CARscan-

Approach 1 -

Segmentation of images by detecting contours. It failed because in images with elements along with cars were also getting detected , and were difficult to separate because few of them were having greater area.

Approach 2 -

Detection of Cars and using bounding box to create mask layer and segment them to separate from the background. However bounding boxes are not accurate and can be bigger or smaller than the actual car. Thus this failed.

Approach 3 -

Semantic Segmentation of images using PixelLib with help of Pascalvoc dataset trained with Deeplabv3+ framework. With just a few lines of code , a tiresome job is done with the help of PixelLib library.

Input (provided)

Output (No background)

Output (New background)

Dependencies :

Download the H5 file of the xception model trained on pascal voc for segmenting objects from Here. Put the downloaded model in the same directory as the python file.

opencv :

$ pip install opencv-python

tensorflow : 2.0 or higher

$ pip install tensorflow

scikit-image :

$ pip install scikit-image

PIL:

$ pip install pillow

PixelLib:

$ pip install pixellib

How to start :

• Clone the repo

git clone https://github.com/BlueBlaze6335/CARscan.git

• Create venv

python -m venv env

• change directory

$ cd CARscan

• Download dependencies
• Run python file

python carscan.py