yubrshen / CarND-Semantic-Segmentation

Perform semantic segmentation of photo captured of the front view of a driver identifying the road surface. It's for the purpose of self-driving-car. It's performed by full convolution neural network.

Semantic Segmentation

Introduction

In this project, I will label the pixels of a road in images using a Fully Convolutional Network (FCN).

To review my work of the project, please read Semantic Segmentation Design Workbook

Below is the original content of the README.md

Setup

Frameworks and Packages

Make sure you have the following is installed:

• Python 3
• TensorFlow
• NumPy
• SciPy

Dataset

Download the Kitti Road dataset from here. Extract the dataset in the data folder. This will create the folder data_road with all the training a test images.

Start

Implement

Implement the code in the main.py module indicated by the "TODO" comments. The comments indicated with "OPTIONAL" tag are not required to complete.

Run

Run the following command to run the project:

python main.py

Note If running this in Jupyter Notebook system messages, such as those regarding test status, may appear in the terminal rather than the notebook.

Submission

1. Ensure you've passed all the unit tests.
2. Ensure you pass all points on the rubric.
3. Submit the following in a zip file.

• helper.py
• main.py
• project_tests.py
• Newest inference images from runs folder (all images from the most recent run)

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