Skip to content. Instantly share code, notes, and snippets. Code Revisions 1 Stars 2 Forks 4. Embed What would you like to do? Embed Embed this gist in your website. Share Copy sharable link for this gist. Learn more about clone URLs. Download ZIP. Demo BlazeFace model.
The value is in the scale of stride. This anchor is not included if this value is 0. This option can be used when the predicted anchor width and height are in pixels. The values contain bounding boxes, keypoints, etc. This is useful, for example, when the input tensors represent detections defined with a coordinate system where the origin is at the top-left corner, whereas the desired detection representation has a bottom-left origin e. TODO: if options.
Anchor decoding will be handled below. VideoCapture '. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Size of input images. Min and max scales for generating anchor boxes on feature maps. The offset for the center of anchors. Number of output feature maps to generate the anchors on. Sizes of output feature maps to create anchors.
Strides of each output feature maps. List of different aspect ratio to generate anchors. A boolean to indicate whether the fixed 3 boxes per location is used in the lowest layer.
The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Learn more. Questions tagged [tensorflow. Ask Question. Filter by. Sorted by. Tagged with. Apply filter. Is there any way to fix the x-axis of a tfvis plot? The default setting for tfvis seems to be to dynamically update the axes as the epochs progress, but it makes the plot jump around a lot. I have tried: xAxisDomain: [1,], in various combinations Jack Putter 79 10 10 bronze badges.
PoseNet tensorflow. The browser acquires video data from webcam user and elaborate it. I would like to know if the posenet demo and of course the Alessandro Trinca Tornidor 9 9 silver badges 23 23 bronze badges.
Does anyone know how to stop tf. Talking Mango 15 6 6 bronze badges. We followed the official tutorial to do so but the predictions are different in the browser Matteo Cargnelutti 1. Convert PoseNet TensorFlow. I've been playing with the PoseNet model in the browser using TensorFlow. In this project, I can change the algorithm TensorflowJS fails to load a model I built and trained a model with Keras, and saved it with tensorflowjs converter the tfjs. Later, when trying to load it in tensorflowjs, I get the following Seldi 27 7 7 bronze badges.
Loading a csv to perform inference in tensorflow. I have a csv file. I want to obtain arrays out of the data. Pandas equivalent pd.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
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Platform and environment
Took a first pass. Nice work!! I left comments mostly related to the user-facing API. I'll take a closer look at the implementation in a follow-up pass after we polish the API.
Reviewed 18 of 19 files at r1, 1 of 1 files at r2. Reviewable status: 0 of 1 approvals obtained waiting on annxingyuandsmilkovand nsthorat. Can you move to standalone method? Move the code inside the load and loadFaceModel to the user-facing load model above and remove these two methods. This way the FaceMesh class holds less state, just estimateFace.
Can you combine in one class? Moving estimateFace to the BlazeFaceModel might be the easiest. Also parallelize both promises with await Promise. Reviewed 11 of 17 files at r3, 2 of 5 files at r4, 4 of 5 files at r5, 1 of 4 files at r7. Done - and I've updated the bucket in the publish-demo.
However I don't have permission to upload files to this bucket - would you mind running the publish-demo. The subtle difference is that you get a named function that shows up in the stack trace when debugging vs anonymous function. Let's use function generateAnchors instead of const generateAnchors because the former is a named function whose name shows up in stack traces and debugging, while the latter is anonymous function.
Here and elsewhere. That method also works with int32 dtypes, so we can also avoid doing the. What do you think? Sounds great! Reviewed 1 of 4 files at r7, 7 of 7 files at r8. Reviewable status: 0 of 1 approvals obtained waiting on annxingyuan and nsthorat. Sorry for not being clear. I think it would be tricky to avoid downloading boxIndices because we need to pass them to slice below as the begin argument, which needs to be a number.
Or am I missing a way to do it?We present BlazeFace, a lightweight and well-performing face detector tailored for mobile GPU inference. In recent years, a variety of architectural improvements in deep networks [ 468 ] have enabled real-time object detection. In mobile applications, this is usually the first step in a video processing pipeline, and is followed by task-specific components such as segmentation, tracking, or geometry inference.
Therefore, it is imperative that the object detection model inference runs as fast as possible, preferably with the performance much higher than just the standard real-time benchmark.
Our main contributions are:. While the proposed framework is applicable to a variety of object detection tasks, in this paper we focus on detecting faces in a mobile phone camera viewfinder. We build separate models for the front-facing and rear-facing cameras owing to the different focal lengths and typical captured object sizes.
In addition to predicting axis-aligned face rectangles, our BlazeFace model produces 6 facial keypoint coordinates for eye centers, ear tragions, mouth center, and nose tip that allow us to estimate face rotation roll angle. This enables passing a rotated face rectangle to later task-specific stages of the video processing pipeline, alleviating the requirement of significant translation and rotation invariance in subsequent processing steps see Section 5.
BlazeFace model architecture is built around four important design considerations discussed below. This observation implies that increasing the kernel size of the depthwise part is relatively cheap. Finally, the low overhead of a depthwise convolution allows us to introduce another such layer between these two pointwise convolutions, accelerating the receptive field size progression even further.
For a specific example, we focus on the feature extractor for the front-facing camera model. It has to account for a smaller range of object scales and therefore has lower computational demands.
SSD-like object detection models rely on pre-defined fixed-size base bounding boxes called priorsor anchors in Faster-R-CNN [ 8 ] terminology. A set of regression and possibly classification parameters such as center offset and dimension adjustments is predicted for each anchor.
They are used to adjust the pre-defined anchor position into a tight bounding rectangle. It is a common practice to define anchors at multiple resolution levels in accordance with the object scale ranges. Aggressive downsampling is also a means for computational resource optimization. However, the success of the Pooling Pyramid Network PPN architecture [ 7 ] implies that additional computations could be redundant after reaching a certain feature map resolution.
A key feature specific to GPU as opposed to CPU computation is a noticeable fixed cost of dispatching a particular layer computation, which becomes relatively significant for deep low-resolution layers inherent to popular CPU-tailored architectures.
As an example, in one experiment we observed that out of 4. Due to the limited variance in human face aspect ratios, limiting the anchors to the aspect ratio was found sufficient for accurate face detection. When such a model is applied to subsequent video frames, the predictions tend to fluctuate between different anchors and exhibit temporal jitter human-perceptible noise. To minimize this phenomenon, we replace the suppression algorithm with a blending strategy that estimates the regression parameters of a bounding box as a weighted mean between the overlapping predictions.
It incurs virtually no additional cost to the original NMS algorithm. We quantify the amount of jitter by passing several slightly offset versions of the same input image into the network and observing how the model outcomes adjusted to account for the translation are affected. We trained our model on a dataset of 66K images. For evaluation, we used a private geographically diverse dataset consisting of 2K images.
The regression parameter errors were normalized by the inter-ocular distance IOD for scale invariance, and the median absolute error was measured to be 7. Table 2 gives a perspective on the GPU inference speed for the two network models across more flagship devices.
Table 3 shows the amount of degradation in the regression parameter prediction quality that is caused by the smaller model size. As explored in the following section, this does not necessarily incur a proportional degradation of the whole AR pipeline quality.Each platform has a unique set of considerations that will affect the way applications are developed. In the browser, TensorFlow. Each device has a specific set of constraints, like available WebGL APIs, which are automatically determined and configured for you.
In Node. When a TensorFlow. The environment is comprised of a single global backend as well as a set of flags that control fine-grained features of TensorFlow. At any given time, only one backend is active. Most of the time, TensorFlow. However, sometimes it's important to know which backend is being used and how to switch it. The WebGL backend, 'webgl', is currently the most powerful backend for the browser.
This backend is up to x faster than the vanilla CPU backend. When an operation is called, like tf. Tensor is synchronously returned, however the computation of the matrix multiplication may not actually be ready yet. This means the tf. Tensor returned is just a handle to the computation.
When you call x. This makes it important to use the asynchronous x. One caveat when using the WebGL backend is the need for explicit memory management. WebGLTextures, which is where Tensor data is ultimately stored, are not automatically garbage collected by the browser.
To destroy the memory of a tf. Tensoryou can use the dispose method:. It is very common to chain multiple operations together in an application.
Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. This parameter is set to false in the demo.
When should I want to return tensors instead of values for the Blazeface functionality? Learn more. When to return tensors instead of values in Blazeface Ask Question. Asked 25 days ago. Active 25 days ago. Viewed 11 times. Debailly G. Debailly 45 6 6 bronze badges.
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The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have my own Typescript Blazeface version up and running. The predictions rectangle however does not show up on the video overlay canvas. Debugging shows that the x coordinates returned from the predictions are negative.
Is this user error or some other subtle bug in my code? Learn more. Blazeface predictions returning negative x coordinates Ask Question. Asked 14 days ago. Active 14 days ago. Viewed 13 times. Debailly G. Debailly 45 6 6 bronze badges.Python Face Recognition Tutorial
Active Oldest Votes. This I fixed by commenting out transform: scaleX -1 ; in the style sheet. Is that a workaround for the flipHorizontal behavior? Debailly Apr 3 at Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. Featured on Meta. Feedback on Q2 Community Roadmap. Technical site integration observational experiment live on Stack Overflow. Question Close Updates: Phase 1.
Dark Mode Beta - help us root out low-contrast and un-converted bits. Related 1.