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GitOrigin-RevId: 27c70b5fe62ab71189d358ca122ee4b19c817a8f
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MediaPipe Team
+107
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@@ -224,29 +224,33 @@ where object detection simply runs on every image. Default to `0.99`.
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#### model_name
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Name of the model to use for predicting 3D bounding box landmarks. Currently supports
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`{'Shoe', 'Chair', 'Cup', 'Camera'}`.
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Name of the model to use for predicting 3D bounding box landmarks. Currently
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supports `{'Shoe', 'Chair', 'Cup', 'Camera'}`. Default to `Shoe`.
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#### focal_length
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Camera focal length `(fx, fy)`, by default is defined in
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[NDC space](#ndc-space). To use focal length `(fx_pixel, fy_pixel)` in
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[pixel space](#pixel-space), users should provide `image_size` = `(image_width,
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image_height)` to enable conversions inside the API. For further details about
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NDC and pixel space, please see [Coordinate Systems](#coordinate-systems).
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By default, camera focal length defined in [NDC space](#ndc-space), i.e., `(fx,
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fy)`. Default to `(1.0, 1.0)`. To specify focal length in
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[pixel space](#pixel-space) instead, i.e., `(fx_pixel, fy_pixel)`, users should
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provide [`image_size`](#image_size) = `(image_width, image_height)` to enable
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conversions inside the API. For further details about NDC and pixel space,
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please see [Coordinate Systems](#coordinate-systems).
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#### principal_point
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Camera principal point `(px, py)`, by default is defined in
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[NDC space](#ndc-space). To use principal point `(px_pixel, py_pixel)` in
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[pixel space](#pixel-space), users should provide `image_size` = `(image_width,
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image_height)` to enable conversions inside the API. For further details about
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NDC and pixel space, please see [Coordinate Systems](#coordinate-systems).
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By default, camera principal point defined in [NDC space](#ndc-space), i.e.,
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`(px, py)`. Default to `(0.0, 0.0)`. To specify principal point in
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[pixel space](#pixel-space), i.e.,`(px_pixel, py_pixel)`, users should provide
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[`image_size`](#image_size) = `(image_width, image_height)` to enable
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conversions inside the API. For further details about NDC and pixel space,
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please see [Coordinate Systems](#coordinate-systems).
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#### image_size
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(**Optional**) size `(image_width, image_height)` of the input image, **ONLY**
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needed when use `focal_length` and `principal_point` in pixel space.
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**Specify only when [`focal_length`](#focal_length) and
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[`principal_point`](#principal_point) are specified in pixel space.**
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Size of the input image, i.e., `(image_width, image_height)`.
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### Output
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@@ -356,6 +360,89 @@ with mp_objectron.Objectron(static_image_mode=False,
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cap.release()
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```
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## JavaScript Solution API
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Please first see general [introduction](../getting_started/javascript.md) on
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MediaPipe in JavaScript, then learn more in the companion [web demo](#resources)
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and the following usage example.
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Supported configuration options:
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* [staticImageMode](#static_image_mode)
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* [maxNumObjects](#max_num_objects)
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* [minDetectionConfidence](#min_detection_confidence)
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* [minTrackingConfidence](#min_tracking_confidence)
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* [modelName](#model_name)
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* [focalLength](#focal_length)
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* [principalPoint](#principal_point)
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* [imageSize](#image_size)
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```html
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/control_utils_3d.js" crossorigin="anonymous"></script>
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/objectron/objectron.js" crossorigin="anonymous"></script>
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</head>
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<body>
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<div class="container">
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<video class="input_video"></video>
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<canvas class="output_canvas" width="1280px" height="720px"></canvas>
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</div>
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</body>
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</html>
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```
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```javascript
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<script type="module">
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const videoElement = document.getElementsByClassName('input_video')[0];
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const canvasElement = document.getElementsByClassName('output_canvas')[0];
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const canvasCtx = canvasElement.getContext('2d');
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function onResults(results) {
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canvasCtx.save();
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canvasCtx.drawImage(
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results.image, 0, 0, canvasElement.width, canvasElement.height);
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if (!!results.objectDetections) {
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for (const detectedObject of results.objectDetections) {
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// Reformat keypoint information as landmarks, for easy drawing.
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const landmarks: mpObjectron.Point2D[] =
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detectedObject.keypoints.map(x => x.point2d);
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// Draw bounding box.
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drawingUtils.drawConnectors(canvasCtx, landmarks,
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mpObjectron.BOX_CONNECTIONS, {color: '#FF0000'});
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// Draw centroid.
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drawingUtils.drawLandmarks(canvasCtx, [landmarks[0]], {color: '#FFFFFF'});
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}
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}
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canvasCtx.restore();
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}
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const objectron = new Objectron({locateFile: (file) => {
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return `https://cdn.jsdelivr.net/npm/@mediapipe/objectron/${file}`;
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}});
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objectron.setOptions({
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modelName: 'Chair',
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maxNumObjects: 3,
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});
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objectron.onResults(onResults);
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const camera = new Camera(videoElement, {
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onFrame: async () => {
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await objectron.send({image: videoElement});
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},
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width: 1280,
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height: 720
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});
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camera.start();
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</script>
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```
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## Example Apps
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Please first see general instructions for
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@@ -561,11 +648,15 @@ py = -py_pixel * 2.0 / image_height + 1.0
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[Announcing the Objectron Dataset](https://ai.googleblog.com/2020/11/announcing-objectron-dataset.html)
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* Google AI Blog:
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[Real-Time 3D Object Detection on Mobile Devices with MediaPipe](https://ai.googleblog.com/2020/03/real-time-3d-object-detection-on-mobile.html)
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* Paper: [Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations](https://arxiv.org/abs/2012.09988), to appear in CVPR 2021
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* Paper: [Objectron: A Large Scale Dataset of Object-Centric Videos in the
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Wild with Pose Annotations](https://arxiv.org/abs/2012.09988), to appear in
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CVPR 2021
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* Paper: [MobilePose: Real-Time Pose Estimation for Unseen Objects with Weak
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Shape Supervision](https://arxiv.org/abs/2003.03522)
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* Paper:
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[Instant 3D Object Tracking with Applications in Augmented Reality](https://drive.google.com/open?id=1O_zHmlgXIzAdKljp20U_JUkEHOGG52R8)
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([presentation](https://www.youtube.com/watch?v=9ndF1AIo7h0)), Fourth Workshop on Computer Vision for AR/VR, CVPR 2020
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([presentation](https://www.youtube.com/watch?v=9ndF1AIo7h0)), Fourth
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Workshop on Computer Vision for AR/VR, CVPR 2020
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* [Models and model cards](./models.md#objectron)
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* [Web demo](https://code.mediapipe.dev/codepen/objectron)
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* [Python Colab](https://mediapipe.page.link/objectron_py_colab)
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@@ -96,6 +96,7 @@ Supported configuration options:
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```python
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import cv2
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import mediapipe as mp
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import numpy as np
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mp_drawing = mp.solutions.drawing_utils
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mp_selfie_segmentation = mp.solutions.selfie_segmentation
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@@ -29,7 +29,7 @@ has_toc: false
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[Object Detection](https://google.github.io/mediapipe/solutions/object_detection) | ✅ | ✅ | ✅ | | | ✅
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[Box Tracking](https://google.github.io/mediapipe/solutions/box_tracking) | ✅ | ✅ | ✅ | | |
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[Instant Motion Tracking](https://google.github.io/mediapipe/solutions/instant_motion_tracking) | ✅ | | | | |
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[Objectron](https://google.github.io/mediapipe/solutions/objectron) | ✅ | | ✅ | ✅ | |
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[Objectron](https://google.github.io/mediapipe/solutions/objectron) | ✅ | | ✅ | ✅ | ✅ |
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[KNIFT](https://google.github.io/mediapipe/solutions/knift) | ✅ | | | | |
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[AutoFlip](https://google.github.io/mediapipe/solutions/autoflip) | | | ✅ | | |
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[MediaSequence](https://google.github.io/mediapipe/solutions/media_sequence) | | | ✅ | | |
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