In this project we are going to create a simple perception stack for self-driving cars (SDCs.) Although a typical perception stack for a self-driving car may contain different data sources from different sensors (ex.: cameras, lidar, radar, etc…), we’re only going to be focusing on video streams from cameras for simplicity. We’re mainly going to be analyzing the road ahead, detecting the lane lines, detecting other cars/agents on the road, and estimating some useful information that may help other SDCs stacks. The project is split into two phases. We’ll be going into each of them in the following parts.
In this first phase, our goal is to write a software pipeline to identify the lane boundaries in a video from a front-facing camera on a car
In this stage we are locating and identifying the cars on the road
in this phase object detecion doesn't work with Debugging Mode
The jupyter Notebook contains the code and Presentaion , we have written the actual codes plus some comments explaining what we were doing through out the project
the .py file contains the file that will run in the CMD
test_image_detection.ipynb this is the notebook
test_image_detection.py this is the python script that will run through CMD
- Input (Video or Image) path
- Output (Video or Image) path
- Flag that indicates , the input whether it was an image or video
- Flag that indicates the Debugging Mode
Third Parameter has only two values
1 that indicates the input is Video
0 that indicates the input is Image
Fourth Parameter has only two values
1 that indicates Debugging Mode Activated
0 tha indicates Debugging Mode Deactivated
python test_image_detection.py #First_Parameter #Second_parameter #Third_parameter #Fourth_parameter
Sample Example:
python test_image_detection.py test_images/test1.jpg test_images 0 1
Name | ID |
---|---|
Shereen Reda Sayed Mohamed | 1804990 |
Mahmoud Khaled Abdelaal Aly | 1801004 |
Maram Nabil Ibrahim Ali | 1803746 |