corresponding output image from any input image you give it. The idea is straight from the pix2pix paperwhich is a good read. Trained on about 2k stock cat photos and edges automatically generated from those photos. Generates cat-colored objects, some with nightmare faces. The best one I've seen yet was a cat-beholder. Some of the pictures look especially creepy, I think because it's easier to notice when an animal looks wrong, especially around the eyes.
The auto-detected edges are not very good and in many cases didn't detect the cat's eyes, making it a bit worse for training the image translation model. Trained on a database of building facades to labeled building facades. It doesn't seem sure about what to do with a large empty area, but if you put enough windows on there it often has reasonable results. Draw "wall" color rectangles to erase things. I didn't have the names of the different parts of building facades so I just guessed what they were called.
If you're really good at drawing the edges of shoes, you can try to produce some new designs. Keep in mind it's trained on real objects, so if you can draw more 3D things, it seems to work better. If you draw a shoe here instead of a handbag, you get a very oddly textured shoe. The models were trained and exported with the pix2pix.