wehub-resource-sync
468fc26eb0
CodeQL Advanced / Analyze (actions) (push) Has been cancelled
CodeQL Advanced / Analyze (javascript-typescript) (push) Has been cancelled
CodeQL Advanced / Analyze (python) (push) Has been cancelled
Scorecard supply-chain security / Scorecard analysis (push) Has been cancelled
20 lines
1.1 KiB
Markdown
20 lines
1.1 KiB
Markdown
# Human Body Segmentation
|
|
|
|
Lab Assignment from [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners).
|
|
|
|
## Task
|
|
|
|
In video production, for example, in weather forecasts, we often need to cut out a human image from camera and place it on top of some other footage. This is typically done using **chroma key** techniques, when a human is filmed in front of a uniform color background, which is then removed. In this lab, we will train a neural network model to cut out the human silhouette.
|
|
|
|
## The Dataset
|
|
|
|
We will be using [Segmentation Full Body MADS Dataset](https://www.kaggle.com/datasets/tapakah68/segmentation-full-body-mads-dataset) from Kaggle. Download the dataset manually from Kaggle.
|
|
|
|
## Stating Notebook
|
|
|
|
Start the lab by opening [BodySegmentation.ipynb](BodySegmentation.ipynb)
|
|
|
|
## Takeaway
|
|
|
|
Body segmentation is just one of the common tasks that we can do with images of people. Another important tasks include **skeleton detection** and **pose detection**. Look into [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose) library to see how those tasks can be implemented.
|