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* Removed submodules.

* Add back submodules using https://

* simplified plotting functions

* fixed most tests

* fixed test

* fixed unit test

* small text edits to the 02 notebook

* added fct description

* tiny cleanup on notebook

* Add pretrained keypoint model (#453)

* Add pretrained keypoint model

* Fix bugs in tests

* Add 03 notebook in conftest.py

* Minor revision

* Reformat code using black

* if folder exists, remove (#448)

* Add mask annotation tool (#447)

* Add mask annotation tool

* Update mask annotation explanation and add converion scripts

* Add screenshots of Labelbox annotation

* Rearrange screenshots

* Move convertion script into functions in data.py

* Point out annotation conversion scripts clearly in notebook

* Refine annotation conversion scripts

* Fix bugs

* Add tests for labelbox format conversion methods

* Move r2p1d from contrib to scenarios.

* Update .gitignore.

* Add README.md

* Remove the folder /scenario/action_recognition/data/samples; update notebook to use web url for sample data.

* Move data split files to data/misc; update notebook accordingly.

* Update data path.

* Add keypoint detection with tuned model (#454)

* Add keypoint detetion with tuned model

* Add tests

* Minor revision

* Update tests

* Fix bugs in tests

* Use GPU device if available

* Update tests

* Fix bug: 'not idx' will be 'True' if 'idx=0'

* Fix bugs

* Move toy keypoint meta into notebook

* Fix bugs

* Fix bugs

* Fix bugs in notebook

* Add descriptions for keypoint meta data

* Raise exception when RandomHorizontalFlip is used without specifying hflip_inds

* Add NOTICE file.

* Add keypoint detection model tuning with top and bottom keypoints (#456)

* Add keypoint detection model tuning with top and bottom keypoints

* Fix undefined unzip_url

* Resolved undefined od_urls

* Add annotation tool to scenarios.

* Plot keypoints as round dots to make them noticeable (#458)

* Plot keypoints as dots

* Change variable naming

* Resolve test machine failure (#460)

This is due to the latest PyTorch (version 1.3) from conda is built on
CUDA 10.1 while the version on the test machine is CUDA 10.0.

* Remove unused imports in 02_mask_rcnn.ipynb (#463)

* Remove unused imports in 02_mask_rcnn.ipynb

* Add missing imports

* Simplify binary_mask() (#464)

* clean up of keypoint detection notebook

* minor clean-up

* remove conflict code (#471)

* Update README.md (#472)

* updated readmes

* added images

* updated readmes

* added intro figure

* modified new picture

* update figure

* Move to bare Windows GPU VMs and fix build issues (#475)

* Updating AzureDevOps pipeline agent pool to use a barebones Windows GPU VM.

* Added a AzureDevOps pipeline for windows.

* Added a conda init step for powershell.

* Adding conda to PATH as a separate step

* Pinning pillow to 6.1 to fix issues listed in: python-pillow/Pillow#4130

* chained powershell commands

* Updated AzureDevOps yml file to use the inline powershell script syntax.

* Moved to using inline commands + Invoke-Expression syntax

* Adding indentation for chained commands.

* Moving to the literal block style indicator for yaml for chained commands.

* Using call activate instead of source activate when activating the conda environment through powershell.

* Switching to script steps instead of using powershell steps for conda operations.

* Fixed the numpy.float64 issue by moving to a fork with the upstream fixes for the issue.

* Fixed the numpy.float64 issue in utils/cv/detection/plot.py

Co-authored-by: PatrickBue <[email protected]>
Co-authored-by: Simon Zhao <[email protected]>
Co-authored-by: Miguel González-Fierro <[email protected]>
Co-authored-by: JS <[email protected]>
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5 changes: 4 additions & 1 deletion .gitignore
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Expand Up @@ -115,9 +115,12 @@ output.ipynb

# don't save any data
classification/data/*
data/
/data/*
!/data/misc
!contrib/action_recognition/r2p1d/**
!contrib/crowd_counting/crowdcounting/data/
!scenarios/action_recognition/data


# don't save .swp files
*.swp
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285 changes: 285 additions & 0 deletions NOTICE.txt
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13 changes: 9 additions & 4 deletions README.md
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# Computer Vision

In recent years, we've see an extra-ordinary growth in Computer Vision, with applications in face recognition, image understanding, search, drones, mapping, semi-autonomous and autonomous vehicles. A key part to many of these applications are visual recognition tasks such as image classification, object detection and image similarity.
In recent years, we've see an extra-ordinary growth in Computer Vision, with applications in face recognition, image understanding, search, drones, mapping, semi-autonomous and autonomous vehicles. A key part to many of these applications are visual recognition tasks such as image classification, object detection and image similarity.

This repository provides examples and best practice guidelines for building computer vision systems. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in Computer Vision algorithms, neural architectures, and operationalizing such systems. Rather than creating implementions from scratch, we draw from existing state-of-the-art libraries and build additional utility around loading image data, optimizing and evaluating models, and scaling up to the cloud. In addition, having worked in this space for many years, we aim to answer common questions, point out frequently observed pitfalls, and show how to use the cloud for training and deployment.

We hope that these examples and utilities can significantly reduce the “time to market” by simplifying the experience from defining the business problem to development of solution by orders of magnitude. In addition, the example notebooks would serve as guidelines and showcase best practices and usage of the tools in a wide variety of languages.

These examples are provided as [Jupyter notebooks](scenarios) and common [utility functions](utils_cv). All examples use PyTorch as the underlying deep learning library.

<p align="center">
<img src="./scenarios/media/cv_overview.jpg" height="350" alt="Some supported CV scenarios"/>
</p>


## Target Audience

Our target audience for this repository includes data scientists and machine learning engineers with varying levels of Computer Vision knowledge as our content is source-only and targets custom machine learning modelling. The utilities and examples provided are intended to be solution accelerators for real-world vision problems.
Our target audience for this repository includes data scientists and machine learning engineers with varying levels of Computer Vision knowledge as our content is source-only and targets custom machine learning modelling. The utilities and examples provided are intended to be solution accelerators for real-world vision problems.

## Get Started

Expand All @@ -30,7 +35,8 @@ The following is a summary of commonly used Computer Vision scenarios that are c
| -------- | ----------- | ----------- |
| [Classification](scenarios/classification) | Base | Image Classification is a supervised machine learning technique that allows you to learn and predict the category of a given image. |
| [Similarity](scenarios/similarity) | Base | Image Similarity is a way to compute a similarity score given a pair of images. Given an image, it allows you to identify the most similar image in a given dataset. |
| [Detection](scenarios/detection) | Base | Object Detection is a supervised machine learning technique that allows you to detect the bounding box of an object within an image. |
| [Detection](scenarios/detection) | Base | Object Detection is a technique that allows you to detect the bounding box of an object within an image. |
| [Keypoints](scenarios/keypoints) | Base | Keypoint detection can be used to detect specific points on an object. A pre-trained model is provided to detect body joints for human pose estimation. |
| [Action recognition](contrib/action_recognition) | Contrib | Action recognition to identify in video/webcam footage what actions are performed (e.g. "running", "opening a bottle") and at what respective start/end times.|
| [Crowd counting](contrib/crowd_counting) | Contrib | Counting the number of people in low-crowd-density (e.g. less than 10 people) and high-crowd-density (e.g. thousands of people) scenarios.|

Expand Down Expand Up @@ -77,4 +83,3 @@ provide a set of examples (backed by code) of how to build common AI-oriented wo

## Contributing
This project welcomes contributions and suggestions. Please see our [contribution guidelines](CONTRIBUTING.md).

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