Repo link

Contributers:

• Fady Ahmed Mostafa Ali فادي احمد مصطفي على, code: 1700957, Section: 3
• Omar Gamal Hamed Agami عمر جمال حامد عجمي, code: 1700860, Section: 3,
• Ziad Mostafa Abd El-Aziz Mostafa زياد مصطفى عبد العزيز مصطفى, code: 1700572, Section: 2

Prerequisites:

install needed libraries

• pip install jupyter
• pip install numpy
• pip install matplot
• pip install opencv-python
• pip install moviepy
• pip install scikit-image
• pip install scikit-learn

pip install jupyter numpy matplot opencv-python moviepy scikit-image scikit-learn

To run the script:

Phase 1:

• open power shell or cmd
• write this command to get output video:

.\script './src/challenge_video.mp4' './dst/output.mp4'

• to enable debugging mode add -d or --debug to the end of the command

.\script './src/challenge_video.mp4' './dst/output.mp4' -d

• to run python file directly

python .\lane_detection.py './src/challenge_video.mp4' './dst/output.mp4' -d

Phase 2:

• open linux terminal or WSL
• write this command to get output video:

HOG

./car_detection.sh 'src/project_video.mp4' './dst/output.mp4' hog

YOLO

./car_detection.sh 'src/project_video.mp4' './dst/output.mp4' yolo

To run the notebook:

When reaching debugging mode, the video will start automatically:

• Press "L" to get next frame.
• Press "Q" to close video.